diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/CNAME b/CNAME new file mode 100644 index 0000000..da3cd55 --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +andrewlook.com \ No newline at end of file diff --git a/about/index.html b/about/index.html new file mode 100644 index 0000000..240921e --- /dev/null +++ b/about/index.html @@ -0,0 +1,603 @@ + + + + + + + + + +andrewlook - about me + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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about me

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Hi, I’m Andrew Look.

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I’m a machine learning engineer, ex-startup founder, and part-time artist based in Brooklyn, NY.

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Work

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I recently left my most recent startup, Aesthetic (Y Combinator Summer 2018).

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Before that, I did ML engineering at Pinterest from 2016-2018.

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Previously, I worked on a mobile ad startup called URX from 2013 until 2016 when we were acquired by Pinterest.

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Side Projects

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I’m also an AI advisor to Long Journey Ventures, occasionally helping with technical due diligence on companies with an AI/ML focus.

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In spare time, I’m building audio-reactive digital art and training new team members as Head of Software for the Titanic’s End Mutant Vehicle.

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Art

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I’ve been painting since 2013 (when I really needed a hobby to unwind from the stress of startup life).

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I’ve been experimenting with ML art and generative AI since 2016 (when I started playing with DeepDreams and GANs and making physical paintings from the outputs).

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I’ve been making single-line drawings since 2018 (when I decided to experiment with adding a constraint to my art practice).

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I’ve been making pottery since 2022 (when I wanted to experiment with making art in three dimensions rather than two).

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+ + + + + \ No newline at end of file diff --git a/blog/index.html b/blog/index.html new file mode 100644 index 0000000..249676e --- /dev/null +++ b/blog/index.html @@ -0,0 +1,710 @@ + + + + + + + + + +andrewlook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+Title + +Date +
+SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering + +2024-01-04 +
+SLML Part 5 - SketchRNN Experiments: Path Preprocessing + +2023-12-14 +
+SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization + +2023-12-03 +
+SLML Part 3 - JPEG to SVG to Stroke-3 + +2023-11-07 +
+SLML Part 2 - Assembling the Dataset + +2023-11-01 +
+SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings + +2023-10-27 +
+SLML Part 0 - My Single-Line Drawing Practice + +2023-10-20 +
+A Tour of AI Art in 2018 + +2018-04-18 +
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+No matching items +
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+ + + + + \ No newline at end of file diff --git a/blog/index.xml b/blog/index.xml new file mode 100644 index 0000000..f25cd3f --- /dev/null +++ b/blog/index.xml @@ -0,0 +1,1903 @@ + + + +andrewlook +https://andrewlook.com/notebooks/blog/ + +Andrew Look Personal Website +quarto-1.4.551 +Thu, 04 Jan 2024 00:00:00 GMT + + SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part6.html + +

SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering

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This post is part 6 of “SLML” - Single-Line Machine Learning.

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To read the previous post, check out part 5.

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If you want to keep reading, check out part 7.

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New Dataset: 20240104

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Though I’d grown my training dataset by bounding-box separating single pages into multiple drawings, I was concerned about the tradeoff of filtering drawings out versus having a more coherent dataset with similar subject matter.

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To make up for more aggressive filtering, I decided to incorporate several additional sketchbooks I scanned and labeled into a new dataset epoch 20240104.

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Differences in dataset 20240104 compared to dataset 20231214:

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  1. More raw input drawings
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  2. Same preprocessing, with a modified “adaptive” RDP simplification 1.
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RDP and Sequence Length

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In previous datasets, I had chosen the same strength of RDP line simplification for the whole dataset. Some drawings had been simplified reasonably, but other had been simple to begin with and ended up as a series of straight lines much sharper than the original curves.

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+Figure 1: 30 points +
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For the remaining drawings, I ran the RDP algorithm with varying values for its epsilon parameter, until the number of points dipped under 250. Then I saved the result as a zipped numpy file.

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Training on 20240104

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+Figure 2: Training and validation loss metrics from models trained on 20240104 using visual filtering on the bounding-box separated drawings, with maxiumum sequence lengths of 200 (gray) and 250 (blue). +
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After training on 20240104, the validation losses (gray and blue lines in Figure 2) seemed substantially lower than the validation losses from the models trained on the previous dataset (beige, light green).

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Overly Complex Drawings

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One failure mode I noticed in the results generated after training on the bounding-box separated dataset 20231214-filtered was that some generated drawings had knotted, gnarled lines as in Figure 3.

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+Figure 3: Generated examples with too much complexity. +
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Reviewing the bounding box-separated dataset I noticed that some drawings were of one figure, and some drawings were patterned or chained with many faces.

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+(a) 1093 points +
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+(b) 1127 points +
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+(c) 329 points +
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+Figure 4: Drawings with over 300 points. +
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Sometimes I make patterns by chaining repeating sequences of faces into long continuous lines. I wondered whether the presence of this kind of drawing in the training data was occasionally encouraging the model to make long continuous chains rather than drawing a single person.

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chains example
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+Figure 5: Example of a “diagonal chain” single-line pattern I draw. +
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I wanted to exclude those patterns/chains from my training data, so I could give my model the best chance of learning to draw one person at a time.

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Similarity-Filtered Dataset

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I decided to make a subset 20240104-furtherfiltered of dataset 20240104.

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My plan was to compute embeddings for every bounding-box separated drawing in dataset 20240104. Then I could K-Means clustering on them, and decide which clusters I wanted to exclude in bulk. 2

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Right away I spotted the “too complex” chained line drawings in cluster 0 (Figure 6 (a)). There were also several chained line drawings in cluster 3 (Figure 6 (b)) mixed in with some squarish horizontal drawings that I was happy to exclude from my training set, as they looked too different from my more typical standalone drawings of individual faces/people.

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+(a) Cluster 0 +
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+Figure 6: Clusters with drawings that were “too complex”. +
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I also noticed some clusters with drawings that were “too simple”. It seems like many of the drawings in cluster 13 (Figure 7 (a)) were stray lines accidentally separated from any context by the bounding-box preprocessing. Cluster 9 (Figure 7 (b)) had many similar nonsensical lines, though they were mixed in with some false positives - valid drawings that I’d prefer to keep in the dataset.

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+Figure 7: Clusters with drawings that were “too simple”. +
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I was excited to notice some distinct categories in my drawings, seeing them from a distance.

In the future, as I add more drawings, it’d be great to explicitly label these drawing categories and even train separate models on them. For now, given that I don’t have enough drawings scanned yet, I’m choosing to keep them in one dataset.
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Clusters 1, 4, and 11 (in Figure 8 (a), Figure 8 (c), and Figure 8 (i), respectively) all have vertical, narrow, whole-body figures.

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Cluster 2, in Figure 8 (b), mostly has rounder compositions of individual faces without a complete body.

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Clusters 8 and 15, in Figure 8 (g) and Figure 8 (l), seem to have more complex drawings but mostly still contain drawings of standalone people.

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The remaining clusters contain reasonably uniform drawings of standalone people, in vertical compositions, that are not too narrow. Hovering your mouse over these links Figure 8 (d), Figure 8 (e), Figure 8 (f), Figure 7 (b), Figure 8 (h), Figure 8 (j), Figure 8 (k).

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+(a) Cluster 1 +
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+(i) Cluster 11 +
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+Figure 8: Clusters with drawings that looked good to me. +
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Training on Filtered Dataset

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The remainder of the clusters, in Figure 8, looked “good enough” for me to include in my training set. I all other clusters, and saved a filtered-down dataset as 20240104-furtherfiltered.

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Compared to dataset 20240104, it’s clear in the top row of Figure 9 that in the filtered dataset variant, the distribution of number of strokes has shifted away from the long tail of many-stroke drawings.

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+Figure 9: Comparing to unfiltered dataset 20240104 (2100 drawings) to the filtered dataset 20240104-furtherfiltered (1300 drawings). +
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Comparing the training metrics in Figure 10 for the model trained on filtered dataset 20240104-furtherfiltered (in red) with the previous model runs on unfiltered dataset 20240104 (in gray and blue) is not a perfect comparison. Since the validation set for 20240104-furtherfiltered was also filtered, it’s a smaller (and likely noisier) validation set. Still, the new model’s validation loss was roughly within the bounds of what I expected.

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+Figure 10: Training and validation loss metrics from models trained on 20240104-furtherfiltered using visual filtering on the bounding-box separated drawings (red). +
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Qualitatively, the generated results after visual similarity filtering were significantly improved.

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+Figure 11: Generated samples after training with visual filtering on bbox-separated dataset. +
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Even the generated results that looked less like people/faces to me still had appealing curves and flowing patterns, which I recognize from my own drawing style.

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+Figure 12: Generated samples after training with visual filtering on bbox-separated dataset. +
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If you want to keep reading, check out part 7 of my SLML series.

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Footnotes

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  1. based on what I observed when filtering the bounding-box dataset by number of points↩︎

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  2. similar to what I did with full sketchbook pages in part 1↩︎

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]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part6.html + Thu, 04 Jan 2024 00:00:00 GMT + + + + SLML Part 5 - SketchRNN Experiments: Path Preprocessing + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part5.html + +

SLML Part 5 - SketchRNN Experiments: Path Preprocessing

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This post is part 5 of “SLML” - Single-Line Machine Learning.

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To read the previous post, check out part 4. If you want to keep reading, check out part 6.

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Path Joining

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Despite some improvement within the strokes, my latest models were still producing many short strokes. So I figured that improving my dataset would give better results than fiddling with hyper-parameters or my model.

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Looking at my training data, I returned to an issue from the stroke3-conversion process: autotrace hadn’t realized that the different segments of my lines were actually connected. It was producing a series of small, centerline-traced segments, as seen in Figure 1 (a).

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I’d need some preprocessing to connect the smaller strokes into larger strokes, so that my SketchRNN model would learn to generate long contiguous single-line drawings.

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+(a) Original: 22 strokes +
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+Figure 1: Comparison of number of strokes before and after preprocessing, with one color per stroke. +
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My first preprocesing improvement was simple algorithm that I called “path joining”.

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Each of my drawings is represented as a collection of line segments, each with a start and end point. I try to find the shortest connection I can draw to connect two segments. Starting with the longest stroke, I compare it each of the other strokes and calculate the minimum distance between the start and end points of the line segments. After going through all the strokes in a drawing, I connect the two strokes with the shortest gap between their endpoints. I repeat this process, joining the strokes until no strokes remain with endpoints more than 30 pixels apart.

I set an upper bound on the path-joining distance of 30 pixels (in a 200x200 pixel image) was the maximum distance I observed made sense before I started to erroneously join strokes that were too far apart.
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Though path-joining improved the average stroke length as seen in Figure 1 (b), I noticed some drawings had large contiguous strokes that weren’t getting connected. I realized that while the strokes were close together and in some cases touching, their start and end points were far apart from each other.

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Path Splicing

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My next preprocessing improvement, “path splicing”, would run after “path joining” and attempt to address this problem.

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After path joining leaves a smaller number of strokes, I want to find the shortest connections to combine multiple longer strokes. Starting with the longest path, I look for a smaller stroke that I could “splice” into the middle of the larger stroke. For each candidate stroke, I’d step through each point on the larger stroke and compare its distance from the start and end points of the shorter paths. When I found the smallest gap, I would “splice” the shorter line into the longer path at the point with the smallest distance.

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While not every drawing was turned into a single stroke, this was a big improvement, as seen in Figure 1 (c).

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+Figure 2: Previous dataset look_i16__minn10, left, compared to path-spliced dataset v2-splicedata, right. +
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Based my earlier experiment showing the benefits of short-stroke exclusion, I wanted to try training a model on this new dataset.

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Training after Path-Splicing

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I ran the preprocessing on the whole dataset. Next, I filtered the original 1300 drawings to exclude any drawings with more than 6 strokes, resulting in a new dataset of 1200 drawings that I named v2-splicedata. Then I trained a new set of models, keeping the layernorm and recurrent dropout enabled.

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+Figure 3: Training and validation loss metrics of recurrent dropout model (light green) alongside models trained on this joined/spliced dataset (turquoise/dark green). +
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After training some models on a path-spliced dataset the training metrics aren’t a perfect comparison, since the content of the validation also changed when I applied path-splicing. Still, I can see from the validation loss graph that the model started to overfit around 1000 steps. The roughly similar shapes of the early train and validation loss curves at least convinced me that the model hadn’t gotten dramatically worse.

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+Figure 4: Generated samples from a model trained on path-spliced dataset. +
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Qualitatively, the generated drawings showed a big improvement. The model had learned to generate longer unbroken strokes. I started to notice results that looked more like single-line drawings of people. In some cases they look surreal, but I started to see some more recognizable face elements and in some cases full bodies.

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+Figure 5: Iterating through full dataset, with 3 frames per drawing: original, path-joined, and path-spliced. I liked the slight jitter in the animation as I watched the drawings go from colorful (many strokes) to fully blue (single stroke). +
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Bounding Boxes

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My last remaining problem: there were some pages where I’d make 4 or 5 separate drawings that had nothing to do with each other, and had a lot of space between them.

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I wanted to separate those into separate examples before making a training dataset, for 2 reasons:

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If the model sometimes learngs to make a complete drawing and then start a second unrelated drawing, how does the model know when to finish a drawing vs. when to start a second drawing alongside the first one?

My intuition for my third preprocessing improvement, “bounding box separation”, came when I noticed how unrelated strokes within a drawing didn’t overlap much, and tended to be spaced far apart. For each of the stroke within a drawing, I’d determine its top/bottom/left/right extremes and consider a box around each stroke as in Figure 6.

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+Figure 6: Example drawing with multiple unrelated strokes. +
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Then for each combination of bounding boxes, I’d compute a ratio of the area of their overlap compared to the area of the non-overlapping parts as in Figure 7.

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+Figure 7: Intersection over Union (“IOU”) Metric. +
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If the ratio exceeds some threshold, I consider them to be part of the same drawing, and I merge the bounding boxes as in Figure 9 (a). Combining that merged bounding box along with all the remaining bounding boxes, I repeat the process until no bounding-box intersections remain that exceed the threshold.

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+Figure 8: Comparison of high-IOU vs. low-IOU bounding box intersections. +
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Also, if any bounding boxes have a really small area, I just drop them. It turns out this helps exclude small scribbles of text that were ending up in my training data as separate strokes - for example, the page number at the bottom right of Figure 9 (a).

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Original: 4 Strokes
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Merge: 3 Strokes
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+Figure 9: Example from training set of a very small stroke being removed. +
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Dataset 20231214

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Once I have all the separated strokes, I save then into a new dataset as separate drawings. While the previous dataset v2-splicedata only has 1200 drawings, the new bounding-box separated dataset 20231214 has 2400 drawings.

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+Figure 10: Comparison of previous dataset v2-splicedata with 20231214 and 20231214-filtered. +
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The dataset grew from 1200 to 2400 drawings because pages containing multiple distinct drawings (such as Figure 11 (a)) were divided into separate rows in the new training set (like Figure 11 (b), Figure 11 (c), Figure 11 (d)).

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+Figure 11: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214. +
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The new separated drawings looked more visually consistent with the average drawing out of the training set as a whole. The new dataset contains far more single-character drawings, so I expect that the RNN will benefit on learning from a set of drawings with more similar subject matter.

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I hypothesized that the bbox-separated dataset will be a big improvement because of the consistency of the resulting drawings. Before, the model was learning that sometimes drawings end after one person is drawn, but sometimes we move the pen and start a new person.

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Filtering by Number of Points

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Looking at the distribution of number of points per drawing in the new dataset 20231214 in the bottom-middle chart in Figure 10, I noticed a long tail of drawings with more than 500 points. To explore this, I created a variant dataset 20231214-filtered.

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Dataset 20231214-filtered which was filtered down to 1800 drawings, keeping only drawings with more than 50 and less than 300 points as you can see in the bottom-right chart in Figure 10.

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Wondering if drawings with many points were less likely to have consistent subject matter (individual people) than the rest of the training set, I sampled some drawings with over 300 points. While drawings such as Figure 12 (a) and Figure 12 (b) were obvious candidates to exlcude, there were valid drawings near the margin such as Figure 12 (c) that I would be excluding after I picked a threshold.

Possible Improvement: Filtering by visual embedding might be more reliable to exclude complex drawings
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+Figure 12: Drawings with over 300 points. +
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I also looked at the low end of the distribution and found drawings with under 50 points. There were nonsensical squiggles such as Figure 13 (a) that I was happy to exclude. There were cases below the 50 point threshold such as Figure 13 (b) and Figure 13 (c) that looked recognizable as my drawings, but had been simplified by RDP too aggressively.

Possible Improvement: Applying RDP after bounding box separation rather than before.
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+Figure 13: Drawings with under 50 points. +
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Training after Bounding Boxes

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+Figure 14: Training and validation loss metrics comparing model trained on 20231214 (beige) compared with models trained on dataset 20231214-filtered with and without stroke augmentation (green, burgundy). +
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After training on the unfiltered dataset 20231214, I noticed that some drawings were devolving into a sequence of repeated face features without forming a cohesive person or face, as in Figure 15.

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+Figure 15: Generated results after training on unfiltered dataset 20231214. +
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The model results in Figure 16 after training on filtered dataset 20231214-filtered appear qualitatively better to me. The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.

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+Figure 16: Generated results after training on filtered dataset 20231214-filtered. +
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The model results in Figure 17 after training with stroke-augmentation on filtered dataset 20231214-filtered appear to be roughly of similar quality to . The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.

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+Figure 17: Generated results after training on filtered dataset 20231214-filtered, with stroke augmentation enabled. +
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If you want to keep reading, check out part 6 of my SLML series.

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+ + + ]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part5.html + Thu, 14 Dec 2023 00:00:00 GMT + + + + SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part4.html + +

SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization

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This post is part 4 of “SLML” - Single-Line Machine Learning.

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To read the previous post, check out part 3. If you want to keep reading, check out part 5.

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Now that I had a repeatable process to convert JPEGs into stroke-3, I decided to start training models with my first dataset (which I called look_i16).

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Filtering out Short Strokes

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The training data in my first dataset, look_i16, had lots of tiny strokes mixed in with longer ones, and no inherent order to them.

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+Figure 1: Samples from dataset look_i16. +
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Unsurprisingly, my first models produced odd smatterings composed of many small strokes.

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+Figure 2: Generated outputs from SketchRNN trained on dataset look_i16. +
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As a quick experiment, I tried just filtering out any really short strokes out of the dataset - I decided to iterate through each of the 1300 drawings in the training set, and filter out any strokes with less than 10 points - I called this dataset look_i16__minn10.

I recognized that I’d need a more systematic solution to improve my training data, but I wanted to try a quick improvement before going deeper. See part 5 for how I updated my preprocessing to reduce the number of strokes in my training data.
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+Figure 3: Distribution of num_strokes and num_points in datasets look_i16 and look_i16__minn10. Both datasets have 1300 entries in the training set. +
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Note that the average number of strokes in look_i16 is around 40, while it’s closer to 10 in look_i16__minn10. It seems that there were many very short strokes in the training data. I also simplified the drawings more aggressively for look_i16__minn10 by increasing RDP’s epsilon parameter to 1.0 when preprocessing the data, which further reduced the number of points per drawing.

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+Figure 4: Training and validation loss metrics for look_i16 (pink) and look_i16__minn10 (yellow). +
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When training models, I’m measuring the reconstruction loss. I feed in a drawing, the model encodes it into an embedding vector, and then decodes the embedding vector back into a drawing. I can compute the loss at each step in the reconstructed drawing compare to the original. Periodically, after processing several batches of training data, I compute the reconstruction metrics on a validation set. This is a portion of the dataset I’m not using to actually update the weights of the model during training.

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By comparing the reconstruction loss on the training set vs. the validation set over time, I can identify when the model starts “overfitting”. Intuitively, if the model is learning to perform better on the training data while performing worse on the validation data, that means it is effectively memorizing the training set rather than learning to generalize its learnings to drawings it wasn’t trained on.

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The model trained on look_i16__minn10 performed slightly better than the model trained on look_i16 in terms of the error when reconstructing a drawing. It’s visible in Figure 4 that the loss values were lower, and the validation loss didn’t start to increase until slightly later.

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+Figure 5: Generated outputs from SketchRNN trained on dataset look_i16__minn10. +
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The results produced after training on look_i16__minn10 were much less chaotic. While they didn’t resemble coherent drawings, this was the first time I spotted some elements of my drawing style (head shape, eye style, lips, chin).

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Layer Normalization

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The Magenta team had recommended using Layer Normalization and Recurrent Dropout with Memory Loss.

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I noticed that when I let the trainer run overnight, I’d get wild spikes in the training loss. I decided to start with Layer Normalization.

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+Figure 6: Training and validation loss metrics from training on look_i16__minn10 without layernorm (yellow) and with layernorm (burgundy: learnable=True, gray: learnable=False). The yellow line without layernorm enabled is the same as in Figure 4, but this graph shows more than 150,000 train steps while the previous graph showed only 7,000 train steps. +
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Adding layer normalization showed a dramatic difference, visible in Figure 6. The yellow line (without layer norm) has a massive increase in validation loss, and many big spikes, while the burgundy and gray lines (with layer norm integrated) have much lower validation loss and don’t have any comparable spikes.

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+Figure 7: Generated samples from model trained with Layer Normalization. +
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Recurrent Dropout

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The results from the layernorm models in Figure 7 had some hints of my drawing style, while still struggling to form coherent drawings.

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Next, I kept layernorm enabled and enabled recurrent dropout. I ran one separate runs with and without stroke augmentation enabled.

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+Figure 8: Training and validation loss metrics for layernorm-only models (burgundy and gray) alongside models trained with recurrent dropout (green) and with stroke augmentation (pink). +
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Compared to the layernorm-only models (burgundy and gray), the one recurrent dropout model (green) achieved a lower validation loss relatively quickly, before starting to overfit.

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The model trained with recurrent dropout and stroke augmentation (pink) clearly performed worse than with recurrent dropout (in terms of higher validation loss).

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+Figure 9: Generated samples from model trained with Recurrent Dropout as well as Layer Normalization. +
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The resulting generations from model with layernorm and recurrent dropout in Figure 9 weren’t obviously better or worse than those from the layernorm-only model in Figure 7.

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If you want to keep reading, check out part 5 of my SLML series.

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+ + + ]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part4.html + Sun, 03 Dec 2023 00:00:00 GMT + + + + SLML Part 3 - JPEG to SVG to Stroke-3 + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part3.html + +

SLML Part 3 - JPEG to SVG to Stroke-3

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This post is part 3 of “SLML” - Single-Line Machine Learning.

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To read the previous post, check out part 2.

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If you want to keep reading, here is part 4.

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Most computer vision algorithms represent images as a rectangular grid of pixels on a screen. The model that the Magenta team trained, SketchRNN, instead interprets the drawings as a sequence of movements of a pen. They call this “stroke-3 format”, since each step in the sequence is represented by 3 values:

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  • delta_x: how much did it move left-to-right?
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  • delta_y: how much did it move up-and-down?
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  • lift_pen: was the pen down (continuing the current stroke) or was the pen lifted (moving to the start of a new stroke)
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First, I had to convert my JPEG scans into the “stroke-3” format. This would involve:

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  1. converting the files from JPEG to SVG
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  2. converting SVG to stroke-3
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  3. simplifying the drawings to reduce the number of points
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JPEG to SVG

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When I first started converting to SVG, I had trouble finding a tool that would give me a single, clean stroke for each line. Eventually I found a tool called autotrace that was able to correctly do a “centerline trace”.

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+Figure 1: Comparison of Vectorization Tools. +
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SVG to Points

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Then I used a python library called svgpathtools to take the resulting SVG files, and convert each of the paths to a sequence of points. This step is necessary because SVG paths are often represented as Bezier curves.

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One problem I noticed was that the drawings were represented as many separate strokes rather than one continuous line. For example, in the image below, each color represents a separate pen stroke.

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separate strokes
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Line Simplification

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Finally, I’d apply the Ramer-Douglas-Pecker (“RDP”) algorithm on the resulting points, which uses an adjustable “epsilon” parameter to simplify down the drawings by reducing the number of points in a line’s path.

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RDP example
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This is important because the SketchRNN model has difficulty with sequences longer than a few hundred points, so it’s helpful to simplify the drawings down by removing some of the very fine details while preserving the overall shape.

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Next in my SLML series is part 4, where I experiment with hyperparams and datasets in training SketchRNN.

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+ + + ]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part3.html + Tue, 07 Nov 2023 00:00:00 GMT + + + + SLML Part 2 - Assembling the Dataset + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part2.html + +

SLML Part 2 - Assembling the Dataset

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This post is part 2 of “SLML” - Single-Line Machine Learning.

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Check out the previous post, part 1, for some background on why I got started training SketchRNN on my own drawings.

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Next in the series is part 3, where I convert my JPEGs into stroke-3 format in preparation for model training.

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For years, I’ve maintained a daily practice of making single-line drawings in my sketchbooks.

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sb48p072sb67p077
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When I finish a sketchbook, I scan its pages, number them, and put them into a similar folder structure. I marked which pages were diagrams/mindmaps/etc and put them in a subfolder called notes - the drawings (and watercolors) are in a folder called art. Unfortunately, I didn’t tag watercolors in advance.

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Some detail on the subfolders:

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  • art subfolders contain drawings / watercolors.
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  • notes subfolders contain diagrams, mindmaps, and anything else technical I’m working on. I used to have a separate notebook for these, but I got tired of carrying around an extra sketchbook everywhere I go, so at some point they just ended up merging with my art practice.
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  • xtra contains any bad scans or funny bloopers from my scanning process. I kind of like the scans with my hands in them and thought they might be useful for a future project, so I kept them in their own folder.
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  • cover contains a scan of the notebook cover. Each cover at least has a post-it with the sketchbook ID number and the start/end dates of when I used that sketchbook. Often I’ll make a little collage on the front of the notebook with airplane tickets / stickers / scraps of paper relevant to somewhere I traveled when I used that sketchbook.
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The Problem: Filtering

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My first problem is that the “art” sections of my sketchbooks don’t just contain single-line drawings. They also contain watercolor paintings, since I don’t exclusively fill my sketchbooks with single-line drawings.

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sb69p086-wcsb67p051-sparkysb69p006sb35p013
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Measuring Visual Similarity with Embeddings

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Looking for a scalable way to filter the drawings out of my art scans, I decided to try computing visual embeddings.

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I decided to fine-tune a vision model on my dataset to distinguish “art” pages from “notes” pages. My hope was that the model would learn to distinguish some features within the visual domain of my scanned sketchbook pages. Even though I don’t intend to use the predictions of an algorithm trained to classify art-vs-notes, this will produce more meaningful embeddings.

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Ultimately, what I want to see is that when I take the embeddings of my sketchbook pages and cluster them, the notes and drawings should neatly separate into separate clusters. Also, I want the watercolors to cleanly separate into their own clusters, away from the single-line drawings.

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Inspecting the Embeddings

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After I fine-tuned my vision model, I computed the embeddings for all the sketchbook pages I had on hand. As a sanity check, I want to verify that the embedding space has cleanly separated regions for the labels is was trained on.

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It’s hard to visualize 512 dimensions, so the T-SNE dimensionality reduction is a good visualization technique to view the data. T-SNE projects the higher number of dimensions into 2D or 3D, with the goal that any 2 points close together in high-dimensional space are close together in lower-dimensional space.

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I colorized the points in the resulting graph based on their label, so it’s easy to see at a glance that embedding space has learned something useful about this dataset, as I hoped.

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T-SNE representation of sketchbook pages. art pages are blue, notes pages are orange, and cover pages are brown.
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Clustering to Identify Sub-Groups

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Now I’m hoping to find the watercolors in the dataset. Given that the embedding space captures visual similarity, I want to identify groups of pages that are similar to each other. This lets me label data in bulk, rather than going through the images one-by-one. K-Means Clustering was a quick solution for this. I can decide to ask for K distinct “clusters”, and the algorithm picks K “centroids”.

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Given a request for K clusters, pick K random points as the starting centroids. Then, repeat these steps for N iterations: For each point, select the nearest centroid point and compute the distance between these two. Adjusts the centroids with the goal of minimizing the total distance from all points to their nearest centroids.
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Using Clusters to Classify

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After some experimentation, I found that using K=16 produced groupings that would be useful to me. Each row in the image below consists of the top images from one cluster.

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Since each cluster’s centroid is a vector in the same space as the image embeddings, I can “classify” any image embedding by doing a nearest-neighbor search of comparing the embedding of the query image to each of the cluster centroid vectors, and choosing the cluster centroid with the smallest distance. This is also called a K-Nearest Neighbor classifier.

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When I applied this process to each of the images, I glanced at a t-SNE colorized by these clusters:

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T-SNE representation of sketchbook pages, colored by which of the 16 clusters they were classified by K-Means using the pages’ visual embeddings.
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Making Sense of the Clusters

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I went through and grouped these clusters based on what I wanted to identify automatically. In particular, I wanted to take only the drawings and separate them from the watercolors. After inspecting the clusters, I also realized that there were a number of bad scans / incorrectly cropped images that I wanted to exclude as well.

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I went through the dataset again, and based on the cluster assigned to each image, I assigned it a higher-level group based on the hand-mapping of clusters above.

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When I looked at the t-SNE colorized by this higher-level cluster mapping, it was encouraging to see that my embedding space had neatly separated the groups I cared about:

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T-SNE representation of pages, colored by cluster group. drawings are purple, notes are dark green, watercolors are yellow, bad scans are blue, covers are light green.
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Since my images were tagged with these higher-level groups, I was able to select only the drawings, and begin converting them into usable training data.

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Next in my SLML series is part 3, where I convert my JPEGs into stroke-3 format in preparation for model training.

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+ + + ]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part2.html + Wed, 01 Nov 2023 00:00:00 GMT + + + + SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part1.html + +

SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings

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This post is part 1 of “SLML” - Single-Line Machine Learning.

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If you want some context on how I got into single-line drawings, check out the part 0.

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Next in the series is part 2 where I assemble the training data, and filter it using visual embeddings.

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Discovering SketchRNN

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David Ha and Doug Eck from the Magenta team at Google had crowdsourced over 100,000 drawings using a game call “Quick, Draw”. The game would give users a prompt such as “Yoga” or “Pig” and users would have 20 seconds to draw it in the browser. This produced a dataset of line drawings for each category. Then, they trained a model called SketchRNN on these drawings. They trained a separate model for each category that users were prompted to draw, so there’s a separate model trained for “yoga”, “pig”, etc.

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I was fascinated by Magenta’s SketchRNN demo where you start a drawing, and the SketchRNN model tries continuously to complete it in new ways. It makes me think about the conditional probability that evolves as a drawing progresses. Given that one line exists on the page, how does that influence what line the artist is most likely to draw next?

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My favorite example is to select the yoga model, and then draw a rhombus shape simulating a yoga mat. I love watching stick figures emerge in various poses, all centered on and interacting with the yoga mat.

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Some of the same authors from the Magenta paper collaborated with distill.pub, using the same SketchRNN model trained on a handwriting dataset, to publish an explorable explanation called Experiments in Handwriting with a Neural Network.

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Why train a model on my single-line drawings?

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I wanted to apply SketchRNN to my own single-line drawings.

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I’d considered training an ML model on my single-line drawings, but the pixel-based GAN’s and VAE’s available in 2017/2018 when I started thinking about this didn’t seem like they’d yield good results on images that are mostly composed of white pixels with narrow black lines interspersed. What SketchRNN produces sequences, the generated results could be animated. I see single-line drawing as a sort of performance. A video of me drawing a single-line is inherently a proof that I didn’t lift my pen from the page.

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It struck me that it could give a new window into my own drawing style. If my own drawing style evolves slowly over time, would I be able to notice the difference between a model trained on drawings I made recently from one trained on drawings I made several years ago?

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How cool would it be to have an interface, similar to Andy Muatuschak’s scrying pen, where I could start drawing and see live completions, showing me how the probability space of subsequent strokes in my drawing is changing?

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scrying pen demo
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What new ideas might I get from turning the “variability” up, like the slider in distill.pub’s handwriting demo, and generating new drawings based on my old ones? distill-variation Or running stroke prediction along the length of a drawing:

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Getting Started

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When I reached out to the authors of SketchRNN, they estimated that I’d need several thousand examples in order to train a model. I only had a few hundred at the time. But I kept making new drawings in my sketchbooks, and numbering the pages and the sketchbooks so that I could scan them and store them in a consistent way. In the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.

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Several years went by. More sketchbooks accumulated.

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Eventually, I ran a file count and saw a few thousand distinct drawings. I was finally ready to get started. I was going to need:

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  1. at least a thousand drawings
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  2. the ability to convert my scanned drawings into stroke-3 format
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  3. to train a model on my drawings, and experiment until I found a configuration producing results that I liked.
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Next in my SLML series is part 2, where I convert my JPEG scans into vector representations in preparation for training SketchRNN.

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+ + + ]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part1.html + Fri, 27 Oct 2023 00:00:00 GMT + + + + SLML Part 0 - My Single-Line Drawing Practice + Andrew Look + https://andrewlook.com/notebooks/blog/posts/slml_part0.html + +
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My Single-Line Drawing Practice

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This post is the background to my series “SLML” - Single-Line Machine Learning.

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Next in the series is part 1, where I discovered SketchRNN and got curious about applying it to my own artwork.

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I started experimenting with single-line drawings around 2017. I’d been painting and drawing and experimenting with Machine Learning art for around 5 years at that point, but my art hobby had started to feel like a chore. I was taking on more and more ambitious painting projects, and there wasn’t a lot of simplicity or repetition in my art practice. A lot of my art felt premeditated, as I’d print out what I wanted to paint and then “execute” it on the canvas, painstakingly trying to represent what I’d decided to paint as accurately as possible.

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I got tired of painting but wanted to keep up my art practice, so I started just carrying around a sketchbook. Since I’d noticed a tendency to overthink my drawings I thought a pencil would give me too much freedom to erase, so I only brought a pen with me. I found myself wanting to practice the challenging parts of paintings that I’d struggled with, particularly drawing people and faces.

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Friends advised me that adding a constraint could help me to iterate quickly and not overthink my drawings. Since one of my patterns had been to spend too much time on individual drawings, I decided to adopt an extreme constraint: as soon as I lifted the pen from the page, I’d call the drawing done.

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I was drawn at first to the technical challenge. As a painter I’d already been learning about facial anatomy in an attempt to improve my drawings. Now I needed not only to know enough about anatomy to visualize a person or face I wanted to draw in three dimensions, but also to pick a path across that surface to make a finished drawing.

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As a painter I’d grown accustomed to shading and blending to add or remove contrast. Once I started working only with lines, I had to find new tricks. Negative space in a drawing can create a good shadow. Concentric circles creates an illusion of depth. Putting lines close together makes something look darker.

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Over time, other aspects of single-line drawing pulled me in further. I found how much beauty there was to be found in what to leave out of a drawing. I learned how satisfying it is to choose just the essential lines needed to imply a facial expression or a pose.

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If you want to keep reading, check out part 1 of my SLML series, where I discovered SketchRNN and got curious about applying it to my own artwork.

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+ + ]]> + singleline + machinelearning + slml + https://andrewlook.com/notebooks/blog/posts/slml_part0.html + Fri, 20 Oct 2023 00:00:00 GMT + + + A Tour of AI Art in 2018 + Andrew Look + https://andrewlook.com/notebooks/blog/posts/2018_ai_art_tour.html + By the end of this tour, my goal is to help you understand how some existing artists are incorporating AI tools and concepts into their work, and how using AI art tools might augment your creativity.

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Disclaimer: I’m not a real expert on computer vision, neuroscience or art. But I have spent a couple years learning about the space, so I’ll do my best to explain things as I understand them so far.

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For those more familiar with AI, I plan on making some broad generalizations in the interest of brevity. If you’re interested in the technical details, I’ve done my best to include relevant links on where to go deeper.

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What is AI art?

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Many artistic tools come from people striving to understand how algorithms affect our lives. I’ve distilled a framework to understand different types of AI Art, and am sharing it in the hopes that it makes the concepts easier to discuss and compare.

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Here are 3 ways I can think of to define “AI Art”:

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  1. Making creative use of outputs from an AI Tool
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  2. Leveraging how AI represents information to deliberately craft an Effect
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  3. Exploring the Concepts within AI and what they mean for us
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Some Definitions

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Given the amount of cultural baggage we all associate with words like “AI” and “art”, it’s worth clarifying some definitions up front. “AI Art” has stuck as a phrase to describe an emerging category of creative work, but it might be more accurate to call it “Machine Learning Art.”

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The art I’m discussing here is mostly made by humans using statistical methods, not by sentient robots with their own creative ideas. Sorry to disappoint, but this post isn’t dedicated to anthropomorphic machines that paint:

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SPIRAL by Deepmind (blog, pdf, youtube)

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AI

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AI is a term used in so many contexts, that it’s a bit of a loaded term. Most mentions of AI can be bucketed into one of two broad categories:

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  • General AI: The futuristic aspiration of computers that experience consciousness
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  • Specialized AI: Systems using statistics to learn patterns from data in order to make predictions.
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I’ll be focused on artistic applications of techniques that have sprung out of real-world research from the category of Specialized AI, also known as Machine Learning.

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Machine Learning

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Machine Learning (usually) refers to a type of algorithm that (1) is built to perform a clearly-defined task, and (2) learns patterns and relationships from training data

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For example, imagine that we want to predict housing prices. We’d probably start with a spreadsheet of prices we know to be true, alongside features for each house that we expect to be related to the price. If we plot price vs. size on this toy dataset, we can start to see a slope.

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housing prices table housing prices scatter plot

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If we were to draw a line through these points, the “slope” would be what you multiply the size by in order to get the price (plus a “bias” term, if the slope doesn’t meet the Y-axis right at zero). The objective of an ML algorithm is to find a line to draw through these points that minimizes the error in its predictions. You can visualize the error as the distance from each real data point’s Y coordinate to the prediction: the line’s value at the corresponding X coordinate.

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error bars iterative learning

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“Art is everything that you don’t have to do.” - brian eno

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Whole books could be written in an attempt to define art, so I won’t attempt an exhaustive definition.

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“Fountain,” Marcel Duchamp, 1917 (wikipedia)

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The truth is that art can be defined by whoever is creating it, and art history is full of boundary-pushing acts leading onlookers to ask “but is it art?”

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Back to AI Art

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Now I’ll step through three possible ways to define AI art and provide some examples along the way.

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  1. Making creative use of outputs from an AI Tool
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  2. Leveraging how AI represents information to deliberately craft an Effect
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  3. Exploring the Concepts within AI and what they mean for us
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Definition #1: Making creative use of outputs from an AI Tool

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The simplest definition of AI art is any artifact generated from a tool the makes use of AI, whether or not the artist makes this a central part of the work.

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Apps such as Prisma, for example, make it easy for anyone to take a photo on their phone and render it in a painterly style.

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Even without much context on how they work, easy-to-use tools such as pix2pix can be fun to play with and yield weird results.

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bread_cat?>

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“Image-to-Image Translation with Conditional Adversarial Networks”, Isola et al, 2017 (pdf)

+
+

We’ll look at some more in-depth approaches later (time permitting), but for now this is just fun.

+
+
+
+

+
pix2pix_pyramid_cat
+
+
+
+
+
+
+

Definition #2: Leveraging how AI represents information to deliberately craft an effect

+

A slightly more complex definition of AI art is one in which an artist uses his or her understanding of the machine learning internals to achieve a specific effect. For example, it’s possible to make some interesting visual artifacts by inspecting how machine learning algorithms represent information.

+
+

+

source

+
+

In fact, the spooky faces came from a visualization of Eigenfaces, which sought to “learn” how to represent faces in a “latent space”. For me, this early facila recognition output brings to mind one of my favorite quotes by Brian Eno (who I’m quoting more than once in this piece since he’s had so much to say about the overlap of technology and art).

+
+

“Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit, all of these will be cherished and emulated as soon as they can be avoided. It’s the sound of failure. So much modern art is the sound of things going out of control. Out of a medium, pushing to its limits and breaking apart.” - Brian Eno

+
+
+

Recent generative algorithms have rapidly improved their ability both to learn latent spaces, and to generate images from any point in these latent spaces.

+
+
+
+

+
facespace
+
+
+

Face Recognition using Eigenfaces, Turk et al, 1991 pdf

+
+

Methods of encoding real images into vectors representing a point in latent space have since improved, as have the methods of decoding latent vectors back into realistic images.

+

When we interpolate between two points in the latent space, we can smoothly generate images at each point along the way. In the uncanny example below, I find it striking that (almost) every point in between looks like a person. Looking at this work, I can’t help thinking that these algorithms are showing us something about our innate similarity to all other humans.

+
+
+
+

+
celeb_1hr_man
+
+
+

Progressive Growing of GANs for Improved Quality, Stability, and Variation, Karras et al, 2018 (pdf, youtube)

+
+

One striking example of a completely new type of tool leveraging this understanding is called Toposketch. It allows visual navigation of latent spaces in order to gain fine-grained control over the generated artifacts. This makes me wonder what kinds of creative tools could be built to leverage the information that machines are able to derive from the increaing volume of data that’s available to us today.

+
+
+

+
toposketch_man_loop
+
+
+
+
+
+

Definition #3: Exploring the Concepts within AI and what they means for us

+

Among other things, this could encompass exploring AI’s relationships to:

+
    +
  • our society
    • +
  • the individual
    • +
  • the nature of consciousness
    • +
+
+

Example: Treachery of Imagenet

+

Tom White made some beautifully designed prints, each of which fools a computer vision network into believing it’s actually a picture of a specific object (see the predicted categories to the right of the image below).

+
+
+
+

+
img
+
+
+

source

+
+

There’s a new field of security concerned with adversarial machine learning, concerned with how attackers can fool neural networks into misinterpreting what they see. It’s not intuitive, but the consequences can be serious - imagine a self-driving car fooled into missing a stop sign. I admire how Tom White took a complex concept and used it to make a visually appealing piece that sparks curiosity and raises awareness about concepts that may affect our lives but don’t have easy conversational entry points for outsiders.

+
+
+

Example: Simulating How Humans Draw

+

I’m fascinated when researchers attempt to mimic how humans draw. In particular, I’m interested in how the researchers set up an algorithm to mimic the process of drawing - they have to choose what happens next, how much the first part of the drawing affects what happens next, and how to define success.

+
+
+
+

+
simulating how humans draw
+
+
+

source

+
+

In my analog artworks, I’ve noticed that sometimes I start a drawing not knowing how it will end up. Small accidents or random movements early in the drawing lead me to see something unexpected, adjust my course and capture what I saw. Oddly enough, thinking about the rigid framing of machine learning problems (input: view of the canvas, output: X,Y,Z coordinates of brush movement) has led to some fun experiments. For example, what happens if i close my eyes and draw?

+
+
+
+
+

In Conclusion

+

Given that there are several ways to define AI art (and I’m sure my list is not comprehensive), my hope is that we can broaden our view of what AI art means. While it does include fun images made using new mobile apps, I’d also like to think that there’s some more profound works out there. Works that can surprise people, help them understand the role of new technologies in their daily lives, and stoke their curiosity to learn more.

+
+

“Stop thinking about art works as objects, and start thinking about them as triggers for experiences. … what makes a work of art ‘good’ for you is not something that is already ‘inside’ it, but something that happens inside you — so the value of the work lies in the degree to which it can help you have the kind of experience that you call art.”

+

Brian Eno, via brainpickings

+
+ + +
+ + ]]> + machinelearning + art + https://andrewlook.com/notebooks/blog/posts/2018_ai_art_tour.html + Wed, 18 Apr 2018 00:00:00 GMT + + + diff --git a/blog/posts/2018_ai_art_tour.html b/blog/posts/2018_ai_art_tour.html new file mode 100644 index 0000000..3e8c747 --- /dev/null +++ b/blog/posts/2018_ai_art_tour.html @@ -0,0 +1,818 @@ + + + + + + + + + + + + +andrewlook - A Tour of AI Art in 2018 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

A Tour of AI Art in 2018

+
+
machinelearning
+
art
+
+
+ +
+
+ some-description +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

April 18, 2018

+
+
+ + +
+ + + +
+ + +

By the end of this tour, my goal is to help you understand how some existing artists are incorporating AI tools and concepts into their work, and how using AI art tools might augment your creativity.

+

Disclaimer: I’m not a real expert on computer vision, neuroscience or art. But I have spent a couple years learning about the space, so I’ll do my best to explain things as I understand them so far.

+

For those more familiar with AI, I plan on making some broad generalizations in the interest of brevity. If you’re interested in the technical details, I’ve done my best to include relevant links on where to go deeper.

+
+

What is AI art?

+

Many artistic tools come from people striving to understand how algorithms affect our lives. I’ve distilled a framework to understand different types of AI Art, and am sharing it in the hopes that it makes the concepts easier to discuss and compare.

+

Here are 3 ways I can think of to define “AI Art”:

+
    +
  1. Making creative use of outputs from an AI Tool
    2. +
  2. Leveraging how AI represents information to deliberately craft an Effect
    3. +
  3. Exploring the Concepts within AI and what they mean for us
    4. +
+
+
+

Some Definitions

+

Given the amount of cultural baggage we all associate with words like “AI” and “art”, it’s worth clarifying some definitions up front. “AI Art” has stuck as a phrase to describe an emerging category of creative work, but it might be more accurate to call it “Machine Learning Art.”

+

The art I’m discussing here is mostly made by humans using statistical methods, not by sentient robots with their own creative ideas. Sorry to disappoint, but this post isn’t dedicated to anthropomorphic machines that paint:

+
+
+
+

+
not this
+
+
+

SPIRAL by Deepmind (blog, pdf, youtube)

+
+
+

AI

+

AI is a term used in so many contexts, that it’s a bit of a loaded term. Most mentions of AI can be bucketed into one of two broad categories:

+
    +
  • General AI: The futuristic aspiration of computers that experience consciousness
    • +
  • Specialized AI: Systems using statistics to learn patterns from data in order to make predictions.
    • +
+

I’ll be focused on artistic applications of techniques that have sprung out of real-world research from the category of Specialized AI, also known as Machine Learning.

+
+
+
+

Machine Learning

+

Machine Learning (usually) refers to a type of algorithm that (1) is built to perform a clearly-defined task, and (2) learns patterns and relationships from training data

+ +

For example, imagine that we want to predict housing prices. We’d probably start with a spreadsheet of prices we know to be true, alongside features for each house that we expect to be related to the price. If we plot price vs. size on this toy dataset, we can start to see a slope.

+
+

housing prices table housing prices scatter plot

+
+

If we were to draw a line through these points, the “slope” would be what you multiply the size by in order to get the price (plus a “bias” term, if the slope doesn’t meet the Y-axis right at zero). The objective of an ML algorithm is to find a line to draw through these points that minimizes the error in its predictions. You can visualize the error as the distance from each real data point’s Y coordinate to the prediction: the line’s value at the corresponding X coordinate.

+
+

error bars iterative learning

+
+

“Art is everything that you don’t have to do.” - brian eno

+

Whole books could be written in an attempt to define art, so I won’t attempt an exhaustive definition.

+
+

+

“Fountain,” Marcel Duchamp, 1917 (wikipedia)

+
+

The truth is that art can be defined by whoever is creating it, and art history is full of boundary-pushing acts leading onlookers to ask “but is it art?”

+
+
+

Back to AI Art

+

Now I’ll step through three possible ways to define AI art and provide some examples along the way.

+
    +
  1. Making creative use of outputs from an AI Tool
    2. +
  2. Leveraging how AI represents information to deliberately craft an Effect
    3. +
  3. Exploring the Concepts within AI and what they mean for us
    4. +
+ +
+
+
+

Definition #1: Making creative use of outputs from an AI Tool

+

The simplest definition of AI art is any artifact generated from a tool the makes use of AI, whether or not the artist makes this a central part of the work.

+

Apps such as Prisma, for example, make it easy for anyone to take a photo on their phone and render it in a painterly style.

+

Even without much context on how they work, easy-to-use tools such as pix2pix can be fun to play with and yield weird results.

+
+

bread_cat?>

+

“Image-to-Image Translation with Conditional Adversarial Networks”, Isola et al, 2017 (pdf)

+
+

We’ll look at some more in-depth approaches later (time permitting), but for now this is just fun.

+
+
+
+

+
pix2pix_pyramid_cat
+
+
+
+
+
+
+

Definition #2: Leveraging how AI represents information to deliberately craft an effect

+

A slightly more complex definition of AI art is one in which an artist uses his or her understanding of the machine learning internals to achieve a specific effect. For example, it’s possible to make some interesting visual artifacts by inspecting how machine learning algorithms represent information.

+
+

+

source

+
+

In fact, the spooky faces came from a visualization of Eigenfaces, which sought to “learn” how to represent faces in a “latent space”. For me, this early facila recognition output brings to mind one of my favorite quotes by Brian Eno (who I’m quoting more than once in this piece since he’s had so much to say about the overlap of technology and art).

+
+

“Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit, all of these will be cherished and emulated as soon as they can be avoided. It’s the sound of failure. So much modern art is the sound of things going out of control. Out of a medium, pushing to its limits and breaking apart.” - Brian Eno

+
+
+

Recent generative algorithms have rapidly improved their ability both to learn latent spaces, and to generate images from any point in these latent spaces.

+
+
+
+

+
facespace
+
+
+

Face Recognition using Eigenfaces, Turk et al, 1991 pdf

+
+

Methods of encoding real images into vectors representing a point in latent space have since improved, as have the methods of decoding latent vectors back into realistic images.

+

When we interpolate between two points in the latent space, we can smoothly generate images at each point along the way. In the uncanny example below, I find it striking that (almost) every point in between looks like a person. Looking at this work, I can’t help thinking that these algorithms are showing us something about our innate similarity to all other humans.

+
+
+
+

+
celeb_1hr_man
+
+
+

Progressive Growing of GANs for Improved Quality, Stability, and Variation, Karras et al, 2018 (pdf, youtube)

+
+

One striking example of a completely new type of tool leveraging this understanding is called Toposketch. It allows visual navigation of latent spaces in order to gain fine-grained control over the generated artifacts. This makes me wonder what kinds of creative tools could be built to leverage the information that machines are able to derive from the increaing volume of data that’s available to us today.

+
+
+

+
toposketch_man_loop
+
+
+
+
+
+

Definition #3: Exploring the Concepts within AI and what they means for us

+

Among other things, this could encompass exploring AI’s relationships to:

+
    +
  • our society
    • +
  • the individual
    • +
  • the nature of consciousness
    • +
+
+

Example: Treachery of Imagenet

+

Tom White made some beautifully designed prints, each of which fools a computer vision network into believing it’s actually a picture of a specific object (see the predicted categories to the right of the image below).

+
+
+
+

+
img
+
+
+

source

+
+

There’s a new field of security concerned with adversarial machine learning, concerned with how attackers can fool neural networks into misinterpreting what they see. It’s not intuitive, but the consequences can be serious - imagine a self-driving car fooled into missing a stop sign. I admire how Tom White took a complex concept and used it to make a visually appealing piece that sparks curiosity and raises awareness about concepts that may affect our lives but don’t have easy conversational entry points for outsiders.

+
+
+

Example: Simulating How Humans Draw

+

I’m fascinated when researchers attempt to mimic how humans draw. In particular, I’m interested in how the researchers set up an algorithm to mimic the process of drawing - they have to choose what happens next, how much the first part of the drawing affects what happens next, and how to define success.

+
+
+
+

+
simulating how humans draw
+
+
+

source

+
+

In my analog artworks, I’ve noticed that sometimes I start a drawing not knowing how it will end up. Small accidents or random movements early in the drawing lead me to see something unexpected, adjust my course and capture what I saw. Oddly enough, thinking about the rigid framing of machine learning problems (input: view of the canvas, output: X,Y,Z coordinates of brush movement) has led to some fun experiments. For example, what happens if i close my eyes and draw?

+
+
+
+
+

In Conclusion

+

Given that there are several ways to define AI art (and I’m sure my list is not comprehensive), my hope is that we can broaden our view of what AI art means. While it does include fun images made using new mobile apps, I’d also like to think that there’s some more profound works out there. Works that can surprise people, help them understand the role of new technologies in their daily lives, and stoke their curiosity to learn more.

+
+

“Stop thinking about art works as objects, and start thinking about them as triggers for experiences. … what makes a work of art ‘good’ for you is not something that is already ‘inside’ it, but something that happens inside you — so the value of the work lies in the degree to which it can help you have the kind of experience that you call art.”

+

Brian Eno, via brainpickings

+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/blog/posts/js_deno.html b/blog/posts/js_deno.html new file mode 100644 index 0000000..9f3a2ee --- /dev/null +++ b/blog/posts/js_deno.html @@ -0,0 +1,1277 @@ + + + + + + + + + + + + +andrewlook - JS playground + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

JS playground

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ some-description +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

December 1, 2023

+
+
+ + +
+ + + +
+ + + +

useful: - rgbkrk/denotebooks: 🦕 Deno by Example, in Notebooks! - Jupyter Kernel for Deno | Deno Docs

+
+
import * as pl from "npm:nodejs-polars";
+
+
+
let df = pl.DataFrame({"A": [1,2], "B": [3,4]});
+
+
+
console.table(df.rows())
+
+
┌───────┬───┬───┐
+│ (idx) │ 0 │ 1 │
+├───────┼───┼───┤
+│     0 │ 1 │ 3 │
+│     1 │ 2 │ 4 │
+└───────┴───┴───┘
+
+
+
+
import {display} from "https://deno.land/x/display@v0.1.1/mod.ts"
+
+
+
import pl from "npm:nodejs-polars@0.8.3"; // note: version needed for tab-sep fix https://github.com/pola-rs/nodejs-polars/issues/155
+let response = await fetch(
+  "https://cdn.jsdelivr.net/npm/world-atlas@1/world/110m.tsv",
+);
+let data = await response.text();
+let df = pl.readCSV(data, { sep: "\t" });
+df.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
scalerankfeatureclalabelranksovereigntsov_a3adm0_difleveltypeadminadm0_a3geou_difgeounitgu_a3su_difsubunitsu_a3brk_diffnamename_longbrk_a3brk_namebrk_groupabbrevpostalformal_enformal_frnote_adm0note_brkname_sortname_altmapcolor7mapcolor8mapcolor9mapcolor13pop_estgdp_md_estpop_yearlastcensusgdp_yeareconomyincome_grpwikipediafips_10iso_a2iso_a3iso_n3un_a3wb_a2wb_a3woe_idadm0_a3_isadm0_a3_usadm0_a3_unadm0_a3_wbcontinentregion_unsubregionregion_wbname_lenlong_lenabbrev_lentinyhomepart
1Admin-0 country6KosovoKOS02Sovereign countryKosovoKOS0KosovoKOS0KosovoKOS1KosovoKosovoB57KosovonullKos.KORepublic of KosovonullnullSelf admin.; Claimed by SerbiaKosovonull2231118048385352-991981-996. Developing region4. Lower middle income-99null-99-99-99-99KVKSV-99SRBKOS-99-99EuropeEuropeSouthern EuropeEurope & Central Asia664-991
1Admin-0 country5SomalilandSOL02IndeterminateSomalilandSOL0SomalilandSOL0SomalilandSOL1SomalilandSomalilandB30SomalilandnullSolnd.SLRepublic of SomalilandnullSelf admin.Self admin.; Claimed by SomaliaSomalilandnull3652350000012250-99-99-996. Developing region4. Lower middle income-99null-99-99-99-99-99-99-99SOMSOM-99-99AfricaAfricaEastern AfricaSub-Saharan Africa10106-991
1Admin-0 country6Northern CyprusCYN02Sovereign countryNorthern CyprusCYN0Northern CyprusCYN0Northern CyprusCYN1N. CyprusNorthern CyprusB20N. CyprusnullN. Cy.CNTurkish Republic of Northern CyprusnullSelf admin.Self admin.; Claimed by CyprusCyprus, Northernnull31482651003600-99-99-996. Developing region3. Upper middle income-99null-99-99-99-99-99-99-99CYPCYP-99-99AsiaAsiaWestern AsiaEurope & Central Asia9156-991
1Admin-0 country3AfghanistanAFG02Sovereign countryAfghanistanAFG0AfghanistanAFG0AfghanistanAFG0AfghanistanAfghanistanAFGAfghanistannullAfg.AFIslamic State of AfghanistannullnullnullAfghanistannull56872840000022270-991979-997. Least developed region5. Low income-99nullAFAFG44AFAFG-99AFGAFG-99-99AsiaAsiaSouthern AsiaSouth Asia11114-991
1Admin-0 country3AngolaAGO02Sovereign countryAngolaAGO0AngolaAGO0AngolaAGO0AngolaAngolaAGOAngolanullAng.AOPeople's Republic of AngolanullnullnullAngolanull326112799293110300-991970-997. Least developed region3. Upper middle income-99nullAOAGO2424AOAGO-99AGOAGO-99-99AfricaAfricaMiddle AfricaSub-Saharan Africa664-991
+
+
+
+
+
df = df
+    .groupBy("continent")
+    .agg(pl.col("pop_est").sum().alias("total_pop_est"))
+
+df = df.sort("continent")
+
+display(df)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
continenttotal_pop_est
Africa993281878
Antarctica3802
Asia4085852698
Europe728131201
North America539350981
Oceania33519610
Seven seas (open ocean)140
South America394355478
+
+
+
+
+
import * as d3 from "npm:d3"
+
+const canvasHeight = 400;
+const max = d3.max(df.total_pop_est)
+const xScaleSorted = d3.scaleLinear().domain([0,max]).range([0,300])
+const yScaleSorted = d3.scaleBand().domain(df.continent).range([0, canvasHeight]).padding(0.1);
+
+
+
// import {createCanvas, Image, Path2D} from "https://deno.land/x/skia_canvas/mod.ts"
+
+
+
// const canvas = createCanvas(400, 300);
+// const ctx = canvas.getContext("2d");
+
+// df.toRecords().forEach((record, i) => {
+//     const y = yScaleSorted(record.continent)
+//     const width = xScaleSorted(record.total_pop_est)
+//     const height = yScaleSorted.bandwidth()
+
+//     ctx.fillStyle = width < xScaleSorted(max) ? "#8787A7" : "575787"
+//     ctx.fillRect(0,y,width,height)
+//     ctx.fillStyle = "#000"
+//     ctx.fillText(record.continent, width+5, y + height/2)
+// })
+
+

above, skia-canvas didn’t seem to want to install.

+

Trying a different approach from here: d3-tslab-example.ipynb

+
+
import { JSDOM } from "npm:jsdom";
+import * as d3 from "npm:d3";
+
+
+
const dom = new JSDOM(`<!DOCTYPE html><body></body>`);
+let body = d3.select(dom.window.document.querySelector("body"))
+let svg = body.append('svg').attr('width', 200).attr('height', 200).attr('xmlns', 'http://www.w3.org/2000/svg');
+svg.append("circle")
+    .attr("cx", 100)
+    .attr("cy", 100)
+    .attr("r", 90)
+    .style("fill", "black")
+body.html()
+
+
+
'<svg width="200" height="200" xmlns="http://www.w3.org/2000/svg"><circle cx="100" cy="100" r="90" st'... 34 more characters
+
+
+
+
+
await Deno.jupyter.display(body.html())
+
+
+
'<svg width="200" height="200" xmlns="http://www.w3.org/2000/svg"><circle cx="100" cy="100" r="90" st'... 34 more characters
+
+
+
+
+
console.log(body.html())
+
+
<svg width="200" height="200" xmlns="http://www.w3.org/2000/svg"><circle cx="100" cy="100" r="90" style="fill: black;"></circle></svg>
+
+
+
+
// https://deno.land/api@v1.38.1?unstable=&s=Deno.jupyter.svg
+Deno.jupyter.svg`${body.html()}`
+
+
+
+

+
+
+
+
+
+
function showsvg(block) {
+   const dom = new JSDOM(`<!DOCTYPE html><body></body>`);
+   let body = d3.select(dom.window.document.querySelector("body"))
+   let svg = body.append('svg').attr('width', 200).attr('height', 200).attr('xmlns', 'http://www.w3.org/2000/svg');
+   block(svg)
+   return Deno.jupyter.svg`${body.html()}`;
+}
+showsvg(svg => { 
+    svg.append("circle")
+    .attr("cx", 100)
+    .attr("cy", 100)
+    .attr("r", 90)
+    .style("fill", "blue")
+});
+
+
+
+

+
+
+
+
+
+
const N = 1000;
+const bound = Math.PI * 2;
+const theta = [...Array(N).keys()].map(x => x * bound/N)
+
+const x = d3.scaleLinear().domain([-1,1]).range([0,200])
+const y = x
+
+const line = d3.line<number>()
+    .x(th =>  x(Math.sin(10 * th) * Math.cos(th)))
+    .y(th =>  y(Math.sin(10 * th) * Math.sin(th)))
+
+showsvg(svg => { 
+    svg.append("path")
+       .attr("d", line(theta))
+       .style("fill", "black")
+       .style("stroke", "black")
+})
+
+
+
+

+
+
+
+
+
+
- related to my issue: https://github.com/denoland/deno/issues/22508
+
+https://github.com/samizdatco/skia-canvas
+
+
+
// import { createCanvas } from "https://deno.land/x/skia_canvas@0.5.4/mod.ts";
+
+
Downloading https://github.com/DjDeveloperr/skia_canvas/releases/download/0.5.4/libnative_canvas_aarch64.dylib
+
+
+
TypeError: Deno.dlopen is not a function
+
+
+

alt: canvas@v1.4.1 | Deno

+
+
import { createCanvas } from "https://deno.land/x/canvas/mod.ts";
+
+ + + +
+ + +
+ + + + + \ No newline at end of file diff --git a/blog/posts/js_deno_files/figure-html/cell-20-output-1.svg b/blog/posts/js_deno_files/figure-html/cell-20-output-1.svg new file mode 100644 index 0000000..e9c1a6f --- /dev/null +++ b/blog/posts/js_deno_files/figure-html/cell-20-output-1.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/blog/posts/js_deno_files/figure-html/cell-21-output-1.svg b/blog/posts/js_deno_files/figure-html/cell-21-output-1.svg new file mode 100644 index 0000000..f9b6185 --- /dev/null +++ b/blog/posts/js_deno_files/figure-html/cell-21-output-1.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/blog/posts/js_deno_files/figure-html/cell-22-output-1.svg b/blog/posts/js_deno_files/figure-html/cell-22-output-1.svg new file mode 100644 index 0000000..86334cd --- /dev/null +++ b/blog/posts/js_deno_files/figure-html/cell-22-output-1.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/blog/posts/slml_part0.html b/blog/posts/slml_part0.html new file mode 100644 index 0000000..0710c66 --- /dev/null +++ b/blog/posts/slml_part0.html @@ -0,0 +1,657 @@ + + + + + + + + + + + + +andrewlook - SLML Part 0 - My Single-Line Drawing Practice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SLML Part 0 - My Single-Line Drawing Practice

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ How I learned to love minimalism through my art. +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

October 20, 2023

+
+
+ + +
+ + + +
+ + + +
+

My Single-Line Drawing Practice

+
+

This post is the background to my series “SLML” - Single-Line Machine Learning.

+

Next in the series is part 1, where I discovered SketchRNN and got curious about applying it to my own artwork.

+
+

I started experimenting with single-line drawings around 2017. I’d been painting and drawing and experimenting with Machine Learning art for around 5 years at that point, but my art hobby had started to feel like a chore. I was taking on more and more ambitious painting projects, and there wasn’t a lot of simplicity or repetition in my art practice. A lot of my art felt premeditated, as I’d print out what I wanted to paint and then “execute” it on the canvas, painstakingly trying to represent what I’d decided to paint as accurately as possible.

+

I got tired of painting but wanted to keep up my art practice, so I started just carrying around a sketchbook. Since I’d noticed a tendency to overthink my drawings I thought a pencil would give me too much freedom to erase, so I only brought a pen with me. I found myself wanting to practice the challenging parts of paintings that I’d struggled with, particularly drawing people and faces.

+

Friends advised me that adding a constraint could help me to iterate quickly and not overthink my drawings. Since one of my patterns had been to spend too much time on individual drawings, I decided to adopt an extreme constraint: as soon as I lifted the pen from the page, I’d call the drawing done.

+

I was drawn at first to the technical challenge. As a painter I’d already been learning about facial anatomy in an attempt to improve my drawings. Now I needed not only to know enough about anatomy to visualize a person or face I wanted to draw in three dimensions, but also to pick a path across that surface to make a finished drawing.

+

As a painter I’d grown accustomed to shading and blending to add or remove contrast. Once I started working only with lines, I had to find new tricks. Negative space in a drawing can create a good shadow. Concentric circles creates an illusion of depth. Putting lines close together makes something look darker.

+

Over time, other aspects of single-line drawing pulled me in further. I found how much beauty there was to be found in what to leave out of a drawing. I learned how satisfying it is to choose just the essential lines needed to imply a facial expression or a pose.

+
+

If you want to keep reading, check out part 1 of my SLML series, where I discovered SketchRNN and got curious about applying it to my own artwork.

+
+ + + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/blog/posts/slml_part1.html b/blog/posts/slml_part1.html new file mode 100644 index 0000000..e7e4e85 --- /dev/null +++ b/blog/posts/slml_part1.html @@ -0,0 +1,698 @@ + + + + + + + + + + + + +andrewlook - SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ Using visual embeddings to filter thousands of images. +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

October 27, 2023

+
+
+ + +
+ + + +
+ + +
+

SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings

+
+

This post is part 1 of “SLML” - Single-Line Machine Learning.

+

If you want some context on how I got into single-line drawings, check out the part 0.

+

Next in the series is part 2 where I assemble the training data, and filter it using visual embeddings.

+
+
+

Discovering SketchRNN

+

David Ha and Doug Eck from the Magenta team at Google had crowdsourced over 100,000 drawings using a game call “Quick, Draw”. The game would give users a prompt such as “Yoga” or “Pig” and users would have 20 seconds to draw it in the browser. This produced a dataset of line drawings for each category. Then, they trained a model called SketchRNN on these drawings. They trained a separate model for each category that users were prompted to draw, so there’s a separate model trained for “yoga”, “pig”, etc.

+

I was fascinated by Magenta’s SketchRNN demo where you start a drawing, and the SketchRNN model tries continuously to complete it in new ways. It makes me think about the conditional probability that evolves as a drawing progresses. Given that one line exists on the page, how does that influence what line the artist is most likely to draw next?

+

My favorite example is to select the yoga model, and then draw a rhombus shape simulating a yoga mat. I love watching stick figures emerge in various poses, all centered on and interacting with the yoga mat.

+
+
+

+
magenta-demo-01-draw-and-gen
+
+
+

Some of the same authors from the Magenta paper collaborated with distill.pub, using the same SketchRNN model trained on a handwriting dataset, to publish an explorable explanation called Experiments in Handwriting with a Neural Network.

+
+
+

Why train a model on my single-line drawings?

+

I wanted to apply SketchRNN to my own single-line drawings.

+

I’d considered training an ML model on my single-line drawings, but the pixel-based GAN’s and VAE’s available in 2017/2018 when I started thinking about this didn’t seem like they’d yield good results on images that are mostly composed of white pixels with narrow black lines interspersed. What SketchRNN produces sequences, the generated results could be animated. I see single-line drawing as a sort of performance. A video of me drawing a single-line is inherently a proof that I didn’t lift my pen from the page.

+

It struck me that it could give a new window into my own drawing style. If my own drawing style evolves slowly over time, would I be able to notice the difference between a model trained on drawings I made recently from one trained on drawings I made several years ago?

+

How cool would it be to have an interface, similar to Andy Muatuschak’s scrying pen, where I could start drawing and see live completions, showing me how the probability space of subsequent strokes in my drawing is changing?

+
+
+

+
scrying pen demo
+
+
+ +

What new ideas might I get from turning the “variability” up, like the slider in distill.pub’s handwriting demo, and generating new drawings based on my old ones? distill-variation Or running stroke prediction along the length of a drawing:

+
+
+

+
distill-vary-strokes
+
+
+
+
+

+
distill-vary-strokes-legend
+
+
+ +
+
+

Getting Started

+

When I reached out to the authors of SketchRNN, they estimated that I’d need several thousand examples in order to train a model. I only had a few hundred at the time. But I kept making new drawings in my sketchbooks, and numbering the pages and the sketchbooks so that I could scan them and store them in a consistent way. In the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.

+

Several years went by. More sketchbooks accumulated.

+

Eventually, I ran a file count and saw a few thousand distinct drawings. I was finally ready to get started. I was going to need:

+
    +
  1. at least a thousand drawings
    2. +
  2. the ability to convert my scanned drawings into stroke-3 format
    3. +
  3. to train a model on my drawings, and experiment until I found a configuration producing results that I liked.
    4. +
+
+

Next in my SLML series is part 2, where I convert my JPEG scans into vector representations in preparation for training SketchRNN.

+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/blog/posts/slml_part2.html b/blog/posts/slml_part2.html new file mode 100644 index 0000000..35b8b3c --- /dev/null +++ b/blog/posts/slml_part2.html @@ -0,0 +1,776 @@ + + + + + + + + + + + + +andrewlook - SLML Part 2 - Assembling the Dataset + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SLML Part 2 - Assembling the Dataset

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ Using visual embeddings to filter thousands of images. +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

November 1, 2023

+
+
+ + +
+ + + +
+ + +
+

SLML Part 2 - Assembling the Dataset

+
+

This post is part 2 of “SLML” - Single-Line Machine Learning.

+

Check out the previous post, part 1, for some background on why I got started training SketchRNN on my own drawings.

+

Next in the series is part 3, where I convert my JPEGs into stroke-3 format in preparation for model training.

+
+

For years, I’ve maintained a daily practice of making single-line drawings in my sketchbooks.

+
+ ++++++ + + + + + + + + + + +
sb48p072sb67p077
sb67p004sb38p073-gown2
+
+

When I finish a sketchbook, I scan its pages, number them, and put them into a similar folder structure. I marked which pages were diagrams/mindmaps/etc and put them in a subfolder called notes - the drawings (and watercolors) are in a folder called art. Unfortunately, I didn’t tag watercolors in advance.

+

Some detail on the subfolders:

+
    +
  • art subfolders contain drawings / watercolors.
    • +
  • notes subfolders contain diagrams, mindmaps, and anything else technical I’m working on. I used to have a separate notebook for these, but I got tired of carrying around an extra sketchbook everywhere I go, so at some point they just ended up merging with my art practice.
    • +
  • xtra contains any bad scans or funny bloopers from my scanning process. I kind of like the scans with my hands in them and thought they might be useful for a future project, so I kept them in their own folder.
    • +
  • cover contains a scan of the notebook cover. Each cover at least has a post-it with the sketchbook ID number and the start/end dates of when I used that sketchbook. Often I’ll make a little collage on the front of the notebook with airplane tickets / stickers / scraps of paper relevant to somewhere I traveled when I used that sketchbook.
    • +
+
+

+
+ +
+

The Problem: Filtering

+

My first problem is that the “art” sections of my sketchbooks don’t just contain single-line drawings. They also contain watercolor paintings, since I don’t exclusively fill my sketchbooks with single-line drawings.

+
+ ++++++ + + + + + + + + + + +
sb69p086-wcsb67p051-sparkysb69p006sb35p013
+
+
+
+

Measuring Visual Similarity with Embeddings

+

Looking for a scalable way to filter the drawings out of my art scans, I decided to try computing visual embeddings.

+ +

I decided to fine-tune a vision model on my dataset to distinguish “art” pages from “notes” pages. My hope was that the model would learn to distinguish some features within the visual domain of my scanned sketchbook pages. Even though I don’t intend to use the predictions of an algorithm trained to classify art-vs-notes, this will produce more meaningful embeddings.

+ +

Ultimately, what I want to see is that when I take the embeddings of my sketchbook pages and cluster them, the notes and drawings should neatly separate into separate clusters. Also, I want the watercolors to cleanly separate into their own clusters, away from the single-line drawings.

+
+
+

Inspecting the Embeddings

+

After I fine-tuned my vision model, I computed the embeddings for all the sketchbook pages I had on hand. As a sanity check, I want to verify that the embedding space has cleanly separated regions for the labels is was trained on.

+

It’s hard to visualize 512 dimensions, so the T-SNE dimensionality reduction is a good visualization technique to view the data. T-SNE projects the higher number of dimensions into 2D or 3D, with the goal that any 2 points close together in high-dimensional space are close together in lower-dimensional space.

+

I colorized the points in the resulting graph based on their label, so it’s easy to see at a glance that embedding space has learned something useful about this dataset, as I hoped.

+
+
+

+
T-SNE representation of sketchbook pages. art pages are blue, notes pages are orange, and cover pages are brown.
+
+
+
+
+

Clustering to Identify Sub-Groups

+

Now I’m hoping to find the watercolors in the dataset. Given that the embedding space captures visual similarity, I want to identify groups of pages that are similar to each other. This lets me label data in bulk, rather than going through the images one-by-one. K-Means Clustering was a quick solution for this. I can decide to ask for K distinct “clusters”, and the algorithm picks K “centroids”.

+
+
+

+
Given a request for K clusters, pick K random points as the starting centroids. Then, repeat these steps for N iterations: For each point, select the nearest centroid point and compute the distance between these two. Adjusts the centroids with the goal of minimizing the total distance from all points to their nearest centroids.
+
+
+ +
+
+

Using Clusters to Classify

+

After some experimentation, I found that using K=16 produced groupings that would be useful to me. Each row in the image below consists of the top images from one cluster.

+
+

+
+

Since each cluster’s centroid is a vector in the same space as the image embeddings, I can “classify” any image embedding by doing a nearest-neighbor search of comparing the embedding of the query image to each of the cluster centroid vectors, and choosing the cluster centroid with the smallest distance. This is also called a K-Nearest Neighbor classifier.

+

When I applied this process to each of the images, I glanced at a t-SNE colorized by these clusters:

+
+
+

+
T-SNE representation of sketchbook pages, colored by which of the 16 clusters they were classified by K-Means using the pages’ visual embeddings.
+
+
+ +
+
+

Making Sense of the Clusters

+

I went through and grouped these clusters based on what I wanted to identify automatically. In particular, I wanted to take only the drawings and separate them from the watercolors. After inspecting the clusters, I also realized that there were a number of bad scans / incorrectly cropped images that I wanted to exclude as well.

+
+

+

+

+

+

+
+

I went through the dataset again, and based on the cluster assigned to each image, I assigned it a higher-level group based on the hand-mapping of clusters above.

+

When I looked at the t-SNE colorized by this higher-level cluster mapping, it was encouraging to see that my embedding space had neatly separated the groups I cared about:

+
+
+

+
T-SNE representation of pages, colored by cluster group. drawings are purple, notes are dark green, watercolors are yellow, bad scans are blue, covers are light green.
+
+
+

Since my images were tagged with these higher-level groups, I was able to select only the drawings, and begin converting them into usable training data.

+
+

Next in my SLML series is part 3, where I convert my JPEGs into stroke-3 format in preparation for model training.

+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/blog/posts/slml_part3.html b/blog/posts/slml_part3.html new file mode 100644 index 0000000..d93b91c --- /dev/null +++ b/blog/posts/slml_part3.html @@ -0,0 +1,728 @@ + + + + + + + + + + + + +andrewlook - SLML Part 3 - JPEG to SVG to Stroke-3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SLML Part 3 - JPEG to SVG to Stroke-3

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ Converting JPEG scans to vector paths +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

November 7, 2023

+
+
+ + +
+ + + +
+ + +
+

SLML Part 3 - JPEG to SVG to Stroke-3

+
+

This post is part 3 of “SLML” - Single-Line Machine Learning.

+

To read the previous post, check out part 2.

+

If you want to keep reading, here is part 4.

+
+

Most computer vision algorithms represent images as a rectangular grid of pixels on a screen. The model that the Magenta team trained, SketchRNN, instead interprets the drawings as a sequence of movements of a pen. They call this “stroke-3 format”, since each step in the sequence is represented by 3 values:

+
    +
  • delta_x: how much did it move left-to-right?
    • +
  • delta_y: how much did it move up-and-down?
    • +
  • lift_pen: was the pen down (continuing the current stroke) or was the pen lifted (moving to the start of a new stroke)
    • +
+
+

+
+

First, I had to convert my JPEG scans into the “stroke-3” format. This would involve:

+
    +
  1. converting the files from JPEG to SVG
    2. +
  2. converting SVG to stroke-3
    3. +
  3. simplifying the drawings to reduce the number of points
    4. +
+
+

JPEG to SVG

+

When I first started converting to SVG, I had trouble finding a tool that would give me a single, clean stroke for each line. Eventually I found a tool called autotrace that was able to correctly do a “centerline trace”.

+
+
+
+
+
+
+
+
+ +
+
+(a) potrace +
+
+
+
+
+
+
+
+ +
+
+(b) autotrace +
+
+
+
+
+
+
+Figure 1: Comparison of Vectorization Tools. +
+
+
+
+
+

SVG to Points

+

Then I used a python library called svgpathtools to take the resulting SVG files, and convert each of the paths to a sequence of points. This step is necessary because SVG paths are often represented as Bezier curves.

+

One problem I noticed was that the drawings were represented as many separate strokes rather than one continuous line. For example, in the image below, each color represents a separate pen stroke.

+
+
+

+
separate strokes
+
+
+
+
+

Line Simplification

+

Finally, I’d apply the Ramer-Douglas-Pecker (“RDP”) algorithm on the resulting points, which uses an adjustable “epsilon” parameter to simplify down the drawings by reducing the number of points in a line’s path.

+
+
+

+
RDP example
+
+
+

This is important because the SketchRNN model has difficulty with sequences longer than a few hundred points, so it’s helpful to simplify the drawings down by removing some of the very fine details while preserving the overall shape.

+
+
+

+
phase1-sample-0177-epoch-01700-orig
+
+
+
+

Next in my SLML series is part 4, where I experiment with hyperparams and datasets in training SketchRNN.

+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/blog/posts/slml_part4.html b/blog/posts/slml_part4.html new file mode 100644 index 0000000..07a3c35 --- /dev/null +++ b/blog/posts/slml_part4.html @@ -0,0 +1,841 @@ + + + + + + + + + + + + +andrewlook - SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ Experiments training SketchRNN on my dataset of single-line drawings. +
+
+ + +
+ +
+
Author
+
+

Andrew Look

+
+
+ +
+
Published
+
+

December 3, 2023

+
+
+ + +
+ + + +
+ + +
+

SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization

+
+

This post is part 4 of “SLML” - Single-Line Machine Learning.

+

To read the previous post, check out part 3. If you want to keep reading, check out part 5.

+
+

Now that I had a repeatable process to convert JPEGs into stroke-3, I decided to start training models with my first dataset (which I called look_i16).

+
+

Filtering out Short Strokes

+

The training data in my first dataset, look_i16, had lots of tiny strokes mixed in with longer ones, and no inherent order to them.

+
+
+
+
+
+

+
+
+

+
+
+
+
+Figure 1: Samples from dataset look_i16. +
+
+
+

Unsurprisingly, my first models produced odd smatterings composed of many small strokes.

+
+
+
+
+
+

+
+
+

+
+
+

+
+
+
+
+Figure 2: Generated outputs from SketchRNN trained on dataset look_i16. +
+
+
+

As a quick experiment, I tried just filtering out any really short strokes out of the dataset - I decided to iterate through each of the 1300 drawings in the training set, and filter out any strokes with less than 10 points - I called this dataset look_i16__minn10.

I recognized that I’d need a more systematic solution to improve my training data, but I wanted to try a quick improvement before going deeper. See part 5 for how I updated my preprocessing to reduce the number of strokes in my training data.
+
+
+
+ +
+
+Figure 3: Distribution of num_strokes and num_points in datasets look_i16 and look_i16__minn10. Both datasets have 1300 entries in the training set. +
+
+
+

Note that the average number of strokes in look_i16 is around 40, while it’s closer to 10 in look_i16__minn10. It seems that there were many very short strokes in the training data. I also simplified the drawings more aggressively for look_i16__minn10 by increasing RDP’s epsilon parameter to 1.0 when preprocessing the data, which further reduced the number of points per drawing.

+
+
+
+ +
+
+Figure 4: Training and validation loss metrics for look_i16 (pink) and look_i16__minn10 (yellow). +
+
+
+

When training models, I’m measuring the reconstruction loss. I feed in a drawing, the model encodes it into an embedding vector, and then decodes the embedding vector back into a drawing. I can compute the loss at each step in the reconstructed drawing compare to the original. Periodically, after processing several batches of training data, I compute the reconstruction metrics on a validation set. This is a portion of the dataset I’m not using to actually update the weights of the model during training.

+

By comparing the reconstruction loss on the training set vs. the validation set over time, I can identify when the model starts “overfitting”. Intuitively, if the model is learning to perform better on the training data while performing worse on the validation data, that means it is effectively memorizing the training set rather than learning to generalize its learnings to drawings it wasn’t trained on.

+

The model trained on look_i16__minn10 performed slightly better than the model trained on look_i16 in terms of the error when reconstructing a drawing. It’s visible in Figure 4 that the loss values were lower, and the validation loss didn’t start to increase until slightly later.

+
+
+
+
+
+

+
+
+

+
+
+

+
+
+
+
+Figure 5: Generated outputs from SketchRNN trained on dataset look_i16__minn10. +
+
+
+

The results produced after training on look_i16__minn10 were much less chaotic. While they didn’t resemble coherent drawings, this was the first time I spotted some elements of my drawing style (head shape, eye style, lips, chin).

+
+
+

Layer Normalization

+ +

The Magenta team had recommended using Layer Normalization and Recurrent Dropout with Memory Loss.

+

I noticed that when I let the trainer run overnight, I’d get wild spikes in the training loss. I decided to start with Layer Normalization.

+
+
+
+ +
+
+Figure 6: Training and validation loss metrics from training on look_i16__minn10 without layernorm (yellow) and with layernorm (burgundy: learnable=True, gray: learnable=False). The yellow line without layernorm enabled is the same as in Figure 4, but this graph shows more than 150,000 train steps while the previous graph showed only 7,000 train steps. +
+
+
+

Adding layer normalization showed a dramatic difference, visible in Figure 6. The yellow line (without layer norm) has a massive increase in validation loss, and many big spikes, while the burgundy and gray lines (with layer norm integrated) have much lower validation loss and don’t have any comparable spikes.

+
+
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+Figure 7: Generated samples from model trained with Layer Normalization. +
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Recurrent Dropout

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The results from the layernorm models in Figure 7 had some hints of my drawing style, while still struggling to form coherent drawings.

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Next, I kept layernorm enabled and enabled recurrent dropout. I ran one separate runs with and without stroke augmentation enabled.

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+Figure 8: Training and validation loss metrics for layernorm-only models (burgundy and gray) alongside models trained with recurrent dropout (green) and with stroke augmentation (pink). +
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Compared to the layernorm-only models (burgundy and gray), the one recurrent dropout model (green) achieved a lower validation loss relatively quickly, before starting to overfit.

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The model trained with recurrent dropout and stroke augmentation (pink) clearly performed worse than with recurrent dropout (in terms of higher validation loss).

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+Figure 9: Generated samples from model trained with Recurrent Dropout as well as Layer Normalization. +
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The resulting generations from model with layernorm and recurrent dropout in Figure 9 weren’t obviously better or worse than those from the layernorm-only model in Figure 7.

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+

If you want to keep reading, check out part 5 of my SLML series.

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+ + + + + \ No newline at end of file diff --git a/blog/posts/slml_part5.html b/blog/posts/slml_part5.html new file mode 100644 index 0000000..801619d --- /dev/null +++ b/blog/posts/slml_part5.html @@ -0,0 +1,1201 @@ + + + + + + + + + + + + +andrewlook - SLML Part 5 - SketchRNN Experiments: Path Preprocessing + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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SLML Part 5 - SketchRNN Experiments: Path Preprocessing

+
+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ Experiments training SketchRNN on my dataset of single-line drawings. +
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+ + +
+ +
+
Author
+
+

Andrew Look

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+ +
+
Published
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+

December 14, 2023

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+ + +
+

SLML Part 5 - SketchRNN Experiments: Path Preprocessing

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+

This post is part 5 of “SLML” - Single-Line Machine Learning.

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To read the previous post, check out part 4. If you want to keep reading, check out part 6.

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+
+

Path Joining

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Despite some improvement within the strokes, my latest models were still producing many short strokes. So I figured that improving my dataset would give better results than fiddling with hyper-parameters or my model.

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Looking at my training data, I returned to an issue from the stroke3-conversion process: autotrace hadn’t realized that the different segments of my lines were actually connected. It was producing a series of small, centerline-traced segments, as seen in Figure 1 (a).

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I’d need some preprocessing to connect the smaller strokes into larger strokes, so that my SketchRNN model would learn to generate long contiguous single-line drawings.

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+(a) Original: 22 strokes +
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+(b) After path-joining: 4 strokes +
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+(c) After path-splicing: 2 strokes +
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+Figure 1: Comparison of number of strokes before and after preprocessing, with one color per stroke. +
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My first preprocesing improvement was simple algorithm that I called “path joining”.

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Each of my drawings is represented as a collection of line segments, each with a start and end point. I try to find the shortest connection I can draw to connect two segments. Starting with the longest stroke, I compare it each of the other strokes and calculate the minimum distance between the start and end points of the line segments. After going through all the strokes in a drawing, I connect the two strokes with the shortest gap between their endpoints. I repeat this process, joining the strokes until no strokes remain with endpoints more than 30 pixels apart.

I set an upper bound on the path-joining distance of 30 pixels (in a 200x200 pixel image) was the maximum distance I observed made sense before I started to erroneously join strokes that were too far apart.
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Though path-joining improved the average stroke length as seen in Figure 1 (b), I noticed some drawings had large contiguous strokes that weren’t getting connected. I realized that while the strokes were close together and in some cases touching, their start and end points were far apart from each other.

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Path Splicing

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My next preprocessing improvement, “path splicing”, would run after “path joining” and attempt to address this problem.

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After path joining leaves a smaller number of strokes, I want to find the shortest connections to combine multiple longer strokes. Starting with the longest path, I look for a smaller stroke that I could “splice” into the middle of the larger stroke. For each candidate stroke, I’d step through each point on the larger stroke and compare its distance from the start and end points of the shorter paths. When I found the smallest gap, I would “splice” the shorter line into the longer path at the point with the smallest distance.

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While not every drawing was turned into a single stroke, this was a big improvement, as seen in Figure 1 (c).

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+Figure 2: Previous dataset look_i16__minn10, left, compared to path-spliced dataset v2-splicedata, right. +
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Based my earlier experiment showing the benefits of short-stroke exclusion, I wanted to try training a model on this new dataset.

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Training after Path-Splicing

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I ran the preprocessing on the whole dataset. Next, I filtered the original 1300 drawings to exclude any drawings with more than 6 strokes, resulting in a new dataset of 1200 drawings that I named v2-splicedata. Then I trained a new set of models, keeping the layernorm and recurrent dropout enabled.

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+Figure 3: Training and validation loss metrics of recurrent dropout model (light green) alongside models trained on this joined/spliced dataset (turquoise/dark green). +
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After training some models on a path-spliced dataset the training metrics aren’t a perfect comparison, since the content of the validation also changed when I applied path-splicing. Still, I can see from the validation loss graph that the model started to overfit around 1000 steps. The roughly similar shapes of the early train and validation loss curves at least convinced me that the model hadn’t gotten dramatically worse.

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+Figure 4: Generated samples from a model trained on path-spliced dataset. +
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Qualitatively, the generated drawings showed a big improvement. The model had learned to generate longer unbroken strokes. I started to notice results that looked more like single-line drawings of people. In some cases they look surreal, but I started to see some more recognizable face elements and in some cases full bodies.

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+Figure 5: Iterating through full dataset, with 3 frames per drawing: original, path-joined, and path-spliced. I liked the slight jitter in the animation as I watched the drawings go from colorful (many strokes) to fully blue (single stroke). +
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Bounding Boxes

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My last remaining problem: there were some pages where I’d make 4 or 5 separate drawings that had nothing to do with each other, and had a lot of space between them.

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I wanted to separate those into separate examples before making a training dataset, for 2 reasons:

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  1. To get more training examples from my limited number of scanned drawings.
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  2. To avoid confusing the model when some examples are complex drawings with multiple people, and other drawings are just one person.
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If the model sometimes learngs to make a complete drawing and then start a second unrelated drawing, how does the model know when to finish a drawing vs. when to start a second drawing alongside the first one?

My intuition for my third preprocessing improvement, “bounding box separation”, came when I noticed how unrelated strokes within a drawing didn’t overlap much, and tended to be spaced far apart. For each of the stroke within a drawing, I’d determine its top/bottom/left/right extremes and consider a box around each stroke as in Figure 6.

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+Figure 6: Example drawing with multiple unrelated strokes. +
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Then for each combination of bounding boxes, I’d compute a ratio of the area of their overlap compared to the area of the non-overlapping parts as in Figure 7.

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+Figure 7: Intersection over Union (“IOU”) Metric. +
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If the ratio exceeds some threshold, I consider them to be part of the same drawing, and I merge the bounding boxes as in Figure 9 (a). Combining that merged bounding box along with all the remaining bounding boxes, I repeat the process until no bounding-box intersections remain that exceed the threshold.

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IOU = 0.03
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IOU = 0.12
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+(a) Merged BBoxes +
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+Figure 8: Comparison of high-IOU vs. low-IOU bounding box intersections. +
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Also, if any bounding boxes have a really small area, I just drop them. It turns out this helps exclude small scribbles of text that were ending up in my training data as separate strokes - for example, the page number at the bottom right of Figure 9 (a).

