diff --git a/blockDocs/Blocks/Blur.html b/blockDocs/Blocks/Blur.html deleted file mode 100644 index 826a3c56..00000000 --- a/blockDocs/Blocks/Blur.html +++ /dev/null @@ -1,372 +0,0 @@ - - - - - - - Blocks.Blur API documentation - - - - - - - - - - - - -
-
-

-Blocks.Blur

- - - - - - - 
 1import cv2
- 2import numpy as np
- 3
- 4def main(inputs, outputs, parameters, synchronise):
- 5    """
- 6    ## Blurs an Object\n
- 7    The object to be blurred is read through the inputs.
- 8    We have multiple available blurs including Gaussian, Averaging and Median Blur. 
- 9    We can change these blurs by changing the name given in the parameter block
-10
-11    `while` loop is the part of the program that is executed continuously.
-12    It is enabled by default but can be disabled by passing in 0 through the enable wire.
-13
-14    Outputs the blurred image through the `share_image()` function
-15    """
-16    # Blur Type
-17    blur_type : str = parameters.read_string("BlurType") 
-18    """Type of blur, reads from a parameter"""
-19    # Kernel Size
-20    kernel = tuple([int(x.strip()) for x in parameters.read_string("Kernel").split(',')])
-21    auto_enable = False
-22    try:
-23        enable = inputs.read_number("Enable")
-24    except Exception:
-25        auto_enable = True
-26
-27    while(auto_enable or inputs.read_number('Enable')):
-28        frame = inputs.read_image("Img")
-29        if frame is None:
-30            continue
-31
-32        if blur_type == 'Gaussian':
-33            blurred_img = cv2.GaussianBlur(frame, kernel, 0)
-34                        
-35        elif blur_type == 'Averaging':
-36            blurred_img = cv2.blur(frame, kernel)
-37                        
-38        elif blur_type == 'Median':
-39            blurred_img = cv2.medianBlur(frame, kernel[0])
-40
-41        outputs.share_image('Out', blurred_img)
-42        synchronise()
-
- - -
-
- -
- - def - main(inputs, outputs, parameters, synchronise) - - - -
- - 
 5def main(inputs, outputs, parameters, synchronise):
- 6    """
- 7    ## Blurs an Object\n
- 8    The object to be blurred is read through the inputs.
- 9    We have multiple available blurs including Gaussian, Averaging and Median Blur. 
-10    We can change these blurs by changing the name given in the parameter block
-11
-12    `while` loop is the part of the program that is executed continuously.
-13    It is enabled by default but can be disabled by passing in 0 through the enable wire.
-14
-15    Outputs the blurred image through the `share_image()` function
-16    """
-17    # Blur Type
-18    blur_type : str = parameters.read_string("BlurType") 
-19    """Type of blur, reads from a parameter"""
-20    # Kernel Size
-21    kernel = tuple([int(x.strip()) for x in parameters.read_string("Kernel").split(',')])
-22    auto_enable = False
-23    try:
-24        enable = inputs.read_number("Enable")
-25    except Exception:
-26        auto_enable = True
-27
-28    while(auto_enable or inputs.read_number('Enable')):
-29        frame = inputs.read_image("Img")
-30        if frame is None:
-31            continue
-32
-33        if blur_type == 'Gaussian':
-34            blurred_img = cv2.GaussianBlur(frame, kernel, 0)
-35                        
-36        elif blur_type == 'Averaging':
-37            blurred_img = cv2.blur(frame, kernel)
-38                        
-39        elif blur_type == 'Median':
-40            blurred_img = cv2.medianBlur(frame, kernel[0])
-41
-42        outputs.share_image('Out', blurred_img)
-43        synchronise()
-
- - -

Block Description

-

Blurs an Object

- -

The object to be blurred is read through the inputs. -We have multiple available blurs including Gaussian, Averaging and Median Blur. -We can change these blurs by changing the name given in the parameter block

- -

while loop is the part of the program that is executed continuously. -It is enabled by default but can be disabled by passing in 0 through the enable wire.

