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NicolasCailotto · Nov 2, 2017 · 05471f8 · 05471f8
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diff --git a/raspberry/getpics.sh b/raspberry/getpics.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+
+while true
+do
+    /home/pi/webcam.sh
+    sleep 5
+done
diff --git a/raspberry/webcam.sh b/raspberry/webcam.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+DATE=$(date +”%Y-%m-%d_%H%M%S”)
+
+fswebcam -r 640×480 –no-banner /home/pi/webcam/$DATE.jpg
diff --git a/taxiornot.ipynb b/taxiornot.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Taxiornot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importamos la clase vgg16 y luego la instanciamos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from vgg16 import Vgg16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vgg = Vgg16()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "batch_size=4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "path = \"data/tachornot/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 236 images belonging to 2 classes.\n",
+      "Found 62 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "batches = vgg.get_batches(path+'train', batch_size=batch_size)\n",
+    "val_batches = vgg.get_batches(path+'valid', batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling *finetune()* modifies the model such that it will be trained based on the data in the batches provided - in this case, to predict either 'dog' or 'cat'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vgg.finetune(batches)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we *fit()* the parameters of the model using the training data, reporting the accuracy on the validation set after every epoch. (An *epoch* is one full pass through the training data.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "236/236 [==============================] - 4s - loss: 0.9080 - acc: 0.7500 - val_loss: 0.2648 - val_acc: 0.8710\n",
+      "Epoch 2/10\n",
+      "236/236 [==============================] - 3s - loss: 0.4089 - acc: 0.8475 - val_loss: 0.1603 - val_acc: 0.9516\n",
+      "Epoch 3/10\n",
+      "236/236 [==============================] - 3s - loss: 0.2968 - acc: 0.9068 - val_loss: 0.2070 - val_acc: 0.9355\n",
+      "Epoch 4/10\n",
+      "236/236 [==============================] - 3s - loss: 0.3114 - acc: 0.8983 - val_loss: 0.2002 - val_acc: 0.9516\n",
+      "Epoch 5/10\n",
+      "236/236 [==============================] - 3s - loss: 0.1200 - acc: 0.9449 - val_loss: 0.0704 - val_acc: 0.9839\n",
+      "Epoch 6/10\n",
+      "236/236 [==============================] - 3s - loss: 0.2147 - acc: 0.9237 - val_loss: 0.0279 - val_acc: 1.0000\n",
+      "Epoch 7/10\n",
+      "236/236 [==============================] - 3s - loss: 0.1917 - acc: 0.9364 - val_loss: 0.0688 - val_acc: 0.9516\n",
+      "Epoch 8/10\n",
+      "236/236 [==============================] - 3s - loss: 0.1763 - acc: 0.9449 - val_loss: 0.0359 - val_acc: 0.9839\n",
+      "Epoch 9/10\n",
+      "236/236 [==============================] - 3s - loss: 0.2478 - acc: 0.9153 - val_loss: 0.0245 - val_acc: 1.0000\n",
+      "Epoch 10/10\n",
+      "236/236 [==============================] - 3s - loss: 0.1889 - acc: 0.9364 - val_loss: 0.0390 - val_acc: 0.9839\n"
+     ]
+    }
+   ],
+   "source": [
+    "vgg.fit(batches, val_batches, nb_epoch=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "imgs,labels = next(val_batches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plots' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-ecc52a7a9690>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'plots' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "plots(imgs, titles=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
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