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Original: 4 Strokes
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+(a) Tiny page number: 1 stroke +
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Merge: 3 Strokes
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Result
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+Figure 9: Example from training set of a very small stroke being removed. +
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Dataset 20231214

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Once I have all the separated strokes, I save then into a new dataset as separate drawings. While the previous dataset v2-splicedata only has 1200 drawings, the new bounding-box separated dataset 20231214 has 2400 drawings.

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+Figure 10: Comparison of previous dataset v2-splicedata with 20231214 and 20231214-filtered. +
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The dataset grew from 1200 to 2400 drawings because pages containing multiple distinct drawings (such as Figure 11 (a)) were divided into separate rows in the new training set (like Figure 11 (b), Figure 11 (c), Figure 11 (d)).

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+(a) Original +
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+Figure 11: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214. +
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The new separated drawings looked more visually consistent with the average drawing out of the training set as a whole. The new dataset contains far more single-character drawings, so I expect that the RNN will benefit on learning from a set of drawings with more similar subject matter.

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I hypothesized that the bbox-separated dataset will be a big improvement because of the consistency of the resulting drawings. Before, the model was learning that sometimes drawings end after one person is drawn, but sometimes we move the pen and start a new person.

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Filtering by Number of Points

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Looking at the distribution of number of points per drawing in the new dataset 20231214 in the bottom-middle chart in Figure 10, I noticed a long tail of drawings with more than 500 points. To explore this, I created a variant dataset 20231214-filtered.

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Dataset 20231214-filtered which was filtered down to 1800 drawings, keeping only drawings with more than 50 and less than 300 points as you can see in the bottom-right chart in Figure 10.

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Wondering if drawings with many points were less likely to have consistent subject matter (individual people) than the rest of the training set, I sampled some drawings with over 300 points. While drawings such as Figure 12 (a) and Figure 12 (b) were obvious candidates to exlcude, there were valid drawings near the margin such as Figure 12 (c) that I would be excluding after I picked a threshold.

Possible Improvement: Filtering by visual embedding might be more reliable to exclude complex drawings
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+(a) 1093 points +
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+(b) 1127 points +
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+(c) 329 points +
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+Figure 12: Drawings with over 300 points. +
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I also looked at the low end of the distribution and found drawings with under 50 points. There were nonsensical squiggles such as Figure 13 (a) that I was happy to exclude. There were cases below the 50 point threshold such as Figure 13 (b) and Figure 13 (c) that looked recognizable as my drawings, but had been simplified by RDP too aggressively.

Possible Improvement: Applying RDP after bounding box separation rather than before.
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+(a) 21 points +
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+(b) 30 points +
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+(c) 41 points +
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+Figure 13: Drawings with under 50 points. +
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Training after Bounding Boxes

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+Figure 14: Training and validation loss metrics comparing model trained on 20231214 (beige) compared with models trained on dataset 20231214-filtered with and without stroke augmentation (green, burgundy). +
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After training on the unfiltered dataset 20231214, I noticed that some drawings were devolving into a sequence of repeated face features without forming a cohesive person or face, as in Figure 15.

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+Figure 15: Generated results after training on unfiltered dataset 20231214. +
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The model results in Figure 16 after training on filtered dataset 20231214-filtered appear qualitatively better to me. The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.

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+Figure 16: Generated results after training on filtered dataset 20231214-filtered. +
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The model results in Figure 17 after training with stroke-augmentation on filtered dataset 20231214-filtered appear to be roughly of similar quality to . The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.

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+Figure 17: Generated results after training on filtered dataset 20231214-filtered, with stroke augmentation enabled. +
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If you want to keep reading, check out part 6 of my SLML series.

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+ + + + + + \ No newline at end of file diff --git a/blog/posts/slml_part6.html b/blog/posts/slml_part6.html new file mode 100644 index 0000000..a074bf9 --- /dev/null +++ b/blog/posts/slml_part6.html @@ -0,0 +1,1104 @@ + + + + + + + + + + + + +andrewlook - SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering

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+
singleline
+
machinelearning
+
slml
+
+
+ +
+
+ Experiments training SketchRNN on my dataset of single-line drawings. +
+
+ + +
+ +
+
Author
+
+

Andrew Look

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+
+ +
+
Published
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+

January 4, 2024

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+ + +
+ + + +
+ + +
+

SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering

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This post is part 6 of “SLML” - Single-Line Machine Learning.

+

To read the previous post, check out part 5.

+

If you want to keep reading, check out part 7.

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+
+

New Dataset: 20240104

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Though I’d grown my training dataset by bounding-box separating single pages into multiple drawings, I was concerned about the tradeoff of filtering drawings out versus having a more coherent dataset with similar subject matter.

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To make up for more aggressive filtering, I decided to incorporate several additional sketchbooks I scanned and labeled into a new dataset epoch 20240104.

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Differences in dataset 20240104 compared to dataset 20231214:

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  1. More raw input drawings
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  2. Same preprocessing, with a modified “adaptive” RDP simplification 1.
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RDP and Sequence Length

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In previous datasets, I had chosen the same strength of RDP line simplification for the whole dataset. Some drawings had been simplified reasonably, but other had been simple to begin with and ended up as a series of straight lines much sharper than the original curves.

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+Figure 1: 30 points +
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For the remaining drawings, I ran the RDP algorithm with varying values for its epsilon parameter, until the number of points dipped under 250. Then I saved the result as a zipped numpy file.

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Training on 20240104

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+Figure 2: Training and validation loss metrics from models trained on 20240104 using visual filtering on the bounding-box separated drawings, with maxiumum sequence lengths of 200 (gray) and 250 (blue). +
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After training on 20240104, the validation losses (gray and blue lines in Figure 2) seemed substantially lower than the validation losses from the models trained on the previous dataset (beige, light green).

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Overly Complex Drawings

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One failure mode I noticed in the results generated after training on the bounding-box separated dataset 20231214-filtered was that some generated drawings had knotted, gnarled lines as in Figure 3.

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+Figure 3: Generated examples with too much complexity. +
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Reviewing the bounding box-separated dataset I noticed that some drawings were of one figure, and some drawings were patterned or chained with many faces.

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+Figure 4: Drawings with over 300 points. +
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Sometimes I make patterns by chaining repeating sequences of faces into long continuous lines. I wondered whether the presence of this kind of drawing in the training data was occasionally encouraging the model to make long continuous chains rather than drawing a single person.

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chains example
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+Figure 5: Example of a “diagonal chain” single-line pattern I draw. +
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I wanted to exclude those patterns/chains from my training data, so I could give my model the best chance of learning to draw one person at a time.

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Similarity-Filtered Dataset

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I decided to make a subset 20240104-furtherfiltered of dataset 20240104.

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My plan was to compute embeddings for every bounding-box separated drawing in dataset 20240104. Then I could K-Means clustering on them, and decide which clusters I wanted to exclude in bulk. 2

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Right away I spotted the “too complex” chained line drawings in cluster 0 (Figure 6 (a)). There were also several chained line drawings in cluster 3 (Figure 6 (b)) mixed in with some squarish horizontal drawings that I was happy to exclude from my training set, as they looked too different from my more typical standalone drawings of individual faces/people.

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+Figure 6: Clusters with drawings that were “too complex”. +
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I also noticed some clusters with drawings that were “too simple”. It seems like many of the drawings in cluster 13 (Figure 7 (a)) were stray lines accidentally separated from any context by the bounding-box preprocessing. Cluster 9 (Figure 7 (b)) had many similar nonsensical lines, though they were mixed in with some false positives - valid drawings that I’d prefer to keep in the dataset.

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+Figure 7: Clusters with drawings that were “too simple”. +
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I was excited to notice some distinct categories in my drawings, seeing them from a distance.

In the future, as I add more drawings, it’d be great to explicitly label these drawing categories and even train separate models on them. For now, given that I don’t have enough drawings scanned yet, I’m choosing to keep them in one dataset.
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Clusters 1, 4, and 11 (in Figure 8 (a), Figure 8 (c), and Figure 8 (i), respectively) all have vertical, narrow, whole-body figures.

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Cluster 2, in Figure 8 (b), mostly has rounder compositions of individual faces without a complete body.

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Clusters 8 and 15, in Figure 8 (g) and Figure 8 (l), seem to have more complex drawings but mostly still contain drawings of standalone people.

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The remaining clusters contain reasonably uniform drawings of standalone people, in vertical compositions, that are not too narrow. Hovering your mouse over these links Figure 8 (d), Figure 8 (e), Figure 8 (f), Figure 7 (b), Figure 8 (h), Figure 8 (j), Figure 8 (k).

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+Figure 8: Clusters with drawings that looked good to me. +
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Training on Filtered Dataset

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The remainder of the clusters, in Figure 8, looked “good enough” for me to include in my training set. I all other clusters, and saved a filtered-down dataset as 20240104-furtherfiltered.

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Compared to dataset 20240104, it’s clear in the top row of Figure 9 that in the filtered dataset variant, the distribution of number of strokes has shifted away from the long tail of many-stroke drawings.

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+Figure 9: Comparing to unfiltered dataset 20240104 (2100 drawings) to the filtered dataset 20240104-furtherfiltered (1300 drawings). +
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Comparing the training metrics in Figure 10 for the model trained on filtered dataset 20240104-furtherfiltered (in red) with the previous model runs on unfiltered dataset 20240104 (in gray and blue) is not a perfect comparison. Since the validation set for 20240104-furtherfiltered was also filtered, it’s a smaller (and likely noisier) validation set. Still, the new model’s validation loss was roughly within the bounds of what I expected.

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+Figure 10: Training and validation loss metrics from models trained on 20240104-furtherfiltered using visual filtering on the bounding-box separated drawings (red). +
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Qualitatively, the generated results after visual similarity filtering were significantly improved.

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+Figure 11: Generated samples after training with visual filtering on bbox-separated dataset. +
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Even the generated results that looked less like people/faces to me still had appealing curves and flowing patterns, which I recognize from my own drawing style.

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+Figure 12: Generated samples after training with visual filtering on bbox-separated dataset. +
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If you want to keep reading, check out part 7 of my SLML series.

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Footnotes

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  1. based on what I observed when filtering the bounding-box dataset by number of points↩︎

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  2. similar to what I did with full sketchbook pages in part 1↩︎

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SLML Part 7 - SketchRNN Experiments: Data Augmentation

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singleline
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machinelearning
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slml
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+ Experiments training SketchRNN on my dataset of single-line drawings. +
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Andrew Look

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January 4, 2024

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SLML Part 7 - SketchRNN Experiments: Data Augmentation

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This post is part 7 of “SLML” - Single-Line Machine Learning.

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To read the previous post, check out part 6.

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Preprocessing for Data Augmentation

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I was curious about why my earlier experiment using stroke augmentation didn’t show benefits (and in some cases made the models perform much worse on validation metrics). In Figure 1, it’s clear that the pink line (without stroke augmentation) has a faster-growing validation loss after the point of overfitting.

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+Figure 1: Training and validation loss metrics for layernorm-only models (burgundy and gray) alongside models trained with recurrent dropout (green) and with stroke augmentation (pink). +
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Those experiments used stroke augmentation as a hyperparameter on the dataset - so at runtime when the model started training, it would modify the drawings before assembling them into batches for the trainer.

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I decided to create a dataset where I ran the augmentation ahead of time and saved the result so I could inspect the results more closely. For each entry in the dataset, I included the original drawing as well as a seperate entry for the drawing in reverse with a “flipped” sequence. This doubled the size of the dataset.

Another idea I’d like to try is applying local distortions or dilations, since that would change the directions certain strokes take without losing the overal subject of the drawing. Radial Basis Functions or Warp Grids seem like promising approaches to try.
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Then I took each drawing and randomly:

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  • Applied stroke agumentation to drop points, with a probability up to 0.5.
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  • Randomly rotated -15 to 15 degrees
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  • Randomly scaled between 100% to 120% of original size.
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Some examples of the augmentations are visible in Figure 2.

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Original drawing
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After “Stroke Augmentation” drops points from lines at random
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Randomly rotated and scaled
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+Figure 2: Examples of data augmentation. +
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Training With Augmented Dataset

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Comparing the validation loss metrics in Figure 3 from the models trained on augmented dataset (purple) with my previous round of best-performing models, the augmented dataset takes longer to converge but the validation loss keeps sloping downwards. This is encouraging to me since it seems like the more diverse drawings in the augmented dataset are helping the model learn to generalize more than pervious models trained on non-augmented datasets.

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+Figure 3: Training and validation loss metrics from augmented dataset 20240221-dataaug10x (purple). +
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As a small side experiment, I wanted to confirm my finding in part 4 that layer norm caused a meaningful improvement. Looking at the loss metrics in Figure 4, it appears that disabling layernorm (light green) causes a significant drop in performance. Disabling recurrent dropout doesn’t have a significant effect, as far as I can tell.

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+Figure 4: Training and validation loss metrics from augmented dataset 20240221-dataaug10x (purple) compared to variants without layernorm (light green) and without recurrent dropout (magenta / dark red). +
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SLML Part X - xyz

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singleline
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machinelearning
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slml
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+ Using visual embeddings to filter thousands of images. +
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Andrew Look

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November 1, 2023

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For years, I’ve maintained a daily practice of making single-line drawings in my sketchbooks.

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home

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Hi, I’m Andrew Look.

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I’m an ML engineer, ex-startup founder, and part-time artist based in Brooklyn, NY.

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You can learn more about me on my “about” page, or you can check out my “resume” page.

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Projects

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+Single-Line Machine Learning +
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Posts

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SLML Datasets Overview + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

SLML Datasets Overview

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

The Datasets

+
https://storage.googleapis.com/singleline-datasets/v1/look_f16.npz
+https://storage.googleapis.com/singleline-datasets/v1/look_f16__minn10_epsilon1.npz
+https://storage.googleapis.com/singleline-datasets/v1/look_i16.npz
+https://storage.googleapis.com/singleline-datasets/v1/look_i16__minn10_epsilon1.npz
+
+https://storage.googleapis.com/singleline-datasets/v1/v2-splice-maxstrokes5.npz
+https://storage.googleapis.com/singleline-datasets/v1/v2-splice-maxstrokes6.npz
+
+https://storage.googleapis.com/singleline-datasets/v1/epoch-20231214-trainval.npz
+https://storage.googleapis.com/singleline-datasets/v1/epoch-20231214-filtered-trainval.npz
+
+https://storage.googleapis.com/singleline-datasets/v1/epoch20240104_trainval08.npz
+https://storage.googleapis.com/singleline-datasets/v1/epoch20240104_trainval09.npz
+
+https://storage.googleapis.com/singleline-datasets/v1/epoch20240104_furtherfiltered_trainval08.npz
+https://storage.googleapis.com/singleline-datasets/v1/epoch20240104_furtherfiltered_trainval09.npz
+
+https://storage.googleapis.com/singleline-datasets/v1/epoch20240221_expanded10x_trainval.npz
+
+
DS_0 = 'look_i16.npz'
+
+DS_1 = 'look_i16__minn10_epsilon1.npz'
+
+DS_2 = 'v2-splice-maxstrokes6.npz'
+
+DS_3 = 'epoch-20231214-trainval.npz'
+DS_4 = 'epoch-20231214-filtered-trainval.npz'
+
+DS_5 = 'epoch20240104_trainval09.npz'
+
+DS_6 = 'epoch20240104_furtherfiltered_trainval09.npz'
+
+DS_7 = 'epoch20240221_expanded10x_trainval.npz'
+
+
+
BASE_PATH = '/Users/al/code/_svg/singleline_models/data/look'
+
+def load_ds(fname, base_path=BASE_PATH):
+    payload = np.load(Path(base_path) / fname, encoding='latin1', allow_pickle=True)
+    return payload['train'], payload['valid']
+
+
+
train0, valid0 = load_ds(DS_0)
+train1, valid1 = load_ds(DS_1)
+train2, valid2 = load_ds(DS_2)
+train3, valid3 = load_ds(DS_3)
+train4, valid4 = load_ds(DS_4)
+train5, valid5 = load_ds(DS_5)
+train6, valid6 = load_ds(DS_6)
+
+
+
len(train5), len(train6)
+
+
(2100, 1300)
+
+
+
+
len(train3), len(train4)
+
+
(2400, 1800)
+
+
+
+
len(train1), len(train2)
+
+
(1300, 1200)
+
+
+
+
from singleline_dataset.transforms import *
+
+
+def stroke_summary_df(dataset):
+    summary = [
+        {
+            "idx": i,
+            "num_points": len(deltas),
+            "num_strokes": len(deltas_to_strokes(deltas)),
+        }
+        for i, deltas in enumerate(dataset)
+    ]
+    # by_num_strokes = sorted(summary, key=lambda k: k["num_strokes"], reverse=True)
+    df = pd.DataFrame(summary)
+    return df
+
+
+
df0 = stroke_summary_df(train0)
+df1 = stroke_summary_df(train1)
+df2 = stroke_summary_df(train2)
+df3 = stroke_summary_df(train3)
+df4 = stroke_summary_df(train4)
+df5 = stroke_summary_df(train5)
+df6 = stroke_summary_df(train6)
+
+
+
df0.num_points.hist()
+# df0.num_strokes.hist()
+
+
+
+

+
+
+
+
+
+
df1.num_points.hist()
+
+
+
+

+
+
+
+
+
+
df2.num_points.hist()
+
+
+
+

+
+
+
+
+
+
df3.num_points.hist()
+
+
+
+

+
+
+
+
+
+
df4.num_points.hist()
+
+
+
+

+
+
+
+
+
+
df5.num_points.hist()
+
+
+
+

+
+
+
+
+
+
df6.num_points.hist()
+
+
+
+

+
+
+
+
+
+
import matplotlib.pyplot as plt
+
+fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))
+
+ax1.set_title('num_strokes (epoch=20231214)')
+df3.num_strokes.hist(ax=ax1)
+ax2.set_title('num_points (epoch=20231214)')
+df3.num_points.hist(ax=ax2)
+ax3.set_title('num_strokes (epoch=20231214-filtered)')
+df4.num_strokes.hist(ax=ax3)
+ax4.set_title('num_points (epoch=20231214-filtered)')
+df4.num_points.hist(ax=ax4)
+
+
+
+

+
+
+
+
+
+
len(df3), len(df4)
+
+
(2400, 1800)
+
+
+
+
df3[df3.num_points > 300]
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
idxnum_pointsnum_strokes
8810348
99112716
16163294
26263204
33333314
............
237223723152
23762376109316
238923893234
23952395138916
23962396123416
+ +

313 rows × 3 columns

+
+
+
+
+
+
plot_strokes(deltas_to_strokes(train3[9]))
+
+
+
+

+
+
+
+
+
+
plot_strokes(deltas_to_strokes(train3[16]))
+
+
+
+

+
+
+
+
+
+
plot_strokes(deltas_to_strokes(train3[2376]))
+
+
+
+

+
+
+
+
+
+
df3[df3.num_points < 50]
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
idxnum_pointsnum_strokes
2121231
2424281
3232201
5252491
6060351
............
23702370411
23822382201
23832383321
23922392381
23942394301
+ +

379 rows × 3 columns

+
+
+
+
+
+
plot_strokes(deltas_to_strokes(train3[21]))
+
+
+
+

+
+
+
+
+
+
plot_strokes(deltas_to_strokes(train3[2394]))
+
+
+
+

+
+
+
+
+
+
plot_strokes(deltas_to_strokes(train3[2370]))
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/jupyter/index.html b/jupyter/index.html new file mode 100644 index 0000000..dcc25f3 --- /dev/null +++ b/jupyter/index.html @@ -0,0 +1,662 @@ + + + + + + + + + +andrewlook - jupyter notebooks + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

jupyter notebooks

+
+ + + +
+ + + + +
+ + + +
+ + + + + + +
+ + + + + + + + + +
+Title + +Date +
+
+No matching items +
+
+ +
+ + + + + \ No newline at end of file diff --git a/jupyter/index.xml b/jupyter/index.xml new file mode 100644 index 0000000..a2e432b --- /dev/null +++ b/jupyter/index.xml @@ -0,0 +1,15 @@ + + + +andrewlook +https://andrewlook.com/notebooks/jupyter/ + +Andrew Look Personal Website +quarto-1.4.551 +Wed, 13 Mar 2024 18:49:38 GMT + + diff --git a/jupyter/overview.html b/jupyter/overview.html new file mode 100644 index 0000000..b9cc412 --- /dev/null +++ b/jupyter/overview.html @@ -0,0 +1,743 @@ + + + + + + + + + + +andrewlook - Overview: ML Single-Lines + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

Overview: ML Single-Lines

+
+ +
+
+ Training a sequence model on a dataset of my single-line drawings. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

Introduction to the Intersection of Machine Learning and Art

+ +

I’ve always been fascinated by machine learning and art. There’s a magic to me in how machines represent information. I believe that making art to explore this can teach us something about how our own minds work, or at least make us think.

+

I started painting when I was stressed out during my first startup. Teaching myself, I wanted to constantly make more ambitious and difficult paintings. My goal was to never repeat myself, always do something bigger and better. I’d rarely get in flow since it felt like I was always pushing uphill. Soon afterwards, I was working with machine learning and discovered deepdreams, style transfer, GANs. I found myself drawn to art projects using these technologies, making paintings from their outputs.

+
+
+

Shift to single-line drawing as a creative constraint.

+ +

Eventually my paintings of ML outputs became cumbersome, and I needed a constraint to keep my art practice fun and interesting. I found single-line drawing to be challenging, engaging, and expressive.

+
+
+

Concept of Training SketchRNN with Personal Art

+ +

When SketchRNN came out from Google, I was struck by a demo that lets you start a drawing, and a model trained on the “collective consciousness” of thousands of people can complete your drawing in diverse ways.

+

But the model was trained on 20-second sketches from non-artists, so the model mainly draws stick figures (or drawings of similar skill level). I thought it’d be awesome to train this on a dataset of my own drawings.

+

Particularly for single-line drawings, the trajectory of the drawing is so essential to the medium that SketchRNN seemed like the perfect representation. By comparison, GANs (and later, diffusion models) generate all pixels simultaneously - this misses the aspect of single-line drawings flowing and taking into account the previous path of the line in deciding where to go next.

+

When I looked further into SketchRNN and spoke with its creator, David Ha, he told me I’d “only” need a few thousand drawings. I had maybe 100 scans on my computer at the time. I forgot about the SketchRNN goal, but continued filling my sketchbooks.

+
+
+

Sketchbook Management During COVID

+ +

My sketchbook scanning had become a little ritual that I’d do. It was funny to take one of the most offline, internal, organic things I do - sitting quietly, drawing in my sketchbook - and systematically digitize it. In the back of my mind, I imagined that being able to visually search them or cluster them would help me see patterns in my drawings over time. But it’d be a lot of engineering work, so I never quite got around to it.

+
+
+

Personal Rituals and Systematic Organization of Artwork

+ +

But at the same time, it’s this personal ritual. I usually make a collage on the cover of my sketchbook, with plane tickets or scraps of paper capturing what was going on in my life while that sketchbook was in progress. I put a post-it note with the start and end dates of the sketchbook, and give it a number (like “SB48” for sketchbook number 48). I number each of the pages in the bottom corner closest to the binder rings (this is a practical step, so that after I scan all the pages I can easily see which page it is within the sketchbook).

+

I bought a book scanner, since using a flatbed scanner took too long. I just turn it on, flip through the pages, and I automatically get separate images per page. I scan the pages, and throw them all in a folder.

+

I name the folder “sb48”. Then I flip through the files, naming the pages to correspond to the page numbers - for example, “005.jpg” for page 5, since I want the file names to sort nicely. If I want to refer to a drawing, I can just type “sb48p005.jpg” and find what I’m looking for.

+

+

Then I go through and use “tags”. Yellow for “notes” (diagrams/mindmaps), Red for anything “private”, Orange for “extras” (bad scans, happy accidents, alternate scans), Green for favorite drawings, and anything untagged is assumed to be art. I separate the covers too.

+

Then, I create 4 sub-folders inside “sb48”: - cover - art - notes - xtra - priv

+

+

And I sort by tags so it’s easy to move them into the right sub-folders. Then I move the folder for sketchbook 48 into a big folder alongside all of my other sketchbooks.

+

+

Engineer friends have asked why I don’t automate this. I’ve streamlined it somewhat, so that I have keyboard shortcuts and can move fairly quickly.

+

I don’t mind the manual effort though - I find that spending a little extra time seeing my old drawings helps me fall back in love with my art practice, especially when it’s started to feel stagnant or burdensome. I flag my favorites, get new ideas. Or I remember what problems I was working through when I see my notes and engineering diagrams.

+
+
+

Revisiting the SketchRNN Goal and Dataset Preparation

+ +

3 or 4 years went by. I kept up my ritual of scanning my sketchbooks whenever I finished one, every few weeks or months (sometimes in a matter of days, if I was in a drawing frenzy).

+

At some point I remembered SketchRNN, and did a file count. I saw that I had at least a couple thousand drawings scanned already - it might actually be possible to train this model I’d been dreaming about!

+

The first step (and often the most time-consuming in machine learning projects) was to prepare the dataset.

+
+
+

Separating Artwork Using Machine Learning

+ +

My sketchbook filing system let me separate out notes and diagrams from artwork - which was great. However, I still had a lot of watercolor paintings mixed in with my single-line drawings. Reluctant to go through and hand-sort the watercolors one by one, I thought I could use machine learning to make this process faster.

+

I took a computer vision model (resnet34) trained to identify photographs of everyday objects and animals (think cat, dog, truck, toaster). I used this to compute “embeddings” for each of the images in the “art” folders of my sketchbooks. Embeddings are like a compressed version of the essential information need for the model to identify what it’s trained to look for, and images with similar embeddings tend to contain similar content.

+

My goal was to get embeddings of all my images, and then group them by similarity - the hope being that my watercolors would separate cleanly from the thousands of single-line drawings.

+

Since the off-the-shelf computer vision model was trained on photographs, it didn’t do as well with black-and-white scans. So I fine-tuned the model on my own data, in order to teach the model to work within the context of scanned pages. Since I had lots of pages already bucketed into folders labeled art, notes, and covers, I made a dataset of images along with their category. I trained a model on this data.

+

Then I took those embeddings.

+

When I visualized the embedding space, the notes were all grouped together and the art also appeared in a cleanly separated group. This was a good start, and meant that the model had learned to represent the difference between these scanned pages in a meaningful way.

+

Next, I ran K-Means clustering on these embeddings. This finds a number of groupings that minimize the distance of each point to its nearest group, for whatever number of groups was requested. I tried 16 groups to start.

+

When I looked through each of the groups, I was thrilled - several of the groups contained only watercolors, many contained only drawings.

+

I gave each of the clusters a label (0_drawings, 1_watercolors, etc) and then assigned each page to its cluster and attached a label based on the cluster. Then I moved each page into a folder corresponding to its label. I went through every single page manually to see if it had been mis-categorized. A surprisingly small number were out of place, but I moved them into a new folder that I called “hand-labeled” - these would be my ground-truth.

+

Now I had well over a thousand scans of single-line drawings in my folder 0_drawings, with all of the watercolors separated out.

+ +
+
+

Challenges in Vectorizing Scans for SketchRNN

+ +

Vector graphics are notorious in the machine learning world for being a pain to work with. Instead of a simple grid of pixel colors, as you get in a PNG or JPEG file, SVG’s have paths composed of various key points.

+

SketchRNN requires input in a format called stroke-3. The idea is to represent pen strokes as a series of triplets, where each triplet in the sequence contains: - delta_x: how much did the pen move left or right? - delta_y: how much did the pen move upwards or downwards? - lift_pen: does this move continue the active stroke, or “lift the pen off the page” and move to the beginning of a new stroke?

+

But before I could convert my JPEG scans into stroke-3, I had to vectorize them (convert them into SVG paths, which could then be transformed into stroke-3).

+

My first obstacle was that all of the automated ways of vectorizing scans represented my lines as a closed shape - so one line would be represented by two paths with no thickness around the outside of the line, filled in with black. I discovered that what I wanted was a “centerline trace” - a single path, with line thickness.

+

After trying a variety of approaches, I found an open-source library called autotrace. It’s quite painful to work with. I found an Inkscape plugin that used autotrace in a way that worked for my drawings. Adobe Illustrator has something called “Live Trace” that could work, but I ruled out the idea of Live Tracing several thousand drawings.

+

I found that I could automate the code from that inkscape plugin (it’s ugly, but I wrote a wrapper script that can apply it to my drawings and get passable results).

+

When I saw drawings come out that more or less mirrored the scanned pages, I was excited and decided to start trying to train a model.

+
+
+

Final Preparations for Training the Model

+ +

What came out was pure gibberish. A flurry of small separate lines.

+

I realized that I had a problem: autotrace hadn’t realized that the different segments of my lines were actually connected. It was producing a series of small, centerline-traced segments.

+

I’d have to go back and somehow connect them. So, I tried a simple little algorithm that I called “path joining.”

+

The idea: each of my drawings was a collection of line segments, each with a start and end point. I’d start with the longest one. Then, for each of the other line segments, I’d find the minimum distance between the start and end points of the line segments. Put simply, what’s the shortest connection I can draw to connect two segments? I found that I needed an upper bound on the distance - I’d resized all of my drawings to 200x200 pixels, so something like 15-30 pixels was the maximum distance I observed made sense before I started to get irrelevant lines connecting to each other.

+

This worked remarkably well. Still, I noticed some drawings had large contiguous strokes that weren’t getting connected. I realized that while the strokes were close together and in some cases touching, their start and end points were far apart from each other.

+

So I wrote a second algorithm, which I called “path splicing.”

+

After I’d applied path joining and had a smaller number of long strokes left over, I wanted to see which strokes should be joined together. So, I’d take the longest path, then for each of the remaining shorter paths, I’d step through each point on the longest path and compare its distance from the start and end points of the shorter paths. When I found the smallest gap, I would “insert” the shorter line into the longer path at the point with the smallest distance.

+

I reviewed the results of this - it came out better than I’d expected.

+

My last remaining problem: there were some pages where I’d make 4 or 5 separate drawings that had nothing to do with each other, and had a lot of space between them. I wanted to separate those into separate examples before making a training dataset, for 2 reasons: 1) To get more training examples from the scans I had 2) To avoid confusing the model I was training - if some examples make a complete drawing and then start a second unrelated drawing, how does the model know when to finish a drawing vs. when to start a second drawing alongside the first one?

+

So I wrote a third algorithm, which i called “bounding box separation”.

+

My intuition was that separate drawings had some space between them, and didn’t overlap much. So I’d take each of the strokes within a drawing, and determine its top/bottom/left/right extremes, so I could draw a box around each stroke. Then for each combination of bounding boxes, I’d compute a ratio of the area of their overlap compared to the area of the non-overlapping parts. If the ratio exceeds some threshold, I consider them to be part of the same drawing, and I merge the bounding boxes. Then I take that larger bounding box along with all the remaining bounding boxes, and repeat the process until no overlaps remain that exceed the threshold. Also, if any bounding boxes have a really small area, I just drop them. It turns out this helps exclude little page numbers / bits of text from ending up in my training data.

+

Once I have all the separated drawings, I save them out as separate files.

+

I noticed that some drawings were of one figure, and some drawings were patterned or chained with many faces. I wanted to exclude those patterns/chains from my training data, so I could give my model the best chance of learning to draw one person at a time.

+

So, I computed embeddings for these bounding-box separated drawings, clustered them, and got reasonably coherent groups.

+

Finally, I excluded the ones that didn’t fit the composition I wanted, and saved a filtered-down dataset.

+

To train the model, I had to pick a maximum number of points in a given drawing. 250 was the recommended default.

+

I looked at the distribution of number of points in all the drawings. At the very low end, some drawings had snuck in that were just little squiggles, and at the upper end, some really convoluted messes of lines were in there. I cut out the top and bottom 5% of drawings by number of points.

+

For the remaining drawings, I ran the RDP algorithm with varying values for its epsilon parameter, until the number of points dipped under 250. Then I saved the result as a zipped numpy file.

+
+
+

+ + + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/jupyter/part1_computing_embeddings.html b/jupyter/part1_computing_embeddings.html new file mode 100644 index 0000000..81196e9 --- /dev/null +++ b/jupyter/part1_computing_embeddings.html @@ -0,0 +1,69837 @@ + + + + + + + + + +andrewlook - Part 1: Computing Embeddings + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

Part 1: Computing Embeddings

+
+ + + +
+ + + + +
+ + + +
+ + + +

I have several thousand image files scattered across all of the sketchbook pages that I’ve scanned. My goal is to get all of the single-line drawings in one place so that I can convert them to the correct vector format needed to train a model that will produce new single-line drawings.

+
+
+

+
sketchbook folder icons
+
+
+
+

Sketchbook Data

+

My sketchbooks contain lots of single-line drawings I’ve made over the past few years.

+
+
+Code +
_imgs = [
+    'sb48p072.jpg',
+    'sb67p077.jpg',
+    'sb67p004.jpg',
+    'sb38p073--gown2.jpg', 
+]
+
+image_urls([f"./assets/00-sample-pages/art/{f}" for f in _imgs],
+           labels=[os.path.basename(f) for f in _imgs],
+            w=180
+)
+
+
+
+

sb48p072.jpg

+
+ +
+

sb67p077.jpg

+
+ +
+

sb67p004.jpg

+
+ +
+

sb38p073--gown2.jpg

+
+ +
+
+
+

My first problem is that the sketchbooks don’t just contain single-line drawings. They also contain watercolor paintings, since I don’t exclusively fill my sketchbooks with single-line drawings.

+
+
+Code +
_imgs = [
+    'sb26p068--purple-hair.jpg',
+    'sb55p069-color.jpg',
+    'sb26p098-pickle-toast.jpg',
+    'sb26p069--red-nose.jpg',
+]
+image_urls([f"./assets/00-sample-pages/art/{f}" for f in _imgs],
+           labels=[os.path.basename(f) for f in _imgs],
+            w=180
+)
+
+
+
+

sb26p068--purple-hair.jpg

+
+ +
+

sb55p069-color.jpg

+
+ +
+

sb26p098-pickle-toast.jpg

+
+ +
+

sb26p069--red-nose.jpg

+
+ +
+
+
+
+
+Code +
_imgs = [
+    'sb69p086--wc.jpg',
+    'sb67p051--sparky.jpg',
+    'sb69p006.jpg',
+    'sb35p013.jpg',
+]
+image_urls([f"./assets/00-sample-pages/art/{f}" for f in _imgs],
+           labels=[os.path.basename(f) for f in _imgs],
+            w=180
+)
+
+
+
+

sb69p086--wc.jpg

+
+ +
+

sb67p051--sparky.jpg

+
+ +
+

sb69p006.jpg

+
+ +
+

sb35p013.jpg

+
+ +
+
+
+
+

About the subfolders

+ +

Unfortunately, I didn’t tag watercolors in advance. However, I marked which pages were diagrams/mindmaps/etc and put them in a subfolder called notes - the drawings (and watercolors) are in a folder called art.

+

My hunch is that I can use visual embeddings to group the images by similarity. Hopefully, clustering will make it easy to separate the watercolors.

+ +

Some detail on the subfolders: - art subfolders contain drawings / watercolors. - notes subfolders contain diagrams, mindmaps, and anything else technical I’m working on. I used to have a separate notebook for these, but I got tired of carrying around an extra sketchbook everywhere I go, so at some point they just ended up merging with my art practice. - xtra contains any bad scans or funny bloopers from my scanning process. I kind of like the scans with my hands in them and thought they might be useful for a future project, so I kept them in their own folder. - cover contains a scan of the notebook cover. Each cover at least has a post-it with the sketchbook ID number and the start/end dates of when I used that sketchbook. Often I’ll make a little collage on the front of the notebook with airplane tickets / stickers / scraps of paper relevant to somewhere I traveled when I used that sketchbook.

+
+
EXTERNAL_PARENT_DIR = Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks')
+EXTERNAL_SKETCHBOOKS_DIR = EXTERNAL_PARENT_DIR / 'sketchbooks'
+
+for sketchbook_dir in EXTERNAL_SKETCHBOOKS_DIR.ls()[:3]:
+    print(os.path.basename(sketchbook_dir))
+    for subdir in sketchbook_dir.ls():
+        if not os.path.isdir(subdir):
+            continue
+        print(f"\t{os.path.basename(subdir)}: {len(subdir.ls())} files")
+
+
sb67
+    xtra: 48 files
+    art: 117 files
+    notes: 9 files
+    cover: 1 files
+sb69
+    xtra: 6 files
+    art: 126 files
+    notes: 15 files
+    cover: 4 files
+sb05
+    xtra: 26 files
+    art: 83 files
+    notes: 40 files
+    cover: 1 files
+
+
+
+
+
+

Computing Embeddings

+

My first decision is which model I want to use to compute visual embeddings of my single-line drawings.

+

In the past, I’ve found that vision models trained on standard datasets like ImageNet can produce nonsensical results when they’re shown black-and-white scanned images. ImageNet consists mostly of photographs, which contain a wide distribution of colors - there aren’t a lot of images in there where the majority of pixels are white.

+

So, my solution was to fine-tune a vision model on my dataset, so that the model would learn to distinguish some features within the visual domain of my scanned sketchbook pages. The hope is, this will produce more meaningful embeddings.

+

Ultimately, what I want to see is that when I take the embeddings of my sketchbook pages and cluster them, the notes and drawings should neatly separate into separate clusters. Also, I want the watercolors to cleanly separate into their own clusters, away from the single-line drawings.

+
+
EXTERNAL_DATA_HOME = EXTERNAL_PARENT_DIR / 'singleline_data'
+EPOCH = SketchbookEpoch(epoch='20231214', data_home=EXTERNAL_DATA_HOME)
+
+CURR_01_FLAT = EPOCH.dir_01_FLAT()
+CURR_02_CATEGORIZED = EPOCH.dir_02_CATEGORIZED()
+CURR_03_HAND = EPOCH.dir_03_HANDLABELED()
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
# flatten_sketchbooks(EXTERNAL_SKETCHBOOKS_DIR, CURR_01_FLAT)
+
+
+
""" code to train original model """
+
+dataloaders = sketchbook_dataloaders(CURR_01_FLAT)
+print(dataloaders.vocab)
+learn = vision_learner(dataloaders, resnet34, metrics=error_rate)
+# learn = sketchbook_resnet34(SAMPLE_SKETCHBOOKS_DIR)
+# learn.fine_tune(6)
+# learn.recorder.plot_loss()
+# learn.save(PRETRAINED_CHECKPOINT)
+
+

Using this pretrained model, the next step is to actually save an embedding vector for each filename. Looking at the final few layers in the model, the Linear layer will contain the embedding vector, which will have 512 dimensions.

+
+
learn = load_resnet(
+    data_home=EXTERNAL_DATA_HOME,
+    model_path=PRETRAINED_MODEL_PATH,
+    model_checkpoint=PRETRAINED_CHECKPOINT,
+)
+
+learn.model[-1]
+
+
Sequential(
+  (0): AdaptiveConcatPool2d(
+    (ap): AdaptiveAvgPool2d(output_size=1)
+    (mp): AdaptiveMaxPool2d(output_size=1)
+  )
+  (1): fastai.layers.Flatten(full=False)
+  (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (3): Dropout(p=0.25, inplace=False)
+  (4): Linear(in_features=1024, out_features=512, bias=False)
+  (5): ReLU(inplace=True)
+  (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (7): Dropout(p=0.5, inplace=False)
+  (8): Linear(in_features=512, out_features=5, bias=False)
+)
+
+
+

Eventually I want to save a TSV file with these embeddings, so I don’t need to do an expensive re-computation of them every time that I use them. I’m going to make decisions about which images to exclude from the training set based on the analysis of these embeddings, so it’s important to tie the embeddings back to the original filenames. So as I load batches of image data to run through the vision model, I’ll need corresponding batches of the same image files.

+
+
# dir_to_embed = DATA_HOME / "raster/epoch-20231214/01_FLAT"
+dir_to_embed = CURR_01_FLAT
+
+path = Path(dir_to_embed)
+files = get_image_files(path)
+len(files)
+
+batched_fnames, ordered_dls = batch_fnames_and_images(dir_to_embed)
+
+
total items: 2922, num batches: 46
+
+
+

To actually extract the embedding vector rather than the classifier output that the model was trained for, I register a “hook” in the pytorch model, targeting the specific layer that I want.

+
+
class Hook:
+    def __init__(self, m):
+        self.hook = m.register_forward_hook(self.hook_func)
+
+    def hook_func(self, m, i, o):
+        self.stored = o.detach().clone()
+
+    def __enter__(self, *args):
+        return self
+
+    def __exit__(self, *args):
+        self.hook.remove()
+
+
+def predict_embeddings(model, xb):
+    with torch.no_grad():
+        with Hook(model[-1][-2]) as hook:
+            output = model.to("cpu").eval()(xb.to("cpu"))
+            act = hook.stored
+    return act.cpu().numpy()
+
+

I can quickly test that function to make sure it works as expected:

+
+
train_iter = iter(ordered_dls.train)
+x0, y0 = next(train_iter)
+print(x0.shape, y0.shape)
+
+activations = predict_embeddings(learn.model, x0)
+activations.shape
+
+
torch.Size([64, 3, 224, 224]) torch.Size([64])
+
+
+
(64, 512)
+
+
+

Now I call a helper function that runs this for the whole directory, and includes file metadata that I want to save.

+
+
rows = []
+for x in embed_dir(dir_to_embed, learn, strip_dir=EXTERNAL_DATA_HOME):
+    rows.append(x)
+df = pd.DataFrame(rows)
+print(f"num rows: {len(df)}")
+
+
+
embeddings_fname = f"{str(dir_to_embed)}.tsv"
+df.to_csv(embeddings_fname, index=False, sep="\t", header=True)
+print(f"wrote {embeddings_fname}")
+
+
wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20231214/01_FLAT.tsv
+
+
+
+
df = pd.read_csv(embeddings_fname, delimiter="\t")
+df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
idxindiv_fnameorig_fnamelabelemb_csv
00sb77p043.jpgart/sb77p043.jpgart0.104207695,-0.18638375,-0.62591493,-0.6553513,-0.5701394,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.38013077,1.5405647,-0.72562873,-0.662768,-0.6266761,0.97834426,0.9655045,-0.5467274,-0.6520286,-0.4104699,1.0914154,1.0141778,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,0.6779934,0.0863238,-0.73986673,-0.45211998,0.061522484,-0.67007643,-0.52171326,-0.4592305,1.2624313,-0.6186755,0.93794376,-0.57338566,0.25945204,-0.21217108,-0.56674594,0.9833558,-0.6189488,-0.6682474,1.5392866,-0.59257406,1.6529765,-0.6001429,-0.45996642,-0.27613547,-0.2818516,-0.66552746,-0.6482407,-0.4...
11sb67p021.jpgart/sb67p021.jpgart0.7969849,0.14494091,-0.73929733,-0.6553513,1.9535823,-0.65341413,0.36580223,-0.36136246,-0.07100159,-0.42715576,1.369601,-0.72562873,-0.26127928,-0.3483695,-0.06697661,0.4524386,-0.29447836,0.8182244,0.7332382,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.45040703,-0.53302765,-0.45211998,0.42632103,-0.67007643,1.0613238,-0.4592305,-0.600525,0.39376843,-0.59931153,-0.2633891,2.0686517,1.8864914,-0.61049384,0.04706061,0.2791052,-0.6682474,-0.75794554,-0.59257406,0.92550987,-0.6001429,-0.651804,-0.13118449,1.0723509,-0.66552746,-0.6482407,0.3424...
22sb06p023.jpgnotes/sb06p023.jpgnotes1.8547797,1.8846574,-0.73929733,-0.21922109,3.3641608,-0.65341413,0.26967233,1.4491367,-0.7537334,0.631469,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.9564223,0.033899486,1.4874573,0.2574559,-0.6497359,-0.64108604,1.6134343,0.63970476,-0.6779002,0.054212928,-0.6690378,-0.4518578,2.188378,-0.73986673,0.37282923,-0.68509877,0.15817368,1.4280679,1.0238429,0.2699657,0.42746747,-0.14477086,-0.57338566,2.2932181,1.8153762,-0.61049384,1.8594978,1.3366861,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.3015023,-0.18820012,1.3682914,-0.49842834,-0.66552746,0.6213522,1.689276,-0.7311...
33sb25p227.jpgart/sb25p227.jpgart-0.11056262,-0.5714155,-0.7312232,-0.6553513,0.781162,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.21062706,0.27820683,-0.72562873,1.1432679,-0.24305794,1.4008591,1.22613,-0.5467274,0.52194935,0.63351226,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.029345095,-0.6690378,-0.4518578,0.19455552,-0.73986673,0.0066122413,-0.68509877,-0.67007643,-0.52171326,-0.4359758,-0.600525,-0.6186755,0.11884242,-0.57338566,-0.65925777,1.2907594,-0.61049384,0.060923636,0.3174371,-0.6682474,-0.75794554,-0.59257406,0.04120022,-0.6001429,-0.651804,0.037656665,-0.4933368,0.6555009,-0.6482407,-0....
44sb07p104.jpgart/sb07p104.jpgart0.21664959,1.2388747,-0.73929733,0.36272156,1.23564,-0.44915906,-0.7267234,-0.29234704,-0.7537334,-0.42715576,-0.51833826,-0.72562873,-0.3837577,-0.6266761,-0.68991846,0.54637694,-0.5467274,-0.63006383,-0.39589638,-0.6497359,-0.2676324,0.34809577,-0.5446661,-0.6779002,0.34786582,-0.30389354,-0.4518578,1.3147106,-0.43326455,-0.45211998,0.81599486,-0.67007643,0.2869113,-0.2872985,-0.600525,0.09329122,0.31422192,-0.57338566,0.77486813,2.8450775,-0.61049384,0.67306954,-0.59963334,-0.6682474,-0.75794554,-0.59257406,-0.6157326,0.63789755,0.13689959,1.135535,-0.03640884,-0.4392346,0.30233687,0.40...
+ +
+
+
+
+
+
# Before running T-SNE or clustering, the embedding vectors need to be structured as a numpy array.
+embs = pd_series_to_embs(df.emb_csv)
+print(embs.shape)
+
+
(2922, 512)
+
+
+
+

Bird’s Eye View of Embedding Space

+

As a sanity check, I want to verify that the embedding space has cleanly separated regions for the labels is was trained on. It’s hard to visualize 512 dimensions, so the T-SNE dimensionality reduction is a good visualization technique to view the data. T-SNE projects the higher number of dimensions into 2D or 3D, with the goal that any 2 points close together in high-dimensional space are close together in lower-dimensional space. I’ll colorize the points in the resulting graph based on their label (art, notes, cover) so it’s easy to see at a glance if the embedding space has learned something useful about this dataset.

+
+
from sklearn.manifold import TSNE
+
+y = list(df.label)
+labels = list(set(y))
+label_mapping = {label: i for i, label in enumerate(labels)}
+y_idx = [label_mapping[label] for label in y]
+print(labels, y_idx[:5])
+
+X = embs
+tsne = TSNE(n_components=2, random_state=42)
+X_tsne = tsne.fit_transform(X)
+
+fig = plt.scatter(x=X_tsne[:, 0], y=X_tsne[:, 1], c=y_idx, s=2)
+
+
['art', 'notes', 'cover'] [0, 0, 1, 0, 0]
+
+
+
+
+

+
+
+
+
+
+
+

Clustering the Embeddings

+

Now I’m hoping to find the watercolors in the dataset. Given that the embedding space captures visual similarity, I want to identify groups of pages that are similar to each other. This lets me label data in bulk, rather than going through the images one-by-one.

+

K-Means clustering is an easy tool to apply here. I can decide to ask for K distinct “clusters”, and the algorithm picks K “centroids”. For each point (in this case, for each sketchbook page’s embedding vector), it finds the nearest centroid point. Then the algorithm adjusts the centroids iteratively, with the goal of minimizing the total distance from each point to its nearest centroid.

+
+
+

+
Example of KMeans iterations, source: https://shabal.in/visuals/kmeans/5.html
+
+
+
+
kmeans = train_kmeans(embs, ncentroids=16)
+
+# note: these will need to be populated and re-saved once we've labeled the clusters.
+cluster_to_label = {}
+
+save_centroids(centroids=kmeans.centroids, cluster_to_label=cluster_to_label, data_home=EXTERNAL_DATA_HOME)
+
+
wrote to /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_centroids.json
+wrote to /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_labels.json
+
+
+
+
centroids, cluster_to_label = load_centroids(
+    data_home=EXTERNAL_DATA_HOME,
+    model_path=PRETRAINED_MODEL_PATH
+)
+assigner = cluster_assigner(centroids, cluster_to_label)
+
+
reading /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_centroids.json
+reading /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_labels.json
+
+
+
+
+

Assign Clusters

+

This effectively works like a K Nearest Neighbors (KNN) classifier now. For each point, I compute the embedding vector’s distance to each of the cluster centroids. I assign a cluster label based on the cluster of nearest centroid. I’ve also saved the centroids, so that if I add new images I can map them to the same set of clusters.

+
+
knn_dist, knn_clusterid, knn_label = assigner(embs)
+knn_label[:5], knn_clusterid[:5]
+
+
([None, None, None, None, None],
+ array([[5],
+        [9],
+        [3],
+        [5],
+        [5]]))
+
+
+
+
categorized_fname = EPOCH.tsv_02_CATEGORIZED()
+categorized_dir = EPOCH.dir_02_CATEGORIZED()
+
+df["knn_dist"] = knn_dist
+df["knn_clusterid"] = knn_clusterid
+df["knn_label"] = knn_label
+
+## these steps are only for after 'cluster_to_label' is populated
+# df["categorized_path"] = df.apply(
+#     lambda row: os.path.join(row["knn_label"], row["indiv_fname"]), axis=1
+# )
+
+if 'emb_csv' in df.columns:
+    del df['emb_csv']
+
+df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
idxindiv_fnameorig_fnamelabelknn_distknn_clusteridknn_label
00sb77p043.jpgart/sb77p043.jpgart210.4341135None
11sb67p021.jpgart/sb67p021.jpgart145.4699409None
22sb06p023.jpgnotes/sb06p023.jpgnotes247.6583863None
33sb25p227.jpgart/sb25p227.jpgart218.3357705None
44sb07p104.jpgart/sb07p104.jpgart214.9852295None
+ +
+
+
+
+
+
clusters_fname = f"{dir_to_embed}_clusters.tsv"
+
+# NOTE: when saving embeddings CSV, idx was a regular column (not a pandas 'index').
+# When reloaded, 'idx' was made the index column. TODO: make original DF use 'idx' as index,
+# so saving and reloading code is identical.
+df.to_csv(clusters_fname, index=True, index_label="idx", sep="\t", header=True)
+
+cdf = pd.read_csv(clusters_fname, delimiter="\t", index_col="idx")
+
+
+
+
+

Cluster Analysis

+

To get an overview, my first step is to look at a T-SNE of the clusters.

+
+
fig = plt.scatter(
+    x=X_tsne[:, 0], y=X_tsne[:, 1], c=cdf.knn_clusterid, s=3.0, cmap=plt.get_cmap("tab20")
+)
+
+
+
+

+
+
+
+
+

The colors are mostly well-separated, which is good news. Some of the colors are a bit more interleaved than the T-SNE of classifier labels art/notes/etc, but there are enough distinct groupings of color that I’m interested to see what the groupings look like.

+

As another quick spot-check, I’ll look at a histogram to get a feel for how many points are in each cluster:

+
+
cdf.knn_clusterid.hist()
+
+
+
+

+
+
+
+
+
+

Naming the Clusters

+ +

I pulled up 16 images from each cluster and laid them out in a grid. Each row is one cluster, so the first row is knn_clusterid == 0, and the last row is knn_clusterid == 15.

+
+
+

+
Samples from each cluster
+
+
+
+
show_all_clusters(clusters_df=df, clusters=knn_clusterid, prefix=CURR_01_FLAT)
+
+

It worked! At a glance, I can see that: - clusters 1, 3, 12, and 13 are watercolors - clusters 2 and 10 are bad scans (not cropped, bad color balance, blank pages, etc) - cluster 8 is notes - cluster 15 is sketchbook covers - the remainder seem to be single-line drawings

+
+
DRAWING_CLUSTERS = [0, 2, 3, 4, 8, 13, 14, 15]
+WATERCOLOR_CLUSTERS = [5, 7, 11]
+NOTES_CLUSTERS = [9, 12]
+BAD_SCANS = [1, 6, 10]
+COVERS = []
+
+
+
show_all_clusters(
+    clusters_df=df, clusters=knn_clusterid, cluster_idxs=WATERCOLOR_CLUSTERS, title="Watercolors"
+)
+
+
+
+

+
+
+
+
+
+
show_all_clusters(
+    clusters_df=df, clusters=knn_clusterid, cluster_idxs=DRAWING_CLUSTERS, title="Drawings"
+)
+
+
+
+

+
+
+
+
+
+
show_all_clusters(
+    clusters_df=df, clusters=knn_clusterid, cluster_idxs=NOTES_CLUSTERS, title="Notes"
+)
+
+
+
+

+
+
+
+
+
+
show_all_clusters(clusters_df=df, clusters=knn_clusterid, cluster_idxs=BAD_SCANS, title="Bad Scans")
+
+
+
+

+
+
+
+
+

Now I want to translate these “metaclusters” into labels back in my dataframe and TSV file.

+

I’m giving them names like 0_drawings, 1_watercolors, 2_notes, etc. Since “drawings” are the main thing I care about here, I want them to show up first whenever I sort the labels.

+
+
def metacluster(x):
+    if x in DRAWING_CLUSTERS:
+        return 0
+    if x in WATERCOLOR_CLUSTERS:
+        return 1
+    if x in NOTES_CLUSTERS:
+        return 2
+    if x in BAD_SCANS:
+        return 3
+    if x in COVERS:
+        return 4
+    else:
+        raise Exception(f"invalid cluster: {x}")
+
+
+metacluster_to_label = {
+    0: "0_drawings",
+    1: "1_watercolors",
+    2: "2_notes",
+    3: "3_badscans",
+    4: "4_covers",
+}
+
+label_to_metacluster = {v: k for k, v in metacluster_to_label.items()}
+label_to_metacluster
+
+
{'0_drawings': 0,
+ '1_watercolors': 1,
+ '2_notes': 2,
+ '3_badscans': 3,
+ '4_covers': 4}
+
+
+
+
df["metacluster"] = [metacluster(x) for x in list(df.cluster)]
+df["metacluster_name"] = [metacluster_id_to_name(i) for i in list(df.metacluster)]
+
+df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetaclustermetacluster_name
idx
0/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb77p043.jpgart/sb77p043.jpgartxtra40.168532,0.008473,0.008068,0.016940,0.797986-0.6198182,-0.5714155,-0.6871516,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,1.4904574,-0.72562873,-0.662768,-0.6266761,0.6913696,0.054546177,-0.5467274,-0.6520286,0.117179275,0.94330966,0.5815062,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.4584825,-0.3818994,-0.5750927,-0.73986673,-0.45211998,-0.6141643,-0.67007643,-0.52171326,-0.4592305,1.4788612,-0.6186755,0.799198,-0.57338566,-0.17778617,0.10336053,0.093812525,0.17924881,-0.6189488,-0.6682474,2.1040225,-0.59257406,1.6553459,-0.6001429,0.5109105,0.19352531,-0.2517513,-0.66552746,-0.6482407,-0.4732...11199.2296111_watercolors
1/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb67p021.jpgart/sb67p021.jpgartxtra40.170976,0.008673,0.008480,0.017028,0.7948431.1073987,0.3512016,-0.73929733,-0.6553513,1.7477593,-0.65341413,0.32179314,-0.4896673,-0.1797334,-0.42715576,1.3163843,-0.72562873,-0.61387855,-0.6266761,-0.35361218,0.37611246,-0.08771938,-0.5262803,0.9261699,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.32559583,-0.6690378,-0.4518578,0.51828945,-0.4238917,-0.45211998,-0.16527033,-0.67007643,1.0136865,-0.4592305,-0.600525,-0.060860336,-0.19446748,-0.24127388,2.2282472,1.8003348,-0.61049384,0.39795423,0.2626753,-0.6682474,-0.75794554,-0.59257406,0.80257696,-0.6001429,-0.651804,-0.028328001,0.53641737,-0.6501347,-0.6482407,0....3132.2611100_drawings
2/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/notes/sb06p023.jpgnotes/sb06p023.jpgnotesxtra40.171455,0.008462,0.007966,0.016696,0.7954212.071539,1.9655468,-0.73929733,0.5748948,3.170734,-0.65341413,0.63399774,1.729534,-0.7537334,0.84828806,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.328189,0.38060355,1.1197739,0.73272705,-0.6497359,-0.64108604,1.9738588,0.9751267,-0.6779002,0.62476915,-0.6690378,-0.4518578,1.4331162,-0.73986673,0.5611434,-0.68509877,0.6532827,2.040183,0.9425397,0.18940705,-0.27290884,0.1475305,-0.57338566,1.9694935,1.2107165,-0.61049384,1.9219763,-0.047254384,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.17440769,-0.18534619,1.6759431,-0.59497476,-0.66552746,0.67768025,2.0296078,-0.731109...6223.1566833_badscans
3/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb25p227.jpgart/sb25p227.jpgartxtra40.158396,0.008277,0.007424,0.016584,0.8093191.3776337,-0.5714155,-0.2587232,-0.6553513,1.3532461,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.25077122,0.050992966,-0.72562873,0.1406585,-0.6266761,1.0653365,0.96198404,-0.5467274,0.30261123,0.6327609,-0.6497359,-0.64108604,-0.28434965,-0.35651857,-0.6779002,-0.28245115,-0.6690378,-0.4518578,0.030088186,-0.73986673,-0.18931103,-0.041653574,-0.67007643,-0.52171326,-0.29881266,-0.600525,-0.23231092,-0.73933,-0.57338566,-0.65925777,1.8833572,-0.61049384,0.44560486,0.14075392,-0.6682474,-0.75794554,-0.59257406,-0.07511848,-0.6001429,-0.651804,-0.5315948,0.023931146,0.723624,-0.6482407,-...8188.5807500_drawings
4/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb07p104.jpgart/sb07p104.jpgartxtra40.177406,0.008799,0.008245,0.017177,0.788373-0.39105213,0.96309596,-0.73929733,0.4489988,0.8297614,-0.3937308,-0.7267234,-0.32461396,-0.7537334,-0.42715576,-0.62038505,-0.72562873,0.3037846,-0.6266761,-0.5213815,-0.31669965,-0.5467274,-0.6520286,-0.22544897,-0.6497359,-0.64108604,0.6374447,-0.5446661,-0.6779002,-0.14143991,-0.6690378,-0.44753593,0.66945183,-0.73986673,-0.45211998,-0.05996394,-0.21525535,0.82928157,-0.04412651,-0.600525,-0.04148197,-0.73825717,-0.57338566,-0.25227422,2.3566113,-0.61049384,1.0027854,-0.40134108,1.3733256,-0.75794554,-0.59257406,-0.4395525,-0.6001429,1.0358506,1.1911299,0.8162154,0.032713354,-0.6482407...5238.3317011_watercolors
+ +
+
+
+
+

Finally, I’m going to view a T-SNE colored by metacluster. This is a sanity check to make sure that the embedding space groupings match my intuitive labeling of the clusters.

+
+
fig = plt.scatter(
+    x=X_tsne[:, 0], y=X_tsne[:, 1], c=df.metacluster, s=3.0, cmap=plt.get_cmap("tab20")
+)
+
+
+
+

+
+
+
+
+
+
+

categorizing clusters, bucketing images into new dir

+

Thinking out loud: - how to make these scripts idempotent? - ideally, when I get these images bucketed by directory, I can manually “re-label” any incorrect ones by moving them to a new dir.

+

when I add more files at the input stage, it’d be nice if I could re-run the scripts: - flatten should copy over the new files (maybe it doesn’t need to replace - that’d be faster) - vis-cluster not recategorizing the ones I’ve already labeled would be helpful. - do I need to save kmeans.faiss and/or index.faiss for this to work?

+
+
# # dir_to_embed = singleline_data_home() / "raster/epoch-20231214/01_FLAT"
+# dest_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/02_VISUAL_CATEGORIES"
+# )
+
+# # add useful metadata about where we're going to move the files to
+# df["indiv_fname"] = df.rel_fname.map(lambda f: os.path.basename(f))
+# df["visual_rel_path"] = df.apply(
+#     lambda row: os.path.join(row["metacluster_name"], row["indiv_fname"]), axis=1
+# )
+# df["visual_abs_path"] = df.visual_rel_path.map(lambda f: os.path.join(dest_dir, f))
+
+# # print(df["indiv_fname"][0:3])
+# # print(df["visual_rel_path"][0:3])
+# # print(df["visual_abs_path"][0:3])
+
+
+
# metaclusters_fname = f"{dir_to_embed}/metaclusters.tsv"
+# df.to_csv(metaclusters_fname, index=True, index_label="idx", sep="\t", header=True)
+
+# metaclusters_df = pd.read_csv(metaclusters_fname, delimiter="\t", index_col="idx")
+# metaclusters_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetaclustermetacluster_nameindiv_fnamevisual_rel_pathvisual_abs_path
idx
0/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb77p043.jpgart/sb77p043.jpgartxtra40.168532,0.008473,0.008068,0.016940,0.797986-0.6198182,-0.5714155,-0.6871516,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,1.4904574,-0.72562873,-0.662768,-0.6266761,0.6913696,0.054546177,-0.5467274,-0.6520286,0.117179275,0.94330966,0.5815062,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.4584825,-0.3818994,-0.5750927,-0.73986673,-0.45211998,-0.6141643,-0.67007643,-0.52171326,-0.4592305,1.4788612,-0.6186755,0.799198,-0.57338566,-0.17778617,0.10336053,0.093812525,0.17924881,-0.6189488,-0.6682474,2.1040225,-0.59257406,1.6553459,-0.6001429,0.5109105,0.19352531,-0.2517513,-0.66552746,-0.6482407,-0.4732...11199.2296111_watercolorssb77p043.jpg1_watercolors/sb77p043.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/1_watercolors/sb77p043.jpg
1/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb67p021.jpgart/sb67p021.jpgartxtra40.170976,0.008673,0.008480,0.017028,0.7948431.1073987,0.3512016,-0.73929733,-0.6553513,1.7477593,-0.65341413,0.32179314,-0.4896673,-0.1797334,-0.42715576,1.3163843,-0.72562873,-0.61387855,-0.6266761,-0.35361218,0.37611246,-0.08771938,-0.5262803,0.9261699,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.32559583,-0.6690378,-0.4518578,0.51828945,-0.4238917,-0.45211998,-0.16527033,-0.67007643,1.0136865,-0.4592305,-0.600525,-0.060860336,-0.19446748,-0.24127388,2.2282472,1.8003348,-0.61049384,0.39795423,0.2626753,-0.6682474,-0.75794554,-0.59257406,0.80257696,-0.6001429,-0.651804,-0.028328001,0.53641737,-0.6501347,-0.6482407,0....3132.2611100_drawingssb67p021.jpg0_drawings/sb67p021.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/0_drawings/sb67p021.jpg
2/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/notes/sb06p023.jpgnotes/sb06p023.jpgnotesxtra40.171455,0.008462,0.007966,0.016696,0.7954212.071539,1.9655468,-0.73929733,0.5748948,3.170734,-0.65341413,0.63399774,1.729534,-0.7537334,0.84828806,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.328189,0.38060355,1.1197739,0.73272705,-0.6497359,-0.64108604,1.9738588,0.9751267,-0.6779002,0.62476915,-0.6690378,-0.4518578,1.4331162,-0.73986673,0.5611434,-0.68509877,0.6532827,2.040183,0.9425397,0.18940705,-0.27290884,0.1475305,-0.57338566,1.9694935,1.2107165,-0.61049384,1.9219763,-0.047254384,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.17440769,-0.18534619,1.6759431,-0.59497476,-0.66552746,0.67768025,2.0296078,-0.731109...6223.1566833_badscanssb06p023.jpg3_badscans/sb06p023.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/3_badscans/sb06p023.jpg
3/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb25p227.jpgart/sb25p227.jpgartxtra40.158396,0.008277,0.007424,0.016584,0.8093191.3776337,-0.5714155,-0.2587232,-0.6553513,1.3532461,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.25077122,0.050992966,-0.72562873,0.1406585,-0.6266761,1.0653365,0.96198404,-0.5467274,0.30261123,0.6327609,-0.6497359,-0.64108604,-0.28434965,-0.35651857,-0.6779002,-0.28245115,-0.6690378,-0.4518578,0.030088186,-0.73986673,-0.18931103,-0.041653574,-0.67007643,-0.52171326,-0.29881266,-0.600525,-0.23231092,-0.73933,-0.57338566,-0.65925777,1.8833572,-0.61049384,0.44560486,0.14075392,-0.6682474,-0.75794554,-0.59257406,-0.07511848,-0.6001429,-0.651804,-0.5315948,0.023931146,0.723624,-0.6482407,-...8188.5807500_drawingssb25p227.jpg0_drawings/sb25p227.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/0_drawings/sb25p227.jpg
4/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb07p104.jpgart/sb07p104.jpgartxtra40.177406,0.008799,0.008245,0.017177,0.788373-0.39105213,0.96309596,-0.73929733,0.4489988,0.8297614,-0.3937308,-0.7267234,-0.32461396,-0.7537334,-0.42715576,-0.62038505,-0.72562873,0.3037846,-0.6266761,-0.5213815,-0.31669965,-0.5467274,-0.6520286,-0.22544897,-0.6497359,-0.64108604,0.6374447,-0.5446661,-0.6779002,-0.14143991,-0.6690378,-0.44753593,0.66945183,-0.73986673,-0.45211998,-0.05996394,-0.21525535,0.82928157,-0.04412651,-0.600525,-0.04148197,-0.73825717,-0.57338566,-0.25227422,2.3566113,-0.61049384,1.0027854,-0.40134108,1.3733256,-0.75794554,-0.59257406,-0.4395525,-0.6001429,1.0358506,1.1911299,0.8162154,0.032713354,-0.6482407...5238.3317011_watercolorssb07p104.jpg1_watercolors/sb07p104.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/1_watercolors/sb07p104.jpg
+ +
+
+
+
+
+
+

moving files

+
+
# test_files = get_image_files(dir_to_embed)
+# test_files
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
# L(os.path.basename(f) for f in test_files)
+
+
+
# "NON_EXISTENT" in L(os.path.basename(f) for f in test_files), "sb38p036.jpg" in L(
+#     os.path.basename(f) for f in test_files
+# )
+
+
+
# existing_files = get_image_files(dest_dir)
+
+
+
# existing_files
+
+
+
# import math
+# import os
+# import shutil
+
+# from PIL import Image
+
+# """
+# inputs:
+# - dest_dir
+# - metaclusters_df
+# """
+
+
+# existing_files = L(os.path.basename(f) for f in get_image_files(dest_dir))
+
+# for idx in range(len(metaclusters_df)):
+#     row = metaclusters_df.iloc[idx]
+
+#     indiv_fname = row.indiv_fname
+#     if indiv_fname in existing_files:
+#         print(f"skipping {indiv_fname}")
+#         continue
+
+#     orig_abs_path = row.abs_fname
+#     visual_abs_path = row.visual_abs_path
+
+#     visual_dir = os.path.dirname(visual_abs_path)
+#     if not os.path.isdir(visual_dir):
+#         os.makedirs(visual_dir)
+
+#     print(f"writing to {visual_abs_path}")
+#     shutil.copy(orig_abs_path, visual_abs_path)
+#     # try:
+#     #     img = Image.open(f"/Users/al/Dropbox/2-Areas/2-Sketchbooks/{row.fname}")
+#     #     crop_amt = math.floor(img.size[0] * 20 / 224)
+#     #     cropped_img = img.crop((crop_amt, 0, img.size[0] - crop_amt, img.size[1]))
+#     #     # cropped_img
+#     #     cropped_img.save(out_path)
+#     # except Exception as e:
+#     #     print(e)
+
+
+
+
+

hand-labeling (+ braindump)

+

Since the clusters weren’t 100% accurate, I hand-edited the clusters (by going through each folder, tagging anything that belonged in a different folder, then grouping the folder by tags and moving out any tagged files).

+

First I copied this directory:

+
full-v2/02_VISUAL_CATEGORIES
+

to

+
full-v2/03_VISUAL_HAND_LABELED
+

So, I should probably - [ ] iterate through the files in metaclusters_df and mark what their updated relpaths are - [ ] save the result to a TSV file (in 03_VISUAL_HAND_LABELED?) - [ ] update my cropping code below to use the more accurately selected files when populating 10_SIMPLE_CROP - [ ] (maybe) also save the filepath of the cropped files?

+

Then, if I want, I could - re-cluster within the watercolors and within the drawings. Perhaps cluster on the cropped versions? That might give me a starting point for filtering down to a more similar set of drawings. - Alternatively, I might get more bang for my buck by going into vector land and splitting the non-overlapping strokes into separate drawings - Also: can i find and filter out all diagchains / tower drawings? those might clarify the signal a bit

+

And it’d be really fun (!) to… - Visualizing Data using the Embedding Projector in TensorBoard  |  TensorFlow - Taking the TensorBoard Embedding Projector to the Next Level - though I’ll want clear PNG’s for the sprites. perhaps this is easier to do after I’ve vectorized and re-saved to SVG.

+

I’m also curious whether there’s anything automated I can do to: - do “face detection”, so I’m only working with actual drawings of faces? - full-train on quickdraw faces dataset, then fine-tune on my face drawings? - detect bounding boxes for handwriting on the page (potentially crop it out at vectorization time?) - detect edge of sketchbook pages? - detect poorly scanned / crop to just the page?

+

Side note re: the handlabeling process: - why didn’t “covers” get their own cluster? - where did all the “xtra” ones go? were they in ‘badcovers’ (which I didn’t manually sort through yet)

+
+

first, get a mapping of indiv_fname to the relative path within 03_VISUAL_HAND_LABELED

+
+
# handlabeled_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/03_VISUAL_HAND_LABELED"
+# )
+
+# handlabeled_files = get_image_files(handlabeled_dir)
+# handlabeled_files
+
+
+
# def basename_and_parent(f):
+#     base = os.path.basename(f)
+#     parent_abs = os.path.dirname(f)
+#     parent_base = os.path.basename(parent_abs)
+#     handlabeled_relpath = os.path.join(parent_base, base)
+#     return base, handlabeled_relpath
+
+
+# label_mapping = {
+#     base: parent for base, parent in [basename_and_parent(f) for f in handlabeled_files]
+# }
+
+
+
+

infer hand-chosen label

+
+
# metaclusters_df.head()
+
+
+
# metaclusters_df["handlabeled_rel_path"] = metaclusters_df.indiv_fname.map(
+#     lambda f: label_mapping[f]
+# )
+# print(metaclusters_df.handlabeled_rel_path[:5])
+
+# metaclusters_df["handlabeled_abs_path"] = metaclusters_df.handlabeled_rel_path.map(
+#     lambda p: os.path.join(handlabeled_dir, p)
+# )
+# print(metaclusters_df.handlabeled_abs_path[:5])
+
+# metaclusters_df[
+#     "handlabeled_metacluster_name"
+# ] = metaclusters_df.handlabeled_rel_path.map(lambda p: os.path.dirname(p))
+# print(metaclusters_df.handlabeled_metacluster_name[:5])
+
+# metaclusters_df[
+#     "handlabeled_metacluster_id"
+# ] = metaclusters_df.handlabeled_metacluster_name.map(
+#     lambda n: metacluster_name_to_id[n]
+# )
+
+
+
# # note to self: give this a better name
+# # - not calling it "metacluster", instead -> "visual category"? "category"?
+# # - "metacluster_id"
+
+# # comparing the id's
+# metaclusters_df[["handlabeled_metacluster_id", "metacluster"]][:5]
+
+
+
# # did the label change when I had to hand-label?
+# metaclusters_df["handlabeled_metacluster_was_correct"] = metaclusters_df.apply(
+#     lambda row: row.metacluster == row.handlabeled_metacluster_id, axis=1
+# )[:20]
+# print(metaclusters_df.handlabeled_metacluster_was_correct[:10])
+
+
+
+

precompute path to which the image is going to get preprocessed/cropped, save DF as handlabeled.tsv

+
+
# preprocessing_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/10_SIMPLE_CROP"
+# )
+
+# metaclusters_df["preprocessed_abs_path"] = metaclusters_df.handlabeled_rel_path.map(
+#     lambda p: os.path.join(preprocessing_dir, p)
+# )
+# metaclusters_df.preprocessed_abs_path[:5]
+
+
+
# metaclusters_df.head()
+
+
+
# handlabeled_tsv_fname = f"{handlabeled_dir}/handlabeled.tsv"
+
+# metaclusters_df.to_csv(
+#     handlabeled_tsv_fname, index=True, index_label="idx", sep="\t", header=True
+# )
+
+# handlabeled_df = pd.read_csv(handlabeled_tsv_fname, delimiter="\t", index_col="idx")
+
+# hdf = handlabeled_df
+# hdf.head()
+
+
+
+

side quest: how accurate was clustering for separating the categories I care about?

+

Time to make a classification report!

+ +
+
# from sklearn.metrics import classification_report
+
+# target_names = list(metacluster_id_to_name.values())
+
+# y_true = metaclusters_df.handlabeled_metacluster_id
+# y_pred = metaclusters_df.metacluster
+
+# print(classification_report(y_true, y_pred, target_names=target_names))
+
+
               precision    recall  f1-score   support
+
+   0_drawings       0.95      0.96      0.96      1923
+1_watercolors       0.83      0.93      0.88       404
+      2_notes       0.86      0.79      0.82       287
+   3_badscans       1.00      0.99      0.99       269
+     4_covers       0.00      0.00      0.00        39
+
+     accuracy                           0.93      2922
+    macro avg       0.73      0.73      0.73      2922
+ weighted avg       0.92      0.93      0.92      2922
+
+
+
+
/Users/al/miniconda3_m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/Users/al/miniconda3_m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/Users/al/miniconda3_m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+
+
+
+
# from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
+
+# cm = confusion_matrix(y_true, y_pred)
+# disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=target_names)
+# disp.plot()
+# # plt.show()
+
+
+
# for idx in range(len(metaclusters_df)):
+
+#     if idx > 10:
+#         break
+
+#     row = metaclusters_df.iloc[idx]
+
+#     indiv_fname = row.indiv_fname
+
+#     handlabeled_rel_path = label_mapping[indiv_fname]
+#     handlabeled_metacluster_name = os.path.dirname(handlabeled_rel_path)
+#     print(handlabeled_rel_path, handlabeled_metacluster_name, row.metacluster_name)
+
+
+
+
+

processing

+
+

v0: dummy crop (both sides, by a fixed percentage)

+
+
# preprocessing_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/10_SIMPLE_CROP"
+# )  # <-- this was defined earlier.
+existing_files = L(os.path.basename(f) for f in get_image_files(preprocessing_dir))
+
+crop_frac = 20.0 / 224
+print(f"using crop_frac={crop_frac}")
+
+for idx in range(len(metaclusters_df)):
+
+    # if idx > 10:
+    #     break
+
+    row = metaclusters_df.iloc[idx]
+    # if row.metacluster_name != "0_drawings":
+    #     # print(f"ignore {row.metacluster_name}")
+    #     continue
+
+    indiv_fname = row.indiv_fname
+    if indiv_fname in existing_files:
+        print(f"skipping {indiv_fname}")
+        continue
+
+    # handlabeled_rel_path = row.handlabeled_rel_path
+    # preproc_abs_path = os.path.join(preprocessing_dir, handlabeled_rel_path)
+    preproc_abs_path = row.preprocessed_abs_path
+    # print(preproc_abs_path)
+
+    preproc_dir = os.path.dirname(preproc_abs_path)
+    if not os.path.isdir(preproc_dir):
+        os.makedirs(preproc_dir)
+
+    handlabeled_abs_path = row.handlabeled_abs_path
+    print(f"writing from {handlabeled_abs_path} to {preproc_abs_path}")
+
+    try:
+        img = Image.open(handlabeled_abs_path)
+        print(img.size)
+        crop_amt = math.floor(
+            img.size[0] * crop_frac
+        )  # compute number of pixels to crop (roughly 8% of page size)
+        cropped_img = img.crop((crop_amt, 0, img.size[0] - crop_amt, img.size[1]))
+        # cropped_img
+        cropped_img.save(preproc_abs_path)
+    except Exception as e:
+        print(e)
+
+
using crop_frac=0.08928571428571429
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb77p043.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb77p043.jpg
+(1920, 2461)
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+(2006, 2597)
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+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb25p227.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p227.jpg
+(1364, 2024)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/1_watercolors/sb07p104.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb07p104.jpg
+(1700, 2338)
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+(1914, 2469)
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+(1931, 2600)
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+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb35p094.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb35p094.jpg
+(1948, 2502)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb31p050.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb31p050.jpg
+(1935, 2473)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/4_covers/sb28pcover.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/4_covers/sb28pcover.jpg
+(1995, 2553)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb07p037.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb07p037.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb68p050.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb68p050.jpg
+(1879, 2447)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb78p065.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb78p065.jpg
+(1937, 2439)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb42p081.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb42p081.jpg
+(1927, 2480)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb32p105.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb32p105.jpg
+(1886, 2440)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb68p077.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb68p077.jpg
+(1914, 2452)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb00p104.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb00p104.jpg
+(1403, 1970)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb35p120.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb35p120.jpg
+(1994, 2534)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb26p024.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb26p024.jpg
+(1878, 2509)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb31p072.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb31p072.jpg
+(1999, 2473)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb48p042.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb48p042.jpg
+(1888, 2485)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/2_notes/sb46p087-modules.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb46p087-modules.jpg
+(1924, 2509)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb31p099.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb31p099.jpg
+(1866, 2490)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb11p061.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb11p061.jpg
+(1930, 2451)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb42p013.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb42p013.jpg
+(1954, 2510)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb68p081.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb68p081.jpg
+(1904, 2455)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p088.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p088.jpg
+(1979, 2506)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb26p039.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb26p039.jpg
+(1953, 2487)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb69p115.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb69p115.jpg
+(1929, 2513)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb38p013.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb38p013.jpg
+(1965, 2519)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb68p090.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb68p090.jpg
+(1905, 2468)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb69p105.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb69p105.jpg
+(1912, 2503)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb00p034.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb00p034.jpg
+(1405, 1968)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb11p060.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb11p060.jpg
+(1948, 2451)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb69p107--coffee.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb69p107--coffee.jpg
+(1925, 2506)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/2_notes/sb73p059--brainstorm.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb73p059--brainstorm.jpg
+(1628, 2480)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb32p116.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb32p116.jpg
+(1964, 2501)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p110.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p110.jpg
+(1988, 2530)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb73p078.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb73p078.jpg
+(1738, 2531)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/1_watercolors/sb26p011.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb26p011.jpg
+(1962, 2514)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb38p099.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb38p099.jpg
+(1918, 2505)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb07p022.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb07p022.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb44p053.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb44p053.jpg
+(1942, 2482)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb78p073.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb78p073.jpg
+(1888, 2434)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb28p035.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb28p035.jpg
+(1988, 2465)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb25p150.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p150.jpg
+(1436, 2028)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb07p009.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb07p009.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb68p080.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb68p080.jpg
+(1914, 2455)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb07p016.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb07p016.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb32p078.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb32p078.jpg
+(1920, 2474)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb46p057.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb46p057.jpg
+(1951, 2517)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p023.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p023.jpg
+(2018, 2589)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb46p004.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb46p004.jpg
+(1888, 2547)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/2_notes/sb25p118.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb25p118.jpg
+(1293, 2022)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb42p100.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb42p100.jpg
+(1813, 2496)
+
+
+
+
+

get embeddings for the cropped images

+
+
cropped_emb_rows = []
+for x in embed_dir(
+    preprocessing_dir, learn
+):  # important: use "preprocessing_dir" so we get embeds of the cropped imgs
+    cropped_emb_rows.append(x)
+cropped_embd_df = pd.DataFrame(cropped_emb_rows)
+print(f"num rows: {len(cropped_embd_df)}")
+cropped_embd_df.head()
+
+
total items: 2922, num batches: 46
+num rows: 2922
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idxabs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csv
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+ +
+
+
+
+

join the dataframes.

+

from pandas.DataFrame.join — pandas 2.1.4 documentation:

+
+

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result. Note: default is a “left” join, preserving the left’s index space.

+
+

rsuffix to add _cropped to any overlapping column names.

+

Here are the expected columns:

+
idx        # totally different from index scheme already being used - kill it
+abs_fname  # should be same as preprocessed_abs_path
+rel_fname  # same as humanlabeled_rel_path (JOIN KEY)
+label      # same as humanlabeled_metacluster_name
+pred_label # DROP
+pred_idx   # DROP
+pred_probs # DROP
+emb_csv    # keep this!
+
+
del cropped_embd_df["idx"]
+del cropped_embd_df["pred_label"]
+del cropped_embd_df["pred_probs"]
+del cropped_embd_df["pred_idx"]
+cropped_embd_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelemb_csv
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+ +
+
+
+
+
+
joined_df = metaclusters_df.join(
+    cropped_embd_df.set_index("rel_fname"),
+    on="handlabeled_rel_path",
+    # validate='one_to_one',
+    rsuffix="_cropped",
+)
+joined_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetacluster...visual_abs_pathhandlabeled_rel_pathhandlabeled_metacluster_namehandlabeled_metacluster_idhandlabeled_metacluster_was_correctpreprocessed_abs_pathhandlabeled_abs_pathabs_fname_croppedlabel_croppedemb_csv_cropped
idx
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+ +

5 rows × 23 columns

+
+
+
+
+
+
# validation
+assert len(metaclusters_df) == len(joined_df), "some rows are missing"
+
+
+
assert not joined_df.apply(
+    lambda row: row.abs_fname_cropped == row.preprocessed_abs_path, axis=1
+).any(), "some row had a mismatch between expected filenames"
+del joined_df["abs_fname_cropped"]
+
+
+
# assert not joined_df.apply(
+#     lambda row: row.label_cropped == row.handlabeled_metacluster_name, axis=1
+# ).any(), "some row had a mismatch between expected metacluster_name"
+# # del joined_df["label_cropped"]
+
+
+
joined_df[["label_cropped", "handlabeled_metacluster_name"]].head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
label_croppedhandlabeled_metacluster_name
idx
00_drawings0_drawings
10_drawings0_drawings
23_badscans3_badscans
30_drawings0_drawings
41_watercolors1_watercolors
+ +
+
+
+
+
+
joined_df["labels_match"] = joined_df.apply(
+    lambda row: row.label_cropped == row.handlabeled_metacluster_name, axis=1
+)
+
+
+
joined_df[joined_df.labels_match == False]
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetacluster...visual_abs_pathhandlabeled_rel_pathhandlabeled_metacluster_namehandlabeled_metacluster_idhandlabeled_metacluster_was_correctpreprocessed_abs_pathhandlabeled_abs_pathlabel_croppedemb_csv_croppedlabels_match
idx
+ +

0 rows × 23 columns

+
+
+
+
+
+
# del joined_df['labels_match']
+
+
+
# del joined_df['label_cropped']
+
+
+
cropped_tsv_fname = f"{preprocessing_dir}/cropped.tsv"
+
+joined_df.to_csv(
+    cropped_tsv_fname, index=True, index_label="idx", sep="\t", header=True
+)
+
+cropped_df = pd.read_csv(cropped_tsv_fname, delimiter="\t", index_col="idx")
+
+cdf = cropped_df
+cdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetacluster...indiv_fnamevisual_rel_pathvisual_abs_pathhandlabeled_rel_pathhandlabeled_metacluster_namehandlabeled_metacluster_idhandlabeled_metacluster_was_correctpreprocessed_abs_pathhandlabeled_abs_pathemb_csv_cropped
idx
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+ +

5 rows × 21 columns

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+ + +
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+ +
+ +
+ + + + + \ No newline at end of file diff --git a/jupyter/part2_exploring_embeddings.html b/jupyter/part2_exploring_embeddings.html new file mode 100644 index 0000000..fcaad16 --- /dev/null +++ b/jupyter/part2_exploring_embeddings.html @@ -0,0 +1,69073 @@ + + + + + + + + + +andrewlook - Part 2: Exploring Embeddings + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ +
+ + + + +
+ +
+
+

Part 2: Exploring Embeddings

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
EXTERNAL_PARENT_DIR = Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks')
+EXTERNAL_DATA_HOME = EXTERNAL_PARENT_DIR / 'singleline_data'
+EPOCH = SketchbookEpoch(epoch='20231214', data_home=EXTERNAL_DATA_HOME)
+
+CURR_01_FLAT = EPOCH.dir_01_FLAT()
+CURR_02_CATEGORIZED = EPOCH.dir_02_CATEGORIZED()
+CURR_03_HAND = EPOCH.dir_03_HANDLABELED()
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
# learn = load_resnet(
+#     data_home=EXTERNAL_DATA_HOME,
+#     model_path=PRETRAINED_MODEL_PATH,
+#     model_checkpoint=PRETRAINED_CHECKPOINT,
+# )
+
+# learn.model[-1]
+
+
+
embeddings_fname = EPOCH.tsv_01_FLAT()
+df = pd.read_csv(embeddings_fname, delimiter="\t", index_col="idx")
+df.head()
+
+embs = pd_series_to_embs(df.emb_csv)
+print(embs.shape)
+
+
(2922, 512)
+
+
+
+
centroids, cluster_to_label = load_centroids(
+    data_home=EXTERNAL_DATA_HOME,
+    model_path=PRETRAINED_MODEL_PATH
+)
+assigner = cluster_assigner(centroids, cluster_to_label)
+
+
reading /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_centroids.json
+reading /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_labels.json
+
+
+
+
# clusters_fname = f"{dir_to_embed}_clusters.tsv"
+
+# # NOTE: when saving embeddings CSV, idx was a regular column (not a pandas 'index').
+# # When reloaded, 'idx' was made the index column. TODO: make original DF use 'idx' as index,
+# # so saving and reloading code is identical.
+# df.to_csv(clusters_fname, index=True, index_label="idx", sep="\t", header=True)
+
+# cdf = pd.read_csv(clusters_fname, delimiter="\t", index_col="idx")
+# cdf.knn_clusterid.hist()
+
+
+

categorizing clusters, bucketing images into new dir

+

Thinking out loud: - how to make these scripts idempotent? - ideally, when I get these images bucketed by directory, I can manually “re-label” any incorrect ones by moving them to a new dir.

+

when I add more files at the input stage, it’d be nice if I could re-run the scripts: - flatten should copy over the new files (maybe it doesn’t need to replace - that’d be faster) - vis-cluster not recategorizing the ones I’ve already labeled would be helpful. - do I need to save kmeans.faiss and/or index.faiss for this to work?

+
+
# # dir_to_embed = singleline_data_home() / "raster/epoch-20231214/01_FLAT"
+# dest_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/02_VISUAL_CATEGORIES"
+# )
+
+# # add useful metadata about where we're going to move the files to
+# df["indiv_fname"] = df.rel_fname.map(lambda f: os.path.basename(f))
+# df["visual_rel_path"] = df.apply(
+#     lambda row: os.path.join(row["metacluster_name"], row["indiv_fname"]), axis=1
+# )
+# df["visual_abs_path"] = df.visual_rel_path.map(lambda f: os.path.join(dest_dir, f))
+
+# # print(df["indiv_fname"][0:3])
+# # print(df["visual_rel_path"][0:3])
+# # print(df["visual_abs_path"][0:3])
+
+
+
# metaclusters_fname = f"{dir_to_embed}/metaclusters.tsv"
+# df.to_csv(metaclusters_fname, index=True, index_label="idx", sep="\t", header=True)
+
+# metaclusters_df = pd.read_csv(metaclusters_fname, delimiter="\t", index_col="idx")
+# metaclusters_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetaclustermetacluster_nameindiv_fnamevisual_rel_pathvisual_abs_path
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3/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb25p227.jpgart/sb25p227.jpgartxtra40.158396,0.008277,0.007424,0.016584,0.8093191.3776337,-0.5714155,-0.2587232,-0.6553513,1.3532461,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.25077122,0.050992966,-0.72562873,0.1406585,-0.6266761,1.0653365,0.96198404,-0.5467274,0.30261123,0.6327609,-0.6497359,-0.64108604,-0.28434965,-0.35651857,-0.6779002,-0.28245115,-0.6690378,-0.4518578,0.030088186,-0.73986673,-0.18931103,-0.041653574,-0.67007643,-0.52171326,-0.29881266,-0.600525,-0.23231092,-0.73933,-0.57338566,-0.65925777,1.8833572,-0.61049384,0.44560486,0.14075392,-0.6682474,-0.75794554,-0.59257406,-0.07511848,-0.6001429,-0.651804,-0.5315948,0.023931146,0.723624,-0.6482407,-...8188.5807500_drawingssb25p227.jpg0_drawings/sb25p227.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/0_drawings/sb25p227.jpg
4/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb07p104.jpgart/sb07p104.jpgartxtra40.177406,0.008799,0.008245,0.017177,0.788373-0.39105213,0.96309596,-0.73929733,0.4489988,0.8297614,-0.3937308,-0.7267234,-0.32461396,-0.7537334,-0.42715576,-0.62038505,-0.72562873,0.3037846,-0.6266761,-0.5213815,-0.31669965,-0.5467274,-0.6520286,-0.22544897,-0.6497359,-0.64108604,0.6374447,-0.5446661,-0.6779002,-0.14143991,-0.6690378,-0.44753593,0.66945183,-0.73986673,-0.45211998,-0.05996394,-0.21525535,0.82928157,-0.04412651,-0.600525,-0.04148197,-0.73825717,-0.57338566,-0.25227422,2.3566113,-0.61049384,1.0027854,-0.40134108,1.3733256,-0.75794554,-0.59257406,-0.4395525,-0.6001429,1.0358506,1.1911299,0.8162154,0.032713354,-0.6482407...5238.3317011_watercolorssb07p104.jpg1_watercolors/sb07p104.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/1_watercolors/sb07p104.jpg
+ +
+
+
+
+
+
+

moving files

+
+
# test_files = get_image_files(dir_to_embed)
+# test_files
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
# L(os.path.basename(f) for f in test_files)
+
+
+
# "NON_EXISTENT" in L(os.path.basename(f) for f in test_files), "sb38p036.jpg" in L(
+#     os.path.basename(f) for f in test_files
+# )
+
+
+
# existing_files = get_image_files(dest_dir)
+
+
+
# existing_files
+
+
+
# import math
+# import os
+# import shutil
+
+# from PIL import Image
+
+# """
+# inputs:
+# - dest_dir
+# - metaclusters_df
+# """
+
+
+# existing_files = L(os.path.basename(f) for f in get_image_files(dest_dir))
+
+# for idx in range(len(metaclusters_df)):
+#     row = metaclusters_df.iloc[idx]
+
+#     indiv_fname = row.indiv_fname
+#     if indiv_fname in existing_files:
+#         print(f"skipping {indiv_fname}")
+#         continue
+
+#     orig_abs_path = row.abs_fname
+#     visual_abs_path = row.visual_abs_path
+
+#     visual_dir = os.path.dirname(visual_abs_path)
+#     if not os.path.isdir(visual_dir):
+#         os.makedirs(visual_dir)
+
+#     print(f"writing to {visual_abs_path}")
+#     shutil.copy(orig_abs_path, visual_abs_path)
+#     # try:
+#     #     img = Image.open(f"/Users/al/Dropbox/2-Areas/2-Sketchbooks/{row.fname}")
+#     #     crop_amt = math.floor(img.size[0] * 20 / 224)
+#     #     cropped_img = img.crop((crop_amt, 0, img.size[0] - crop_amt, img.size[1]))
+#     #     # cropped_img
+#     #     cropped_img.save(out_path)
+#     # except Exception as e:
+#     #     print(e)
+
+
+
+

hand-labeling (+ braindump)

+

Since the clusters weren’t 100% accurate, I hand-edited the clusters (by going through each folder, tagging anything that belonged in a different folder, then grouping the folder by tags and moving out any tagged files).