- -

Outputs the blurred image through the share_image() function

-
- -
-

Example Usage

- -

Output

- - - - - - - - - -
NormalAfter Median Blur
-
- - - -
-
- - - - \ No newline at end of file diff --git a/docs/Blocks/module.html.jinja2 b/docs/Blocks/theme/module.html.jinja2 similarity index 100% rename from docs/Blocks/module.html.jinja2 rename to docs/Blocks/theme/module.html.jinja2 diff --git a/docs/Blocks/theme/syntax-highlighting.css b/docs/Blocks/theme/syntax-highlighting.css new file mode 100644 index 00000000..c758fca2 --- /dev/null +++ b/docs/Blocks/theme/syntax-highlighting.css @@ -0,0 +1,80 @@ +* monokai color scheme, see pdoc/template/README.md */ +pre { line-height: 125%; } +span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 20px; } +.pdoc-code .hll { background-color: #49483e } +.pdoc-code { background: #272822; color: #f8f8f2 } +.pdoc-code .c { color: #75715e } /* Comment */ +.pdoc-code .err { color: #960050; background-color: #1e0010 } /* Error */ +.pdoc-code .esc { color: #f8f8f2 } /* Escape */ +.pdoc-code .g { color: #f8f8f2 } /* Generic */ +.pdoc-code .k { color: #66d9ef } /* Keyword */ +.pdoc-code .l { color: #ae81ff } /* Literal */ +.pdoc-code .n { color: #f8f8f2 } /* Name */ +.pdoc-code .o { color: #f92672 } /* Operator */ +.pdoc-code .x { color: #f8f8f2 } /* Other */ +.pdoc-code .p { color: #f8f8f2 } /* Punctuation */ +.pdoc-code .ch { color: #75715e } /* Comment.Hashbang */ +.pdoc-code .cm { color: #75715e } /* Comment.Multiline */ +.pdoc-code .cp { color: #75715e } /* Comment.Preproc */ +.pdoc-code .cpf { color: #75715e } /* Comment.PreprocFile */ +.pdoc-code .c1 { color: #75715e } /* Comment.Single */ +.pdoc-code .cs { color: #75715e } /* Comment.Special */ +.pdoc-code .gd { color: #f92672 } /* Generic.Deleted */ +.pdoc-code .ge { color: #f8f8f2; font-style: italic } /* Generic.Emph */ +.pdoc-code .gr { color: #f8f8f2 } /* Generic.Error */ +.pdoc-code .gh { color: #f8f8f2 } /* Generic.Heading */ +.pdoc-code .gi { color: #a6e22e } /* Generic.Inserted */ +.pdoc-code .go { color: #66d9ef } /* Generic.Output */ +.pdoc-code .gp { color: #f92672; font-weight: bold } /* Generic.Prompt */ +.pdoc-code .gs { color: #f8f8f2; font-weight: bold } /* Generic.Strong */ +.pdoc-code .gu { color: #75715e } /* Generic.Subheading */ +.pdoc-code .gt { color: #f8f8f2 } /* Generic.Traceback */ +.pdoc-code .kc { color: #66d9ef } /* Keyword.Constant */ +.pdoc-code .kd { color: #66d9ef } /* Keyword.Declaration */ +.pdoc-code .kn { color: #f92672 } /* Keyword.Namespace */ +.pdoc-code .kp { color: #66d9ef } /* Keyword.Pseudo */ +.pdoc-code .kr { color: #66d9ef } /* Keyword.Reserved */ +.pdoc-code .kt { color: #66d9ef } /* Keyword.Type */ +.pdoc-code .ld { color: #e6db74 } /* Literal.Date */ +.pdoc-code .m { color: #ae81ff } /* Literal.Number */ +.pdoc-code .s { color: #e6db74 } /* Literal.String */ +.pdoc-code .na { color: #a6e22e } /* Name.Attribute */ +.pdoc-code .nb { color: #f8f8f2 } /* Name.Builtin */ +.pdoc-code .nc { color: #a6e22e } /* Name.Class */ +.pdoc-code .no { color: #66d9ef } /* Name.Constant */ +.pdoc-code .nd { color: #a6e22e } /* Name.Decorator */ +.pdoc-code .ni { color: #f8f8f2 } /* Name.Entity */ +.pdoc-code .ne { color: #a6e22e } /* Name.Exception */ +.pdoc-code .nf { color: #a6e22e } /* Name.Function */ +.pdoc-code .nl { color: #f8f8f2 } /* Name.Label */ +.pdoc-code .nn { color: #f8f8f2 } /* Name.Namespace */ +.pdoc-code .nx { color: #a6e22e } /* Name.Other */ +.pdoc-code .py { color: #f8f8f2 } /* Name.Property */ +.pdoc-code .nt { color: #f92672 } /* Name.Tag */ +.pdoc-code .nv { color: #f8f8f2 } /* Name.Variable */ +.pdoc-code .ow { color: #f92672 } /* Operator.Word */ +.pdoc-code .w { color: #f8f8f2 } /* Text.Whitespace */ +.pdoc-code .mb { color: #ae81ff } /* Literal.Number.Bin */ +.pdoc-code .mf { color: #ae81ff } /* Literal.Number.Float */ +.pdoc-code .mh { color: #ae81ff } /* Literal.Number.Hex */ +.pdoc-code .mi { color: #ae81ff } /* Literal.Number.Integer */ +.pdoc-code .mo { color: #ae81ff } /* Literal.Number.Oct */ +.pdoc-code .sa { color: #e6db74 } /* Literal.String.Affix */ +.pdoc-code .sb { color: #e6db74 } /* Literal.String.Backtick */ +.pdoc-code .sc { color: #e6db74 } /* Literal.String.Char */ +.pdoc-code .dl { color: #e6db74 } /* Literal.String.Delimiter */ +.pdoc-code .sd { color: #e6db74 } /* Literal.String.Doc */ +.pdoc-code .s2 { color: #e6db74 } /* Literal.String.Double */ +.pdoc-code .se { color: #ae81ff } /* Literal.String.Escape */ +.pdoc-code .sh { color: #e6db74 } /* Literal.String.Heredoc */ +.pdoc-code .si { color: #e6db74 } /* Literal.String.Interpol */ +.pdoc-code .sx { color: #e6db74 } /* Literal.String.Other */ +.pdoc-code .sr { color: #e6db74 } /* Literal.String.Regex */ +.pdoc-code .s1 { color: #e6db74 } /* Literal.String.Single */ +.pdoc-code .ss { color: #e6db74 } /* Literal.String.Symbol */ +.pdoc-code .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */ +.pdoc-code .fm { color: #a6e22e } /* Name.Function.Magic */ +.pdoc-code .vc { color: #f8f8f2 } /* Name.Variable.Class */ +.pdoc-code .vg { color: #f8f8f2 } /* Name.Variable.Global */ +.pdoc-code .vi { color: #f8f8f2 } /* Name.Variable.Instance */ +.pdoc-code .vm { color: #f8f8f2 } /* Name.Variable.Magic */ diff --git a/docs/Blocks/theme/theme.css b/docs/Blocks/theme/theme.css new file mode 100644 index 00000000..1dfe7c4a --- /dev/null +++ b/docs/Blocks/theme/theme.css @@ -0,0 +1,20 @@ +:root { + --pdoc-background: #212529; +} + +.pdoc { + --text: #f7f7f7; + --muted: #9d9d9d; + --link: #58a6ff; + --link-hover: #3989ff; + --code: #333; + --active: #555; + + --accent: #343434; + --accent2: #555; + + --nav-hover: rgba(0, 0, 0, 0.1); + --name: #77C1FF; + --def: #0cdd0c; + --annotation: #00c037; +} diff --git a/docs/blockDocs/Blocks.html b/docs/blockDocs/Blocks.html index 3c48ebd4..8eb39116 100644 --- a/docs/blockDocs/Blocks.html +++ b/docs/blockDocs/Blocks.html @@ -7,8 +7,8 @@ Blocks API documentation - - + + @@ -25,12 +25,10 @@