+

First I copied this directory:

+
full-v2/02_VISUAL_CATEGORIES
+

to

+
full-v2/03_VISUAL_HAND_LABELED
+

So, I should probably - [ ] iterate through the files in metaclusters_df and mark what their updated relpaths are - [ ] save the result to a TSV file (in 03_VISUAL_HAND_LABELED?) - [ ] update my cropping code below to use the more accurately selected files when populating 10_SIMPLE_CROP - [ ] (maybe) also save the filepath of the cropped files?

+

Then, if I want, I could - re-cluster within the watercolors and within the drawings. Perhaps cluster on the cropped versions? That might give me a starting point for filtering down to a more similar set of drawings. - Alternatively, I might get more bang for my buck by going into vector land and splitting the non-overlapping strokes into separate drawings - Also: can i find and filter out all diagchains / tower drawings? those might clarify the signal a bit

+

And it’d be really fun (!) to… - Visualizing Data using the Embedding Projector in TensorBoard  |  TensorFlow - Taking the TensorBoard Embedding Projector to the Next Level - though I’ll want clear PNG’s for the sprites. perhaps this is easier to do after I’ve vectorized and re-saved to SVG.

+

I’m also curious whether there’s anything automated I can do to: - do “face detection”, so I’m only working with actual drawings of faces? - full-train on quickdraw faces dataset, then fine-tune on my face drawings? - detect bounding boxes for handwriting on the page (potentially crop it out at vectorization time?) - detect edge of sketchbook pages? - detect poorly scanned / crop to just the page?

+

Side note re: the handlabeling process: - why didn’t “covers” get their own cluster? - where did all the “xtra” ones go? were they in ‘badcovers’ (which I didn’t manually sort through yet)

+
+

first, get a mapping of indiv_fname to the relative path within 03_VISUAL_HAND_LABELED

+
+
# handlabeled_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/03_VISUAL_HAND_LABELED"
+# )
+
+# handlabeled_files = get_image_files(handlabeled_dir)
+# handlabeled_files
+
+
+
# def basename_and_parent(f):
+#     base = os.path.basename(f)
+#     parent_abs = os.path.dirname(f)
+#     parent_base = os.path.basename(parent_abs)
+#     handlabeled_relpath = os.path.join(parent_base, base)
+#     return base, handlabeled_relpath
+
+
+# label_mapping = {
+#     base: parent for base, parent in [basename_and_parent(f) for f in handlabeled_files]
+# }
+
+
+
+

infer hand-chosen label

+
+
# metaclusters_df.head()
+
+
+
# metaclusters_df["handlabeled_rel_path"] = metaclusters_df.indiv_fname.map(
+#     lambda f: label_mapping[f]
+# )
+# print(metaclusters_df.handlabeled_rel_path[:5])
+
+# metaclusters_df["handlabeled_abs_path"] = metaclusters_df.handlabeled_rel_path.map(
+#     lambda p: os.path.join(handlabeled_dir, p)
+# )
+# print(metaclusters_df.handlabeled_abs_path[:5])
+
+# metaclusters_df[
+#     "handlabeled_metacluster_name"
+# ] = metaclusters_df.handlabeled_rel_path.map(lambda p: os.path.dirname(p))
+# print(metaclusters_df.handlabeled_metacluster_name[:5])
+
+# metaclusters_df[
+#     "handlabeled_metacluster_id"
+# ] = metaclusters_df.handlabeled_metacluster_name.map(
+#     lambda n: metacluster_name_to_id[n]
+# )
+
+
+
# # note to self: give this a better name
+# # - not calling it "metacluster", instead -> "visual category"? "category"?
+# # - "metacluster_id"
+
+# # comparing the id's
+# metaclusters_df[["handlabeled_metacluster_id", "metacluster"]][:5]
+
+
+
# # did the label change when I had to hand-label?
+# metaclusters_df["handlabeled_metacluster_was_correct"] = metaclusters_df.apply(
+#     lambda row: row.metacluster == row.handlabeled_metacluster_id, axis=1
+# )[:20]
+# print(metaclusters_df.handlabeled_metacluster_was_correct[:10])
+
+
+
+

precompute path to which the image is going to get preprocessed/cropped, save DF as handlabeled.tsv

+
+
# preprocessing_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/10_SIMPLE_CROP"
+# )
+
+# metaclusters_df["preprocessed_abs_path"] = metaclusters_df.handlabeled_rel_path.map(
+#     lambda p: os.path.join(preprocessing_dir, p)
+# )
+# metaclusters_df.preprocessed_abs_path[:5]
+
+
+
# metaclusters_df.head()
+
+
+
# handlabeled_tsv_fname = f"{handlabeled_dir}/handlabeled.tsv"
+
+# metaclusters_df.to_csv(
+#     handlabeled_tsv_fname, index=True, index_label="idx", sep="\t", header=True
+# )
+
+# handlabeled_df = pd.read_csv(handlabeled_tsv_fname, delimiter="\t", index_col="idx")
+
+# hdf = handlabeled_df
+# hdf.head()
+
+
+
+

side quest: how accurate was clustering for separating the categories I care about?

+

Time to make a classification report!

+ +
+
# from sklearn.metrics import classification_report
+
+# target_names = list(metacluster_id_to_name.values())
+
+# y_true = metaclusters_df.handlabeled_metacluster_id
+# y_pred = metaclusters_df.metacluster
+
+# print(classification_report(y_true, y_pred, target_names=target_names))
+
+
               precision    recall  f1-score   support
+
+   0_drawings       0.95      0.96      0.96      1923
+1_watercolors       0.83      0.93      0.88       404
+      2_notes       0.86      0.79      0.82       287
+   3_badscans       1.00      0.99      0.99       269
+     4_covers       0.00      0.00      0.00        39
+
+     accuracy                           0.93      2922
+    macro avg       0.73      0.73      0.73      2922
+ weighted avg       0.92      0.93      0.92      2922
+
+
+
+
/Users/al/miniconda3_m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/Users/al/miniconda3_m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/Users/al/miniconda3_m1/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+
+
+
+
# from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
+
+# cm = confusion_matrix(y_true, y_pred)
+# disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=target_names)
+# disp.plot()
+# # plt.show()
+
+
+
# for idx in range(len(metaclusters_df)):
+
+#     if idx > 10:
+#         break
+
+#     row = metaclusters_df.iloc[idx]
+
+#     indiv_fname = row.indiv_fname
+
+#     handlabeled_rel_path = label_mapping[indiv_fname]
+#     handlabeled_metacluster_name = os.path.dirname(handlabeled_rel_path)
+#     print(handlabeled_rel_path, handlabeled_metacluster_name, row.metacluster_name)
+
+
+
+
+

processing

+
+

v0: dummy crop (both sides, by a fixed percentage)

+
+
# preprocessing_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/10_SIMPLE_CROP"
+# )  # <-- this was defined earlier.
+existing_files = L(os.path.basename(f) for f in get_image_files(preprocessing_dir))
+
+crop_frac = 20.0 / 224
+print(f"using crop_frac={crop_frac}")
+
+for idx in range(len(metaclusters_df)):
+
+    # if idx > 10:
+    #     break
+
+    row = metaclusters_df.iloc[idx]
+    # if row.metacluster_name != "0_drawings":
+    #     # print(f"ignore {row.metacluster_name}")
+    #     continue
+
+    indiv_fname = row.indiv_fname
+    if indiv_fname in existing_files:
+        print(f"skipping {indiv_fname}")
+        continue
+
+    # handlabeled_rel_path = row.handlabeled_rel_path
+    # preproc_abs_path = os.path.join(preprocessing_dir, handlabeled_rel_path)
+    preproc_abs_path = row.preprocessed_abs_path
+    # print(preproc_abs_path)
+
+    preproc_dir = os.path.dirname(preproc_abs_path)
+    if not os.path.isdir(preproc_dir):
+        os.makedirs(preproc_dir)
+
+    handlabeled_abs_path = row.handlabeled_abs_path
+    print(f"writing from {handlabeled_abs_path} to {preproc_abs_path}")
+
+    try:
+        img = Image.open(handlabeled_abs_path)
+        print(img.size)
+        crop_amt = math.floor(
+            img.size[0] * crop_frac
+        )  # compute number of pixels to crop (roughly 8% of page size)
+        cropped_img = img.crop((crop_amt, 0, img.size[0] - crop_amt, img.size[1]))
+        # cropped_img
+        cropped_img.save(preproc_abs_path)
+    except Exception as e:
+        print(e)
+
+
using crop_frac=0.08928571428571429
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb77p043.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb77p043.jpg
+(1920, 2461)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p021.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p021.jpg
+(2006, 2597)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb06p023.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb06p023.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb25p227.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p227.jpg
+(1364, 2024)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/1_watercolors/sb07p104.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb07p104.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/1_watercolors/sb31p017.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb31p017.jpg
+(1959, 2518)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb48p057.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb48p057.jpg
+(1939, 2503)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb01p007.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb01p007.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb77p044.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb77p044.jpg
+(1850, 2462)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/2_notes/sb06p076.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb06p076.jpg
+(1700, 2338)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb11p024.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb11p024.jpg
+(1914, 2469)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p006--kitchen-nap.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p006--kitchen-nap.jpg
+(1931, 2600)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb68p093.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb68p093.jpg
+(1896, 2460)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/2_notes/sb25p119.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb25p119.jpg
+(1352, 2022)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb45p121.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb45p121.jpg
+(1865, 2563)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb69p013.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb69p013.jpg
+(2018, 2510)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb48p068.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb48p068.jpg
+(1945, 2483)
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+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p023.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p023.jpg
+(2018, 2589)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb46p004.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb46p004.jpg
+(1888, 2547)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/2_notes/sb25p118.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb25p118.jpg
+(1293, 2022)
+writing from /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb42p100.jpg to /Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb42p100.jpg
+(1813, 2496)
+
+
+
+
+

get embeddings for the cropped images

+
+
cropped_emb_rows = []
+for x in embed_dir(
+    preprocessing_dir, learn
+):  # important: use "preprocessing_dir" so we get embeds of the cropped imgs
+    cropped_emb_rows.append(x)
+cropped_embd_df = pd.DataFrame(cropped_emb_rows)
+print(f"num rows: {len(cropped_embd_df)}")
+cropped_embd_df.head()
+
+
total items: 2922, num batches: 46
+num rows: 2922
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idxabs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csv
00/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p199.jpg0_drawings/sb25p199.jpg0_drawingsxtra40.163658,0.008260,0.008051,0.016511,0.803520-0.5330713,-0.5714155,0.73641866,-0.6553513,-0.6254566,-0.65341413,0.13287973,-0.4896673,-0.7537334,0.25619665,-0.18803719,-0.72562873,-0.662768,-0.6266761,0.64458674,-0.57515705,-0.5467274,-0.071858704,1.1719493,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.46651447,0.3104514,0.90333235,-0.44427776,-0.46260503,1.2030442,-0.45211998,-0.2965303,-0.67007643,0.9364619,-0.4592305,-0.600525,-0.49382913,-0.73933,-0.57338566,-0.5953745,0.43034816,-0.61049384,0.8340209,-0.6189488,-0.1796374,-0.30213186,-0.59257406,0.2503296,-0.6001429,-0.4512694,-0.5315948,-0.59497476,-0.66552746,0.7661344,-0.1...
11/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p043.jpg0_drawings/sb25p043.jpg0_drawingsxtra40.153572,0.008271,0.007657,0.016232,0.814268-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.3503988,-0.4896673,-0.7537334,0.6263685,0.6809032,-0.72562873,0.24799824,-0.6266761,0.94325465,0.30273575,-0.5467274,0.12154883,0.7854639,-0.6497359,-0.510615,-0.55193967,-0.5446661,-0.6779002,-0.57759243,0.5402647,-0.4518578,-0.5982413,-0.35663128,-0.45211998,-0.68509877,-0.67007643,0.21951097,-0.4592305,-0.14663172,-0.6186755,0.7839405,-0.57338566,0.08800787,1.0596322,-0.61049384,0.058719218,-0.0042130947,-0.6682474,0.108083844,-0.59257406,0.63600713,-0.6001429,-0.651804,-0.5315948,-0.5008225,0.18616694,-0.4135767,-0....
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33/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb46p090-fundraising-deck-illustrations.jpg2_notes/sb46p090-fundraising-deck-illustrations.jpg2_notesxtra40.171107,0.008683,0.008175,0.016800,0.795234-0.52853656,2.5605347,-0.73929733,-0.6553513,2.4787,-0.65341413,-0.7267234,1.532997,-0.7537334,2.886949,2.3408055,-0.72562873,-0.29813898,-0.6266761,-0.68991846,0.0828715,2.9314456,-0.6520286,3.5069184,-0.6497359,-0.64108604,0.39781398,-0.5446661,-0.6779002,-0.57759243,-0.6690378,0.77024436,-0.36507833,3.7055478,2.4240553,-0.68509877,-0.67007643,2.9676533,1.0786949,0.7419685,-0.6186755,1.2447245,1.4471076,0.9406444,-0.6179389,-0.61049384,2.6705651,-0.30582196,-0.6682474,-0.75794554,-0.4556011,2.5623467,-0.6001429,-0.651804,2.429375,-0.59497476,-0.66552746,-0.6482407,4.322685,-0.37778312,2....
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+ +
+
+
+
+

join the dataframes.

+

from pandas.DataFrame.join — pandas 2.1.4 documentation:

+
+

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result. Note: default is a “left” join, preserving the left’s index space.

+
+

rsuffix to add _cropped to any overlapping column names.

+

Here are the expected columns:

+
idx        # totally different from index scheme already being used - kill it
+abs_fname  # should be same as preprocessed_abs_path
+rel_fname  # same as humanlabeled_rel_path (JOIN KEY)
+label      # same as humanlabeled_metacluster_name
+pred_label # DROP
+pred_idx   # DROP
+pred_probs # DROP
+emb_csv    # keep this!
+
+
del cropped_embd_df["idx"]
+del cropped_embd_df["pred_label"]
+del cropped_embd_df["pred_probs"]
+del cropped_embd_df["pred_idx"]
+cropped_embd_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelemb_csv
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2/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb26p011.jpg1_watercolors/sb26p011.jpg1_watercolors1.3847659,-0.5714155,-0.73929733,1.9980699,-0.38369656,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.09318411,-0.5577197,-0.72562873,-0.662768,1.3684493,-0.6397908,-0.57515705,-0.5467274,-0.6520286,1.2087092,-0.6497359,-0.64108604,-0.55193967,1.1091483,1.2902013,0.6211384,-0.2669702,-0.4518578,0.72892654,1.2568704,0.11155799,0.59543765,-0.67007643,0.14250022,-0.4006025,-0.600525,1.2841779,-0.73933,0.03668952,-0.65925777,1.0671545,-0.61049384,-0.58021957,0.48637342,0.40816802,1.7963738,-0.51651776,0.10670686,-0.6001429,-0.651804,-0.5315948,0.9057182,1.036983,0.6402601,-0.39563733,-0.43874...
3/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/2_notes/sb46p090-fundraising-deck-illustrations.jpg2_notes/sb46p090-fundraising-deck-illustrations.jpg2_notes-0.52853656,2.5605347,-0.73929733,-0.6553513,2.4787,-0.65341413,-0.7267234,1.532997,-0.7537334,2.886949,2.3408055,-0.72562873,-0.29813898,-0.6266761,-0.68991846,0.0828715,2.9314456,-0.6520286,3.5069184,-0.6497359,-0.64108604,0.39781398,-0.5446661,-0.6779002,-0.57759243,-0.6690378,0.77024436,-0.36507833,3.7055478,2.4240553,-0.68509877,-0.67007643,2.9676533,1.0786949,0.7419685,-0.6186755,1.2447245,1.4471076,0.9406444,-0.6179389,-0.61049384,2.6705651,-0.30582196,-0.6682474,-0.75794554,-0.4556011,2.5623467,-0.6001429,-0.651804,2.429375,-0.59497476,-0.66552746,-0.6482407,4.322685,-0.37778312,2....
4/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb38p002.jpg0_drawings/sb38p002.jpg0_drawings2.1299553,0.7444822,-0.73929733,-0.6553513,0.15277767,-0.04387927,-0.49867624,-0.4896673,-0.7537334,0.088731796,1.2834122,-0.72562873,1.3512818,-0.6266761,0.7380045,0.66530037,-0.5467274,-0.6520286,0.6048254,-0.6497359,-0.64108604,-0.55193967,-0.5409544,-0.6779002,1.4321909,-0.6551965,-0.4518578,-0.5982413,0.24350339,-0.45211998,-0.34182537,-0.67007643,0.96453965,-0.2583686,-0.600525,-0.6186755,-0.327084,-0.34915227,1.854417,1.7647287,-0.61049384,0.027278483,1.9321591,-0.6682474,-0.75794554,-0.59257406,-0.51042324,-0.6001429,-0.651804,0.5398095,-0.59497476,-0.3940103,1.0658383,0.6436641,0....
+ +
+
+
+
+
+
joined_df = metaclusters_df.join(
+    cropped_embd_df.set_index("rel_fname"),
+    on="handlabeled_rel_path",
+    # validate='one_to_one',
+    rsuffix="_cropped",
+)
+joined_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetacluster...visual_abs_pathhandlabeled_rel_pathhandlabeled_metacluster_namehandlabeled_metacluster_idhandlabeled_metacluster_was_correctpreprocessed_abs_pathhandlabeled_abs_pathabs_fname_croppedlabel_croppedemb_csv_cropped
idx
0/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb77p043.jpgart/sb77p043.jpgartxtra40.168532,0.008473,0.008068,0.016940,0.797986-0.6198182,-0.5714155,-0.6871516,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,1.4904574,-0.72562873,-0.662768,-0.6266761,0.6913696,0.054546177,-0.5467274,-0.6520286,0.117179275,0.94330966,0.5815062,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.4584825,-0.3818994,-0.5750927,-0.73986673,-0.45211998,-0.6141643,-0.67007643,-0.52171326,-0.4592305,1.4788612,-0.6186755,0.799198,-0.57338566,-0.17778617,0.10336053,0.093812525,0.17924881,-0.6189488,-0.6682474,2.1040225,-0.59257406,1.6553459,-0.6001429,0.5109105,0.19352531,-0.2517513,-0.66552746,-0.6482407,-0.4732...11199.229611.../Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/1_watercolors/sb77p043.jpg0_drawings/sb77p043.jpg0_drawings0False/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb77p043.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb77p043.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb77p043.jpg0_drawings-0.6198182,-0.5714155,0.2937309,0.2691822,-0.6254566,-0.46855158,-0.7267234,-0.4896673,-0.7537334,-0.42715576,2.241211,-0.013144195,-0.30681813,-0.6266761,0.43516117,-0.57515705,-0.5467274,-0.5245168,1.3334948,2.1400952,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,0.38178188,-0.5982413,-0.50063276,-0.21540937,0.4948988,-0.67007643,-0.52171326,-0.4592305,1.8570362,-0.6186755,0.94023055,-0.57338566,0.5270257,-0.6179389,2.302543,1.1004333,0.15528035,-0.6682474,1.890626,-0.59257406,1.331521,-0.6001429,1.5220151,0.93749076,-0.59497476,-0.66552746,-0.6482407,-0.4732165,0....
1/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb67p021.jpgart/sb67p021.jpgartxtra40.170976,0.008673,0.008480,0.017028,0.7948431.1073987,0.3512016,-0.73929733,-0.6553513,1.7477593,-0.65341413,0.32179314,-0.4896673,-0.1797334,-0.42715576,1.3163843,-0.72562873,-0.61387855,-0.6266761,-0.35361218,0.37611246,-0.08771938,-0.5262803,0.9261699,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.32559583,-0.6690378,-0.4518578,0.51828945,-0.4238917,-0.45211998,-0.16527033,-0.67007643,1.0136865,-0.4592305,-0.600525,-0.060860336,-0.19446748,-0.24127388,2.2282472,1.8003348,-0.61049384,0.39795423,0.2626753,-0.6682474,-0.75794554,-0.59257406,0.80257696,-0.6001429,-0.651804,-0.028328001,0.53641737,-0.6501347,-0.6482407,0....3132.261110.../Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/0_drawings/sb67p021.jpg0_drawings/sb67p021.jpg0_drawings0True/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p021.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb67p021.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb67p021.jpg0_drawings-0.20756221,-0.24843562,0.29454154,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,0.9697293,-0.72562873,-0.662768,-0.6266761,-0.23836556,0.4814458,-0.5467274,-0.6520286,0.838902,-0.6497359,-0.57338166,-0.55193967,-0.5446661,-0.28419846,1.6012006,-0.6690378,-0.4518578,-0.0104218125,-0.73986673,-0.3002332,-0.68509877,-0.67007643,-0.36297536,-0.4592305,-0.600525,-0.6186755,-0.73933,-0.57338566,-0.65925777,1.6904012,-0.61049384,-0.58021957,0.508085,0.14767355,0.8082305,-0.59257406,-0.6157326,-0.6001429,-0.651804,-0.30467254,0.17396247,-0.66552746,-0.6482407,-0.0...
2/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/notes/sb06p023.jpgnotes/sb06p023.jpgnotesxtra40.171455,0.008462,0.007966,0.016696,0.7954212.071539,1.9655468,-0.73929733,0.5748948,3.170734,-0.65341413,0.63399774,1.729534,-0.7537334,0.84828806,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.328189,0.38060355,1.1197739,0.73272705,-0.6497359,-0.64108604,1.9738588,0.9751267,-0.6779002,0.62476915,-0.6690378,-0.4518578,1.4331162,-0.73986673,0.5611434,-0.68509877,0.6532827,2.040183,0.9425397,0.18940705,-0.27290884,0.1475305,-0.57338566,1.9694935,1.2107165,-0.61049384,1.9219763,-0.047254384,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.17440769,-0.18534619,1.6759431,-0.59497476,-0.66552746,0.67768025,2.0296078,-0.731109...6223.156683.../Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/3_badscans/sb06p023.jpg3_badscans/sb06p023.jpg3_badscans3True/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb06p023.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb06p023.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb06p023.jpg3_badscans-0.6198182,3.0373774,-0.73929733,-0.6553513,2.4003017,-0.65341413,-0.7267234,1.4674367,-0.7537334,1.7004633,1.0316664,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.0147724,0.8003675,1.5867116,1.4791383,-0.6497359,-0.64108604,-0.017381668,-0.5446661,-0.6779002,0.8590278,-0.6690378,0.8003452,0.64224637,-0.73986673,0.95411706,-0.68509877,-0.67007643,3.127275,0.82505405,-0.600525,-0.6186755,-0.26080996,0.11058271,0.19624358,1.2965859,-0.61049384,1.7284827,-0.6189488,-0.6682474,-0.75794554,0.3355127,2.5574257,-0.6001429,-0.651804,1.8285801,-0.59497476,-0.66552746,-0.6482407,2.4977334,-0.731109...
3/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb25p227.jpgart/sb25p227.jpgartxtra40.158396,0.008277,0.007424,0.016584,0.8093191.3776337,-0.5714155,-0.2587232,-0.6553513,1.3532461,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.25077122,0.050992966,-0.72562873,0.1406585,-0.6266761,1.0653365,0.96198404,-0.5467274,0.30261123,0.6327609,-0.6497359,-0.64108604,-0.28434965,-0.35651857,-0.6779002,-0.28245115,-0.6690378,-0.4518578,0.030088186,-0.73986673,-0.18931103,-0.041653574,-0.67007643,-0.52171326,-0.29881266,-0.600525,-0.23231092,-0.73933,-0.57338566,-0.65925777,1.8833572,-0.61049384,0.44560486,0.14075392,-0.6682474,-0.75794554,-0.59257406,-0.07511848,-0.6001429,-0.651804,-0.5315948,0.023931146,0.723624,-0.6482407,-...8188.580750.../Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/0_drawings/sb25p227.jpg0_drawings/sb25p227.jpg0_drawings0True/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p227.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/0_drawings/sb25p227.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/0_drawings/sb25p227.jpg0_drawings-0.6198182,-0.428226,-0.73929733,-0.6553513,-0.49369657,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.28293163,-0.62038505,-0.32824886,2.5504894,-0.6266761,0.8397537,0.85716057,-0.5467274,-0.058725297,0.64954805,-0.6497359,-0.64108604,-0.36876833,-0.5446661,-0.29563427,0.08626467,-0.6690378,-0.4518578,0.64337015,-0.73986673,0.018805295,-0.68509877,-0.67007643,1.5876667,-0.4592305,-0.600525,-0.6186755,0.28688914,-0.57338566,-0.65925777,1.6810645,-0.61049384,1.8564332,-0.6189488,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.6001429,-0.63579994,-0.45296612,-0.29925343,-0.0263654,-0.64824...
4/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/art/sb07p104.jpgart/sb07p104.jpgartxtra40.177406,0.008799,0.008245,0.017177,0.788373-0.39105213,0.96309596,-0.73929733,0.4489988,0.8297614,-0.3937308,-0.7267234,-0.32461396,-0.7537334,-0.42715576,-0.62038505,-0.72562873,0.3037846,-0.6266761,-0.5213815,-0.31669965,-0.5467274,-0.6520286,-0.22544897,-0.6497359,-0.64108604,0.6374447,-0.5446661,-0.6779002,-0.14143991,-0.6690378,-0.44753593,0.66945183,-0.73986673,-0.45211998,-0.05996394,-0.21525535,0.82928157,-0.04412651,-0.600525,-0.04148197,-0.73825717,-0.57338566,-0.25227422,2.3566113,-0.61049384,1.0027854,-0.40134108,1.3733256,-0.75794554,-0.59257406,-0.4395525,-0.6001429,1.0358506,1.1911299,0.8162154,0.032713354,-0.6482407...5238.331701.../Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/1_watercolors/sb07p104.jpg1_watercolors/sb07p104.jpg1_watercolors1True/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb07p104.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/1_watercolors/sb07p104.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/1_watercolors/sb07p104.jpg1_watercolors-0.6198182,0.8895926,-0.73929733,0.6418346,0.46164584,1.0085131,-0.7267234,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.72562873,1.4399182,-0.6266761,0.474914,-0.57515705,-0.5467274,-0.6520286,1.2274435,-0.6497359,-0.27622125,-0.45870692,-0.5446661,-0.6779002,1.3343718,-0.6690378,-0.4518578,0.42277813,-0.73986673,-0.45211998,1.6291834,-0.67007643,-0.09817025,-0.00090405345,-0.600525,-0.6186755,-0.73933,-0.57338566,0.006386578,1.034553,-0.61049384,-0.58021957,-0.09483403,0.78779536,-0.73080033,0.10947651,0.3367887,-0.5382227,-0.26548737,0.5630663,2.15088,0.2266624,-0.6482407,0.049348265...
+ +

5 rows × 23 columns

+
+
+
+
+
+
# validation
+assert len(metaclusters_df) == len(joined_df), "some rows are missing"
+
+
+
assert not joined_df.apply(
+    lambda row: row.abs_fname_cropped == row.preprocessed_abs_path, axis=1
+).any(), "some row had a mismatch between expected filenames"
+del joined_df["abs_fname_cropped"]
+
+
+
# assert not joined_df.apply(
+#     lambda row: row.label_cropped == row.handlabeled_metacluster_name, axis=1
+# ).any(), "some row had a mismatch between expected metacluster_name"
+# # del joined_df["label_cropped"]
+
+
+
joined_df[["label_cropped", "handlabeled_metacluster_name"]].head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
label_croppedhandlabeled_metacluster_name
idx
00_drawings0_drawings
10_drawings0_drawings
23_badscans3_badscans
30_drawings0_drawings
41_watercolors1_watercolors
+ +
+
+
+
+
+
joined_df["labels_match"] = joined_df.apply(
+    lambda row: row.label_cropped == row.handlabeled_metacluster_name, axis=1
+)
+
+
+
joined_df[joined_df.labels_match == False]
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetacluster...visual_abs_pathhandlabeled_rel_pathhandlabeled_metacluster_namehandlabeled_metacluster_idhandlabeled_metacluster_was_correctpreprocessed_abs_pathhandlabeled_abs_pathlabel_croppedemb_csv_croppedlabels_match
idx
+ +

0 rows × 23 columns

+
+
+
+
+
+
# del joined_df['labels_match']
+
+
+
# del joined_df['label_cropped']
+
+
+
cropped_tsv_fname = f"{preprocessing_dir}/cropped.tsv"
+
+joined_df.to_csv(
+    cropped_tsv_fname, index=True, index_label="idx", sep="\t", header=True
+)
+
+cropped_df = pd.read_csv(cropped_tsv_fname, delimiter="\t", index_col="idx")
+
+cdf = cropped_df
+cdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
abs_fnamerel_fnamelabelpred_labelpred_idxpred_probsemb_csvclustercluster_distmetacluster...indiv_fnamevisual_rel_pathvisual_abs_pathhandlabeled_rel_pathhandlabeled_metacluster_namehandlabeled_metacluster_idhandlabeled_metacluster_was_correctpreprocessed_abs_pathhandlabeled_abs_pathemb_csv_cropped
idx
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2/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/01_FLAT/notes/sb06p023.jpgnotes/sb06p023.jpgnotesxtra40.171455,0.008462,0.007966,0.016696,0.7954212.071539,1.9655468,-0.73929733,0.5748948,3.170734,-0.65341413,0.63399774,1.729534,-0.7537334,0.84828806,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.328189,0.38060355,1.1197739,0.73272705,-0.6497359,-0.64108604,1.9738588,0.9751267,-0.6779002,0.62476915,-0.6690378,-0.4518578,1.4331162,-0.73986673,0.5611434,-0.68509877,0.6532827,2.040183,0.9425397,0.18940705,-0.27290884,0.1475305,-0.57338566,1.9694935,1.2107165,-0.61049384,1.9219763,-0.047254384,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.17440769,-0.18534619,1.6759431,-0.59497476,-0.66552746,0.67768025,2.0296078,-0.731109...6223.156683...sb06p023.jpg3_badscans/sb06p023.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/02_VISUAL_CATEGORIES/3_badscans/sb06p023.jpg3_badscans/sb06p023.jpg3_badscans3True/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/10_SIMPLE_CROP/3_badscans/sb06p023.jpg/Users/al/Dropbox/2-Areas/2-Sketchbooks/datasets/full-v2/03_VISUAL_HAND_LABELED/3_badscans/sb06p023.jpg-0.6198182,3.0373774,-0.73929733,-0.6553513,2.4003017,-0.65341413,-0.7267234,1.4674367,-0.7537334,1.7004633,1.0316664,-0.72562873,-0.662768,-0.6266761,-0.68991846,2.0147724,0.8003675,1.5867116,1.4791383,-0.6497359,-0.64108604,-0.017381668,-0.5446661,-0.6779002,0.8590278,-0.6690378,0.8003452,0.64224637,-0.73986673,0.95411706,-0.68509877,-0.67007643,3.127275,0.82505405,-0.600525,-0.6186755,-0.26080996,0.11058271,0.19624358,1.2965859,-0.61049384,1.7284827,-0.6189488,-0.6682474,-0.75794554,0.3355127,2.5574257,-0.6001429,-0.651804,1.8285801,-0.59497476,-0.66552746,-0.6482407,2.4977334,-0.731109...
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+ +

5 rows × 21 columns

+
+
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/jupyter/singleline_dataset_second_attempt.html b/jupyter/singleline_dataset_second_attempt.html new file mode 100644 index 0000000..4cedf82 --- /dev/null +++ b/jupyter/singleline_dataset_second_attempt.html @@ -0,0 +1,7564 @@ + + + + + + + + + + +andrewlook - clustering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+ +
+
+

clustering

+
+ +
+
+ Fill in a module description here +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
import math
+
+import pandas as pd
+from fastai.vision.all import *
+
+from singleline_stroke3.embeddings import *
+from singleline_stroke3.fileorg import *
+
+
+
EXTERNAL_PARENT_DIR = Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks')
+EXTERNAL_SKETCHBOOKS_DIR = EXTERNAL_PARENT_DIR / 'sketchbooks'
+EXTERNAL_DATA_HOME = EXTERNAL_PARENT_DIR / 'singleline_data'
+
+
+
PREV_EPOCH = SketchbookEpoch(epoch='20231214', data_home=EXTERNAL_DATA_HOME)
+EPOCH = SketchbookEpoch(epoch='20240104', data_home=EXTERNAL_DATA_HOME)
+
+PREV_01_FLAT = PREV_EPOCH.dir_01_FLAT()
+PREV_02_CATEGORIZED = PREV_EPOCH.dir_02_CATEGORIZED()
+PREV_03_HAND = PREV_EPOCH.dir_03_HANDLABELED()
+
+CURR_01_FLAT = EPOCH.dir_01_FLAT()
+CURR_02_CATEGORIZED = EPOCH.dir_02_CATEGORIZED()
+CURR_03_HAND = EPOCH.dir_03_HANDLABELED()
+CURR_04_CROP = EPOCH.dir_04_CROP()
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+no env var SINGLELINE_DATA_HOME, defaulting to: None
+no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
EXTERNAL_SKETCHBOOKS_DIR.ls()
+
+
(#33) [Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb67'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb69'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb05'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb68'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb50'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb35'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb32'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb04'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/.DS_Store'),Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/sketchbooks/sb26')...]
+
+
+
+
# TODO: quieter output
+flatten_sketchbooks(EXTERNAL_SKETCHBOOKS_DIR, CURR_01_FLAT)
+
+
+
CURR_01_FLAT
+
+
Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/01_FLAT')
+
+
+
+

Predict embeddings on new dataset

+
+
+
+
total items: 3032, num batches: 48
+
+
+
+
# dir_to_embed = DATA_HOME / "raster/epoch-20231214/01_FLAT"
+dir_to_embed = CURR_01_FLAT
+
+path = Path(dir_to_embed)
+files = get_image_files(path)
+len(files)
+
+batched_fnames, ordered_dls = batch_fnames_and_images(dir_to_embed)
+
+learn = load_resnet(
+    data_home=EXTERNAL_DATA_HOME,
+    model_path=PRETRAINED_MODEL_PATH,
+    model_checkpoint=PRETRAINED_CHECKPOINT,
+)
+
+#learn.model[-1]
+
+rows = []
+for x in embed_dir(dir_to_embed, learn, strip_dir=DATA_HOME):
+    rows.append(x)
+df = pd.DataFrame(rows)
+print(f"num rows: {len(df)}")
+df.head()
+
+embeddings_fname = f"{str(dir_to_embed)}.tsv"
+df.to_csv(embeddings_fname, index=False, sep="\t", header=True)
+print(f"wrote {embeddings_fname}")
+
+df = pd.read_csv(embeddings_fname, delimiter="\t", index_col="idx")
+df.head()
+
+embs = pd_series_to_embs(df.emb_csv)
+print(embs.shape)
+
+
Sequential(
+  (0): AdaptiveConcatPool2d(
+    (ap): AdaptiveAvgPool2d(output_size=1)
+    (mp): AdaptiveMaxPool2d(output_size=1)
+  )
+  (1): fastai.layers.Flatten(full=False)
+  (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (3): Dropout(p=0.25, inplace=False)
+  (4): Linear(in_features=1024, out_features=512, bias=False)
+  (5): ReLU(inplace=True)
+  (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (7): Dropout(p=0.5, inplace=False)
+  (8): Linear(in_features=512, out_features=5, bias=False)
+)
+
+
+
+
+
+
total items: 3032, num batches: 48
+num rows: 3032
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
idxindiv_fnameorig_fnamelabelemb_csv
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11sb77prear-cover.jpgcover/sb77prear-cover.jpgcover1.9835572,0.88216716,-0.73929733,-0.6553513,0.95597816,-0.65341413,-0.6597224,1.1695832,-0.7537334,0.30802843,-0.62038505,-0.72562873,-0.662768,-0.01779759,-0.68991846,-0.57515705,-0.07535747,-0.6520286,1.559851,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.3563297,-0.6690378,-0.17245707,-0.4265231,-0.73986673,-0.45211998,-0.5716219,-0.67007643,1.3844777,-0.01508677,-0.600525,-0.18866462,-0.73933,-0.28364885,-0.65925777,0.6516924,-0.61049384,1.7278569,0.21176928,-0.37875053,-0.75794554,0.16353595,1.3892753,-0.6001429,-0.6438874,2.9371314,0.9321619,-0.66552746,-0.6482407,0.6786...
22sb55p003.jpgart/sb55p003.jpgart1.622292,-0.2968188,-0.73929733,0.6021224,1.4958,-0.21535778,-0.7267234,-0.4896673,-0.7537334,-0.10785735,1.0314178,-0.72562873,-0.662768,-0.6266761,-0.68991846,1.5773908,-0.5467274,-0.63079214,-0.6898426,-0.6497359,-0.64108604,-0.55193967,0.290758,-0.6779002,-0.57759243,0.6712836,-0.4518578,0.73998094,1.4330137,0.009113848,-0.68509877,-0.67007643,1.9814153,-0.4592305,-0.600525,-0.6186755,-0.73933,-0.33041948,0.57097626,1.1697849,-0.61049384,0.01691258,0.56803906,-0.27057087,-0.75794554,-0.59257406,0.42289907,1.921669,-0.651804,-0.26263413,-0.42323184,-0.66552746,0.81260383,0.13302967,-0.7...
33sb73p061.jpgart/sb73p061.jpgart0.16305512,1.4613445,-0.73929733,-0.6553513,0.8147204,-0.65341413,-0.17605859,0.19489205,-0.7537334,1.671761,1.0696301,-0.72562873,-0.662768,-0.6266761,-0.68991846,0.94930255,1.1227012,-0.6520286,-0.6898426,-0.6497359,-0.64108604,-0.55193967,0.07409674,-0.6779002,-0.57759243,-0.6690378,-0.07547301,0.60642123,-0.57024753,0.70669293,-0.68509877,-0.67007643,4.085253,0.099163026,-0.31986937,-0.6186755,1.3904073,1.0975487,1.2184626,0.08304453,-0.60790604,0.49650353,-0.24247655,-0.6682474,-0.75794554,1.084398,-0.6157326,-0.6001429,-0.651804,0.34641546,-0.59497476,-0.66552746,-0.6482407,1.158359,...
44sb73p041.jpgart/sb73p041.jpgart1.9151492,0.6609892,-0.73929733,-0.6553513,1.1880168,-0.65341413,-0.7267234,-0.14307764,-0.7537334,-0.34104526,0.3361588,-0.72562873,-0.35769063,-0.42719716,-0.4528957,-0.26852533,0.41437465,-0.6520286,-0.40210772,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.020544767,-0.6346607,-0.4518578,-0.26067856,0.2364592,-0.34402665,-0.68509877,-0.67007643,1.3040129,-0.39724284,-0.600525,-0.6186755,-0.73933,0.4320628,-0.19739038,-0.42163545,-0.61049384,0.97026175,-0.5976355,-0.6682474,-0.75794554,0.3709892,1.178539,-0.6001429,-0.651804,-0.269921,-0.49530712,-0.66552746,-0.5894381,1.357...
+ +
+
+
+
+
+

saving/loading embeddings

+
+
+
+
wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/01_FLAT.tsv
+(3032, 512)
+
+
+
+
+
+

KMeans centroids

+
+
kmeans = train_kmeans(embs, ncentroids=16)
+
+centroids, cluster_to_label = load_centroids(
+    data_home=singleline_data_home('../data_home'),
+    model_path=PRETRAINED_MODEL_PATH
+)
+assigner = cluster_assigner(centroids, cluster_to_label)
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+reading ../data_home/models/epoch-20231128/01_FLAT_cluster_centroids.json
+reading ../data_home/models/epoch-20231128/01_FLAT_cluster_labels.json
+
+
+
+
# assign nearest centroid to each
+embs = pd_series_to_embs(df.emb_csv)
+knn_dist, knn_clusterid, knn_label = assigner(embs)
+knn_label[:5], knn_clusterid[:5]
+
+
(['1_watercolors', '1_watercolors', '0_drawings', '2_notes', '0_drawings'],
+ array([[ 5],
+        [ 5],
+        [15],
+        [12],
+        [13]]))
+
+
+
+
categorized_fname = EPOCH.tsv_02_CATEGORIZED()
+categorized_dir = EPOCH.dir_02_CATEGORIZED()
+
+df["knn_dist"] = knn_dist
+df["knn_clusterid"] = knn_clusterid
+df["knn_label"] = knn_label
+
+df["categorized_path"] = df.apply(
+    lambda row: os.path.join(row["knn_label"], row["indiv_fname"]), axis=1
+)
+
+if 'emb_csv' in df.columns:
+    del df['emb_csv']
+
+df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_path
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.31735251_watercolors1_watercolors/sb73p010-color.jpg
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.05957051_watercolors1_watercolors/sb77prear-cover.jpg
2sb55p003.jpgart/sb55p003.jpgart141.103607150_drawings0_drawings/sb55p003.jpg
3sb73p061.jpgart/sb73p061.jpgart272.285706122_notes2_notes/sb73p061.jpg
4sb73p041.jpgart/sb73p041.jpgart151.181458130_drawings0_drawings/sb73p041.jpg
+ +
+
+
+
+
+
clusters_fname = f"{dir_to_embed}_clusters.tsv"
+
+# NOTE: when saving embeddings CSV, idx was a regular column (not a pandas 'index').
+# When reloaded, 'idx' was made the index column. TODO: make original DF use 'idx' as index,
+# so saving and reloading code is identical.
+df.to_csv(clusters_fname, index=True, index_label="idx", sep="\t", header=True)
+
+cdf = pd.read_csv(clusters_fname, delimiter="\t", index_col="idx")
+cdf.knn_clusterid.hist()
+
+
+
+

+
+
+
+
+
+
cdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_path
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.3173551_watercolors1_watercolors/sb73p010-color.jpg
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.0595751_watercolors1_watercolors/sb77prear-cover.jpg
2sb55p003.jpgart/sb55p003.jpgart141.10360150_drawings0_drawings/sb55p003.jpg
3sb73p061.jpgart/sb73p061.jpgart272.28570122_notes2_notes/sb73p061.jpg
4sb73p041.jpgart/sb73p041.jpgart151.18146130_drawings0_drawings/sb73p041.jpg
+ +
+
+
+
+
+
cdf.knn_label.hist()
+
+
+
+

+
+
+
+
+
+
# TODO: quieter output
+categorize_files(cdf, epoch=EPOCH, prev_epoch=PREV_EPOCH)
+
+
+
# next step: merge hand-categorized files into 03_HANDLABELED to form a new epoch.
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
cdf
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_path
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.3173551_watercolors1_watercolors/sb73p010-color.jpg
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.0595751_watercolors1_watercolors/sb77prear-cover.jpg
2sb55p003.jpgart/sb55p003.jpgart141.10360150_drawings0_drawings/sb55p003.jpg
3sb73p061.jpgart/sb73p061.jpgart272.28570122_notes2_notes/sb73p061.jpg
4sb73p041.jpgart/sb73p041.jpgart151.18146130_drawings0_drawings/sb73p041.jpg
........................
1147sb35p107.jpgart/sb35p107.jpgart213.2633871_watercolors1_watercolors/sb35p107.jpg
1148sb25p085.jpgart/sb25p085.jpgart183.25726130_drawings0_drawings/sb25p085.jpg
1149sb46p112.jpgart/sb46p112.jpgart165.7680430_drawings0_drawings/sb46p112.jpg
1150sb44p055.jpgart/sb44p055.jpgart132.14764130_drawings0_drawings/sb44p055.jpg
1151sb73p042.jpgart/sb73p042.jpgart451.91785130_drawings0_drawings/sb73p042.jpg
+ +

3032 rows × 7 columns

+
+
+
+
+
+
def merge_categorized_files(cdf, epoch, prev_epoch=None):
+    categorized_dir = epoch.dir_02_CATEGORIZED()
+    existing_categorized = {os.path.basename(f): str(f).replace(f"{categorized_dir}/", "") for f in get_image_files(categorized_dir)}
+    
+    prev_handlabeled_dir = prev_epoch.dir_03_HANDLABELED()
+    prev_handlabeled = {os.path.basename(f): str(f).replace(f"{prev_handlabeled_dir}/", "") for f in get_image_files(prev_handlabeled_dir)} if prev_epoch else []
+
+    prev_handlabeled_paths = []
+    handlabeled_paths = []
+    handlabel_different = []
+    is_new = []
+    
+    for idx in range(len(cdf)):
+        row = cdf.iloc[idx]   
+        indiv_fname = row.indiv_fname
+
+        prev_handlabeled_path = None
+        handlabeled_path = None
+        is_file_new_to_epoch = False
+        
+        if indiv_fname in existing_categorized:
+            handlabeled_path = existing_categorized[indiv_fname]
+            is_file_new_to_epoch = True
+            # print(f"already copied to 02_CATEGORIZED (curr epoch) - skipping {indiv_fname}")
+            # continue
+        elif indiv_fname in prev_handlabeled:
+            prev_handlabeled_path = prev_handlabeled[indiv_fname]
+            handlabeled_path = prev_handlabeled_path
+        else:
+            raise Exception(f"file '{indiv_fname}' neither in previous epoch's 03_HANDLABELED dir nor current epoch's 02_CATEGORIZED dir")
+
+        prev_handlabeled_paths.append(prev_handlabeled_path)
+        handlabeled_paths.append(handlabeled_path)
+        handlabel_different.append(handlabeled_path != row.categorized_path)
+        is_new.append(is_file_new_to_epoch)
+
+    cdf['handlabeled_path'] = handlabeled_paths
+    cdf['is_handlabel_different_from_classifier'] = handlabel_different
+    cdf['prev_handlabeled_path'] = prev_handlabeled_paths
+    cdf['is_new'] = is_new
+    
+
+    for idx in range(len(cdf)):
+        row = cdf.iloc[idx]   
+        indiv_fname = row.indiv_fname
+        
+        orig_abs_path = epoch.dir_01_FLAT() / row.orig_fname
+        if not os.path.isfile(orig_abs_path):
+            raise Exception(f"expected original file to exist at: {orig_abs_path}")
+
+        
+        handlabeled_abs_path = epoch.dir_03_HANDLABELED() / row.handlabeled_path
+        handlabeled_dir = os.path.dirname(handlabeled_abs_path)
+        if not os.path.isdir(handlabeled_dir):
+            os.makedirs(handlabeled_dir)
+    
+        print(f"writing to {handlabeled_abs_path}")
+        shutil.copy(orig_abs_path, handlabeled_abs_path)
+    return cdf
+
+
+
# TODO: quieter output
+cdf2 = merge_categorized_files(cdf, epoch=EPOCH, prev_epoch=PREV_EPOCH)
+
+
+
cdf2.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_pathhandlabeled_pathis_handlabel_different_from_classifierprev_handlabeled_pathis_new
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.3173551_watercolors1_watercolors/sb73p010-color.jpg1_watercolors/sb73p010-color.jpgFalse1_watercolors/sb73p010-color.jpgFalse
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.0595751_watercolors1_watercolors/sb77prear-cover.jpg4_covers/sb77prear-cover.jpgTrue4_covers/sb77prear-cover.jpgFalse
2sb55p003.jpgart/sb55p003.jpgart141.10360150_drawings0_drawings/sb55p003.jpg0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.jpgFalse
3sb73p061.jpgart/sb73p061.jpgart272.28570122_notes2_notes/sb73p061.jpg0_drawings/sb73p061.jpgTrue0_drawings/sb73p061.jpgFalse
4sb73p041.jpgart/sb73p041.jpgart151.18146130_drawings0_drawings/sb73p041.jpg0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.jpgFalse
+ +
+
+
+
+
+
cdf2[cdf2.is_new & cdf2.is_handlabel_different_from_classifier]
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_pathhandlabeled_pathis_handlabel_different_from_classifierprev_handlabeled_pathis_new
idx
198sb50pcover.jpgcover/sb50pcover.jpgcover640.35535122_notes2_notes/sb50pcover.jpg4_covers/sb50pcover.jpgTrueNoneTrue
1011sb50p109-color-postit.jpgnotes/sb50p109-color-postit.jpgnotes390.1732551_watercolors1_watercolors/sb50p109-color-postit.jpg2_notes/sb50p109-color-postit.jpgTrueNoneTrue
1137sb50p089.jpgart/sb50p089.jpgart138.40935111_watercolors1_watercolors/sb50p089.jpg0_drawings/sb50p089.jpgTrueNoneTrue
1881sb50p093.jpgart/sb50p093.jpgart256.20300111_watercolors1_watercolors/sb50p093.jpg0_drawings/sb50p093.jpgTrueNoneTrue
2307sb50pcover-color.jpgcover/sb50pcover-color.jpgcover379.17596122_notes2_notes/sb50pcover-color.jpg4_covers/sb50pcover-color.jpgTrueNoneTrue
+ +
+
+
+
+
+
EPOCH.tsv_03_HANDLABELED()
+
+
Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/03_HANDLABELED.tsv')
+
+
+
+
handlabeled_fname = EPOCH.tsv_03_HANDLABELED()
+print(handlabeled_fname)
+
+# NOTE: when saving embeddings CSV, idx was a regular column (not a pandas 'index').
+# When reloaded, 'idx' was made the index column. TODO: make original DF use 'idx' as index,
+# so saving and reloading code is identical.
+cdf2.to_csv(handlabeled_fname, index=True, index_label="idx", sep="\t", header=True)
+
+hdf = pd.read_csv(handlabeled_fname, delimiter="\t", index_col="idx")
+
+
/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/03_HANDLABELED.tsv
+
+
+
+
hdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_pathhandlabeled_pathis_handlabel_different_from_classifierprev_handlabeled_pathis_new
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.3173551_watercolors1_watercolors/sb73p010-color.jpg1_watercolors/sb73p010-color.jpgFalse1_watercolors/sb73p010-color.jpgFalse
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.0595751_watercolors1_watercolors/sb77prear-cover.jpg4_covers/sb77prear-cover.jpgTrue4_covers/sb77prear-cover.jpgFalse
2sb55p003.jpgart/sb55p003.jpgart141.10360150_drawings0_drawings/sb55p003.jpg0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.jpgFalse
3sb73p061.jpgart/sb73p061.jpgart272.28570122_notes2_notes/sb73p061.jpg0_drawings/sb73p061.jpgTrue0_drawings/sb73p061.jpgFalse
4sb73p041.jpgart/sb73p041.jpgart151.18146130_drawings0_drawings/sb73p041.jpg0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.jpgFalse
+ +
+
+
+
+
+
+

processing

+
+

v0: dummy crop (both sides, by a fixed percentage)

+
+
CURR_03_HAND
+
+
Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/03_HANDLABELED')
+
+
+
+
CURR_04_CROP
+
+
Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/04_CROP')
+
+
+
+
#TODO: quieter output
+input_dir = CURR_03_HAND
+output_dir = CURR_04_CROP
+df = hdf
+
+# preprocessing_dir = (
+#     singleline_data_home() / "raster/epoch-20231214/10_SIMPLE_CROP"
+# )  # <-- this was defined earlier.
+existing_files = L(os.path.basename(f) for f in get_image_files(output_dir))
+
+crop_frac = 20.0 / 224
+print(f"using crop_frac={crop_frac}")
+
+for idx in range(len(df)):
+    row = df.iloc[idx]
+    indiv_fname = row.indiv_fname
+    if indiv_fname in existing_files:
+        print(f"skipping {indiv_fname}")
+        continue
+
+    handlabeled_abs_path = input_dir / row.handlabeled_path
+    if not os.path.isfile(handlabeled_abs_path):
+        raise Exception(f"input file doesnt exist: {handlabeled_abs_path}")
+
+    crop_abs_path = output_dir / row.handlabeled_path
+    crop_dir = os.path.dirname(crop_abs_path)
+    if not os.path.isdir(crop_dir):
+        os.makedirs(crop_dir)
+    
+    print(f"writing from {handlabeled_abs_path} to {crop_abs_path}")
+
+    # TODO: check aspect ratio of image and decide which direction to crop,
+    # to account for binder rings in landscape orientation (for pages that
+    # have been rotated).
+    try:
+        img = Image.open(handlabeled_abs_path)
+        print(img.size)
+        crop_amt = math.floor(
+            img.size[0] * crop_frac
+        )  # compute number of pixels to crop (roughly 8% of page size)
+        cropped_img = img.crop((crop_amt, 0, img.size[0] - crop_amt, img.size[1]))
+        # cropped_img
+        cropped_img.save(crop_abs_path)
+    except Exception as e:
+        print(e)
+
+
+
+

vectorize

+
+
# TODO: quieter output
+TARGET_DIR=CURR_04_CROP/'0_drawings'
+
+
+
TARGET_DIR=$1
+CENTERLINE_TRACE_DIR="/Users/al/code/_svg/inkscape-centerline-trace"
+
+for file in $(ls $TARGET_DIR); do
+    export idx=$(basename $file .jpg)
+    export inpath=$TARGET_DIR/$file
+    export outpath=$TARGET_DIR/$idx.svg
+    if ! [ -f $outpath ]; then
+        echo "$outpath does not exist."
+        $CENTERLINE_TRACE_DIR/centerline-trace.sh $inpath > $outpath
+    else
+        echo "$outpath exists; skipping."
+    fi
+done
+
+
+
!ls {TARGET_DIR}/*.svg | wc -l
+
+
    2017
+
+
+
+
+
+

stroke3

+
+
import os
+
+import numpy as np
+import pandas as pd
+
+from singleline_stroke3.display import *
+from singleline_stroke3.strokes import *
+from singleline_stroke3.svg import *
+from singleline_stroke3.transforms import *
+
+
+
TARGET_DIR
+
+
Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/04_CROP/0_drawings')
+
+
+
+
def enumerate_svgs(input_dir):
+    """Find all the files within a directory (non-recursively)"""
+    files = []
+    for file in os.listdir(input_dir):
+        if os.path.isfile(os.path.join(input_dir, file)) and os.path.splitext(file)[1] == '.svg':
+            files.append(file)
+    return files
+
+
+
hdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_pathhandlabeled_pathis_handlabel_different_from_classifierprev_handlabeled_pathis_new
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.3173551_watercolors1_watercolors/sb73p010-color.jpg1_watercolors/sb73p010-color.jpgFalse1_watercolors/sb73p010-color.jpgFalse
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.0595751_watercolors1_watercolors/sb77prear-cover.jpg4_covers/sb77prear-cover.jpgTrue4_covers/sb77prear-cover.jpgFalse
2sb55p003.jpgart/sb55p003.jpgart141.10360150_drawings0_drawings/sb55p003.jpg0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.jpgFalse
3sb73p061.jpgart/sb73p061.jpgart272.28570122_notes2_notes/sb73p061.jpg0_drawings/sb73p061.jpgTrue0_drawings/sb73p061.jpgFalse
4sb73p041.jpgart/sb73p041.jpgart151.18146130_drawings0_drawings/sb73p041.jpg0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.jpgFalse
+ +
+
+
+
+
+
hdf['handlabeled_category'] = hdf.handlabeled_path.map(lambda s: os.path.dirname(s))
+
+
+
svg_df = hdf.copy()
+svg_df['svg_autotrace_path'] = svg_df.handlabeled_path.map(lambda s: s.replace('.jpg', '.svg'))
+
+
+
svg_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelknn_distknn_clusteridknn_labelcategorized_pathhandlabeled_pathis_handlabel_different_from_classifierprev_handlabeled_pathis_newsvg_autotrace_pathhandlabeled_category
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart264.3173551_watercolors1_watercolors/sb73p010-color.jpg1_watercolors/sb73p010-color.jpgFalse1_watercolors/sb73p010-color.jpgFalse1_watercolors/sb73p010-color.svg1_watercolors
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover273.0595751_watercolors1_watercolors/sb77prear-cover.jpg4_covers/sb77prear-cover.jpgTrue4_covers/sb77prear-cover.jpgFalse4_covers/sb77prear-cover.svg4_covers
2sb55p003.jpgart/sb55p003.jpgart141.10360150_drawings0_drawings/sb55p003.jpg0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings
3sb73p061.jpgart/sb73p061.jpgart272.28570122_notes2_notes/sb73p061.jpg0_drawings/sb73p061.jpgTrue0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings
4sb73p041.jpgart/sb73p041.jpgart151.18146130_drawings0_drawings/sb73p041.jpg0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings
+ +
+
+
+
+
+
DEL_COLS = [
+    'knn_dist',
+    'knn_clusterid',
+    'knn_label',
+    'categorized_path',
+    'is_handlabel_different_from_classifier',
+    'prev_handlabeled_path',
+]
+for c in DEL_COLS:
+    if c in svg_df.columns:
+        del svg_df[c]
+
+
+
svg_df.columns
+
+
Index(['indiv_fname', 'orig_fname', 'label', 'handlabeled_path', 'is_new',
+       'svg_autotrace_path', 'handlabeled_category'],
+      dtype='object')
+
+
+
+
svg_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_category
idx
0sb73p010-color.jpgart/sb73p010-color.jpgart1_watercolors/sb73p010-color.jpgFalse1_watercolors/sb73p010-color.svg1_watercolors
1sb77prear-cover.jpgcover/sb77prear-cover.jpgcover4_covers/sb77prear-cover.jpgFalse4_covers/sb77prear-cover.svg4_covers
2sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings
3sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings
4sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings
+ +
+
+
+
+
+
# filter to drawings only
+svg_df = svg_df[svg_df.handlabeled_category == '0_drawings']
+
+
+
svg_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_category
idx
2sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings
3sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings
4sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings
5sb55p008--diagchains.jpgart/sb55p008--diagchains.jpgart0_drawings/sb55p008--diagchains.jpgFalse0_drawings/sb55p008--diagchains.svg0_drawings
6sb38p028.jpgart/sb38p028.jpgart0_drawings/sb38p028.jpgFalse0_drawings/sb38p028.svg0_drawings
+ +
+
+
+
+
+
# important: reset index column so it lines up to dataframe
+svg_df = svg_df.reset_index()
+
+
+
if 'idx' in svg_df.columns:
+    del svg_df['idx']
+svg_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_category
0sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings
1sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings
2sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings
3sb55p008--diagchains.jpgart/sb55p008--diagchains.jpgart0_drawings/sb55p008--diagchains.jpgFalse0_drawings/sb55p008--diagchains.svg0_drawings
4sb38p028.jpgart/sb38p028.jpgart0_drawings/sb38p028.jpgFalse0_drawings/sb38p028.svg0_drawings
+ +
+
+
+
+
+
svg_autotrace_fname = EPOCH.tsv_04_CROP()
+print(svg_autotrace_fname)
+
+# NOTE: when saving embeddings CSV, idx was a regular column (not a pandas 'index').
+# When reloaded, 'idx' was made the index column. TODO: make original DF use 'idx' as index,
+# so saving and reloading code is identical.
+svg_df.to_csv(svg_autotrace_fname, index=True, index_label="idx", sep="\t", header=True)
+
+svg_df = pd.read_csv(svg_autotrace_fname, delimiter="\t", index_col="idx")
+svg_df.head()
+
+
/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/04_CROP.tsv
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_category
idx
0sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings
1sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings
2sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings
3sb55p008--diagchains.jpgart/sb55p008--diagchains.jpgart0_drawings/sb55p008--diagchains.jpgFalse0_drawings/sb55p008--diagchains.svg0_drawings
4sb38p028.jpgart/sb38p028.jpgart0_drawings/sb38p028.jpgFalse0_drawings/sb38p028.svg0_drawings
+ +
+
+
+
+
+
len(svg_df)
+
+
2017
+
+
+
+
svg_df
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_category
idx
0sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings
1sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings
2sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings
3sb55p008--diagchains.jpgart/sb55p008--diagchains.jpgart0_drawings/sb55p008--diagchains.jpgFalse0_drawings/sb55p008--diagchains.svg0_drawings
4sb38p028.jpgart/sb38p028.jpgart0_drawings/sb38p028.jpgFalse0_drawings/sb38p028.svg0_drawings
........................
2012sb69p098--diagchains.jpgart/sb69p098--diagchains.jpgart0_drawings/sb69p098--diagchains.jpgFalse0_drawings/sb69p098--diagchains.svg0_drawings
2013sb25p085.jpgart/sb25p085.jpgart0_drawings/sb25p085.jpgFalse0_drawings/sb25p085.svg0_drawings
2014sb46p112.jpgart/sb46p112.jpgart0_drawings/sb46p112.jpgFalse0_drawings/sb46p112.svg0_drawings
2015sb44p055.jpgart/sb44p055.jpgart0_drawings/sb44p055.jpgFalse0_drawings/sb44p055.svg0_drawings
2016sb73p042.jpgart/sb73p042.jpgart0_drawings/sb73p042.jpgFalse0_drawings/sb73p042.svg0_drawings
+ +

2017 rows × 7 columns

+
+
+
+
+
+
def svgs_to_deltas(
+    input_df,
+    input_dir,
+    stroke3_dir=None,
+    target_size=200,
+    total_n=1000,
+    min_n=3,
+    # epsilon=1.0,
+    limit=None,
+):
+    if not os.path.isdir(stroke3_dir):
+        os.makedirs(stroke3_dir)
+
+    dataset = []
+    metadata = []
+    for idx in range(len(input_df)):
+        if limit and idx >= limit:
+            break
+        row = input_df.iloc[idx]
+        fname = row.svg_autotrace_path
+        input_abs_path = os.path.join(input_dir, fname)
+
+        print(input_abs_path)
+
+        had_error = False
+        err_msg = ""
+        try:
+            rescaled_strokes = svg_to_strokes(input_abs_path, total_n=total_n, min_n=min_n)
+            num_raw_points = np.vstack(rescaled_strokes).shape[0]
+            
+            joined_strokes, _ = merge_until(rescaled_strokes, dist_threshold=15.0)
+            spliced_strokes, _ = splice_until(joined_strokes, dist_threshold=40.0)
+
+            print(
+                f"{fname}: {len(rescaled_strokes)} strokes -> {len(joined_strokes)} joined -> {len(spliced_strokes)} spliced"
+            )
+
+            deltas = strokes_to_deltas(spliced_strokes) #stroke_rdp_deltas(spliced_strokes, epsilon=epsilon)
+            
+            # rdp_points = deltas.shape[0]
+            
+            #deltas = stroke_rdp_deltas(spliced_strokes, epsilon=epsilon)
+            ## monitor number of points before/after applying RDP path simplification algorithm
+            # raw_points = np.vstack(rescaled_strokes).shape[0]  
+            #print(f"{input_fname} points: raw={raw_points}, rdp={rdp_points}")
+
+            if stroke3_dir:
+                def new_suffix(dir, fname, suffix):
+                    sd = dir
+                    if not os.path.isdir(sd):
+                        os.makedirs(sd)
+                    fullpath = os.path.join(sd, fname.replace(".svg", suffix))
+                    if not os.path.isdir(os.path.dirname(fullpath)):
+                        os.makedirs(os.path.dirname(fullpath))
+                    return fullpath
+
+                final_n_strokes = len(spliced_strokes)
+                dir = stroke3_dir
+                # dir = f"png/{final_n_strokes:02d}"
+                
+                # plot_strokes(
+                #     rescaled_strokes, fname=new_suffix(dir, fname, ".0_strokes.png")
+                # )
+                # plot_strokes(
+                #     joined_strokes, fname=new_suffix(dir, fname, ".1_joined.png")
+                # )
+                # plot_strokes(
+                #     spliced_strokes, fname=new_suffix(dir, fname, ".2_spliced.png")
+                # )
+                # plot_strokes(
+                #     deltas_to_strokes(deltas),
+                #     fname=new_suffix(dir, fname, ".3_deltas.png"),
+                # )
+
+                # raw_output_fname = new_suffix('svg', fname, ".raw.svg")
+                # with open(raw_output_fname, "w", encoding="utf-8") as raw_out:
+                #     raw_dwg = render_strokes(rescaled_strokes, target_size=target_size)
+                #     raw_dwg.write(raw_out, pretty=True)
+                #     print(f"\twrote {raw_output_fname}")
+
+                # preproc_output_fname = new_suffix('svg', fname, ".preproc.svg")
+                # with open(preproc_output_fname, "w", encoding="utf-8") as preproc_out:
+                #     preproc_dwg = render_deltas(deltas, target_size=target_size)
+                #     preproc_dwg.save(preproc_output_fname)
+                #     print(f"\twrote {preproc_output_fname}")     
+            dataset.append(deltas)
+            metadata.append({
+                'idx': idx,
+                **(row.to_dict()),
+                'total_strokes': len(rescaled_strokes),
+                'joined_strokes': len(joined_strokes), 
+                'spliced_strokes': len(spliced_strokes),
+                'total_points': num_raw_points,
+                'had_error': had_error,
+                'err_msg': err_msg,
+            })
+        except Exception as e:
+            had_error = True
+            err_msg = e
+            print(f"error processing idx={idx} input_fname={input_abs_path}: {e}")
+            # raise e
+    return dataset, metadata
+
+
+
#TODO: quieter output
+
+# SVG paths are relative to this dir, e.g. '0_drawings/sb45p076.svg'
+SVG_INPUT_DIR = EPOCH.dir_04_CROP() 
+
+STROKE3_OUTPUT_DIR = EPOCH.dir_05_STROKE3()
+STROKE3_TSV_FNAME = EPOCH.tsv_05_STROKE3()
+STROKE3_NPZ_FNAME = str(STROKE3_TSV_FNAME).replace('.tsv', '.npz')
+BBOXSEP_OUTPUT_DIR = EPOCH.dir_06_BBOXSEP()
+BBOXSEP_TSV_FNAME = EPOCH.tsv_06_BBOXSEP()
+
+dataset, metadata = svgs_to_deltas(
+    input_df=svg_df,
+    input_dir=SVG_INPUT_DIR,
+    stroke3_dir=STROKE3_OUTPUT_DIR,
+    target_size=200,
+    total_n=1000,
+    min_n=3,
+    # limit=5,
+)
+
+stroke3_ds = np.array(dataset, dtype=object)
+np.savez(STROKE3_NPZ_FNAME, stroke3_ds, encoding='latin1', allow_pickle=True)
+
+# stroke3_df = input_df.copy()
+# if limit:
+#     stroke3_df = stroke3_df.iloc[:limit+1]
+
+# meta_cols = [
+#     'total_points',
+#     'had_error',
+#     'err_msg',    
+#     'total_strokes',
+#     'joined_strokes',
+#     'spliced_strokes',
+# ]
+# for c in meta_cols:
+#     stroke3_df[c] = [m[c] for m in metadata]
+
+stroke3_df = pd.DataFrame(metadata)
+
+
+
+stroke3_df.to_csv(STROKE3_TSV_FNAME, index=True, index_label="idx", sep="\t", header=True)
+stroke3_df = pd.read_csv(STROKE3_TSV_FNAME, delimiter="\t", index_col="idx")
+stroke3_df.head()
+
+
+
if 'idx.1' in stroke3_df.columns:
+    del stroke3_df['idx.1']
+stroke3_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_categorytotal_strokesjoined_strokesspliced_strokestotal_pointshad_errorerr_msg
idx
0sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings61103932FalseNaN
1sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
2sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse0_drawings/sb73p041.svg0_drawings78154907FalseNaN
3sb55p008--diagchains.jpgart/sb55p008--diagchains.jpgart0_drawings/sb55p008--diagchains.jpgFalse0_drawings/sb55p008--diagchains.svg0_drawings77178875FalseNaN
4sb38p028.jpgart/sb38p028.jpgart0_drawings/sb38p028.jpgFalse0_drawings/sb38p028.svg0_drawings4444951FalseNaN
+ +
+
+
+
+
+

bounding box separation

+
+
from singleline_stroke3.bounding_boxes import *
+
+
+
BBOXSEP_OUTPUT_DIR = EPOCH.dir_06_BBOXSEP()
+BBOXSEP_TSV_FNAME = EPOCH.tsv_06_BBOXSEP()
+BBOXSEP_NPZ_FNAME = str(BBOXSEP_TSV_FNAME).replace('.tsv', '.npz')
+
+
+
stroke3_dataset = np.load(
+    STROKE3_NPZ_FNAME,
+    encoding="latin1",
+    allow_pickle=True,
+)["arr_0"]
+
+
+
len(stroke3_dataset)
+
+
2015
+
+
+
+
def sep_stroke3_with_bounding_boxes(
+    input_df,
+    input_arr,
+    bboxsep_dir=None,
+    # target_size=200,
+    limit=None,
+    # # TODO: apply uniform RDP? or repeatedly till under max_seq_len?
+    # epsilon=1.0,
+    # max_seq_len=250,
+):
+    if not os.path.isdir(bboxsep_dir):
+        os.makedirs(bboxsep_dir)
+
+    new_idx = 0
+    dataset = []
+    metadata = []
+    for idx in range(len(input_df)):
+        if limit and idx >= limit:
+            break
+        row = input_df.iloc[idx]
+        fname = row.svg_autotrace_path
+        expected_num_points = row.total_points
+        expected_num_strokes = row.spliced_strokes
+
+        had_error = False
+        err_msg = ""
+        try:
+            strokes = deltas_to_strokes(input_arr[idx])
+            assert expected_num_strokes == len(strokes), f"{idx} num points mismatch: expected {expected_num_points}, actual {num_raw_points}"
+            num_raw_points = np.vstack(strokes).shape[0]
+            assert expected_num_points == num_raw_points, f"{idx} num points mismatch: expected {expected_num_points}, actual {num_raw_points}"
+
+            for j, s in enumerate(group_and_rescale_overlapping_strokes(strokes)):
+                group_id = j
+                output_png_fname = f"{os.path.splitext(fname)[0]}g{group_id:02d}.png"
+                
+                output_png_abs_path = os.path.join(bboxsep_dir, output_png_fname)
+                if not os.path.isdir(os.path.dirname(output_png_abs_path)):
+                    os.makedirs(os.path.dirname(output_png_abs_path))
+                
+                plot_strokes(s, fname=output_png_abs_path, transparent=True)
+                
+                dataset.append(s)
+                metadata.append({
+                    'new_idx': new_idx,
+                    'group_fname': output_png_fname,
+                    'group_id': group_id,
+                    'group_num_strokes': len(s),
+                    'group_num_points': np.vstack(s).shape[0],
+                    **row.to_dict(),
+                })
+                
+                new_idx += 1
+        except Exception as e:
+            had_error = True
+            err_msg = e
+            print(f"error processing idx={idx} input_fname={fname}: {e}")
+            # raise e
+    return dataset, metadata   
+                
+            
+    # output_items = []
+    # for i in range(len(full_dataset)):
+    #     item = deltas_to_strokes(full_dataset[i])
+        
+    #         plot_strokes(s, fname=debug_dir / f"{i:04d}_group-{j:02d}.png")
+    #         plot_strokes(
+    #             s, fname=final_dir / f"{i:04d}_group-{j:02d}.png", transparent=True
+    #         )
+    #         output_items.append(s)
+    #     # show the original for comparison
+    #     plot_strokes(item, bounding_boxes=True, fname=debug_dir / f"{i:04d}__overview.png")
+    
+    # new_dataset = np.array(output_items, dtype=object)
+    # new_dataset_deltas = np.array([strokes_to_deltas(s) for s in new_dataset], dtype=object)
+    
+    # np.savez(npz_dir / "all.npz", new_dataset_deltas, encoding="latin1", allow_pickle=True)
+
+
+
dataset, metadata = sep_stroke3_with_bounding_boxes(
+    input_df=stroke3_df,
+    input_arr=stroke3_dataset,
+    bboxsep_dir=BBOXSEP_OUTPUT_DIR,
+    # limit=5
+)
+
+bboxsep_ds = np.array(dataset, dtype=object)
+np.savez(BBOXSEP_NPZ_FNAME, bboxsep_ds, encoding='latin1', allow_pickle=True)
+
+bboxsep_df = pd.DataFrame(metadata)
+bboxsep_df.to_csv(BBOXSEP_TSV_FNAME, index=True, index_label="idx", sep="\t", header=True)
+bboxsep_df = pd.read_csv(BBOXSEP_TSV_FNAME, delimiter="\t", index_col="idx")
+bboxsep_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_categorytotal_strokesjoined_strokesspliced_strokestotal_pointshad_errorerr_msg
idx
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings61103932FalseNaN
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
220_drawings/sb73p061g01.png12219sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
330_drawings/sb73p061g02.png21214sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
440_drawings/sb73p061g03.png31141sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
+ +
+
+
+
+
+
len(bboxsep_df)
+
+
3146
+
+
+
+
# dir_to_embed = DATA_HOME / "raster/epoch-20231214/01_FLAT"
+dir_to_embed = BBOXSEP_OUTPUT_DIR
+
+path = Path(dir_to_embed)
+files = get_image_files(path)
+len(files)
+
+batched_fnames, ordered_dls = batch_fnames_and_images(dir_to_embed)
+
+learn = load_resnet(
+    data_home=EXTERNAL_DATA_HOME,
+    model_path=PRETRAINED_MODEL_PATH,
+    model_checkpoint=PRETRAINED_CHECKPOINT,
+)
+
+#learn.model[-1]
+
+rows = []
+for x in embed_dir(dir_to_embed, learn, strip_dir=BBOXSEP_OUTPUT_DIR):
+    rows.append(x)
+df = pd.DataFrame(rows)
+print(f"num rows: {len(df)}")
+df.head()
+
+bbox_embeddings_fname = f"{str(BBOXSEP_OUTPUT_DIR)}_embs.tsv"
+df.to_csv(bbox_embeddings_fname, index=False, sep="\t", header=True)
+print(f"wrote {bbox_embeddings_fname}")
+
+
+
df = pd.read_csv(bbox_embeddings_fname, delimiter="\t", index_col="idx")
+df.head()
+
+embs = pd_series_to_embs(df.emb_csv)
+print(embs.shape)
+
+
(3146, 512)
+
+
+
+
kmeans = train_kmeans(embs, ncentroids=16)
+
+

+Clustering 3146 points in 512D to 16 clusters, redo 1 times, 20 iterations
+  Preprocessing in 0.00 s
+  Iteration 19 (0.02 s, search 0.02 s): objective=506165 imbalance=1.180 nsplit=0
+
+
+
+
kmeans.centroids
+
+
array([[ 0.40956366,  0.4539906 , -0.7392965 , ..., -0.593978  ,
+        -0.01202063,  0.82983655],
+       [-0.48970124, -0.40134948, -0.7189984 , ..., -0.48196766,
+        -0.41706023,  0.1788576 ],
+       [-0.52298546, -0.56843925, -0.6605481 , ..., -0.5846314 ,
+        -0.46725053, -0.2719769 ],
+       ...,
+       [-0.50658494, -0.4525644 , -0.6467315 , ..., -0.5658877 ,
+        -0.48807985, -0.32663694],
+       [ 0.05594135, -0.39736596, -0.6444024 , ..., -0.5038025 ,
+        -0.41823232,  1.186483  ],
+       [-0.26665202, -0.47642395, -0.71609116, ..., -0.6128117 ,
+        -0.45602712,  0.13645345]], dtype=float32)
+
+
+
+
embs.shape
+
+
(3146, 512)
+
+
+
+
# centroids, cluster_to_label = load_centroids(
+#     data_home=singleline_data_home('../data_home'),
+#     model_path=PRETRAINED_MODEL_PATH
+# )
+
+centroids = kmeans.centroids
+cluster_labels = {}
+
+save_centroids(
+    centroids,
+    cluster_labels,
+    data_home=singleline_data_home('../data_home'),
+    model_path=PRETRAINED_MODEL_PATH,
+    extra_suffix='_bboxsep'
+)
+
+assigner = cluster_assigner(centroids, cluster_to_label)
+
+knn_dist, knn_clusterid, knn_label = assigner(embs)
+knn_label[:5], knn_clusterid[:5]
+
+df["knn_dist"] = knn_dist
+df["knn_clusterid"] = knn_clusterid
+df["knn_label"] = knn_label
+
+# df["categorized_path"] = df.apply(
+#     lambda row: os.path.join(row["knn_label"], row["indiv_fname"]), axis=1
+# )
+
+# if 'emb_csv' in df.columns:
+#     del df['emb_csv']
+
+df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelemb_csvknn_distknn_clusteridknn_label
idx
0sb73p062g00.png0_drawings/sb73p062g00.png0_drawings-0.4085784,1.077941,-0.73929733,-0.6553513,-0.33674708,0.44653428,-0.7267234,-0.16337693,-0.7537334,2.58098,0.70077145,-0.72562873,-0.662768,0.3356592,-0.68991846,0.22388738,-0.5467274,-0.46443364,-0.4460039,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.5208976,-0.4252375,-0.35008803,-0.5982413,-0.73986673,-0.2656212,-0.68509877,-0.019111216,0.6072817,0.9031346,-0.5710402,-0.6186755,-0.73933,1.291467,-0.65925777,-0.2905227,-0.61049384,0.86185104,3.5578523,-0.6682474,-0.75794554,-0.59257406,-0.56912696,-0.6001429,1.3671005,0.48722607,0.19623727,-0.66552746,-0.6482407,1.5543242,...338.4016113None
1sb25p021g00.png0_drawings/sb25p021g00.png0_drawings-0.6198182,0.07978231,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.5267999,0.22521895,-0.45169276,0.4765754,0.9923676,0.07570249,0.10221094,-0.5629848,-0.68991846,-0.57515705,-0.5467274,2.5525615,0.45225585,-0.06349939,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.9686263,-0.6690378,-0.4518578,1.3688718,-0.73986673,-0.16642767,-0.68509877,-0.67007643,0.72362816,-0.16392156,-0.600525,-0.6186755,-0.73933,-0.57338566,-0.65925777,0.37941074,-0.61049384,0.5526003,0.38675356,-0.6682474,-0.75794554,-0.59257406,0.48467332,-0.6001429,0.06385499,0.43829525,0.17835289,-0.34760472,-0.39676708,1....238.1714173None
2sb48p025g00.png0_drawings/sb48p025g00.png0_drawings0.27033854,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.009221405,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.5080385,-0.57515705,-0.5467274,1.5457456,2.1699433,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.659942,-0.4518578,0.43428838,-0.73986673,-0.07844362,-0.68509877,-0.67007643,-0.52171326,0.12707904,0.82075787,0.00014060736,-0.66719526,-0.39957428,-0.26531035,-0.6179389,-0.61049384,-0.58021957,-0.23677179,-0.6682474,0.99923706,-0.59257406,1.9531038,-0.6001429,-0.054986358,-0.11816922,0.64457357,-0.66552746,...135.4247132None
3sb44p098g00.png0_drawings/sb44p098g00.png0_drawings0.7062113,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,1.9157894,-0.4896673,-0.7537334,0.54395044,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.58417577,-0.24514529,-0.5467274,0.88600737,2.1812363,-0.19237944,-0.64108604,-0.55193967,0.90909463,-0.6779002,2.1914794,-0.6690378,0.2622062,-0.11539039,-0.73986673,0.39760068,-0.68509877,-0.67007643,-0.52171326,-0.19909361,-0.014922798,1.1047375,-0.73933,-0.57338566,0.79833126,-0.6179389,2.8530078,0.099111676,1.3431637,-0.6682474,3.1076033,-0.59257406,-0.6157326,-0.6001429,-0.651804,0.9190846,0.1499474,-0.3282049,1.5581564,-0.0157680...181.65789811None
4sb38p096--bundledg00.png0_drawings/sb38p096--bundledg00.png0_drawings-0.6198182,-0.16951999,-0.73929733,-0.6553513,0.04408127,-0.65341413,0.23919284,-0.4896673,-0.7537334,-0.123981416,-0.62038505,-0.72562873,-0.09523386,-0.6266761,1.4427767,-0.4129038,-0.5467274,-0.6520286,3.6913576,0.16698122,-0.64108604,-0.55193967,-0.5446661,0.09032714,1.3312979,-0.6690378,-0.17945293,0.10221714,-0.73986673,-0.33327153,-0.68509877,-0.67007643,-0.4788125,-0.35560656,-0.05634737,1.1187687,-0.73933,0.19565272,0.54220104,-0.6179389,-0.61049384,0.7409752,-0.53702986,-0.6682474,0.29792833,-0.59257406,0.7453012,-0.6001429,-0.651804,-0.038021386,0.9696573,-0.66552746,1.3529996,1...193.70834414None
+ +
+
+
+
+
+
bbox_clusters_fname = f"{str(BBOXSEP_OUTPUT_DIR)}_clusters.tsv"
+
+df.to_csv(bbox_clusters_fname, index=False, sep="\t", header=True)
+print(f"wrote {bbox_clusters_fname}")
+
+
wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters.tsv
+
+
+
+
# !head {bbox_clusters_fname}
+
+
+
cdf = pd.read_csv(bbox_clusters_fname, delimiter="\t") #, index_col="idx")
+cdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelemb_csvknn_distknn_clusteridknn_label
0sb73p062g00.png0_drawings/sb73p062g00.png0_drawings-0.4085784,1.077941,-0.73929733,-0.6553513,-0.33674708,0.44653428,-0.7267234,-0.16337693,-0.7537334,2.58098,0.70077145,-0.72562873,-0.662768,0.3356592,-0.68991846,0.22388738,-0.5467274,-0.46443364,-0.4460039,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.5208976,-0.4252375,-0.35008803,-0.5982413,-0.73986673,-0.2656212,-0.68509877,-0.019111216,0.6072817,0.9031346,-0.5710402,-0.6186755,-0.73933,1.291467,-0.65925777,-0.2905227,-0.61049384,0.86185104,3.5578523,-0.6682474,-0.75794554,-0.59257406,-0.56912696,-0.6001429,1.3671005,0.48722607,0.19623727,-0.66552746,-0.6482407,1.5543242,...338.401603NaN
1sb25p021g00.png0_drawings/sb25p021g00.png0_drawings-0.6198182,0.07978231,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.5267999,0.22521895,-0.45169276,0.4765754,0.9923676,0.07570249,0.10221094,-0.5629848,-0.68991846,-0.57515705,-0.5467274,2.5525615,0.45225585,-0.06349939,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.9686263,-0.6690378,-0.4518578,1.3688718,-0.73986673,-0.16642767,-0.68509877,-0.67007643,0.72362816,-0.16392156,-0.600525,-0.6186755,-0.73933,-0.57338566,-0.65925777,0.37941074,-0.61049384,0.5526003,0.38675356,-0.6682474,-0.75794554,-0.59257406,0.48467332,-0.6001429,0.06385499,0.43829525,0.17835289,-0.34760472,-0.39676708,1....238.171423NaN
2sb48p025g00.png0_drawings/sb48p025g00.png0_drawings0.27033854,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.009221405,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.5080385,-0.57515705,-0.5467274,1.5457456,2.1699433,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.659942,-0.4518578,0.43428838,-0.73986673,-0.07844362,-0.68509877,-0.67007643,-0.52171326,0.12707904,0.82075787,0.00014060736,-0.66719526,-0.39957428,-0.26531035,-0.6179389,-0.61049384,-0.58021957,-0.23677179,-0.6682474,0.99923706,-0.59257406,1.9531038,-0.6001429,-0.054986358,-0.11816922,0.64457357,-0.66552746,...135.424712NaN
3sb44p098g00.png0_drawings/sb44p098g00.png0_drawings0.7062113,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,1.9157894,-0.4896673,-0.7537334,0.54395044,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.58417577,-0.24514529,-0.5467274,0.88600737,2.1812363,-0.19237944,-0.64108604,-0.55193967,0.90909463,-0.6779002,2.1914794,-0.6690378,0.2622062,-0.11539039,-0.73986673,0.39760068,-0.68509877,-0.67007643,-0.52171326,-0.19909361,-0.014922798,1.1047375,-0.73933,-0.57338566,0.79833126,-0.6179389,2.8530078,0.099111676,1.3431637,-0.6682474,3.1076033,-0.59257406,-0.6157326,-0.6001429,-0.651804,0.9190846,0.1499474,-0.3282049,1.5581564,-0.0157680...181.6579011NaN
4sb38p096--bundledg00.png0_drawings/sb38p096--bundledg00.png0_drawings-0.6198182,-0.16951999,-0.73929733,-0.6553513,0.04408127,-0.65341413,0.23919284,-0.4896673,-0.7537334,-0.123981416,-0.62038505,-0.72562873,-0.09523386,-0.6266761,1.4427767,-0.4129038,-0.5467274,-0.6520286,3.6913576,0.16698122,-0.64108604,-0.55193967,-0.5446661,0.09032714,1.3312979,-0.6690378,-0.17945293,0.10221714,-0.73986673,-0.33327153,-0.68509877,-0.67007643,-0.4788125,-0.35560656,-0.05634737,1.1187687,-0.73933,0.19565272,0.54220104,-0.6179389,-0.61049384,0.7409752,-0.53702986,-0.6682474,0.29792833,-0.59257406,0.7453012,-0.6001429,-0.651804,-0.038021386,0.9696573,-0.66552746,1.3529996,1...193.7083414NaN
+ +
+
+
+
+
+
embs = pd_series_to_embs(cdf.emb_csv)
+print(embs.shape)
+
+
(3146, 512)
+
+
+
+
+

emb projector

+
+
import io
+import json
+import math
+import os
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorboard.plugins import projector
+
+
+
import csv
+from PIL import Image
+
+tf_logs = f"{str(BBOXSEP_OUTPUT_DIR)}_tflogs"
+if not os.path.isdir(tf_logs):
+    os.makedirs(tf_logs)
+metadata_fname = f"{tf_logs}/metadata.tsv"
+
+with open(metadata_fname, 'w') as outfile:
+    for idx in range(len(df)):
+        row = cdf.iloc[idx]
+        outfile.write(f"{row.indiv_fname}\t{row.knn_clusterid}\n")
+!head {metadata_fname}
+
+vecs_fname = f"{tf_logs}/feature_vecs.tsv"
+with open(vecs_fname, 'w') as outfile:
+    csv_writer = csv.writer(outfile, delimiter="\t")
+    csv_writer.writerows([e.split(',') for e in list(cdf.emb_csv)])
+
+
sb73p062g00.png 3
+sb25p021g00.png 3
+sb48p025g00.png 2
+sb44p098g00.png 11
+sb38p096--bundledg00.png    14
+sb67p097g00.png 14
+sb46p018g00.png 11
+sb05p069g00.png 10
+sb77p009g03.png 2
+sb77p063g00.png 7
+
+
+
+
def bbox_fname(row):
+    return BBOXSEP_OUTPUT_DIR / row.orig_fname
+
+def image_grid(df, row_to_fname_fn, output_fname, dim=100):
+    one_square_size = int(np.ceil(np.sqrt(len(df))))
+    master_width = dim * one_square_size
+    master_height = dim * one_square_size
+    spriteimage = Image.new(
+        mode='RGBA',
+        size=(master_width, master_height),
+        color=(0,0,0,0) # fully transparent
+    )
+    for idx in range(len(df)):
+        row = df.iloc[idx]
+        img_path = row_to_fname_fn(row)
+        image = Image.open(img_path).resize((dim, dim))
+        div, mod = divmod(idx, one_square_size)
+        h_loc = dim * div
+        w_loc = dim * mod
+        spriteimage.paste(image, (w_loc, h_loc))
+    
+    # important: tensorboard looks for 'jpeg' not 'jpg' when setting mimetime
+    spriteimage.convert('RGBA').save(output_fname)
+
+image_grid(cdf, bbox_fname, f'{tf_logs}/sprite.png')
+
+
+
print("""
+(base) As-MacBook-Pro-2  portfolio_tsne_projector (bboxsep)  $ cat projector_config.pbtxt
+embeddings {
+  metadata_path: "oss_data/metadata.tsv"
+  tensor_path: "oss_data/feature_vecs.tsv"
+  sprite: {
+    image_path: "oss_data/sprite.png"
+    single_image_dim: [ 100, 100 ]
+  }
+}
+(base) As-MacBook-Pro-2  portfolio_tsne_projector (bboxsep)  $ cat oss_data/
+feature_vecs.tsv                metadata.tsv                    oss_demo_projector_config.json  sprite.jpeg                     sprite.png
+(base) As-MacBook-Pro-2  portfolio_tsne_projector (bboxsep)  $ cat oss_data/oss_demo_projector_config.json
+{
+  "embeddings": [
+    {
+      "tensorName": "SingleLines v1 (2023-12-14)",
+      "tensorPath": "oss_data/feature_vecs.tsv",
+      "tensorShape": [1923, 512],
+      "metadataPath": "oss_data/metadata.tsv",
+      "sprite": {
+        "imagePath": "oss_data/sprite.png",
+        "singleImageDim": [100, 100]
+      }
+    }
+  ],
+  "modelCheckpointPath": "Demo datasets"
+}
+""")
+
+

+(base) As-MacBook-Pro-2  portfolio_tsne_projector (bboxsep)  $ cat projector_config.pbtxt
+embeddings {
+  metadata_path: "oss_data/metadata.tsv"
+  tensor_path: "oss_data/feature_vecs.tsv"
+  sprite: {
+    image_path: "oss_data/sprite.png"
+    single_image_dim: [ 100, 100 ]
+  }
+}
+(base) As-MacBook-Pro-2  portfolio_tsne_projector (bboxsep)  $ cat oss_data/
+feature_vecs.tsv                metadata.tsv                    oss_demo_projector_config.json  sprite.jpeg                     sprite.png
+(base) As-MacBook-Pro-2  portfolio_tsne_projector (bboxsep)  $ cat oss_data/oss_demo_projector_config.json
+{
+  "embeddings": [
+    {
+      "tensorName": "SingleLines v1 (2023-12-14)",
+      "tensorPath": "oss_data/feature_vecs.tsv",
+      "tensorShape": [1923, 512],
+      "metadataPath": "oss_data/metadata.tsv",
+      "sprite": {
+        "imagePath": "oss_data/sprite.png",
+        "singleImageDim": [100, 100]
+      }
+    }
+  ],
+  "modelCheckpointPath": "Demo datasets"
+}
+
+
+
+
+
+

using non-matplotlib image_grid to view clusters without waiting an hour for render

+
+
CLUSTER_OUTPUT_DIR = f"{str(BBOXSEP_OUTPUT_DIR)}_clusters"
+
+if not os.path.isdir(CLUSTER_OUTPUT_DIR):
+    os.makedirs(CLUSTER_OUTPUT_DIR)
+    
+for cid in sorted(cdf.knn_clusterid.unique()):
+    filtered_df = cdf[cdf.knn_clusterid == cid].sort_values(by='knn_dist')
+    cluster_summary_fname = f"{CLUSTER_OUTPUT_DIR}/{cid:03d}.png"
+    image_grid(filtered_df, bbox_fname, cluster_summary_fname)
+    print(f"wrote {cluster_summary_fname}")
+
+
wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/000.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/001.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/002.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/003.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/004.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/005.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/006.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/007.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/008.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/009.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/010.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/011.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/012.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/013.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/014.png
+wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/06_BBOXSEP_clusters/015.png
+
+
+
+
filtered_df = filtered_df.sort_values(by='knn_dist')
+filtered_df
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelemb_csvknn_distknn_clusteridknn_label
1998sb28p056g02.png0_drawings/sb28p056g02.png0_drawings-0.60455513,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.054002315,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.112467945,-0.57515705,-0.5467274,-0.32089174,1.1871707,-0.4789152,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.809992,-0.6690378,-0.4518578,0.19717878,-0.21986055,-0.2707454,-0.68509877,-0.67007643,-0.52171326,-0.4592305,-0.3509087,-0.3690443,-0.73933,-0.57338566,-0.65925777,0.21065515,0.11473888,0.23341817,-0.47544438,-0.6682474,0.983819,-0.59257406,0.5196174,-0.6001429,0.84356177,0.33397973,-0.59497476,-0.66552746,-0.6482407,...67.36813015NaN
547sb35p120g00.png0_drawings/sb35p120g00.png0_drawings-0.17950505,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.5731437,-0.5005919,-0.6266761,0.36701328,-0.32113102,-0.5467274,1.4307499,0.7395865,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.2718384,-0.6690378,-0.4518578,0.178128,-0.7282027,-0.45211998,-0.68509877,-0.67007643,-0.3344276,-0.4592305,-0.600525,-0.5688742,-0.14499563,-0.57338566,-0.0829373,0.5705925,-0.047367632,0.29262394,-0.04999113,-0.6682474,-0.19065416,-0.59257406,0.9725562,-0.6001429,0.6409602,-0.3554126,-0.59497476,-0.43712783,-0.57496345,-...72.07480015NaN
2663sb25p226g01.png0_drawings/sb25p226g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,0.38995147,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.72562873,0.84423506,-0.6266761,0.2957921,-0.57515705,-0.5467274,0.55877405,0.7099236,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.31666198,-0.43482745,-0.4518578,0.2568329,-0.73986673,-0.29413116,-0.68509877,-0.67007643,-0.5082777,-0.4592305,-0.31847417,-0.5157136,-0.73933,-0.57338566,0.16691482,0.39247966,-0.30317673,1.0235901,0.5535735,-0.6682474,0.08928716,-0.59257406,-0.034588695,-0.6001429,-0.651804,0.46177804,0.7148857,-0.66552746,-0.6482407,0...75.19583015NaN
428sb26p061--negative-spaceg02.png0_drawings/sb26p061--negative-spaceg02.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.64288265,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.72562873,-0.45596313,-0.6266761,0.5445996,-0.0221017,-0.5467274,0.085103035,0.6716062,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.7787115,-0.6690378,-0.4518578,0.8602971,-0.73986673,-0.45211998,-0.68509877,-0.67007643,-0.4294455,-0.3206294,-0.600525,-0.6107425,-0.73933,-0.57338566,-0.42801154,0.24852055,-0.5459173,0.18204749,0.05127096,-0.6682474,0.7538444,-0.59257406,-0.5558812,-0.6001429,-0.651804,-0.18433052,0.6542469,-0.66552746,-0.6482407,0.68...75.43741015NaN
1669sb69p029g01.png0_drawings/sb69p029g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.47509778,-0.4123549,-0.7267234,-0.4896673,-0.7537334,-0.24194966,0.3949381,-0.72562873,-0.24519569,-0.6266761,1.0265491,-0.57515705,-0.5467274,0.21744221,1.4432005,0.33792824,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.249109,-0.6690378,-0.4518578,0.11238462,-0.73986673,-0.45211998,-0.68509877,-0.67007643,-0.3981428,-0.4592305,-0.28042543,-0.17581123,-0.09715998,-0.57338566,-0.53148556,-0.5123446,0.40966898,0.6758582,-0.0038056374,-0.6682474,-0.75794554,-0.59257406,-0.09032333,-0.6001429,-0.651804,0.1426571,0.7723752,-0.66552746,-0.648240...78.75088515NaN
........................
2593sb44p071g00.png0_drawings/sb44p071g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,0.3143853,-0.36085165,0.675473,-0.72562873,-0.662768,-0.08208591,0.3102712,-0.57515705,-0.5467274,0.12552947,-0.6898426,-0.6497359,-0.64108604,-0.55193967,-0.5446661,0.7327621,-0.19695726,-0.6690378,-0.19914126,1.8982725,1.0865909,-0.07935959,0.4636613,-0.67007643,-0.26040968,-0.3171153,-0.600525,-0.35587394,-0.43931392,-0.57338566,-0.65925777,-0.6179389,-0.61049384,-0.27569664,-0.6189488,-0.6682474,-0.75794554,-0.59257406,-0.16646585,-0.6001429,-0.54031205,-0.12916845,0.30850637,-0.53874755,-0.648240...225.80280015NaN
1005sb69p072g00.png0_drawings/sb69p072g00.png0_drawings-0.6198182,-0.5714155,0.1447035,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.72562873,0.23293644,0.42267382,1.6605217,-0.57515705,-0.5467274,0.6652698,1.4203566,-0.6497359,-0.5364696,-0.55193967,-0.5446661,0.022121608,0.50169533,-0.6690378,-0.4518578,1.7610838,-0.17648542,-0.45211998,-0.68509877,-0.67007643,-0.52171326,-0.1115548,-0.600525,0.4432236,-0.7083354,-0.57338566,-0.65925777,-0.6179389,0.94870335,-0.58021957,0.90502226,-0.6682474,1.4069695,-0.59257406,-0.08827418,-0.6001429,-0.23213962,-0.5315948,0.55634606,0.5508274,-0.6482407,-0.4...226.61770015NaN
2101sb48p067g00.png0_drawings/sb48p067g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.19732387,-0.46356404,-0.72562873,0.13454342,-0.6266761,-0.32887515,-0.57515705,-0.5467274,-0.6520286,1.6473716,-0.6497359,-0.64108604,-0.49987292,-0.5446661,-0.6779002,-0.57759243,-0.6690378,0.037450433,0.18190187,-0.73986673,0.020722866,-0.68509877,-0.67007643,-0.52171326,-0.23677915,-0.15668502,0.22680593,-0.73933,-0.57338566,1.2647043,-0.6179389,-0.61049384,2.1807945,1.0678242,-0.6682474,0.6942992,-0.59257406,0.036304057,-0.6001429,-0.5346943,1.1561732,2.56619,-0.66552746,0.9787934,0....233.34900015NaN
86sb38p026g00.png0_drawings/sb38p026g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,0.55048406,-0.62038505,-0.72562873,-0.662768,-0.6266761,1.3854902,-0.57515705,-0.5467274,0.94242877,0.8782532,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.4352187,-0.6690378,-0.4518578,1.1342419,-0.62232804,-0.2273321,-0.68509877,-0.67007643,-0.1273719,-0.4592305,-0.600525,-0.6019784,-0.73933,-0.57338566,-0.65925777,-0.32364675,0.19919872,-0.58021957,2.3526206,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.6001429,-0.008537114,-0.41088784,1.677799,-0.66552746,-0.6482407,0.5...234.17490015NaN
567sb00p013g01.png0_drawings/sb00p013g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,0.16399276,-0.18260321,-0.35923368,-0.7267234,-0.4896673,-0.19637728,-0.42715576,0.79644907,-0.72562873,-0.036955297,-0.3767244,0.54874784,-0.57515705,-0.5467274,-0.6520286,0.27685016,0.2026313,1.6490104,-0.55193967,-0.5446661,-0.3825759,0.15093857,-0.6690378,0.27503973,0.6990508,0.77585995,-0.45211998,1.4613954,-0.67007643,-0.52171326,-0.043664634,-0.600525,-0.45836574,0.70107275,-0.57338566,-0.65925777,1.3871087,0.29972345,-0.2813233,-0.009079695,-0.6682474,1.9028971,-0.59257406,-0.12389892,-0.6001429,-0.651804,0.29877716,1.229559,-0.66552746,-0.62418795...237.12683015NaN
+ +

263 rows × 7 columns

+
+
+
+
+
+
cdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
indiv_fnameorig_fnamelabelemb_csvknn_distknn_clusteridknn_label
0sb73p062g00.png0_drawings/sb73p062g00.png0_drawings-0.4085784,1.077941,-0.73929733,-0.6553513,-0.33674708,0.44653428,-0.7267234,-0.16337693,-0.7537334,2.58098,0.70077145,-0.72562873,-0.662768,0.3356592,-0.68991846,0.22388738,-0.5467274,-0.46443364,-0.4460039,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.5208976,-0.4252375,-0.35008803,-0.5982413,-0.73986673,-0.2656212,-0.68509877,-0.019111216,0.6072817,0.9031346,-0.5710402,-0.6186755,-0.73933,1.291467,-0.65925777,-0.2905227,-0.61049384,0.86185104,3.5578523,-0.6682474,-0.75794554,-0.59257406,-0.56912696,-0.6001429,1.3671005,0.48722607,0.19623727,-0.66552746,-0.6482407,1.5543242,...338.401603NaN
1sb25p021g00.png0_drawings/sb25p021g00.png0_drawings-0.6198182,0.07978231,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.5267999,0.22521895,-0.45169276,0.4765754,0.9923676,0.07570249,0.10221094,-0.5629848,-0.68991846,-0.57515705,-0.5467274,2.5525615,0.45225585,-0.06349939,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.9686263,-0.6690378,-0.4518578,1.3688718,-0.73986673,-0.16642767,-0.68509877,-0.67007643,0.72362816,-0.16392156,-0.600525,-0.6186755,-0.73933,-0.57338566,-0.65925777,0.37941074,-0.61049384,0.5526003,0.38675356,-0.6682474,-0.75794554,-0.59257406,0.48467332,-0.6001429,0.06385499,0.43829525,0.17835289,-0.34760472,-0.39676708,1....238.171423NaN
2sb48p025g00.png0_drawings/sb48p025g00.png0_drawings0.27033854,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.009221405,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.5080385,-0.57515705,-0.5467274,1.5457456,2.1699433,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.659942,-0.4518578,0.43428838,-0.73986673,-0.07844362,-0.68509877,-0.67007643,-0.52171326,0.12707904,0.82075787,0.00014060736,-0.66719526,-0.39957428,-0.26531035,-0.6179389,-0.61049384,-0.58021957,-0.23677179,-0.6682474,0.99923706,-0.59257406,1.9531038,-0.6001429,-0.054986358,-0.11816922,0.64457357,-0.66552746,...135.424712NaN
3sb44p098g00.png0_drawings/sb44p098g00.png0_drawings0.7062113,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,1.9157894,-0.4896673,-0.7537334,0.54395044,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.58417577,-0.24514529,-0.5467274,0.88600737,2.1812363,-0.19237944,-0.64108604,-0.55193967,0.90909463,-0.6779002,2.1914794,-0.6690378,0.2622062,-0.11539039,-0.73986673,0.39760068,-0.68509877,-0.67007643,-0.52171326,-0.19909361,-0.014922798,1.1047375,-0.73933,-0.57338566,0.79833126,-0.6179389,2.8530078,0.099111676,1.3431637,-0.6682474,3.1076033,-0.59257406,-0.6157326,-0.6001429,-0.651804,0.9190846,0.1499474,-0.3282049,1.5581564,-0.0157680...181.6579011NaN
4sb38p096--bundledg00.png0_drawings/sb38p096--bundledg00.png0_drawings-0.6198182,-0.16951999,-0.73929733,-0.6553513,0.04408127,-0.65341413,0.23919284,-0.4896673,-0.7537334,-0.123981416,-0.62038505,-0.72562873,-0.09523386,-0.6266761,1.4427767,-0.4129038,-0.5467274,-0.6520286,3.6913576,0.16698122,-0.64108604,-0.55193967,-0.5446661,0.09032714,1.3312979,-0.6690378,-0.17945293,0.10221714,-0.73986673,-0.33327153,-0.68509877,-0.67007643,-0.4788125,-0.35560656,-0.05634737,1.1187687,-0.73933,0.19565272,0.54220104,-0.6179389,-0.61049384,0.7409752,-0.53702986,-0.6682474,0.29792833,-0.59257406,0.7453012,-0.6001429,-0.651804,-0.038021386,0.9696573,-0.66552746,1.3529996,1...193.7083414NaN
+ +
+
+
+
+
+
bboxsep_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnameorig_fnamelabelhandlabeled_pathis_newsvg_autotrace_pathhandlabeled_categorytotal_strokesjoined_strokesspliced_strokestotal_pointshad_errorerr_msg
idx
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse0_drawings/sb55p003.svg0_drawings61103932FalseNaN
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
220_drawings/sb73p061g01.png12219sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
330_drawings/sb73p061g02.png21214sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
440_drawings/sb73p061g03.png31141sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse0_drawings/sb73p061.svg0_drawings72157719FalseNaN
+ +
+
+
+
+
+
# bboxsep_df.group_num_points.hist(bins=100)
+
+
+
+
+

prepare for filtering bboxsep

+
+

join dataframes: bboxsep embeddings plus stroke3 metadata

+
+
joined_df = bboxsep_df.join(cdf.set_index('orig_fname'), on='group_fname', lsuffix='bboxsep_')
+
+
+
joined_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...spliced_strokestotal_pointshad_errorerr_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_label
idx
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse...3932FalseNaNsb55p003g00.png0_drawings0.04728329,-0.5714155,-0.73929733,-0.35399058,0.22400337,-0.65341413,0.11710799,-0.4896673,-0.7537334,-0.27084306,-0.43680793,-0.72562873,0.26104796,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,2.2132683,-0.63401383,-0.64108604,0.0068377256,-0.5446661,-0.6779002,-0.40022308,-0.6690378,-0.25018138,-0.5982413,-0.73986673,1.0058696,-0.68509877,-0.67007643,0.5276551,-0.4592305,-0.600525,-0.3091834,-0.73933,-0.57338566,0.84570634,-0.6179389,-0.52985966,0.5803701,0.10534239,-0.6682474,-0.1908136,-0.59257406,-0.6157326,-0.6001429,1.3485551,1.1220317,0.10932493,-0.66552746,0.8113811,1....196.4447614NaN
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...7719FalseNaNsb73p061g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.46724933,-0.7537334,0.9455167,-0.62038505,-0.72562873,0.18925023,-0.6266761,0.33996195,-0.15316445,-0.5467274,0.03829068,1.0683823,0.23568732,-0.64108604,-0.55193967,-0.5446661,-0.022136033,1.4963098,-0.4510746,-0.4518578,-0.5982413,-0.28755826,0.1980134,-0.68509877,-0.67007643,-0.52171326,-0.19965711,-0.0069928765,-0.46161294,-0.73933,-0.50798476,0.03861904,0.68692446,-0.5830822,0.46990138,1.1344135,-0.6682474,0.6321523,-0.59257406,0.156174,-0.6001429,2.36085,0.16527855,-0.13570255,-0.63657653,0.61075664,0.7...119.7111510NaN
220_drawings/sb73p061g01.png12219sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...7719FalseNaNsb73p061g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.42268687,-0.7537334,0.75527334,-0.62038505,-0.72562873,-0.5534677,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.20404798,1.4840671,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.6336739,-0.6690378,0.54027987,-0.5982413,-0.73986673,1.5201781,-0.68509877,-0.67007643,-0.50883394,0.4712809,1.783369,0.16019982,-0.73933,-0.3392509,-0.65925777,0.47834373,2.4338524,0.62893397,1.8294188,0.30422044,1.2648056,-0.59257406,0.18224514,-0.6001429,1.8846588,1.0936818,-0.1367777,-0.66552746,-0.6482407,1.7473626...265.478039NaN
330_drawings/sb73p061g02.png21214sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...7719FalseNaNsb73p061g02.png0_drawings0.5602926,-0.5714155,-0.52156335,-0.6553513,0.57978284,-0.65341413,-0.7267234,-0.14383027,-0.7537334,0.60118496,-0.62038505,-0.72562873,1.6132213,-0.6266761,-0.68991846,1.344243,-0.5467274,-0.005348623,3.4557934,-0.6497359,-0.64108604,0.116793215,-0.29555666,-0.6779002,-0.57759243,-0.6690378,-0.3580075,0.43256426,-0.73986673,0.14702079,-0.68509877,-0.67007643,0.91594005,-0.12435901,-0.18599117,-0.6186755,-0.73933,-0.57338566,-0.65925777,-0.026621401,-0.61049384,2.1561542,1.0271561,-0.6682474,-0.75794554,-0.59257406,0.018729866,0.459763,1.4212353,0.93373936,-0.1690771,-0.3115216,-0.6482407,...196.712313NaN
440_drawings/sb73p061g03.png31141sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...7719FalseNaNsb73p061g03.png0_drawings0.5994661,-0.27441767,-0.73929733,-0.6553513,-0.4899227,-0.65341413,-0.011652231,-0.42220512,-0.7537334,0.8362489,-0.34087157,-0.72562873,0.88858306,-0.6266761,-0.68991846,-0.21294108,-0.5467274,-0.082508564,0.64590657,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.25070423,-0.6690378,-0.4518578,-0.336111,-0.73986673,0.2852489,-0.68509877,-0.67007643,0.40476,-0.28328538,-0.600525,-0.6186755,-0.68293667,-0.57338566,0.11821991,3.1746101,-0.053979993,1.7328179,1.7004575,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.6001429,0.45167196,0.39032936,-0.076191604,0.106552124,-0.64824...194.215213NaN
+ +

5 rows × 24 columns

+
+
+
+
+
+
joined_df.knn_clusterid.hist()
+
+
+
+

+
+
+
+
+
+
joined_df[joined_df.knn_clusterid==0].group_num_strokes.hist()
+
+
+
+

+
+
+
+
+
+
from IPython import display
+
+for cid in sorted(cdf.knn_clusterid.unique()):
+    cluster_summary_fname = f"{CLUSTER_OUTPUT_DIR}/{cid:03d}.png"
+    display.display(display.Image(cluster_summary_fname))
+
+
+
+

+
+
+
+
+
+
+

+
+
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+
+
+
+

+
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+

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+

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+

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+

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+

+
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+

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+

+
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+

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+

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+

+
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+

+
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+

+
+
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+
+
+
+

+
+
+
+
+
+
+

label clusters, save centroids+labels for bbox-sep

+
+
GOOD_CLUSTERS = [
+    1,
+    2,
+    4,
+    5,
+    6,
+    7,
+    8,
+    10,
+    11,
+    12,
+    14,
+    15,
+]
+TOO_SIMPLE = [9, 13]
+TOO_COMPLEX = [0, 3]
+
+cluster_labels = {}
+
+for c in GOOD_CLUSTERS:
+    cluster_labels[c] = 'good'
+for c in TOO_SIMPLE:
+    cluster_labels[c] = 'simple'
+for c in TOO_COMPLEX:
+    cluster_labels[c] = 'complex'
+
+
+
save_centroids(
+    centroids,
+    cluster_labels,
+    data_home=EXTERNAL_DATA_HOME,
+    # data_home=singleline_data_home(default='../data_home'),
+    model_path=PRETRAINED_MODEL_PATH,
+    extra_suffix='_bboxsep'
+)
+
+_centroids, _cluster_labels = load_centroids(
+    data_home=EXTERNAL_DATA_HOME,
+    # data_home=singleline_data_home(default='../data_home'),
+    model_path=PRETRAINED_MODEL_PATH,
+    extra_suffix='_bboxsep'
+)
+
+
wrote to /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_centroids_bboxsep.json
+wrote to /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_labels_bboxsep.json
+reading /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_centroids_bboxsep.json
+reading /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/models/epoch-20231128/01_FLAT_cluster_labels_bboxsep.json
+
+
+
+
_centroids
+
+
array([[ 0.40956366,  0.4539906 , -0.7392965 , ..., -0.593978  ,
+        -0.01202063,  0.82983655],
+       [-0.48970124, -0.40134948, -0.7189984 , ..., -0.48196766,
+        -0.41706023,  0.1788576 ],
+       [-0.52298546, -0.56843925, -0.6605481 , ..., -0.5846314 ,
+        -0.46725053, -0.2719769 ],
+       ...,
+       [-0.50658494, -0.4525644 , -0.6467315 , ..., -0.5658877 ,
+        -0.48807985, -0.32663694],
+       [ 0.05594135, -0.39736596, -0.6444024 , ..., -0.5038025 ,
+        -0.41823232,  1.186483  ],
+       [-0.26665202, -0.47642395, -0.71609116, ..., -0.6128117 ,
+        -0.45602712,  0.13645345]], dtype=float32)
+
+
+
+
_cluster_labels
+
+
{'1': 'good',
+ '2': 'good',
+ '4': 'good',
+ '5': 'good',
+ '6': 'good',
+ '7': 'good',
+ '8': 'good',
+ '10': 'good',
+ '11': 'good',
+ '12': 'good',
+ '14': 'good',
+ '15': 'good',
+ '9': 'simple',
+ '13': 'simple',
+ '0': 'complex',
+ '3': 'complex'}
+
+
+
+
+

assign labels to bboxsep clusters

+
+
joined_df['cluster_label'] = joined_df.knn_clusterid.map(lambda cid: cluster_labels[cid])
+
+
+
joined_df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...total_pointshad_errorerr_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_label
idx
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse...932FalseNaNsb55p003g00.png0_drawings0.04728329,-0.5714155,-0.73929733,-0.35399058,0.22400337,-0.65341413,0.11710799,-0.4896673,-0.7537334,-0.27084306,-0.43680793,-0.72562873,0.26104796,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,2.2132683,-0.63401383,-0.64108604,0.0068377256,-0.5446661,-0.6779002,-0.40022308,-0.6690378,-0.25018138,-0.5982413,-0.73986673,1.0058696,-0.68509877,-0.67007643,0.5276551,-0.4592305,-0.600525,-0.3091834,-0.73933,-0.57338566,0.84570634,-0.6179389,-0.52985966,0.5803701,0.10534239,-0.6682474,-0.1908136,-0.59257406,-0.6157326,-0.6001429,1.3485551,1.1220317,0.10932493,-0.66552746,0.8113811,1....196.4447614NaNgood
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.46724933,-0.7537334,0.9455167,-0.62038505,-0.72562873,0.18925023,-0.6266761,0.33996195,-0.15316445,-0.5467274,0.03829068,1.0683823,0.23568732,-0.64108604,-0.55193967,-0.5446661,-0.022136033,1.4963098,-0.4510746,-0.4518578,-0.5982413,-0.28755826,0.1980134,-0.68509877,-0.67007643,-0.52171326,-0.19965711,-0.0069928765,-0.46161294,-0.73933,-0.50798476,0.03861904,0.68692446,-0.5830822,0.46990138,1.1344135,-0.6682474,0.6321523,-0.59257406,0.156174,-0.6001429,2.36085,0.16527855,-0.13570255,-0.63657653,0.61075664,0.7...119.7111510NaNgood
220_drawings/sb73p061g01.png12219sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.42268687,-0.7537334,0.75527334,-0.62038505,-0.72562873,-0.5534677,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.20404798,1.4840671,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.6336739,-0.6690378,0.54027987,-0.5982413,-0.73986673,1.5201781,-0.68509877,-0.67007643,-0.50883394,0.4712809,1.783369,0.16019982,-0.73933,-0.3392509,-0.65925777,0.47834373,2.4338524,0.62893397,1.8294188,0.30422044,1.2648056,-0.59257406,0.18224514,-0.6001429,1.8846588,1.0936818,-0.1367777,-0.66552746,-0.6482407,1.7473626...265.478039NaNsimple
330_drawings/sb73p061g02.png21214sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g02.png0_drawings0.5602926,-0.5714155,-0.52156335,-0.6553513,0.57978284,-0.65341413,-0.7267234,-0.14383027,-0.7537334,0.60118496,-0.62038505,-0.72562873,1.6132213,-0.6266761,-0.68991846,1.344243,-0.5467274,-0.005348623,3.4557934,-0.6497359,-0.64108604,0.116793215,-0.29555666,-0.6779002,-0.57759243,-0.6690378,-0.3580075,0.43256426,-0.73986673,0.14702079,-0.68509877,-0.67007643,0.91594005,-0.12435901,-0.18599117,-0.6186755,-0.73933,-0.57338566,-0.65925777,-0.026621401,-0.61049384,2.1561542,1.0271561,-0.6682474,-0.75794554,-0.59257406,0.018729866,0.459763,1.4212353,0.93373936,-0.1690771,-0.3115216,-0.6482407,...196.712313NaNcomplex
440_drawings/sb73p061g03.png31141sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g03.png0_drawings0.5994661,-0.27441767,-0.73929733,-0.6553513,-0.4899227,-0.65341413,-0.011652231,-0.42220512,-0.7537334,0.8362489,-0.34087157,-0.72562873,0.88858306,-0.6266761,-0.68991846,-0.21294108,-0.5467274,-0.082508564,0.64590657,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.25070423,-0.6690378,-0.4518578,-0.336111,-0.73986673,0.2852489,-0.68509877,-0.67007643,0.40476,-0.28328538,-0.600525,-0.6186755,-0.68293667,-0.57338566,0.11821991,3.1746101,-0.053979993,1.7328179,1.7004575,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.6001429,0.45167196,0.39032936,-0.076191604,0.106552124,-0.64824...194.215213NaNcomplex
+ +

5 rows × 25 columns

+
+
+
+
+
+
FILTER_OUTPUT_DIR = EPOCH.dir_07_FILTER()
+if not os.path.isdir(FILTER_OUTPUT_DIR):
+    os.makedirs(FILTER_OUTPUT_DIR)
+FILTER_TSV_FNAME = EPOCH.tsv_07_FILTER()
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
+
joined_df.to_csv(FILTER_TSV_FNAME, index=False, sep="\t", header=True)
+print(f"wrote {FILTER_TSV_FNAME}")
+
+
wrote /Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/07_FILTER.tsv
+
+
+
+
jdf = pd.read_csv(FILTER_TSV_FNAME, delimiter="\t") #, index_col="idx")
+jdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...total_pointshad_errorerr_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_label
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse...932FalseNaNsb55p003g00.png0_drawings0.04728329,-0.5714155,-0.73929733,-0.35399058,0.22400337,-0.65341413,0.11710799,-0.4896673,-0.7537334,-0.27084306,-0.43680793,-0.72562873,0.26104796,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,2.2132683,-0.63401383,-0.64108604,0.0068377256,-0.5446661,-0.6779002,-0.40022308,-0.6690378,-0.25018138,-0.5982413,-0.73986673,1.0058696,-0.68509877,-0.67007643,0.5276551,-0.4592305,-0.600525,-0.3091834,-0.73933,-0.57338566,0.84570634,-0.6179389,-0.52985966,0.5803701,0.10534239,-0.6682474,-0.1908136,-0.59257406,-0.6157326,-0.6001429,1.3485551,1.1220317,0.10932493,-0.66552746,0.8113811,1....196.4447614NaNgood
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.46724933,-0.7537334,0.9455167,-0.62038505,-0.72562873,0.18925023,-0.6266761,0.33996195,-0.15316445,-0.5467274,0.03829068,1.0683823,0.23568732,-0.64108604,-0.55193967,-0.5446661,-0.022136033,1.4963098,-0.4510746,-0.4518578,-0.5982413,-0.28755826,0.1980134,-0.68509877,-0.67007643,-0.52171326,-0.19965711,-0.0069928765,-0.46161294,-0.73933,-0.50798476,0.03861904,0.68692446,-0.5830822,0.46990138,1.1344135,-0.6682474,0.6321523,-0.59257406,0.156174,-0.6001429,2.36085,0.16527855,-0.13570255,-0.63657653,0.61075664,0.7...119.7111510NaNgood
220_drawings/sb73p061g01.png12219sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.42268687,-0.7537334,0.75527334,-0.62038505,-0.72562873,-0.5534677,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.20404798,1.4840671,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.6336739,-0.6690378,0.54027987,-0.5982413,-0.73986673,1.5201781,-0.68509877,-0.67007643,-0.50883394,0.4712809,1.783369,0.16019982,-0.73933,-0.3392509,-0.65925777,0.47834373,2.4338524,0.62893397,1.8294188,0.30422044,1.2648056,-0.59257406,0.18224514,-0.6001429,1.8846588,1.0936818,-0.1367777,-0.66552746,-0.6482407,1.7473626...265.478039NaNsimple
330_drawings/sb73p061g02.png21214sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g02.png0_drawings0.5602926,-0.5714155,-0.52156335,-0.6553513,0.57978284,-0.65341413,-0.7267234,-0.14383027,-0.7537334,0.60118496,-0.62038505,-0.72562873,1.6132213,-0.6266761,-0.68991846,1.344243,-0.5467274,-0.005348623,3.4557934,-0.6497359,-0.64108604,0.116793215,-0.29555666,-0.6779002,-0.57759243,-0.6690378,-0.3580075,0.43256426,-0.73986673,0.14702079,-0.68509877,-0.67007643,0.91594005,-0.12435901,-0.18599117,-0.6186755,-0.73933,-0.57338566,-0.65925777,-0.026621401,-0.61049384,2.1561542,1.0271561,-0.6682474,-0.75794554,-0.59257406,0.018729866,0.459763,1.4212353,0.93373936,-0.1690771,-0.3115216,-0.6482407,...196.712313NaNcomplex
440_drawings/sb73p061g03.png31141sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g03.png0_drawings0.5994661,-0.27441767,-0.73929733,-0.6553513,-0.4899227,-0.65341413,-0.011652231,-0.42220512,-0.7537334,0.8362489,-0.34087157,-0.72562873,0.88858306,-0.6266761,-0.68991846,-0.21294108,-0.5467274,-0.082508564,0.64590657,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.25070423,-0.6690378,-0.4518578,-0.336111,-0.73986673,0.2852489,-0.68509877,-0.67007643,0.40476,-0.28328538,-0.600525,-0.6186755,-0.68293667,-0.57338566,0.11821991,3.1746101,-0.053979993,1.7328179,1.7004575,-0.6682474,-0.75794554,-0.59257406,-0.6157326,-0.6001429,0.45167196,0.39032936,-0.076191604,0.106552124,-0.64824...194.215213NaNcomplex
+ +

5 rows × 25 columns

+
+
+
+
+
+
ds = np.load(BBOXSEP_NPZ_FNAME, encoding='latin1', allow_pickle=True)['arr_0']
+ds.shape
+
+
(3146,)
+
+
+
+

sanity-check

+

make sure NPZ contents and dataframe contents are aligned

+
+
# note: NPZ is strokes, not delta/stroke3
+plot_strokes(ds[0])
+
+
+
+

+
+
+
+
+
+
Image.open(BBOXSEP_OUTPUT_DIR / jdf.iloc[0].group_fname)
+
+
+
+

+
+
+
+
+
+
jdf.cluster_label.hist()
+
+
+
+

+
+
+
+
+
+
+
+
+

filtering

+
+

filter to clusters labeled “good”

+
+
good_jdf = jdf[jdf.cluster_label == 'good'].sort_values(by='group_num_points')
+
+
+
good_jdf
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...total_pointshad_errorerr_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_label
111811180_drawings/sb25p185g02.png2131sb25p185.jpgart/sb25p185.jpgart0_drawings/sb25p185.jpgFalse...913FalseNaNsb25p185g02.png0_drawings-0.6198182,-0.5714155,-0.73929733,0.1365276,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.37663308,-0.62038505,-0.72562873,0.43479574,-0.6266761,-0.68991846,-0.48708817,-0.5467274,2.1529212,2.6845608,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.642818,-0.73986673,-0.17095494,-0.68509877,-0.67007643,-0.52171326,-0.2838103,1.6439883,-0.6186755,-0.14346069,-0.57338566,0.35708773,-0.6179389,-0.61049384,-0.58021957,0.673053,-0.27862772,-0.31951192,-0.59257406,0.20935732,-0.6001429,-0.30986813,-0.18282452,-0.18923333,-0.66552746,-0.648...198.162632NaNgood
191619160_drawings/sb25p125g04.png4134sb25p125.jpgart/sb25p125.jpgart0_drawings/sb25p125.jpgFalse...902FalseNaNsb25p125g04.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,0.69653773,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.0280326,-0.57515705,-0.5467274,0.6625003,0.36793756,-0.6497359,-0.64108604,-0.55193967,-0.32117873,-0.6779002,2.1312556,-0.6690378,-0.4518578,-0.5982413,-0.0033613443,0.30348197,-0.68509877,-0.67007643,-0.40359753,-0.3060785,-0.600525,0.27992672,-0.73933,-0.57338566,1.2887836,-0.19923326,1.8492811,-0.58021957,0.9560714,-0.40003663,0.7672442,-0.59257406,-0.21736711,-0.6001429,0.9196389,-0.41073623,0.45491612,0.33916223,-0.35884154,0.0...211.2204310NaNgood
5705700_drawings/sb25p179-whats-in-a-sketchbookg02.png2135sb25p179-whats-in-a-sketchbook.jpgnotes/sb25p179-whats-in-a-sketchbook.jpgnotes0_drawings/sb25p179-whats-in-a-sketchbook.jpgFalse...701FalseNaNsb25p179-whats-in-a-sketchbookg02.png0_drawings-0.59012836,0.2323553,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.5119321,0.1717692,-0.7537334,0.21706411,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.28297073,3.2042804,-0.5467274,-0.6520286,1.5840913,-0.6497359,-0.64108604,0.50751585,-0.08541769,-0.6779002,1.7682481,-0.6690378,0.5983945,-0.5982413,-0.73986673,1.4396008,-0.68509877,-0.67007643,0.46568382,-0.25951743,-0.600525,-0.6186755,-0.73933,-0.16946498,-0.65925777,-0.6179389,-0.61049384,-0.29438102,1.5495194,-0.6682474,0.33356762,-0.59257406,-0.6157326,-0.6001429,1.0514184,0.29193193,0.40280968,-0.6068183,0.32230842,1.9357555,...275.685551NaNgood
234023400_drawings/sb38p081g01.png1135sb38p081.jpgart/sb38p081.jpgart0_drawings/sb38p081.jpgFalse...858FalseNaNsb38p081g01.png0_drawings-0.6198182,-0.46731,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.08140719,-0.4896673,-0.7537334,1.2413944,-0.62038505,-0.72562873,-0.662768,-0.6266761,0.23866087,-0.57515705,-0.5467274,-0.2956484,1.7022036,-0.6497359,-0.64108604,-0.55193967,0.30344278,-0.6779002,2.337994,-0.6690378,0.34578347,-0.46154124,-0.46280393,1.2992494,-0.68509877,-0.67007643,-0.52171326,-0.10349408,-0.39859495,-0.09947389,-0.73933,-0.57338566,0.76756275,-0.6179389,1.5889049,-0.58021957,0.65297556,-0.60780674,2.821436,-0.59257406,0.7684048,-0.6001429,0.34948182,0.19200855,-0.59497476,-0.66552746,0.84186804,0.618...121.6177411NaNgood
278527850_drawings/sb77p017g04.png4137sb77p017.jpgart/sb77p017.jpgart0_drawings/sb77p017.jpgFalse...928FalseNaNsb77p017g04.png0_drawings0.105097234,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.22639048,-0.39247805,-0.7537334,0.7720407,-0.62038505,-0.72562873,0.5624113,-0.6266761,-0.68991846,-0.35765618,-0.5467274,-0.41597736,-0.48053342,-0.5174302,-0.64108604,-0.55193967,-0.5446661,0.8542391,3.1147468,-0.6690378,0.4601984,-0.5982413,-0.73986673,0.22383282,-0.68509877,-0.67007643,0.21348906,-0.4592305,-0.600525,1.7134764,-0.73933,-0.57338566,0.64829993,0.3909546,-0.61049384,0.5155402,1.9053141,-0.6682474,0.8197565,0.018003523,-0.26761976,-0.6001429,2.067383,-0.10543817,1.1247433,0.8416481,2.5156565,1.2849805,...185.516971NaNgood
..................................................................
152315230_drawings/sb26p000g00.png0163627sb26p000.jpgart/sb26p000.jpgart0_drawings/sb26p000.jpgFalse...993FalseNaNsb26p000g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.32616854,-0.24150081,-0.7537334,0.7222707,-0.3538895,-0.72562873,1.5557338,-0.5117979,-0.68991846,-0.57515705,-0.5467274,1.0215497,0.5765364,-0.6497359,0.896859,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,-0.5982413,-0.040418804,0.106363446,-0.68509877,-0.67007643,-0.52171326,-0.010058641,-0.50279045,-0.13343102,-0.73933,-0.57338566,2.4999657,1.55893,-0.61049384,0.19597775,1.3642957,-0.6682474,1.1820146,-0.59257406,0.20204073,-0.6001429,2.0034428,-0.014275789,-0.59497476,2.254665,-0.24811566,0.70...229.924358NaNgood
193019300_drawings/sb73p057g00.png0163828sb73p057.jpgart/sb73p057.jpgart0_drawings/sb73p057.jpgFalse...969FalseNaNsb73p057g00.png0_drawings-0.16135406,-0.5714155,-0.30271015,-0.6553513,-0.31833506,-0.65341413,-0.7267234,-0.28267032,-0.7537334,0.37118575,0.87385976,-0.72562873,-0.57952887,0.6362833,0.5084452,-0.22724995,-0.5467274,0.35449284,0.7794676,-0.6497359,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.21479678,-0.6690378,-0.4518578,-0.5982413,-0.73986673,-0.4490037,-0.68509877,-0.67007643,-0.2994364,0.07610384,-0.600525,-0.6186755,0.06570494,0.067295015,-0.65925777,0.39152288,-0.61049384,1.1142881,1.2409655,-0.6682474,-0.75794554,-0.59257406,-0.20920894,-0.6001429,0.60176325,0.13885683,0.7273855,-0.662378,1.3855385,1....185.506778NaNgood
184318430_drawings/sb72p036g00.png0154234sb72p036.jpgart/sb72p036.jpgart0_drawings/sb72p036.jpgFalse...944FalseNaNsb72p036g00.png0_drawings0.23770028,-0.5714155,-0.4465557,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.30734026,-0.7537334,0.15001783,0.035733283,-0.72562873,1.1390157,-0.6266761,-0.21557641,0.27054513,-0.5467274,-0.6520286,0.385108,-0.6497359,-0.64108604,0.26666003,-0.5446661,-0.6779002,-0.57759243,-0.3434411,-0.4518578,-0.5982413,-0.73986673,0.69509554,-0.68509877,-0.67007643,-0.3029986,0.17242959,1.6237646,-0.6186755,-0.73933,-0.5510175,0.669368,0.7787843,-0.61049384,0.5778777,0.33693397,-0.4432963,-0.75794554,-0.59257406,3.391646,-0.6001429,2.1483743,-0.5315948,0.5318872,-0.66552746,0.1902036,1.1923716,0.91...165.029427NaNgood
151515150_drawings/sb25p025g00.png0284471sb25p025.jpgart/sb25p025.jpgart0_drawings/sb25p025.jpgFalse...841FalseNaNsb25p025g00.png0_drawings0.07654363,-0.5148912,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.22904922,-0.62038505,-0.72562873,1.128666,-0.6266761,0.87664753,-0.57515705,-0.5467274,-0.6520286,1.7907459,0.64530444,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.1420083,-0.6690378,-0.4518578,0.24943048,-0.73986673,0.21349719,-0.68509877,-0.67007643,-0.0551562,-0.4592305,-0.600525,0.53528905,-0.73933,-0.57338566,1.1262103,-0.6179389,-0.61049384,1.0941081,-0.17765257,-0.21084523,0.26777744,-0.59257406,0.7225874,-0.6001429,-0.651804,0.26665884,0.010279417,-0.66552746,0.4149146,1.85261...169.7134414NaNgood
266226620_drawings/sb11p018g00.png0154750sb11p018.jpgart/sb11p018.jpgart0_drawings/sb11p018.jpgFalse...960FalseNaNsb11p018g00.png0_drawings-0.30707094,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,0.05596289,-0.62038505,-0.72562873,1.2189244,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,0.6512716,0.76152265,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.19231415,1.4908922,0.51145494,-0.68509877,-0.67007643,0.31971788,-0.254141,-0.600525,-0.17450035,-0.73933,-0.57338566,-0.65925777,-0.6179389,-0.61049384,1.3415778,0.89640486,-0.6682474,1.1076254,-0.59257406,1.4866126,-0.33183065,2.6855114,-0.5315948,0.70867467,-0.66552746,0.5583911,0.4219...152.121068NaNgood
+ +

2557 rows × 25 columns

+
+
+
+
+
+
+

pick max-min num points to filter out overly simple, overly complex

+
+
good_jdf.group_num_points.hist()
+
+
+
+

+
+
+
+
+
+
good_jdf.group_num_strokes.hist()
+
+
+
+

+
+
+
+
+
+
image_grid(good_jdf, lambda row: BBOXSEP_OUTPUT_DIR / row.group_fname, FILTER_OUTPUT_DIR / 'grid_good.png')
+Image.open(FILTER_OUTPUT_DIR / 'grid_good.png')
+
+
+
+
+
+
+

+
+
+
+
+
+
good_jdf[41:]
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...total_pointshad_errorerr_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_label
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151515150_drawings/sb25p025g00.png0284471sb25p025.jpgart/sb25p025.jpgart0_drawings/sb25p025.jpgFalse...841FalseNaNsb25p025g00.png0_drawings0.07654363,-0.5148912,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.22904922,-0.62038505,-0.72562873,1.128666,-0.6266761,0.87664753,-0.57515705,-0.5467274,-0.6520286,1.7907459,0.64530444,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.1420083,-0.6690378,-0.4518578,0.24943048,-0.73986673,0.21349719,-0.68509877,-0.67007643,-0.0551562,-0.4592305,-0.600525,0.53528905,-0.73933,-0.57338566,1.1262103,-0.6179389,-0.61049384,1.0941081,-0.17765257,-0.21084523,0.26777744,-0.59257406,0.7225874,-0.6001429,-0.651804,0.26665884,0.010279417,-0.66552746,0.4149146,1.85261...169.7134414NaNgood
266226620_drawings/sb11p018g00.png0154750sb11p018.jpgart/sb11p018.jpgart0_drawings/sb11p018.jpgFalse...960FalseNaNsb11p018g00.png0_drawings-0.30707094,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.4896673,-0.7537334,0.05596289,-0.62038505,-0.72562873,1.2189244,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,0.6512716,0.76152265,-0.64108604,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.19231415,1.4908922,0.51145494,-0.68509877,-0.67007643,0.31971788,-0.254141,-0.600525,-0.17450035,-0.73933,-0.57338566,-0.65925777,-0.6179389,-0.61049384,1.3415778,0.89640486,-0.6682474,1.1076254,-0.59257406,1.4866126,-0.33183065,2.6855114,-0.5315948,0.70867467,-0.66552746,0.5583911,0.4219...152.121068NaNgood
+ +

80 rows × 25 columns

+
+
+
+
+
+
# careful: an earlier DF was called "filtered_df", not "filtered_jdf"
+filtered_jdf = good_jdf[(good_jdf.group_num_points >= 84) & (good_jdf.group_num_points <= 1753)]
+
+
+
len(filtered_jdf)
+
+
2437
+
+
+
+
image_grid(filtered_jdf, lambda row: BBOXSEP_OUTPUT_DIR / row.group_fname, FILTER_OUTPUT_DIR / 'grid_filtered.png')
+Image.open(FILTER_OUTPUT_DIR / 'grid_filtered.png')
+
+
+
+

+
+
+
+
+
+
+
+

persist new TSV

+
+
# to be safe, sort by new_idx - we're going to select these indices 
+# out of the numpy array of raw data, so I want to make sure they're
+# ordered properly (unsure if numpy would return selected indices
+# based on their original order, or the order given in my list).
+filtered_jdf = filtered_jdf.sort_values(by='new_idx')
+
+
+
filtered_jdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...total_pointshad_errorerr_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_label
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse...932FalseNaNsb55p003g00.png0_drawings0.04728329,-0.5714155,-0.73929733,-0.35399058,0.22400337,-0.65341413,0.11710799,-0.4896673,-0.7537334,-0.27084306,-0.43680793,-0.72562873,0.26104796,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,2.2132683,-0.63401383,-0.64108604,0.0068377256,-0.5446661,-0.6779002,-0.40022308,-0.6690378,-0.25018138,-0.5982413,-0.73986673,1.0058696,-0.68509877,-0.67007643,0.5276551,-0.4592305,-0.600525,-0.3091834,-0.73933,-0.57338566,0.84570634,-0.6179389,-0.52985966,0.5803701,0.10534239,-0.6682474,-0.1908136,-0.59257406,-0.6157326,-0.6001429,1.3485551,1.1220317,0.10932493,-0.66552746,0.8113811,1....196.4447614NaNgood
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...719FalseNaNsb73p061g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.46724933,-0.7537334,0.9455167,-0.62038505,-0.72562873,0.18925023,-0.6266761,0.33996195,-0.15316445,-0.5467274,0.03829068,1.0683823,0.23568732,-0.64108604,-0.55193967,-0.5446661,-0.022136033,1.4963098,-0.4510746,-0.4518578,-0.5982413,-0.28755826,0.1980134,-0.68509877,-0.67007643,-0.52171326,-0.19965711,-0.0069928765,-0.46161294,-0.73933,-0.50798476,0.03861904,0.68692446,-0.5830822,0.46990138,1.1344135,-0.6682474,0.6321523,-0.59257406,0.156174,-0.6001429,2.36085,0.16527855,-0.13570255,-0.63657653,0.61075664,0.7...119.7111510NaNgood
550_drawings/sb73p041g00.png03702sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse...907FalseNaNsb73p041g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.4395254,-0.65341413,-0.7267234,-0.30100727,-0.7537334,0.6979399,0.5823597,-0.72562873,-0.662768,0.702158,-0.6232817,-0.57515705,-0.22156492,-0.07576728,-0.25915077,0.7949797,-0.5030652,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.8875855,-0.73986673,0.2878165,-0.68509877,-0.67007643,-0.52171326,-0.20293939,-0.600525,0.28690326,-0.73933,0.13321829,-0.65925777,-0.6179389,-0.61049384,-0.58021957,-0.6189488,-0.6682474,-0.75794554,-0.59257406,0.2206502,-0.6001429,-0.3428496,-0.37840372,-0.59497476,-0.66552746,-0.6482407,0....285.931528NaNgood
660_drawings/sb73p041g01.png11205sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse...907FalseNaNsb73p041g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,0.058876395,-0.65341413,-0.7267234,-0.42173523,-0.7537334,0.7433976,0.19966531,-0.72562873,0.62771916,-0.6266761,-0.68991846,-0.57515705,-0.5467274,1.5417054,2.402433,-0.6497359,-0.64108604,-0.4386818,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.11702931,-0.73986673,-0.45211998,-0.68509877,-0.67007643,-0.52171326,0.34430972,-0.48163074,-0.6186755,-0.73933,0.040438354,-0.65925777,0.7474549,-0.61049384,0.30740798,1.8930382,-0.6682474,-0.75794554,-0.59257406,-0.3586682,0.89375025,-0.651804,0.08712256,1.415978,-0.66552746,-0.6482407,-0....179.577218NaNgood
10100_drawings/sb55p080g00.png041462sb55p080.jpgart/sb55p080.jpgart0_drawings/sb55p080.jpgFalse...995FalseNaNsb55p080g00.png0_drawings-0.6198182,-0.5714155,-0.12864059,-0.6553513,0.03367281,-0.3521561,-0.7267234,-0.47485968,-0.7537334,-0.42715576,1.136474,-0.72562873,2.6017675,-0.6266761,-0.68991846,-0.57515705,-0.5467274,1.0752423,-0.02930379,1.118658,0.39956325,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.16788304,-0.42450532,-0.45211998,-0.68509877,-0.67007643,-0.23189741,0.37339267,1.0403941,0.11935049,-0.73933,-0.19533288,1.3258636,-0.53331745,-0.61049384,1.4947255,0.25517285,-0.59559596,-0.75794554,-0.59257406,0.2976374,-0.6001429,-0.651804,0.09863156,1.5771475,-0.66552746,-0.15108436,0.443...143.851075NaNgood
+ +

5 rows × 25 columns

+
+
+
+
+
+
len(filtered_jdf)
+
+
2437
+
+
+
+
list(filtered_jdf.new_idx)[:5]
+
+
[0, 1, 5, 6, 10]
+
+
+
+
ds.shape
+
+
(3146,)
+
+
+
+
filtered_ds = ds[list(filtered_jdf.new_idx)]
+
+
+
#filtered_ds[0]
+
+
+
plot_strokes(filtered_ds[3])
+
+
+
+

+
+
+
+
+
+
Image.open(BBOXSEP_OUTPUT_DIR / filtered_jdf.iloc[3].group_fname)
+
+
+
+

+
+
+
+
+
+
filtered_jdf.iloc[3].group_num_points
+
+
205
+
+
+
+
filtered_jdf.group_num_points.hist()
+
+
+
+

+
+
+
+
+

to do: - [ ] save the TSV - [ ] save NPX pre-RDP (in case I want to mess with it later) - [ ] run RDP to simplify the drawings down to 250 points (max_seq_len) - [ ] save NPZ post-RDP

+
+
def iterative_RDP(input_strokes, initial_epsilon=0.5, target_length=250, verbose=False):
+    new_strokes = input_strokes
+    epsilon = initial_epsilon
+    if verbose:
+        print(f"initial length: {np.vstack(new_strokes).shape[0]}")
+    while np.vstack(new_strokes).shape[0] > target_length:
+        new_strokes = rdp_strokes(new_strokes, epsilon=epsilon)
+        epsilon += 0.2
+        if verbose:
+            print(f"\tRDP pass complete: {np.vstack(new_strokes).shape[0]}")
+    return new_strokes, np.vstack(new_strokes).shape[0], epsilon
+
+
+
for i in range(len(filtered_jdf)):
+    if i > 1:
+        break
+    row = filtered_jdf.iloc[i]
+    strokes = filtered_ds[i]
+    plot_strokes(strokes)
+    _strokes, rdp_num_points, epsilon = iterative_RDP(strokes, verbose=True)
+    plot_strokes(_strokes)
+
+
+
+

+
+
+
+
+
initial length: 1510
+    RDP pass complete: 781
+    RDP pass complete: 679
+    RDP pass complete: 581
+    RDP pass complete: 545
+    RDP pass complete: 502
+    RDP pass complete: 469
+    RDP pass complete: 439
+    RDP pass complete: 407
+    RDP pass complete: 382
+    RDP pass complete: 366
+    RDP pass complete: 344
+    RDP pass complete: 321
+    RDP pass complete: 303
+    RDP pass complete: 287
+    RDP pass complete: 277
+    RDP pass complete: 267
+    RDP pass complete: 261
+    RDP pass complete: 248
+initial length: 128
+
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+
# init some columns to update in a for-loop
+filtered_jdf['rdp_num_points'] = filtered_jdf['group_num_points']
+filtered_jdf['rdp_epsilon'] = [0.0] * len(filtered_jdf)
+
+all_rdp_deltas = []
+
+for i in range(len(filtered_jdf)):
+    row = filtered_jdf.iloc[i]
+    strokes = filtered_ds[i]
+    _strokes, rdp_num_points, epsilon = iterative_RDP(strokes, verbose=False)
+
+    rdp_deltas = strokes_to_deltas(_strokes)
+
+    all_rdp_deltas.append(rdp_deltas)
+
+
+
len(all_rdp_deltas), len(filtered_jdf)
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+
+
+
(2437, 2437)
+
+
+
+
+
+
Path('/Users/al/Dropbox/2-Areas/2-Sketchbooks/singleline_data/raster/epoch-20240104/08_THRESHOLDED')
+
+
+
+
THESHOLDED_TSV_FNAME = EPOCH.tsv_08_THRESHOLDED()
+
+filtered_jdf.to_csv(THESHOLDED_TSV_FNAME, index=True, index_label="idx", sep="\t", header=True)
+
+
+
fjdf = pd.read_csv(THESHOLDED_TSV_FNAME, delimiter="\t", index_col="idx")
+fjdf.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...err_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_labelrdp_num_pointsrdp_epsilon
idx
000_drawings/sb55p003g00.png041510sb55p003.jpgart/sb55p003.jpgart0_drawings/sb55p003.jpgFalse...NaNsb55p003g00.png0_drawings0.04728329,-0.5714155,-0.73929733,-0.35399058,0.22400337,-0.65341413,0.11710799,-0.4896673,-0.7537334,-0.27084306,-0.43680793,-0.72562873,0.26104796,-0.6266761,-0.68991846,-0.57515705,-0.5467274,-0.6520286,2.2132683,-0.63401383,-0.64108604,0.0068377256,-0.5446661,-0.6779002,-0.40022308,-0.6690378,-0.25018138,-0.5982413,-0.73986673,1.0058696,-0.68509877,-0.67007643,0.5276551,-0.4592305,-0.600525,-0.3091834,-0.73933,-0.57338566,0.84570634,-0.6179389,-0.52985966,0.5803701,0.10534239,-0.6682474,-0.1908136,-0.59257406,-0.6157326,-0.6001429,1.3485551,1.1220317,0.10932493,-0.66552746,0.8113811,1....196.4447614NaNgood15100.0
110_drawings/sb73p061g00.png02128sb73p061.jpgart/sb73p061.jpgart0_drawings/sb73p061.jpgFalse...NaNsb73p061g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.46724933,-0.7537334,0.9455167,-0.62038505,-0.72562873,0.18925023,-0.6266761,0.33996195,-0.15316445,-0.5467274,0.03829068,1.0683823,0.23568732,-0.64108604,-0.55193967,-0.5446661,-0.022136033,1.4963098,-0.4510746,-0.4518578,-0.5982413,-0.28755826,0.1980134,-0.68509877,-0.67007643,-0.52171326,-0.19965711,-0.0069928765,-0.46161294,-0.73933,-0.50798476,0.03861904,0.68692446,-0.5830822,0.46990138,1.1344135,-0.6682474,0.6321523,-0.59257406,0.156174,-0.6001429,2.36085,0.16527855,-0.13570255,-0.63657653,0.61075664,0.7...119.7111510NaNgood1280.0
550_drawings/sb73p041g00.png03702sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse...NaNsb73p041g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.4395254,-0.65341413,-0.7267234,-0.30100727,-0.7537334,0.6979399,0.5823597,-0.72562873,-0.662768,0.702158,-0.6232817,-0.57515705,-0.22156492,-0.07576728,-0.25915077,0.7949797,-0.5030652,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.8875855,-0.73986673,0.2878165,-0.68509877,-0.67007643,-0.52171326,-0.20293939,-0.600525,0.28690326,-0.73933,0.13321829,-0.65925777,-0.6179389,-0.61049384,-0.58021957,-0.6189488,-0.6682474,-0.75794554,-0.59257406,0.2206502,-0.6001429,-0.3428496,-0.37840372,-0.59497476,-0.66552746,-0.6482407,0....285.931528NaNgood7020.0
660_drawings/sb73p041g01.png11205sb73p041.jpgart/sb73p041.jpgart0_drawings/sb73p041.jpgFalse...NaNsb73p041g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,0.058876395,-0.65341413,-0.7267234,-0.42173523,-0.7537334,0.7433976,0.19966531,-0.72562873,0.62771916,-0.6266761,-0.68991846,-0.57515705,-0.5467274,1.5417054,2.402433,-0.6497359,-0.64108604,-0.4386818,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.11702931,-0.73986673,-0.45211998,-0.68509877,-0.67007643,-0.52171326,0.34430972,-0.48163074,-0.6186755,-0.73933,0.040438354,-0.65925777,0.7474549,-0.61049384,0.30740798,1.8930382,-0.6682474,-0.75794554,-0.59257406,-0.3586682,0.89375025,-0.651804,0.08712256,1.415978,-0.66552746,-0.6482407,-0....179.577218NaNgood2050.0
10100_drawings/sb55p080g00.png041462sb55p080.jpgart/sb55p080.jpgart0_drawings/sb55p080.jpgFalse...NaNsb55p080g00.png0_drawings-0.6198182,-0.5714155,-0.12864059,-0.6553513,0.03367281,-0.3521561,-0.7267234,-0.47485968,-0.7537334,-0.42715576,1.136474,-0.72562873,2.6017675,-0.6266761,-0.68991846,-0.57515705,-0.5467274,1.0752423,-0.02930379,1.118658,0.39956325,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.4518578,0.16788304,-0.42450532,-0.45211998,-0.68509877,-0.67007643,-0.23189741,0.37339267,1.0403941,0.11935049,-0.73933,-0.19533288,1.3258636,-0.53331745,-0.61049384,1.4947255,0.25517285,-0.59559596,-0.75794554,-0.59257406,0.2976374,-0.6001429,-0.651804,0.09863156,1.5771475,-0.66552746,-0.15108436,0.443...143.851075NaNgood14620.0
+ +

5 rows × 27 columns

+
+
+
+
+
+
np.array(all_rdp_deltas[:2], dtype=object)
+
+
array([array([[ 1.36805539e+02,  1.16311483e+02,  0.00000000e+00],
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+              [-1.16154509e+00, -8.44669185e+00,  0.00000000e+00],
+              [-7.86753454e-01, -8.50183305e+00,  0.00000000e+00],
+              [-6.75480470e-01, -8.51457382e+00,  0.00000000e+00],
+              [-8.27726134e-01, -8.48491417e+00,  0.00000000e+00],
+              [-6.35158682e-01, -8.50943159e+00,  0.00000000e+00],
+              [-3.24569522e+00, -5.52263976e+00,  0.00000000e+00],
+              [-2.36015203e+00, -5.87436607e+00,  0.00000000e+00],
+              [-1.49643702e+00, -8.34669068e+00,  0.00000000e+00],
+              [-1.31500190e+00, -8.47218887e+00,  0.00000000e+00],
+              [-8.91471111e-01, -8.53136064e+00,  0.00000000e+00],
+              [-3.28208544e-01, -8.47802695e+00,  0.00000000e+00],
+              [ 1.04044995e+00, -1.04044995e+00,  0.00000000e+00],
+              [ 8.33385332e+00, -1.76681121e+00,  0.00000000e+00],
+              [ 8.50708289e+00, -1.29853091e-01,  0.00000000e+00],
+              [ 8.52031322e+00, -1.20677350e+00,  0.00000000e+00],
+              [ 8.32690514e+00, -1.36891865e+00,  0.00000000e+00],
+              [ 8.54317028e+00,  4.69313996e-02,  0.00000000e+00],
+              [ 8.48259707e+00, -3.67817323e-02,  0.00000000e+00],
+              [ 2.89608064e+00,  6.82743275e+00,  0.00000000e+00],
+              [ 1.65325261e-01,  8.52801188e+00,  0.00000000e+00],
+              [ 1.06570268e+00,  8.45738777e+00,  0.00000000e+00],
+              [ 1.32918677e+00,  8.57374021e+00,  0.00000000e+00],
+              [-7.08839380e+00,  3.23973687e+00,  0.00000000e+00],
+              [-8.40148109e+00,  4.87323164e-01,  0.00000000e+00],
+              [-1.14324430e+00,  1.36081469e+01,  0.00000000e+00],
+              [-6.50799769e-01,  8.38624653e+00,  0.00000000e+00],
+              [ 2.08053490e+00,  8.42233282e+00,  0.00000000e+00],
+              [ 3.67807341e+00,  7.65713385e+00,  0.00000000e+00],
+              [-1.12685000e+01,  3.32276779e+00,  0.00000000e+00],
+              [-5.11214133e+00, -6.49189746e+00,  0.00000000e+00],
+              [-2.17955396e+00, -8.50164160e+00,  0.00000000e+00],
+              [ 7.06874831e-01, -8.37302963e+00,  0.00000000e+00],
+              [ 5.57280391e+00, -1.60548671e+01,  0.00000000e+00],
+              [-8.34308696e+00,  1.74182930e+00,  0.00000000e+00],
+              [-6.47211651e+00,  3.71959161e+00,  0.00000000e+00],
+              [-1.82401556e-01,  8.57479279e+00,  0.00000000e+00],
+              [-7.03032023e-01,  8.50041746e+00,  0.00000000e+00],
+              [-8.62498193e-01,  8.49079909e+00,  0.00000000e+00],
+              [-6.79693485e-01,  8.49895452e+00,  0.00000000e+00],
+              [-1.20006011e-01,  8.52727206e+00,  0.00000000e+00],
+              [ 8.61186251e-02,  8.53106072e+00,  0.00000000e+00],
+              [-2.17013254e+00,  1.98818827e+00,  0.00000000e+00],
+              [ 1.82959570e+01,  1.21055837e+01,  0.00000000e+00],
+              [-1.56657777e+00,  8.38662088e+00,  0.00000000e+00],
+              [-1.85331176e+00,  8.32658719e+00,  0.00000000e+00],
+              [-1.92934469e+00,  8.31066813e+00,  0.00000000e+00],
+              [-6.35582771e+00,  1.42531094e+01,  0.00000000e+00],
+              [ 5.25123588e+00,  4.26762892e+00,  0.00000000e+00],
+              [ 3.61316640e+00,  7.70653938e+00,  0.00000000e+00],
+              [ 1.82403866e+00,  8.36842068e+00,  0.00000000e+00],
+              [ 1.62358748e+00,  6.88225657e+00,  0.00000000e+00],
+              [ 1.65175264e+00, -5.24895395e+00,  0.00000000e+00],
+              [ 3.40155675e-01,  8.79902970e+00,  0.00000000e+00],
+              [ 6.70253303e-01,  8.38456989e+00,  0.00000000e+00],
+              [ 1.14886816e+00,  8.45367760e+00,  1.00000000e+00],
+              [ 4.08415765e+01, -1.55819964e+02,  0.00000000e+00],
+              [ 2.58283775e+00,  7.82091790e+00,  0.00000000e+00],
+              [-5.98197805e+00,  9.24509932e-03,  0.00000000e+00],
+              [ 7.45822777e-01,  5.89736986e+00,  0.00000000e+00],
+              [ 6.59623791e+00,  3.77843174e+00,  0.00000000e+00],
+              [-8.21570536e-01,  1.52682088e+01,  0.00000000e+00],
+              [ 7.38867329e+00,  4.21129738e+00,  0.00000000e+00],
+              [ 7.55844908e+00,  4.00574567e+00,  0.00000000e+00],
+              [ 7.49174964e+00,  4.12370916e+00,  0.00000000e+00],
+              [ 7.18857494e+00,  4.56518785e+00,  0.00000000e+00],
+              [ 5.31026027e+00,  6.44063356e+00,  0.00000000e+00],
+              [-3.70852319e-01,  8.19788717e+00,  0.00000000e+00],
+              [-2.03922630e+01,  3.60395989e+00,  0.00000000e+00],
+              [ 7.78995687e+00,  3.18142763e+00,  0.00000000e+00],
+              [ 2.67949623e+00,  5.44787361e+00,  0.00000000e+00],
+              [-7.02146209e+00,  4.56760653e+00,  0.00000000e+00],
+              [-7.78235928e+00, -3.10502293e+00,  0.00000000e+00],
+              [-7.55457435e+00, -3.97241953e+00,  0.00000000e+00],
+              [-7.97575263e+00, -2.92483392e+00,  0.00000000e+00],
+              [-8.34597082e+00, -2.00732834e+00,  0.00000000e+00],
+              [-8.16109481e+00, -2.19652603e+00,  0.00000000e+00],
+              [-5.45022373e+00, -6.21892848e+00,  0.00000000e+00],
+              [-7.04868470e-01, -8.41183268e+00,  0.00000000e+00],
+              [ 2.21095614e+01,  2.13292240e+01,  0.00000000e+00],
+              [ 7.52167571e-02,  8.53134914e+00,  0.00000000e+00],
+              [ 7.52167571e-02,  8.53134914e+00,  0.00000000e+00],
+              [ 7.52167571e-02,  8.53134914e+00,  0.00000000e+00],
+              [ 3.04032591e-02,  8.53157030e+00,  0.00000000e+00],
+              [ 1.30462139e+00, -5.08818648e-02,  0.00000000e+00],
+              [ 6.05559267e+00, -2.85096120e+00,  0.00000000e+00],
+              [ 6.59487237e+00,  5.48929089e+00,  0.00000000e+00],
+              [ 5.24732225e+00,  6.67753471e+00,  0.00000000e+00],
+              [ 2.91121170e+00,  2.38919753e+00,  0.00000000e+00],
+              [ 4.25344541e+00,  7.41138603e+00,  0.00000000e+00],
+              [ 2.53960876e+00,  8.03260040e+00,  0.00000000e+00],
+              [ 1.86144410e+00,  8.37906504e+00,  0.00000000e+00],
+              [ 1.35802120e+00,  8.43684362e+00,  0.00000000e+00],
+              [ 6.33608312e-01,  8.50081848e+00,  0.00000000e+00],
+              [ 3.40652231e+00, -4.83067653e+00,  0.00000000e+00],
+              [-1.22404289e+00, -8.43670550e+00,  0.00000000e+00],
+              [-8.94387151e-01, -8.50934414e+00,  0.00000000e+00],
+              [-1.41792493e+00, -8.39771261e+00,  0.00000000e+00],
+              [-3.20276450e+00, -7.85796967e+00,  0.00000000e+00],
+              [-4.47777252e+00, -7.24531669e+00,  0.00000000e+00],
+              [-4.63905392e+00, -7.14682341e+00,  0.00000000e+00],
+              [-6.49765439e+00, -5.48565059e+00,  0.00000000e+00],
+              [-7.28833332e+00, -4.48140826e+00,  0.00000000e+00],
+              [-4.17926548e+00, -6.20114232e+00,  0.00000000e+00],
+              [-1.35893838e+01,  2.61915351e+01,  0.00000000e+00],
+              [-8.53823333e+00, -1.26771502e-01,  0.00000000e+00],
+              [-4.13889076e+00, -6.18728621e+00,  0.00000000e+00],
+              [ 3.63672150e+00, -7.65219047e+00,  0.00000000e+00],
+              [ 3.97767615e+00,  2.48681049e+00,  0.00000000e+00],
+              [-2.32189913e+00,  7.07148375e+00,  0.00000000e+00],
+              [ 5.25881607e+00, -3.26506211e-01,  0.00000000e+00],
+              [ 2.70956894e+00, -6.89077564e+00,  0.00000000e+00],
+              [ 5.48359713e-01, -3.36345660e+00,  0.00000000e+00],
+              [-8.12197485e+00, -2.62291243e+00,  0.00000000e+00],
+              [-8.33401749e+00, -2.30985973e+00,  0.00000000e+00],
+              [-8.35734997e+00, -7.90891292e-01,  0.00000000e+00],
+              [-8.18475286e+00,  1.94063023e+00,  0.00000000e+00],
+              [-6.82725012e+00,  5.06558992e+00,  0.00000000e+00],
+              [ 4.68202477e+00, -2.08089990e+00,  0.00000000e+00],
+              [ 2.89771534e+00,  7.08342225e+00,  0.00000000e+00],
+              [ 3.21651807e+00, -1.71273521e-01,  0.00000000e+00],
+              [-3.68678145e-01, -7.87033876e+00,  0.00000000e+00],
+              [ 5.43928169e+00, -1.90304732e+00,  0.00000000e+00],
+              [-2.43835173e+00,  7.64004025e+00,  0.00000000e+00],
+              [ 4.92384559e+00,  6.52515168e+00,  0.00000000e+00],
+              [ 6.99008510e+00,  2.26830395e+00,  0.00000000e+00],
+              [-4.15874338e+00, -6.63472011e+00,  0.00000000e+00],
+              [ 3.21682404e+00, -7.06583239e+00,  1.00000000e+00]])],
+      dtype=object)
+
+
+
+
THRESHOLDED_OUTPUT_DIR = EPOCH.dir_08_THRESHOLDED()
+if not os.path.isdir(THRESHOLDED_OUTPUT_DIR):
+    os.makedirs(THRESHOLDED_OUTPUT_DIR)
+    
+THRESHOLDED_NPZ_ALL = THRESHOLDED_OUTPUT_DIR / 'all.npz'
+np.savez(THRESHOLDED_NPZ_ALL, np.array(all_rdp_deltas, dtype=object), encoding='latin1', allow_pickle=True)
+
+thresholded_ds = np.load(THRESHOLDED_NPZ_ALL, encoding='latin1', allow_pickle=True)['arr_0']
+print(len(thresholded_ds))
+
+
2437
+
+
+
+
from singleline_stroke3.dataset import split_train_val
+
+THRESHOLDED_NPZ_TRAINVAL_08 = THRESHOLDED_OUTPUT_DIR / 'epoch20240104_trainval08.npz'
+split_train_val(thresholded_ds, THRESHOLDED_NPZ_TRAINVAL_08, split_ratio=0.8)
+
+
no env var SINGLELINE_DATA_HOME, defaulting to: None
+1900 537 2437
+1900 537
+
+
+
+
THRESHOLDED_NPZ_TRAINVAL_09 = THRESHOLDED_OUTPUT_DIR / 'epoch20240104_trainval09.npz'
+split_train_val(thresholded_ds, THRESHOLDED_NPZ_TRAINVAL_09, split_ratio=0.9)
+
+
2100 337 2437
+2100 337
+
+
+
+
filtered_jdf[]
+
+
+
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new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...err_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_labelrdp_num_pointsrdp_epsilon
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..................................................................
313431340_drawings/sb68p065g00.png01996sb68p065.jpgart/sb68p065.jpgart0_drawings/sb68p065.jpgFalse...NaNsb68p065g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,0.47267532,-0.65341413,-0.7267234,-0.4896673,-0.7537334,-0.42715576,0.61208475,-0.72562873,0.48144948,-0.19021246,0.17331672,-0.57515705,-0.5467274,-0.044855714,-0.29174072,0.41907477,0.5425865,-0.55193967,-0.5446661,-0.6779002,-0.57759243,-0.6690378,-0.2936861,0.10596323,0.06448883,-0.45211998,0.20688862,-0.67007643,-0.52171326,-0.010420322,-0.08097559,-0.6186755,-0.73933,-0.57338566,-0.457012,-0.12355664,-0.056488395,0.8602169,0.13501674,-0.6682474,-0.32604802,-0.59257406,-0.036618352,-0.6001429,-0.651804,0.33704346,1.1910194,-0.50803477,-0.64...121.9156215NaNgood9960.0
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313831380_drawings/sb25p085g01.png11289sb25p085.jpgart/sb25p085.jpgart0_drawings/sb25p085.jpgFalse...NaNsb25p085g01.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,0.89491075,-0.4896673,-0.7537334,0.24895486,-0.62038505,-0.72562873,-0.662768,-0.6266761,-0.68991846,-0.46607763,-0.5467274,-0.6520286,3.5338778,1.8079176,-0.64108604,-0.55193967,-0.30309936,-0.5675203,1.7857099,-0.6690378,0.66355884,-0.5982413,-0.73986673,0.81931067,-0.68509877,-0.67007643,-0.12419522,-0.4592305,-0.600525,1.4066145,-0.73933,-0.46856436,0.21541041,-0.6179389,-0.38059765,1.2664366,0.20014733,-0.6682474,3.5115676,-0.59257406,-0.4498409,-0.6001429,0.89373386,0.77392715,0.267698,-0.5093546,2.2536607,0.4036819,...206.9922514NaNgood2890.0
314031400_drawings/sb46p112g00.png02999sb46p112.jpgart/sb46p112.jpgart0_drawings/sb46p112.jpgFalse...NaNsb46p112g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.29618046,-0.7537334,0.5394305,-0.62038505,-0.72562873,0.29681575,-0.6266761,-0.14870733,-0.57515705,-0.5467274,0.09095949,0.6520004,0.018526673,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.8378592,-0.6690378,-0.4518578,-0.5982413,-0.73986673,-0.067587644,-0.68509877,-0.67007643,-0.52171326,-0.4592305,-0.15650398,-0.6186755,-0.73933,-0.38130352,0.75700474,-0.1524789,-0.61049384,0.34709376,-0.6189488,-0.6682474,0.3949592,-0.59257406,1.2957525,-0.6001429,0.5395025,-0.41562328,-0.24885559,-0.66552746,-0.358585...129.7961412NaNgood9990.0
314131410_drawings/sb44p055g00.png01971sb44p055.jpgart/sb44p055.jpgart0_drawings/sb44p055.jpgFalse...NaNsb44p055g00.png0_drawings-0.6198182,0.38865608,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.5761457,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.72562873,2.8744497,-0.6266761,0.6088895,-0.57515705,-0.5467274,-0.6520286,0.8363658,1.797528,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.92518836,-0.6690378,-0.047510684,1.3537724,0.9186529,0.5943966,-0.68509877,-0.67007643,0.65859795,0.20812324,-0.600525,-0.6186755,-0.73933,-0.57338566,-0.65925777,-0.6179389,-0.61049384,1.2459471,-0.14557356,-0.6682474,-0.75794554,-0.59257406,0.73717755,-0.6001429,-0.651804,-0.27754274,0.65142286,-0.66552746,1.9023553,1.41640...174.948616NaNgood9710.0
+ +

2437 rows × 27 columns

+
+
+
+
+
+
further_filtered_jdf = filtered_jdf[filtered_jdf.knn_clusterid.isin([5,6,7,10,11,12,14])]
+
+
+
further_filtered_jdf
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
new_idxgroup_fnamegroup_idgroup_num_strokesgroup_num_pointsindiv_fnamebboxsep_orig_fnamelabelbboxsep_handlabeled_pathis_new...err_msgindiv_fnamelabelemb_csvknn_distknn_clusteridknn_labelcluster_labelrdp_num_pointsrdp_epsilon
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..................................................................
313231320_drawings/sb42p069g00.png02556sb42p069.jpgart/sb42p069.jpgart0_drawings/sb42p069.jpgFalse...NaNsb42p069g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,0.1714744,-0.12372601,-0.53709203,-0.4896673,-0.72522235,-0.42715576,-0.0006441474,-0.72562873,1.9680816,-0.6266761,0.9233274,-0.57515705,-0.5467274,-0.35323882,-0.6898426,-0.17163047,-0.43495637,-0.14689967,-0.15963706,-0.6779002,-0.34607226,-0.6690378,0.0037623048,-0.08322686,-0.73986673,-0.36665216,-0.68509877,-0.67007643,-0.45042124,-0.27984118,-0.600525,1.1806307,-0.73933,-0.57338566,0.04949963,-0.6179389,-0.61049384,2.1094356,1.1033976,0.12534255,-0.1549204,-0.59257406,-0.22932369,-0.6001429,-0.651804,0.5429686,1.3666267,-0.66552746,-0.246...124.653876NaNgood5560.0
313331330_drawings/sb42p069g01.png12379sb42p069.jpgart/sb42p069.jpgart0_drawings/sb42p069.jpgFalse...NaNsb42p069g01.png0_drawings-0.32089156,-0.17657954,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.31888568,-0.46326503,-0.7537334,0.026750982,-0.62038505,-0.72562873,1.737588,-0.6266761,0.36475402,-0.57515705,-0.5467274,-0.6520286,0.09774345,0.42654252,-0.64108604,-0.55193967,-0.5446661,-0.3546108,0.7581286,-0.6690378,-0.4518578,-0.11281887,-0.73986673,0.1760861,-0.68509877,-0.67007643,0.8764119,-0.4592305,-0.600525,0.27783585,-0.73933,-0.57338566,0.62643707,-0.6179389,-0.61049384,0.64185077,-0.36368576,-0.6682474,-0.75794554,-0.2386437,-0.6157326,-0.6001429,0.33158153,0.26073122,1.0236527,-0.66552746,1.2014505,1....114.983286NaNgood3790.0
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314031400_drawings/sb46p112g00.png02999sb46p112.jpgart/sb46p112.jpgart0_drawings/sb46p112.jpgFalse...NaNsb46p112g00.png0_drawings-0.6198182,-0.5714155,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.7267234,-0.29618046,-0.7537334,0.5394305,-0.62038505,-0.72562873,0.29681575,-0.6266761,-0.14870733,-0.57515705,-0.5467274,0.09095949,0.6520004,0.018526673,-0.64108604,-0.55193967,-0.5446661,-0.6779002,1.8378592,-0.6690378,-0.4518578,-0.5982413,-0.73986673,-0.067587644,-0.68509877,-0.67007643,-0.52171326,-0.4592305,-0.15650398,-0.6186755,-0.73933,-0.38130352,0.75700474,-0.1524789,-0.61049384,0.34709376,-0.6189488,-0.6682474,0.3949592,-0.59257406,1.2957525,-0.6001429,0.5395025,-0.41562328,-0.24885559,-0.66552746,-0.358585...129.7961412NaNgood9990.0
314131410_drawings/sb44p055g00.png01971sb44p055.jpgart/sb44p055.jpgart0_drawings/sb44p055.jpgFalse...NaNsb44p055g00.png0_drawings-0.6198182,0.38865608,-0.73929733,-0.6553513,-0.6254566,-0.65341413,-0.5761457,-0.4896673,-0.7537334,-0.42715576,-0.62038505,-0.72562873,2.8744497,-0.6266761,0.6088895,-0.57515705,-0.5467274,-0.6520286,0.8363658,1.797528,-0.64108604,-0.55193967,-0.5446661,-0.6779002,0.92518836,-0.6690378,-0.047510684,1.3537724,0.9186529,0.5943966,-0.68509877,-0.67007643,0.65859795,0.20812324,-0.600525,-0.6186755,-0.73933,-0.57338566,-0.65925777,-0.6179389,-0.61049384,1.2459471,-0.14557356,-0.6682474,-0.75794554,-0.59257406,0.73717755,-0.6001429,-0.651804,-0.27754274,0.65142286,-0.66552746,1.9023553,1.41640...174.948616NaNgood9710.0
+ +

1550 rows × 27 columns

+
+
+
+
+
+
image_grid(further_filtered_jdf, lambda row: BBOXSEP_OUTPUT_DIR / row.group_fname, FILTER_OUTPUT_DIR / 'grid_further_filtered.png')
+Image.open(FILTER_OUTPUT_DIR / 'grid_further_filtered.png')
+
+
+
+

+
+
+
+
+
+
further_filtered_ds = ds[list(further_filtered_jdf.new_idx)]
+
+
+
# # init some columns to update in a for-loop
+# further_filtered_jdf.loc[:,'rdp_num_points'] = further_filtered_jdf['group_num_points']
+# further_filtered_jdf.loc[:,'rdp_epsilon'] = [0.0] * len(further_filtered_jdf)
+
+all_rdp_deltas = []
+
+for i in range(len(further_filtered_jdf)):
+    row = further_filtered_jdf.iloc[i]
+    strokes = further_filtered_ds[i]
+    _strokes, rdp_num_points, epsilon = iterative_RDP(strokes, verbose=False)
+
+    rdp_deltas = strokes_to_deltas(_strokes)
+
+    all_rdp_deltas.append(rdp_deltas)
+
+
+
THRESHOLDED_NPZ_ALL = THRESHOLDED_OUTPUT_DIR / 'furtherfiltered_all.npz'
+
+np.savez(THRESHOLDED_NPZ_ALL, np.array(all_rdp_deltas, dtype=object), encoding='latin1', allow_pickle=True)
+
+thresholded_ds = np.load(THRESHOLDED_NPZ_ALL, encoding='latin1', allow_pickle=True)['arr_0']
+print(len(thresholded_ds))
+
+THRESHOLDED_NPZ_TRAINVAL_08 = THRESHOLDED_OUTPUT_DIR / 'epoch20240104_furtherfiltered_trainval08.npz'
+split_train_val(thresholded_ds, THRESHOLDED_NPZ_TRAINVAL_08, split_ratio=0.8)
+
+
1550
+1200 350 1550
+1200 350
+
+
+
+
THRESHOLDED_NPZ_TRAINVAL_09 = THRESHOLDED_OUTPUT_DIR / 'epoch20240104_furtherfiltered_trainval09.npz'
+split_train_val(thresholded_ds, THRESHOLDED_NPZ_TRAINVAL_09, split_ratio=0.9)
+
+
1300 250 1550
+1300 250
+
+
+
+

scratch

+
+
### get embeddings for the cropped images
+
+
+
# cropped_emb_rows = []
+# for x in embed_dir(
+#     preprocessing_dir, learn
+# ):  # important: use "preprocessing_dir" so we get embeds of the cropped imgs
+#     cropped_emb_rows.append(x)
+# cropped_embd_df = pd.DataFrame(cropped_emb_rows)
+# print(f"num rows: {len(cropped_embd_df)}")
+# cropped_embd_df.head()
+
+

join the dataframes.

+

from pandas.DataFrame.join — pandas 2.1.4 documentation:

+
+

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result. Note: default is a “left” join, preserving the left’s index space.

+
+

rsuffix to add _cropped to any overlapping column names.

+

Here are the expected columns:

+
idx        # totally different from index scheme already being used - kill it
+abs_fname  # should be same as preprocessed_abs_path
+rel_fname  # same as humanlabeled_rel_path (JOIN KEY)
+label      # same as humanlabeled_metacluster_name
+pred_label # DROP
+pred_idx   # DROP
+pred_probs # DROP
+emb_csv    # keep this!
+
+
# del cropped_embd_df["idx"]
+# del cropped_embd_df["pred_label"]
+# del cropped_embd_df["pred_probs"]
+# del cropped_embd_df["pred_idx"]
+# cropped_embd_df.head()
+
+
+
# joined_df = metaclusters_df.join(
+#     cropped_embd_df.set_index("rel_fname"),
+#     on="handlabeled_rel_path",
+#     # validate='one_to_one',
+#     rsuffix="_cropped",
+# )
+# joined_df.head()
+
+
+
# # validation
+# assert len(metaclusters_df) == len(joined_df), "some rows are missing"
+
+
+
# assert not joined_df.apply(
+#     lambda row: row.abs_fname_cropped == row.preprocessed_abs_path, axis=1
+# ).any(), "some row had a mismatch between expected filenames"
+# del joined_df["abs_fname_cropped"]
+
+
+
# assert not joined_df.apply(
+#     lambda row: row.label_cropped == row.handlabeled_metacluster_name, axis=1
+# ).any(), "some row had a mismatch between expected metacluster_name"
+# # del joined_df["label_cropped"]
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# joined_df[["label_cropped", "handlabeled_metacluster_name"]].head()
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# joined_df["labels_match"] = joined_df.apply(
+#     lambda row: row.label_cropped == row.handlabeled_metacluster_name, axis=1
+# )
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# joined_df[joined_df.labels_match == False]
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# del joined_df['labels_match']
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# del joined_df['label_cropped']
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# cropped_tsv_fname = f"{preprocessing_dir}/cropped.tsv"
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+# joined_df.to_csv(
+#     cropped_tsv_fname, index=True, index_label="idx", sep="\t", header=True
+# )
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+# cropped_df = pd.read_csv(cropped_tsv_fname, delimiter="\t", index_col="idx")
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+# cdf = cropped_df
+# cdf.head()
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+ + + + + \ No newline at end of file diff --git a/listings.json b/listings.json new file mode 100644 index 0000000..8853996 --- /dev/null +++ b/listings.json @@ -0,0 +1,45 @@ +[ + { + "listing": "/blog/index.html", + "items": [ + "/blog/posts/slml_part6.html", + "/blog/posts/slml_part5.html", + "/blog/posts/slml_part4.html", + "/blog/posts/slml_part3.html", + "/blog/posts/slml_part2.html", + "/blog/posts/slml_part1.html", + "/blog/posts/slml_part0.html", + "/blog/posts/2018_ai_art_tour.html" + ] + }, + { + "listing": "/index.html", + "items": [ + "/projects/slml.html", + "/blog/posts/slml_part6.html", + "/blog/posts/slml_part5.html", + "/blog/posts/slml_part4.html", + "/blog/posts/slml_part3.html", + "/blog/posts/slml_part2.html", + "/blog/posts/slml_part1.html", + "/blog/posts/slml_part0.html", + "/blog/posts/2018_ai_art_tour.html" + ] + }, + { + "listing": "/projects/slml.html", + "items": [ + "/blog/posts/slml_part0.html", + "/blog/posts/slml_part1.html", + "/blog/posts/slml_part2.html", + "/blog/posts/slml_part3.html", + "/blog/posts/slml_part4.html", + "/blog/posts/slml_part5.html", + "/blog/posts/slml_part6.html" + ] + }, + { + "listing": "/jupyter/index.html", + "items": [] + } +] \ No newline at end of file diff --git a/projects/slml.html b/projects/slml.html new file mode 100644 index 0000000..aad85a1 --- /dev/null +++ b/projects/slml.html @@ -0,0 +1,1329 @@ + + + + + + + + + + + + +andrewlook - Single-Line Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Single-Line Machine Learning

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singleline
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machinelearning
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slml
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+ Overview of my project training ML models on a dataset of single-line drawings. +
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Author
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Andrew Look

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Published
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October 25, 2023

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Modified
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March 6, 2024

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Single-Line Machine Learning

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Lately I’ve been working on training ML models to generate single-line drawings in my style. I’ve open-sourced the code for my models and the code I used to prepare the dataset.

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I’ve started writing deep dives for each phase of the project.

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This page includes a broader overview of the story as a whole, linking to the individual sections for more detail.

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Why This Project

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In part 0 I share how I got started making single-line drawings, and why I found them interesting enough to make them as a daily practice.

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In part 1 - Discovering SketchRNN I cover how SketchRNN captured my imagination and why I decided to try training it on my own data. For example, I imagined turning the “variability” up, and generating new drawings based on my old ones?

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distill.pub’s handwriting demo

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distill.pub’s handwriting demo
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I hit my first roadbloack when I reached out to the authors of SketchRNN. They estimated that I’d need thousands of examples in order to train a model, but I only had a few hundred at the time.

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I decided to keep making drawings in my sketchbooks, numbering the pages, and scanning them to store with a standardized file naming scheme.

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In the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.

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Several years went by. More sketchbooks accumulated. Eventually, I ran a file count and saw a few thousand distinct drawings.

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+Figure 1: Some example single-line drawings from my sketchbooks. +
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Preparing the Dataset

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I was finally ready to get started. I was going to need:

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  1. a thousand JPEGs of my drawings (at least)
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  2. a stroke-3 conversion process for my JPEG scans
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  3. a SketchRNN model and trainer
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  4. the patience to experiment with hyperparameters
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I started by collecting my sketchbook page JPEGs into usable training data.

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sb26p068-purple-hair
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sb55p069-color
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+Figure 2: Some watercolor examples from my sketchbooks. +
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In part 2 - Embedding Filtering, I cover how I’m using embeddings to filter my dataset of drawings. I used embeddings to solve the problem of filtering everything out of my sketchbook data that wasn’t a single-line drawing - particularly my watercolors.