Contents

@@ -73,59 +71,71 @@

Submodules

Blocks

-

Jekyll Local Installation

+
-

Prerequisites

+

-

Installing Ruby on Ubuntu

+

Visual Circuit

-

First of all, we need to install all the dependencies typing:

+

Contributors +Forks +Stargazers +Issues +License

-
sudo apt-get install ruby-full build-essential zlib1g-dev
-
+

Visual Circuit is an open source tool to develop robotic applications. It aims to make developing applications for ROS and Gazebo simple and user friendly by its intuitive block-based interface. Users have the ablity to drag and drop blocks to develop their logic. Users are also able to build completely custom blocks as well as edit code in the existing blocks, this makes Visual Circuit a robust and powerful tool to develop even complicated applications.

-

After that, we need to set up a gem installation directory for your user account. The following commands will add environment variables to your ~/.bashrc file to configure the gem installation path. Run them now:

+

For more information visit our site VisualCircuit

-
echo '# Install Ruby Gems to ~/gems' >> ~/.bashrc
-echo 'export GEM_HOME="$HOME/gems"' >> ~/.bashrc
-echo 'export PATH="$HOME/gems/bin:$PATH"' >> ~/.bashrc
-source ~/.bashrc
-
+

Setup

-

Finally, we install Jekyll:

+

Front-end

-
gem install jekyll bundler
-
+

For more specific instructions check the frontend readme

-

Notice that we don't use the root user :-)

+
    +
  1. Clone the repository https://github.com/JdeRobot/VisualCircuit.git
    2. +
  2. Change directory to frontend
    3. +
  3. Run npm install
    4. +
  4. Run npm start
    5. +
  5. Open http://localhost:3000/ in browser.
    6. +
-

Installing Ruby and Jekyll on Mac OS X

+

Back-end

-

Follow the Jekyll page installation guide.

+

For more specific instructions check the backend readme

-

Running Jekyll Serve

+
    +
  1. Clone the repository https://github.com/JdeRobot/VisualCircuit.git
    2. +
  2. Change directory to backend
    3. +
  3. Create a Python3 virtual environment using venv. +For eg. python -m venv .venv
    4. +
  4. After activating the virtual environment, install the dependencies by running +pip install -r requirements.txt
    5. +
  5. Add .env file to the backend folder. And add the variables as defined in .env.template
    6. +
  6. Create the static files to serve during execution by python manage.py collectstatic
    7. +
  7. Start the server by running python manage.py runserver 8000
    8. +
-

By default, the Jekyll server is launched with the following command (which is the one indicated on your website).

+ -
bundle exec jekyll serve
-
+

Contributing

-

If in the process of building the server there is a dependency problem, for example, there is a missing library to install, it is necessary to delete the Gemfile.lock file so that it is rebuilt with the installed dependency. This list of dependencies is found in the Gemfile file (in Python it would be equivalent to the requirements.txt file) and the version of each of the installed gems (packages) is specified. Having a list of dependencies is important for future updates as well as knowing the libraries needed to run the server. Once the Gemfile.lock file is deleted, the command shown above is launched again and the dependency errors should end.

+

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. For more info on how to design a block, refer to this link

-

Notes for exercise cards.

+

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". +Don't forget to give the project a star! Thanks again!

- - -

FAQ

- - +
    +
  1. Fork the Project
    2. +
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
    3. +
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
    4. +
  4. Push to the Branch (git push origin feature/AmazingFeature)
    5. +
  5. Open a Pull Request
    6. +
-
jekyll build --incremental --verbose
-
+

+

diff --git a/docs/blockDocs/Blocks/Blur.html b/docs/blockDocs/Blocks/Blur.html index 7978bd08..2c1c46f8 100644 --- a/docs/blockDocs/Blocks/Blur.html +++ b/docs/blockDocs/Blocks/Blur.html @@ -7,8 +7,8 @@ Blocks.Blur API documentation - - + + @@ -186,22 +186,6 @@