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I also made an exploratory browser to visualize the embedding space of the drawings, and published it at projector.andrewlook.com. Here’s a demo video:

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In part 3 - Dataset Vectorization, I cover how I’m vectorizing the scans to prepare them for RNN/transformer training. At this stage in the process, my drawings were converted to centerline-traced vector paths, but they were represented as a series of separate strokes. It’s visible in Figure 3 how the strokes are out of order, since strokes don’t start where the previous stroke left off.

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+Figure 3: Example of separate strokes after my vectorization process. +
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SketchRNN Experiments

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Part 4 covers how the dataset I used to train my first model contained drawings with too many short strokes, as in Figure 3. Models trained on this dataset produced frenetic drawings such as Figure 4 (a). I experimented with some RNN training improvements and after filtering short strokes out of my first datasets and training a new model I saw a big improvment, as in Figure 4 (b).

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+(a) Before Short-Stroke Exclusion +
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+Figure 4: Sample generated results from models trained before and after filtering out strokes with less than 10 points. +
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Part 5 covers improvements I made to my preprocessing of the dataset by joining SVG paths up into continuous lines so that the model could learn from longer sequences.

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+(a) Original: 22 strokes +
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+(b) After path-joining: 4 strokes +
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+(c) After path-splicing: 2 strokes +
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+Figure 5: Comparison of number of strokes before and after preprocessing, with one color per stroke. +
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The generated results at this stage were uncanny, but showed a big improvement from the initial results. There were at least some recognizable faces and bodies, as seen in Figure 6.

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+Figure 6: Generated samples from a model trained on path-spliced dataset. +
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Also in Part 5, I a preprocessing step to decompose strokes into seperate drawings if their bounding boxes didn’t overlap sufficiently. The size of the dataset roughly doubled, since sketchbook pages containing multiple drawings were broken out into separate training examples as in Figure 7.

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+(a) Original +
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+Figure 7: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214. +
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Part 6 covers how I filtered drawings by visual similarity. Though I did an earlier pass on visual similarity to filter watercolors out from my single-line drawings, this time I wanted to explore the embedding space of the individual drawings after bounding-box separation had been applied. I found that clustering the drawing embeddings identified clusters I wanted to exlcude from the training data like Figure 8 (a) and Figure 8 (b), and helped me identify clusters that I wanted to include in the training data like Figure 8 (c).

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+(a) Cluster of complex drawings I wanted to filter out +
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+(b) Cluster of simple drawings I wanted to filter out +
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+(c) Cluster of good drawings I wanted to include +
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+Figure 8: Example groupings after running K-Means on visual embeddings of individual drawings. +
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The drawings generated by models trained on this visually-filtered dataset started to look recognizable as containing distinct people or faces, as in Figure 9. Still odd and convoluted, but interesting enough to give me new ideas for drawings or paintings.

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+Figure 9: Generated samples after training with visual filtering on bbox-separated dataset. +
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Part 7 covers experiments I tried using data augmentation, using tricks to take my existing set of drawings and create a more diverse set of training data. For regular computer vision algorithms looking at images in terms of pixels, it’s common to randomly crop the images, flip them horizontally, and change the colors. For vector drawings like I’m working with, there are a different set of techniques available.

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Original drawing
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After “Stroke Augmentation” drops points from lines at random
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Randomly rotated and scaled
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+Figure 10: Examples of data augmentation. +
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Interpreting the Results

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It has also been fun to connect the model’s generation function to an animation builder, so I can watch the machine “draw” in real time. Compared with viewing a static output, the animations reminds me that part of what I love about single-line drawings is the surprise as a viewer.

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The drawing might start off nonsensical, and then end up with something recognizable enough to be an abstract figure drawing. Even when I’m drawing, I don’t always know where the drawing is going to end up.

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I’m adjusting as my hand moves, and adapting to any unexpected moves or mistakes to try and arrive at a drawing that I like. This is not so different from how SketchRNN generates drawings. One way to look at it is that I’m sampling from a probability distribution of possible next moves, and my decision is made by the muscle memory of what’s happened so far.

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Looking at some of the results generated from my SketchRNN models such as Figure 9, they remind me of an experiment I’ve tried in drawing with my eyes closed.

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I was curious about how much I’m adjusting drawings based on what I see while I’m drawing. I wanted to see how much of my drawings come exclusively from the muscle memory I’ve developed in drawing faces.

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Drawing with eyes closed is a great analogy for how SketchRNN draws. The model is only receiving information about the path has traveled so far. No visual information is available about what the final drawing looks like in pixel space as a real image.

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Errors like the ones in my eyes-closed drawings made me think about a common issue with models like SketchRNN that rely on recurrent neural networks.

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The problem of “long-term dependencies” refers to the poor performance RNN’s exhibit in understand things that are too far apart in a sequence.

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In the case of a drawing, long term dependencies would be things that are far apart in terms of the path the pen takes.

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Recurrent neural networks read a sequence and update the “hidden” layer. The hidden layers act as a kind of memory, allowing later steps in the sequence to incorporate information from earlier parts of the sequence. Each step incorporates more information to the same vector before passing it to the next step in the sequnce. As the RNN model steps through the sequence, the signal from earlier in the sequence tends to “decay” in favor of more recent information later in the sequence.

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Recurrent neural networks read a sequence and update the “hidden” layer. The hidden layers act as a kind of memory, allowing later steps in the sequence to incorporate information from earlier parts of the sequence. Each step incorporates more information to the same vector before passing it to the next step in the sequnce. As the RNN model steps through the sequence, the signal from earlier in the sequence tends to “decay” in favor of more recent information later in the sequence.
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The long-term dependency problem makes intuitive sense to me when I consider my eyes-closed drawings.

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Apparently I have muscle memory when I draw eyes and a note in close proximity, and in drawings lips and a chin, but without looking it’s hard to swoop down from eyes to lips and have them be aligned to the nose I drew earlier.

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This got me interested in how Transformer models use attention mechanisms to let each step in the sequence take into account the entire sequence at once.

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I came across a paper called Sketchformer: Transformer-based Representation for Sketched Structure, which made a transformer model based on SketchRNN. I decided to try adapting that model for my dataset, and seeing how it compares on handling long-term dependencies.

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Architecture of the SketchFormer model.

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Architecture of the SketchFormer model.
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In my next section on “Training Sketchformer” (coming soon), I cover my experiments using a transformer model instead of an RNN, to see if the model can better handle long-term dependencies within the drawings.

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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/resume/index.html b/resume/index.html new file mode 100644 index 0000000..4920b01 --- /dev/null +++ b/resume/index.html @@ -0,0 +1,671 @@ + + + + + + + + + +andrewlook - resume + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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resume

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Summary

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15+ years experience building technology, teams, companies.

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Passionate about machine learning and creativity.

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Looking for a technical role in AI / ML, especially in generative AI and creativity.

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Y Combinator and UCLA CS alum.

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Experience

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Aesthetic (YC S’18)

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LLM-powered Language Learning Tools (June 2023 - October 2023)

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  • Developed a prototype for an audio-centric language learning application, leveraging LLMs to generate multilingual educational content.
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  • Implemented a prompt testing system to evaluate and enhance the quality of prompt engineering modifications incrementally.
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AI Assistant for Artists / Designers (July 2022 - May 2023)

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  • Created a user-friendly app enabling artists to fine-tune generative models with their portfolios, facilitating rapid creation of new images using their own style.
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  • Managed backend processes to train and deploy models to an inference API.
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  • Automated analysis of LoRA hyper-parameters for artistic style fine-tuning.
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  • Expanded the capabilities of the Dreambooth trainer to accommodate various concepts.
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Token-Gated Platform for NFT creators (Feb 2021 - June 2022)

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  • Developed a self-service platform enabling NFT creators to form their communities, conduct token sales, and offer exclusive features to token holders.
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  • Authored versatile smart contracts allowing creators to independently manage collections and sales beyond conventional exchanges.
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API for Generating Design Assets (Jan 2020 - Jan 2021)

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  • Crafted a “Zapier for Design” API to produce on-demand templated social media images and animations using React for templating and Puppeteer/ffmpeg for rendering.
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Software-augmented design agency (June 2018 - October 2019)

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  • Developed a workflow system to monitor tasks and payments, catering to freelancers involved in design projects as the business scaled to a $1M ARR.
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  • Hired and managed a 12-person remote engineering team.
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  • Established remote hiring and onboarding processes with a 100% interview-to-acceptance rate for new hires.
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Long Journey Ventures

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AI Advisor (May 2023 - Present)

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  • Conducting due diligence on AI startups, delving into their technology to guide investment strategies.
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Pinterest

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ML Engineer (June 2016 - June 2018)

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  • Spearheaded the “Taste Graph” initiative, redesigning the ontology of user interests and developing classifiers for pins and queries.
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  • Enhanced overall revenue by 2.5% and boosted ecommerce conversion rates by 20% by substituting legacy user interests with taste graph data for ad targeting.
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  • Increased revenue by 2% with the implementation of a FastText text classifier to align search queries with ad interests.
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  • Oversaw data labeling initiatives and developed offline evaluation systems for interest classification models.
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  • Designed tools for data science teams to perform statistical analysis on ad interest A/B testing outcomes.
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URX (YC S’13, Acq. Pinterest)

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Co-Founder / CTO (Jan 2013 - May 2016)

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  • Engineered a contextual search engine to expose pertinent content across mobile applications, capitalizing on deep-link traffic.
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  • Designed a distributed system to index 2 billion pages of mobile deep-link content.
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  • Led machine learning projects to augment index entries, focusing on classification, disambiguation, and knowledge graph integration.
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  • Grew and nurtured a 25-member engineering team spanning engineering, infrastructure, and machine learning.
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Shopzilla

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Senior Software Engineer (September 2011 - January 2013)

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  • Developed and scaled an SEM ad revenue prediction pipeline to assess 5MM keywords daily.
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  • Implemented a Solr-based recommendation engine for SEO, optimizing search relevancy and performance while streamlining index build and deployment.
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Software Engineer (August 2010 - September 2011)

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  • Led the transition of company-wide analytical workflows to a MapReduce environment, evaluating and prototyping relevant technologies.
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Data Engineering Intern (June 2007 - Feb 2009)

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  • Developed Ruby on Rails applications to enhance efficiency in labeling processes.
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Education

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University of California, Los Angeles - BS. Computer Science (Sept 2006 - June 2010)

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+ + + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..57730ad --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://andrewlook.com/notebooks/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 0000000..d6f838e --- /dev/null +++ b/search.json @@ -0,0 +1,722 @@ +[ + { + "objectID": "blog/index.html", + "href": "blog/index.html", + "title": "andrewlook", + "section": "", + "text": "Title\n\n\nDate\n\n\n\n\n\n\nSLML Part 6 - SketchRNN Experiments: Granular Visual Filtering\n\n\n2024-01-04\n\n\n\n\nSLML Part 5 - SketchRNN Experiments: Path Preprocessing\n\n\n2023-12-14\n\n\n\n\nSLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization\n\n\n2023-12-03\n\n\n\n\nSLML Part 3 - JPEG to SVG to Stroke-3\n\n\n2023-11-07\n\n\n\n\nSLML Part 2 - Assembling the Dataset\n\n\n2023-11-01\n\n\n\n\nSLML Part 1 - Why I Decided to Train SketchRNN on My Drawings\n\n\n2023-10-27\n\n\n\n\nSLML Part 0 - My Single-Line Drawing Practice\n\n\n2023-10-20\n\n\n\n\nA Tour of AI Art in 2018\n\n\n2018-04-18\n\n\n\n\n\nNo matching items", + "crumbs": [ + "blog" + ] + }, + { + "objectID": "blog/posts/slml_part1.html", + "href": "blog/posts/slml_part1.html", + "title": "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings", + "section": "", + "text": "This post is part 1 of “SLML” - Single-Line Machine Learning.\nIf you want some context on how I got into single-line drawings, check out the part 0.\nNext in the series is part 2 where I assemble the training data, and filter it using visual embeddings.\n\n\n\nDavid Ha and Doug Eck from the Magenta team at Google had crowdsourced over 100,000 drawings using a game call “Quick, Draw”. The game would give users a prompt such as “Yoga” or “Pig” and users would have 20 seconds to draw it in the browser. This produced a dataset of line drawings for each category. Then, they trained a model called SketchRNN on these drawings. They trained a separate model for each category that users were prompted to draw, so there’s a separate model trained for “yoga”, “pig”, etc.\nI was fascinated by Magenta’s SketchRNN demo where you start a drawing, and the SketchRNN model tries continuously to complete it in new ways. It makes me think about the conditional probability that evolves as a drawing progresses. Given that one line exists on the page, how does that influence what line the artist is most likely to draw next?\nMy favorite example is to select the yoga model, and then draw a rhombus shape simulating a yoga mat. I love watching stick figures emerge in various poses, all centered on and interacting with the yoga mat.\n\n\n\nmagenta-demo-01-draw-and-gen\n\n\nSome of the same authors from the Magenta paper collaborated with distill.pub, using the same SketchRNN model trained on a handwriting dataset, to publish an explorable explanation called Experiments in Handwriting with a Neural Network.\n\n\n\nI wanted to apply SketchRNN to my own single-line drawings.\nI’d considered training an ML model on my single-line drawings, but the pixel-based GAN’s and VAE’s available in 2017/2018 when I started thinking about this didn’t seem like they’d yield good results on images that are mostly composed of white pixels with narrow black lines interspersed. What SketchRNN produces sequences, the generated results could be animated. I see single-line drawing as a sort of performance. A video of me drawing a single-line is inherently a proof that I didn’t lift my pen from the page.\nIt struck me that it could give a new window into my own drawing style. If my own drawing style evolves slowly over time, would I be able to notice the difference between a model trained on drawings I made recently from one trained on drawings I made several years ago?\nHow cool would it be to have an interface, similar to Andy Muatuschak’s scrying pen, where I could start drawing and see live completions, showing me how the probability space of subsequent strokes in my drawing is changing?\n\n\n\nscrying pen demo\n\n\n\nWhat new ideas might I get from turning the “variability” up, like the slider in distill.pub’s handwriting demo, and generating new drawings based on my old ones? Or running stroke prediction along the length of a drawing:\n\n\n\ndistill-vary-strokes\n\n\n\n\n\ndistill-vary-strokes-legend\n\n\n\n\n\n\nWhen I reached out to the authors of SketchRNN, they estimated that I’d need several thousand examples in order to train a model. I only had a few hundred at the time. But I kept making new drawings in my sketchbooks, and numbering the pages and the sketchbooks so that I could scan them and store them in a consistent way. In the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.\nSeveral years went by. More sketchbooks accumulated.\nEventually, I ran a file count and saw a few thousand distinct drawings. I was finally ready to get started. I was going to need:\n\nat least a thousand drawings\nthe ability to convert my scanned drawings into stroke-3 format\nto train a model on my drawings, and experiment until I found a configuration producing results that I liked.\n\n\nNext in my SLML series is part 2, where I convert my JPEG scans into vector representations in preparation for training SketchRNN.", + "crumbs": [ + "blog", + "posts", + "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings" + ] + }, + { + "objectID": "blog/posts/slml_part1.html#discovering-sketchrnn", + "href": "blog/posts/slml_part1.html#discovering-sketchrnn", + "title": "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings", + "section": "", + "text": "David Ha and Doug Eck from the Magenta team at Google had crowdsourced over 100,000 drawings using a game call “Quick, Draw”. The game would give users a prompt such as “Yoga” or “Pig” and users would have 20 seconds to draw it in the browser. This produced a dataset of line drawings for each category. Then, they trained a model called SketchRNN on these drawings. They trained a separate model for each category that users were prompted to draw, so there’s a separate model trained for “yoga”, “pig”, etc.\nI was fascinated by Magenta’s SketchRNN demo where you start a drawing, and the SketchRNN model tries continuously to complete it in new ways. It makes me think about the conditional probability that evolves as a drawing progresses. Given that one line exists on the page, how does that influence what line the artist is most likely to draw next?\nMy favorite example is to select the yoga model, and then draw a rhombus shape simulating a yoga mat. I love watching stick figures emerge in various poses, all centered on and interacting with the yoga mat.\n\n\n\nmagenta-demo-01-draw-and-gen\n\n\nSome of the same authors from the Magenta paper collaborated with distill.pub, using the same SketchRNN model trained on a handwriting dataset, to publish an explorable explanation called Experiments in Handwriting with a Neural Network.", + "crumbs": [ + "blog", + "posts", + "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings" + ] + }, + { + "objectID": "blog/posts/slml_part1.html#why-train-a-model-on-my-single-line-drawings", + "href": "blog/posts/slml_part1.html#why-train-a-model-on-my-single-line-drawings", + "title": "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings", + "section": "", + "text": "I wanted to apply SketchRNN to my own single-line drawings.\nI’d considered training an ML model on my single-line drawings, but the pixel-based GAN’s and VAE’s available in 2017/2018 when I started thinking about this didn’t seem like they’d yield good results on images that are mostly composed of white pixels with narrow black lines interspersed. What SketchRNN produces sequences, the generated results could be animated. I see single-line drawing as a sort of performance. A video of me drawing a single-line is inherently a proof that I didn’t lift my pen from the page.\nIt struck me that it could give a new window into my own drawing style. If my own drawing style evolves slowly over time, would I be able to notice the difference between a model trained on drawings I made recently from one trained on drawings I made several years ago?\nHow cool would it be to have an interface, similar to Andy Muatuschak’s scrying pen, where I could start drawing and see live completions, showing me how the probability space of subsequent strokes in my drawing is changing?\n\n\n\nscrying pen demo\n\n\n\nWhat new ideas might I get from turning the “variability” up, like the slider in distill.pub’s handwriting demo, and generating new drawings based on my old ones? Or running stroke prediction along the length of a drawing:\n\n\n\ndistill-vary-strokes\n\n\n\n\n\ndistill-vary-strokes-legend", + "crumbs": [ + "blog", + "posts", + "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings" + ] + }, + { + "objectID": "blog/posts/slml_part1.html#getting-started", + "href": "blog/posts/slml_part1.html#getting-started", + "title": "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings", + "section": "", + "text": "When I reached out to the authors of SketchRNN, they estimated that I’d need several thousand examples in order to train a model. I only had a few hundred at the time. But I kept making new drawings in my sketchbooks, and numbering the pages and the sketchbooks so that I could scan them and store them in a consistent way. In the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.\nSeveral years went by. More sketchbooks accumulated.\nEventually, I ran a file count and saw a few thousand distinct drawings. I was finally ready to get started. I was going to need:\n\nat least a thousand drawings\nthe ability to convert my scanned drawings into stroke-3 format\nto train a model on my drawings, and experiment until I found a configuration producing results that I liked.\n\n\nNext in my SLML series is part 2, where I convert my JPEG scans into vector representations in preparation for training SketchRNN.", + "crumbs": [ + "blog", + "posts", + "SLML Part 1 - Why I Decided to Train SketchRNN on My Drawings" + ] + }, + { + "objectID": "blog/posts/slml_part4.html", + "href": "blog/posts/slml_part4.html", + "title": "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization", + "section": "", + "text": "This post is part 4 of “SLML” - Single-Line Machine Learning.\nTo read the previous post, check out part 3. If you want to keep reading, check out part 5.\n\nNow that I had a repeatable process to convert JPEGs into stroke-3, I decided to start training models with my first dataset (which I called look_i16).\n\n\nThe training data in my first dataset, look_i16, had lots of tiny strokes mixed in with longer ones, and no inherent order to them.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Samples from dataset look_i16.\n\n\n\nUnsurprisingly, my first models produced odd smatterings composed of many small strokes.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Generated outputs from SketchRNN trained on dataset look_i16.\n\n\n\nAs a quick experiment, I tried just filtering out any really short strokes out of the dataset - I decided to iterate through each of the 1300 drawings in the training set, and filter out any strokes with less than 10 points - I called this dataset look_i16__minn10. I recognized that I’d need a more systematic solution to improve my training data, but I wanted to try a quick improvement before going deeper. See part 5 for how I updated my preprocessing to reduce the number of strokes in my training data.\n\n\n\n\n\n\nFigure 3: Distribution of num_strokes and num_points in datasets look_i16 and look_i16__minn10. Both datasets have 1300 entries in the training set.\n\n\n\nNote that the average number of strokes in look_i16 is around 40, while it’s closer to 10 in look_i16__minn10. It seems that there were many very short strokes in the training data. I also simplified the drawings more aggressively for look_i16__minn10 by increasing RDP’s epsilon parameter to 1.0 when preprocessing the data, which further reduced the number of points per drawing.\n\n\n\n\n\n\nFigure 4: Training and validation loss metrics for look_i16 (pink) and look_i16__minn10 (yellow).\n\n\n\nWhen training models, I’m measuring the reconstruction loss. I feed in a drawing, the model encodes it into an embedding vector, and then decodes the embedding vector back into a drawing. I can compute the loss at each step in the reconstructed drawing compare to the original. Periodically, after processing several batches of training data, I compute the reconstruction metrics on a validation set. This is a portion of the dataset I’m not using to actually update the weights of the model during training.\nBy comparing the reconstruction loss on the training set vs. the validation set over time, I can identify when the model starts “overfitting”. Intuitively, if the model is learning to perform better on the training data while performing worse on the validation data, that means it is effectively memorizing the training set rather than learning to generalize its learnings to drawings it wasn’t trained on.\nThe model trained on look_i16__minn10 performed slightly better than the model trained on look_i16 in terms of the error when reconstructing a drawing. It’s visible in Figure 4 that the loss values were lower, and the validation loss didn’t start to increase until slightly later.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Generated outputs from SketchRNN trained on dataset look_i16__minn10.\n\n\n\nThe results produced after training on look_i16__minn10 were much less chaotic. While they didn’t resemble coherent drawings, this was the first time I spotted some elements of my drawing style (head shape, eye style, lips, chin).\n\n\n\n\nThe Magenta team had recommended using Layer Normalization and Recurrent Dropout with Memory Loss.\nI noticed that when I let the trainer run overnight, I’d get wild spikes in the training loss. I decided to start with Layer Normalization.\n\n\n\n\n\n\nFigure 6: Training and validation loss metrics from training on look_i16__minn10 without layernorm (yellow) and with layernorm (burgundy: learnable=True, gray: learnable=False). The yellow line without layernorm enabled is the same as in Figure 4, but this graph shows more than 150,000 train steps while the previous graph showed only 7,000 train steps.\n\n\n\nAdding layer normalization showed a dramatic difference, visible in Figure 6. The yellow line (without layer norm) has a massive increase in validation loss, and many big spikes, while the burgundy and gray lines (with layer norm integrated) have much lower validation loss and don’t have any comparable spikes.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Generated samples from model trained with Layer Normalization.\n\n\n\n\n\n\n\nThe results from the layernorm models in Figure 7 had some hints of my drawing style, while still struggling to form coherent drawings.\nNext, I kept layernorm enabled and enabled recurrent dropout. I ran one separate runs with and without stroke augmentation enabled.\n\n\n\n\n\n\nFigure 8: Training and validation loss metrics for layernorm-only models (burgundy and gray) alongside models trained with recurrent dropout (green) and with stroke augmentation (pink).\n\n\n\nCompared to the layernorm-only models (burgundy and gray), the one recurrent dropout model (green) achieved a lower validation loss relatively quickly, before starting to overfit.\nThe model trained with recurrent dropout and stroke augmentation (pink) clearly performed worse than with recurrent dropout (in terms of higher validation loss).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Generated samples from model trained with Recurrent Dropout as well as Layer Normalization.\n\n\n\nThe resulting generations from model with layernorm and recurrent dropout in Figure 9 weren’t obviously better or worse than those from the layernorm-only model in Figure 7.\n\n\nIf you want to keep reading, check out part 5 of my SLML series.", + "crumbs": [ + "blog", + "posts", + "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization" + ] + }, + { + "objectID": "blog/posts/slml_part4.html#filtering-out-short-strokes", + "href": "blog/posts/slml_part4.html#filtering-out-short-strokes", + "title": "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization", + "section": "", + "text": "The training data in my first dataset, look_i16, had lots of tiny strokes mixed in with longer ones, and no inherent order to them.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Samples from dataset look_i16.\n\n\n\nUnsurprisingly, my first models produced odd smatterings composed of many small strokes.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Generated outputs from SketchRNN trained on dataset look_i16.\n\n\n\nAs a quick experiment, I tried just filtering out any really short strokes out of the dataset - I decided to iterate through each of the 1300 drawings in the training set, and filter out any strokes with less than 10 points - I called this dataset look_i16__minn10. I recognized that I’d need a more systematic solution to improve my training data, but I wanted to try a quick improvement before going deeper. See part 5 for how I updated my preprocessing to reduce the number of strokes in my training data.\n\n\n\n\n\n\nFigure 3: Distribution of num_strokes and num_points in datasets look_i16 and look_i16__minn10. Both datasets have 1300 entries in the training set.\n\n\n\nNote that the average number of strokes in look_i16 is around 40, while it’s closer to 10 in look_i16__minn10. It seems that there were many very short strokes in the training data. I also simplified the drawings more aggressively for look_i16__minn10 by increasing RDP’s epsilon parameter to 1.0 when preprocessing the data, which further reduced the number of points per drawing.\n\n\n\n\n\n\nFigure 4: Training and validation loss metrics for look_i16 (pink) and look_i16__minn10 (yellow).\n\n\n\nWhen training models, I’m measuring the reconstruction loss. I feed in a drawing, the model encodes it into an embedding vector, and then decodes the embedding vector back into a drawing. I can compute the loss at each step in the reconstructed drawing compare to the original. Periodically, after processing several batches of training data, I compute the reconstruction metrics on a validation set. This is a portion of the dataset I’m not using to actually update the weights of the model during training.\nBy comparing the reconstruction loss on the training set vs. the validation set over time, I can identify when the model starts “overfitting”. Intuitively, if the model is learning to perform better on the training data while performing worse on the validation data, that means it is effectively memorizing the training set rather than learning to generalize its learnings to drawings it wasn’t trained on.\nThe model trained on look_i16__minn10 performed slightly better than the model trained on look_i16 in terms of the error when reconstructing a drawing. It’s visible in Figure 4 that the loss values were lower, and the validation loss didn’t start to increase until slightly later.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Generated outputs from SketchRNN trained on dataset look_i16__minn10.\n\n\n\nThe results produced after training on look_i16__minn10 were much less chaotic. While they didn’t resemble coherent drawings, this was the first time I spotted some elements of my drawing style (head shape, eye style, lips, chin).", + "crumbs": [ + "blog", + "posts", + "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization" + ] + }, + { + "objectID": "blog/posts/slml_part4.html#layer-normalization", + "href": "blog/posts/slml_part4.html#layer-normalization", + "title": "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization", + "section": "", + "text": "The Magenta team had recommended using Layer Normalization and Recurrent Dropout with Memory Loss.\nI noticed that when I let the trainer run overnight, I’d get wild spikes in the training loss. I decided to start with Layer Normalization.\n\n\n\n\n\n\nFigure 6: Training and validation loss metrics from training on look_i16__minn10 without layernorm (yellow) and with layernorm (burgundy: learnable=True, gray: learnable=False). The yellow line without layernorm enabled is the same as in Figure 4, but this graph shows more than 150,000 train steps while the previous graph showed only 7,000 train steps.\n\n\n\nAdding layer normalization showed a dramatic difference, visible in Figure 6. The yellow line (without layer norm) has a massive increase in validation loss, and many big spikes, while the burgundy and gray lines (with layer norm integrated) have much lower validation loss and don’t have any comparable spikes.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Generated samples from model trained with Layer Normalization.", + "crumbs": [ + "blog", + "posts", + "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization" + ] + }, + { + "objectID": "blog/posts/slml_part4.html#recurrent-dropout", + "href": "blog/posts/slml_part4.html#recurrent-dropout", + "title": "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization", + "section": "", + "text": "The results from the layernorm models in Figure 7 had some hints of my drawing style, while still struggling to form coherent drawings.\nNext, I kept layernorm enabled and enabled recurrent dropout. I ran one separate runs with and without stroke augmentation enabled.\n\n\n\n\n\n\nFigure 8: Training and validation loss metrics for layernorm-only models (burgundy and gray) alongside models trained with recurrent dropout (green) and with stroke augmentation (pink).\n\n\n\nCompared to the layernorm-only models (burgundy and gray), the one recurrent dropout model (green) achieved a lower validation loss relatively quickly, before starting to overfit.\nThe model trained with recurrent dropout and stroke augmentation (pink) clearly performed worse than with recurrent dropout (in terms of higher validation loss).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Generated samples from model trained with Recurrent Dropout as well as Layer Normalization.\n\n\n\nThe resulting generations from model with layernorm and recurrent dropout in Figure 9 weren’t obviously better or worse than those from the layernorm-only model in Figure 7.\n\n\nIf you want to keep reading, check out part 5 of my SLML series.", + "crumbs": [ + "blog", + "posts", + "SLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization" + ] + }, + { + "objectID": "blog/posts/slml_part6.html", + "href": "blog/posts/slml_part6.html", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "This post is part 6 of “SLML” - Single-Line Machine Learning.\nTo read the previous post, check out part 5.\nIf you want to keep reading, check out part 7.\n\n\n\nThough I’d grown my training dataset by bounding-box separating single pages into multiple drawings, I was concerned about the tradeoff of filtering drawings out versus having a more coherent dataset with similar subject matter.\nTo make up for more aggressive filtering, I decided to incorporate several additional sketchbooks I scanned and labeled into a new dataset epoch 20240104.\nDifferences in dataset 20240104 compared to dataset 20231214:\n\nMore raw input drawings\nSame preprocessing, with a modified “adaptive” RDP simplification 1.\n\n\n\n\nIn previous datasets, I had chosen the same strength of RDP line simplification for the whole dataset. Some drawings had been simplified reasonably, but other had been simple to begin with and ended up as a series of straight lines much sharper than the original curves.\n\n\n\n\n\n\nFigure 1: 30 points\n\n\n\nFor the remaining drawings, I ran the RDP algorithm with varying values for its epsilon parameter, until the number of points dipped under 250. Then I saved the result as a zipped numpy file.\n\n\n\n\n\n\n\n\n\nFigure 2: Training and validation loss metrics from models trained on 20240104 using visual filtering on the bounding-box separated drawings, with maxiumum sequence lengths of 200 (gray) and 250 (blue).\n\n\n\nAfter training on 20240104, the validation losses (gray and blue lines in Figure 2) seemed substantially lower than the validation losses from the models trained on the previous dataset (beige, light green).\n\n\n\nOne failure mode I noticed in the results generated after training on the bounding-box separated dataset 20231214-filtered was that some generated drawings had knotted, gnarled lines as in Figure 3.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Generated examples with too much complexity.\n\n\n\nReviewing the bounding box-separated dataset I noticed that some drawings were of one figure, and some drawings were patterned or chained with many faces.\n\n\n\n\n\n\n\n\n\n\n\n(a) 1093 points\n\n\n\n\n\n\n\n\n\n\n\n(b) 1127 points\n\n\n\n\n\n\n\n\n\n\n\n(c) 329 points\n\n\n\n\n\n\n\nFigure 4: Drawings with over 300 points.\n\n\n\nSometimes I make patterns by chaining repeating sequences of faces into long continuous lines. I wondered whether the presence of this kind of drawing in the training data was occasionally encouraging the model to make long continuous chains rather than drawing a single person.\n\n\n\n\n\n\nchains example\n\n\n\n\nFigure 5: Example of a “diagonal chain” single-line pattern I draw.\n\n\n\nI wanted to exclude those patterns/chains from my training data, so I could give my model the best chance of learning to draw one person at a time.\n\n\n\nI decided to make a subset 20240104-furtherfiltered of dataset 20240104.\nMy plan was to compute embeddings for every bounding-box separated drawing in dataset 20240104. Then I could K-Means clustering on them, and decide which clusters I wanted to exclude in bulk. 2\nRight away I spotted the “too complex” chained line drawings in cluster 0 (Figure 6 (a)). There were also several chained line drawings in cluster 3 (Figure 6 (b)) mixed in with some squarish horizontal drawings that I was happy to exclude from my training set, as they looked too different from my more typical standalone drawings of individual faces/people.\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster 0\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster 3\n\n\n\n\n\n\n\nFigure 6: Clusters with drawings that were “too complex”.\n\n\n\nI also noticed some clusters with drawings that were “too simple”. It seems like many of the drawings in cluster 13 (Figure 7 (a)) were stray lines accidentally separated from any context by the bounding-box preprocessing. Cluster 9 (Figure 7 (b)) had many similar nonsensical lines, though they were mixed in with some false positives - valid drawings that I’d prefer to keep in the dataset.\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster 13\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster 9\n\n\n\n\n\n\n\nFigure 7: Clusters with drawings that were “too simple”.\n\n\n\nI was excited to notice some distinct categories in my drawings, seeing them from a distance. In the future, as I add more drawings, it’d be great to explicitly label these drawing categories and even train separate models on them. For now, given that I don’t have enough drawings scanned yet, I’m choosing to keep them in one dataset.\nClusters 1, 4, and 11 (in Figure 8 (a), Figure 8 (c), and Figure 8 (i), respectively) all have vertical, narrow, whole-body figures.\nCluster 2, in Figure 8 (b), mostly has rounder compositions of individual faces without a complete body.\nClusters 8 and 15, in Figure 8 (g) and Figure 8 (l), seem to have more complex drawings but mostly still contain drawings of standalone people.\nThe remaining clusters contain reasonably uniform drawings of standalone people, in vertical compositions, that are not too narrow. Hovering your mouse over these links Figure 8 (d), Figure 8 (e), Figure 8 (f), Figure 7 (b), Figure 8 (h), Figure 8 (j), Figure 8 (k).\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster 1\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster 2\n\n\n\n\n\n\n\n\n\n\n\n(c) Cluster 4\n\n\n\n\n\n\n\n\n\n\n\n(d) Cluster 5\n\n\n\n\n\n\n\n\n\n\n\n\n\n(e) Cluster 6\n\n\n\n\n\n\n\n\n\n\n\n(f) Cluster 7\n\n\n\n\n\n\n\n\n\n\n\n(g) Cluster 8\n\n\n\n\n\n\n\n\n\n\n\n(h) Cluster 10\n\n\n\n\n\n\n\n\n\n\n\n\n\n(i) Cluster 11\n\n\n\n\n\n\n\n\n\n\n\n(j) Cluster 12\n\n\n\n\n\n\n\n\n\n\n\n(k) Cluster 14\n\n\n\n\n\n\n\n\n\n\n\n(l) Cluster 15\n\n\n\n\n\n\n\nFigure 8: Clusters with drawings that looked good to me.\n\n\n\n\n\n\nThe remainder of the clusters, in Figure 8, looked “good enough” for me to include in my training set. I all other clusters, and saved a filtered-down dataset as 20240104-furtherfiltered.\nCompared to dataset 20240104, it’s clear in the top row of Figure 9 that in the filtered dataset variant, the distribution of number of strokes has shifted away from the long tail of many-stroke drawings.\n\n\n\n\n\n\nFigure 9: Comparing to unfiltered dataset 20240104 (2100 drawings) to the filtered dataset 20240104-furtherfiltered (1300 drawings).\n\n\n\nComparing the training metrics in Figure 10 for the model trained on filtered dataset 20240104-furtherfiltered (in red) with the previous model runs on unfiltered dataset 20240104 (in gray and blue) is not a perfect comparison. Since the validation set for 20240104-furtherfiltered was also filtered, it’s a smaller (and likely noisier) validation set. Still, the new model’s validation loss was roughly within the bounds of what I expected.\n\n\n\n\n\n\nFigure 10: Training and validation loss metrics from models trained on 20240104-furtherfiltered using visual filtering on the bounding-box separated drawings (red).\n\n\n\n\nQualitatively, the generated results after visual similarity filtering were significantly improved.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Generated samples after training with visual filtering on bbox-separated dataset.\n\n\n\nEven the generated results that looked less like people/faces to me still had appealing curves and flowing patterns, which I recognize from my own drawing style.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 12: Generated samples after training with visual filtering on bbox-separated dataset.\n\n\n\n\nIf you want to keep reading, check out part 7 of my SLML series.", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#new-dataset-20240104", + "href": "blog/posts/slml_part6.html#new-dataset-20240104", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "Though I’d grown my training dataset by bounding-box separating single pages into multiple drawings, I was concerned about the tradeoff of filtering drawings out versus having a more coherent dataset with similar subject matter.\nTo make up for more aggressive filtering, I decided to incorporate several additional sketchbooks I scanned and labeled into a new dataset epoch 20240104.\nDifferences in dataset 20240104 compared to dataset 20231214:\n\nMore raw input drawings\nSame preprocessing, with a modified “adaptive” RDP simplification 1.", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#rdp-and-sequence-length", + "href": "blog/posts/slml_part6.html#rdp-and-sequence-length", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "In previous datasets, I had chosen the same strength of RDP line simplification for the whole dataset. Some drawings had been simplified reasonably, but other had been simple to begin with and ended up as a series of straight lines much sharper than the original curves.\n\n\n\n\n\n\nFigure 1: 30 points\n\n\n\nFor the remaining drawings, I ran the RDP algorithm with varying values for its epsilon parameter, until the number of points dipped under 250. Then I saved the result as a zipped numpy file.", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#training-on-20240104", + "href": "blog/posts/slml_part6.html#training-on-20240104", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "Figure 2: Training and validation loss metrics from models trained on 20240104 using visual filtering on the bounding-box separated drawings, with maxiumum sequence lengths of 200 (gray) and 250 (blue).\n\n\n\nAfter training on 20240104, the validation losses (gray and blue lines in Figure 2) seemed substantially lower than the validation losses from the models trained on the previous dataset (beige, light green).", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#overly-complex-drawings", + "href": "blog/posts/slml_part6.html#overly-complex-drawings", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "One failure mode I noticed in the results generated after training on the bounding-box separated dataset 20231214-filtered was that some generated drawings had knotted, gnarled lines as in Figure 3.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Generated examples with too much complexity.\n\n\n\nReviewing the bounding box-separated dataset I noticed that some drawings were of one figure, and some drawings were patterned or chained with many faces.\n\n\n\n\n\n\n\n\n\n\n\n(a) 1093 points\n\n\n\n\n\n\n\n\n\n\n\n(b) 1127 points\n\n\n\n\n\n\n\n\n\n\n\n(c) 329 points\n\n\n\n\n\n\n\nFigure 4: Drawings with over 300 points.\n\n\n\nSometimes I make patterns by chaining repeating sequences of faces into long continuous lines. I wondered whether the presence of this kind of drawing in the training data was occasionally encouraging the model to make long continuous chains rather than drawing a single person.\n\n\n\n\n\n\nchains example\n\n\n\n\nFigure 5: Example of a “diagonal chain” single-line pattern I draw.\n\n\n\nI wanted to exclude those patterns/chains from my training data, so I could give my model the best chance of learning to draw one person at a time.", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#similarity-filtered-dataset", + "href": "blog/posts/slml_part6.html#similarity-filtered-dataset", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "I decided to make a subset 20240104-furtherfiltered of dataset 20240104.\nMy plan was to compute embeddings for every bounding-box separated drawing in dataset 20240104. Then I could K-Means clustering on them, and decide which clusters I wanted to exclude in bulk. 2\nRight away I spotted the “too complex” chained line drawings in cluster 0 (Figure 6 (a)). There were also several chained line drawings in cluster 3 (Figure 6 (b)) mixed in with some squarish horizontal drawings that I was happy to exclude from my training set, as they looked too different from my more typical standalone drawings of individual faces/people.\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster 0\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster 3\n\n\n\n\n\n\n\nFigure 6: Clusters with drawings that were “too complex”.\n\n\n\nI also noticed some clusters with drawings that were “too simple”. It seems like many of the drawings in cluster 13 (Figure 7 (a)) were stray lines accidentally separated from any context by the bounding-box preprocessing. Cluster 9 (Figure 7 (b)) had many similar nonsensical lines, though they were mixed in with some false positives - valid drawings that I’d prefer to keep in the dataset.\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster 13\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster 9\n\n\n\n\n\n\n\nFigure 7: Clusters with drawings that were “too simple”.\n\n\n\nI was excited to notice some distinct categories in my drawings, seeing them from a distance. In the future, as I add more drawings, it’d be great to explicitly label these drawing categories and even train separate models on them. For now, given that I don’t have enough drawings scanned yet, I’m choosing to keep them in one dataset.\nClusters 1, 4, and 11 (in Figure 8 (a), Figure 8 (c), and Figure 8 (i), respectively) all have vertical, narrow, whole-body figures.\nCluster 2, in Figure 8 (b), mostly has rounder compositions of individual faces without a complete body.\nClusters 8 and 15, in Figure 8 (g) and Figure 8 (l), seem to have more complex drawings but mostly still contain drawings of standalone people.\nThe remaining clusters contain reasonably uniform drawings of standalone people, in vertical compositions, that are not too narrow. Hovering your mouse over these links Figure 8 (d), Figure 8 (e), Figure 8 (f), Figure 7 (b), Figure 8 (h), Figure 8 (j), Figure 8 (k).\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster 1\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster 2\n\n\n\n\n\n\n\n\n\n\n\n(c) Cluster 4\n\n\n\n\n\n\n\n\n\n\n\n(d) Cluster 5\n\n\n\n\n\n\n\n\n\n\n\n\n\n(e) Cluster 6\n\n\n\n\n\n\n\n\n\n\n\n(f) Cluster 7\n\n\n\n\n\n\n\n\n\n\n\n(g) Cluster 8\n\n\n\n\n\n\n\n\n\n\n\n(h) Cluster 10\n\n\n\n\n\n\n\n\n\n\n\n\n\n(i) Cluster 11\n\n\n\n\n\n\n\n\n\n\n\n(j) Cluster 12\n\n\n\n\n\n\n\n\n\n\n\n(k) Cluster 14\n\n\n\n\n\n\n\n\n\n\n\n(l) Cluster 15\n\n\n\n\n\n\n\nFigure 8: Clusters with drawings that looked good to me.", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#training-on-filtered-dataset", + "href": "blog/posts/slml_part6.html#training-on-filtered-dataset", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "", + "text": "The remainder of the clusters, in Figure 8, looked “good enough” for me to include in my training set. I all other clusters, and saved a filtered-down dataset as 20240104-furtherfiltered.\nCompared to dataset 20240104, it’s clear in the top row of Figure 9 that in the filtered dataset variant, the distribution of number of strokes has shifted away from the long tail of many-stroke drawings.\n\n\n\n\n\n\nFigure 9: Comparing to unfiltered dataset 20240104 (2100 drawings) to the filtered dataset 20240104-furtherfiltered (1300 drawings).\n\n\n\nComparing the training metrics in Figure 10 for the model trained on filtered dataset 20240104-furtherfiltered (in red) with the previous model runs on unfiltered dataset 20240104 (in gray and blue) is not a perfect comparison. Since the validation set for 20240104-furtherfiltered was also filtered, it’s a smaller (and likely noisier) validation set. Still, the new model’s validation loss was roughly within the bounds of what I expected.\n\n\n\n\n\n\nFigure 10: Training and validation loss metrics from models trained on 20240104-furtherfiltered using visual filtering on the bounding-box separated drawings (red).\n\n\n\n\nQualitatively, the generated results after visual similarity filtering were significantly improved.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Generated samples after training with visual filtering on bbox-separated dataset.\n\n\n\nEven the generated results that looked less like people/faces to me still had appealing curves and flowing patterns, which I recognize from my own drawing style.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 12: Generated samples after training with visual filtering on bbox-separated dataset.\n\n\n\n\nIf you want to keep reading, check out part 7 of my SLML series.", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part6.html#footnotes", + "href": "blog/posts/slml_part6.html#footnotes", + "title": "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering", + "section": "Footnotes", + "text": "Footnotes\n\n\nbased on what I observed when filtering the bounding-box dataset by number of points↩︎\nsimilar to what I did with full sketchbook pages in part 1↩︎", + "crumbs": [ + "blog", + "posts", + "SLML Part 6 - SketchRNN Experiments: Granular Visual Filtering" + ] + }, + { + "objectID": "blog/posts/slml_part0.html", + "href": "blog/posts/slml_part0.html", + "title": "SLML Part 0 - My Single-Line Drawing Practice", + "section": "", + "text": "My Single-Line Drawing Practice\n\nThis post is the background to my series “SLML” - Single-Line Machine Learning.\nNext in the series is part 1, where I discovered SketchRNN and got curious about applying it to my own artwork.\n\nI started experimenting with single-line drawings around 2017. I’d been painting and drawing and experimenting with Machine Learning art for around 5 years at that point, but my art hobby had started to feel like a chore. I was taking on more and more ambitious painting projects, and there wasn’t a lot of simplicity or repetition in my art practice. A lot of my art felt premeditated, as I’d print out what I wanted to paint and then “execute” it on the canvas, painstakingly trying to represent what I’d decided to paint as accurately as possible.\nI got tired of painting but wanted to keep up my art practice, so I started just carrying around a sketchbook. Since I’d noticed a tendency to overthink my drawings I thought a pencil would give me too much freedom to erase, so I only brought a pen with me. I found myself wanting to practice the challenging parts of paintings that I’d struggled with, particularly drawing people and faces.\nFriends advised me that adding a constraint could help me to iterate quickly and not overthink my drawings. Since one of my patterns had been to spend too much time on individual drawings, I decided to adopt an extreme constraint: as soon as I lifted the pen from the page, I’d call the drawing done.\nI was drawn at first to the technical challenge. As a painter I’d already been learning about facial anatomy in an attempt to improve my drawings. Now I needed not only to know enough about anatomy to visualize a person or face I wanted to draw in three dimensions, but also to pick a path across that surface to make a finished drawing.\nAs a painter I’d grown accustomed to shading and blending to add or remove contrast. Once I started working only with lines, I had to find new tricks. Negative space in a drawing can create a good shadow. Concentric circles creates an illusion of depth. Putting lines close together makes something look darker.\nOver time, other aspects of single-line drawing pulled me in further. I found how much beauty there was to be found in what to leave out of a drawing. I learned how satisfying it is to choose just the essential lines needed to imply a facial expression or a pose.\n\nIf you want to keep reading, check out part 1 of my SLML series, where I discovered SketchRNN and got curious about applying it to my own artwork.", + "crumbs": [ + "blog", + "posts", + "SLML Part 0 - My Single-Line Drawing Practice" + ] + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "home", + "section": "", + "text": "I’m an ML engineer, ex-startup founder, and part-time artist based in Brooklyn, NY.\nYou can learn more about me on my “about” page, or you can check out my “resume” page.", + "crumbs": [ + "blog", + "home" + ] + }, + { + "objectID": "index.html#hi-im-andrew-look.", + "href": "index.html#hi-im-andrew-look.", + "title": "home", + "section": "", + "text": "I’m an ML engineer, ex-startup founder, and part-time artist based in Brooklyn, NY.\nYou can learn more about me on my “about” page, or you can check out my “resume” page.", + "crumbs": [ + "blog", + "home" + ] + }, + { + "objectID": "index.html#projects", + "href": "index.html#projects", + "title": "home", + "section": "Projects", + "text": "Projects\n\n\n\n\n\nTitle\n\n\n\n\n\n\nSingle-Line Machine Learning\n\n\n\n\n\nNo matching items", + "crumbs": [ + "blog", + "home" + ] + }, + { + "objectID": "index.html#posts", + "href": "index.html#posts", + "title": "home", + "section": "Posts", + "text": "Posts\n\n\n\n\n\nTitle\n\n\n\n\n\n\nSLML Part 6 - SketchRNN Experiments: Granular Visual Filtering\n\n\n\n\nSLML Part 5 - SketchRNN Experiments: Path Preprocessing\n\n\n\n\nSLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization\n\n\n\n\nSLML Part 3 - JPEG to SVG to Stroke-3\n\n\n\n\nSLML Part 2 - Assembling the Dataset\n\n\n\n\nSLML Part 1 - Why I Decided to Train SketchRNN on My Drawings\n\n\n\n\nSLML Part 0 - My Single-Line Drawing Practice\n\n\n\n\nA Tour of AI Art in 2018\n\n\n\n\n\nNo matching items", + "crumbs": [ + "blog", + "home" + ] + }, + { + "objectID": "about/index.html", + "href": "about/index.html", + "title": "about me", + "section": "", + "text": "Hi, I’m Andrew Look.\nI’m a machine learning engineer, ex-startup founder, and part-time artist based in Brooklyn, NY.\n\nWork\nI recently left my most recent startup, Aesthetic (Y Combinator Summer 2018).\nBefore that, I did ML engineering at Pinterest from 2016-2018.\nPreviously, I worked on a mobile ad startup called URX from 2013 until 2016 when we were acquired by Pinterest.\n\n\nSide Projects\nI’m also an AI advisor to Long Journey Ventures, occasionally helping with technical due diligence on companies with an AI/ML focus.\nIn spare time, I’m building audio-reactive digital art and training new team members as Head of Software for the Titanic’s End Mutant Vehicle.\n\n\nArt\nI’ve been painting since 2013 (when I really needed a hobby to unwind from the stress of startup life).\nI’ve been experimenting with ML art and generative AI since 2016 (when I started playing with DeepDreams and GANs and making physical paintings from the outputs).\nI’ve been making single-line drawings since 2018 (when I decided to experiment with adding a constraint to my art practice).\nI’ve been making pottery since 2022 (when I wanted to experiment with making art in three dimensions rather than two).", + "crumbs": [ + "blog", + "about" + ] + }, + { + "objectID": "projects/slml.html", + "href": "projects/slml.html", + "title": "Single-Line Machine Learning", + "section": "", + "text": "Lately I’ve been working on training ML models to generate single-line drawings in my style. I’ve open-sourced the code for my models and the code I used to prepare the dataset.\nI’ve started writing deep dives for each phase of the project.\n\n\n\n\n\nTitle\n\n\n\n\n\n\nSLML Part 0 - My Single-Line Drawing Practice\n\n\n\n\nSLML Part 1 - Why I Decided to Train SketchRNN on My Drawings\n\n\n\n\nSLML Part 2 - Assembling the Dataset\n\n\n\n\nSLML Part 3 - JPEG to SVG to Stroke-3\n\n\n\n\nSLML Part 4 - SketchRNN Experiments: Minimum Stroke Length and RNN Regularization\n\n\n\n\nSLML Part 5 - SketchRNN Experiments: Path Preprocessing\n\n\n\n\nSLML Part 6 - SketchRNN Experiments: Granular Visual Filtering\n\n\n\n\n\nNo matching items\n\n\nThis page includes a broader overview of the story as a whole, linking to the individual sections for more detail.\n\n\nIn part 0 I share how I got started making single-line drawings, and why I found them interesting enough to make them as a daily practice.\n\n\n\nIn part 1 - Discovering SketchRNN I cover how SketchRNN captured my imagination and why I decided to try training it on my own data. For example, I imagined turning the “variability” up, and generating new drawings based on my old ones?\n\n\n\n\ndistill.pub’s handwriting demo\n\n\n\nI hit my first roadbloack when I reached out to the authors of SketchRNN. They estimated that I’d need thousands of examples in order to train a model, but I only had a few hundred at the time.\nI decided to keep making drawings in my sketchbooks, numbering the pages, and scanning them to store with a standardized file naming scheme.\n\n\n\nIn the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.\nSeveral years went by. More sketchbooks accumulated. Eventually, I ran a file count and saw a few thousand distinct drawings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Some example single-line drawings from my sketchbooks.\n\n\n\n\n\n\nI was finally ready to get started. I was going to need:\n\na thousand JPEGs of my drawings (at least)\na stroke-3 conversion process for my JPEG scans\na SketchRNN model and trainer\nthe patience to experiment with hyperparameters\n\nI started by collecting my sketchbook page JPEGs into usable training data.\n\n\n\n\n\n\n\n\nsb26p068-purple-hair\n\n\n\n\n\n\n\nsb55p069-color\n\n\n\n\n\n\n\nsb26p098-pickle-toast\n\n\n\n\n\n\n\nsb26p069-red-nose\n\n\n\n\n\n\nFigure 2: Some watercolor examples from my sketchbooks.\n\n\n\nIn part 2 - Embedding Filtering, I cover how I’m using embeddings to filter my dataset of drawings. I used embeddings to solve the problem of filtering everything out of my sketchbook data that wasn’t a single-line drawing - particularly my watercolors.\nI also made an exploratory browser to visualize the embedding space of the drawings, and published it at projector.andrewlook.com. Here’s a demo video:\n\n\n\nIn part 3 - Dataset Vectorization, I cover how I’m vectorizing the scans to prepare them for RNN/transformer training. At this stage in the process, my drawings were converted to centerline-traced vector paths, but they were represented as a series of separate strokes. It’s visible in Figure 3 how the strokes are out of order, since strokes don’t start where the previous stroke left off.\n\n\n\n\n\n\nFigure 3: Example of separate strokes after my vectorization process.\n\n\n\n\n\n\n\n\nPart 4 covers how the dataset I used to train my first model contained drawings with too many short strokes, as in Figure 3. Models trained on this dataset produced frenetic drawings such as Figure 4 (a). I experimented with some RNN training improvements and after filtering short strokes out of my first datasets and training a new model I saw a big improvment, as in Figure 4 (b).\n\n\n\n\n\n\n\n\n\n\n\n(a) Before Short-Stroke Exclusion\n\n\n\n\n\n\n\n\n\n\n\n(b) After Short-Stroke Exclusion\n\n\n\n\n\n\n\nFigure 4: Sample generated results from models trained before and after filtering out strokes with less than 10 points.\n\n\n\n\nPart 5 covers improvements I made to my preprocessing of the dataset by joining SVG paths up into continuous lines so that the model could learn from longer sequences.\n\n\n\n\n\n\n\n\n\n\n\n(a) Original: 22 strokes\n\n\n\n\n\n\n\n\n\n\n\n(b) After path-joining: 4 strokes\n\n\n\n\n\n\n\n\n\n\n\n(c) After path-splicing: 2 strokes\n\n\n\n\n\n\n\nFigure 5: Comparison of number of strokes before and after preprocessing, with one color per stroke.\n\n\n\n\nThe generated results at this stage were uncanny, but showed a big improvement from the initial results. There were at least some recognizable faces and bodies, as seen in Figure 6.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Generated samples from a model trained on path-spliced dataset.\n\n\n\nAlso in Part 5, I a preprocessing step to decompose strokes into seperate drawings if their bounding boxes didn’t overlap sufficiently. The size of the dataset roughly doubled, since sketchbook pages containing multiple drawings were broken out into separate training examples as in Figure 7.\n\n\n\n\n\n\n\n\n\n\n\n(a) Original\n\n\n\n\n\n\n\n\n\n\n\n(b)\n\n\n\n\n\n\n\n\n\n\n\n(c)\n\n\n\n\n\n\n\n\n\n\n\n(d)\n\n\n\n\n\n\n\nFigure 7: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214.\n\n\n\nPart 6 covers how I filtered drawings by visual similarity. Though I did an earlier pass on visual similarity to filter watercolors out from my single-line drawings, this time I wanted to explore the embedding space of the individual drawings after bounding-box separation had been applied. I found that clustering the drawing embeddings identified clusters I wanted to exlcude from the training data like Figure 8 (a) and Figure 8 (b), and helped me identify clusters that I wanted to include in the training data like Figure 8 (c).\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster of complex drawings I wanted to filter out\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster of simple drawings I wanted to filter out\n\n\n\n\n\n\n\n\n\n\n\n(c) Cluster of good drawings I wanted to include\n\n\n\n\n\n\n\nFigure 8: Example groupings after running K-Means on visual embeddings of individual drawings.\n\n\n\nThe drawings generated by models trained on this visually-filtered dataset started to look recognizable as containing distinct people or faces, as in Figure 9. Still odd and convoluted, but interesting enough to give me new ideas for drawings or paintings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\n\n\n\nPart 7 covers experiments I tried using data augmentation, using tricks to take my existing set of drawings and create a more diverse set of training data. For regular computer vision algorithms looking at images in terms of pixels, it’s common to randomly crop the images, flip them horizontally, and change the colors. For vector drawings like I’m working with, there are a different set of techniques available.\n\n\n\n\n\n\n\n\nOriginal drawing\n\n\n\n\n\n\n\nAfter “Stroke Augmentation” drops points from lines at random\n\n\n\n\n\n\n\nRandomly rotated and scaled\n\n\n\n\n\n\nFigure 10: Examples of data augmentation.\n\n\n\n\n\n\n\nIt has also been fun to connect the model’s generation function to an animation builder, so I can watch the machine “draw” in real time. Compared with viewing a static output, the animations reminds me that part of what I love about single-line drawings is the surprise as a viewer.\n\nThe drawing might start off nonsensical, and then end up with something recognizable enough to be an abstract figure drawing. Even when I’m drawing, I don’t always know where the drawing is going to end up.\n\nI’m adjusting as my hand moves, and adapting to any unexpected moves or mistakes to try and arrive at a drawing that I like. This is not so different from how SketchRNN generates drawings. One way to look at it is that I’m sampling from a probability distribution of possible next moves, and my decision is made by the muscle memory of what’s happened so far.\nLooking at some of the results generated from my SketchRNN models such as Figure 9, they remind me of an experiment I’ve tried in drawing with my eyes closed.\nI was curious about how much I’m adjusting drawings based on what I see while I’m drawing. I wanted to see how much of my drawings come exclusively from the muscle memory I’ve developed in drawing faces.\nDrawing with eyes closed is a great analogy for how SketchRNN draws. The model is only receiving information about the path has traveled so far. No visual information is available about what the final drawing looks like in pixel space as a real image.\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\nErrors like the ones in my eyes-closed drawings made me think about a common issue with models like SketchRNN that rely on recurrent neural networks.\nThe problem of “long-term dependencies” refers to the poor performance RNN’s exhibit in understand things that are too far apart in a sequence.\nIn the case of a drawing, long term dependencies would be things that are far apart in terms of the path the pen takes.\n\n\n\nRecurrent neural networks read a sequence and update the “hidden” layer. The hidden layers act as a kind of memory, allowing later steps in the sequence to incorporate information from earlier parts of the sequence. Each step incorporates more information to the same vector before passing it to the next step in the sequnce. As the RNN model steps through the sequence, the signal from earlier in the sequence tends to “decay” in favor of more recent information later in the sequence.\n\n\nThe long-term dependency problem makes intuitive sense to me when I consider my eyes-closed drawings.\nApparently I have muscle memory when I draw eyes and a note in close proximity, and in drawings lips and a chin, but without looking it’s hard to swoop down from eyes to lips and have them be aligned to the nose I drew earlier.\n\n\nThis got me interested in how Transformer models use attention mechanisms to let each step in the sequence take into account the entire sequence at once.\n\nI came across a paper called Sketchformer: Transformer-based Representation for Sketched Structure, which made a transformer model based on SketchRNN. I decided to try adapting that model for my dataset, and seeing how it compares on handling long-term dependencies.\n\n\n\n\nArchitecture of the SketchFormer model.\n\n\n\nIn my next section on “Training Sketchformer” (coming soon), I cover my experiments using a transformer model instead of an RNN, to see if the model can better handle long-term dependencies within the drawings.\n\n\n\n\ndistill.pub’s handwriting demo\nFigure 1: Some example single-line drawings from my sketchbooks.\nFigure 1: Some example single-line drawings from my sketchbooks.\nFigure 1: Some example single-line drawings from my sketchbooks.\nFigure 1: Some example single-line drawings from my sketchbooks.\nsb26p068-purple-hair\nsb55p069-color\nsb26p098-pickle-toast\nsb26p069-red-nose\nFigure 4 (a): Before Short-Stroke Exclusion\nFigure 4 (b): After Short-Stroke Exclusion\nFigure 5 (a): Original: 22 strokes\nFigure 5 (b): After path-joining: 4 strokes\nFigure 5 (c): After path-splicing: 2 strokes\nFigure 6: Generated samples from a model trained on path-spliced dataset.\nFigure 6: Generated samples from a model trained on path-spliced dataset.\nFigure 6: Generated samples from a model trained on path-spliced dataset.\nFigure 7 (a): Original\nFigure 7 (b): \nFigure 7 (c): \nFigure 7 (d): \nFigure 8 (a): Cluster of complex drawings I wanted to filter out\nFigure 8 (b): Cluster of simple drawings I wanted to filter out\nFigure 8 (c): Cluster of good drawings I wanted to include\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\nOriginal drawing\nAfter “Stroke Augmentation” drops points from lines at random\nRandomly rotated and scaled\nRecurrent neural networks read a sequence and update the “hidden” layer. The hidden layers act as a kind of memory, allowing later steps in the sequence to incorporate information from earlier parts of the sequence. Each step incorporates more information to the same vector before passing it to the next step in the sequnce. As the RNN model steps through the sequence, the signal from earlier in the sequence tends to “decay” in favor of more recent information later in the sequence.\nArchitecture of the SketchFormer model.", + "crumbs": [ + "blog", + "projects", + "Single-Line Machine Learning" + ] + }, + { + "objectID": "projects/slml.html#why-this-project", + "href": "projects/slml.html#why-this-project", + "title": "Single-Line Machine Learning", + "section": "", + "text": "In part 0 I share how I got started making single-line drawings, and why I found them interesting enough to make them as a daily practice.\n\n\n\nIn part 1 - Discovering SketchRNN I cover how SketchRNN captured my imagination and why I decided to try training it on my own data. For example, I imagined turning the “variability” up, and generating new drawings based on my old ones?\n\n\n\n\ndistill.pub’s handwriting demo\n\n\n\nI hit my first roadbloack when I reached out to the authors of SketchRNN. They estimated that I’d need thousands of examples in order to train a model, but I only had a few hundred at the time.\nI decided to keep making drawings in my sketchbooks, numbering the pages, and scanning them to store with a standardized file naming scheme.\n\n\n\nIn the back of my mind, I held on to the idea that one day I’d have enough drawings to train a model on them.\nSeveral years went by. More sketchbooks accumulated. Eventually, I ran a file count and saw a few thousand distinct drawings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Some example single-line drawings from my sketchbooks.", + "crumbs": [ + "blog", + "projects", + "Single-Line Machine Learning" + ] + }, + { + "objectID": "projects/slml.html#preparing-the-dataset", + "href": "projects/slml.html#preparing-the-dataset", + "title": "Single-Line Machine Learning", + "section": "", + "text": "I was finally ready to get started. I was going to need:\n\na thousand JPEGs of my drawings (at least)\na stroke-3 conversion process for my JPEG scans\na SketchRNN model and trainer\nthe patience to experiment with hyperparameters\n\nI started by collecting my sketchbook page JPEGs into usable training data.\n\n\n\n\n\n\n\n\nsb26p068-purple-hair\n\n\n\n\n\n\n\nsb55p069-color\n\n\n\n\n\n\n\nsb26p098-pickle-toast\n\n\n\n\n\n\n\nsb26p069-red-nose\n\n\n\n\n\n\nFigure 2: Some watercolor examples from my sketchbooks.\n\n\n\nIn part 2 - Embedding Filtering, I cover how I’m using embeddings to filter my dataset of drawings. I used embeddings to solve the problem of filtering everything out of my sketchbook data that wasn’t a single-line drawing - particularly my watercolors.\nI also made an exploratory browser to visualize the embedding space of the drawings, and published it at projector.andrewlook.com. Here’s a demo video:\n\n\n\nIn part 3 - Dataset Vectorization, I cover how I’m vectorizing the scans to prepare them for RNN/transformer training. At this stage in the process, my drawings were converted to centerline-traced vector paths, but they were represented as a series of separate strokes. It’s visible in Figure 3 how the strokes are out of order, since strokes don’t start where the previous stroke left off.\n\n\n\n\n\n\nFigure 3: Example of separate strokes after my vectorization process.", + "crumbs": [ + "blog", + "projects", + "Single-Line Machine Learning" + ] + }, + { + "objectID": "projects/slml.html#sketchrnn-experiments", + "href": "projects/slml.html#sketchrnn-experiments", + "title": "Single-Line Machine Learning", + "section": "", + "text": "Part 4 covers how the dataset I used to train my first model contained drawings with too many short strokes, as in Figure 3. Models trained on this dataset produced frenetic drawings such as Figure 4 (a). I experimented with some RNN training improvements and after filtering short strokes out of my first datasets and training a new model I saw a big improvment, as in Figure 4 (b).\n\n\n\n\n\n\n\n\n\n\n\n(a) Before Short-Stroke Exclusion\n\n\n\n\n\n\n\n\n\n\n\n(b) After Short-Stroke Exclusion\n\n\n\n\n\n\n\nFigure 4: Sample generated results from models trained before and after filtering out strokes with less than 10 points.\n\n\n\n\nPart 5 covers improvements I made to my preprocessing of the dataset by joining SVG paths up into continuous lines so that the model could learn from longer sequences.\n\n\n\n\n\n\n\n\n\n\n\n(a) Original: 22 strokes\n\n\n\n\n\n\n\n\n\n\n\n(b) After path-joining: 4 strokes\n\n\n\n\n\n\n\n\n\n\n\n(c) After path-splicing: 2 strokes\n\n\n\n\n\n\n\nFigure 5: Comparison of number of strokes before and after preprocessing, with one color per stroke.\n\n\n\n\nThe generated results at this stage were uncanny, but showed a big improvement from the initial results. There were at least some recognizable faces and bodies, as seen in Figure 6.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Generated samples from a model trained on path-spliced dataset.\n\n\n\nAlso in Part 5, I a preprocessing step to decompose strokes into seperate drawings if their bounding boxes didn’t overlap sufficiently. The size of the dataset roughly doubled, since sketchbook pages containing multiple drawings were broken out into separate training examples as in Figure 7.\n\n\n\n\n\n\n\n\n\n\n\n(a) Original\n\n\n\n\n\n\n\n\n\n\n\n(b)\n\n\n\n\n\n\n\n\n\n\n\n(c)\n\n\n\n\n\n\n\n\n\n\n\n(d)\n\n\n\n\n\n\n\nFigure 7: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214.\n\n\n\nPart 6 covers how I filtered drawings by visual similarity. Though I did an earlier pass on visual similarity to filter watercolors out from my single-line drawings, this time I wanted to explore the embedding space of the individual drawings after bounding-box separation had been applied. I found that clustering the drawing embeddings identified clusters I wanted to exlcude from the training data like Figure 8 (a) and Figure 8 (b), and helped me identify clusters that I wanted to include in the training data like Figure 8 (c).\n\n\n\n\n\n\n\n\n\n\n\n(a) Cluster of complex drawings I wanted to filter out\n\n\n\n\n\n\n\n\n\n\n\n(b) Cluster of simple drawings I wanted to filter out\n\n\n\n\n\n\n\n\n\n\n\n(c) Cluster of good drawings I wanted to include\n\n\n\n\n\n\n\nFigure 8: Example groupings after running K-Means on visual embeddings of individual drawings.\n\n\n\nThe drawings generated by models trained on this visually-filtered dataset started to look recognizable as containing distinct people or faces, as in Figure 9. Still odd and convoluted, but interesting enough to give me new ideas for drawings or paintings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\n\n\n\nPart 7 covers experiments I tried using data augmentation, using tricks to take my existing set of drawings and create a more diverse set of training data. For regular computer vision algorithms looking at images in terms of pixels, it’s common to randomly crop the images, flip them horizontally, and change the colors. For vector drawings like I’m working with, there are a different set of techniques available.\n\n\n\n\n\n\n\n\nOriginal drawing\n\n\n\n\n\n\n\nAfter “Stroke Augmentation” drops points from lines at random\n\n\n\n\n\n\n\nRandomly rotated and scaled\n\n\n\n\n\n\nFigure 10: Examples of data augmentation.", + "crumbs": [ + "blog", + "projects", + "Single-Line Machine Learning" + ] + }, + { + "objectID": "projects/slml.html#interpreting-the-results", + "href": "projects/slml.html#interpreting-the-results", + "title": "Single-Line Machine Learning", + "section": "", + "text": "It has also been fun to connect the model’s generation function to an animation builder, so I can watch the machine “draw” in real time. Compared with viewing a static output, the animations reminds me that part of what I love about single-line drawings is the surprise as a viewer.\n\nThe drawing might start off nonsensical, and then end up with something recognizable enough to be an abstract figure drawing. Even when I’m drawing, I don’t always know where the drawing is going to end up.\n\nI’m adjusting as my hand moves, and adapting to any unexpected moves or mistakes to try and arrive at a drawing that I like. This is not so different from how SketchRNN generates drawings. One way to look at it is that I’m sampling from a probability distribution of possible next moves, and my decision is made by the muscle memory of what’s happened so far.\nLooking at some of the results generated from my SketchRNN models such as Figure 9, they remind me of an experiment I’ve tried in drawing with my eyes closed.\nI was curious about how much I’m adjusting drawings based on what I see while I’m drawing. I wanted to see how much of my drawings come exclusively from the muscle memory I’ve developed in drawing faces.\nDrawing with eyes closed is a great analogy for how SketchRNN draws. The model is only receiving information about the path has traveled so far. No visual information is available about what the final drawing looks like in pixel space as a real image.\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\nErrors like the ones in my eyes-closed drawings made me think about a common issue with models like SketchRNN that rely on recurrent neural networks.\nThe problem of “long-term dependencies” refers to the poor performance RNN’s exhibit in understand things that are too far apart in a sequence.\nIn the case of a drawing, long term dependencies would be things that are far apart in terms of the path the pen takes.\n\n\n\nRecurrent neural networks read a sequence and update the “hidden” layer. The hidden layers act as a kind of memory, allowing later steps in the sequence to incorporate information from earlier parts of the sequence. Each step incorporates more information to the same vector before passing it to the next step in the sequnce. As the RNN model steps through the sequence, the signal from earlier in the sequence tends to “decay” in favor of more recent information later in the sequence.\n\n\nThe long-term dependency problem makes intuitive sense to me when I consider my eyes-closed drawings.\nApparently I have muscle memory when I draw eyes and a note in close proximity, and in drawings lips and a chin, but without looking it’s hard to swoop down from eyes to lips and have them be aligned to the nose I drew earlier.\n\n\nThis got me interested in how Transformer models use attention mechanisms to let each step in the sequence take into account the entire sequence at once.\n\nI came across a paper called Sketchformer: Transformer-based Representation for Sketched Structure, which made a transformer model based on SketchRNN. I decided to try adapting that model for my dataset, and seeing how it compares on handling long-term dependencies.\n\n\n\n\nArchitecture of the SketchFormer model.\n\n\n\nIn my next section on “Training Sketchformer” (coming soon), I cover my experiments using a transformer model instead of an RNN, to see if the model can better handle long-term dependencies within the drawings.\n\n\n\n\ndistill.pub’s handwriting demo\nFigure 1: Some example single-line drawings from my sketchbooks.\nFigure 1: Some example single-line drawings from my sketchbooks.\nFigure 1: Some example single-line drawings from my sketchbooks.\nFigure 1: Some example single-line drawings from my sketchbooks.\nsb26p068-purple-hair\nsb55p069-color\nsb26p098-pickle-toast\nsb26p069-red-nose\nFigure 4 (a): Before Short-Stroke Exclusion\nFigure 4 (b): After Short-Stroke Exclusion\nFigure 5 (a): Original: 22 strokes\nFigure 5 (b): After path-joining: 4 strokes\nFigure 5 (c): After path-splicing: 2 strokes\nFigure 6: Generated samples from a model trained on path-spliced dataset.\nFigure 6: Generated samples from a model trained on path-spliced dataset.\nFigure 6: Generated samples from a model trained on path-spliced dataset.\nFigure 7 (a): Original\nFigure 7 (b): \nFigure 7 (c): \nFigure 7 (d): \nFigure 8 (a): Cluster of complex drawings I wanted to filter out\nFigure 8 (b): Cluster of simple drawings I wanted to filter out\nFigure 8 (c): Cluster of good drawings I wanted to include\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\nFigure 9: Generated samples after training with visual filtering on bbox-separated dataset.\nOriginal drawing\nAfter “Stroke Augmentation” drops points from lines at random\nRandomly rotated and scaled\nRecurrent neural networks read a sequence and update the “hidden” layer. The hidden layers act as a kind of memory, allowing later steps in the sequence to incorporate information from earlier parts of the sequence. Each step incorporates more information to the same vector before passing it to the next step in the sequnce. As the RNN model steps through the sequence, the signal from earlier in the sequence tends to “decay” in favor of more recent information later in the sequence.\nArchitecture of the SketchFormer model.", + "crumbs": [ + "blog", + "projects", + "Single-Line Machine Learning" + ] + }, + { + "objectID": "jupyter/index.html", + "href": "jupyter/index.html", + "title": "jupyter notebooks", + "section": "", + "text": "Title\n\n\nDate\n\n\n\n\n\n\n\nNo matching items", + "crumbs": [ + "blog", + "jupyter" + ] + }, + { + "objectID": "resume/index.html", + "href": "resume/index.html", + "title": "resume", + "section": "", + "text": "15+ years experience building technology, teams, companies.\nPassionate about machine learning and creativity.\nLooking for a technical role in AI / ML, especially in generative AI and creativity.\nY Combinator and UCLA CS alum.", + "crumbs": [ + "blog", + "resume" + ] + }, + { + "objectID": "resume/index.html#summary", + "href": "resume/index.html#summary", + "title": "resume", + "section": "", + "text": "15+ years experience building technology, teams, companies.\nPassionate about machine learning and creativity.\nLooking for a technical role in AI / ML, especially in generative AI and creativity.\nY Combinator and UCLA CS alum.", + "crumbs": [ + "blog", + "resume" + ] + }, + { + "objectID": "resume/index.html#experience", + "href": "resume/index.html#experience", + "title": "resume", + "section": "Experience", + "text": "Experience\n\nAesthetic (YC S’18)\nLLM-powered Language Learning Tools (June 2023 - October 2023)\n\nDeveloped a prototype for an audio-centric language learning application, leveraging LLMs to generate multilingual educational content.\nImplemented a prompt testing system to evaluate and enhance the quality of prompt engineering modifications incrementally.\n\nAI Assistant for Artists / Designers (July 2022 - May 2023)\n\nCreated a user-friendly app enabling artists to fine-tune generative models with their portfolios, facilitating rapid creation of new images using their own style.\nManaged backend processes to train and deploy models to an inference API.\nAutomated analysis of LoRA hyper-parameters for artistic style fine-tuning.\nExpanded the capabilities of the Dreambooth trainer to accommodate various concepts.\n\nToken-Gated Platform for NFT creators (Feb 2021 - June 2022)\n\nDeveloped a self-service platform enabling NFT creators to form their communities, conduct token sales, and offer exclusive features to token holders.\nAuthored versatile smart contracts allowing creators to independently manage collections and sales beyond conventional exchanges.\n\nAPI for Generating Design Assets (Jan 2020 - Jan 2021)\n\nCrafted a “Zapier for Design” API to produce on-demand templated social media images and animations using React for templating and Puppeteer/ffmpeg for rendering.\n\nSoftware-augmented design agency (June 2018 - October 2019)\n\nDeveloped a workflow system to monitor tasks and payments, catering to freelancers involved in design projects as the business scaled to a $1M ARR.\nHired and managed a 12-person remote engineering team.\nEstablished remote hiring and onboarding processes with a 100% interview-to-acceptance rate for new hires.\n\n\n\nLong Journey Ventures\nAI Advisor (May 2023 - Present)\n\nConducting due diligence on AI startups, delving into their technology to guide investment strategies.\n\n\n\nPinterest\nML Engineer (June 2016 - June 2018)\n\nSpearheaded the “Taste Graph” initiative, redesigning the ontology of user interests and developing classifiers for pins and queries.\nEnhanced overall revenue by 2.5% and boosted ecommerce conversion rates by 20% by substituting legacy user interests with taste graph data for ad targeting.\nIncreased revenue by 2% with the implementation of a FastText text classifier to align search queries with ad interests.\nOversaw data labeling initiatives and developed offline evaluation systems for interest classification models.\nDesigned tools for data science teams to perform statistical analysis on ad interest A/B testing outcomes.\n\n\n\nURX (YC S’13, Acq. Pinterest)\nCo-Founder / CTO (Jan 2013 - May 2016)\n\nEngineered a contextual search engine to expose pertinent content across mobile applications, capitalizing on deep-link traffic.\nDesigned a distributed system to index 2 billion pages of mobile deep-link content.\nLed machine learning projects to augment index entries, focusing on classification, disambiguation, and knowledge graph integration.\nGrew and nurtured a 25-member engineering team spanning engineering, infrastructure, and machine learning.\n\n\n\nShopzilla\nSenior Software Engineer (September 2011 - January 2013)\n\nDeveloped and scaled an SEM ad revenue prediction pipeline to assess 5MM keywords daily.\nImplemented a Solr-based recommendation engine for SEO, optimizing search relevancy and performance while streamlining index build and deployment.\n\nSoftware Engineer (August 2010 - September 2011)\n\nLed the transition of company-wide analytical workflows to a MapReduce environment, evaluating and prototyping relevant technologies.\n\nData Engineering Intern (June 2007 - Feb 2009)\n\nDeveloped Ruby on Rails applications to enhance efficiency in labeling processes.", + "crumbs": [ + "blog", + "resume" + ] + }, + { + "objectID": "resume/index.html#education", + "href": "resume/index.html#education", + "title": "resume", + "section": "Education", + "text": "Education\nUniversity of California, Los Angeles - BS. Computer Science (Sept 2006 - June 2010)", + "crumbs": [ + "blog", + "resume" + ] + }, + { + "objectID": "blog/posts/slml_part3.html", + "href": "blog/posts/slml_part3.html", + "title": "SLML Part 3 - JPEG to SVG to Stroke-3", + "section": "", + "text": "This post is part 3 of “SLML” - Single-Line Machine Learning.\nTo read the previous post, check out part 2.\nIf you want to keep reading, here is part 4.\n\nMost computer vision algorithms represent images as a rectangular grid of pixels on a screen. The model that the Magenta team trained, SketchRNN, instead interprets the drawings as a sequence of movements of a pen. They call this “stroke-3 format”, since each step in the sequence is represented by 3 values:\n\ndelta_x: how much did it move left-to-right?\ndelta_y: how much did it move up-and-down?\nlift_pen: was the pen down (continuing the current stroke) or was the pen lifted (moving to the start of a new stroke)\n\n\n\n\nFirst, I had to convert my JPEG scans into the “stroke-3” format. This would involve:\n\nconverting the files from JPEG to SVG\nconverting SVG to stroke-3\nsimplifying the drawings to reduce the number of points\n\n\n\nWhen I first started converting to SVG, I had trouble finding a tool that would give me a single, clean stroke for each line. Eventually I found a tool called autotrace that was able to correctly do a “centerline trace”.\n\n\n\n\n\n\n\n\n\n\n\n(a) potrace\n\n\n\n\n\n\n\n\n\n\n\n(b) autotrace\n\n\n\n\n\n\n\nFigure 1: Comparison of Vectorization Tools.\n\n\n\n\n\n\nThen I used a python library called svgpathtools to take the resulting SVG files, and convert each of the paths to a sequence of points. This step is necessary because SVG paths are often represented as Bezier curves.\nOne problem I noticed was that the drawings were represented as many separate strokes rather than one continuous line. For example, in the image below, each color represents a separate pen stroke.\n\n\n\nseparate strokes\n\n\n\n\n\nFinally, I’d apply the Ramer-Douglas-Pecker (“RDP”) algorithm on the resulting points, which uses an adjustable “epsilon” parameter to simplify down the drawings by reducing the number of points in a line’s path.\n\n\n\nRDP example\n\n\nThis is important because the SketchRNN model has difficulty with sequences longer than a few hundred points, so it’s helpful to simplify the drawings down by removing some of the very fine details while preserving the overall shape.\n\n\n\nphase1-sample-0177-epoch-01700-orig\n\n\n\nNext in my SLML series is part 4, where I experiment with hyperparams and datasets in training SketchRNN.", + "crumbs": [ + "blog", + "posts", + "SLML Part 3 - JPEG to SVG to Stroke-3" + ] + }, + { + "objectID": "blog/posts/slml_part3.html#jpeg-to-svg", + "href": "blog/posts/slml_part3.html#jpeg-to-svg", + "title": "SLML Part 3 - JPEG to SVG to Stroke-3", + "section": "", + "text": "When I first started converting to SVG, I had trouble finding a tool that would give me a single, clean stroke for each line. Eventually I found a tool called autotrace that was able to correctly do a “centerline trace”.\n\n\n\n\n\n\n\n\n\n\n\n(a) potrace\n\n\n\n\n\n\n\n\n\n\n\n(b) autotrace\n\n\n\n\n\n\n\nFigure 1: Comparison of Vectorization Tools.", + "crumbs": [ + "blog", + "posts", + "SLML Part 3 - JPEG to SVG to Stroke-3" + ] + }, + { + "objectID": "blog/posts/slml_part3.html#svg-to-points", + "href": "blog/posts/slml_part3.html#svg-to-points", + "title": "SLML Part 3 - JPEG to SVG to Stroke-3", + "section": "", + "text": "Then I used a python library called svgpathtools to take the resulting SVG files, and convert each of the paths to a sequence of points. This step is necessary because SVG paths are often represented as Bezier curves.\nOne problem I noticed was that the drawings were represented as many separate strokes rather than one continuous line. For example, in the image below, each color represents a separate pen stroke.\n\n\n\nseparate strokes", + "crumbs": [ + "blog", + "posts", + "SLML Part 3 - JPEG to SVG to Stroke-3" + ] + }, + { + "objectID": "blog/posts/slml_part3.html#line-simplification", + "href": "blog/posts/slml_part3.html#line-simplification", + "title": "SLML Part 3 - JPEG to SVG to Stroke-3", + "section": "", + "text": "Finally, I’d apply the Ramer-Douglas-Pecker (“RDP”) algorithm on the resulting points, which uses an adjustable “epsilon” parameter to simplify down the drawings by reducing the number of points in a line’s path.\n\n\n\nRDP example\n\n\nThis is important because the SketchRNN model has difficulty with sequences longer than a few hundred points, so it’s helpful to simplify the drawings down by removing some of the very fine details while preserving the overall shape.\n\n\n\nphase1-sample-0177-epoch-01700-orig\n\n\n\nNext in my SLML series is part 4, where I experiment with hyperparams and datasets in training SketchRNN.", + "crumbs": [ + "blog", + "posts", + "SLML Part 3 - JPEG to SVG to Stroke-3" + ] + }, + { + "objectID": "blog/posts/slml_part5.html", + "href": "blog/posts/slml_part5.html", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "This post is part 5 of “SLML” - Single-Line Machine Learning.\nTo read the previous post, check out part 4. If you want to keep reading, check out part 6.\n\n\n\nDespite some improvement within the strokes, my latest models were still producing many short strokes. So I figured that improving my dataset would give better results than fiddling with hyper-parameters or my model.\nLooking at my training data, I returned to an issue from the stroke3-conversion process: autotrace hadn’t realized that the different segments of my lines were actually connected. It was producing a series of small, centerline-traced segments, as seen in Figure 1 (a).\nI’d need some preprocessing to connect the smaller strokes into larger strokes, so that my SketchRNN model would learn to generate long contiguous single-line drawings.\n\n\n\n\n\n\n\n\n\n\n\n(a) Original: 22 strokes\n\n\n\n\n\n\n\n\n\n\n\n(b) After path-joining: 4 strokes\n\n\n\n\n\n\n\n\n\n\n\n(c) After path-splicing: 2 strokes\n\n\n\n\n\n\n\nFigure 1: Comparison of number of strokes before and after preprocessing, with one color per stroke.\n\n\n\nMy first preprocesing improvement was simple algorithm that I called “path joining”.\nEach of my drawings is represented as a collection of line segments, each with a start and end point. I try to find the shortest connection I can draw to connect two segments. Starting with the longest stroke, I compare it each of the other strokes and calculate the minimum distance between the start and end points of the line segments. After going through all the strokes in a drawing, I connect the two strokes with the shortest gap between their endpoints. I repeat this process, joining the strokes until no strokes remain with endpoints more than 30 pixels apart. I set an upper bound on the path-joining distance of 30 pixels (in a 200x200 pixel image) was the maximum distance I observed made sense before I started to erroneously join strokes that were too far apart.\nThough path-joining improved the average stroke length as seen in Figure 1 (b), I noticed some drawings had large contiguous strokes that weren’t getting connected. I realized that while the strokes were close together and in some cases touching, their start and end points were far apart from each other.\n\n\n\nMy next preprocessing improvement, “path splicing”, would run after “path joining” and attempt to address this problem.\nAfter path joining leaves a smaller number of strokes, I want to find the shortest connections to combine multiple longer strokes. Starting with the longest path, I look for a smaller stroke that I could “splice” into the middle of the larger stroke. For each candidate stroke, I’d step through each point on the larger stroke and compare its distance from the start and end points of the shorter paths. When I found the smallest gap, I would “splice” the shorter line into the longer path at the point with the smallest distance.\nWhile not every drawing was turned into a single stroke, this was a big improvement, as seen in Figure 1 (c).\n\n\n\n\n\n\nFigure 2: Previous dataset look_i16__minn10, left, compared to path-spliced dataset v2-splicedata, right.\n\n\n\nBased my earlier experiment showing the benefits of short-stroke exclusion, I wanted to try training a model on this new dataset.\n\n\n\nI ran the preprocessing on the whole dataset. Next, I filtered the original 1300 drawings to exclude any drawings with more than 6 strokes, resulting in a new dataset of 1200 drawings that I named v2-splicedata. Then I trained a new set of models, keeping the layernorm and recurrent dropout enabled.\n\n\n\n\n\n\nFigure 3: Training and validation loss metrics of recurrent dropout model (light green) alongside models trained on this joined/spliced dataset (turquoise/dark green).\n\n\n\nAfter training some models on a path-spliced dataset the training metrics aren’t a perfect comparison, since the content of the validation also changed when I applied path-splicing. Still, I can see from the validation loss graph that the model started to overfit around 1000 steps. The roughly similar shapes of the early train and validation loss curves at least convinced me that the model hadn’t gotten dramatically worse.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4: Generated samples from a model trained on path-spliced dataset.\n\n\n\nQualitatively, the generated drawings showed a big improvement. The model had learned to generate longer unbroken strokes. I started to notice results that looked more like single-line drawings of people. In some cases they look surreal, but I started to see some more recognizable face elements and in some cases full bodies.\n\n\n\n\n\n\nFigure 5: Iterating through full dataset, with 3 frames per drawing: original, path-joined, and path-spliced. I liked the slight jitter in the animation as I watched the drawings go from colorful (many strokes) to fully blue (single stroke).\n\n\n\n\n\n\nMy last remaining problem: there were some pages where I’d make 4 or 5 separate drawings that had nothing to do with each other, and had a lot of space between them.\nI wanted to separate those into separate examples before making a training dataset, for 2 reasons:\n\nTo get more training examples from my limited number of scanned drawings.\nTo avoid confusing the model when some examples are complex drawings with multiple people, and other drawings are just one person. \n\nIf the model sometimes learngs to make a complete drawing and then start a second unrelated drawing, how does the model know when to finish a drawing vs. when to start a second drawing alongside the first one?My intuition for my third preprocessing improvement, “bounding box separation”, came when I noticed how unrelated strokes within a drawing didn’t overlap much, and tended to be spaced far apart. For each of the stroke within a drawing, I’d determine its top/bottom/left/right extremes and consider a box around each stroke as in Figure 6.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Example drawing with multiple unrelated strokes. \n\n\n\nThen for each combination of bounding boxes, I’d compute a ratio of the area of their overlap compared to the area of the non-overlapping parts as in Figure 7.\n\n\n\n\n\n\n\nFigure 7: Intersection over Union (“IOU”) Metric.\n\n\nIf the ratio exceeds some threshold, I consider them to be part of the same drawing, and I merge the bounding boxes as in Figure 9 (a). Combining that merged bounding box along with all the remaining bounding boxes, I repeat the process until no bounding-box intersections remain that exceed the threshold.\n\n\n\n\n\n\n\n\nIOU = 0.03\n\n\n\n\n\n\n\nIOU = 0.12\n\n\n\n\n\n\n\n\n\n\n(a) Merged BBoxes\n\n\n\n\n\n\n\nFigure 8: Comparison of high-IOU vs. low-IOU bounding box intersections.\n\n\n\nAlso, if any bounding boxes have a really small area, I just drop them. It turns out this helps exclude small scribbles of text that were ending up in my training data as separate strokes - for example, the page number at the bottom right of Figure 9 (a).\n\n\n\n\n\n\n\n\nOriginal: 4 Strokes\n\n\n\n\n\n\n\n\n\n\n(a) Tiny page number: 1 stroke\n\n\n\n\n\n\n\n\nMerge: 3 Strokes\n\n\n\n\n\n\n\nResult\n\n\n\n\n\n\nFigure 9: Example from training set of a very small stroke being removed.\n\n\n\n\n\n\nOnce I have all the separated strokes, I save then into a new dataset as separate drawings. While the previous dataset v2-splicedata only has 1200 drawings, the new bounding-box separated dataset 20231214 has 2400 drawings.\n\n\n\n\n\n\nFigure 10: Comparison of previous dataset v2-splicedata with 20231214 and 20231214-filtered.\n\n\n\nThe dataset grew from 1200 to 2400 drawings because pages containing multiple distinct drawings (such as Figure 11 (a)) were divided into separate rows in the new training set (like Figure 11 (b), Figure 11 (c), Figure 11 (d)).\n\n\n\n\n\n\n\n\n\n\n\n(a) Original\n\n\n\n\n\n\n\n\n\n\n\n(b)\n\n\n\n\n\n\n\n\n\n\n\n(c)\n\n\n\n\n\n\n\n\n\n\n\n(d)\n\n\n\n\n\n\n\nFigure 11: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214.\n\n\n\nThe new separated drawings looked more visually consistent with the average drawing out of the training set as a whole. The new dataset contains far more single-character drawings, so I expect that the RNN will benefit on learning from a set of drawings with more similar subject matter.\nI hypothesized that the bbox-separated dataset will be a big improvement because of the consistency of the resulting drawings. Before, the model was learning that sometimes drawings end after one person is drawn, but sometimes we move the pen and start a new person.\n\n\n\nLooking at the distribution of number of points per drawing in the new dataset 20231214 in the bottom-middle chart in Figure 10, I noticed a long tail of drawings with more than 500 points. To explore this, I created a variant dataset 20231214-filtered.\nDataset 20231214-filtered which was filtered down to 1800 drawings, keeping only drawings with more than 50 and less than 300 points as you can see in the bottom-right chart in Figure 10.\nWondering if drawings with many points were less likely to have consistent subject matter (individual people) than the rest of the training set, I sampled some drawings with over 300 points. While drawings such as Figure 12 (a) and Figure 12 (b) were obvious candidates to exlcude, there were valid drawings near the margin such as Figure 12 (c) that I would be excluding after I picked a threshold.Possible Improvement: Filtering by visual embedding might be more reliable to exclude complex drawings\n\n\n\n\n\n\n\n\n\n\n\n(a) 1093 points\n\n\n\n\n\n\n\n\n\n\n\n(b) 1127 points\n\n\n\n\n\n\n\n\n\n\n\n(c) 329 points\n\n\n\n\n\n\n\nFigure 12: Drawings with over 300 points.\n\n\n\nI also looked at the low end of the distribution and found drawings with under 50 points. There were nonsensical squiggles such as Figure 13 (a) that I was happy to exclude. There were cases below the 50 point threshold such as Figure 13 (b) and Figure 13 (c) that looked recognizable as my drawings, but had been simplified by RDP too aggressively. Possible Improvement: Applying RDP after bounding box separation rather than before.\n\n\n\n\n\n\n\n\n\n\n\n(a) 21 points\n\n\n\n\n\n\n\n\n\n\n\n(b) 30 points\n\n\n\n\n\n\n\n\n\n\n\n(c) 41 points\n\n\n\n\n\n\n\nFigure 13: Drawings with under 50 points.\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 14: Training and validation loss metrics comparing model trained on 20231214 (beige) compared with models trained on dataset 20231214-filtered with and without stroke augmentation (green, burgundy).\n\n\n\nAfter training on the unfiltered dataset 20231214, I noticed that some drawings were devolving into a sequence of repeated face features without forming a cohesive person or face, as in Figure 15.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 15: Generated results after training on unfiltered dataset 20231214.\n\n\n\n\nThe model results in Figure 16 after training on filtered dataset 20231214-filtered appear qualitatively better to me. The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 16: Generated results after training on filtered dataset 20231214-filtered.\n\n\n\n\nThe model results in Figure 17 after training with stroke-augmentation on filtered dataset 20231214-filtered appear to be roughly of similar quality to . The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 17: Generated results after training on filtered dataset 20231214-filtered, with stroke augmentation enabled.\n\n\n\n\n\n\nIf you want to keep reading, check out part 6 of my SLML series.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#path-joining", + "href": "blog/posts/slml_part5.html#path-joining", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "Despite some improvement within the strokes, my latest models were still producing many short strokes. So I figured that improving my dataset would give better results than fiddling with hyper-parameters or my model.\nLooking at my training data, I returned to an issue from the stroke3-conversion process: autotrace hadn’t realized that the different segments of my lines were actually connected. It was producing a series of small, centerline-traced segments, as seen in Figure 1 (a).\nI’d need some preprocessing to connect the smaller strokes into larger strokes, so that my SketchRNN model would learn to generate long contiguous single-line drawings.\n\n\n\n\n\n\n\n\n\n\n\n(a) Original: 22 strokes\n\n\n\n\n\n\n\n\n\n\n\n(b) After path-joining: 4 strokes\n\n\n\n\n\n\n\n\n\n\n\n(c) After path-splicing: 2 strokes\n\n\n\n\n\n\n\nFigure 1: Comparison of number of strokes before and after preprocessing, with one color per stroke.\n\n\n\nMy first preprocesing improvement was simple algorithm that I called “path joining”.\nEach of my drawings is represented as a collection of line segments, each with a start and end point. I try to find the shortest connection I can draw to connect two segments. Starting with the longest stroke, I compare it each of the other strokes and calculate the minimum distance between the start and end points of the line segments. After going through all the strokes in a drawing, I connect the two strokes with the shortest gap between their endpoints. I repeat this process, joining the strokes until no strokes remain with endpoints more than 30 pixels apart. I set an upper bound on the path-joining distance of 30 pixels (in a 200x200 pixel image) was the maximum distance I observed made sense before I started to erroneously join strokes that were too far apart.\nThough path-joining improved the average stroke length as seen in Figure 1 (b), I noticed some drawings had large contiguous strokes that weren’t getting connected. I realized that while the strokes were close together and in some cases touching, their start and end points were far apart from each other.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#path-splicing", + "href": "blog/posts/slml_part5.html#path-splicing", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "My next preprocessing improvement, “path splicing”, would run after “path joining” and attempt to address this problem.\nAfter path joining leaves a smaller number of strokes, I want to find the shortest connections to combine multiple longer strokes. Starting with the longest path, I look for a smaller stroke that I could “splice” into the middle of the larger stroke. For each candidate stroke, I’d step through each point on the larger stroke and compare its distance from the start and end points of the shorter paths. When I found the smallest gap, I would “splice” the shorter line into the longer path at the point with the smallest distance.\nWhile not every drawing was turned into a single stroke, this was a big improvement, as seen in Figure 1 (c).\n\n\n\n\n\n\nFigure 2: Previous dataset look_i16__minn10, left, compared to path-spliced dataset v2-splicedata, right.\n\n\n\nBased my earlier experiment showing the benefits of short-stroke exclusion, I wanted to try training a model on this new dataset.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#training-after-path-splicing", + "href": "blog/posts/slml_part5.html#training-after-path-splicing", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "I ran the preprocessing on the whole dataset. Next, I filtered the original 1300 drawings to exclude any drawings with more than 6 strokes, resulting in a new dataset of 1200 drawings that I named v2-splicedata. Then I trained a new set of models, keeping the layernorm and recurrent dropout enabled.\n\n\n\n\n\n\nFigure 3: Training and validation loss metrics of recurrent dropout model (light green) alongside models trained on this joined/spliced dataset (turquoise/dark green).\n\n\n\nAfter training some models on a path-spliced dataset the training metrics aren’t a perfect comparison, since the content of the validation also changed when I applied path-splicing. Still, I can see from the validation loss graph that the model started to overfit around 1000 steps. The roughly similar shapes of the early train and validation loss curves at least convinced me that the model hadn’t gotten dramatically worse.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 4: Generated samples from a model trained on path-spliced dataset.\n\n\n\nQualitatively, the generated drawings showed a big improvement. The model had learned to generate longer unbroken strokes. I started to notice results that looked more like single-line drawings of people. In some cases they look surreal, but I started to see some more recognizable face elements and in some cases full bodies.\n\n\n\n\n\n\nFigure 5: Iterating through full dataset, with 3 frames per drawing: original, path-joined, and path-spliced. I liked the slight jitter in the animation as I watched the drawings go from colorful (many strokes) to fully blue (single stroke).", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#bounding-boxes", + "href": "blog/posts/slml_part5.html#bounding-boxes", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "My last remaining problem: there were some pages where I’d make 4 or 5 separate drawings that had nothing to do with each other, and had a lot of space between them.\nI wanted to separate those into separate examples before making a training dataset, for 2 reasons:\n\nTo get more training examples from my limited number of scanned drawings.\nTo avoid confusing the model when some examples are complex drawings with multiple people, and other drawings are just one person. \n\nIf the model sometimes learngs to make a complete drawing and then start a second unrelated drawing, how does the model know when to finish a drawing vs. when to start a second drawing alongside the first one?My intuition for my third preprocessing improvement, “bounding box separation”, came when I noticed how unrelated strokes within a drawing didn’t overlap much, and tended to be spaced far apart. For each of the stroke within a drawing, I’d determine its top/bottom/left/right extremes and consider a box around each stroke as in Figure 6.\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Example drawing with multiple unrelated strokes. \n\n\n\nThen for each combination of bounding boxes, I’d compute a ratio of the area of their overlap compared to the area of the non-overlapping parts as in Figure 7.\n\n\n\n\n\n\n\nFigure 7: Intersection over Union (“IOU”) Metric.\n\n\nIf the ratio exceeds some threshold, I consider them to be part of the same drawing, and I merge the bounding boxes as in Figure 9 (a). Combining that merged bounding box along with all the remaining bounding boxes, I repeat the process until no bounding-box intersections remain that exceed the threshold.\n\n\n\n\n\n\n\n\nIOU = 0.03\n\n\n\n\n\n\n\nIOU = 0.12\n\n\n\n\n\n\n\n\n\n\n(a) Merged BBoxes\n\n\n\n\n\n\n\nFigure 8: Comparison of high-IOU vs. low-IOU bounding box intersections.\n\n\n\nAlso, if any bounding boxes have a really small area, I just drop them. It turns out this helps exclude small scribbles of text that were ending up in my training data as separate strokes - for example, the page number at the bottom right of Figure 9 (a).\n\n\n\n\n\n\n\n\nOriginal: 4 Strokes\n\n\n\n\n\n\n\n\n\n\n(a) Tiny page number: 1 stroke\n\n\n\n\n\n\n\n\nMerge: 3 Strokes\n\n\n\n\n\n\n\nResult\n\n\n\n\n\n\nFigure 9: Example from training set of a very small stroke being removed.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#dataset-20231214", + "href": "blog/posts/slml_part5.html#dataset-20231214", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "Once I have all the separated strokes, I save then into a new dataset as separate drawings. While the previous dataset v2-splicedata only has 1200 drawings, the new bounding-box separated dataset 20231214 has 2400 drawings.\n\n\n\n\n\n\nFigure 10: Comparison of previous dataset v2-splicedata with 20231214 and 20231214-filtered.\n\n\n\nThe dataset grew from 1200 to 2400 drawings because pages containing multiple distinct drawings (such as Figure 11 (a)) were divided into separate rows in the new training set (like Figure 11 (b), Figure 11 (c), Figure 11 (d)).\n\n\n\n\n\n\n\n\n\n\n\n(a) Original\n\n\n\n\n\n\n\n\n\n\n\n(b)\n\n\n\n\n\n\n\n\n\n\n\n(c)\n\n\n\n\n\n\n\n\n\n\n\n(d)\n\n\n\n\n\n\n\nFigure 11: Original drawing (left) was one row in dataset v2-splicedata. The rightmost three drawings are distinct rows in dataset 20231214.\n\n\n\nThe new separated drawings looked more visually consistent with the average drawing out of the training set as a whole. The new dataset contains far more single-character drawings, so I expect that the RNN will benefit on learning from a set of drawings with more similar subject matter.\nI hypothesized that the bbox-separated dataset will be a big improvement because of the consistency of the resulting drawings. Before, the model was learning that sometimes drawings end after one person is drawn, but sometimes we move the pen and start a new person.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#filtering-by-number-of-points", + "href": "blog/posts/slml_part5.html#filtering-by-number-of-points", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "Looking at the distribution of number of points per drawing in the new dataset 20231214 in the bottom-middle chart in Figure 10, I noticed a long tail of drawings with more than 500 points. To explore this, I created a variant dataset 20231214-filtered.\nDataset 20231214-filtered which was filtered down to 1800 drawings, keeping only drawings with more than 50 and less than 300 points as you can see in the bottom-right chart in Figure 10.\nWondering if drawings with many points were less likely to have consistent subject matter (individual people) than the rest of the training set, I sampled some drawings with over 300 points. While drawings such as Figure 12 (a) and Figure 12 (b) were obvious candidates to exlcude, there were valid drawings near the margin such as Figure 12 (c) that I would be excluding after I picked a threshold.Possible Improvement: Filtering by visual embedding might be more reliable to exclude complex drawings\n\n\n\n\n\n\n\n\n\n\n\n(a) 1093 points\n\n\n\n\n\n\n\n\n\n\n\n(b) 1127 points\n\n\n\n\n\n\n\n\n\n\n\n(c) 329 points\n\n\n\n\n\n\n\nFigure 12: Drawings with over 300 points.\n\n\n\nI also looked at the low end of the distribution and found drawings with under 50 points. There were nonsensical squiggles such as Figure 13 (a) that I was happy to exclude. There were cases below the 50 point threshold such as Figure 13 (b) and Figure 13 (c) that looked recognizable as my drawings, but had been simplified by RDP too aggressively. Possible Improvement: Applying RDP after bounding box separation rather than before.\n\n\n\n\n\n\n\n\n\n\n\n(a) 21 points\n\n\n\n\n\n\n\n\n\n\n\n(b) 30 points\n\n\n\n\n\n\n\n\n\n\n\n(c) 41 points\n\n\n\n\n\n\n\nFigure 13: Drawings with under 50 points.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/slml_part5.html#training-after-bounding-boxes", + "href": "blog/posts/slml_part5.html#training-after-bounding-boxes", + "title": "SLML Part 5 - SketchRNN Experiments: Path Preprocessing", + "section": "", + "text": "Figure 14: Training and validation loss metrics comparing model trained on 20231214 (beige) compared with models trained on dataset 20231214-filtered with and without stroke augmentation (green, burgundy).\n\n\n\nAfter training on the unfiltered dataset 20231214, I noticed that some drawings were devolving into a sequence of repeated face features without forming a cohesive person or face, as in Figure 15.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 15: Generated results after training on unfiltered dataset 20231214.\n\n\n\n\nThe model results in Figure 16 after training on filtered dataset 20231214-filtered appear qualitatively better to me. The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 16: Generated results after training on filtered dataset 20231214-filtered.\n\n\n\n\nThe model results in Figure 17 after training with stroke-augmentation on filtered dataset 20231214-filtered appear to be roughly of similar quality to . The best results I could find had long coherent strokes capturing part of a face and sometimes a corresponding body.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 17: Generated results after training on filtered dataset 20231214-filtered, with stroke augmentation enabled.\n\n\n\n\n\n\nIf you want to keep reading, check out part 6 of my SLML series.", + "crumbs": [ + "blog", + "posts", + "SLML Part 5 - SketchRNN Experiments: Path Preprocessing" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html", + "href": "blog/posts/2018_ai_art_tour.html", + "title": "A Tour of AI Art in 2018", + "section": "", + "text": "By the end of this tour, my goal is to help you understand how some existing artists are incorporating AI tools and concepts into their work, and how using AI art tools might augment your creativity.\nDisclaimer: I’m not a real expert on computer vision, neuroscience or art. But I have spent a couple years learning about the space, so I’ll do my best to explain things as I understand them so far.\nFor those more familiar with AI, I plan on making some broad generalizations in the interest of brevity. If you’re interested in the technical details, I’ve done my best to include relevant links on where to go deeper.", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#what-is-ai-art", + "href": "blog/posts/2018_ai_art_tour.html#what-is-ai-art", + "title": "A Tour of AI Art in 2018", + "section": "What is AI art?", + "text": "What is AI art?\nMany artistic tools come from people striving to understand how algorithms affect our lives. I’ve distilled a framework to understand different types of AI Art, and am sharing it in the hopes that it makes the concepts easier to discuss and compare.\nHere are 3 ways I can think of to define “AI Art”:\n\nMaking creative use of outputs from an AI Tool\nLeveraging how AI represents information to deliberately craft an Effect\nExploring the Concepts within AI and what they mean for us", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#some-definitions", + "href": "blog/posts/2018_ai_art_tour.html#some-definitions", + "title": "A Tour of AI Art in 2018", + "section": "Some Definitions", + "text": "Some Definitions\nGiven the amount of cultural baggage we all associate with words like “AI” and “art”, it’s worth clarifying some definitions up front. “AI Art” has stuck as a phrase to describe an emerging category of creative work, but it might be more accurate to call it “Machine Learning Art.”\nThe art I’m discussing here is mostly made by humans using statistical methods, not by sentient robots with their own creative ideas. Sorry to disappoint, but this post isn’t dedicated to anthropomorphic machines that paint:\n\n\n\n\nnot this\n\n\nSPIRAL by Deepmind (blog, pdf, youtube)\n\n\nAI\nAI is a term used in so many contexts, that it’s a bit of a loaded term. Most mentions of AI can be bucketed into one of two broad categories:\n\nGeneral AI: The futuristic aspiration of computers that experience consciousness\nSpecialized AI: Systems using statistics to learn patterns from data in order to make predictions.