Block Description

Parameters: BlurType

- -
-

Example Usage

- -

Output

- - - - - - - - - -
NormalAfter Median Blur
-
diff --git a/docs/blockDocs/Blocks/Camera.html b/docs/blockDocs/Blocks/Camera.html index b3644ced..6da86a42 100644 --- a/docs/blockDocs/Blocks/Camera.html +++ b/docs/blockDocs/Blocks/Camera.html @@ -7,8 +7,8 @@ Blocks.Camera API documentation - - + + diff --git a/docs/blockDocs/Blocks/ColorFilter.html b/docs/blockDocs/Blocks/ColorFilter.html index 49c2ca67..ce25d7c3 100644 --- a/docs/blockDocs/Blocks/ColorFilter.html +++ b/docs/blockDocs/Blocks/ColorFilter.html @@ -7,8 +7,8 @@ Blocks.ColorFilter API documentation - - + + diff --git a/docs/blockDocs/Blocks/ContourDetector.html b/docs/blockDocs/Blocks/ContourDetector.html index ffabcfdd..39c0acc0 100644 --- a/docs/blockDocs/Blocks/ContourDetector.html +++ b/docs/blockDocs/Blocks/ContourDetector.html @@ -7,8 +7,8 @@ Blocks.ContourDetector API documentation - - + + diff --git a/docs/blockDocs/Blocks/Cropper.html b/docs/blockDocs/Blocks/Cropper.html index dea8061d..d4591dcb 100644 --- a/docs/blockDocs/Blocks/Cropper.html +++ b/docs/blockDocs/Blocks/Cropper.html @@ -7,8 +7,8 @@ Blocks.Cropper API documentation - - + + diff --git a/docs/blockDocs/Blocks/Dilation.html b/docs/blockDocs/Blocks/Dilation.html index d62cf383..a6352de0 100644 --- a/docs/blockDocs/Blocks/Dilation.html +++ b/docs/blockDocs/Blocks/Dilation.html @@ -7,8 +7,8 @@ Blocks.Dilation API documentation - - + + diff --git a/docs/blockDocs/Blocks/EdgeDetector.html b/docs/blockDocs/Blocks/EdgeDetector.html index 22a3d00d..fd376d56 100644 --- a/docs/blockDocs/Blocks/EdgeDetector.html +++ b/docs/blockDocs/Blocks/EdgeDetector.html @@ -7,8 +7,8 @@ Blocks.EdgeDetector API documentation - - + + diff --git a/docs/blockDocs/Blocks/Erosion.html b/docs/blockDocs/Blocks/Erosion.html index 45e27ba5..3192864f 100644 --- a/docs/blockDocs/Blocks/Erosion.html +++ b/docs/blockDocs/Blocks/Erosion.html @@ -7,8 +7,8 @@ Blocks.Erosion API documentation - - + + diff --git a/docs/blockDocs/Blocks/FaceDetector.html b/docs/blockDocs/Blocks/FaceDetector.html index 98001f47..7a15ce4b 100644 --- a/docs/blockDocs/Blocks/FaceDetector.html +++ b/docs/blockDocs/Blocks/FaceDetector.html @@ -7,8 +7,8 @@ Blocks.FaceDetector API documentation - - + + diff --git a/docs/blockDocs/Blocks/IMU.html b/docs/blockDocs/Blocks/IMU.html index b29c90be..bdacd76d 100644 --- a/docs/blockDocs/Blocks/IMU.html +++ b/docs/blockDocs/Blocks/IMU.html @@ -7,8 +7,8 @@ Blocks.IMU API documentation - - + + diff --git a/docs/blockDocs/Blocks/ImageRead.html b/docs/blockDocs/Blocks/ImageRead.html index 99b47c66..e3ed9dc2 100644 --- a/docs/blockDocs/Blocks/ImageRead.html +++ b/docs/blockDocs/Blocks/ImageRead.html @@ -7,8 +7,8 @@ Blocks.ImageRead API documentation - - + + diff --git a/docs/blockDocs/Blocks/MotorDriver.html