\n\nI’ll be focused on artistic applications of techniques that have sprung out of real-world research from the category of Specialized AI, also known as Machine Learning.", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#machine-learning", + "href": "blog/posts/2018_ai_art_tour.html#machine-learning", + "title": "A Tour of AI Art in 2018", + "section": "Machine Learning", + "text": "Machine Learning\nMachine Learning (usually) refers to a type of algorithm that (1) is built to perform a clearly-defined task, and (2) learns patterns and relationships from training data\n\nFor example, imagine that we want to predict housing prices. We’d probably start with a spreadsheet of prices we know to be true, alongside features for each house that we expect to be related to the price. If we plot price vs. size on this toy dataset, we can start to see a slope.\n\n \n\nIf we were to draw a line through these points, the “slope” would be what you multiply the size by in order to get the price (plus a “bias” term, if the slope doesn’t meet the Y-axis right at zero). The objective of an ML algorithm is to find a line to draw through these points that minimizes the error in its predictions. You can visualize the error as the distance from each real data point’s Y coordinate to the prediction: the line’s value at the corresponding X coordinate.\n\n \n\n“Art is everything that you don’t have to do.” - brian eno\nWhole books could be written in an attempt to define art, so I won’t attempt an exhaustive definition.\n\n\n“Fountain,” Marcel Duchamp, 1917 (wikipedia)\n\nThe truth is that art can be defined by whoever is creating it, and art history is full of boundary-pushing acts leading onlookers to ask “but is it art?”", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#back-to-ai-art", + "href": "blog/posts/2018_ai_art_tour.html#back-to-ai-art", + "title": "A Tour of AI Art in 2018", + "section": "Back to AI Art", + "text": "Back to AI Art\nNow I’ll step through three possible ways to define AI art and provide some examples along the way.\n\nMaking creative use of outputs from an AI Tool\nLeveraging how AI represents information to deliberately craft an Effect\nExploring the Concepts within AI and what they mean for us", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#definition-1-making-creative-use-of-outputs-from-an-ai-tool", + "href": "blog/posts/2018_ai_art_tour.html#definition-1-making-creative-use-of-outputs-from-an-ai-tool", + "title": "A Tour of AI Art in 2018", + "section": "Definition #1: Making creative use of outputs from an AI Tool", + "text": "Definition #1: Making creative use of outputs from an AI Tool\nThe simplest definition of AI art is any artifact generated from a tool the makes use of AI, whether or not the artist makes this a central part of the work.\nApps such as Prisma, for example, make it easy for anyone to take a photo on their phone and render it in a painterly style.\nEven without much context on how they work, easy-to-use tools such as pix2pix can be fun to play with and yield weird results.\n\n?>\n“Image-to-Image Translation with Conditional Adversarial Networks”, Isola et al, 2017 (pdf)\n\nWe’ll look at some more in-depth approaches later (time permitting), but for now this is just fun.\n\n\n\n\npix2pix_pyramid_cat", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#definition-2-leveraging-how-ai-represents-information-to-deliberately-craft-an-effect", + "href": "blog/posts/2018_ai_art_tour.html#definition-2-leveraging-how-ai-represents-information-to-deliberately-craft-an-effect", + "title": "A Tour of AI Art in 2018", + "section": "Definition #2: Leveraging how AI represents information to deliberately craft an effect", + "text": "Definition #2: Leveraging how AI represents information to deliberately craft an effect\nA slightly more complex definition of AI art is one in which an artist uses his or her understanding of the machine learning internals to achieve a specific effect. For example, it’s possible to make some interesting visual artifacts by inspecting how machine learning algorithms represent information.\n\n\nsource\n\nIn fact, the spooky faces came from a visualization of Eigenfaces, which sought to “learn” how to represent faces in a “latent space”. For me, this early facila recognition output brings to mind one of my favorite quotes by Brian Eno (who I’m quoting more than once in this piece since he’s had so much to say about the overlap of technology and art).\n\n“Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit, all of these will be cherished and emulated as soon as they can be avoided. It’s the sound of failure. So much modern art is the sound of things going out of control. Out of a medium, pushing to its limits and breaking apart.” - Brian Eno\n\n\nRecent generative algorithms have rapidly improved their ability both to learn latent spaces, and to generate images from any point in these latent spaces.\n\n\n\n\nfacespace\n\n\nFace Recognition using Eigenfaces, Turk et al, 1991 pdf\n\nMethods of encoding real images into vectors representing a point in latent space have since improved, as have the methods of decoding latent vectors back into realistic images.\nWhen we interpolate between two points in the latent space, we can smoothly generate images at each point along the way. In the uncanny example below, I find it striking that (almost) every point in between looks like a person. Looking at this work, I can’t help thinking that these algorithms are showing us something about our innate similarity to all other humans.\n\n\n\n\nceleb_1hr_man\n\n\nProgressive Growing of GANs for Improved Quality, Stability, and Variation, Karras et al, 2018 (pdf, youtube)\n\nOne striking example of a completely new type of tool leveraging this understanding is called Toposketch. It allows visual navigation of latent spaces in order to gain fine-grained control over the generated artifacts. This makes me wonder what kinds of creative tools could be built to leverage the information that machines are able to derive from the increaing volume of data that’s available to us today.\n\n\n\ntoposketch_man_loop", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#definition-3-exploring-the-concepts-within-ai-and-what-they-means-for-us", + "href": "blog/posts/2018_ai_art_tour.html#definition-3-exploring-the-concepts-within-ai-and-what-they-means-for-us", + "title": "A Tour of AI Art in 2018", + "section": "Definition #3: Exploring the Concepts within AI and what they means for us", + "text": "Definition #3: Exploring the Concepts within AI and what they means for us\nAmong other things, this could encompass exploring AI’s relationships to:\n\nour society\nthe individual\nthe nature of consciousness\n\n\nExample: Treachery of Imagenet\nTom White made some beautifully designed prints, each of which fools a computer vision network into believing it’s actually a picture of a specific object (see the predicted categories to the right of the image below).\n\n\n\n\nimg\n\n\nsource\n\nThere’s a new field of security concerned with adversarial machine learning, concerned with how attackers can fool neural networks into misinterpreting what they see. It’s not intuitive, but the consequences can be serious - imagine a self-driving car fooled into missing a stop sign. I admire how Tom White took a complex concept and used it to make a visually appealing piece that sparks curiosity and raises awareness about concepts that may affect our lives but don’t have easy conversational entry points for outsiders.\n\n\nExample: Simulating How Humans Draw\nI’m fascinated when researchers attempt to mimic how humans draw. In particular, I’m interested in how the researchers set up an algorithm to mimic the process of drawing - they have to choose what happens next, how much the first part of the drawing affects what happens next, and how to define success.\n\n\n\n\nsimulating how humans draw\n\n\nsource\n\nIn my analog artworks, I’ve noticed that sometimes I start a drawing not knowing how it will end up. Small accidents or random movements early in the drawing lead me to see something unexpected, adjust my course and capture what I saw. Oddly enough, thinking about the rigid framing of machine learning problems (input: view of the canvas, output: X,Y,Z coordinates of brush movement) has led to some fun experiments. For example, what happens if i close my eyes and draw?", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/2018_ai_art_tour.html#in-conclusion", + "href": "blog/posts/2018_ai_art_tour.html#in-conclusion", + "title": "A Tour of AI Art in 2018", + "section": "In Conclusion", + "text": "In Conclusion\nGiven that there are several ways to define AI art (and I’m sure my list is not comprehensive), my hope is that we can broaden our view of what AI art means. While it does include fun images made using new mobile apps, I’d also like to think that there’s some more profound works out there. Works that can surprise people, help them understand the role of new technologies in their daily lives, and stoke their curiosity to learn more.\n\n“Stop thinking about art works as objects, and start thinking about them as triggers for experiences. … what makes a work of art ‘good’ for you is not something that is already ‘inside’ it, but something that happens inside you — so the value of the work lies in the degree to which it can help you have the kind of experience that you call art.”\nBrian Eno, via brainpickings", + "crumbs": [ + "blog", + "posts", + "A Tour of AI Art in 2018" + ] + }, + { + "objectID": "blog/posts/slml_part2.html", + "href": "blog/posts/slml_part2.html", + "title": "SLML Part 2 - Assembling the Dataset", + "section": "", + "text": "This post is part 2 of “SLML” - Single-Line Machine Learning.\nCheck out the previous post, part 1, for some background on why I got started training SketchRNN on my own drawings.\nNext in the series is part 3, where I convert my JPEGs into stroke-3 format in preparation for model training.\n\nFor years, I’ve maintained a daily practice of making single-line drawings in my sketchbooks.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhen I finish a sketchbook, I scan its pages, number them, and put them into a similar folder structure. I marked which pages were diagrams/mindmaps/etc and put them in a subfolder called notes - the drawings (and watercolors) are in a folder called art. Unfortunately, I didn’t tag watercolors in advance.\nSome detail on the subfolders:\n\nart subfolders contain drawings / watercolors.\nnotes subfolders contain diagrams, mindmaps, and anything else technical I’m working on. I used to have a separate notebook for these, but I got tired of carrying around an extra sketchbook everywhere I go, so at some point they just ended up merging with my art practice.\nxtra contains any bad scans or funny bloopers from my scanning process. I kind of like the scans with my hands in them and thought they might be useful for a future project, so I kept them in their own folder.\ncover contains a scan of the notebook cover. Each cover at least has a post-it with the sketchbook ID number and the start/end dates of when I used that sketchbook. Often I’ll make a little collage on the front of the notebook with airplane tickets / stickers / scraps of paper relevant to somewhere I traveled when I used that sketchbook.\n\n\n\n\n\n\n\nMy first problem is that the “art” sections of my sketchbooks don’t just contain single-line drawings. They also contain watercolor paintings, since I don’t exclusively fill my sketchbooks with single-line drawings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLooking for a scalable way to filter the drawings out of my art scans, I decided to try computing visual embeddings.\n\nI decided to fine-tune a vision model on my dataset to distinguish “art” pages from “notes” pages. My hope was that the model would learn to distinguish some features within the visual domain of my scanned sketchbook pages. Even though I don’t intend to use the predictions of an algorithm trained to classify art-vs-notes, this will produce more meaningful embeddings.\n\nUltimately, what I want to see is that when I take the embeddings of my sketchbook pages and cluster them, the notes and drawings should neatly separate into separate clusters. Also, I want the watercolors to cleanly separate into their own clusters, away from the single-line drawings.\n\n\n\nAfter I fine-tuned my vision model, I computed the embeddings for all the sketchbook pages I had on hand. As a sanity check, I want to verify that the embedding space has cleanly separated regions for the labels is was trained on.\nIt’s hard to visualize 512 dimensions, so the T-SNE dimensionality reduction is a good visualization technique to view the data. T-SNE projects the higher number of dimensions into 2D or 3D, with the goal that any 2 points close together in high-dimensional space are close together in lower-dimensional space.\nI colorized the points in the resulting graph based on their label, so it’s easy to see at a glance that embedding space has learned something useful about this dataset, as I hoped.\n\n\n\nT-SNE representation of sketchbook pages. art pages are blue, notes pages are orange, and cover pages are brown.\n\n\n\n\n\nNow I’m hoping to find the watercolors in the dataset. Given that the embedding space captures visual similarity, I want to identify groups of pages that are similar to each other. This lets me label data in bulk, rather than going through the images one-by-one. K-Means Clustering was a quick solution for this. I can decide to ask for K distinct “clusters”, and the algorithm picks K “centroids”.\n\n\n\nGiven a request for K clusters, pick K random points as the starting centroids. Then, repeat these steps for N iterations: For each point, select the nearest centroid point and compute the distance between these two. Adjusts the centroids with the goal of minimizing the total distance from all points to their nearest centroids.\n\n\n\n\n\n\nAfter some experimentation, I found that using K=16 produced groupings that would be useful to me. Each row in the image below consists of the top images from one cluster.\n\n\n\nSince each cluster’s centroid is a vector in the same space as the image embeddings, I can “classify” any image embedding by doing a nearest-neighbor search of comparing the embedding of the query image to each of the cluster centroid vectors, and choosing the cluster centroid with the smallest distance. This is also called a K-Nearest Neighbor classifier.\nWhen I applied this process to each of the images, I glanced at a t-SNE colorized by these clusters:\n\n\n\nT-SNE representation of sketchbook pages, colored by which of the 16 clusters they were classified by K-Means using the pages’ visual embeddings.\n\n\n\n\n\n\nI went through and grouped these clusters based on what I wanted to identify automatically. In particular, I wanted to take only the drawings and separate them from the watercolors. After inspecting the clusters, I also realized that there were a number of bad scans / incorrectly cropped images that I wanted to exclude as well.\n\n\n\n\n\n\n\nI went through the dataset again, and based on the cluster assigned to each image, I assigned it a higher-level group based on the hand-mapping of clusters above.\nWhen I looked at the t-SNE colorized by this higher-level cluster mapping, it was encouraging to see that my embedding space had neatly separated the groups I cared about:\n\n\n\nT-SNE representation of pages, colored by cluster group. drawings are purple, notes are dark green, watercolors are yellow, bad scans are blue, covers are light green.\n\n\nSince my images were tagged with these higher-level groups, I was able to select only the drawings, and begin converting them into usable training data.\n\nNext in my SLML series is part 3, where I convert my JPEGs into stroke-3 format in preparation for model training.", + "crumbs": [ + "blog", + "posts", + "SLML Part 2 - Assembling the Dataset" + ] + }, + { + "objectID": "blog/posts/slml_part2.html#the-problem-filtering", + "href": "blog/posts/slml_part2.html#the-problem-filtering", + "title": "SLML Part 2 - Assembling the Dataset", + "section": "", + "text": "My first problem is that the “art” sections of my sketchbooks don’t just contain single-line drawings. They also contain watercolor paintings, since I don’t exclusively fill my sketchbooks with single-line drawings.", + "crumbs": [ + "blog", + "posts", + "SLML Part 2 - Assembling the Dataset" + ] + }, + { + "objectID": "blog/posts/slml_part2.html#measuring-visual-similarity-with-embeddings", + "href": "blog/posts/slml_part2.html#measuring-visual-similarity-with-embeddings", + "title": "SLML Part 2 - Assembling the Dataset", + "section": "", + "text": "Looking for a scalable way to filter the drawings out of my art scans, I decided to try computing visual embeddings.\n\nI decided to fine-tune a vision model on my dataset to distinguish “art” pages from “notes” pages. My hope was that the model would learn to distinguish some features within the visual domain of my scanned sketchbook pages. Even though I don’t intend to use the predictions of an algorithm trained to classify art-vs-notes, this will produce more meaningful embeddings.\n\nUltimately, what I want to see is that when I take the embeddings of my sketchbook pages and cluster them, the notes and drawings should neatly separate into separate clusters. Also, I want the watercolors to cleanly separate into their own clusters, away from the single-line drawings.", + "crumbs": [ + "blog", + "posts", + "SLML Part 2 - Assembling the Dataset" + ] + }, + { + "objectID": "blog/posts/slml_part2.html#inspecting-the-embeddings", + "href": "blog/posts/slml_part2.html#inspecting-the-embeddings", + "title": "SLML Part 2 - Assembling the Dataset", + "section": "", + "text": "After I fine-tuned my vision model, I computed the embeddings for all the sketchbook pages I had on hand. As a sanity check, I want to verify that the embedding space has cleanly separated regions for the labels is was trained on.\nIt’s hard to visualize 512 dimensions, so the T-SNE dimensionality reduction is a good visualization technique to view the data. 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This lets me label data in bulk, rather than going through the images one-by-one. K-Means Clustering was a quick solution for this. I can decide to ask for K distinct “clusters”, and the algorithm picks K “centroids”.\n\n\n\nGiven a request for K clusters, pick K random points as the starting centroids. Then, repeat these steps for N iterations: For each point, select the nearest centroid point and compute the distance between these two. Adjusts the centroids with the goal of minimizing the total distance from all points to their nearest centroids.", + "crumbs": [ + "blog", + "posts", + "SLML Part 2 - Assembling the Dataset" + ] + }, + { + "objectID": "blog/posts/slml_part2.html#using-clusters-to-classify", + "href": "blog/posts/slml_part2.html#using-clusters-to-classify", + "title": "SLML Part 2 - Assembling the Dataset", + "section": "", + "text": "After some experimentation, I found that using K=16 produced groupings that would be useful to me. Each row in the image below consists of the top images from one cluster.\n\n\n\nSince each cluster’s centroid is a vector in the same space as the image embeddings, I can “classify” any image embedding by doing a nearest-neighbor search of comparing the embedding of the query image to each of the cluster centroid vectors, and choosing the cluster centroid with the smallest distance. This is also called a K-Nearest Neighbor classifier.\nWhen I applied this process to each of the images, I glanced at a t-SNE colorized by these clusters:\n\n\n\nT-SNE representation of sketchbook pages, colored by which of the 16 clusters they were classified by K-Means using the pages’ visual embeddings.", + "crumbs": [ + "blog", + "posts", + "SLML Part 2 - Assembling the Dataset" + ] + }, + { + "objectID": "blog/posts/slml_part2.html#making-sense-of-the-clusters", + "href": "blog/posts/slml_part2.html#making-sense-of-the-clusters", + "title": "SLML Part 2 - Assembling the Dataset", + "section": "", + "text": "I went through and grouped these clusters based on what I wanted to identify automatically. In particular, I wanted to take only the drawings and separate them from the watercolors. 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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #595959;--bs-navbar-hover-color: rgba(46, 87, 158, 0.8);--bs-navbar-disabled-color: rgba(89, 89, 89, 0.75);--bs-navbar-active-color: #2e579e;--bs-navbar-brand-color: #595959;--bs-navbar-brand-hover-color: #2e579e;--bs-navbar-toggler-border-color: rgba(89, 89, 89, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%231c345e'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #a2c1f6;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(69, 130, 236, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #1c345e;--bs-accordion-active-bg: #dae6fb}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1.1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%238fb4f4'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%238fb4f4'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1.1rem;--bs-pagination-color: #4582ec;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #3768bd;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #3768bd;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(69, 130, 236, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #4582ec;--bs-pagination-active-border-color: #4582ec;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:calc(1.2625rem + 0.15vw);--bs-pagination-border-radius: 0.5rem}@media(min-width: 1200px){.pagination-lg{--bs-pagination-font-size:1.375rem}}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.9625rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 1.2em;--bs-badge-padding-y: 0.6em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 400;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.825rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #4582ec;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #4582ec;--bs-list-group-active-border-color: #4582ec;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". 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var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: 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var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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s.drag(e)}),!1),this.img.addEventListener("click",(function(e){return s.slide.classList.contains("dragging-nav")?(s.zoomOut(),!1):s.zoomedIn?void(s.zoomedIn&&!s.dragging&&s.zoomOut()):s.zoomIn()}),!1),this.img.setZoomEvents=!0}return n(e,[{key:"zoomIn",value:function(){var e=this.widowWidth();if(!(this.zoomedIn||e<=768)){var t=this.img;if(t.setAttribute("data-style",t.getAttribute("style")),t.style.maxWidth=t.naturalWidth+"px",t.style.maxHeight=t.naturalHeight+"px",t.naturalWidth>e){var i=e/2-t.naturalWidth/2;this.setTranslate(this.img.parentNode,i,0)}this.slide.classList.add("zoomed"),this.zoomedIn=!0}}},{key:"zoomOut",value:function(){this.img.parentNode.setAttribute("style",""),this.img.setAttribute("style",this.img.getAttribute("data-style")),this.slide.classList.remove("zoomed"),this.zoomedIn=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.onclose&&"function"==typeof this.onclose&&this.onclose()}},{key:"dragStart",value:function(e){e.preventDefault(),this.zoomedIn?("touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset),e.target===this.img&&(this.active=!0,this.img.classList.add("dragging"))):this.active=!1}},{key:"dragEnd",value:function(e){var t=this;e.preventDefault(),this.initialX=this.currentX,this.initialY=this.currentY,this.active=!1,setTimeout((function(){t.dragging=!1,t.img.isDragging=!1,t.img.classList.remove("dragging")}),100)}},{key:"drag",value:function(e){this.active&&(e.preventDefault(),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.img.isDragging=!0,this.dragging=!0,this.setTranslate(this.img,this.currentX,this.currentY))}},{key:"onMove",value:function(e){if(this.zoomedIn){var t=e.clientX-this.img.naturalWidth/2,i=e.clientY-this.img.naturalHeight/2;this.setTranslate(this.img,t,i)}}},{key:"setTranslate",value:function(e,t,i){e.style.transform="translate3d("+t+"px, "+i+"px, 0)"}},{key:"widowWidth",value:function(){return window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth}}]),e}(),V=function(){function e(){var i=this,n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e);var s=n.dragEl,l=n.toleranceX,o=void 0===l?40:l,r=n.toleranceY,a=void 0===r?65:r,h=n.slide,d=void 0===h?null:h,c=n.instance,u=void 0===c?null:c;this.el=s,this.active=!1,this.dragging=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.direction=null,this.lastDirection=null,this.toleranceX=o,this.toleranceY=a,this.toleranceReached=!1,this.dragContainer=this.el,this.slide=d,this.instance=u,this.el.addEventListener("mousedown",(function(e){return i.dragStart(e)}),!1),this.el.addEventListener("mouseup",(function(e){return i.dragEnd(e)}),!1),this.el.addEventListener("mousemove",(function(e){return i.drag(e)}),!1)}return n(e,[{key:"dragStart",value:function(e){if(this.slide.classList.contains("zoomed"))this.active=!1;else{"touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset);var t=e.target.nodeName.toLowerCase();e.target.classList.contains("nodrag")||u(e.target,".nodrag")||-1!==["input","select","textarea","button","a"].indexOf(t)?this.active=!1:(e.preventDefault(),(e.target===this.el||"img"!==t&&u(e.target,".gslide-inline"))&&(this.active=!0,this.el.classList.add("dragging"),this.dragContainer=u(e.target,".ginner-container")))}}},{key:"dragEnd",value:function(e){var t=this;e&&e.preventDefault(),this.initialX=0,this.initialY=0,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.active=!1,this.doSlideChange&&(this.instance.preventOutsideClick=!0,"right"==this.doSlideChange&&this.instance.prevSlide(),"left"==this.doSlideChange&&this.instance.nextSlide()),this.doSlideClose&&this.instance.close(),this.toleranceReached||this.setTranslate(this.dragContainer,0,0,!0),setTimeout((function(){t.instance.preventOutsideClick=!1,t.toleranceReached=!1,t.lastDirection=null,t.dragging=!1,t.el.isDragging=!1,t.el.classList.remove("dragging"),t.slide.classList.remove("dragging-nav"),t.dragContainer.style.transform="",t.dragContainer.style.transition=""}),100)}},{key:"drag",value:function(e){if(this.active){e.preventDefault(),this.slide.classList.add("dragging-nav"),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.el.isDragging=!0,this.dragging=!0,this.doSlideChange=!1,this.doSlideClose=!1;var t=Math.abs(this.currentX),i=Math.abs(this.currentY);if(t>0&&t>=Math.abs(this.currentY)&&(!this.lastDirection||"x"==this.lastDirection)){this.yOffset=0,this.lastDirection="x",this.setTranslate(this.dragContainer,this.currentX,0);var n=this.shouldChange();if(!this.instance.settings.dragAutoSnap&&n&&(this.doSlideChange=n),this.instance.settings.dragAutoSnap&&n)return this.instance.preventOutsideClick=!0,this.toleranceReached=!0,this.active=!1,this.instance.preventOutsideClick=!0,this.dragEnd(null),"right"==n&&this.instance.prevSlide(),void("left"==n&&this.instance.nextSlide())}if(this.toleranceY>0&&i>0&&i>=t&&(!this.lastDirection||"y"==this.lastDirection)){this.xOffset=0,this.lastDirection="y",this.setTranslate(this.dragContainer,0,this.currentY);var s=this.shouldClose();return!this.instance.settings.dragAutoSnap&&s&&(this.doSlideClose=!0),void(this.instance.settings.dragAutoSnap&&s&&this.instance.close())}}}},{key:"shouldChange",value:function(){var e=!1;if(Math.abs(this.currentX)>=this.toleranceX){var t=this.currentX>0?"right":"left";("left"==t&&this.slide!==this.slide.parentNode.lastChild||"right"==t&&this.slide!==this.slide.parentNode.firstChild)&&(e=t)}return e}},{key:"shouldClose",value:function(){var e=!1;return Math.abs(this.currentY)>=this.toleranceY&&(e=!0),e}},{key:"setTranslate",value:function(e,t,i){var n=arguments.length>3&&void 0!==arguments[3]&&arguments[3];e.style.transition=n?"all .2s ease":"",e.style.transform="translate3d(".concat(t,"px, ").concat(i,"px, 0)")}}]),e}();function j(e,t,i,n){var s=e.querySelector(".gslide-media"),l=new Image,o="gSlideTitle_"+i,r="gSlideDesc_"+i;l.addEventListener("load",(function(){T(n)&&n()}),!1),l.src=t.href,""!=t.sizes&&""!=t.srcset&&(l.sizes=t.sizes,l.srcset=t.srcset),l.alt="",I(t.alt)||""===t.alt||(l.alt=t.alt),""!==t.title&&l.setAttribute("aria-labelledby",o),""!==t.description&&l.setAttribute("aria-describedby",r),t.hasOwnProperty("_hasCustomWidth")&&t._hasCustomWidth&&(l.style.width=t.width),t.hasOwnProperty("_hasCustomHeight")&&t._hasCustomHeight&&(l.style.height=t.height),s.insertBefore(l,s.firstChild)}function F(e,t,i,n){var s=this,l=e.querySelector(".ginner-container"),o="gvideo"+i,r=e.querySelector(".gslide-media"),a=this.getAllPlayers();h(l,"gvideo-container"),r.insertBefore(m('
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'.concat(d,"
"))),k(d))){"none"==d.style.display&&(d.style.display="block");var c=document.createElement("div");c.className="ginlined-content",c.appendChild(d),s=c}if(r){var u=document.getElementById(r);if(!u)return!1;var g=u.cloneNode(!0);g.style.height=t.height,g.style.maxWidth=t.width,h(g,"ginlined-content"),s=g}if(!s)return console.error("Unable to append inline slide content",t),!1;o.style.height=t.height,o.style.width=t.width,o.appendChild(s),this.events["inlineclose"+r]=a("click",{onElement:o.querySelectorAll(".gtrigger-close"),withCallback:function(e){e.preventDefault(),l.close()}}),T(n)&&n()}function Z(e,t,i,n){var s=e.querySelector(".gslide-media"),l=function(e){var t=e.url,i=e.allow,n=e.callback,s=e.appendTo,l=document.createElement("iframe");return l.className="vimeo-video gvideo",l.src=t,l.style.width="100%",l.style.height="100%",i&&l.setAttribute("allow",i),l.onload=function(){l.onload=null,h(l,"node-ready"),T(n)&&n()},s&&s.appendChild(l),l}({url:t.href,callback:n});s.parentNode.style.maxWidth=t.width,s.parentNode.style.height=t.height,s.appendChild(l)}var U=function(){function e(){var i=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e),this.defaults={href:"",sizes:"",srcset:"",title:"",type:"",videoProvider:"",description:"",alt:"",descPosition:"bottom",effect:"",width:"",height:"",content:!1,zoomable:!0,draggable:!0},L(i)&&(this.defaults=l(this.defaults,i))}return n(e,[{key:"sourceType",value:function(e){var t=e;if(null!==(e=e.toLowerCase()).match(/\.(jpeg|jpg|jpe|gif|png|apn|webp|avif|svg)/))return"image";if(e.match(/(youtube\.com|youtube-nocookie\.com)\/watch\?v=([a-zA-Z0-9\-_]+)/)||e.match(/youtu\.be\/([a-zA-Z0-9\-_]+)/)||e.match(/(youtube\.com|youtube-nocookie\.com)\/embed\/([a-zA-Z0-9\-_]+)/))return"video";if(e.match(/vimeo\.com\/([0-9]*)/))return"video";if(null!==e.match(/\.(mp4|ogg|webm|mov)/))return"video";if(null!==e.match(/\.(mp3|wav|wma|aac|ogg)/))return"audio";if(e.indexOf("#")>-1&&""!==t.split("#").pop().trim())return"inline";return e.indexOf("goajax=true")>-1?"ajax":"external"}},{key:"parseConfig",value:function(e,t){var i=this,n=l({descPosition:t.descPosition},this.defaults);if(L(e)&&!k(e)){O(e,"type")||(O(e,"content")&&e.content?e.type="inline":O(e,"href")&&(e.type=this.sourceType(e.href)));var s=l(n,e);return this.setSize(s,t),s}var r="",a=e.getAttribute("data-glightbox"),h=e.nodeName.toLowerCase();if("a"===h&&(r=e.href),"img"===h&&(r=e.src,n.alt=e.alt),n.href=r,o(n,(function(s,l){O(t,l)&&"width"!==l&&(n[l]=t[l]);var o=e.dataset[l];I(o)||(n[l]=i.sanitizeValue(o))})),n.content&&(n.type="inline"),!n.type&&r&&(n.type=this.sourceType(r)),I(a)){if(!n.title&&"a"==h){var d=e.title;I(d)||""===d||(n.title=d)}if(!n.title&&"img"==h){var c=e.alt;I(c)||""===c||(n.title=c)}}else{var u=[];o(n,(function(e,t){u.push(";\\s?"+t)})),u=u.join("\\s?:|"),""!==a.trim()&&o(n,(function(e,t){var s=a,l=new RegExp("s?"+t+"s?:s?(.*?)("+u+"s?:|$)"),o=s.match(l);if(o&&o.length&&o[1]){var r=o[1].trim().replace(/;\s*$/,"");n[t]=i.sanitizeValue(r)}}))}if(n.description&&"."===n.description.substring(0,1)){var g;try{g=document.querySelector(n.description).innerHTML}catch(e){if(!(e instanceof DOMException))throw e}g&&(n.description=g)}if(!n.description){var v=e.querySelector(".glightbox-desc");v&&(n.description=v.innerHTML)}return this.setSize(n,t,e),this.slideConfig=n,n}},{key:"setSize",value:function(e,t){var i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,n="video"==e.type?this.checkSize(t.videosWidth):this.checkSize(t.width),s=this.checkSize(t.height);return e.width=O(e,"width")&&""!==e.width?this.checkSize(e.width):n,e.height=O(e,"height")&&""!==e.height?this.checkSize(e.height):s,i&&"image"==e.type&&(e._hasCustomWidth=!!i.dataset.width,e._hasCustomHeight=!!i.dataset.height),e}},{key:"checkSize",value:function(e){return M(e)?"".concat(e,"px"):e}},{key:"sanitizeValue",value:function(e){return"true"!==e&&"false"!==e?e:"true"===e}}]),e}(),$=function(){function e(i,n,s){t(this,e),this.element=i,this.instance=n,this.index=s}return n(e,[{key:"setContent",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(c(t,"loaded"))return!1;var n=this.instance.settings,s=this.slideConfig,l=w();T(n.beforeSlideLoad)&&n.beforeSlideLoad({index:this.index,slide:t,player:!1});var o=s.type,r=s.descPosition,a=t.querySelector(".gslide-media"),d=t.querySelector(".gslide-title"),u=t.querySelector(".gslide-desc"),g=t.querySelector(".gdesc-inner"),v=i,f="gSlideTitle_"+this.index,p="gSlideDesc_"+this.index;if(T(n.afterSlideLoad)&&(v=function(){T(i)&&i(),n.afterSlideLoad({index:e.index,slide:t,player:e.instance.getSlidePlayerInstance(e.index)})}),""==s.title&&""==s.description?g&&g.parentNode.parentNode.removeChild(g.parentNode):(d&&""!==s.title?(d.id=f,d.innerHTML=s.title):d.parentNode.removeChild(d),u&&""!==s.description?(u.id=p,l&&n.moreLength>0?(s.smallDescription=this.slideShortDesc(s.description,n.moreLength,n.moreText),u.innerHTML=s.smallDescription,this.descriptionEvents(u,s)):u.innerHTML=s.description):u.parentNode.removeChild(u),h(a.parentNode,"desc-".concat(r)),h(g.parentNode,"description-".concat(r))),h(a,"gslide-".concat(o)),h(t,"loaded"),"video"!==o){if("external"!==o)return"inline"===o?(G.apply(this.instance,[t,s,this.index,v]),void(s.draggable&&new V({dragEl:t.querySelector(".gslide-inline"),toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:this.instance}))):void("image"!==o?T(v)&&v():j(t,s,this.index,(function(){var i=t.querySelector("img");s.draggable&&new V({dragEl:i,toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:e.instance}),s.zoomable&&i.naturalWidth>i.offsetWidth&&(h(i,"zoomable"),new H(i,t,(function(){e.instance.resize()}))),T(v)&&v()})));Z.apply(this,[t,s,this.index,v])}else F.apply(this.instance,[t,s,this.index,v])}},{key:"slideShortDesc",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:50,i=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=document.createElement("div");n.innerHTML=e;var s=n.innerText,l=i;if((e=s.trim()).length<=t)return e;var o=e.substr(0,t-1);return l?(n=null,o+'... '+i+""):o}},{key:"descriptionEvents",value:function(e,t){var i=this,n=e.querySelector(".desc-more");if(!n)return!1;a("click",{onElement:n,withCallback:function(e,n){e.preventDefault();var s=document.body,l=u(n,".gslide-desc");if(!l)return!1;l.innerHTML=t.description,h(s,"gdesc-open");var o=a("click",{onElement:[s,u(l,".gslide-description")],withCallback:function(e,n){"a"!==e.target.nodeName.toLowerCase()&&(d(s,"gdesc-open"),h(s,"gdesc-closed"),l.innerHTML=t.smallDescription,i.descriptionEvents(l,t),setTimeout((function(){d(s,"gdesc-closed")}),400),o.destroy())}})}})}},{key:"create",value:function(){return m(this.instance.settings.slideHTML)}},{key:"getConfig",value:function(){k(this.element)||this.element.hasOwnProperty("draggable")||(this.element.draggable=this.instance.settings.draggable);var e=new U(this.instance.settings.slideExtraAttributes);return this.slideConfig=e.parseConfig(this.element,this.instance.settings),this.slideConfig}}]),e}(),J=w(),K=null!==w()||void 0!==document.createTouch||"ontouchstart"in window||"onmsgesturechange"in window||navigator.msMaxTouchPoints,Q=document.getElementsByTagName("html")[0],ee={selector:".glightbox",elements:null,skin:"clean",theme:"clean",closeButton:!0,startAt:null,autoplayVideos:!0,autofocusVideos:!0,descPosition:"bottom",width:"900px",height:"506px",videosWidth:"960px",beforeSlideChange:null,afterSlideChange:null,beforeSlideLoad:null,afterSlideLoad:null,slideInserted:null,slideRemoved:null,slideExtraAttributes:null,onOpen:null,onClose:null,loop:!1,zoomable:!0,draggable:!0,dragAutoSnap:!1,dragToleranceX:40,dragToleranceY:65,preload:!0,oneSlidePerOpen:!1,touchNavigation:!0,touchFollowAxis:!0,keyboardNavigation:!0,closeOnOutsideClick:!0,plugins:!1,plyr:{css:"https://cdn.plyr.io/3.6.12/plyr.css",js:"https://cdn.plyr.io/3.6.12/plyr.js",config:{ratio:"16:9",fullscreen:{enabled:!0,iosNative:!0},youtube:{noCookie:!0,rel:0,showinfo:0,iv_load_policy:3},vimeo:{byline:!1,portrait:!1,title:!1,transparent:!1}}},openEffect:"zoom",closeEffect:"zoom",slideEffect:"slide",moreText:"See more",moreLength:60,cssEfects:{fade:{in:"fadeIn",out:"fadeOut"},zoom:{in:"zoomIn",out:"zoomOut"},slide:{in:"slideInRight",out:"slideOutLeft"},slideBack:{in:"slideInLeft",out:"slideOutRight"},none:{in:"none",out:"none"}},svg:{close:'',next:' ',prev:''},slideHTML:'
\n
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',lightboxHTML:''},te=function(){function e(){var i=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e),this.customOptions=i,this.settings=l(ee,i),this.effectsClasses=this.getAnimationClasses(),this.videoPlayers={},this.apiEvents=[],this.fullElementsList=!1}return n(e,[{key:"init",value:function(){var e=this,t=this.getSelector();t&&(this.baseEvents=a("click",{onElement:t,withCallback:function(t,i){t.preventDefault(),e.open(i)}})),this.elements=this.getElements()}},{key:"open",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(0===this.elements.length)return!1;this.activeSlide=null,this.prevActiveSlideIndex=null,this.prevActiveSlide=null;var i=M(t)?t:this.settings.startAt;if(k(e)){var n=e.getAttribute("data-gallery");n&&(this.fullElementsList=this.elements,this.elements=this.getGalleryElements(this.elements,n)),I(i)&&(i=this.getElementIndex(e))<0&&(i=0)}M(i)||(i=0),this.build(),g(this.overlay,"none"===this.settings.openEffect?"none":this.settings.cssEfects.fade.in);var s=document.body,l=window.innerWidth-document.documentElement.clientWidth;if(l>0){var o=document.createElement("style");o.type="text/css",o.className="gcss-styles",o.innerText=".gscrollbar-fixer {margin-right: ".concat(l,"px}"),document.head.appendChild(o),h(s,"gscrollbar-fixer")}h(s,"glightbox-open"),h(Q,"glightbox-open"),J&&(h(document.body,"glightbox-mobile"),this.settings.slideEffect="slide"),this.showSlide(i,!0),1===this.elements.length?(h(this.prevButton,"glightbox-button-hidden"),h(this.nextButton,"glightbox-button-hidden")):(d(this.prevButton,"glightbox-button-hidden"),d(this.nextButton,"glightbox-button-hidden")),this.lightboxOpen=!0,this.trigger("open"),T(this.settings.onOpen)&&this.settings.onOpen(),K&&this.settings.touchNavigation&&B(this),this.settings.keyboardNavigation&&X(this)}},{key:"openAt",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;this.open(null,e)}},{key:"showSlide",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];f(this.loader),this.index=parseInt(t);var n=this.slidesContainer.querySelector(".current");n&&d(n,"current"),this.slideAnimateOut();var s=this.slidesContainer.querySelectorAll(".gslide")[t];if(c(s,"loaded"))this.slideAnimateIn(s,i),p(this.loader);else{f(this.loader);var l=this.elements[t],o={index:this.index,slide:s,slideNode:s,slideConfig:l.slideConfig,slideIndex:this.index,trigger:l.node,player:null};this.trigger("slide_before_load",o),l.instance.setContent(s,(function(){p(e.loader),e.resize(),e.slideAnimateIn(s,i),e.trigger("slide_after_load",o)}))}this.slideDescription=s.querySelector(".gslide-description"),this.slideDescriptionContained=this.slideDescription&&c(this.slideDescription.parentNode,"gslide-media"),this.settings.preload&&(this.preloadSlide(t+1),this.preloadSlide(t-1)),this.updateNavigationClasses(),this.activeSlide=s}},{key:"preloadSlide",value:function(e){var t=this;if(e<0||e>this.elements.length-1)return!1;if(I(this.elements[e]))return!1;var i=this.slidesContainer.querySelectorAll(".gslide")[e];if(c(i,"loaded"))return!1;var n=this.elements[e],s=n.type,l={index:e,slide:i,slideNode:i,slideConfig:n.slideConfig,slideIndex:e,trigger:n.node,player:null};this.trigger("slide_before_load",l),"video"===s||"external"===s?setTimeout((function(){n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}),200):n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}},{key:"prevSlide",value:function(){this.goToSlide(this.index-1)}},{key:"nextSlide",value:function(){this.goToSlide(this.index+1)}},{key:"goToSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(this.prevActiveSlide=this.activeSlide,this.prevActiveSlideIndex=this.index,!this.loop()&&(e<0||e>this.elements.length-1))return!1;e<0?e=this.elements.length-1:e>=this.elements.length&&(e=0),this.showSlide(e)}},{key:"insertSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;t<0&&(t=this.elements.length);var i=new $(e,this,t),n=i.getConfig(),s=l({},n),o=i.create(),r=this.elements.length-1;s.index=t,s.node=!1,s.instance=i,s.slideConfig=n,this.elements.splice(t,0,s);var a=null,h=null;if(this.slidesContainer){if(t>r)this.slidesContainer.appendChild(o);else{var d=this.slidesContainer.querySelectorAll(".gslide")[t];this.slidesContainer.insertBefore(o,d)}(this.settings.preload&&0==this.index&&0==t||this.index-1==t||this.index+1==t)&&this.preloadSlide(t),0===this.index&&0===t&&(this.index=1),this.updateNavigationClasses(),a=this.slidesContainer.querySelectorAll(".gslide")[t],h=this.getSlidePlayerInstance(t),s.slideNode=a}this.trigger("slide_inserted",{index:t,slide:a,slideNode:a,slideConfig:n,slideIndex:t,trigger:null,player:h}),T(this.settings.slideInserted)&&this.settings.slideInserted({index:t,slide:a,player:h})}},{key:"removeSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1;if(e<0||e>this.elements.length-1)return!1;var t=this.slidesContainer&&this.slidesContainer.querySelectorAll(".gslide")[e];t&&(this.getActiveSlideIndex()==e&&(e==this.elements.length-1?this.prevSlide():this.nextSlide()),t.parentNode.removeChild(t)),this.elements.splice(e,1),this.trigger("slide_removed",e),T(this.settings.slideRemoved)&&this.settings.slideRemoved(e)}},{key:"slideAnimateIn",value:function(e,t){var i=this,n=e.querySelector(".gslide-media"),s=e.querySelector(".gslide-description"),l={index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,slideNode:this.prevActiveSlide,slideIndex:this.prevActiveSlide,slideConfig:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].slideConfig,trigger:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].node,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},o={index:this.index,slide:this.activeSlide,slideNode:this.activeSlide,slideConfig:this.elements[this.index].slideConfig,slideIndex:this.index,trigger:this.elements[this.index].node,player:this.getSlidePlayerInstance(this.index)};if(n.offsetWidth>0&&s&&(p(s),s.style.display=""),d(e,this.effectsClasses),t)g(e,this.settings.cssEfects[this.settings.openEffect].in,(function(){i.settings.autoplayVideos&&i.slidePlayerPlay(e),i.trigger("slide_changed",{prev:l,current:o}),T(i.settings.afterSlideChange)&&i.settings.afterSlideChange.apply(i,[l,o])}));else{var r=this.settings.slideEffect,a="none"!==r?this.settings.cssEfects[r].in:r;this.prevActiveSlideIndex>this.index&&"slide"==this.settings.slideEffect&&(a=this.settings.cssEfects.slideBack.in),g(e,a,(function(){i.settings.autoplayVideos&&i.slidePlayerPlay(e),i.trigger("slide_changed",{prev:l,current:o}),T(i.settings.afterSlideChange)&&i.settings.afterSlideChange.apply(i,[l,o])}))}setTimeout((function(){i.resize(e)}),100),h(e,"current")}},{key:"slideAnimateOut",value:function(){if(!this.prevActiveSlide)return!1;var e=this.prevActiveSlide;d(e,this.effectsClasses),h(e,"prev");var t=this.settings.slideEffect,i="none"!==t?this.settings.cssEfects[t].out:t;this.slidePlayerPause(e),this.trigger("slide_before_change",{prev:{index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,slideNode:this.prevActiveSlide,slideIndex:this.prevActiveSlideIndex,slideConfig:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].slideConfig,trigger:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].node,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},current:{index:this.index,slide:this.activeSlide,slideNode:this.activeSlide,slideIndex:this.index,slideConfig:this.elements[this.index].slideConfig,trigger:this.elements[this.index].node,player:this.getSlidePlayerInstance(this.index)}}),T(this.settings.beforeSlideChange)&&this.settings.beforeSlideChange.apply(this,[{index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},{index:this.index,slide:this.activeSlide,player:this.getSlidePlayerInstance(this.index)}]),this.prevActiveSlideIndex>this.index&&"slide"==this.settings.slideEffect&&(i=this.settings.cssEfects.slideBack.out),g(e,i,(function(){var t=e.querySelector(".ginner-container"),i=e.querySelector(".gslide-media"),n=e.querySelector(".gslide-description");t.style.transform="",i.style.transform="",d(i,"greset"),i.style.opacity="",n&&(n.style.opacity=""),d(e,"prev")}))}},{key:"getAllPlayers",value:function(){return this.videoPlayers}},{key:"getSlidePlayerInstance",value:function(e){var t="gvideo"+e,i=this.getAllPlayers();return!(!O(i,t)||!i[t])&&i[t]}},{key:"stopSlideVideo",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}console.log("stopSlideVideo is deprecated, use slidePlayerPause");var i=this.getSlidePlayerInstance(e);i&&i.playing&&i.pause()}},{key:"slidePlayerPause",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}var i=this.getSlidePlayerInstance(e);i&&i.playing&&i.pause()}},{key:"playSlideVideo",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}console.log("playSlideVideo is deprecated, use slidePlayerPlay");var i=this.getSlidePlayerInstance(e);i&&!i.playing&&i.play()}},{key:"slidePlayerPlay",value:function(e){var t;if(!J||null!==(t=this.settings.plyr.config)&&void 0!==t&&t.muted){if(k(e)){var i=e.querySelector(".gvideo-wrapper");i&&(e=i.getAttribute("data-index"))}var n=this.getSlidePlayerInstance(e);n&&!n.playing&&(n.play(),this.settings.autofocusVideos&&n.elements.container.focus())}}},{key:"setElements",value:function(e){var t=this;this.settings.elements=!1;var i=[];e&&e.length&&o(e,(function(e,n){var s=new $(e,t,n),o=s.getConfig(),r=l({},o);r.slideConfig=o,r.instance=s,r.index=n,i.push(r)})),this.elements=i,this.lightboxOpen&&(this.slidesContainer.innerHTML="",this.elements.length&&(o(this.elements,(function(){var e=m(t.settings.slideHTML);t.slidesContainer.appendChild(e)})),this.showSlide(0,!0)))}},{key:"getElementIndex",value:function(e){var t=!1;return o(this.elements,(function(i,n){if(O(i,"node")&&i.node==e)return t=n,!0})),t}},{key:"getElements",value:function(){var e=this,t=[];this.elements=this.elements?this.elements:[],!I(this.settings.elements)&&E(this.settings.elements)&&this.settings.elements.length&&o(this.settings.elements,(function(i,n){var s=new $(i,e,n),o=s.getConfig(),r=l({},o);r.node=!1,r.index=n,r.instance=s,r.slideConfig=o,t.push(r)}));var i=!1;return this.getSelector()&&(i=document.querySelectorAll(this.getSelector())),i?(o(i,(function(i,n){var s=new $(i,e,n),o=s.getConfig(),r=l({},o);r.node=i,r.index=n,r.instance=s,r.slideConfig=o,r.gallery=i.getAttribute("data-gallery"),t.push(r)})),t):t}},{key:"getGalleryElements",value:function(e,t){return e.filter((function(e){return e.gallery==t}))}},{key:"getSelector",value:function(){return!this.settings.elements&&(this.settings.selector&&"data-"==this.settings.selector.substring(0,5)?"*[".concat(this.settings.selector,"]"):this.settings.selector)}},{key:"getActiveSlide",value:function(){return this.slidesContainer.querySelectorAll(".gslide")[this.index]}},{key:"getActiveSlideIndex",value:function(){return this.index}},{key:"getAnimationClasses",value:function(){var e=[];for(var t in this.settings.cssEfects)if(this.settings.cssEfects.hasOwnProperty(t)){var i=this.settings.cssEfects[t];e.push("g".concat(i.in)),e.push("g".concat(i.out))}return e.join(" ")}},{key:"build",value:function(){var e=this;if(this.built)return!1;var t=document.body.childNodes,i=[];o(t,(function(e){e.parentNode==document.body&&"#"!==e.nodeName.charAt(0)&&e.hasAttribute&&!e.hasAttribute("aria-hidden")&&(i.push(e),e.setAttribute("aria-hidden","true"))}));var n=O(this.settings.svg,"next")?this.settings.svg.next:"",s=O(this.settings.svg,"prev")?this.settings.svg.prev:"",l=O(this.settings.svg,"close")?this.settings.svg.close:"",r=this.settings.lightboxHTML;r=m(r=(r=(r=r.replace(/{nextSVG}/g,n)).replace(/{prevSVG}/g,s)).replace(/{closeSVG}/g,l)),document.body.appendChild(r);var d=document.getElementById("glightbox-body");this.modal=d;var g=d.querySelector(".gclose");this.prevButton=d.querySelector(".gprev"),this.nextButton=d.querySelector(".gnext"),this.overlay=d.querySelector(".goverlay"),this.loader=d.querySelector(".gloader"),this.slidesContainer=document.getElementById("glightbox-slider"),this.bodyHiddenChildElms=i,this.events={},h(this.modal,"glightbox-"+this.settings.skin),this.settings.closeButton&&g&&(this.events.close=a("click",{onElement:g,withCallback:function(t,i){t.preventDefault(),e.close()}})),g&&!this.settings.closeButton&&g.parentNode.removeChild(g),this.nextButton&&(this.events.next=a("click",{onElement:this.nextButton,withCallback:function(t,i){t.preventDefault(),e.nextSlide()}})),this.prevButton&&(this.events.prev=a("click",{onElement:this.prevButton,withCallback:function(t,i){t.preventDefault(),e.prevSlide()}})),this.settings.closeOnOutsideClick&&(this.events.outClose=a("click",{onElement:d,withCallback:function(t,i){e.preventOutsideClick||c(document.body,"glightbox-mobile")||u(t.target,".ginner-container")||u(t.target,".gbtn")||c(t.target,"gnext")||c(t.target,"gprev")||e.close()}})),o(this.elements,(function(t,i){e.slidesContainer.appendChild(t.instance.create()),t.slideNode=e.slidesContainer.querySelectorAll(".gslide")[i]})),K&&h(document.body,"glightbox-touch"),this.events.resize=a("resize",{onElement:window,withCallback:function(){e.resize()}}),this.built=!0}},{key:"resize",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if((e=e||this.activeSlide)&&!c(e,"zoomed")){var t=y(),i=e.querySelector(".gvideo-wrapper"),n=e.querySelector(".gslide-image"),s=this.slideDescription,l=t.width,o=t.height;if(l<=768?h(document.body,"glightbox-mobile"):d(document.body,"glightbox-mobile"),i||n){var r=!1;if(s&&(c(s,"description-bottom")||c(s,"description-top"))&&!c(s,"gabsolute")&&(r=!0),n)if(l<=768)n.querySelector("img");else if(r){var a=s.offsetHeight,u=n.querySelector("img");u.setAttribute("style","max-height: calc(100vh - ".concat(a,"px)")),s.setAttribute("style","max-width: ".concat(u.offsetWidth,"px;"))}if(i){var g=O(this.settings.plyr.config,"ratio")?this.settings.plyr.config.ratio:"";if(!g){var v=i.clientWidth,f=i.clientHeight,p=v/f;g="".concat(v/p,":").concat(f/p)}var m=g.split(":"),x=this.settings.videosWidth,b=this.settings.videosWidth,S=(b=M(x)||-1!==x.indexOf("px")?parseInt(x):-1!==x.indexOf("vw")?l*parseInt(x)/100:-1!==x.indexOf("vh")?o*parseInt(x)/100:-1!==x.indexOf("%")?l*parseInt(x)/100:parseInt(i.clientWidth))/(parseInt(m[0])/parseInt(m[1]));if(S=Math.floor(S),r&&(o-=s.offsetHeight),b>l||S>o||ob){var w=i.offsetWidth,T=i.offsetHeight,C=o/T,k={width:w*C,height:T*C};i.parentNode.setAttribute("style","max-width: ".concat(k.width,"px")),r&&s.setAttribute("style","max-width: ".concat(k.width,"px;"))}else i.parentNode.style.maxWidth="".concat(x),r&&s.setAttribute("style","max-width: ".concat(x,";"))}}}}},{key:"reload",value:function(){this.init()}},{key:"updateNavigationClasses",value:function(){var e=this.loop();d(this.nextButton,"disabled"),d(this.prevButton,"disabled"),0==this.index&&this.elements.length-1==0?(h(this.prevButton,"disabled"),h(this.nextButton,"disabled")):0!==this.index||e?this.index!==this.elements.length-1||e||h(this.nextButton,"disabled"):h(this.prevButton,"disabled")}},{key:"loop",value:function(){var e=O(this.settings,"loopAtEnd")?this.settings.loopAtEnd:null;return e=O(this.settings,"loop")?this.settings.loop:e,e}},{key:"close",value:function(){var e=this;if(!this.lightboxOpen){if(this.events){for(var t in this.events)this.events.hasOwnProperty(t)&&this.events[t].destroy();this.events=null}return!1}if(this.closing)return!1;this.closing=!0,this.slidePlayerPause(this.activeSlide),this.fullElementsList&&(this.elements=this.fullElementsList),this.bodyHiddenChildElms.length&&o(this.bodyHiddenChildElms,(function(e){e.removeAttribute("aria-hidden")})),h(this.modal,"glightbox-closing"),g(this.overlay,"none"==this.settings.openEffect?"none":this.settings.cssEfects.fade.out),g(this.activeSlide,this.settings.cssEfects[this.settings.closeEffect].out,(function(){if(e.activeSlide=null,e.prevActiveSlideIndex=null,e.prevActiveSlide=null,e.built=!1,e.events){for(var t in e.events)e.events.hasOwnProperty(t)&&e.events[t].destroy();e.events=null}var i=document.body;d(Q,"glightbox-open"),d(i,"glightbox-open touching gdesc-open glightbox-touch glightbox-mobile gscrollbar-fixer"),e.modal.parentNode.removeChild(e.modal),e.trigger("close"),T(e.settings.onClose)&&e.settings.onClose();var n=document.querySelector(".gcss-styles");n&&n.parentNode.removeChild(n),e.lightboxOpen=!1,e.closing=null}))}},{key:"destroy",value:function(){this.close(),this.clearAllEvents(),this.baseEvents&&this.baseEvents.destroy()}},{key:"on",value:function(e,t){var i=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if(!e||!T(t))throw new TypeError("Event name and callback must be defined");this.apiEvents.push({evt:e,once:i,callback:t})}},{key:"once",value:function(e,t){this.on(e,t,!0)}},{key:"trigger",value:function(e){var t=this,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,n=[];o(this.apiEvents,(function(t,s){var l=t.evt,o=t.once,r=t.callback;l==e&&(r(i),o&&n.push(s))})),n.length&&o(n,(function(e){return t.apiEvents.splice(e,1)}))}},{key:"clearAllEvents",value:function(){this.apiEvents.splice(0,this.apiEvents.length)}},{key:"version",value:function(){return"3.1.0"}}]),e}();return function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=new te(e);return t.init(),t}})); \ No newline at end of file diff --git a/site_libs/quarto-contrib/glightbox/lightbox.css b/site_libs/quarto-contrib/glightbox/lightbox.css new file mode 100644 index 0000000..2c29fe1 --- /dev/null +++ b/site_libs/quarto-contrib/glightbox/lightbox.css @@ -0,0 +1,22 @@ +body:not(.glightbox-mobile) div.gslide div.gslide-description, +body:not(.glightbox-mobile) div.gslide-description .gslide-title, +body:not(.glightbox-mobile) div.gslide-description .gslide-desc { + color: var(--quarto-body-color); + background-color: var(--quarto-body-bg); +} + +.goverlay { + background: rgba(0, 0, 0, 0.7); +} + +div.gslide-description .gslide-title { + margin-top: 0.25em; + margin-bottom: 0.25em; + font-weight: 500; + font-family: inherit; +} + +div.gslide-description .gslide-desc { + padding-bottom: 0.5em; + font-family: inherit; +} diff --git a/site_libs/quarto-contrib/videojs/video-js.css b/site_libs/quarto-contrib/videojs/video-js.css new file mode 100644 index 0000000..60432a2 --- /dev/null +++ b/site_libs/quarto-contrib/videojs/video-js.css @@ -0,0 +1,1770 @@ +@charset "UTF-8"; +.vjs-modal-dialog .vjs-modal-dialog-content, .video-js .vjs-modal-dialog, .vjs-button > .vjs-icon-placeholder:before, .video-js .vjs-big-play-button .vjs-icon-placeholder:before { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.vjs-button > .vjs-icon-placeholder:before, .video-js .vjs-big-play-button .vjs-icon-placeholder:before { + text-align: center; +} + +@font-face { + font-family: VideoJS; + src: url(data:application/font-woff;charset=utf-8;base64,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) format("woff"); + font-weight: normal; + font-style: normal; +} +.vjs-icon-play, .video-js .vjs-play-control .vjs-icon-placeholder, .video-js .vjs-big-play-button .vjs-icon-placeholder:before { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-play:before, .video-js .vjs-play-control .vjs-icon-placeholder:before, .video-js .vjs-big-play-button .vjs-icon-placeholder:before { + content: "\f101"; +} + +.vjs-icon-play-circle { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-play-circle:before { + content: "\f102"; +} + +.vjs-icon-pause, .video-js .vjs-play-control.vjs-playing .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-pause:before, .video-js .vjs-play-control.vjs-playing .vjs-icon-placeholder:before { + content: "\f103"; +} + +.vjs-icon-volume-mute, .video-js .vjs-mute-control.vjs-vol-0 .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-volume-mute:before, .video-js .vjs-mute-control.vjs-vol-0 .vjs-icon-placeholder:before { + content: "\f104"; +} + +.vjs-icon-volume-low, .video-js .vjs-mute-control.vjs-vol-1 .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-volume-low:before, .video-js .vjs-mute-control.vjs-vol-1 .vjs-icon-placeholder:before { + content: "\f105"; +} + +.vjs-icon-volume-mid, .video-js .vjs-mute-control.vjs-vol-2 .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-volume-mid:before, .video-js .vjs-mute-control.vjs-vol-2 .vjs-icon-placeholder:before { + content: "\f106"; +} + +.vjs-icon-volume-high, .video-js .vjs-mute-control .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-volume-high:before, .video-js .vjs-mute-control .vjs-icon-placeholder:before { + content: "\f107"; +} + +.vjs-icon-fullscreen-enter, .video-js .vjs-fullscreen-control .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-fullscreen-enter:before, .video-js .vjs-fullscreen-control .vjs-icon-placeholder:before { + content: "\f108"; +} + +.vjs-icon-fullscreen-exit, .video-js.vjs-fullscreen .vjs-fullscreen-control .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-fullscreen-exit:before, .video-js.vjs-fullscreen .vjs-fullscreen-control .vjs-icon-placeholder:before { + content: "\f109"; +} + +.vjs-icon-square { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-square:before { + content: "\f10a"; +} + +.vjs-icon-spinner { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-spinner:before { + content: "\f10b"; +} + +.vjs-icon-subtitles, .video-js .vjs-subs-caps-button .vjs-icon-placeholder, +.video-js.video-js:lang(en-GB) .vjs-subs-caps-button .vjs-icon-placeholder, +.video-js.video-js:lang(en-IE) .vjs-subs-caps-button .vjs-icon-placeholder, +.video-js.video-js:lang(en-AU) .vjs-subs-caps-button .vjs-icon-placeholder, +.video-js.video-js:lang(en-NZ) .vjs-subs-caps-button .vjs-icon-placeholder, .video-js .vjs-subtitles-button .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-subtitles:before, .video-js .vjs-subs-caps-button .vjs-icon-placeholder:before, +.video-js.video-js:lang(en-GB) .vjs-subs-caps-button .vjs-icon-placeholder:before, +.video-js.video-js:lang(en-IE) .vjs-subs-caps-button .vjs-icon-placeholder:before, +.video-js.video-js:lang(en-AU) .vjs-subs-caps-button .vjs-icon-placeholder:before, +.video-js.video-js:lang(en-NZ) .vjs-subs-caps-button .vjs-icon-placeholder:before, .video-js .vjs-subtitles-button .vjs-icon-placeholder:before { + content: "\f10c"; +} + +.vjs-icon-captions, .video-js:lang(en) .vjs-subs-caps-button .vjs-icon-placeholder, +.video-js:lang(fr-CA) .vjs-subs-caps-button .vjs-icon-placeholder, .video-js .vjs-captions-button .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-captions:before, .video-js:lang(en) .vjs-subs-caps-button .vjs-icon-placeholder:before, +.video-js:lang(fr-CA) .vjs-subs-caps-button .vjs-icon-placeholder:before, .video-js .vjs-captions-button .vjs-icon-placeholder:before { + content: "\f10d"; +} + +.vjs-icon-chapters, .video-js .vjs-chapters-button .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-chapters:before, .video-js .vjs-chapters-button .vjs-icon-placeholder:before { + content: "\f10e"; +} + +.vjs-icon-share { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-share:before { + content: "\f10f"; +} + +.vjs-icon-cog { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-cog:before { + content: "\f110"; +} + +.vjs-icon-circle, .vjs-seek-to-live-control .vjs-icon-placeholder, .video-js .vjs-volume-level, .video-js .vjs-play-progress { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-circle:before, .vjs-seek-to-live-control .vjs-icon-placeholder:before, .video-js .vjs-volume-level:before, .video-js .vjs-play-progress:before { + content: "\f111"; +} + +.vjs-icon-circle-outline { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-circle-outline:before { + content: "\f112"; +} + +.vjs-icon-circle-inner-circle { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-circle-inner-circle:before { + content: "\f113"; +} + +.vjs-icon-hd { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-hd:before { + content: "\f114"; +} + +.vjs-icon-cancel, .video-js .vjs-control.vjs-close-button .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-cancel:before, .video-js .vjs-control.vjs-close-button .vjs-icon-placeholder:before { + content: "\f115"; +} + +.vjs-icon-replay, .video-js .vjs-play-control.vjs-ended .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-replay:before, .video-js .vjs-play-control.vjs-ended .vjs-icon-placeholder:before { + content: "\f116"; +} + +.vjs-icon-facebook { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-facebook:before { + content: "\f117"; +} + +.vjs-icon-gplus { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-gplus:before { + content: "\f118"; +} + +.vjs-icon-linkedin { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-linkedin:before { + content: "\f119"; +} + +.vjs-icon-twitter { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-twitter:before { + content: "\f11a"; +} + +.vjs-icon-tumblr { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-tumblr:before { + content: "\f11b"; +} + +.vjs-icon-pinterest { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-pinterest:before { + content: "\f11c"; +} + +.vjs-icon-audio-description, .video-js .vjs-descriptions-button .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-audio-description:before, .video-js .vjs-descriptions-button .vjs-icon-placeholder:before { + content: "\f11d"; +} + +.vjs-icon-audio, .video-js .vjs-audio-button .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-audio:before, .video-js .vjs-audio-button .vjs-icon-placeholder:before { + content: "\f11e"; +} + +.vjs-icon-next-item { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-next-item:before { + content: "\f11f"; +} + +.vjs-icon-previous-item { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-previous-item:before { + content: "\f120"; +} + +.vjs-icon-picture-in-picture-enter, .video-js .vjs-picture-in-picture-control .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-picture-in-picture-enter:before, .video-js .vjs-picture-in-picture-control .vjs-icon-placeholder:before { + content: "\f121"; +} + +.vjs-icon-picture-in-picture-exit, .video-js.vjs-picture-in-picture .vjs-picture-in-picture-control .vjs-icon-placeholder { + font-family: VideoJS; + font-weight: normal; + font-style: normal; +} +.vjs-icon-picture-in-picture-exit:before, .video-js.vjs-picture-in-picture .vjs-picture-in-picture-control .vjs-icon-placeholder:before { + content: "\f122"; +} + +.video-js { + display: block; + vertical-align: top; + box-sizing: border-box; + color: #fff; + background-color: #000; + position: relative; + padding: 0; + font-size: 10px; + line-height: 1; + font-weight: normal; + font-style: normal; + font-family: Arial, Helvetica, sans-serif; + word-break: initial; +} +.video-js:-moz-full-screen { + position: absolute; +} +.video-js:-webkit-full-screen { + width: 100% !important; + height: 100% !important; +} + +.video-js[tabindex="-1"] { + outline: none; +} + +.video-js *, +.video-js *:before, +.video-js *:after { + box-sizing: inherit; +} + +.video-js ul { + font-family: inherit; + font-size: inherit; + line-height: inherit; + list-style-position: outside; + margin-left: 0; + margin-right: 0; + margin-top: 0; + margin-bottom: 0; +} + +.video-js.vjs-fluid, +.video-js.vjs-16-9, +.video-js.vjs-4-3, +.video-js.vjs-9-16, +.video-js.vjs-1-1 { + width: 100%; + max-width: 100%; +} + +.video-js.vjs-fluid:not(.vjs-audio-only-mode), +.video-js.vjs-16-9:not(.vjs-audio-only-mode), +.video-js.vjs-4-3:not(.vjs-audio-only-mode), +.video-js.vjs-9-16:not(.vjs-audio-only-mode), +.video-js.vjs-1-1:not(.vjs-audio-only-mode) { + height: 0; +} + +.video-js.vjs-16-9:not(.vjs-audio-only-mode) { + padding-top: 56.25%; +} + +.video-js.vjs-4-3:not(.vjs-audio-only-mode) { + padding-top: 75%; +} + +.video-js.vjs-9-16:not(.vjs-audio-only-mode) { + padding-top: 177.7777777778%; +} + +.video-js.vjs-1-1:not(.vjs-audio-only-mode) { + padding-top: 100%; +} + +.video-js.vjs-fill:not(.vjs-audio-only-mode) { + width: 100%; + height: 100%; +} + +.video-js .vjs-tech { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.video-js.vjs-audio-only-mode .vjs-tech { + display: none; +} + +body.vjs-full-window { + padding: 0; + margin: 0; + height: 100%; +} + +.vjs-full-window .video-js.vjs-fullscreen { + position: fixed; + overflow: hidden; + z-index: 1000; + left: 0; + top: 0; + bottom: 0; + right: 0; +} + +.video-js.vjs-fullscreen:not(.vjs-ios-native-fs) { + width: 100% !important; + height: 100% !important; + padding-top: 0 !important; +} + +.video-js.vjs-fullscreen.vjs-user-inactive { + cursor: none; +} + +.vjs-hidden { + display: none !important; +} + +.vjs-disabled { + opacity: 0.5; + cursor: default; +} + +.video-js .vjs-offscreen { + height: 1px; + left: -9999px; + position: absolute; + top: 0; + width: 1px; +} + +.vjs-lock-showing { + display: block !important; + opacity: 1 !important; + visibility: visible !important; +} + +.vjs-no-js { + padding: 20px; + color: #fff; + background-color: #000; + font-size: 18px; + font-family: Arial, Helvetica, sans-serif; + text-align: center; + width: 300px; + height: 150px; + margin: 0px auto; +} + +.vjs-no-js a, +.vjs-no-js a:visited { + color: #66A8CC; +} + +.video-js .vjs-big-play-button { + font-size: 3em; + line-height: 1.5em; + height: 1.63332em; + width: 3em; + display: block; + position: absolute; + top: 10px; + left: 10px; + padding: 0; + cursor: pointer; + opacity: 1; + border: 0.06666em solid #fff; + background-color: #2B333F; + background-color: rgba(43, 51, 63, 0.7); + border-radius: 0.3em; + transition: all 0.4s; +} +.vjs-big-play-centered .vjs-big-play-button { + top: 50%; + left: 50%; + margin-top: -0.81666em; + margin-left: -1.5em; +} + +.video-js:hover .vjs-big-play-button, +.video-js .vjs-big-play-button:focus { + border-color: #fff; + background-color: #73859f; + background-color: rgba(115, 133, 159, 0.5); + transition: all 0s; +} + +.vjs-controls-disabled .vjs-big-play-button, +.vjs-has-started .vjs-big-play-button, +.vjs-using-native-controls .vjs-big-play-button, +.vjs-error .vjs-big-play-button { + display: none; +} + +.vjs-has-started.vjs-paused.vjs-show-big-play-button-on-pause .vjs-big-play-button { + display: block; +} + +.video-js button { + background: none; + border: none; + color: inherit; + display: inline-block; + font-size: inherit; + line-height: inherit; + text-transform: none; + text-decoration: none; + transition: none; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; +} + +.vjs-control .vjs-button { + width: 100%; + height: 100%; +} + +.video-js .vjs-control.vjs-close-button { + cursor: pointer; + height: 3em; + position: absolute; + right: 0; + top: 0.5em; + z-index: 2; +} +.video-js .vjs-modal-dialog { + background: rgba(0, 0, 0, 0.8); + background: linear-gradient(180deg, rgba(0, 0, 0, 0.8), rgba(255, 255, 255, 0)); + overflow: auto; +} + +.video-js .vjs-modal-dialog > * { + box-sizing: border-box; +} + +.vjs-modal-dialog .vjs-modal-dialog-content { + font-size: 1.2em; + line-height: 1.5; + padding: 20px 24px; + z-index: 1; +} + +.vjs-menu-button { + cursor: pointer; +} + +.vjs-menu-button.vjs-disabled { + cursor: default; +} + +.vjs-workinghover .vjs-menu-button.vjs-disabled:hover .vjs-menu { + display: none; +} + +.vjs-menu .vjs-menu-content { + display: block; + padding: 0; + margin: 0; + font-family: Arial, Helvetica, sans-serif; + overflow: auto; +} + +.vjs-menu .vjs-menu-content > * { + box-sizing: border-box; +} + +.vjs-scrubbing .vjs-control.vjs-menu-button:hover .vjs-menu { + display: none; +} + +.vjs-menu li { + list-style: none; + margin: 0; + padding: 0.2em 0; + line-height: 1.4em; + font-size: 1.2em; + text-align: center; + text-transform: lowercase; +} + +.vjs-menu li.vjs-menu-item:focus, +.vjs-menu li.vjs-menu-item:hover, +.js-focus-visible .vjs-menu li.vjs-menu-item:hover { + background-color: #73859f; + background-color: rgba(115, 133, 159, 0.5); +} + +.vjs-menu li.vjs-selected, +.vjs-menu li.vjs-selected:focus, +.vjs-menu li.vjs-selected:hover, +.js-focus-visible .vjs-menu li.vjs-selected:hover { + background-color: #fff; + color: #2B333F; +} + +.video-js .vjs-menu *:not(.vjs-selected):focus:not(:focus-visible), +.js-focus-visible .vjs-menu *:not(.vjs-selected):focus:not(.focus-visible) { + background: none; +} + +.vjs-menu li.vjs-menu-title { + text-align: center; + text-transform: uppercase; + font-size: 1em; + line-height: 2em; + padding: 0; + margin: 0 0 0.3em 0; + font-weight: bold; + cursor: default; +} + +.vjs-menu-button-popup .vjs-menu { + display: none; + position: absolute; + bottom: 0; + width: 10em; + left: -3em; + height: 0em; + margin-bottom: 1.5em; + border-top-color: rgba(43, 51, 63, 0.7); +} + +.vjs-menu-button-popup .vjs-menu .vjs-menu-content { + background-color: #2B333F; + background-color: rgba(43, 51, 63, 0.7); + position: absolute; + width: 100%; + bottom: 1.5em; + max-height: 15em; +} + +.vjs-layout-tiny .vjs-menu-button-popup .vjs-menu .vjs-menu-content, +.vjs-layout-x-small .vjs-menu-button-popup .vjs-menu .vjs-menu-content { + max-height: 5em; +} + +.vjs-layout-small .vjs-menu-button-popup .vjs-menu .vjs-menu-content { + max-height: 10em; +} + +.vjs-layout-medium .vjs-menu-button-popup .vjs-menu .vjs-menu-content { + max-height: 14em; +} + +.vjs-layout-large .vjs-menu-button-popup .vjs-menu .vjs-menu-content, +.vjs-layout-x-large .vjs-menu-button-popup .vjs-menu .vjs-menu-content, +.vjs-layout-huge .vjs-menu-button-popup .vjs-menu .vjs-menu-content { + max-height: 25em; +} + +.vjs-workinghover .vjs-menu-button-popup.vjs-hover .vjs-menu, +.vjs-menu-button-popup .vjs-menu.vjs-lock-showing { + display: block; +} + +.video-js .vjs-menu-button-inline { + transition: all 0.4s; + overflow: hidden; +} + +.video-js .vjs-menu-button-inline:before { + width: 2.222222222em; +} + +.video-js .vjs-menu-button-inline:hover, +.video-js .vjs-menu-button-inline:focus, +.video-js .vjs-menu-button-inline.vjs-slider-active, +.video-js.vjs-no-flex .vjs-menu-button-inline { + width: 12em; +} + +.vjs-menu-button-inline .vjs-menu { + opacity: 0; + height: 100%; + width: auto; + position: absolute; + left: 4em; + top: 0; + padding: 0; + margin: 0; + transition: all 0.4s; +} + +.vjs-menu-button-inline:hover .vjs-menu, +.vjs-menu-button-inline:focus .vjs-menu, +.vjs-menu-button-inline.vjs-slider-active .vjs-menu { + display: block; + opacity: 1; +} + +.vjs-no-flex .vjs-menu-button-inline .vjs-menu { + display: block; + opacity: 1; + position: relative; + width: auto; +} + +.vjs-no-flex .vjs-menu-button-inline:hover .vjs-menu, +.vjs-no-flex .vjs-menu-button-inline:focus .vjs-menu, +.vjs-no-flex .vjs-menu-button-inline.vjs-slider-active .vjs-menu { + width: auto; +} + +.vjs-menu-button-inline .vjs-menu-content { + width: auto; + height: 100%; + margin: 0; + overflow: hidden; +} + +.video-js .vjs-control-bar { + display: none; + width: 100%; + position: absolute; + bottom: 0; + left: 0; + right: 0; + height: 3em; + background-color: #2B333F; + background-color: rgba(43, 51, 63, 0.7); +} + +.vjs-has-started .vjs-control-bar, +.vjs-audio-only-mode .vjs-control-bar { + display: flex; + visibility: visible; + opacity: 1; + transition: visibility 0.1s, opacity 0.1s; +} + +.vjs-has-started.vjs-user-inactive.vjs-playing .vjs-control-bar { + visibility: visible; + opacity: 0; + pointer-events: none; + transition: visibility 1s, opacity 1s; +} + +.vjs-controls-disabled .vjs-control-bar, +.vjs-using-native-controls .vjs-control-bar, +.vjs-error .vjs-control-bar { + display: none !important; +} + +.vjs-audio.vjs-has-started.vjs-user-inactive.vjs-playing .vjs-control-bar, +.vjs-audio-only-mode.vjs-has-started.vjs-user-inactive.vjs-playing .vjs-control-bar { + opacity: 1; + visibility: visible; + pointer-events: auto; +} + +.vjs-has-started.vjs-no-flex .vjs-control-bar { + display: table; +} + +.video-js .vjs-control { + position: relative; + text-align: center; + margin: 0; + padding: 0; + height: 100%; + width: 4em; + flex: none; +} + +.video-js .vjs-control.vjs-visible-text { + width: auto; + padding-left: 1em; + padding-right: 1em; +} + +.vjs-button > .vjs-icon-placeholder:before { + font-size: 1.8em; + line-height: 1.67; +} + +.vjs-button > .vjs-icon-placeholder { + display: block; +} + +.video-js .vjs-control:focus:before, +.video-js .vjs-control:hover:before, +.video-js .vjs-control:focus { + text-shadow: 0em 0em 1em white; +} + +.video-js *:not(.vjs-visible-text) > .vjs-control-text { + border: 0; + clip: rect(0 0 0 0); + height: 1px; + overflow: hidden; + padding: 0; + position: absolute; + width: 1px; +} + +.vjs-no-flex .vjs-control { + display: table-cell; + vertical-align: middle; +} + +.video-js .vjs-custom-control-spacer { + display: none; +} + +.video-js .vjs-progress-control { + cursor: pointer; + flex: auto; + display: flex; + align-items: center; + min-width: 4em; + touch-action: none; +} + +.video-js .vjs-progress-control.disabled { + cursor: default; +} + +.vjs-live .vjs-progress-control { + display: none; +} + +.vjs-liveui .vjs-progress-control { + display: flex; + align-items: center; +} + +.vjs-no-flex .vjs-progress-control { + width: auto; +} + +.video-js .vjs-progress-holder { + flex: auto; + transition: all 0.2s; + height: 0.3em; +} + +.video-js .vjs-progress-control .vjs-progress-holder { + margin: 0 10px; +} + +.video-js .vjs-progress-control:hover .vjs-progress-holder { + font-size: 1.6666666667em; +} + +.video-js .vjs-progress-control:hover .vjs-progress-holder.disabled { + font-size: 1em; +} + +.video-js .vjs-progress-holder .vjs-play-progress, +.video-js .vjs-progress-holder .vjs-load-progress, +.video-js .vjs-progress-holder .vjs-load-progress div { + position: absolute; + display: block; + height: 100%; + margin: 0; + padding: 0; + width: 0; +} + +.video-js .vjs-play-progress { + background-color: #fff; +} +.video-js .vjs-play-progress:before { + font-size: 0.9em; + position: absolute; + right: -0.5em; + top: -0.3333333333em; + z-index: 1; +} + +.video-js .vjs-load-progress { + background: rgba(115, 133, 159, 0.5); +} + +.video-js .vjs-load-progress div { + background: rgba(115, 133, 159, 0.75); +} + +.video-js .vjs-time-tooltip { + background-color: #fff; + background-color: rgba(255, 255, 255, 0.8); + border-radius: 0.3em; + color: #000; + float: right; + font-family: Arial, Helvetica, sans-serif; + font-size: 1em; + padding: 6px 8px 8px 8px; + pointer-events: none; + position: absolute; + top: -3.4em; + visibility: hidden; + z-index: 1; +} + +.video-js .vjs-progress-holder:focus .vjs-time-tooltip { + display: none; +} + +.video-js .vjs-progress-control:hover .vjs-time-tooltip, +.video-js .vjs-progress-control:hover .vjs-progress-holder:focus .vjs-time-tooltip { + display: block; + font-size: 0.6em; + visibility: visible; +} + +.video-js .vjs-progress-control.disabled:hover .vjs-time-tooltip { + font-size: 1em; +} + +.video-js .vjs-progress-control .vjs-mouse-display { + display: none; + position: absolute; + width: 1px; + height: 100%; + background-color: #000; + z-index: 1; +} + +.vjs-no-flex .vjs-progress-control .vjs-mouse-display { + z-index: 0; +} + +.video-js .vjs-progress-control:hover .vjs-mouse-display { + display: block; +} + +.video-js.vjs-user-inactive .vjs-progress-control .vjs-mouse-display { + visibility: hidden; + opacity: 0; + transition: visibility 1s, opacity 1s; +} + +.video-js.vjs-user-inactive.vjs-no-flex .vjs-progress-control .vjs-mouse-display { + display: none; +} + +.vjs-mouse-display .vjs-time-tooltip { + color: #fff; + background-color: #000; + background-color: rgba(0, 0, 0, 0.8); +} + +.video-js .vjs-slider { + position: relative; + cursor: pointer; + padding: 0; + margin: 0 0.45em 0 0.45em; + /* iOS Safari */ + -webkit-touch-callout: none; + /* Safari */ + -webkit-user-select: none; + /* Konqueror HTML */ + /* Firefox */ + -moz-user-select: none; + /* Internet Explorer/Edge */ + -ms-user-select: none; + /* Non-prefixed version, currently supported by Chrome and Opera */ + user-select: none; + background-color: #73859f; + background-color: rgba(115, 133, 159, 0.5); +} + +.video-js .vjs-slider.disabled { + cursor: default; +} + +.video-js .vjs-slider:focus { + text-shadow: 0em 0em 1em white; + box-shadow: 0 0 1em #fff; +} + +.video-js .vjs-mute-control { + cursor: pointer; + flex: none; +} +.video-js .vjs-volume-control { + cursor: pointer; + margin-right: 1em; + display: flex; +} + +.video-js .vjs-volume-control.vjs-volume-horizontal { + width: 5em; +} + +.video-js .vjs-volume-panel .vjs-volume-control { + visibility: visible; + opacity: 0; + width: 1px; + height: 1px; + margin-left: -1px; +} + +.video-js .vjs-volume-panel { + transition: width 1s; +} +.video-js .vjs-volume-panel.vjs-hover .vjs-volume-control, .video-js .vjs-volume-panel:active .vjs-volume-control, .video-js .vjs-volume-panel:focus .vjs-volume-control, .video-js .vjs-volume-panel .vjs-volume-control:active, .video-js .vjs-volume-panel.vjs-hover .vjs-mute-control ~ .vjs-volume-control, .video-js .vjs-volume-panel .vjs-volume-control.vjs-slider-active { + visibility: visible; + opacity: 1; + position: relative; + transition: visibility 0.1s, opacity 0.1s, height 0.1s, width 0.1s, left 0s, top 0s; +} +.video-js .vjs-volume-panel.vjs-hover .vjs-volume-control.vjs-volume-horizontal, .video-js .vjs-volume-panel:active .vjs-volume-control.vjs-volume-horizontal, .video-js .vjs-volume-panel:focus .vjs-volume-control.vjs-volume-horizontal, .video-js .vjs-volume-panel .vjs-volume-control:active.vjs-volume-horizontal, .video-js .vjs-volume-panel.vjs-hover .vjs-mute-control ~ .vjs-volume-control.vjs-volume-horizontal, .video-js .vjs-volume-panel .vjs-volume-control.vjs-slider-active.vjs-volume-horizontal { + width: 5em; + height: 3em; + margin-right: 0; +} +.video-js .vjs-volume-panel.vjs-hover .vjs-volume-control.vjs-volume-vertical, .video-js .vjs-volume-panel:active .vjs-volume-control.vjs-volume-vertical, .video-js .vjs-volume-panel:focus .vjs-volume-control.vjs-volume-vertical, .video-js .vjs-volume-panel .vjs-volume-control:active.vjs-volume-vertical, .video-js .vjs-volume-panel.vjs-hover .vjs-mute-control ~ .vjs-volume-control.vjs-volume-vertical, .video-js .vjs-volume-panel .vjs-volume-control.vjs-slider-active.vjs-volume-vertical { + left: -3.5em; + transition: left 0s; +} +.video-js .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-hover, .video-js .vjs-volume-panel.vjs-volume-panel-horizontal:active, .video-js .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-slider-active { + width: 10em; + transition: width 0.1s; +} +.video-js .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-mute-toggle-only { + width: 4em; +} + +.video-js .vjs-volume-panel .vjs-volume-control.vjs-volume-vertical { + height: 8em; + width: 3em; + left: -3000em; + transition: visibility 1s, opacity 1s, height 1s 1s, width 1s 1s, left 1s 1s, top 1s 1s; +} + +.video-js .vjs-volume-panel .vjs-volume-control.vjs-volume-horizontal { + transition: visibility 1s, opacity 1s, height 1s 1s, width 1s, left 1s 1s, top 1s 1s; +} + +.video-js.vjs-no-flex .vjs-volume-panel .vjs-volume-control.vjs-volume-horizontal { + width: 5em; + height: 3em; + visibility: visible; + opacity: 1; + position: relative; + transition: none; +} + +.video-js.vjs-no-flex .vjs-volume-control.vjs-volume-vertical, +.video-js.vjs-no-flex .vjs-volume-panel .vjs-volume-control.vjs-volume-vertical { + position: absolute; + bottom: 3em; + left: 0.5em; +} + +.video-js .vjs-volume-panel { + display: flex; +} + +.video-js .vjs-volume-bar { + margin: 1.35em 0.45em; +} + +.vjs-volume-bar.vjs-slider-horizontal { + width: 5em; + height: 0.3em; +} + +.vjs-volume-bar.vjs-slider-vertical { + width: 0.3em; + height: 5em; + margin: 1.35em auto; +} + +.video-js .vjs-volume-level { + position: absolute; + bottom: 0; + left: 0; + background-color: #fff; +} +.video-js .vjs-volume-level:before { + position: absolute; + font-size: 0.9em; + z-index: 1; +} + +.vjs-slider-vertical .vjs-volume-level { + width: 0.3em; +} +.vjs-slider-vertical .vjs-volume-level:before { + top: -0.5em; + left: -0.3em; + z-index: 1; +} + +.vjs-slider-horizontal .vjs-volume-level { + height: 0.3em; +} +.vjs-slider-horizontal .vjs-volume-level:before { + top: -0.3em; + right: -0.5em; +} + +.video-js .vjs-volume-panel.vjs-volume-panel-vertical { + width: 4em; +} + +.vjs-volume-bar.vjs-slider-vertical .vjs-volume-level { + height: 100%; +} + +.vjs-volume-bar.vjs-slider-horizontal .vjs-volume-level { + width: 100%; +} + +.video-js .vjs-volume-vertical { + width: 3em; + height: 8em; + bottom: 8em; + background-color: #2B333F; + background-color: rgba(43, 51, 63, 0.7); +} + +.video-js .vjs-volume-horizontal .vjs-menu { + left: -2em; +} + +.video-js .vjs-volume-tooltip { + background-color: #fff; + background-color: rgba(255, 255, 255, 0.8); + border-radius: 0.3em; + color: #000; + float: right; + font-family: Arial, Helvetica, sans-serif; + font-size: 1em; + padding: 6px 8px 8px 8px; + pointer-events: none; + position: absolute; + top: -3.4em; + visibility: hidden; + z-index: 1; +} + +.video-js .vjs-volume-control:hover .vjs-volume-tooltip, +.video-js .vjs-volume-control:hover .vjs-progress-holder:focus .vjs-volume-tooltip { + display: block; + font-size: 1em; + visibility: visible; +} + +.video-js .vjs-volume-vertical:hover .vjs-volume-tooltip, +.video-js .vjs-volume-vertical:hover .vjs-progress-holder:focus .vjs-volume-tooltip { + left: 1em; + top: -12px; +} + +.video-js .vjs-volume-control.disabled:hover .vjs-volume-tooltip { + font-size: 1em; +} + +.video-js .vjs-volume-control .vjs-mouse-display { + display: none; + position: absolute; + width: 100%; + height: 1px; + background-color: #000; + z-index: 1; +} + +.video-js .vjs-volume-horizontal .vjs-mouse-display { + width: 1px; + height: 100%; +} + +.vjs-no-flex .vjs-volume-control .vjs-mouse-display { + z-index: 0; +} + +.video-js .vjs-volume-control:hover .vjs-mouse-display { + display: block; +} + +.video-js.vjs-user-inactive .vjs-volume-control .vjs-mouse-display { + visibility: hidden; + opacity: 0; + transition: visibility 1s, opacity 1s; +} + +.video-js.vjs-user-inactive.vjs-no-flex .vjs-volume-control .vjs-mouse-display { + display: none; +} + +.vjs-mouse-display .vjs-volume-tooltip { + color: #fff; + background-color: #000; + background-color: rgba(0, 0, 0, 0.8); +} + +.vjs-poster { + display: inline-block; + vertical-align: middle; + background-repeat: no-repeat; + background-position: 50% 50%; + background-size: contain; + background-color: #000000; + cursor: pointer; + margin: 0; + padding: 0; + position: absolute; + top: 0; + right: 0; + bottom: 0; + left: 0; + height: 100%; +} + +.vjs-has-started .vjs-poster, +.vjs-using-native-controls .vjs-poster { + display: none; +} + +.vjs-audio.vjs-has-started .vjs-poster, +.vjs-has-started.vjs-audio-poster-mode .vjs-poster { + display: block; +} + +.video-js .vjs-live-control { + display: flex; + align-items: flex-start; + flex: auto; + font-size: 1em; + line-height: 3em; +} + +.vjs-no-flex .vjs-live-control { + display: table-cell; + width: auto; + text-align: left; +} + +.video-js:not(.vjs-live) .vjs-live-control, +.video-js.vjs-liveui .vjs-live-control { + display: none; +} + +.video-js .vjs-seek-to-live-control { + align-items: center; + cursor: pointer; + flex: none; + display: inline-flex; + height: 100%; + padding-left: 0.5em; + padding-right: 0.5em; + font-size: 1em; + line-height: 3em; + width: auto; + min-width: 4em; +} + +.vjs-no-flex .vjs-seek-to-live-control { + display: table-cell; + width: auto; + text-align: left; +} + +.video-js.vjs-live:not(.vjs-liveui) .vjs-seek-to-live-control, +.video-js:not(.vjs-live) .vjs-seek-to-live-control { + display: none; +} + +.vjs-seek-to-live-control.vjs-control.vjs-at-live-edge { + cursor: auto; +} + +.vjs-seek-to-live-control .vjs-icon-placeholder { + margin-right: 0.5em; + color: #888; +} + +.vjs-seek-to-live-control.vjs-control.vjs-at-live-edge .vjs-icon-placeholder { + color: red; +} + +.video-js .vjs-time-control { + flex: none; + font-size: 1em; + line-height: 3em; + min-width: 2em; + width: auto; + padding-left: 1em; + padding-right: 1em; +} + +.vjs-live .vjs-time-control { + display: none; +} + +.video-js .vjs-current-time, +.vjs-no-flex .vjs-current-time { + display: none; +} + +.video-js .vjs-duration, +.vjs-no-flex .vjs-duration { + display: none; +} + +.vjs-time-divider { + display: none; + line-height: 3em; +} + +.vjs-live .vjs-time-divider { + display: none; +} + +.video-js .vjs-play-control { + cursor: pointer; +} + +.video-js .vjs-play-control .vjs-icon-placeholder { + flex: none; +} + +.vjs-text-track-display { + position: absolute; + bottom: 3em; + left: 0; + right: 0; + top: 0; + pointer-events: none; +} + +.video-js.vjs-controls-disabled .vjs-text-track-display, +.video-js.vjs-user-inactive.vjs-playing .vjs-text-track-display { + bottom: 1em; +} + +.video-js .vjs-text-track { + font-size: 1.4em; + text-align: center; + margin-bottom: 0.1em; +} + +.vjs-subtitles { + color: #fff; +} + +.vjs-captions { + color: #fc6; +} + +.vjs-tt-cue { + display: block; +} + +video::-webkit-media-text-track-display { + transform: translateY(-3em); +} + +.video-js.vjs-controls-disabled video::-webkit-media-text-track-display, +.video-js.vjs-user-inactive.vjs-playing video::-webkit-media-text-track-display { + transform: translateY(-1.5em); +} + +.video-js .vjs-picture-in-picture-control { + cursor: pointer; + flex: none; +} +.video-js.vjs-audio-only-mode .vjs-picture-in-picture-control { + display: none; +} + +.video-js .vjs-fullscreen-control { + cursor: pointer; + flex: none; +} +.video-js.vjs-audio-only-mode .vjs-fullscreen-control { + display: none; +} + +.vjs-playback-rate > .vjs-menu-button, +.vjs-playback-rate .vjs-playback-rate-value { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.vjs-playback-rate .vjs-playback-rate-value { + pointer-events: none; + font-size: 1.5em; + line-height: 2; + text-align: center; +} + +.vjs-playback-rate .vjs-menu { + width: 4em; + left: 0em; +} + +.vjs-error .vjs-error-display .vjs-modal-dialog-content { + font-size: 1.4em; + text-align: center; +} + +.vjs-error .vjs-error-display:before { + color: #fff; + content: "X"; + font-family: Arial, Helvetica, sans-serif; + font-size: 4em; + left: 0; + line-height: 1; + margin-top: -0.5em; + position: absolute; + text-shadow: 0.05em 0.05em 0.1em #000; + text-align: center; + top: 50%; + vertical-align: middle; + width: 100%; +} + +.vjs-loading-spinner { + display: none; + position: absolute; + top: 50%; + left: 50%; + margin: -25px 0 0 -25px; + opacity: 0.85; + text-align: left; + border: 6px solid rgba(43, 51, 63, 0.7); + box-sizing: border-box; + background-clip: padding-box; + width: 50px; + height: 50px; + border-radius: 25px; + visibility: hidden; +} + +.vjs-seeking .vjs-loading-spinner, +.vjs-waiting .vjs-loading-spinner { + display: block; + -webkit-animation: vjs-spinner-show 0s linear 0.3s forwards; + animation: vjs-spinner-show 0s linear 0.3s forwards; +} + +.vjs-loading-spinner:before, +.vjs-loading-spinner:after { + content: ""; + position: absolute; + margin: -6px; + box-sizing: inherit; + width: inherit; + height: inherit; + border-radius: inherit; + opacity: 1; + border: inherit; + border-color: transparent; + border-top-color: white; +} + +.vjs-seeking .vjs-loading-spinner:before, +.vjs-seeking .vjs-loading-spinner:after, +.vjs-waiting .vjs-loading-spinner:before, +.vjs-waiting .vjs-loading-spinner:after { + -webkit-animation: vjs-spinner-spin 1.1s cubic-bezier(0.6, 0.2, 0, 0.8) infinite, vjs-spinner-fade 1.1s linear infinite; + animation: vjs-spinner-spin 1.1s cubic-bezier(0.6, 0.2, 0, 0.8) infinite, vjs-spinner-fade 1.1s linear infinite; +} + +.vjs-seeking .vjs-loading-spinner:before, +.vjs-waiting .vjs-loading-spinner:before { + border-top-color: white; +} + +.vjs-seeking .vjs-loading-spinner:after, +.vjs-waiting .vjs-loading-spinner:after { + border-top-color: white; + -webkit-animation-delay: 0.44s; + animation-delay: 0.44s; +} + +@keyframes vjs-spinner-show { + to { + visibility: visible; + } +} +@-webkit-keyframes vjs-spinner-show { + to { + visibility: visible; + } +} +@keyframes vjs-spinner-spin { + 100% { + transform: rotate(360deg); + } +} +@-webkit-keyframes vjs-spinner-spin { + 100% { + -webkit-transform: rotate(360deg); + } +} +@keyframes vjs-spinner-fade { + 0% { + border-top-color: #73859f; + } + 20% { + border-top-color: #73859f; + } + 35% { + border-top-color: white; + } + 60% { + border-top-color: #73859f; + } + 100% { + border-top-color: #73859f; + } +} +@-webkit-keyframes vjs-spinner-fade { + 0% { + border-top-color: #73859f; + } + 20% { + border-top-color: #73859f; + } + 35% { + border-top-color: white; + } + 60% { + border-top-color: #73859f; + } + 100% { + border-top-color: #73859f; + } +} +.video-js.vjs-audio-only-mode .vjs-captions-button { + display: none; +} + +.vjs-chapters-button .vjs-menu ul { + width: 24em; +} + +.video-js.vjs-audio-only-mode .vjs-descriptions-button { + display: none; +} + +.video-js .vjs-subs-caps-button + .vjs-menu .vjs-captions-menu-item .vjs-menu-item-text .vjs-icon-placeholder { + vertical-align: middle; + display: inline-block; + margin-bottom: -0.1em; +} + +.video-js .vjs-subs-caps-button + .vjs-menu .vjs-captions-menu-item .vjs-menu-item-text .vjs-icon-placeholder:before { + font-family: VideoJS; + content: ""; + font-size: 1.5em; + line-height: inherit; +} + +.video-js.vjs-audio-only-mode .vjs-subs-caps-button { + display: none; +} + +.video-js .vjs-audio-button + .vjs-menu .vjs-main-desc-menu-item .vjs-menu-item-text .vjs-icon-placeholder { + vertical-align: middle; + display: inline-block; + margin-bottom: -0.1em; +} + +.video-js .vjs-audio-button + .vjs-menu .vjs-main-desc-menu-item .vjs-menu-item-text .vjs-icon-placeholder:before { + font-family: VideoJS; + content: " "; + font-size: 1.5em; + line-height: inherit; +} + +.video-js.vjs-layout-small .vjs-current-time, +.video-js.vjs-layout-small .vjs-time-divider, +.video-js.vjs-layout-small .vjs-duration, +.video-js.vjs-layout-small .vjs-remaining-time, +.video-js.vjs-layout-small .vjs-playback-rate, +.video-js.vjs-layout-small .vjs-volume-control, .video-js.vjs-layout-x-small .vjs-current-time, +.video-js.vjs-layout-x-small .vjs-time-divider, +.video-js.vjs-layout-x-small .vjs-duration, +.video-js.vjs-layout-x-small .vjs-remaining-time, +.video-js.vjs-layout-x-small .vjs-playback-rate, +.video-js.vjs-layout-x-small .vjs-volume-control, .video-js.vjs-layout-tiny .vjs-current-time, +.video-js.vjs-layout-tiny .vjs-time-divider, +.video-js.vjs-layout-tiny .vjs-duration, +.video-js.vjs-layout-tiny .vjs-remaining-time, +.video-js.vjs-layout-tiny .vjs-playback-rate, +.video-js.vjs-layout-tiny .vjs-volume-control { + display: none; +} +.video-js.vjs-layout-small .vjs-volume-panel.vjs-volume-panel-horizontal:hover, .video-js.vjs-layout-small .vjs-volume-panel.vjs-volume-panel-horizontal:active, .video-js.vjs-layout-small .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-slider-active, .video-js.vjs-layout-small .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-hover, .video-js.vjs-layout-x-small .vjs-volume-panel.vjs-volume-panel-horizontal:hover, .video-js.vjs-layout-x-small .vjs-volume-panel.vjs-volume-panel-horizontal:active, .video-js.vjs-layout-x-small .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-slider-active, .video-js.vjs-layout-x-small .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-hover, .video-js.vjs-layout-tiny .vjs-volume-panel.vjs-volume-panel-horizontal:hover, .video-js.vjs-layout-tiny .vjs-volume-panel.vjs-volume-panel-horizontal:active, .video-js.vjs-layout-tiny .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-slider-active, .video-js.vjs-layout-tiny .vjs-volume-panel.vjs-volume-panel-horizontal.vjs-hover { + width: auto; + width: initial; +} +.video-js.vjs-layout-x-small .vjs-progress-control, .video-js.vjs-layout-tiny .vjs-progress-control { + display: none; +} +.video-js.vjs-layout-x-small .vjs-custom-control-spacer { + flex: auto; + display: block; +} +.video-js.vjs-layout-x-small.vjs-no-flex .vjs-custom-control-spacer { + width: auto; +} + +.vjs-modal-dialog.vjs-text-track-settings { + background-color: #2B333F; + background-color: rgba(43, 51, 63, 0.75); + color: #fff; + height: 70%; +} + +.vjs-text-track-settings .vjs-modal-dialog-content { + display: table; +} + +.vjs-text-track-settings .vjs-track-settings-colors, +.vjs-text-track-settings .vjs-track-settings-font, +.vjs-text-track-settings .vjs-track-settings-controls { + display: table-cell; +} + +.vjs-text-track-settings .vjs-track-settings-controls { + text-align: right; + vertical-align: bottom; +} + +@supports (display: grid) { + .vjs-text-track-settings .vjs-modal-dialog-content { + display: grid; + grid-template-columns: 1fr 1fr; + grid-template-rows: 1fr; + padding: 20px 24px 0px 24px; + } + + .vjs-track-settings-controls .vjs-default-button { + margin-bottom: 20px; + } + + .vjs-text-track-settings .vjs-track-settings-controls { + grid-column: 1/-1; + } + + .vjs-layout-small .vjs-text-track-settings .vjs-modal-dialog-content, +.vjs-layout-x-small .vjs-text-track-settings .vjs-modal-dialog-content, +.vjs-layout-tiny .vjs-text-track-settings .vjs-modal-dialog-content { + grid-template-columns: 1fr; + } +} +.vjs-track-setting > select { + margin-right: 1em; + margin-bottom: 0.5em; +} + +.vjs-text-track-settings fieldset { + margin: 5px; + padding: 3px; + border: none; +} + +.vjs-text-track-settings fieldset span { + display: inline-block; +} + +.vjs-text-track-settings fieldset span > select { + max-width: 7.3em; +} + +.vjs-text-track-settings legend { + color: #fff; + margin: 0 0 5px 0; +} + +.vjs-text-track-settings .vjs-label { + position: absolute; + clip: rect(1px 1px 1px 1px); + clip: rect(1px, 1px, 1px, 1px); + display: block; + margin: 0 0 5px 0; + padding: 0; + border: 0; + height: 1px; + width: 1px; + overflow: hidden; +} + +.vjs-track-settings-controls button:focus, +.vjs-track-settings-controls button:active { + outline-style: solid; + outline-width: medium; + background-image: linear-gradient(0deg, #fff 88%, #73859f 100%); +} + +.vjs-track-settings-controls button:hover { + color: rgba(43, 51, 63, 0.75); +} + +.vjs-track-settings-controls button { + background-color: #fff; + background-image: linear-gradient(-180deg, #fff 88%, #73859f 100%); + color: #2B333F; + cursor: pointer; + border-radius: 2px; +} + +.vjs-track-settings-controls .vjs-default-button { + margin-right: 1em; +} + +@media print { + .video-js > *:not(.vjs-tech):not(.vjs-poster) { + visibility: hidden; + } +} +.vjs-resize-manager { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; + border: none; + z-index: -1000; +} + +.js-focus-visible .video-js *:focus:not(.focus-visible) { + outline: none; +} + +.video-js *:focus:not(:focus-visible) { + outline: none; +} diff --git a/site_libs/quarto-contrib/videojs/video.min.js b/site_libs/quarto-contrib/videojs/video.min.js new file mode 100644 index 0000000..3bc604f --- /dev/null +++ b/site_libs/quarto-contrib/videojs/video.min.js @@ -0,0 +1,25 @@ +/** + * @license + * Video.js 7.20.2 + * Copyright Brightcove, Inc. + * Available under Apache License Version 2.0 + * + * + * Includes vtt.js + * Available under Apache License Version 2.0 + * + */ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).videojs=t()}(this,function(){"use strict";for(var e,u="7.20.2",i={},a=function(e,t){return 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decodeURIComponent(encodeURIComponent(e))}}},Ai.convertCueToDOMTree=function(e,t){return e&&t?Si(e,t):null};Ai.processCues=function(n,r,e){if(!n||!r||!e)return null;for(;e.firstChild;)e.removeChild(e.firstChild);var a=n.document.createElement("div");if(a.style.position="absolute",a.style.left="0",a.style.right="0",a.style.top="0",a.style.bottom="0",a.style.margin="1.5%",e.appendChild(a),function(e){for(var t=0;tt.length;u--)l.el_.removeChild(n[u-1]);n.length=t.length})},e}(pt)),pt.registerComponent("TimeTooltip",function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-time-tooltip"},{"aria-hidden":"true"})},t.update=function(e,t,i){var n=he(this.el_),r=de(this.player_.el()),a=e.width*t;r&&n&&(t=e.left-r.left+a,r=e.width-a+(r.right-e.right),t<(e=n.width/2)?e+=e-t:rn.width&&(e=n.width),e=Math.round(e),this.el_.style.right="-"+e+"px",this.write(i))},t.write=function(e){J(this.el_,e)},t.updateTime=function(n,r,a,s){var o=this;this.requestNamedAnimationFrame("TimeTooltip#updateTime",function(){var e,t,i=o.player_.duration();i=o.player_.liveTracker&&o.player_.liveTracker.isLive()?((t=(e=o.player_.liveTracker.liveWindow())-r*e)<1?"":"-")+ln(t,e):ln(a,i),o.update(n,r,i),s&&s()})},e}(pt));Xt=function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-play-progress vjs-slider-bar"},{"aria-hidden":"true"})},t.update=function(e,t){var i,n=this.getChild("timeTooltip");n&&(i=this.player_.scrubbing()?this.player_.getCache().currentTime:this.player_.currentTime(),n.updateTime(e,t,i))},e}(pt);Xt.prototype.options_={children:[]},q||A||Xt.prototype.options_.children.push("timeTooltip"),pt.registerComponent("PlayProgressBar",Xt);I=function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-mouse-display"})},t.update=function(e,t){var i=this,n=t*this.player_.duration();this.getChild("timeTooltip").updateTime(e,t,n,function(){i.el_.style.left=e.width*t+"px"})},e}(pt);I.prototype.options_={children:["timeTooltip"]},pt.registerComponent("MouseTimeDisplay",I);Bt=function(a){function e(e,t){t=a.call(this,e,t)||this;return t.setEventHandlers_(),t}mt(e,a);var t=e.prototype;return t.setEventHandlers_=function(){var t=this;this.update_=Ve(this,this.update),this.update=We(this.update_,30),this.on(this.player_,["ended","durationchange","timeupdate"],this.update),this.player_.liveTracker&&this.on(this.player_.liveTracker,"liveedgechange",this.update),this.updateInterval=null,this.enableIntervalHandler_=function(e){return t.enableInterval_(e)},this.disableIntervalHandler_=function(e){return t.disableInterval_(e)},this.on(this.player_,["playing"],this.enableIntervalHandler_),this.on(this.player_,["ended","pause","waiting"],this.disableIntervalHandler_),"hidden"in document&&"visibilityState"in document&&this.on(document,"visibilitychange",this.toggleVisibility_)},t.toggleVisibility_=function(e){"hidden"===document.visibilityState?(this.cancelNamedAnimationFrame("SeekBar#update"),this.cancelNamedAnimationFrame("Slider#update"),this.disableInterval_(e)):(this.player_.ended()||this.player_.paused()||this.enableInterval_(),this.update())},t.enableInterval_=function(){this.updateInterval||(this.updateInterval=this.setInterval(this.update,30))},t.disableInterval_=function(e){this.player_.liveTracker&&this.player_.liveTracker.isLive()&&e&&"ended"!==e.type||this.updateInterval&&(this.clearInterval(this.updateInterval),this.updateInterval=null)},t.createEl=function(){return a.prototype.createEl.call(this,"div",{className:"vjs-progress-holder"},{"aria-label":this.localize("Progress Bar")})},t.update=function(e){var n=this;if("hidden"!==document.visibilityState){var r=a.prototype.update.call(this);return this.requestNamedAnimationFrame("SeekBar#update",function(){var e=n.player_.ended()?n.player_.duration():n.getCurrentTime_(),t=n.player_.liveTracker,i=n.player_.duration();t&&t.isLive()&&(i=n.player_.liveTracker.liveCurrentTime()),n.percent_!==r&&(n.el_.setAttribute("aria-valuenow",(100*r).toFixed(2)),n.percent_=r),n.currentTime_===e&&n.duration_===i||(n.el_.setAttribute("aria-valuetext",n.localize("progress bar timing: currentTime={1} duration={2}",[ln(e,i),ln(i,i)],"{1} of {2}")),n.currentTime_=e,n.duration_=i),n.bar&&n.bar.update(de(n.el()),n.getProgress())}),r}},t.userSeek_=function(e){this.player_.liveTracker&&this.player_.liveTracker.isLive()&&this.player_.liveTracker.nextSeekedFromUser(),this.player_.currentTime(e)},t.getCurrentTime_=function(){return this.player_.scrubbing()?this.player_.getCache().currentTime:this.player_.currentTime()},t.getPercent=function(){var e,t=this.getCurrentTime_(),i=this.player_.liveTracker;return 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e=this.getChild("mouseTimeDisplay");e&&e.hide()},t.handleMouseUp=function(e){a.prototype.handleMouseUp.call(this,e),e&&e.stopPropagation(),this.player_.scrubbing(!1),this.player_.trigger({type:"timeupdate",target:this,manuallyTriggered:!0}),this.videoWasPlaying?Et(this.player_.play()):this.update_()},t.stepForward=function(){this.userSeek_(this.player_.currentTime()+5)},t.stepBack=function(){this.userSeek_(this.player_.currentTime()-5)},t.handleAction=function(e){this.player_.paused()?this.player_.play():this.player_.pause()},t.handleKeyDown=function(e){var t,i=this.player_.liveTracker;ht.isEventKey(e,"Space")||ht.isEventKey(e,"Enter")?(e.preventDefault(),e.stopPropagation(),this.handleAction(e)):ht.isEventKey(e,"Home")?(e.preventDefault(),e.stopPropagation(),this.userSeek_(0)):ht.isEventKey(e,"End")?(e.preventDefault(),e.stopPropagation(),i&&i.isLive()?this.userSeek_(i.liveCurrentTime()):this.userSeek_(this.player_.duration())):/^[0-9]$/.test(ht(e))?(e.preventDefault(),e.stopPropagation(),t=10*(ht.codes[ht(e)]-ht.codes[0])/100,i&&i.isLive()?this.userSeek_(i.seekableStart()+i.liveWindow()*t):this.userSeek_(this.player_.duration()*t)):ht.isEventKey(e,"PgDn")?(e.preventDefault(),e.stopPropagation(),this.userSeek_(this.player_.currentTime()-60)):ht.isEventKey(e,"PgUp")?(e.preventDefault(),e.stopPropagation(),this.userSeek_(this.player_.currentTime()+60)):a.prototype.handleKeyDown.call(this,e)},t.dispose=function(){this.disableInterval_(),this.off(this.player_,["ended","durationchange","timeupdate"],this.update),this.player_.liveTracker&&this.off(this.player_.liveTracker,"liveedgechange",this.update),this.off(this.player_,["playing"],this.enableIntervalHandler_),this.off(this.player_,["ended","pause","waiting"],this.disableIntervalHandler_),"hidden"in document&&"visibilityState"in document&&this.off(document,"visibilitychange",this.toggleVisibility_),a.prototype.dispose.call(this)},e}(li);Bt.prototype.options_={children:["loadProgressBar","playProgressBar"],barName:"playProgressBar"},q||A||Bt.prototype.options_.children.splice(1,0,"mouseTimeDisplay"),pt.registerComponent("SeekBar",Bt);Ft=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.handleMouseMove=We(Ve(ft(i),i.handleMouseMove),30),i.throttledHandleMouseSeek=We(Ve(ft(i),i.handleMouseSeek),30),i.handleMouseUpHandler_=function(e){return i.handleMouseUp(e)},i.handleMouseDownHandler_=function(e){return i.handleMouseDown(e)},i.enable(),i}mt(e,n);var t=e.prototype;return t.createEl=function(){return n.prototype.createEl.call(this,"div",{className:"vjs-progress-control vjs-control"})},t.handleMouseMove=function(e){var t,i,n,r,a=this.getChild("seekBar");a&&(t=a.getChild("playProgressBar"),i=a.getChild("mouseTimeDisplay"),(t||i)&&(r=he(n=a.el()),e=pe(n,e).x,e=cn(e,0,1),i&&i.update(r,e),t&&t.update(r,a.getProgress())))},t.handleMouseSeek=function(e){var t=this.getChild("seekBar");t&&t.handleMouseMove(e)},t.enabled=function(){return this.enabled_},t.disable=function(){var e;this.children().forEach(function(e){return e.disable&&e.disable()}),this.enabled()&&(this.off(["mousedown","touchstart"],this.handleMouseDownHandler_),this.off(this.el_,"mousemove",this.handleMouseMove),this.removeListenersAddedOnMousedownAndTouchstart(),this.addClass("disabled"),this.enabled_=!1,this.player_.scrubbing()&&(e=this.getChild("seekBar"),this.player_.scrubbing(!1),e.videoWasPlaying&&Et(this.player_.play())))},t.enable=function(){this.children().forEach(function(e){return e.enable&&e.enable()}),this.enabled()||(this.on(["mousedown","touchstart"],this.handleMouseDownHandler_),this.on(this.el_,"mousemove",this.handleMouseMove),this.removeClass("disabled"),this.enabled_=!0)},t.removeListenersAddedOnMousedownAndTouchstart=function(){var e=this.el_.ownerDocument;this.off(e,"mousemove",this.throttledHandleMouseSeek),this.off(e,"touchmove",this.throttledHandleMouseSeek),this.off(e,"mouseup",this.handleMouseUpHandler_),this.off(e,"touchend",this.handleMouseUpHandler_)},t.handleMouseDown=function(e){var t=this.el_.ownerDocument,i=this.getChild("seekBar");i&&i.handleMouseDown(e),this.on(t,"mousemove",this.throttledHandleMouseSeek),this.on(t,"touchmove",this.throttledHandleMouseSeek),this.on(t,"mouseup",this.handleMouseUpHandler_),this.on(t,"touchend",this.handleMouseUpHandler_)},t.handleMouseUp=function(e){var t=this.getChild("seekBar");t&&t.handleMouseUp(e),this.removeListenersAddedOnMousedownAndTouchstart()},e}(pt);Ft.prototype.options_={children:["seekBar"]},pt.registerComponent("ProgressControl",Ft);jt=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.on(e,["enterpictureinpicture","leavepictureinpicture"],function(e){return i.handlePictureInPictureChange(e)}),i.on(e,["disablepictureinpicturechanged","loadedmetadata"],function(e){return i.handlePictureInPictureEnabledChange(e)}),i.on(e,["loadedmetadata","audioonlymodechange","audiopostermodechange"],function(){"audio"===e.currentType().substring(0,5)||e.audioPosterMode()||e.audioOnlyMode()?(e.isInPictureInPicture()&&e.exitPictureInPicture(),i.hide()):i.show()}),i.disable(),i}mt(e,n);var t=e.prototype;return t.buildCSSClass=function(){return"vjs-picture-in-picture-control "+n.prototype.buildCSSClass.call(this)},t.handlePictureInPictureEnabledChange=function(){document.pictureInPictureEnabled&&!1===this.player_.disablePictureInPicture()?this.enable():this.disable()},t.handlePictureInPictureChange=function(e){this.player_.isInPictureInPicture()?this.controlText("Exit Picture-in-Picture"):this.controlText("Picture-in-Picture"),this.handlePictureInPictureEnabledChange()},t.handleClick=function(e){this.player_.isInPictureInPicture()?this.player_.exitPictureInPicture():this.player_.requestPictureInPicture()},e}(sn);jt.prototype.controlText_="Picture-in-Picture",pt.registerComponent("PictureInPictureToggle",jt);j=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.on(e,"fullscreenchange",function(e){return i.handleFullscreenChange(e)}),!1===document[e.fsApi_.fullscreenEnabled]&&i.disable(),i}mt(e,n);var t=e.prototype;return t.buildCSSClass=function(){return"vjs-fullscreen-control "+n.prototype.buildCSSClass.call(this)},t.handleFullscreenChange=function(e){this.player_.isFullscreen()?this.controlText("Non-Fullscreen"):this.controlText("Fullscreen")},t.handleClick=function(e){this.player_.isFullscreen()?this.player_.exitFullscreen():this.player_.requestFullscreen()},e}(sn);j.prototype.controlText_="Fullscreen",pt.registerComponent("FullscreenToggle",j);pt.registerComponent("VolumeLevel",function(t){function e(){return t.apply(this,arguments)||this}return mt(e,t),e.prototype.createEl=function(){var e=t.prototype.createEl.call(this,"div",{className:"vjs-volume-level"});return e.appendChild(t.prototype.createEl.call(this,"span",{className:"vjs-control-text"})),e},e}(pt)),pt.registerComponent("VolumeLevelTooltip",function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-volume-tooltip"},{"aria-hidden":"true"})},t.update=function(e,t,i,n){if(!i){var r=de(this.el_),a=de(this.player_.el()),i=e.width*t;if(!a||!r)return;t=e.left-a.left+i,a=e.width-i+(a.right-e.right),e=r.width/2;tr.width&&(e=r.width),this.el_.style.right="-"+e+"px"}this.write(n+"%")},t.write=function(e){J(this.el_,e)},t.updateVolume=function(e,t,i,n,r){var a=this;this.requestNamedAnimationFrame("VolumeLevelTooltip#updateVolume",function(){a.update(e,t,i,n.toFixed(0)),r&&r()})},e}(pt));k=function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-mouse-display"})},t.update=function(e,t,i){var n=this,r=100*t;this.getChild("volumeLevelTooltip").updateVolume(e,t,i,r,function(){i?n.el_.style.bottom=e.height*t+"px":n.el_.style.left=e.width*t+"px"})},e}(pt);k.prototype.options_={children:["volumeLevelTooltip"]},pt.registerComponent("MouseVolumeLevelDisplay",k);f=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.on("slideractive",function(e){return i.updateLastVolume_(e)}),i.on(e,"volumechange",function(e){return i.updateARIAAttributes(e)}),e.ready(function(){return i.updateARIAAttributes()}),i}mt(e,n);var t=e.prototype;return t.createEl=function(){return n.prototype.createEl.call(this,"div",{className:"vjs-volume-bar vjs-slider-bar"},{"aria-label":this.localize("Volume Level"),"aria-live":"polite"})},t.handleMouseDown=function(e){_e(e)&&n.prototype.handleMouseDown.call(this,e)},t.handleMouseMove=function(e){var t,i,n,r=this.getChild("mouseVolumeLevelDisplay");r&&(t=de(n=this.el()),i=this.vertical(),n=pe(n,e),n=i?n.y:n.x,n=cn(n,0,1),r.update(t,n,i)),_e(e)&&(this.checkMuted(),this.player_.volume(this.calculateDistance(e)))},t.checkMuted=function(){this.player_.muted()&&this.player_.muted(!1)},t.getPercent=function(){return this.player_.muted()?0:this.player_.volume()},t.stepForward=function(){this.checkMuted(),this.player_.volume(this.player_.volume()+.1)},t.stepBack=function(){this.checkMuted(),this.player_.volume(this.player_.volume()-.1)},t.updateARIAAttributes=function(e){var t=this.player_.muted()?0:this.volumeAsPercentage_();this.el_.setAttribute("aria-valuenow",t),this.el_.setAttribute("aria-valuetext",t+"%")},t.volumeAsPercentage_=function(){return Math.round(100*this.player_.volume())},t.updateLastVolume_=function(){var e=this,t=this.player_.volume();this.one("sliderinactive",function(){0===e.player_.volume()&&e.player_.lastVolume_(t)})},e}(li);f.prototype.options_={children:["volumeLevel"],barName:"volumeLevel"},q||A||f.prototype.options_.children.splice(0,0,"mouseVolumeLevelDisplay"),f.prototype.playerEvent="volumechange",pt.registerComponent("VolumeBar",f);ui=function(a){function e(e,t){var i,n,r;return(t=void 0===t?{}:t).vertical=t.vertical||!1,"undefined"!=typeof t.volumeBar&&!S(t.volumeBar)||(t.volumeBar=t.volumeBar||{},t.volumeBar.vertical=t.vertical),i=a.call(this,e,t)||this,n=ft(i),(r=e).tech_&&!r.tech_.featuresVolumeControl&&n.addClass("vjs-hidden"),n.on(r,"loadstart",function(){r.tech_.featuresVolumeControl?n.removeClass("vjs-hidden"):n.addClass("vjs-hidden")}),i.throttledHandleMouseMove=We(Ve(ft(i),i.handleMouseMove),30),i.handleMouseUpHandler_=function(e){return i.handleMouseUp(e)},i.on("mousedown",function(e){return i.handleMouseDown(e)}),i.on("touchstart",function(e){return i.handleMouseDown(e)}),i.on("mousemove",function(e){return i.handleMouseMove(e)}),i.on(i.volumeBar,["focus","slideractive"],function(){i.volumeBar.addClass("vjs-slider-active"),i.addClass("vjs-slider-active"),i.trigger("slideractive")}),i.on(i.volumeBar,["blur","sliderinactive"],function(){i.volumeBar.removeClass("vjs-slider-active"),i.removeClass("vjs-slider-active"),i.trigger("sliderinactive")}),i}mt(e,a);var t=e.prototype;return t.createEl=function(){var e="vjs-volume-horizontal";return this.options_.vertical&&(e="vjs-volume-vertical"),a.prototype.createEl.call(this,"div",{className:"vjs-volume-control vjs-control "+e})},t.handleMouseDown=function(e){var t=this.el_.ownerDocument;this.on(t,"mousemove",this.throttledHandleMouseMove),this.on(t,"touchmove",this.throttledHandleMouseMove),this.on(t,"mouseup",this.handleMouseUpHandler_),this.on(t,"touchend",this.handleMouseUpHandler_)},t.handleMouseUp=function(e){var t=this.el_.ownerDocument;this.off(t,"mousemove",this.throttledHandleMouseMove),this.off(t,"touchmove",this.throttledHandleMouseMove),this.off(t,"mouseup",this.handleMouseUpHandler_),this.off(t,"touchend",this.handleMouseUpHandler_)},t.handleMouseMove=function(e){this.volumeBar.handleMouseMove(e)},e}(pt);ui.prototype.options_={children:["volumeBar"]},pt.registerComponent("VolumeControl",ui);Xt=function(a){function e(e,t){var i,n,r=a.call(this,e,t)||this;return i=ft(r),(n=e).tech_&&!n.tech_.featuresMuteControl&&i.addClass("vjs-hidden"),i.on(n,"loadstart",function(){n.tech_.featuresMuteControl?i.removeClass("vjs-hidden"):i.addClass("vjs-hidden")}),r.on(e,["loadstart","volumechange"],function(e){return r.update(e)}),r}mt(e,a);var t=e.prototype;return t.buildCSSClass=function(){return"vjs-mute-control "+a.prototype.buildCSSClass.call(this)},t.handleClick=function(e){var t=this.player_.volume(),i=this.player_.lastVolume_();0===t?(this.player_.volume(i<.1?.1:i),this.player_.muted(!1)):this.player_.muted(!this.player_.muted())},t.update=function(e){this.updateIcon_(),this.updateControlText_()},t.updateIcon_=function(){var e=this.player_.volume(),t=3;q&&this.player_.tech_&&this.player_.tech_.el_&&this.player_.muted(this.player_.tech_.el_.muted),0===e||this.player_.muted()?t=0:e<.33?t=1:e<.67&&(t=2);for(var i=0;i<4;i++)ie(this.el_,"vjs-vol-"+i);te(this.el_,"vjs-vol-"+t)},t.updateControlText_=function(){var e=this.player_.muted()||0===this.player_.volume()?"Unmute":"Mute";this.controlText()!==e&&this.controlText(e)},e}(sn);Xt.prototype.controlText_="Mute",pt.registerComponent("MuteToggle",Xt);I=function(n){function e(e,t){var i;return"undefined"!=typeof(t=void 0===t?{}:t).inline?t.inline=t.inline:t.inline=!0,"undefined"!=typeof t.volumeControl&&!S(t.volumeControl)||(t.volumeControl=t.volumeControl||{},t.volumeControl.vertical=!t.inline),(i=n.call(this,e,t)||this).handleKeyPressHandler_=function(e){return i.handleKeyPress(e)},i.on(e,["loadstart"],function(e){return i.volumePanelState_(e)}),i.on(i.muteToggle,"keyup",function(e){return i.handleKeyPress(e)}),i.on(i.volumeControl,"keyup",function(e){return i.handleVolumeControlKeyUp(e)}),i.on("keydown",function(e){return i.handleKeyPress(e)}),i.on("mouseover",function(e){return i.handleMouseOver(e)}),i.on("mouseout",function(e){return i.handleMouseOut(e)}),i.on(i.volumeControl,["slideractive"],i.sliderActive_),i.on(i.volumeControl,["sliderinactive"],i.sliderInactive_),i}mt(e,n);var t=e.prototype;return t.sliderActive_=function(){this.addClass("vjs-slider-active")},t.sliderInactive_=function(){this.removeClass("vjs-slider-active")},t.volumePanelState_=function(){this.volumeControl.hasClass("vjs-hidden")&&this.muteToggle.hasClass("vjs-hidden")&&this.addClass("vjs-hidden"),this.volumeControl.hasClass("vjs-hidden")&&!this.muteToggle.hasClass("vjs-hidden")&&this.addClass("vjs-mute-toggle-only")},t.createEl=function(){var e="vjs-volume-panel-horizontal";return this.options_.inline||(e="vjs-volume-panel-vertical"),n.prototype.createEl.call(this,"div",{className:"vjs-volume-panel vjs-control "+e})},t.dispose=function(){this.handleMouseOut(),n.prototype.dispose.call(this)},t.handleVolumeControlKeyUp=function(e){ht.isEventKey(e,"Esc")&&this.muteToggle.focus()},t.handleMouseOver=function(e){this.addClass("vjs-hover"),Be(document,"keyup",this.handleKeyPressHandler_)},t.handleMouseOut=function(e){this.removeClass("vjs-hover"),Fe(document,"keyup",this.handleKeyPressHandler_)},t.handleKeyPress=function(e){ht.isEventKey(e,"Esc")&&this.handleMouseOut()},e}(pt);I.prototype.options_={children:["muteToggle","volumeControl"]},pt.registerComponent("VolumePanel",I);var hn=function(n){function e(e,t){var i=n.call(this,e,t)||this;return t&&(i.menuButton_=t.menuButton),i.focusedChild_=-1,i.on("keydown",function(e){return i.handleKeyDown(e)}),i.boundHandleBlur_=function(e){return i.handleBlur(e)},i.boundHandleTapClick_=function(e){return i.handleTapClick(e)},i}mt(e,n);var t=e.prototype;return t.addEventListenerForItem=function(e){e instanceof pt&&(this.on(e,"blur",this.boundHandleBlur_),this.on(e,["tap","click"],this.boundHandleTapClick_))},t.removeEventListenerForItem=function(e){e instanceof pt&&(this.off(e,"blur",this.boundHandleBlur_),this.off(e,["tap","click"],this.boundHandleTapClick_))},t.removeChild=function(e){"string"==typeof e&&(e=this.getChild(e)),this.removeEventListenerForItem(e),n.prototype.removeChild.call(this,e)},t.addItem=function(e){e=this.addChild(e);e&&this.addEventListenerForItem(e)},t.createEl=function(){var e=this.options_.contentElType||"ul";this.contentEl_=$(e,{className:"vjs-menu-content"}),this.contentEl_.setAttribute("role","menu");e=n.prototype.createEl.call(this,"div",{append:this.contentEl_,className:"vjs-menu"});return e.appendChild(this.contentEl_),Be(e,"click",function(e){e.preventDefault(),e.stopImmediatePropagation()}),e},t.dispose=function(){this.contentEl_=null,this.boundHandleBlur_=null,this.boundHandleTapClick_=null,n.prototype.dispose.call(this)},t.handleBlur=function(e){var t=e.relatedTarget||document.activeElement;this.children().some(function(e){return e.el()===t})||(e=this.menuButton_)&&e.buttonPressed_&&t!==e.el().firstChild&&e.unpressButton()},t.handleTapClick=function(t){var e;this.menuButton_&&(this.menuButton_.unpressButton(),e=this.children(),!Array.isArray(e)||(e=e.filter(function(e){return e.el()===t.target})[0])&&"CaptionSettingsMenuItem"!==e.name()&&this.menuButton_.focus())},t.handleKeyDown=function(e){ht.isEventKey(e,"Left")||ht.isEventKey(e,"Down")?(e.preventDefault(),e.stopPropagation(),this.stepForward()):(ht.isEventKey(e,"Right")||ht.isEventKey(e,"Up"))&&(e.preventDefault(),e.stopPropagation(),this.stepBack())},t.stepForward=function(){var e=0;void 0!==this.focusedChild_&&(e=this.focusedChild_+1),this.focus(e)},t.stepBack=function(){var e=0;void 0!==this.focusedChild_&&(e=this.focusedChild_-1),this.focus(e)},t.focus=function(e){void 0===e&&(e=0);var t=this.children().slice();t.length&&t[0].hasClass("vjs-menu-title")&&t.shift(),0=t.length&&(e=t.length-1),t[this.focusedChild_=e].el_.focus())},e}(pt);pt.registerComponent("Menu",hn);Bt=function(n){function e(e,t){var i;(i=n.call(this,e,t=void 0===t?{}:t)||this).menuButton_=new sn(e,t),i.menuButton_.controlText(i.controlText_),i.menuButton_.el_.setAttribute("aria-haspopup","true");t=sn.prototype.buildCSSClass();i.menuButton_.el_.className=i.buildCSSClass()+" "+t,i.menuButton_.removeClass("vjs-control"),i.addChild(i.menuButton_),i.update(),i.enabled_=!0;t=function(e){return i.handleClick(e)};return i.handleMenuKeyUp_=function(e){return i.handleMenuKeyUp(e)},i.on(i.menuButton_,"tap",t),i.on(i.menuButton_,"click",t),i.on(i.menuButton_,"keydown",function(e){return i.handleKeyDown(e)}),i.on(i.menuButton_,"mouseenter",function(){i.addClass("vjs-hover"),i.menu.show(),Be(document,"keyup",i.handleMenuKeyUp_)}),i.on("mouseleave",function(e){return i.handleMouseLeave(e)}),i.on("keydown",function(e){return i.handleSubmenuKeyDown(e)}),i}mt(e,n);var t=e.prototype;return t.update=function(){var e=this.createMenu();this.menu&&(this.menu.dispose(),this.removeChild(this.menu)),this.menu=e,this.addChild(e),this.buttonPressed_=!1,this.menuButton_.el_.setAttribute("aria-expanded","false"),this.items&&this.items.length<=this.hideThreshold_?(this.hide(),this.menu.contentEl_.removeAttribute("role")):(this.show(),this.menu.contentEl_.setAttribute("role","menu"))},t.createMenu=function(){var e,t=new hn(this.player_,{menuButton:this});if(this.hideThreshold_=0,this.options_.title&&(e=$("li",{className:"vjs-menu-title",textContent:ut(this.options_.title),tabIndex:-1}),e=new pt(this.player_,{el:e}),t.addItem(e)),this.items=this.createItems(),this.items)for(var i=0;i select",id:"captions-background-color-%s",label:"Color",options:[ui,Bt,jt,Ft,j,C,I,Xt]},backgroundOpacity:{selector:".vjs-bg-opacity > select",id:"captions-background-opacity-%s",label:"Transparency",options:[k,li,f]},color:{selector:".vjs-fg-color > select",id:"captions-foreground-color-%s",label:"Color",options:[Bt,ui,jt,Ft,j,C,I,Xt]},edgeStyle:{selector:".vjs-edge-style > select",id:"%s",label:"Text Edge Style",options:[["none","None"],["raised","Raised"],["depressed","Depressed"],["uniform","Uniform"],["dropshadow","Dropshadow"]]},fontFamily:{selector:".vjs-font-family > select",id:"captions-font-family-%s",label:"Font Family",options:[["proportionalSansSerif","Proportional Sans-Serif"],["monospaceSansSerif","Monospace Sans-Serif"],["proportionalSerif","Proportional Serif"],["monospaceSerif","Monospace Serif"],["casual","Casual"],["script","Script"],["small-caps","Small Caps"]]},fontPercent:{selector:".vjs-font-percent > select",id:"captions-font-size-%s",label:"Font Size",options:[["0.50","50%"],["0.75","75%"],["1.00","100%"],["1.25","125%"],["1.50","150%"],["1.75","175%"],["2.00","200%"],["3.00","300%"],["4.00","400%"]],default:2,parser:function(e){return"1.00"===e?null:Number(e)}},textOpacity:{selector:".vjs-text-opacity > select",id:"captions-foreground-opacity-%s",label:"Transparency",options:[k,li]},windowColor:{selector:".vjs-window-color > select",id:"captions-window-color-%s",label:"Color"},windowOpacity:{selector:".vjs-window-opacity > select",id:"captions-window-opacity-%s",label:"Transparency",options:[f,li,k]}};function wn(e,t){if((e=t?t(e):e)&&"none"!==e)return e}Sn.windowColor.options=Sn.backgroundColor.options,pt.registerComponent("TextTrackSettings",function(n){function e(e,t){var i;return t.temporary=!1,(i=n.call(this,e,t)||this).updateDisplay=i.updateDisplay.bind(ft(i)),i.fill(),i.hasBeenOpened_=i.hasBeenFilled_=!0,i.endDialog=$("p",{className:"vjs-control-text",textContent:i.localize("End of dialog window.")}),i.el().appendChild(i.endDialog),i.setDefaults(),void 0===t.persistTextTrackSettings&&(i.options_.persistTextTrackSettings=i.options_.playerOptions.persistTextTrackSettings),i.on(i.$(".vjs-done-button"),"click",function(){i.saveSettings(),i.close()}),i.on(i.$(".vjs-default-button"),"click",function(){i.setDefaults(),i.updateDisplay()}),_(Sn,function(e){i.on(i.$(e.selector),"change",i.updateDisplay)}),i.options_.persistTextTrackSettings&&i.restoreSettings(),i}mt(e,n);var t=e.prototype;return t.dispose=function(){this.endDialog=null,n.prototype.dispose.call(this)},t.createElSelect_=function(e,t,i){var n=this;void 0===t&&(t=""),void 0===i&&(i="label");var e=Sn[e],r=e.id.replace("%s",this.id_),a=[t,r].join(" ").trim();return["<"+i+' id="'+r+'" class="'+("label"===i?"vjs-label":"")+'">',this.localize(e.label),"",'").join("")},t.createElFgColor_=function(){var e="captions-text-legend-"+this.id_;return['
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"].join("")},t.createElWinColor_=function(){var e="captions-window-"+this.id_;return['
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"].join("")},t.createElColors_=function(){return $("div",{className:"vjs-track-settings-colors",innerHTML:[this.createElFgColor_(),this.createElBgColor_(),this.createElWinColor_()].join("")})},t.createElFont_=function(){return $("div",{className:"vjs-track-settings-font",innerHTML:['
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",'
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Escape will cancel and close the window.")},t.buildCSSClass=function(){return n.prototype.buildCSSClass.call(this)+" vjs-text-track-settings"},t.getValues=function(){var i,n,e,r=this;return n=function(e,t,i){var n,t=(n=r.$(t.selector),t=t.parser,wn(n.options[n.options.selectedIndex].value,t));return void 0!==t&&(e[i]=t),e},void 0===(e={})&&(e=0),v(i=Sn).reduce(function(e,t){return n(e,i[t],t)},e)},t.setValues=function(i){var n=this;_(Sn,function(e,t){!function(e,t,i){if(t)for(var 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a=0;a>>0,o=e.subarray(a+4,a+8);if(0==s)break;var u=a+s;if(u>e.length){if(i)break;u=e.length}s=e.subarray(a+8,u);wr(o,t[0])&&(1===t.length?r.push(s):r.push.apply(r,io(s,t.slice(1),i))),a=u}return r}function no(e,t,i,n){void 0===i&&(i=!0),void 0===n&&(n=!1);var r=function(e){for(var t=1,i=0;i=t.length)return t.length;var n=no(t,i,!1);if(wr(e.bytes,n.bytes))return i;var r=no(t,i+n.length);return ao(e,t,i+r.length+r.value+n.length)}function so(e,t){var i;i=t,t=Array.isArray(i)?i.map(ro):[ro(i)],e=br(e);var n=[];if(!t.length)return n;for(var r=0;re.length?e.length:o+s.value,u=e.subarray(o,u);wr(t[0],a.bytes)&&(1===t.length?n.push(u):n=n.concat(so(u,t.slice(1)))),r+=a.length+s.length+u.length}return n}function oo(e,t,i,n){void 0===n&&(n=1/0),e=br(e),i=[].concat(i);for(var r,a=0,s=0;a>1&63),-1!==i.indexOf(u)&&(r=a+o),a+=o+("h264"===t?1:2)}else a++}return e.subarray(0,0)}var 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e}},bo={"urn:uuid:1077efec-c0b2-4d02-ace3-3c1e52e2fb4b":"org.w3.clearkey","urn:uuid:edef8ba9-79d6-4ace-a3c8-27dcd51d21ed":"com.widevine.alpha","urn:uuid:9a04f079-9840-4286-ab92-e65be0885f95":"com.microsoft.playready","urn:uuid:f239e769-efa3-4850-9c16-a903c6932efb":"com.adobe.primetime"},To=Math.pow(2,32),So=function(e){var t=new DataView(e.buffer,e.byteOffset,e.byteLength);return t.getBigUint64?(e=t.getBigUint64(0))>>7,referencedSize:2147483647&t.getUint32(n),subsegmentDuration:t.getUint32(n+4),startsWithSap:!!(128&e[n+8]),sapType:(112&e[n+8])>>>4,sapDeltaTime:268435455&t.getUint32(n+8)});return 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t=eo(e);return wr(e,[255,16],{offset:t,mask:[255,22]})},mp3:function(e){var t=eo(e);return wr(e,[255,2],{offset:t,mask:[255,6]})},webm:function(e){e=so(e,[ko.EBML,ko.DocType])[0];return wr(e,Po.webm)},mkv:function(e){e=so(e,[ko.EBML,ko.DocType])[0];return wr(e,Po.matroska)},mp4:function(e){return!Lo["3gp"](e)&&!Lo.mov(e)&&(!(!wr(e,Po.mp4,{offset:4})&&!wr(e,Po.fmp4,{offset:4}))||(!(!wr(e,Po.moof,{offset:4})&&!wr(e,Po.moov,{offset:4}))||void 0))},mov:function(e){return wr(e,Po.mov,{offset:4})},"3gp":function(e){return wr(e,Po["3gp"],{offset:4})},ac3:function(e){var t=eo(e);return wr(e,Po.ac3,{offset:t})},ts:function(e){if(e.length<189&&1<=e.length)return 71===e[0];for(var t=0;t+188"):function(){}}function No(e,t){var i,n=[];if(e&&e.length)for(i=0;i "+e.end(i));return t.join(", ")}function jo(e){for(var t=[],i=0;iDate.now()}function Zo(e){return e.excludeUntil&&e.excludeUntil===1/0}function eu(e){var t=Jo(e);return!e.disabled&&!t}function tu(e,t){return 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Uint8Array([0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,(65280&e.width)>>8,255&e.width,(65280&e.height)>>8,255&e.height,0,72,0,0,0,72,0,0,0,0,0,0,0,1,19,118,105,100,101,111,106,115,45,99,111,110,116,114,105,98,45,104,108,115,0,0,0,0,0,0,0,0,0,0,0,0,0,24,17,17]),u(T.avcC,new Uint8Array([1,e.profileIdc,e.profileCompatibility,e.levelIdc,255].concat([n.length],a,[r.length],s))),u(T.btrt,new Uint8Array([0,28,156,128,0,45,198,192,0,45,198,192]))],e.sarRatio&&(i=e.sarRatio[0],e=e.sarRatio[1],t.push(u(T.pasp,new Uint8Array([(4278190080&i)>>24,(16711680&i)>>16,(65280&i)>>8,255&i,(4278190080&e)>>24,(16711680&e)>>16,(65280&e)>>8,255&e])))),u.apply(null,t)},N=function(e){return u(T.mp4a,new Uint8Array([0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,(65280&e.channelcount)>>8,255&e.channelcount,(65280&e.samplesize)>>8,255&e.samplesize,0,0,0,0,(65280&e.samplerate)>>8,255&e.samplerate,0,0]),i(e))},d=function(e){e=new Uint8Array([0,0,0,7,0,0,0,0,0,0,0,0,(4278190080&e.id)>>24,(16711680&e.id)>>16,(65280&e.id)>>8,255&e.id,0,0,0,0,(4278190080&e.duration)>>24,(16711680&e.duration)>>16,(65280&e.duration)>>8,255&e.duration,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,64,0,0,0,(65280&e.width)>>8,255&e.width,0,0,(65280&e.height)>>8,255&e.height,0,0]);return u(T.tkhd,e)},v=function(e){var t,i=u(T.tfhd,new Uint8Array([0,0,0,58,(4278190080&e.id)>>24,(16711680&e.id)>>16,(65280&e.id)>>8,255&e.id,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0])),n=Math.floor(e.baseMediaDecodeTime/V),r=Math.floor(e.baseMediaDecodeTime%V),n=u(T.tfdt,new Uint8Array([1,0,0,0,n>>>24&255,n>>>16&255,n>>>8&255,255&n,r>>>24&255,r>>>16&255,r>>>8&255,255&r]));return"audio"===e.type?(t=b(e,92),u(T.traf,i,n,t)):(r=m(e),t=b(e,r.length+92),u(T.traf,i,n,t,r))},c=function(e){return e.duration=e.duration||4294967295,u(T.trak,d(e),h(e))},_=function(e){var t=new Uint8Array([0,0,0,0,(4278190080&e.id)>>24,(16711680&e.id)>>16,(65280&e.id)>>8,255&e.id,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1]);return"video"!==e.type&&(t[t.length-1]=0),u(T.trex,t)},U=function(e,t){var i=0,n=0,r=0,a=0;return e.length&&(void 0!==e[0].duration&&(i=1),void 0!==e[0].size&&(n=2),void 0!==e[0].flags&&(r=4),void 0!==e[0].compositionTimeOffset&&(a=8)),[0,0,i|n|r|a,1,(4278190080&e.length)>>>24,(16711680&e.length)>>>16,(65280&e.length)>>>8,255&e.length,(4278190080&t)>>>24,(16711680&t)>>>16,(65280&t)>>>8,255&t]},B=function(e,t){var i,n,r,a,s=e.samples||[];for(t+=20+16*s.length,t=U(s,t),(n=new Uint8Array(t.length+16*s.length)).set(t),i=t.length,a=0;a>>24,n[i++]=(16711680&r.duration)>>>16,n[i++]=(65280&r.duration)>>>8,n[i++]=255&r.duration,n[i++]=(4278190080&r.size)>>>24,n[i++]=(16711680&r.size)>>>16,n[i++]=(65280&r.size)>>>8,n[i++]=255&r.size,n[i++]=r.flags.isLeading<<2|r.flags.dependsOn,n[i++]=r.flags.isDependedOn<<6|r.flags.hasRedundancy<<4|r.flags.paddingValue<<1|r.flags.isNonSyncSample,n[i++]=61440&r.flags.degradationPriority,n[i++]=15&r.flags.degradationPriority,n[i++]=(4278190080&r.compositionTimeOffset)>>>24,n[i++]=(16711680&r.compositionTimeOffset)>>>16,n[i++]=(65280&r.compositionTimeOffset)>>>8,n[i++]=255&r.compositionTimeOffset;return u(T.trun,n)},F=function(e,t){var i,n,r,a,s=e.samples||[];for(t+=20+8*s.length,t=U(s,t),(i=new Uint8Array(t.length+8*s.length)).set(t),n=t.length,a=0;a>>24,i[n++]=(16711680&r.duration)>>>16,i[n++]=(65280&r.duration)>>>8,i[n++]=255&r.duration,i[n++]=(4278190080&r.size)>>>24,i[n++]=(16711680&r.size)>>>16,i[n++]=(65280&r.size)>>>8,i[n++]=255&r.size;return u(T.trun,i)},b=function(e,t){return("audio"===e.type?F:B)(e,t)};n=function(){return u(T.ftyp,S,w,S,E)};function W(e,t){var i={size:0,flags:{isLeading:0,dependsOn:1,isDependedOn:0,hasRedundancy:0,degradationPriority:0,isNonSyncSample:1}};return i.dataOffset=t,i.compositionTimeOffset=e.pts-e.dts,i.duration=e.duration,i.size=4*e.length,i.size+=e.byteLength,e.keyFrame&&(i.flags.dependsOn=2,i.flags.isNonSyncSample=0),i}function G(e){for(var t=[];e--;)t.push(0);return t}function z(){var e,i;return X||(e={96e3:[ie,[227,64],G(154),[56]],88200:[ie,[231],G(170),[56]],64e3:[ie,[248,192],G(240),[56]],48e3:[ie,[255,192],G(268),[55,148,128],G(54),[112]],44100:[ie,[255,192],G(268),[55,163,128],G(84),[112]],32e3:[ie,[255,192],G(268),[55,234],G(226),[112]],24e3:[ie,[255,192],G(268),[55,255,128],G(268),[111,112],G(126),[224]],16e3:[ie,[255,192],G(268),[55,255,128],G(268),[111,255],G(269),[223,108],G(195),[1,192]],12e3:[ne,G(268),[3,127,248],G(268),[6,255,240],G(268),[13,255,224],G(268),[27,253,128],G(259),[56]],11025:[ne,G(268),[3,127,248],G(268),[6,255,240],G(268),[13,255,224],G(268),[27,255,192],G(268),[55,175,128],G(108),[112]],8e3:[ne,G(268),[3,121,16],G(47),[7]]},i=e,X=Object.keys(i).reduce(function(e,t){return e[t]=new Uint8Array(i[t].reduce(function(e,t){return e.concat(t)},[])),e},{})),X}var X,K=function(e){return u(T.mdat,e)},Y=We,Q=function(e){var t=n(),i=s(e),e=new Uint8Array(t.byteLength+i.byteLength);return e.set(t),e.set(i,t.byteLength),e},$=function(e){var t,i,n=[],r=[];for(r.byteLength=0,r.nalCount=0,r.duration=0,t=n.byteLength=0;t=i?e:(t.minSegmentDts=1/0,e.filter(function(e){return e.dts>=i&&(t.minSegmentDts=Math.min(t.minSegmentDts,e.dts),t.minSegmentPts=t.minSegmentDts,!0)}))},ge=function(e){for(var t,i=[],n=0;n=this.virtualRowCount&&"function"==typeof this.beforeRowOverflow&&this.beforeRowOverflow(e),0this.virtualRowCount;)this.rows.shift(),this.rowIdx--},Ae.prototype.isEmpty=function(){return 0===this.rows.length||1===this.rows.length&&""===this.rows[0]},Ae.prototype.addText=function(e){this.rows[this.rowIdx]+=e},Ae.prototype.backspace=function(){var e;this.isEmpty()||(e=this.rows[this.rowIdx],this.rows[this.rowIdx]=e.substr(0,e.length-1))};function Le(e,t,i){this.serviceNum=e,this.text="",this.currentWindow=new Ae(-1),this.windows=[],this.stream=i,"string"==typeof t&&this.createTextDecoder(t)}Le.prototype.init=function(e,t){this.startPts=e;for(var i=0;i<8;i++)this.windows[i]=new Ae(i),"function"==typeof t&&(this.windows[i].beforeRowOverflow=t)},Le.prototype.setCurrentWindow=function(e){this.currentWindow=this.windows[e]},Le.prototype.createTextDecoder=function(t){if("undefined"==typeof TextDecoder)this.stream.trigger("log",{level:"warn",message:"The `encoding` option is unsupported without TextDecoder support"});else try{this.textDecoder_=new TextDecoder(t)}catch(e){this.stream.trigger("log",{level:"warn",message:"TextDecoder could not be created with "+t+" encoding. "+e})}};var De=function e(t){t=t||{},e.prototype.init.call(this);var i,n=this,r=t.captionServices||{},a={};Object.keys(r).forEach(function(e){i=r[e],/^SERVICE/.test(e)&&(a[e]=i.encoding)}),this.serviceEncodings=a,this.current708Packet=null,this.services={},this.push=function(e){(3===e.type||null===n.current708Packet)&&n.new708Packet(),n.add708Bytes(e)}};De.prototype=new j,De.prototype.new708Packet=function(){null!==this.current708Packet&&this.push708Packet(),this.current708Packet={data:[],ptsVals:[]}},De.prototype.add708Bytes=function(e){var t=e.ccData,i=t>>>8,t=255&t;this.current708Packet.ptsVals.push(e.pts),this.current708Packet.data.push(i),this.current708Packet.data.push(t)},De.prototype.push708Packet=function(){var e,t=this.current708Packet,i=t.data,n=null,r=0,a=i[r++];for(t.seq=a>>6,t.sizeCode=63&a;r>5)&&0>5,t.rowLock=(16&n)>>4,t.columnLock=(8&n)>>3,t.priority=7&n,n=i[++e],t.relativePositioning=(128&n)>>7,t.anchorVertical=127&n,n=i[++e],t.anchorHorizontal=n,n=i[++e],t.anchorPoint=(240&n)>>4,t.rowCount=15&n,n=i[++e],t.columnCount=63&n,n=i[++e],t.windowStyle=(56&n)>>3,t.penStyle=7&n,t.virtualRowCount=t.rowCount+1,e},De.prototype.setWindowAttributes=function(e,t){var i=this.current708Packet.data,n=i[e],t=t.currentWindow.winAttr,n=i[++e];return t.fillOpacity=(192&n)>>6,t.fillRed=(48&n)>>4,t.fillGreen=(12&n)>>2,t.fillBlue=3&n,n=i[++e],t.borderType=(192&n)>>6,t.borderRed=(48&n)>>4,t.borderGreen=(12&n)>>2,t.borderBlue=3&n,n=i[++e],t.borderType+=(128&n)>>5,t.wordWrap=(64&n)>>6,t.printDirection=(48&n)>>4,t.scrollDirection=(12&n)>>2,t.justify=3&n,n=i[++e],t.effectSpeed=(240&n)>>4,t.effectDirection=(12&n)>>2,t.displayEffect=3&n,e},De.prototype.flushDisplayed=function(e,t){for(var i=[],n=0;n<8;n++)t.windows[n].visible&&!t.windows[n].isEmpty()&&i.push(t.windows[n].getText());t.endPts=e,t.text=i.join("\n\n"),this.pushCaption(t),t.startPts=e},De.prototype.pushCaption=function(e){""!==e.text&&(this.trigger("data",{startPts:e.startPts,endPts:e.endPts,text:e.text,stream:"cc708_"+e.serviceNum}),e.text="",e.startPts=e.endPts)},De.prototype.displayWindows=function(e,t){var i=this.current708Packet.data[++e],n=this.getPts(e);this.flushDisplayed(n,t);for(var r=0;r<8;r++)i&1<>4,t.offset=(12&n)>>2,t.penSize=3&n,n=i[++e],t.italics=(128&n)>>7,t.underline=(64&n)>>6,t.edgeType=(56&n)>>3,t.fontStyle=7&n,e},De.prototype.setPenColor=function(e,t){var i=this.current708Packet.data,n=i[e],t=t.currentWindow.penColor,n=i[++e];return t.fgOpacity=(192&n)>>6,t.fgRed=(48&n)>>4,t.fgGreen=(12&n)>>2,t.fgBlue=3&n,n=i[++e],t.bgOpacity=(192&n)>>6,t.bgRed=(48&n)>>4,t.bgGreen=(12&n)>>2,t.bgBlue=3&n,n=i[++e],t.edgeRed=(48&n)>>4,t.edgeGreen=(12&n)>>2,t.edgeBlue=3&n,e},De.prototype.setPenLocation=function(e,t){var i=this.current708Packet.data,n=i[e],r=t.currentWindow.penLoc;return t.currentWindow.pendingNewLine=!0,n=i[++e],r.row=15&n,n=i[++e],r.column=63&n,e},De.prototype.reset=function(e,t){var i=this.getPts(e);return this.flushDisplayed(i,t),this.initService(t.serviceNum,e)};function Oe(e){return null===e?"":(e=Re[e]||e,String.fromCharCode(e))}function Me(){for(var e=[],t=15;t--;)e.push("");return e}var Re={42:225,92:233,94:237,95:243,96:250,123:231,124:247,125:209,126:241,127:9608,304:174,305:176,306:189,307:191,308:8482,309:162,310:163,311:9834,312:224,313:160,314:232,315:226,316:234,317:238,318:244,319:251,544:193,545:201,546:211,547:218,548:220,549:252,550:8216,551:161,552:42,553:39,554:8212,555:169,556:8480,557:8226,558:8220,559:8221,560:192,561:194,562:199,563:200,564:202,565:203,566:235,567:206,568:207,569:239,570:212,571:217,572:249,573:219,574:171,575:187,800:195,801:227,802:205,803:204,804:236,805:210,806:242,807:213,808:245,809:123,810:125,811:92,812:94,813:95,814:124,815:126,816:196,817:228,818:214,819:246,820:223,821:165,822:164,823:9474,824:197,825:229,826:216,827:248,828:9484,829:9488,830:9492,831:9496},Ne=[4352,4384,4608,4640,5376,5408,5632,5664,5888,5920,4096,4864,4896,5120,5152],Ue=function e(t,i){e.prototype.init.call(this),this.field_=t||0,this.dataChannel_=i||0,this.name_="CC"+(1+(this.field_<<1|this.dataChannel_)),this.setConstants(),this.reset(),this.push=function(e){var t,i,n,r,a=32639&e.ccData;a!==this.lastControlCode_?(4096==(61440&a)?this.lastControlCode_=a:a!==this.PADDING_&&(this.lastControlCode_=null),t=a>>>8,i=255&a,a===this.PADDING_||(a===this.RESUME_CAPTION_LOADING_?this.mode_="popOn":a===this.END_OF_CAPTION_?(this.mode_="popOn",this.clearFormatting(e.pts),this.flushDisplayed(e.pts),r=this.displayed_,this.displayed_=this.nonDisplayed_,this.nonDisplayed_=r,this.startPts_=e.pts):a===this.ROLL_UP_2_ROWS_?(this.rollUpRows_=2,this.setRollUp(e.pts)):a===this.ROLL_UP_3_ROWS_?(this.rollUpRows_=3,this.setRollUp(e.pts)):a===this.ROLL_UP_4_ROWS_?(this.rollUpRows_=4,this.setRollUp(e.pts)):a===this.CARRIAGE_RETURN_?(this.clearFormatting(e.pts),this.flushDisplayed(e.pts),this.shiftRowsUp_(),this.startPts_=e.pts):a===this.BACKSPACE_?"popOn"===this.mode_?this.nonDisplayed_[this.row_]=this.nonDisplayed_[this.row_].slice(0,-1):this.displayed_[this.row_]=this.displayed_[this.row_].slice(0,-1):a===this.ERASE_DISPLAYED_MEMORY_?(this.flushDisplayed(e.pts),this.displayed_=Me()):a===this.ERASE_NON_DISPLAYED_MEMORY_?this.nonDisplayed_=Me():a===this.RESUME_DIRECT_CAPTIONING_?("paintOn"!==this.mode_&&(this.flushDisplayed(e.pts),this.displayed_=Me()),this.mode_="paintOn",this.startPts_=e.pts):this.isSpecialCharacter(t,i)?(n=Oe((t=(3&t)<<8)|i),this[this.mode_](e.pts,n),this.column_++):this.isExtCharacter(t,i)?("popOn"===this.mode_?this.nonDisplayed_[this.row_]=this.nonDisplayed_[this.row_].slice(0,-1):this.displayed_[this.row_]=this.displayed_[this.row_].slice(0,-1),n=Oe((t=(3&t)<<8)|i),this[this.mode_](e.pts,n),this.column_++):this.isMidRowCode(t,i)?(this.clearFormatting(e.pts),this[this.mode_](e.pts," "),this.column_++,14==(14&i)&&this.addFormatting(e.pts,["i"]),1==(1&i)&&this.addFormatting(e.pts,["u"])):this.isOffsetControlCode(t,i)?this.column_+=3&i:this.isPAC(t,i)?(r=Ne.indexOf(7968&a),"rollUp"===this.mode_&&(r-this.rollUpRows_+1<0&&(r=this.rollUpRows_-1),this.setRollUp(e.pts,r)),r!==this.row_&&(this.clearFormatting(e.pts),this.row_=r),1&i&&-1===this.formatting_.indexOf("u")&&this.addFormatting(e.pts,["u"]),16==(16&a)&&(this.column_=4*((14&a)>>1)),this.isColorPAC(i)&&14==(14&i)&&this.addFormatting(e.pts,["i"])):this.isNormalChar(t)&&(0===i&&(i=null),n=Oe(t),n+=Oe(i),this[this.mode_](e.pts,n),this.column_+=n.length))):this.lastControlCode_=null}};Ue.prototype=new j,Ue.prototype.flushDisplayed=function(e){var t=this.displayed_.map(function(e,t){try{return e.trim()}catch(e){return this.trigger("log",{level:"warn",message:"Skipping a malformed 608 caption at index "+t+"."}),""}},this).join("\n").replace(/^\n+|\n+$/g,"");t.length&&this.trigger("data",{startPts:this.startPts_,endPts:e,text:t,stream:this.name_})},Ue.prototype.reset=function(){this.mode_="popOn",this.topRow_=0,this.startPts_=0,this.displayed_=Me(),this.nonDisplayed_=Me(),this.lastControlCode_=null,this.column_=0,this.row_=14,this.rollUpRows_=2,this.formatting_=[]},Ue.prototype.setConstants=function(){0===this.dataChannel_?(this.BASE_=16,this.EXT_=17,this.CONTROL_=(20|this.field_)<<8,this.OFFSET_=23):1===this.dataChannel_&&(this.BASE_=24,this.EXT_=25,this.CONTROL_=(28|this.field_)<<8,this.OFFSET_=31),this.PADDING_=0,this.RESUME_CAPTION_LOADING_=32|this.CONTROL_,this.END_OF_CAPTION_=47|this.CONTROL_,this.ROLL_UP_2_ROWS_=37|this.CONTROL_,this.ROLL_UP_3_ROWS_=38|this.CONTROL_,this.ROLL_UP_4_ROWS_=39|this.CONTROL_,this.CARRIAGE_RETURN_=45|this.CONTROL_,this.RESUME_DIRECT_CAPTIONING_=41|this.CONTROL_,this.BACKSPACE_=33|this.CONTROL_,this.ERASE_DISPLAYED_MEMORY_=44|this.CONTROL_,this.ERASE_NON_DISPLAYED_MEMORY_=46|this.CONTROL_},Ue.prototype.isSpecialCharacter=function(e,t){return e===this.EXT_&&48<=t&&t<=63},Ue.prototype.isExtCharacter=function(e,t){return(e===this.EXT_+1||e===this.EXT_+2)&&32<=t&&t<=63},Ue.prototype.isMidRowCode=function(e,t){return e===this.EXT_&&32<=t&&t<=47},Ue.prototype.isOffsetControlCode=function(e,t){return e===this.OFFSET_&&33<=t&&t<=35},Ue.prototype.isPAC=function(e,t){return e>=this.BASE_&&e"},"");this[this.mode_](e,t)},Ue.prototype.clearFormatting=function(e){var t;this.formatting_.length&&(t=this.formatting_.reverse().reduce(function(e,t){return e+""},""),this.formatting_=[],this[this.mode_](e,t))},Ue.prototype.popOn=function(e,t){var i=this.nonDisplayed_[this.row_];this.nonDisplayed_[this.row_]=i+=t},Ue.prototype.rollUp=function(e,t){var i=this.displayed_[this.row_];this.displayed_[this.row_]=i+=t},Ue.prototype.shiftRowsUp_=function(){for(var e=0;e>>2,s*=4,s+=3&a[7],o.timeStamp=s,void 0===t.pts&&void 0===t.dts&&(t.pts=o.timeStamp,t.dts=o.timeStamp),this.trigger("timestamp",o))),t.frames.push(o),i+=10,(i+=n)>>4&&(i+=e[i]+1),0===t.pid)t.type="pat",n(e.subarray(i),t),this.trigger("data",t);else if(t.pid===this.pmtPid)for(t.type="pmt",n(e.subarray(i),t),this.trigger("data",t);this.packetsWaitingForPmt.length;)this.processPes_.apply(this,this.packetsWaitingForPmt.shift());else void 0===this.programMapTable?this.packetsWaitingForPmt.push([e,i,t]):this.processPes_(e,i,t)},this.processPes_=function(e,t,i){i.pid===this.programMapTable.video?i.streamType=je.H264_STREAM_TYPE:i.pid===this.programMapTable.audio?i.streamType=je.ADTS_STREAM_TYPE:i.streamType=this.programMapTable["timed-metadata"][i.pid],i.type="pes",i.data=e.subarray(t),this.trigger("data",i)}}).prototype=new j,Xe.STREAM_TYPES={h264:27,adts:15},(Ke=function(){function n(e,t,i){var n,r,a,s,o=new Uint8Array(e.size),u={type:t},l=0,c=0;if(e.data.length&&!(e.size<9)){for(u.trackId=e.data[0].pid,l=0;l>>3,a.pts*=4,a.pts+=(6&r[13])>>>1,a.dts=a.pts,64&s&&(a.dts=(14&r[14])<<27|(255&r[15])<<20|(254&r[16])<<12|(255&r[17])<<5|(254&r[18])>>>3,a.dts*=4,a.dts+=(6&r[18])>>>1)),a.data=r.subarray(9+r[8])),t="video"===t||u.packetLength<=e.size,(i||t)&&(e.size=0,e.data.length=0),t&&d.trigger("data",u)}}var t,d=this,r=!1,a={data:[],size:0},s={data:[],size:0},o={data:[],size:0};Ke.prototype.init.call(this),this.push=function(i){({pat:function(){},pes:function(){var e,t;switch(i.streamType){case je.H264_STREAM_TYPE:e=a,t="video";break;case je.ADTS_STREAM_TYPE:e=s,t="audio";break;case je.METADATA_STREAM_TYPE:e=o,t="timed-metadata";break;default:return}i.payloadUnitStartIndicator&&n(e,t,!0),e.data.push(i),e.size+=i.data.byteLength},pmt:function(){var e={type:"metadata",tracks:[]};null!==(t=i.programMapTable).video&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.video,codec:"avc",type:"video"}),null!==t.audio&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.audio,codec:"adts",type:"audio"}),r=!0,d.trigger("data",e)}})[i.type]()},this.reset=function(){a.size=0,a.data.length=0,s.size=0,s.data.length=0,this.trigger("reset")},this.flushStreams_=function(){n(a,"video"),n(s,"audio"),n(o,"timed-metadata")},this.flush=function(){var e;!r&&t&&(e={type:"metadata",tracks:[]},null!==t.video&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.video,codec:"avc",type:"video"}),null!==t.audio&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.audio,codec:"adts",type:"audio"}),d.trigger("data",e)),r=!1,this.flushStreams_(),this.trigger("done")}}).prototype=new j;var Qe,$e={PAT_PID:0,MP2T_PACKET_LENGTH:188,TransportPacketStream:Ye,TransportParseStream:Xe,ElementaryStream:Ke,TimestampRolloverStream:We,CaptionStream:Fe.CaptionStream,Cea608Stream:Fe.Cea608Stream,Cea708Stream:Fe.Cea708Stream,MetadataStream:e};for(Qe in je)je.hasOwnProperty(Qe)&&($e[Qe]=je[Qe]);var Je=$e,Ze=ue,et=[96e3,88200,64e3,48e3,44100,32e3,24e3,22050,16e3,12e3,11025,8e3,7350],tt=function(u){var l,c=0;tt.prototype.init.call(this),this.skipWarn_=function(e,t){this.trigger("log",{level:"warn",message:"adts skiping bytes "+e+" to "+t+" in frame "+c+" outside syncword"})},this.push=function(e){var t,i,n,r,a,s,o=0;if(u||(c=0),"audio"===e.type){for(l&&l.length?(n=l,(l=new Uint8Array(n.byteLength+e.data.byteLength)).set(n),l.set(e.data,n.byteLength)):l=e.data;o+7>5,a=(r=1024*(1+(3&l[o+6])))*Ze/et[(60&l[o+2])>>>2],l.byteLength-o>>6&3),channelcount:(1&l[o+2])<<2|(192&l[o+3])>>>6,samplerate:et[(60&l[o+2])>>>2],samplingfrequencyindex:(60&l[o+2])>>>2,samplesize:16,data:l.subarray(o+7+i,o+t)}),c++,o+=t}else"number"!=typeof s&&(s=o),o++;"number"==typeof s&&(this.skipWarn_(s,o),s=null),l=l.subarray(o)}},this.flush=function(){c=0,this.trigger("done")},this.reset=function(){l=void 0,this.trigger("reset")},this.endTimeline=function(){l=void 0,this.trigger("endedtimeline")}};tt.prototype=new j;var it,nt,rt=tt,at=function(n){var r=n.byteLength,a=0,s=0;this.length=function(){return 8*r},this.bitsAvailable=function(){return 8*r+s},this.loadWord=function(){var e=n.byteLength-r,t=new Uint8Array(4),i=Math.min(4,r);if(0===i)throw new Error("no bytes available");t.set(n.subarray(e,e+i)),a=new DataView(t.buffer).getUint32(0),s=8*i,r-=i},this.skipBits=function(e){var t;e>>32-t;return 0<(s-=t)?a<<=t:0>>e))return a<<=e,s-=e,e;return this.loadWord(),e+this.skipLeadingZeros()},this.skipUnsignedExpGolomb=function(){this.skipBits(1+this.skipLeadingZeros())},this.skipExpGolomb=function(){this.skipBits(1+this.skipLeadingZeros())},this.readUnsignedExpGolomb=function(){var e=this.skipLeadingZeros();return this.readBits(e+1)-1},this.readExpGolomb=function(){var e=this.readUnsignedExpGolomb();return 1&e?1+e>>>1:-1*(e>>>1)},this.readBoolean=function(){return 1===this.readBits(1)},this.readUnsignedByte=function(){return this.readBits(8)},this.loadWord()},st=function(){var n,r,a=0;st.prototype.init.call(this),this.push=function(e){for(var t,i=(r=r?((t=new Uint8Array(r.byteLength+e.data.byteLength)).set(r),t.set(e.data,r.byteLength),t):e.data).byteLength;a>4?i+20:i+10}function ut(e,t){return e.length-t<10||e[t]!=="I".charCodeAt(0)||e[t+1]!=="D".charCodeAt(0)||e[t+2]!=="3".charCodeAt(0)?t:ut(e,t+=ot(e,t))}function lt(e){return e[0]<<21|e[1]<<14|e[2]<<7|e[3]}var e={H264Stream:it,NalByteStream:st},ct=[96e3,88200,64e3,48e3,44100,32e3,24e3,22050,16e3,12e3,11025,8e3,7350],dt={isLikelyAacData:function(e){var t=ut(e,0);return e.length>=t+2&&255==(255&e[t])&&240==(240&e[t+1])&&16==(22&e[t+1])},parseId3TagSize:ot,parseAdtsSize:function(e,t){var i=(224&e[t+5])>>5,n=e[t+4]<<3;return 6144&e[t+3]|n|i},parseType:function(e,t){return e[t]==="I".charCodeAt(0)&&e[t+1]==="D".charCodeAt(0)&&e[t+2]==="3".charCodeAt(0)?"timed-metadata":!0&e[t]&&240==(240&e[t+1])?"audio":null},parseSampleRate:function(e){for(var t=0;t+5>>2];t++}return null},parseAacTimestamp:function(e){var t,i=10;64&e[5]&&(i+=4,i+=lt(e.subarray(10,14)));do{if((t=lt(e.subarray(i+4,i+8)))<1)return null;if("PRIV"===String.fromCharCode(e[i],e[i+1],e[i+2],e[i+3]))for(var n=e.subarray(i+10,i+t+10),r=0;r>>2;return s*=4,s+=3&a[7]}}while(i+=10,(i+=t)a.length)break;t={type:"audio",data:a.subarray(r,r+n),pts:s,dts:s},this.trigger("data",t),r+=n}else{if(a.length-r<10)break;if(r+(n=dt.parseId3TagSize(a,r))>a.length)break;t={type:"timed-metadata",data:a.subarray(r,r+n)},this.trigger("data",t),r+=n}e=a.length-r,a=0i.pts?u++:(t++,a-=n.byteLength,s-=n.nalCount,o-=n.duration);return 0===t?e:t===e.length?null:((r=e.slice(t)).byteLength=a,r.duration=o,r.nalCount=s,r.pts=r[0].pts,r.dts=r[0].dts,r)},this.alignGopsAtEnd_=function(e){for(var t,i,n=l.length-1,r=e.length-1,a=null,s=!1;0<=n&&0<=r;){if(t=l[n],i=e[r],t.pts===i.pts){s=!0;break}t.pts>i.pts?n--:(n===l.length-1&&(a=r),r--)}if(!s&&null===a)return null;if(0===(u=s?r:a))return e;var o=e.slice(u),u=o.reduce(function(e,t){return e.byteLength+=t.byteLength,e.duration+=t.duration,e.nalCount+=t.nalCount,e},{byteLength:0,duration:0,nalCount:0});return o.byteLength=u.byteLength,o.duration=u.duration,o.nalCount=u.nalCount,o.pts=o[0].pts,o.dts=o[0].dts,o},this.alignGopsWith=function(e){l=e}}).prototype=new j,(_t=function(e,t){this.numberOfTracks=0,this.metadataStream=t,"undefined"!=typeof(e=e||{}).remux?this.remuxTracks=!!e.remux:this.remuxTracks=!0,"boolean"==typeof e.keepOriginalTimestamps?this.keepOriginalTimestamps=e.keepOriginalTimestamps:this.keepOriginalTimestamps=!1,this.pendingTracks=[],this.videoTrack=null,this.pendingBoxes=[],this.pendingCaptions=[],this.pendingMetadata=[],this.pendingBytes=0,this.emittedTracks=0,_t.prototype.init.call(this),this.push=function(e){return e.text?this.pendingCaptions.push(e):e.frames?this.pendingMetadata.push(e):(this.pendingTracks.push(e.track),this.pendingBytes+=e.boxes.byteLength,"video"===e.track.type&&(this.videoTrack=e.track,this.pendingBoxes.push(e.boxes)),void("audio"===e.track.type&&(this.audioTrack=e.track,this.pendingBoxes.unshift(e.boxes))))}}).prototype=new j,_t.prototype.flush=function(e){var t,i,n,r=0,a={captions:[],captionStreams:{},metadata:[],info:{}},s=0;if(this.pendingTracks.length=this.numberOfTracks&&(this.trigger("done"),this.emittedTracks=0))}if(this.videoTrack?(s=this.videoTrack.timelineStartInfo.pts,St.forEach(function(e){a.info[e]=this.videoTrack[e]},this)):this.audioTrack&&(s=this.audioTrack.timelineStartInfo.pts,Tt.forEach(function(e){a.info[e]=this.audioTrack[e]},this)),this.videoTrack||this.audioTrack){for(1===this.pendingTracks.length?a.type=this.pendingTracks[0].type:a.type="combined",this.emittedTracks+=this.pendingTracks.length,e=Q(this.pendingTracks),a.initSegment=new Uint8Array(e.byteLength),a.initSegment.set(e),a.data=new Uint8Array(this.pendingBytes),n=0;n=this.numberOfTracks&&(this.trigger("done"),this.emittedTracks=0)},_t.prototype.setRemux=function(e){this.remuxTracks=e},(vt=function(n){var r,a,s=this,i=!0;vt.prototype.init.call(this),this.baseMediaDecodeTime=(n=n||{}).baseMediaDecodeTime||0,this.transmuxPipeline_={},this.setupAacPipeline=function(){var t={};(this.transmuxPipeline_=t).type="aac",t.metadataStream=new Je.MetadataStream,t.aacStream=new bt,t.audioTimestampRolloverStream=new Je.TimestampRolloverStream("audio"),t.timedMetadataTimestampRolloverStream=new Je.TimestampRolloverStream("timed-metadata"),t.adtsStream=new rt,t.coalesceStream=new _t(n,t.metadataStream),t.headOfPipeline=t.aacStream,t.aacStream.pipe(t.audioTimestampRolloverStream).pipe(t.adtsStream),t.aacStream.pipe(t.timedMetadataTimestampRolloverStream).pipe(t.metadataStream).pipe(t.coalesceStream),t.metadataStream.on("timestamp",function(e){t.aacStream.setTimestamp(e.timeStamp)}),t.aacStream.on("data",function(e){"timed-metadata"!==e.type&&"audio"!==e.type||t.audioSegmentStream||(a=a||{timelineStartInfo:{baseMediaDecodeTime:s.baseMediaDecodeTime},codec:"adts",type:"audio"},t.coalesceStream.numberOfTracks++,t.audioSegmentStream=new Ct(a,n),t.audioSegmentStream.on("log",s.getLogTrigger_("audioSegmentStream")),t.audioSegmentStream.on("timingInfo",s.trigger.bind(s,"audioTimingInfo")),t.adtsStream.pipe(t.audioSegmentStream).pipe(t.coalesceStream),s.trigger("trackinfo",{hasAudio:!!a,hasVideo:!!r}))}),t.coalesceStream.on("data",this.trigger.bind(this,"data")),t.coalesceStream.on("done",this.trigger.bind(this,"done")),ft(this,t)},this.setupTsPipeline=function(){var i={};(this.transmuxPipeline_=i).type="ts",i.metadataStream=new Je.MetadataStream,i.packetStream=new Je.TransportPacketStream,i.parseStream=new Je.TransportParseStream,i.elementaryStream=new Je.ElementaryStream,i.timestampRolloverStream=new Je.TimestampRolloverStream,i.adtsStream=new rt,i.h264Stream=new wt,i.captionStream=new Je.CaptionStream(n),i.coalesceStream=new _t(n,i.metadataStream),i.headOfPipeline=i.packetStream,i.packetStream.pipe(i.parseStream).pipe(i.elementaryStream).pipe(i.timestampRolloverStream),i.timestampRolloverStream.pipe(i.h264Stream),i.timestampRolloverStream.pipe(i.adtsStream),i.timestampRolloverStream.pipe(i.metadataStream).pipe(i.coalesceStream),i.h264Stream.pipe(i.captionStream).pipe(i.coalesceStream),i.elementaryStream.on("data",function(e){var t;if("metadata"===e.type){for(t=e.tracks.length;t--;)r||"video"!==e.tracks[t].type?a||"audio"!==e.tracks[t].type||((a=e.tracks[t]).timelineStartInfo.baseMediaDecodeTime=s.baseMediaDecodeTime):(r=e.tracks[t]).timelineStartInfo.baseMediaDecodeTime=s.baseMediaDecodeTime;r&&!i.videoSegmentStream&&(i.coalesceStream.numberOfTracks++,i.videoSegmentStream=new yt(r,n),i.videoSegmentStream.on("log",s.getLogTrigger_("videoSegmentStream")),i.videoSegmentStream.on("timelineStartInfo",function(e){a&&!n.keepOriginalTimestamps&&(a.timelineStartInfo=e,i.audioSegmentStream.setEarliestDts(e.dts-s.baseMediaDecodeTime))}),i.videoSegmentStream.on("processedGopsInfo",s.trigger.bind(s,"gopInfo")),i.videoSegmentStream.on("segmentTimingInfo",s.trigger.bind(s,"videoSegmentTimingInfo")),i.videoSegmentStream.on("baseMediaDecodeTime",function(e){a&&i.audioSegmentStream.setVideoBaseMediaDecodeTime(e)}),i.videoSegmentStream.on("timingInfo",s.trigger.bind(s,"videoTimingInfo")),i.h264Stream.pipe(i.videoSegmentStream).pipe(i.coalesceStream)),a&&!i.audioSegmentStream&&(i.coalesceStream.numberOfTracks++,i.audioSegmentStream=new Ct(a,n),i.audioSegmentStream.on("log",s.getLogTrigger_("audioSegmentStream")),i.audioSegmentStream.on("timingInfo",s.trigger.bind(s,"audioTimingInfo")),i.audioSegmentStream.on("segmentTimingInfo",s.trigger.bind(s,"audioSegmentTimingInfo")),i.adtsStream.pipe(i.audioSegmentStream).pipe(i.coalesceStream)),s.trigger("trackinfo",{hasAudio:!!a,hasVideo:!!r})}}),i.coalesceStream.on("data",this.trigger.bind(this,"data")),i.coalesceStream.on("id3Frame",function(e){e.dispatchType=i.metadataStream.dispatchType,s.trigger("id3Frame",e)}),i.coalesceStream.on("caption",this.trigger.bind(this,"caption")),i.coalesceStream.on("done",this.trigger.bind(this,"done")),ft(this,i)},this.setBaseMediaDecodeTime=function(e){var t=this.transmuxPipeline_;n.keepOriginalTimestamps||(this.baseMediaDecodeTime=e),a&&(a.timelineStartInfo.dts=void 0,a.timelineStartInfo.pts=void 0,_e(a),t.audioTimestampRolloverStream&&t.audioTimestampRolloverStream.discontinuity()),r&&(t.videoSegmentStream&&(t.videoSegmentStream.gopCache_=[]),r.timelineStartInfo.dts=void 0,r.timelineStartInfo.pts=void 0,_e(r),t.captionStream.reset()),t.timestampRolloverStream&&t.timestampRolloverStream.discontinuity()},this.setAudioAppendStart=function(e){a&&this.transmuxPipeline_.audioSegmentStream.setAudioAppendStart(e)},this.setRemux=function(e){var t=this.transmuxPipeline_;n.remux=e,t&&t.coalesceStream&&t.coalesceStream.setRemux(e)},this.alignGopsWith=function(e){r&&this.transmuxPipeline_.videoSegmentStream&&this.transmuxPipeline_.videoSegmentStream.alignGopsWith(e)},this.getLogTrigger_=function(t){var i=this;return function(e){e.stream=t,i.trigger("log",e)}},this.push=function(e){var t;i&&((t=Et(e))&&"aac"!==this.transmuxPipeline_.type?this.setupAacPipeline():t||"ts"===this.transmuxPipeline_.type||this.setupTsPipeline(),i=!1),this.transmuxPipeline_.headOfPipeline.push(e)},this.flush=function(){i=!0,this.transmuxPipeline_.headOfPipeline.flush()},this.endTimeline=function(){this.transmuxPipeline_.headOfPipeline.endTimeline()},this.reset=function(){this.transmuxPipeline_.headOfPipeline&&this.transmuxPipeline_.headOfPipeline.reset()},this.resetCaptions=function(){this.transmuxPipeline_.captionStream&&this.transmuxPipeline_.captionStream.reset()}}).prototype=new j;function It(e,c){var i=Rt(e,["moof","traf"]),e=Rt(e,["mdat"]),d={},n=[];return e.forEach(function(e,t){t=i[t];n.push({mdat:e,traf:t})}),n.forEach(function(e){var t,i,n,r,a,s=e.mdat,o=e.traf,u=Rt(o,["tfhd"]),l=Ht(u[0]),e=l.trackId,u=Rt(o,["tfdt"]),u=0>>4&&(t+=e[4]+1),t}function Lt(e){switch(e){case 5:return"slice_layer_without_partitioning_rbsp_idr";case 6:return"sei_rbsp";case 7:return"seq_parameter_set_rbsp";case 8:return"pic_parameter_set_rbsp";case 9:return"access_unit_delimiter_rbsp";default:return null}}var Dt={Transmuxer:vt,VideoSegmentStream:yt,AudioSegmentStream:Ct,AUDIO_PROPERTIES:Tt,VIDEO_PROPERTIES:St,generateSegmentTimingInfo:gt},e=function(e){return e>>>0},Ot=function(e){var t="";return t+=String.fromCharCode(e[0]),t+=String.fromCharCode(e[1]),t+=String.fromCharCode(e[2]),t+=String.fromCharCode(e[3])},Mt=e,Rt=function e(t,i){var n,r,a,s=[];if(!i.length)return null;for(n=0;n>>2,dependsOn:3&e[0],isDependedOn:(192&e[1])>>>6,hasRedundancy:(48&e[1])>>>4,paddingValue:(14&e[1])>>>1,isNonSyncSample:1&e[1],degradationPriority:e[2]<<8|e[3]}},jt=function(e){var t,i={version:e[0],flags:new Uint8Array(e.subarray(1,4)),samples:[]},n=new DataView(e.buffer,e.byteOffset,e.byteLength),r=1&i.flags[2],a=4&i.flags[2],s=1&i.flags[1],o=2&i.flags[1],u=4&i.flags[1],l=8&i.flags[1],c=n.getUint32(4),d=8;for(r&&(i.dataOffset=n.getInt32(d),d+=4),a&&c&&(t={flags:Ft(e.subarray(d,d+4))},d+=4,s&&(t.duration=n.getUint32(d),d+=4),o&&(t.size=n.getUint32(d),d+=4),l&&(1===i.version?t.compositionTimeOffset=n.getInt32(d):t.compositionTimeOffset=n.getUint32(d),d+=4),i.samples.push(t),c--);c--;)t={},s&&(t.duration=n.getUint32(d),d+=4),o&&(t.size=n.getUint32(d),d+=4),u&&(t.flags=Ft(e.subarray(d,d+4)),d+=4),l&&(1===i.version?t.compositionTimeOffset=n.getInt32(d):t.compositionTimeOffset=n.getUint32(d),d+=4),i.samples.push(t);return i},Ht=function(e){var t=new DataView(e.buffer,e.byteOffset,e.byteLength),i={version:e[0],flags:new Uint8Array(e.subarray(1,4)),trackId:t.getUint32(4)},n=1&i.flags[2],r=2&i.flags[2],a=8&i.flags[2],s=16&i.flags[2],o=32&i.flags[2],u=65536&i.flags[0],l=131072&i.flags[0],e=8;return n&&(e+=4,i.baseDataOffset=t.getUint32(12),e+=4),r&&(i.sampleDescriptionIndex=t.getUint32(e),e+=4),a&&(i.defaultSampleDuration=t.getUint32(e),e+=4),s&&(i.defaultSampleSize=t.getUint32(e),e+=4),o&&(i.defaultSampleFlags=t.getUint32(e)),u&&(i.durationIsEmpty=!0),!n&&l&&(i.baseDataOffsetIsMoof=!0),i},j="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{},j="undefined"!=typeof window?window:"undefined"!=typeof j?j:"undefined"!=typeof self?self:{},qt=j,Vt=ke,Wt=Fe.CaptionStream,Gt=function(){var t,r,a,s,o,i,n=!1;this.isInitialized=function(){return n},this.init=function(e){t=new Wt,n=!0,i=!!e&&e.isPartial,t.on("data",function(e){e.startTime=e.startPts/s,e.endTime=e.endPts/s,o.captions.push(e),o.captionStreams[e.stream]=!0}),t.on("log",function(e){o.logs.push(e)})},this.isNewInit=function(e,t){return!(e&&0===e.length||t&&"object"==typeof t&&0===Object.keys(t).length)&&(a!==e[0]||s!==t[a])},this.parse=function(e,t,i){if(!this.isInitialized())return null;if(!t||!i)return null;if(this.isNewInit(t,i))a=t[0],s=i[a];else if(null===a||!s)return r.push(e),null;for(;0>>2&63).replace(/^0/,"")):t.codec="mp4a.40.2"):t.codec=t.codec.toLowerCase()));e=Rt(e,["mdia","mdhd"])[0];e&&(t.timescale=Yt(e)),s.push(t)}),s},Qt=ke,$t=q,Jt=Ie,Zt={};Zt.ts={parseType:function(e,t){e=xt(e);return 0===e?"pat":e===t?"pmt":t?"pes":null},parsePat:function(e){var t=At(e),i=4+Pt(e);return t&&(i+=e[i]+1),(31&e[i+10])<<8|e[i+11]},parsePmt:function(e){var t={},i=At(e),n=4+Pt(e);if(i&&(n+=e[n]+1),1&e[n+5]){for(var r=3+((15&e[n+1])<<8|e[n+2])-4,a=12+((15&e[n+10])<<8|e[n+11]);a=e.byteLength)return null;var i=null,n=e[t+7];return 192&n&&((i={}).pts=(14&e[t+9])<<27|(255&e[t+10])<<20|(254&e[t+11])<<12|(255&e[t+12])<<5|(254&e[t+13])>>>3,i.pts*=4,i.pts+=(6&e[t+13])>>>1,i.dts=i.pts,64&n&&(i.dts=(14&e[t+14])<<27|(255&e[t+15])<<20|(254&e[t+16])<<12|(255&e[t+17])<<5|(254&e[t+18])>>>3,i.dts*=4,i.dts+=(6&e[t+18])>>>1)),i},videoPacketContainsKeyFrame:function(e){for(var t=4+Pt(e),i=e.subarray(t),n=0,r=0,a=!1;re.length){i=!0;break}null===a&&(t=e.subarray(o,o+s),a=Zt.aac.parseAacTimestamp(t)),o+=s;break;case"audio":if(e.length-o<7){i=!0;break}if((s=Zt.aac.parseAdtsSize(e,o))>e.length){i=!0;break}null===r&&(t=e.subarray(o,o+s),r=Zt.aac.parseSampleRate(t)),n++,o+=s;break;default:o++}if(i)return null}if(null===r||null===a)return null;var u=ii/r;return{audio:[{type:"audio",dts:a,pts:a},{type:"audio",dts:a+1024*n*u,pts:a+1024*n*u}]}}:ti)(e);return r&&(r.audio||r.video)?(e=t,(t=r).audio&&t.audio.length&&("undefined"!=typeof(i=e)&&!isNaN(i)||(i=t.audio[0].dts),t.audio.forEach(function(e){e.dts=Jt(e.dts,i),e.pts=Jt(e.pts,i),e.dtsTime=e.dts/ii,e.ptsTime=e.pts/ii})),t.video&&t.video.length&&("undefined"!=typeof(n=e)&&!isNaN(n)||(n=t.video[0].dts),t.video.forEach(function(e){e.dts=Jt(e.dts,n),e.pts=Jt(e.pts,n),e.dtsTime=e.dts/ii,e.ptsTime=e.pts/ii}),t.firstKeyFrame&&((t=t.firstKeyFrame).dts=Jt(t.dts,n),t.pts=Jt(t.pts,n),t.dtsTime=t.dts/ii,t.ptsTime=t.pts/ii)),r):null},ri=function(){function e(e,t){this.options=t||{},this.self=e,this.init()}var t=e.prototype;return t.init=function(){var i,e;this.transmuxer&&this.transmuxer.dispose(),this.transmuxer=new Dt.Transmuxer(this.options),i=this.self,(e=this.transmuxer).on("data",function(e){var t=e.initSegment;e.initSegment={data:t.buffer,byteOffset:t.byteOffset,byteLength:t.byteLength};t=e.data;e.data=t.buffer,i.postMessage({action:"data",segment:e,byteOffset:t.byteOffset,byteLength:t.byteLength},[e.data])}),e.on("done",function(e){i.postMessage({action:"done"})}),e.on("gopInfo",function(e){i.postMessage({action:"gopInfo",gopInfo:e})}),e.on("videoSegmentTimingInfo",function(e){var t={start:{decode:ce(e.start.dts),presentation:ce(e.start.pts)},end:{decode:ce(e.end.dts),presentation:ce(e.end.pts)},baseMediaDecodeTime:ce(e.baseMediaDecodeTime)};e.prependedContentDuration&&(t.prependedContentDuration=ce(e.prependedContentDuration)),i.postMessage({action:"videoSegmentTimingInfo",videoSegmentTimingInfo:t})}),e.on("audioSegmentTimingInfo",function(e){var t={start:{decode:ce(e.start.dts),presentation:ce(e.start.pts)},end:{decode:ce(e.end.dts),presentation:ce(e.end.pts)},baseMediaDecodeTime:ce(e.baseMediaDecodeTime)};e.prependedContentDuration&&(t.prependedContentDuration=ce(e.prependedContentDuration)),i.postMessage({action:"audioSegmentTimingInfo",audioSegmentTimingInfo:t})}),e.on("id3Frame",function(e){i.postMessage({action:"id3Frame",id3Frame:e})}),e.on("caption",function(e){i.postMessage({action:"caption",caption:e})}),e.on("trackinfo",function(e){i.postMessage({action:"trackinfo",trackInfo:e})}),e.on("audioTimingInfo",function(e){i.postMessage({action:"audioTimingInfo",audioTimingInfo:{start:ce(e.start),end:ce(e.end)}})}),e.on("videoTimingInfo",function(e){i.postMessage({action:"videoTimingInfo",videoTimingInfo:{start:ce(e.start),end:ce(e.end)}})}),e.on("log",function(e){i.postMessage({action:"log",log:e})})},t.pushMp4Captions=function(e){this.captionParser||(this.captionParser=new Gt,this.captionParser.init());var t=new Uint8Array(e.data,e.byteOffset,e.byteLength),e=this.captionParser.parse(t,e.trackIds,e.timescales);this.self.postMessage({action:"mp4Captions",captions:e&&e.captions||[],logs:e&&e.logs||[],data:t.buffer},[t.buffer])},t.probeMp4StartTime=function(e){var t=e.timescales,e=e.data,t=Qt(t,e);this.self.postMessage({action:"probeMp4StartTime",startTime:t,data:e},[e.buffer])},t.probeMp4Tracks=function(e){var t=e.data,e=$t(t);this.self.postMessage({action:"probeMp4Tracks",tracks:e,data:t},[t.buffer])},t.probeTs=function(e){var t=e.data,i=e.baseStartTime,e="number"!=typeof i||isNaN(i)?void 0:i*ue,i=ni(t,e),e=null;i&&((e={hasVideo:i.video&&2===i.video.length||!1,hasAudio:i.audio&&2===i.audio.length||!1}).hasVideo&&(e.videoStart=i.video[0].ptsTime),e.hasAudio&&(e.audioStart=i.audio[0].ptsTime)),this.self.postMessage({action:"probeTs",result:e,data:t},[t.buffer])},t.clearAllMp4Captions=function(){this.captionParser&&this.captionParser.clearAllCaptions()},t.clearParsedMp4Captions=function(){this.captionParser&&this.captionParser.clearParsedCaptions()},t.push=function(e){e=new Uint8Array(e.data,e.byteOffset,e.byteLength);this.transmuxer.push(e)},t.reset=function(){this.transmuxer.reset()},t.setTimestampOffset=function(e){e=e.timestampOffset||0;this.transmuxer.setBaseMediaDecodeTime(Math.round(le(e)))},t.setAudioAppendStart=function(e){this.transmuxer.setAudioAppendStart(Math.ceil(le(e.appendStart)))},t.setRemux=function(e){this.transmuxer.setRemux(e.remux)},t.flush=function(e){this.transmuxer.flush(),self.postMessage({action:"done",type:"transmuxed"})},t.endTimeline=function(){this.transmuxer.endTimeline(),self.postMessage({action:"endedtimeline",type:"transmuxed"})},t.alignGopsWith=function(e){this.transmuxer.alignGopsWith(e.gopsToAlignWith.slice())},e}();self.onmessage=function(e){"init"===e.data.action&&e.data.options?this.messageHandlers=new ri(self,e.data.options):(this.messageHandlers||(this.messageHandlers=new ri(self)),e.data&&e.data.action&&"init"!==e.data.action&&this.messageHandlers[e.data.action]&&this.messageHandlers[e.data.action](e.data))}}))),Cl=function(e){e.currentTransmux=null,e.transmuxQueue.length&&(e.currentTransmux=e.transmuxQueue.shift(),"function"==typeof e.currentTransmux?e.currentTransmux():Du(e.currentTransmux))},Il=function(e){Mu("reset",e)},xl=function(e){var t=new kl;t.currentTransmux=null,t.transmuxQueue=[];var i=t.terminate;return t.terminate=function(){return t.currentTransmux=null,t.transmuxQueue.length=0,i.call(t)},t.postMessage({action:"init",options:e}),t},Al=2,Pl=-101,Ll=-102,Dl=Ro("CodecUtils"),Ol=Ro("PlaylistSelector"),ar=function(){var e=this.useDevicePixelRatio&&window.devicePixelRatio||1;return il(this.playlists.master,this.systemBandwidth,parseInt(Zu(this.tech_.el(),"width"),10)*e,parseInt(Zu(this.tech_.el(),"height"),10)*e,this.limitRenditionByPlayerDimensions,this.masterPlaylistController_)},Ml=function(n){function e(e,t){var i=n.call(this)||this;if(!e)throw new TypeError("Initialization settings are required");if("function"!=typeof e.currentTime)throw new TypeError("No currentTime getter specified");if(!e.mediaSource)throw new TypeError("No MediaSource specified");return i.bandwidth=e.bandwidth,i.throughput={rate:0,count:0},i.roundTrip=NaN,i.resetStats_(),i.mediaIndex=null,i.partIndex=null,i.hasPlayed_=e.hasPlayed,i.currentTime_=e.currentTime,i.seekable_=e.seekable,i.seeking_=e.seeking,i.duration_=e.duration,i.mediaSource_=e.mediaSource,i.vhs_=e.vhs,i.loaderType_=e.loaderType,i.currentMediaInfo_=void 0,i.startingMediaInfo_=void 0,i.segmentMetadataTrack_=e.segmentMetadataTrack,i.goalBufferLength_=e.goalBufferLength,i.sourceType_=e.sourceType,i.sourceUpdater_=e.sourceUpdater,i.inbandTextTracks_=e.inbandTextTracks,i.state_="INIT",i.timelineChangeController_=e.timelineChangeController,i.shouldSaveSegmentTimingInfo_=!0,i.parse708captions_=e.parse708captions,i.useDtsForTimestampOffset_=e.useDtsForTimestampOffset,i.captionServices_=e.captionServices,i.experimentalExactManifestTimings=e.experimentalExactManifestTimings,i.checkBufferTimeout_=null,i.error_=void 0,i.currentTimeline_=-1,i.pendingSegment_=null,i.xhrOptions_=null,i.pendingSegments_=[],i.audioDisabled_=!1,i.isPendingTimestampOffset_=!1,i.gopBuffer_=[],i.timeMapping_=0,i.safeAppend_=11<=tr.browser.IE_VERSION,i.appendInitSegment_={audio:!0,video:!0},i.playlistOfLastInitSegment_={audio:null,video:null},i.callQueue_=[],i.loadQueue_=[],i.metadataQueue_={id3:[],caption:[]},i.waitingOnRemove_=!1,i.quotaExceededErrorRetryTimeout_=null,i.activeInitSegmentId_=null,i.initSegments_={},i.cacheEncryptionKeys_=e.cacheEncryptionKeys,i.keyCache_={},i.decrypter_=e.decrypter,i.syncController_=e.syncController,i.syncPoint_={segmentIndex:0,time:0},i.transmuxer_=i.createTransmuxer_(),i.triggerSyncInfoUpdate_=function(){return i.trigger("syncinfoupdate")},i.syncController_.on("syncinfoupdate",i.triggerSyncInfoUpdate_),i.mediaSource_.addEventListener("sourceopen",function(){i.isEndOfStream_()||(i.ended_=!1)}),i.fetchAtBuffer_=!1,i.logger_=Ro("SegmentLoader["+i.loaderType_+"]"),Object.defineProperty(ft(i),"state",{get:function(){return this.state_},set:function(e){e!==this.state_&&(this.logger_(this.state_+" -> "+e),this.state_=e,this.trigger("statechange"))}}),i.sourceUpdater_.on("ready",function(){i.hasEnoughInfoToAppend_()&&i.processCallQueue_()}),"main"===i.loaderType_&&i.timelineChangeController_.on("pendingtimelinechange",function(){i.hasEnoughInfoToAppend_()&&i.processCallQueue_()}),"audio"===i.loaderType_&&i.timelineChangeController_.on("timelinechange",function(){i.hasEnoughInfoToLoad_()&&i.processLoadQueue_(),i.hasEnoughInfoToAppend_()&&i.processCallQueue_()}),i}mt(e,n);var t=e.prototype;return t.createTransmuxer_=function(){return xl({remux:!1,alignGopsAtEnd:this.safeAppend_,keepOriginalTimestamps:!0,parse708captions:this.parse708captions_,captionServices:this.captionServices_})},t.resetStats_=function(){this.mediaBytesTransferred=0,this.mediaRequests=0,this.mediaRequestsAborted=0,this.mediaRequestsTimedout=0,this.mediaRequestsErrored=0,this.mediaTransferDuration=0,this.mediaSecondsLoaded=0,this.mediaAppends=0},t.dispose=function(){this.trigger("dispose"),this.state="DISPOSED",this.pause(),this.abort_(),this.transmuxer_&&this.transmuxer_.terminate(),this.resetStats_(),this.checkBufferTimeout_&&window.clearTimeout(this.checkBufferTimeout_),this.syncController_&&this.triggerSyncInfoUpdate_&&this.syncController_.off("syncinfoupdate",this.triggerSyncInfoUpdate_),this.off()},t.setAudio=function(e){this.audioDisabled_=!e,e?this.appendInitSegment_.audio=!0:this.sourceUpdater_.removeAudio(0,this.duration_())},t.abort=function(){"WAITING"===this.state?(this.abort_(),this.state="READY",this.paused()||this.monitorBuffer_()):this.pendingSegment_&&(this.pendingSegment_=null)},t.abort_=function(){this.pendingSegment_&&this.pendingSegment_.abortRequests&&this.pendingSegment_.abortRequests(),this.pendingSegment_=null,this.callQueue_=[],this.loadQueue_=[],this.metadataQueue_.id3=[],this.metadataQueue_.caption=[],this.timelineChangeController_.clearPendingTimelineChange(this.loaderType_),this.waitingOnRemove_=!1,window.clearTimeout(this.quotaExceededErrorRetryTimeout_),this.quotaExceededErrorRetryTimeout_=null},t.checkForAbort_=function(e){return"APPENDING"!==this.state||this.pendingSegment_?!this.pendingSegment_||this.pendingSegment_.requestId!==e:(this.state="READY",!0)},t.error=function(e){return"undefined"!=typeof e&&(this.logger_("error occurred:",e),this.error_=e),this.pendingSegment_=null,this.error_},t.endOfStream=function(){this.ended_=!0,this.transmuxer_&&Il(this.transmuxer_),this.gopBuffer_.length=0,this.pause(),this.trigger("ended")},t.buffered_=function(){var e=this.getMediaInfo_();if(!this.sourceUpdater_||!e)return tr.createTimeRanges();if("main"===this.loaderType_){var t=e.hasAudio,i=e.hasVideo,e=e.isMuxed;if(i&&t&&!this.audioDisabled_&&!e)return this.sourceUpdater_.buffered();if(i)return this.sourceUpdater_.videoBuffered()}return this.sourceUpdater_.audioBuffered()},t.initSegmentForMap=function(e,t){if(void 0===t&&(t=!1),!e)return null;var i=Su(e),n=this.initSegments_[i];return t&&!n&&e.bytes&&(this.initSegments_[i]=n={resolvedUri:e.resolvedUri,byterange:e.byterange,bytes:e.bytes,tracks:e.tracks,timescales:e.timescales}),n||e},t.segmentKey=function(e,t){if(void 0===t&&(t=!1),!e)return null;var i=wu(e),n=this.keyCache_[i];this.cacheEncryptionKeys_&&t&&!n&&e.bytes&&(this.keyCache_[i]=n={resolvedUri:e.resolvedUri,bytes:e.bytes});e={resolvedUri:(n||e).resolvedUri};return n&&(e.bytes=n.bytes),e},t.couldBeginLoading_=function(){return this.playlist_&&!this.paused()},t.load=function(){if(this.monitorBuffer_(),this.playlist_)return"INIT"===this.state&&this.couldBeginLoading_()?this.init_():void(!this.couldBeginLoading_()||"READY"!==this.state&&"INIT"!==this.state||(this.state="READY"))},t.init_=function(){return this.state="READY",this.resetEverything(),this.monitorBuffer_()},t.playlist=function(e,t){if(void 0===t&&(t={}),e){var i=this.playlist_,n=this.pendingSegment_;this.playlist_=e,this.xhrOptions_=t,"INIT"===this.state&&(e.syncInfo={mediaSequence:e.mediaSequence,time:0},"main"===this.loaderType_&&this.syncController_.setDateTimeMappingForStart(e));var r=null;if(i&&(i.id?r=i.id:i.uri&&(r=i.uri)),this.logger_("playlist update ["+r+" => "+(e.id||e.uri)+"]"),this.trigger("syncinfoupdate"),"INIT"===this.state&&this.couldBeginLoading_())return this.init_();if(!i||i.uri!==e.uri)return null!==this.mediaIndex&&(e.endList?this.resyncLoader():this.resetLoader()),this.currentMediaInfo_=void 0,void this.trigger("playlistupdate");t=e.mediaSequence-i.mediaSequence;this.logger_("live window shift ["+t+"]"),null!==this.mediaIndex&&(this.mediaIndex-=t,this.mediaIndex<0?(this.mediaIndex=null,this.partIndex=null):(r=this.playlist_.segments[this.mediaIndex],!this.partIndex||r.parts&&r.parts.length&&r.parts[this.partIndex]||(r=this.mediaIndex,this.logger_("currently processing part (index "+this.partIndex+") no longer exists."),this.resetLoader(),this.mediaIndex=r))),n&&(n.mediaIndex-=t,n.mediaIndex<0?(n.mediaIndex=null,n.partIndex=null):(0<=n.mediaIndex&&(n.segment=e.segments[n.mediaIndex]),0<=n.partIndex&&n.segment.parts&&(n.part=n.segment.parts[n.partIndex]))),this.syncController_.saveExpiredSegmentInfo(i,e)}},t.pause=function(){this.checkBufferTimeout_&&(window.clearTimeout(this.checkBufferTimeout_),this.checkBufferTimeout_=null)},t.paused=function(){return null===this.checkBufferTimeout_},t.resetEverything=function(e){this.ended_=!1,this.appendInitSegment_={audio:!0,video:!0},this.resetLoader(),this.remove(0,1/0,e),this.transmuxer_&&(this.transmuxer_.postMessage({action:"clearAllMp4Captions"}),this.transmuxer_.postMessage({action:"reset"}))},t.resetLoader=function(){this.fetchAtBuffer_=!1,this.resyncLoader()},t.resyncLoader=function(){this.transmuxer_&&Il(this.transmuxer_),this.mediaIndex=null,this.partIndex=null,this.syncPoint_=null,this.isPendingTimestampOffset_=!1,this.callQueue_=[],this.loadQueue_=[],this.metadataQueue_.id3=[],this.metadataQueue_.caption=[],this.abort(),this.transmuxer_&&this.transmuxer_.postMessage({action:"clearParsedMp4Captions"})},t.remove=function(e,t,i,n){if(void 0===i&&(i=function(){}),void 0===n&&(n=!1),(t=t===1/0?this.duration_():t)<=e)this.logger_("skipping remove because end ${end} is <= start ${start}");else if(this.sourceUpdater_&&this.getMediaInfo_()){var r,a=1,s=function(){0===--a&&i()};for(r in!n&&this.audioDisabled_||(a++,this.sourceUpdater_.removeAudio(e,t,s)),!n&&"main"!==this.loaderType_||(this.gopBuffer_=function(e,t,i,n){for(var r=Math.ceil((t-n)*hl),a=Math.ceil((i-n)*hl),n=e.slice(),s=e.length;s--&&!(e[s].pts<=a););if(-1===s)return n;for(var o=s+1;o--&&!(e[o].pts<=r););return o=Math.max(o,0),n.splice(o,s-o+1),n}(this.gopBuffer_,e,t,this.timeMapping_),a++,this.sourceUpdater_.removeVideo(e,t,s)),this.inbandTextTracks_)rl(e,t,this.inbandTextTracks_[r]);rl(e,t,this.segmentMetadataTrack_),s()}else this.logger_("skipping remove because no source updater or starting media info")},t.monitorBuffer_=function(){this.checkBufferTimeout_&&window.clearTimeout(this.checkBufferTimeout_),this.checkBufferTimeout_=window.setTimeout(this.monitorBufferTick_.bind(this),1)},t.monitorBufferTick_=function(){"READY"===this.state&&this.fillBuffer_(),this.checkBufferTimeout_&&window.clearTimeout(this.checkBufferTimeout_),this.checkBufferTimeout_=window.setTimeout(this.monitorBufferTick_.bind(this),500)},t.fillBuffer_=function(){var e;this.sourceUpdater_.updating()||(e=this.chooseNextRequest_())&&("number"==typeof e.timestampOffset&&(this.isPendingTimestampOffset_=!1,this.timelineChangeController_.pendingTimelineChange({type:this.loaderType_,from:this.currentTimeline_,to:e.timeline})),this.loadSegment_(e))},t.isEndOfStream_=function(e,t,i){if(void 0===e&&(e=this.mediaIndex),void 0===t&&(t=this.playlist_),void 0===i&&(i=this.partIndex),!t||!this.mediaSource_)return!1;var n="number"==typeof e&&t.segments[e],e=e+1===t.segments.length,n=!n||!n.parts||i+1===n.parts.length;return t.endList&&"open"===this.mediaSource_.readyState&&e&&n},t.chooseNextRequest_=function(){var e=this.buffered_(),t=Ho(e)||0,i=qo(e,this.currentTime_()),n=!this.hasPlayed_()&&1<=i,r=i>=this.goalBufferLength_(),e=this.playlist_.segments;if(!e.length||n||r)return null;this.syncPoint_=this.syncPoint_||this.syncController_.getSyncPoint(this.playlist_,this.duration_(),this.currentTimeline_,this.currentTime_());var a,n={partIndex:null,mediaIndex:null,startOfSegment:null,playlist:this.playlist_,isSyncRequest:Boolean(!this.syncPoint_)};n.isSyncRequest?n.mediaIndex=function(e,t,i){t=t||[];for(var n=[],r=0,a=0;a=e.length-1&&s&&!this.seeking_()?null:this.generateSegmentInfo_(n)},t.generateSegmentInfo_=function(e){var t=e.independent,i=e.playlist,n=e.mediaIndex,r=e.startOfSegment,a=e.isSyncRequest,s=e.partIndex,o=e.forceTimestampOffset,u=e.getMediaInfoForTime,l=i.segments[n],e="number"==typeof s&&l.parts[s],t={requestId:"segment-loader-"+Math.random(),uri:e&&e.resolvedUri||l.resolvedUri,mediaIndex:n,partIndex:e?s:null,isSyncRequest:a,startOfSegment:r,playlist:i,bytes:null,encryptedBytes:null,timestampOffset:null,timeline:l.timeline,duration:e&&e.duration||l.duration,segment:l,part:e,byteLength:0,transmuxer:this.transmuxer_,getMediaInfoForTime:u,independent:t},o="undefined"!=typeof o?o:this.isPendingTimestampOffset_;t.timestampOffset=this.timestampOffsetForSegment_({segmentTimeline:l.timeline,currentTimeline:this.currentTimeline_,startOfSegment:r,buffered:this.buffered_(),overrideCheck:o});o=Ho(this.sourceUpdater_.audioBuffered());return"number"==typeof o&&(t.audioAppendStart=o-this.sourceUpdater_.audioTimestampOffset()),this.sourceUpdater_.videoBuffered().length&&(t.gopsToAlignWith=function(e,t,i){if("undefined"==typeof t||null===t||!e.length)return[];for(var n=Math.ceil((t-i+3)*hl),r=0;rn);r++);return e.slice(r)}(this.gopBuffer_,this.currentTime_()-this.sourceUpdater_.videoTimestampOffset(),this.timeMapping_)),t},t.timestampOffsetForSegment_=function(e){return i=(t=e).segmentTimeline,n=t.currentTimeline,r=t.startOfSegment,e=t.buffered,t.overrideCheck||i!==n?!(i "+p+" for "+e),t=m,i=v.vhs_.tech_,t[n=e]||(i.trigger({type:"usage",name:"vhs-608"}),i.trigger({type:"usage",name:"hls-608"}),/^cc708_/.test(r=n)&&(r="SERVICE"+n.split("_")[1]),(o=i.textTracks().getTrackById(r))?t[n]=o:(s=a=n,d=!1,(o=(i.options_.vhs&&i.options_.vhs.captionServices||{})[r])&&(a=o.label,s=o.language,d=o.default),t[n]=i.addRemoteTextTrack({kind:"captions",id:r,default:d,label:a,language:s},!1).track)),rl(h,p,m[e]),l=(f={captionArray:f,inbandTextTracks:m,timestampOffset:g}).inbandTextTracks,m=f.captionArray,c=f.timestampOffset,m&&(u=window.WebKitDataCue||window.VTTCue,m.forEach(function(e){var t=e.stream;l[t].addCue(new u(e.startTime+c,e.endTime+c,e.text))}))}),this.transmuxer_&&this.transmuxer_.postMessage({action:"clearParsedMp4Captions"})):this.metadataQueue_.caption.push(this.handleCaptions_.bind(this,e,t)):this.logger_("SegmentLoader received no captions from a caption event"))},t.handleId3_=function(e,t,i){var n,r,a,s;this.earlyAbortWhenNeeded_(e.stats),this.checkForAbort_(e.requestId)||(this.pendingSegment_.hasAppendedData_?(n=null===this.sourceUpdater_.videoTimestampOffset()?this.sourceUpdater_.audioTimestampOffset():this.sourceUpdater_.videoTimestampOffset(),r=this.inbandTextTracks_,a=i,s=this.vhs_.tech_,r.metadataTrack_||(r.metadataTrack_=s.addRemoteTextTrack({kind:"metadata",label:"Timed Metadata"},!1).track,r.metadataTrack_.inBandMetadataTrackDispatchType=a),nl({inbandTextTracks:this.inbandTextTracks_,metadataArray:t,timestampOffset:n,videoDuration:this.duration_()})):this.metadataQueue_.id3.push(this.handleId3_.bind(this,e,t,i)))},t.processMetadataQueue_=function(){this.metadataQueue_.id3.forEach(function(e){return e()}),this.metadataQueue_.caption.forEach(function(e){return e()}),this.metadataQueue_.id3=[],this.metadataQueue_.caption=[]},t.processCallQueue_=function(){var e=this.callQueue_;this.callQueue_=[],e.forEach(function(e){return e()})},t.processLoadQueue_=function(){var e=this.loadQueue_;this.loadQueue_=[],e.forEach(function(e){return e()})},t.hasEnoughInfoToLoad_=function(){if("audio"!==this.loaderType_)return!0;var e=this.pendingSegment_;return!!e&&(!this.getCurrentMediaInfo_()||!ul({timelineChangeController:this.timelineChangeController_,currentTimeline:this.currentTimeline_,segmentTimeline:e.timeline,loaderType:this.loaderType_,audioDisabled:this.audioDisabled_}))},t.getCurrentMediaInfo_=function(e){return(e=void 0===e?this.pendingSegment_:e)&&e.trackInfo||this.currentMediaInfo_},t.getMediaInfo_=function(e){return void 0===e&&(e=this.pendingSegment_),this.getCurrentMediaInfo_(e)||this.startingMediaInfo_},t.hasEnoughInfoToAppend_=function(){if(!this.sourceUpdater_.ready())return!1;if(this.waitingOnRemove_||this.quotaExceededErrorRetryTimeout_)return!1;var e=this.pendingSegment_,t=this.getCurrentMediaInfo_();if(!e||!t)return!1;var i=t.hasAudio,n=t.hasVideo,t=t.isMuxed;return!(n&&!e.videoTimingInfo)&&(!(i&&!this.audioDisabled_&&!t&&!e.audioTimingInfo)&&!ul({timelineChangeController:this.timelineChangeController_,currentTimeline:this.currentTimeline_,segmentTimeline:e.timeline,loaderType:this.loaderType_,audioDisabled:this.audioDisabled_}))},t.handleData_=function(e,t){if(this.earlyAbortWhenNeeded_(e.stats),!this.checkForAbort_(e.requestId))if(!this.callQueue_.length&&this.hasEnoughInfoToAppend_()){var i,n=this.pendingSegment_;if(this.setTimeMapping_(n.timeline),this.updateMediaSecondsLoaded_(n.part||n.segment),"closed"!==this.mediaSource_.readyState){if(e.map&&(e.map=this.initSegmentForMap(e.map,!0),n.segment.map=e.map),e.key&&this.segmentKey(e.key,!0),n.isFmp4=e.isFmp4,n.timingInfo=n.timingInfo||{},n.isFmp4?(this.trigger("fmp4"),n.timingInfo.start=n[ol(t.type)].start):(i=this.getCurrentMediaInfo_(),(i="main"===this.loaderType_&&i&&i.hasVideo)&&(r=n.videoTimingInfo.start),n.timingInfo.start=this.trueSegmentStart_({currentStart:n.timingInfo.start,playlist:n.playlist,mediaIndex:n.mediaIndex,currentVideoTimestampOffset:this.sourceUpdater_.videoTimestampOffset(),useVideoTimingInfo:i,firstVideoFrameTimeForData:r,videoTimingInfo:n.videoTimingInfo,audioTimingInfo:n.audioTimingInfo})),this.updateAppendInitSegmentStatus(n,t.type),this.updateSourceBufferTimestampOffset_(n),n.isSyncRequest){this.updateTimingInfoEnd_(n),this.syncController_.saveSegmentTimingInfo({segmentInfo:n,shouldSaveTimelineMapping:"main"===this.loaderType_});var r=this.chooseNextRequest_();if(r.mediaIndex!==n.mediaIndex||r.partIndex!==n.partIndex)return void this.logger_("sync segment was incorrect, not appending");this.logger_("sync segment was correct, appending")}n.hasAppendedData_=!0,this.processMetadataQueue_(),this.appendData_(n,t)}}else this.callQueue_.push(this.handleData_.bind(this,e,t))},t.updateAppendInitSegmentStatus=function(e,t){"main"!==this.loaderType_||"number"!=typeof e.timestampOffset||e.changedTimestampOffset||(this.appendInitSegment_={audio:!0,video:!0}),this.playlistOfLastInitSegment_[t]!==e.playlist&&(this.appendInitSegment_[t]=!0)},t.getInitSegmentAndUpdateState_=function(e){var t=e.type,i=e.initSegment,n=e.map,r=e.playlist;if(n){e=Su(n);if(this.activeInitSegmentId_===e)return null;i=this.initSegmentForMap(n,!0).bytes,this.activeInitSegmentId_=e}return i&&this.appendInitSegment_[t]?(this.playlistOfLastInitSegment_[t]=r,this.appendInitSegment_[t]=!1,this.activeInitSegmentId_=null,i):null},t.handleQuotaExceededError_=function(e,t){var i=this,n=e.segmentInfo,r=e.type,a=e.bytes,s=this.sourceUpdater_.audioBuffered(),o=this.sourceUpdater_.videoBuffered();1=n);r++);return e.slice(0,r).concat(t)}(this.gopBuffer_,i.gopInfo,this.safeAppend_)),this.state="APPENDING",this.trigger("appending"),this.waitForAppendsToComplete_(e)}},t.setTimeMapping_=function(e){e=this.syncController_.mappingForTimeline(e);null!==e&&(this.timeMapping_=e)},t.updateMediaSecondsLoaded_=function(e){"number"==typeof e.start&&"number"==typeof e.end?this.mediaSecondsLoaded+=e.end-e.start:this.mediaSecondsLoaded+=e.duration},t.shouldUpdateTransmuxerTimestampOffset_=function(e){return null!==e&&("main"===this.loaderType_&&e!==this.sourceUpdater_.videoTimestampOffset()||!this.audioDisabled_&&e!==this.sourceUpdater_.audioTimestampOffset())},t.trueSegmentStart_=function(e){var t=e.currentStart,i=e.playlist,n=e.mediaIndex,r=e.firstVideoFrameTimeForData,a=e.currentVideoTimestampOffset,s=e.useVideoTimingInfo,o=e.videoTimingInfo,e=e.audioTimingInfo;if("undefined"!=typeof t)return t;if(!s)return e.start;i=i.segments[n-1];return 0!==n&&i&&"undefined"!=typeof i.start&&i.end===r+a?o.start:r},t.waitForAppendsToComplete_=function(e){var t=this.getCurrentMediaInfo_(e);if(!t)return this.error({message:"No starting media returned, likely due to an unsupported media format.",blacklistDuration:1/0}),void this.trigger("error");var i=t.hasAudio,n=t.hasVideo,t=t.isMuxed,n="main"===this.loaderType_&&n,t=!this.audioDisabled_&&i&&!t;if(e.waitingOnAppends=0,!e.hasAppendedData_)return e.timingInfo||"number"!=typeof e.timestampOffset||(this.isPendingTimestampOffset_=!0),e.timingInfo={start:0},e.waitingOnAppends++,this.isPendingTimestampOffset_||(this.updateSourceBufferTimestampOffset_(e),this.processMetadataQueue_()),void this.checkAppendsDone_(e);n&&e.waitingOnAppends++,t&&e.waitingOnAppends++,n&&this.sourceUpdater_.videoQueueCallback(this.checkAppendsDone_.bind(this,e)),t&&this.sourceUpdater_.audioQueueCallback(this.checkAppendsDone_.bind(this,e))},t.checkAppendsDone_=function(e){this.checkForAbort_(e.requestId)||(e.waitingOnAppends--,0===e.waitingOnAppends&&this.handleAppendsDone_())},t.checkForIllegalMediaSwitch=function(e){var t,i,e=(t=this.loaderType_,i=this.getCurrentMediaInfo_(),e=e,"main"===t&&i&&e?e.hasAudio||e.hasVideo?i.hasVideo&&!e.hasVideo?"Only audio found in segment when we expected video. We can't switch to audio only from a stream that had video. To get rid of this message, please add codec information to the manifest.":!i.hasVideo&&e.hasVideo?"Video found in segment when we expected only audio. We can't switch to a stream with video from an audio only stream. To get rid of this message, please add codec information to the manifest.":null:"Neither audio nor video found in segment.":null);return!!e&&(this.error({message:e,blacklistDuration:1/0}),this.trigger("error"),!0)},t.updateSourceBufferTimestampOffset_=function(e){var t;null===e.timestampOffset||"number"!=typeof e.timingInfo.start||e.changedTimestampOffset||"main"!==this.loaderType_||(t=!1,e.timestampOffset-=this.getSegmentStartTimeForTimestampOffsetCalculation_({videoTimingInfo:e.segment.videoTimingInfo,audioTimingInfo:e.segment.audioTimingInfo,timingInfo:e.timingInfo}),e.changedTimestampOffset=!0,e.timestampOffset!==this.sourceUpdater_.videoTimestampOffset()&&(this.sourceUpdater_.videoTimestampOffset(e.timestampOffset),t=!0),e.timestampOffset!==this.sourceUpdater_.audioTimestampOffset()&&(this.sourceUpdater_.audioTimestampOffset(e.timestampOffset),t=!0),t&&this.trigger("timestampoffset"))},t.getSegmentStartTimeForTimestampOffsetCalculation_=function(e){var t=e.videoTimingInfo,i=e.audioTimingInfo,e=e.timingInfo;return this.useDtsForTimestampOffset_?t&&"number"==typeof t.transmuxedDecodeStart?t.transmuxedDecodeStart:i&&"number"==typeof i.transmuxedDecodeStart?i.transmuxedDecodeStart:e.start:e.start},t.updateTimingInfoEnd_=function(e){e.timingInfo=e.timingInfo||{};var t=this.getMediaInfo_(),t="main"===this.loaderType_&&t&&t.hasVideo&&e.videoTimingInfo?e.videoTimingInfo:e.audioTimingInfo;t&&(e.timingInfo.end="number"==typeof t.end?t.end:t.start+e.duration)},t.handleAppendsDone_=function(){if(this.pendingSegment_&&this.trigger("appendsdone"),!this.pendingSegment_)return this.state="READY",void(this.paused()||this.monitorBuffer_());var e=this.pendingSegment_;this.updateTimingInfoEnd_(e),this.shouldSaveSegmentTimingInfo_&&this.syncController_.saveSegmentTimingInfo({segmentInfo:e,shouldSaveTimelineMapping:"main"===this.loaderType_});var t=cl(e,this.sourceType_);if(t&&("warn"===t.severity?tr.log.warn(t.message):this.logger_(t.message)),this.recordThroughput_(e),this.pendingSegment_=null,this.state="READY",!e.isSyncRequest||(this.trigger("syncinfoupdate"),e.hasAppendedData_)){this.logger_("Appended "+sl(e)),this.addSegmentMetadataCue_(e),this.fetchAtBuffer_=!0,this.currentTimeline_!==e.timeline&&(this.timelineChangeController_.lastTimelineChange({type:this.loaderType_,from:this.currentTimeline_,to:e.timeline}),"main"!==this.loaderType_||this.audioDisabled_||this.timelineChangeController_.lastTimelineChange({type:"audio",from:this.currentTimeline_,to:e.timeline})),this.currentTimeline_=e.timeline,this.trigger("syncinfoupdate");var i=e.segment,t=e.part,i=i.end&&this.currentTime_()-i.end>3*e.playlist.targetDuration,t=t&&t.end&&this.currentTime_()-t.end>3*e.playlist.partTargetDuration;if(i||t)return this.logger_("bad "+(i?"segment":"part")+" "+sl(e)),void this.resetEverything();null!==this.mediaIndex&&this.trigger("bandwidthupdate"),this.trigger("progress"),this.mediaIndex=e.mediaIndex,this.partIndex=e.partIndex,this.isEndOfStream_(e.mediaIndex,e.playlist,e.partIndex)&&this.endOfStream(),this.trigger("appended"),e.hasAppendedData_&&this.mediaAppends++,this.paused()||this.monitorBuffer_()}else this.logger_("Throwing away un-appended sync request "+sl(e))},t.recordThroughput_=function(e){var t,i;e.duration<1/60?this.logger_("Ignoring segment's throughput because its duration of "+e.duration+" is less than the min to record "+1/60):(t=this.throughput.rate,i=Date.now()-e.endOfAllRequests+1,i=Math.floor(e.byteLength/i*8*1e3),this.throughput.rate+=(i-t)/++this.throughput.count)},t.addSegmentMetadataCue_=function(e){var t,i,n,r;this.segmentMetadataTrack_&&(i=(t=e.segment).start,r=t.end,al(i)&&al(r)&&(rl(i,r,this.segmentMetadataTrack_),n=window.WebKitDataCue||window.VTTCue,e={custom:t.custom,dateTimeObject:t.dateTimeObject,dateTimeString:t.dateTimeString,bandwidth:e.playlist.attributes.BANDWIDTH,resolution:e.playlist.attributes.RESOLUTION,codecs:e.playlist.attributes.CODECS,byteLength:e.byteLength,uri:e.uri,timeline:e.timeline,playlist:e.playlist.id,start:i,end:r},(r=new n(i,r,JSON.stringify(e))).value=e,this.segmentMetadataTrack_.addCue(r)))},e}(tr.EventTarget);function Rl(){}function Nl(e){return"string"!=typeof e?e:e.replace(/./,function(e){return e.toUpperCase()})}function Ul(e,t){var i=t[e+"Buffer"];return i&&i.updating||t.queuePending[e]}function Bl(e,t){if(0!==t.queue.length){var i=0,n=t.queue[i];if("mediaSource"!==n.type){if("mediaSource"!==e&&t.ready()&&"closed"!==t.mediaSource.readyState&&!Ul(e,t)){if(n.type!==e){if(null===(i=function(e,t){for(var i=0;i=e.playlist.segments.length){e=null;break}e=this.generateSegmentInfo_({playlist:e.playlist,mediaIndex:e.mediaIndex+1,startOfSegment:e.startOfSegment+e.duration,isSyncRequest:e.isSyncRequest})}return e},t.stopForError=function(e){this.error(e),this.state="READY",this.pause(),this.trigger("error")},t.segmentRequestFinished_=function(e,t,i){var n=this;if(this.subtitlesTrack_){if(this.saveTransferStats_(t.stats),!this.pendingSegment_)return this.state="READY",void(this.mediaRequestsAborted+=1);if(e)return e.code===Pl&&this.handleTimeout_(),e.code===Ll?this.mediaRequestsAborted+=1:this.mediaRequestsErrored+=1,void this.stopForError(e);var r=this.pendingSegment_;this.saveBandwidthRelatedStats_(r.duration,t.stats),this.state="APPENDING",this.trigger("appending");var a=r.segment;if(a.map&&(a.map.bytes=t.map.bytes),r.bytes=t.bytes,"function"!=typeof window.WebVTT&&this.subtitlesTrack_&&this.subtitlesTrack_.tech_){var s=function(){n.subtitlesTrack_.tech_.off("vttjsloaded",o),n.stopForError({message:"Error loading vtt.js"})},o=function(){n.subtitlesTrack_.tech_.off("vttjserror",s),n.segmentRequestFinished_(e,t,i)};return this.state="WAITING_ON_VTTJS",this.subtitlesTrack_.tech_.one("vttjsloaded",o),void this.subtitlesTrack_.tech_.one("vttjserror",s)}a.requested=!0;try{this.parseVTTCues_(r)}catch(e){return void this.stopForError({message:e.message})}if(this.updateTimeMapping_(r,this.syncController_.timelines[r.timeline],this.playlist_),r.cues.length?r.timingInfo={start:r.cues[0].startTime,end:r.cues[r.cues.length-1].endTime}:r.timingInfo={start:r.startOfSegment,end:r.startOfSegment+r.duration},r.isSyncRequest)return this.trigger("syncinfoupdate"),this.pendingSegment_=null,void(this.state="READY");r.byteLength=r.bytes.byteLength,this.mediaSecondsLoaded+=a.duration,r.cues.forEach(function(e){n.subtitlesTrack_.addCue(n.featuresNativeTextTracks_?new 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i=0;i>7))^f]=f;for(e=t=0;!c[e];e^=i||1,t=p[t]||1)for(s=16843009*h[n=h[i=h[d[c[e]=r=(r=t^t<<1^t<<2^t<<3^t<<4)>>8^255&r^99]=e]]]^65537*n^257*i^16843008*e,a=257*h[r]^16843008*r,f=0;f<4;f++)u[f][e]=a=a<<24^a>>>8,l[f][r]=s=s<<24^s>>>8;for(f=0;f<5;f++)u[f]=u[f].slice(0),l[f]=l[f].slice(0);return o}(),this._tables=[[c[0][0].slice(),c[0][1].slice(),c[0][2].slice(),c[0][3].slice(),c[0][4].slice()],[c[1][0].slice(),c[1][1].slice(),c[1][2].slice(),c[1][3].slice(),c[1][4].slice()]];var r=this._tables[0][4],a=this._tables[1],s=e.length,o=1;if(4!==s&&6!==s&&8!==s)throw new Error("Invalid aes key size");var u=e.slice(0),l=[];for(this._key=[u,l],t=s;t<4*s+28;t++)n=u[t-1],(t%s==0||8===s&&t%s==4)&&(n=r[n>>>24]<<24^r[n>>16&255]<<16^r[n>>8&255]<<8^r[255&n],t%s==0&&(n=n<<8^n>>>24^o<<24,o=o<<1^283*(o>>7))),u[t]=u[t-s]^n;for(i=0;t;i++,t--)n=u[3&i?t:t-4],l[i]=t<=4||i<4?n:a[0][r[n>>>24]]^a[1][r[n>>16&255]]^a[2][r[n>>8&255]]^a[3][r[255&n]]}return e.prototype.decrypt=function(e,t,i,n,r,a){for(var s,o,u,l=this._key[1],c=e^l[0],d=n^l[1],h=i^l[2],p=t^l[3],f=l.length/4-2,m=4,t=this._tables[1],g=t[0],y=t[1],v=t[2],_=t[3],b=t[4],T=0;T>>24]^y[d>>16&255]^v[h>>8&255]^_[255&p]^l[m],o=g[d>>>24]^y[h>>16&255]^v[p>>8&255]^_[255&c]^l[m+1],u=g[h>>>24]^y[p>>16&255]^v[c>>8&255]^_[255&d]^l[m+2],p=g[p>>>24]^y[c>>16&255]^v[d>>8&255]^_[255&h]^l[m+3],m+=4,c=s,d=o,h=u;for(T=0;T<4;T++)r[(3&-T)+a]=b[c>>>24]<<24^b[d>>16&255]<<16^b[h>>8&255]<<8^b[255&p]^l[m++],s=c,c=d,d=h,h=p,p=s},e}(),l=function(t){function e(){var e=t.call(this,a)||this;return e.jobs=[],e.delay=1,e.timeout_=null,e}r(e,t);var i=e.prototype;return i.processJob_=function(){this.jobs.shift()(),this.jobs.length?this.timeout_=setTimeout(this.processJob_.bind(this),this.delay):this.timeout_=null},i.push=function(e){this.jobs.push(e),this.timeout_||(this.timeout_=setTimeout(this.processJob_.bind(this),this.delay))},e}(a),g=function(e){return e<<24|(65280&e)<<8|(16711680&e)>>8|e>>>24},s=function(){function u(e,t,i,n){var r=u.STEP,a=new Int32Array(e.buffer),s=new Uint8Array(e.byteLength),o=0;for(this.asyncStream_=new l,this.asyncStream_.push(this.decryptChunk_(a.subarray(o,o+r),t,i,s)),o=r;o>2),u=new m(Array.prototype.slice.call(t)),e=new Uint8Array(e.byteLength),l=new Int32Array(e.buffer),c=i[0],d=i[1],h=i[2],p=i[3],f=0;f "+n+" from "+t),this.tech_.trigger({type:"usage",name:"vhs-rendition-change-"+t})),this.masterPlaylistLoader_.media(e,i)},t.startABRTimer_=function(){var e=this;this.stopABRTimer_(),this.abrTimer_=window.setInterval(function(){return e.checkABR_()},250)},t.stopABRTimer_=function(){this.tech_.scrubbing&&this.tech_.scrubbing()||(window.clearInterval(this.abrTimer_),this.abrTimer_=null)},t.getAudioTrackPlaylists_=function(){var e=this.master(),t=e&&e.playlists||[];if(!e||!e.mediaGroups||!e.mediaGroups.AUDIO)return t;var i,n=e.mediaGroups.AUDIO,r=Object.keys(n);if(Object.keys(this.mediaTypes_.AUDIO.groups).length)i=this.mediaTypes_.AUDIO.activeTrack();else{var a,s=n.main||r.length&&n[r[0]];for(a in s)if(s[a].default){i={label:a};break}}if(!i)return t;var o,u=[];for(o in n)if(n[o][i.label]){var l=n[o][i.label];if(l.playlists&&l.playlists.length)u.push.apply(u,l.playlists);else if(l.uri)u.push(l);else if(e.playlists.length)for(var c=0;c "+r.id;if(!t)return l(c+" as current playlist is not set"),!0;if(r.id===t.id)return!1;e=Boolean(Uo(i,n).length);if(!t.endList)return e||"number"!=typeof t.partTargetDuration?(l(c+" as current playlist is live"),!0):(l("not "+c+" as current playlist is live llhls, but currentTime isn't in buffered."),!1);i=qo(i,n),n=u?El.EXPERIMENTAL_MAX_BUFFER_LOW_WATER_LINE:El.MAX_BUFFER_LOW_WATER_LINE;if(o= bufferLowWaterLine ("+i+" >= "+a+")";return u&&(a+=" and next bandwidth > current bandwidth ("+n+" > "+r+")"),l(a),!0}return l("not "+c+" as no switching criteria met"),!1}({buffered:this.tech_.buffered(),currentTime:i,currentPlaylist:t,nextPlaylist:e,bufferLowWaterLine:n,bufferHighWaterLine:r,duration:this.duration(),experimentalBufferBasedABR:this.experimentalBufferBasedABR,log:this.logger_})},t.setupSegmentLoaderListeners_=function(){var t=this;this.experimentalBufferBasedABR||(this.mainSegmentLoader_.on("bandwidthupdate",function(){var e=t.selectPlaylist();t.shouldSwitchToMedia_(e)&&t.switchMedia_(e,"bandwidthupdate"),t.tech_.trigger("bandwidthupdate")}),this.mainSegmentLoader_.on("progress",function(){t.trigger("progress")})),this.mainSegmentLoader_.on("error",function(){t.blacklistCurrentPlaylist(t.mainSegmentLoader_.error())}),this.mainSegmentLoader_.on("appenderror",function(){t.error=t.mainSegmentLoader_.error_,t.trigger("error")}),this.mainSegmentLoader_.on("syncinfoupdate",function(){t.onSyncInfoUpdate_()}),this.mainSegmentLoader_.on("timestampoffset",function(){t.tech_.trigger({type:"usage",name:"vhs-timestamp-offset"}),t.tech_.trigger({type:"usage",name:"hls-timestamp-offset"})}),this.audioSegmentLoader_.on("syncinfoupdate",function(){t.onSyncInfoUpdate_()}),this.audioSegmentLoader_.on("appenderror",function(){t.error=t.audioSegmentLoader_.error_,t.trigger("error")}),this.mainSegmentLoader_.on("ended",function(){t.logger_("main segment loader ended"),t.onEndOfStream()}),this.mainSegmentLoader_.on("earlyabort",function(e){t.experimentalBufferBasedABR||(t.delegateLoaders_("all",["abort"]),t.blacklistCurrentPlaylist({message:"Aborted early because there isn't enough bandwidth to complete the request without rebuffering."},120))});function e(){if(!t.sourceUpdater_.hasCreatedSourceBuffers())return t.tryToCreateSourceBuffers_();var e=t.getCodecsOrExclude_();e&&t.sourceUpdater_.addOrChangeSourceBuffers(e)}this.mainSegmentLoader_.on("trackinfo",e),this.audioSegmentLoader_.on("trackinfo",e),this.mainSegmentLoader_.on("fmp4",function(){t.triggeredFmp4Usage||(t.tech_.trigger({type:"usage",name:"vhs-fmp4"}),t.tech_.trigger({type:"usage",name:"hls-fmp4"}),t.triggeredFmp4Usage=!0)}),this.audioSegmentLoader_.on("fmp4",function(){t.triggeredFmp4Usage||(t.tech_.trigger({type:"usage",name:"vhs-fmp4"}),t.tech_.trigger({type:"usage",name:"hls-fmp4"}),t.triggeredFmp4Usage=!0)}),this.audioSegmentLoader_.on("ended",function(){t.logger_("audioSegmentLoader ended"),t.onEndOfStream()})},t.mediaSecondsLoaded_=function(){return Math.max(this.audioSegmentLoader_.mediaSecondsLoaded+this.mainSegmentLoader_.mediaSecondsLoaded)},t.load=function(){this.mainSegmentLoader_.load(),this.mediaTypes_.AUDIO.activePlaylistLoader&&this.audioSegmentLoader_.load(),this.mediaTypes_.SUBTITLES.activePlaylistLoader&&this.subtitleSegmentLoader_.load()},t.smoothQualityChange_=function(e){void 0===e&&(e=this.selectPlaylist()),this.fastQualityChange_(e)},t.fastQualityChange_=function(e){var t=this;(e=void 0===e?this.selectPlaylist():e)!==this.masterPlaylistLoader_.media()?(this.switchMedia_(e,"fast-quality"),this.mainSegmentLoader_.resetEverything(function(){tr.browser.IE_VERSION||tr.browser.IS_EDGE?t.tech_.setCurrentTime(t.tech_.currentTime()+.04):t.tech_.setCurrentTime(t.tech_.currentTime())})):this.logger_("skipping fastQualityChange because new media is same as old")},t.play=function(){if(!this.setupFirstPlay()){this.tech_.ended()&&this.tech_.setCurrentTime(0),this.hasPlayed_&&this.load();var e=this.tech_.seekable();return this.tech_.duration()===1/0&&this.tech_.currentTime()this.maxPlaylistRetries?1/0:Date.now()+1e3*t,i.excludeUntil=a,e.reason&&(i.lastExcludeReason_=e.reason),this.tech_.trigger("blacklistplaylist"),this.tech_.trigger({type:"usage",name:"vhs-rendition-blacklisted"}),this.tech_.trigger({type:"usage",name:"hls-rendition-blacklisted"});r=this.selectPlaylist();if(!r)return this.error="Playback cannot continue. No available working or supported playlists.",void this.trigger("error");t=e.internal?this.logger_:tr.log.warn,a=e.message?" "+e.message:"";t((e.internal?"Internal problem":"Problem")+" encountered with playlist "+i.id+"."+a+" Switching to playlist "+r.id+"."),r.attributes.AUDIO!==i.attributes.AUDIO&&this.delegateLoaders_("audio",["abort","pause"]),r.attributes.SUBTITLES!==i.attributes.SUBTITLES&&this.delegateLoaders_("subtitle",["abort","pause"]),this.delegateLoaders_("main",["abort","pause"]);a=r.targetDuration/2*1e3||5e3,a="number"==typeof r.lastRequest&&Date.now()-r.lastRequest<=a;return this.switchMedia_(r,"exclude",s||a)},t.pauseLoading=function(){this.delegateLoaders_("all",["abort","pause"]),this.stopABRTimer_()},t.delegateLoaders_=function(i,e){var n=this,r=[],t="all"===i;!t&&"main"!==i||r.push(this.masterPlaylistLoader_);var a=[];!t&&"audio"!==i||a.push("AUDIO"),!t&&"subtitle"!==i||(a.push("CLOSED-CAPTIONS"),a.push("SUBTITLES")),a.forEach(function(e){e=n.mediaTypes_[e]&&n.mediaTypes_[e].activePlaylistLoader;e&&r.push(e)}),["main","audio","subtitle"].forEach(function(e){var t=n[e+"SegmentLoader_"];!t||i!==e&&"all"!==i||r.push(t)}),r.forEach(function(t){return e.forEach(function(e){"function"==typeof t[e]&&t[e]()})})},t.setCurrentTime=function(e){var t=Uo(this.tech_.buffered(),e);return this.masterPlaylistLoader_&&this.masterPlaylistLoader_.media()&&this.masterPlaylistLoader_.media().segments?t&&t.length?e:(this.mainSegmentLoader_.resetEverything(),this.mainSegmentLoader_.abort(),this.mediaTypes_.AUDIO.activePlaylistLoader&&(this.audioSegmentLoader_.resetEverything(),this.audioSegmentLoader_.abort()),this.mediaTypes_.SUBTITLES.activePlaylistLoader&&(this.subtitleSegmentLoader_.resetEverything(),this.subtitleSegmentLoader_.abort()),void this.load()):0},t.duration=function(){if(!this.masterPlaylistLoader_)return 0;var e=this.masterPlaylistLoader_.media();return e?e.endList?this.mediaSource?this.mediaSource.duration:Ql.Playlist.duration(e):1/0:0},t.seekable=function(){return this.seekable_},t.onSyncInfoUpdate_=function(){var e;if(this.masterPlaylistLoader_){var t=this.masterPlaylistLoader_.media();if(t){var i=this.syncController_.getExpiredTime(t,this.duration());if(null!==i){var n,r,a=this.masterPlaylistLoader_.master,s=Ql.Playlist.seekable(t,i,Ql.Playlist.liveEdgeDelay(a,t));if(0!==s.length){if(this.mediaTypes_.AUDIO.activePlaylistLoader){if(t=this.mediaTypes_.AUDIO.activePlaylistLoader.media(),null===(i=this.syncController_.getExpiredTime(t,this.duration())))return;if(0===(e=Ql.Playlist.seekable(t,i,Ql.Playlist.liveEdgeDelay(a,t))).length)return}this.seekable_&&this.seekable_.length&&(n=this.seekable_.end(0),r=this.seekable_.start(0)),!e||e.start(0)>s.end(0)||s.start(0)>e.end(0)?this.seekable_=s:this.seekable_=tr.createTimeRanges([[(e.start(0)>s.start(0)?e:s).start(0),(e.end(0) "'+a[e]+'"')}),u.length)return void this.blacklistCurrentPlaylist({playlist:this.media(),message:"Codec switching not supported: "+u.join(", ")+".",blacklistDuration:1/0,internal:!0})}return a}t=Object.keys(o).reduce(function(e,t){return e&&(e+=", "),e+=t+' does not support codec(s): "'+o[t].join(",")+'"'},"")+".";this.blacklistCurrentPlaylist({playlist:this.media(),internal:!0,message:t,blacklistDuration:1/0})}else this.blacklistCurrentPlaylist({playlist:this.media(),message:"Could not determine codecs for playlist.",blacklistDuration:1/0})},t.tryToCreateSourceBuffers_=function(){var e;"open"!==this.mediaSource.readyState||this.sourceUpdater_.hasCreatedSourceBuffers()||!this.areMediaTypesKnown_()||(e=this.getCodecsOrExclude_())&&(this.sourceUpdater_.createSourceBuffers(e),e=[e.video,e.audio].filter(Boolean).join(","),this.excludeIncompatibleVariants_(e))},t.excludeUnsupportedVariants_=function(){var n=this,r=this.master().playlists,a=[];Object.keys(r).forEach(function(e){var t,i=r[e];-1===a.indexOf(i.id)&&(a.push(i.id),t=[],!(e=$u(n.master,i)).audio||yr(e.audio)||gr(e.audio)||t.push("audio codec "+e.audio),!e.video||yr(e.video)||gr(e.video)||t.push("video codec "+e.video),e.text&&"stpp.ttml.im1t"===e.text&&t.push("text codec "+e.text),t.length&&(i.excludeUntil=1/0,n.logger_("excluding "+i.id+" for unsupported: "+t.join(", "))))})},t.excludeIncompatibleVariants_=function(e){var r=this,a=[],s=this.master().playlists,e=Yu(pr(e)),o=Qu(e),u=e.video&&pr(e.video)[0]||null,l=e.audio&&pr(e.audio)[0]||null;Object.keys(s).forEach(function(e){var t,i,n=s[e];-1===a.indexOf(n.id)&&n.excludeUntil!==1/0&&(a.push(n.id),t=[],i=$u(r.masterPlaylistLoader_.master,n),e=Qu(i),(i.audio||i.video)&&(e!==o&&t.push('codec count "'+e+'" !== "'+o+'"'),r.sourceUpdater_.canChangeType()||(e=i.video&&pr(i.video)[0]||null,i=i.audio&&pr(i.audio)[0]||null,e&&u&&e.type.toLowerCase()!==u.type.toLowerCase()&&t.push('video codec "'+e.type+'" !== "'+u.type+'"'),i&&l&&i.type.toLowerCase()!==l.type.toLowerCase()&&t.push('audio codec "'+i.type+'" !== "'+l.type+'"')),t.length&&(n.excludeUntil=1/0,r.logger_("blacklisting "+n.id+": "+t.join(" && ")))))})},t.updateAdCues_=function(e){var t=0,i=this.seekable();i.length&&(t=i.start(0)),function(e,t,i){if(void 0===i&&(i=0),e.segments)for(var n=i,r=0;r=r.adStartTime&&t<=r.adEndTime)return r}return null}(t,n+u.duration/2)){if("cueIn"in u){o.endTime=n,o.adEndTime=n,n+=u.duration,o=null;continue}if(n=t.end(t.length-1)))return this.techWaiting_();5<=this.consecutiveUpdates&&e===this.lastRecordedTime?(this.consecutiveUpdates++,this.waiting_()):e===this.lastRecordedTime?this.consecutiveUpdates++:(this.consecutiveUpdates=0,this.lastRecordedTime=e)}},t.cancelTimer_=function(){this.consecutiveUpdates=0,this.timer_&&(this.logger_("cancelTimer_"),clearTimeout(this.timer_)),this.timer_=null},t.fixesBadSeeks_=function(){if(!this.tech_.seeking())return!1;var e,t=this.seekable(),i=this.tech_.currentTime();if(this.afterSeekableWindow_(t,i,this.media(),this.allowSeeksWithinUnsafeLiveWindow)&&(e=t.end(t.length-1)),"undefined"!=typeof(e=this.beforeSeekableWindow_(t,i)?(a=t.start(0))+(a===t.end(0)?0:.1):e))return this.logger_("Trying to seek outside of seekable at time "+i+" with seekable range "+Fo(t)+". Seeking to "+e+"."),this.tech_.setCurrentTime(e),!0;for(var n=this.masterPlaylistController_.sourceUpdater_,r=this.tech_.buffered(),a=n.audioBuffer?n.audioBuffered():null,t=n.videoBuffer?n.videoBuffered():null,n=this.media(),s=n.partTargetDuration||2*(n.targetDuration-fl),o=[a,t],u=0;u "+t.end(0)+"]. Attempting to resume playback by seeking to the current time."),this.tech_.trigger({type:"usage",name:"vhs-unknown-waiting"}),this.tech_.trigger({type:"usage",name:"hls-unknown-waiting"})))},t.techWaiting_=function(){var e=this.seekable(),t=this.tech_.currentTime();if(this.tech_.seeking()||null!==this.timer_)return!0;if(this.beforeSeekableWindow_(e,t)){var i=e.end(e.length-1);return this.logger_("Fell out of live window at time "+t+". 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center 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b/site_libs/quarto-listing/list.min.js new file mode 100644 index 0000000..8131881 --- /dev/null +++ b/site_libs/quarto-listing/list.min.js @@ -0,0 +1,2 @@ +var List;List=function(){var t={"./src/add-async.js":function(t){t.exports=function(t){return function e(r,n,s){var i=r.splice(0,50);s=(s=s||[]).concat(t.add(i)),r.length>0?setTimeout((function(){e(r,n,s)}),1):(t.update(),n(s))}}},"./src/filter.js":function(t){t.exports=function(t){return t.handlers.filterStart=t.handlers.filterStart||[],t.handlers.filterComplete=t.handlers.filterComplete||[],function(e){if(t.trigger("filterStart"),t.i=1,t.reset.filter(),void 0===e)t.filtered=!1;else{t.filtered=!0;for(var r=t.items,n=0,s=r.length;nv.page,a=new g(t[s],void 0,n),v.items.push(a),r.push(a)}return v.update(),r}m(t.slice(0),e)}},this.show=function(t,e){return this.i=t,this.page=e,v.update(),v},this.remove=function(t,e,r){for(var n=0,s=0,i=v.items.length;s-1&&r.splice(n,1),v},this.trigger=function(t){for(var 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+ setCategoryHash(category); + } +}; + +window["quarto-listing-loaded"] = () => { + // Process any existing hash + const hash = getHash(); + + if (hash) { + // If there is a category, switch to that + if (hash.category) { + activateCategory(hash.category); + } + // Paginate a specific listing + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const page = hash[getListingPageKey(listingId)]; + if (page) { + showPage(listingId, page); + } + } + } + + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + // The actual list + const list = window["quarto-listings"][listingId]; + + // Update the handlers for pagination events + refreshPaginationHandlers(listingId); + + // Render any visible items that need it + renderVisibleProgressiveImages(list); + + // Whenever the list is updated, we also need to + // attach handlers to the new pagination elements + // and refresh any newly visible items. + list.on("updated", function () { + renderVisibleProgressiveImages(list); + setTimeout(() => refreshPaginationHandlers(listingId)); + + // Show or hide the no matching message + toggleNoMatchingMessage(list); + }); + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Attach click handlers to categories + const categoryEls = window.document.querySelectorAll( + ".quarto-listing-category .category" + ); + + for (const categoryEl of categoryEls) { + const category = categoryEl.getAttribute("data-category"); + categoryEl.onclick = () => { + activateCategory(category); + setCategoryHash(category); + }; + } + + // Attach a click handler to the category title + // (there should be only one, but since it is a class name, handle N) + const categoryTitleEls = window.document.querySelectorAll( + ".quarto-listing-category-title" + ); + for (const categoryTitleEl of categoryTitleEls) { + categoryTitleEl.onclick = () => { + activateCategory(""); + setCategoryHash(""); + }; + } + + categoriesLoaded = true; +}); + +function toggleNoMatchingMessage(list) { + const selector = `#${list.listContainer.id} .listing-no-matching`; + const noMatchingEl = window.document.querySelector(selector); + if (noMatchingEl) { + if (list.visibleItems.length === 0) { + noMatchingEl.classList.remove("d-none"); + } else { + if (!noMatchingEl.classList.contains("d-none")) { + noMatchingEl.classList.add("d-none"); + } + } + } +} + +function setCategoryHash(category) { + setHash({ category }); +} + +function setPageHash(listingId, page) { + const currentHash = getHash() || {}; + currentHash[getListingPageKey(listingId)] = page; + setHash(currentHash); +} + +function getListingPageKey(listingId) { + return `${listingId}-page`; +} + +function refreshPaginationHandlers(listingId) { + const listingEl = window.document.getElementById(listingId); + const paginationEls = listingEl.querySelectorAll( + ".pagination li.page-item:not(.disabled) .page.page-link" + ); + for (const paginationEl of paginationEls) { + paginationEl.onclick = (sender) => { + setPageHash(listingId, sender.target.getAttribute("data-i")); + showPage(listingId, sender.target.getAttribute("data-i")); + return false; + }; + } +} + +function renderVisibleProgressiveImages(list) { + // Run through the visible items and render any progressive images + for (const item of list.visibleItems) { + const itemEl = item.elm; + if (itemEl) { + const progressiveImgs = itemEl.querySelectorAll( + `img[${kProgressiveAttr}]` + ); + for (const progressiveImg of progressiveImgs) { + const srcValue = progressiveImg.getAttribute(kProgressiveAttr); + if (srcValue) { + progressiveImg.setAttribute("src", srcValue); + } + progressiveImg.removeAttribute(kProgressiveAttr); + } + } + } +} + +function getHash() { + // Hashes are of the form + // #name:value|name1:value1|name2:value2 + const currentUrl = new URL(window.location); + const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined; + return parseHash(hashRaw); +} + +const kAnd = "&"; +const kEquals = "="; + +function parseHash(hash) { + if (!hash) { + return undefined; + } + const hasValuesStrs = hash.split(kAnd); + const hashValues = hasValuesStrs + .map((hashValueStr) => { + const vals = hashValueStr.split(kEquals); + if (vals.length === 2) { + return { name: vals[0], value: vals[1] }; + } else { + return undefined; + } + }) + .filter((value) => { + return value !== undefined; + }); + + const hashObj = {}; + hashValues.forEach((hashValue) => { + hashObj[hashValue.name] = decodeURIComponent(hashValue.value); + }); + return hashObj; +} + +function makeHash(obj) { + return Object.keys(obj) + .map((key) => { + return `${key}${kEquals}${obj[key]}`; + }) + .join(kAnd); +} + +function setHash(obj) { + const hash = makeHash(obj); + window.history.pushState(null, null, `#${hash}`); +} + +function showPage(listingId, page) { + const list = window["quarto-listings"][listingId]; + if (list) { + list.show((page - 1) * list.page + 1, list.page); + } +} + +function activateCategory(category) { + // Deactivate existing categories + const activeEls = window.document.querySelectorAll( + ".quarto-listing-category .category.active" + ); + for (const activeEl of activeEls) { + activeEl.classList.remove("active"); + } + + // Activate this category + const categoryEl = window.document.querySelector( + `.quarto-listing-category .category[data-category='${category}'` + ); + if (categoryEl) { + categoryEl.classList.add("active"); + } + + // Filter the listings to this category + filterListingCategory(category); +} + +function filterListingCategory(category) { + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const list = window["quarto-listings"][listingId]; + if (list) { + if (category === "") { + // resets the filter + list.filter(); + } else { + // filter to this category + list.filter(function (item) { + const itemValues = item.values(); + if (itemValues.categories !== null) { + const categories = itemValues.categories.split(","); + return categories.includes(category); + } else { + return false; + } + }); + } + } + } +} diff --git a/site_libs/quarto-nav/headroom.min.js b/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. 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"))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s}); diff --git a/site_libs/quarto-nav/quarto-nav.js b/site_libs/quarto-nav/quarto-nav.js new file mode 100644 index 0000000..f6a53b1 --- /dev/null +++ b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,289 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 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0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 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s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..5f723d7 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1286 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if (now - then > kFuseMaxWait && subSearchFuse === undefined) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..6ad3d75 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,59 @@ + + + + https://andrewlook.com/notebooks/blog/index.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part1.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part4.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part6.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part0.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/index.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/about/index.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/projects/slml.html + 2024-03-13T18:47:28.940Z + + + https://andrewlook.com/notebooks/jupyter/index.html + 2024-03-13T18:47:28.940Z + + + https://andrewlook.com/notebooks/resume/index.html + 2024-03-13T18:47:28.940Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part3.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part5.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/2018_ai_art_tour.html + 2024-03-13T18:47:28.732Z + + + https://andrewlook.com/notebooks/blog/posts/slml_part2.html + 2024-03-13T18:47:28.732Z + + diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..4e39703 --- /dev/null +++ b/styles.css @@ -0,0 +1,113 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, +.cell-output > .sourceCode > pre, +.cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + padding-bottom: 5px; + font-style: italic; + /* font-size: 135%; */ + opacity: 70%; +} + +.quarto-category { + font-size: 0.45em !important; +} + +.quarto-title-meta { + /* display: grid !important; */ + display: inline !important; + grid-template-columns: repeat(3, 1fr) !important; + text-align: right; +} + +.quarto-title-meta-heading { + display: inline; + padding-right: 3px; + opacity: 30% !important; +} + +.quarto-title-meta-contents { + display: inline; + opacity: 50%; +} + +.quarto-title-meta-contents > p { + display: inline; +} + +.quarto-title-block { + display: none; +} + +.quarto-title-block .description { + border-bottom: 1px solid rgba(0, 0, 0, 0.1); +} + +.quarto-title .quarto-categories { + display: none !important; +} + +/* listing tables */ +.quarto-listing-table > thead { + display: none; +} + +.quarto-listing-table { + border-top: none; + border-bottom: none; +} + +.quarto-listing-table > :not(:first-child) { + border-top: none; +} + +.quarto-listing-table > :not(caption) > * > * { + padding: 0.1rem 0.1rem; + border-bottom-width: 0px !important; +} + +/* figures */ +.figure-caption { + text-align: center; +} + +/* toc */ +.sidebar nav[role="doc-toc"] ul { + font-size: 0.65rem; +} +.sidebar nav[role="doc-toc"] > h2 { + font-size: 0.65rem; +} +.sidebar nav[role="doc-toc"] .toc-actions p { + font-size: 0.65rem !important; +}