b/docs/blockDocs/Blocks/MotorDriver.html index edceedeb..022f2808 100644 --- a/docs/blockDocs/Blocks/MotorDriver.html +++ b/docs/blockDocs/Blocks/MotorDriver.html @@ -7,8 +7,8 @@ Blocks.MotorDriver API documentation - - + + diff --git a/docs/blockDocs/Blocks/Odometer.html b/docs/blockDocs/Blocks/Odometer.html index 41c8416e..2fe7135a 100644 --- a/docs/blockDocs/Blocks/Odometer.html +++ b/docs/blockDocs/Blocks/Odometer.html @@ -7,8 +7,8 @@ Blocks.Odometer API documentation - - + + diff --git a/docs/blockDocs/Blocks/PID.html b/docs/blockDocs/Blocks/PID.html index 35e91e9b..960424d0 100644 --- a/docs/blockDocs/Blocks/PID.html +++ b/docs/blockDocs/Blocks/PID.html @@ -7,8 +7,8 @@ Blocks.PID API documentation - - + + diff --git a/docs/blockDocs/Blocks/ROSCamera.html b/docs/blockDocs/Blocks/ROSCamera.html index 3b89bf6d..8ebac968 100644 --- a/docs/blockDocs/Blocks/ROSCamera.html +++ b/docs/blockDocs/Blocks/ROSCamera.html @@ -7,8 +7,8 @@ Blocks.ROSCamera API documentation - - + + diff --git a/docs/blockDocs/Blocks/Screen.html b/docs/blockDocs/Blocks/Screen.html index 69e4946d..4db3562a 100644 --- a/docs/blockDocs/Blocks/Screen.html +++ b/docs/blockDocs/Blocks/Screen.html @@ -7,8 +7,8 @@ Blocks.Screen API documentation - - + + diff --git a/docs/blockDocs/Blocks/Teleoperator.html b/docs/blockDocs/Blocks/Teleoperator.html index c483935d..5538f26f 100644 --- a/docs/blockDocs/Blocks/Teleoperator.html +++ b/docs/blockDocs/Blocks/Teleoperator.html @@ -7,8 +7,8 @@ Blocks.Teleoperator API documentation - - + + diff --git a/docs/blockDocs/Blocks/Threshold.html b/docs/blockDocs/Blocks/Threshold.html index b019978a..14344b74 100644 --- a/docs/blockDocs/Blocks/Threshold.html +++ b/docs/blockDocs/Blocks/Threshold.html @@ -7,8 +7,8 @@ Blocks.Threshold API documentation - - + + diff --git a/docs/blockDocs/Blocks/VideoStreamer.html b/docs/blockDocs/Blocks/VideoStreamer.html index 49d7791c..9765613f 100644 --- a/docs/blockDocs/Blocks/VideoStreamer.html +++ b/docs/blockDocs/Blocks/VideoStreamer.html @@ -7,8 +7,8 @@ Blocks.VideoStreamer API documentation - - + + diff --git a/docs/blockDocs/Blocks/utils.html b/docs/blockDocs/Blocks/utils.html index ea576bba..9716c674 100644 --- a/docs/blockDocs/Blocks/utils.html +++ b/docs/blockDocs/Blocks/utils.html @@ -7,8 +7,8 @@ Blocks.utils API documentation - - + + diff --git a/docs/blockDocs/Blocks/utils/models.html b/docs/blockDocs/Blocks/utils/models.html index 542cb618..af4c6c37 100644 --- a/docs/blockDocs/Blocks/utils/models.html +++ b/docs/blockDocs/Blocks/utils/models.html @@ -7,8 +7,8 @@ Blocks.utils.models API documentation - - + + diff --git a/docs/blockDocs/search.js b/docs/blockDocs/search.js index cd46f322..8cfdfbc7 100644 --- a/docs/blockDocs/search.js +++ b/docs/blockDocs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oJekyll Local Installation\n\n

Prerequisites

\n\n

Installing Ruby on Ubuntu

\n\n

First of all, we need to install all the dependencies typing:

\n\n
sudo apt-get install ruby-full build-essential zlib1g-dev\n
\n\n

After that, we need to set up a gem installation directory for your user account. The following commands will add environment variables to your ~/.bashrc file to configure the gem installation path. Run them now:

\n\n
echo '# Install Ruby Gems to ~/gems' >> ~/.bashrc\necho 'export GEM_HOME="$HOME/gems"' >> ~/.bashrc\necho 'export PATH="$HOME/gems/bin:$PATH"' >> ~/.bashrc\nsource ~/.bashrc\n
\n\n

Finally, we install Jekyll:

\n\n
gem install jekyll bundler\n
\n\n

Notice that we don't use the root user :-)

\n\n

Installing Ruby and Jekyll on Mac OS X

\n\n

Follow the Jekyll page installation guide.

\n\n

Running Jekyll Serve

\n\n

By default, the Jekyll server is launched with the following command (which is the one indicated on your website).

\n\n
bundle exec jekyll serve\n
\n\n

If in the process of building the server there is a dependency problem, for example, there is a missing library to install, it is necessary to delete the Gemfile.lock file so that it is rebuilt with the installed dependency. This list of dependencies is found in the Gemfile file (in Python it would be equivalent to the requirements.txt file) and the version of each of the installed gems (packages) is specified. Having a list of dependencies is important for future updates as well as knowing the libraries needed to run the server. Once the Gemfile.lock file is deleted, the command shown above is launched again and the dependency errors should end.

\n\n

Notes for exercise cards.

\n\n
    \n
  • Teaser Images size: multiple of 600x400px
    • \n
\n\n

FAQ

\n\n
    \n
  • Error building Jekyll server:
    • \n
\n\n
jekyll build --incremental --verbose\n
\n"}, "Blocks.Blur": {"fullname": "Blocks.Blur", "modulename": "Blocks.Blur", "type": "module", "doc": "

\n"}, "Blocks.Blur.main": {"fullname": "Blocks.Blur.main", "modulename": "Blocks.Blur", "qualname": "main", "type": "function", "doc": "

Blurs an Object

\n\n

The object to be blurred is read through the inputs.\nWe have multiple available blurs including Gaussian, Averaging and Median Blur. \nWe can change these blurs by changing the name given in the parameter block

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.

\n\n

Outputs the blurred image through the share_image() function

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: BlurType

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Camera": {"fullname": "Blocks.Camera", "modulename": "Blocks.Camera", "type": "module", "doc": "

\n"}, "Blocks.Camera.main": {"fullname": "Blocks.Camera.main", "modulename": "Blocks.Camera", "qualname": "main", "type": "function", "doc": "

Opens your Camera using OpenCV

\n\n

The Camera block opens your webcam using OpenCV and begins capturing the video feed.\nThis video feed is then propagated forward through the share_image() function

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: None

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ColorFilter": {"fullname": "Blocks.ColorFilter", "modulename": "Blocks.ColorFilter", "type": "module", "doc": "

\n"}, "Blocks.ColorFilter.main": {"fullname": "Blocks.ColorFilter.main", "modulename": "Blocks.ColorFilter", "qualname": "main", "type": "function", "doc": "

Filters Colour according to given parameters

\n\n

The image to be filtered is read through the inputs.\nWe can give a Filter between any HSV range by changing the range of the parameters LowerRGB and UpperRGB.

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire .

\n\n

Here the image is tranformed from BGR to HSV and then the filter is applied through the cv2.inRange()\nfunction. Finally the filtered image is overlayed on the orignal by the means of the\ncv2.bitwise_and() function. This filtered image is then shared through the share_image() function.

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: LowerHSV, UpperHSV

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ContourDetector": {"fullname": "Blocks.ContourDetector", "modulename": "Blocks.ContourDetector", "type": "module", "doc": "

\n"}, "Blocks.ContourDetector.main": {"fullname": "Blocks.ContourDetector.main", "modulename": "Blocks.ContourDetector", "qualname": "main", "type": "function", "doc": "

Detects Contours in an Image

\n\n

The image in which contours are to be detected is read through the inputs.\nFirst the image is converted from BGR to Grayscale, the thresholding values are 60, 255.\nThe function used is cv2.threshold().\nOnce it is thersholded, the contours are detected in the image using cv2.findContours()

\n\n

The program then detects the biggest contour present in the image and finds the co-ordinates of its center\nusing the cv2.moments() function.

\n\n

This co-ords of the center alongwith the contour characteristics are part of the output array.\nTHis array is shared through share_array()

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: Array [x, y, width, height, angle of rotation], BGR Image

\n\n

Parameters: None

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Cropper": {"fullname": "Blocks.Cropper", "modulename": "Blocks.Cropper", "type": "module", "doc": "

\n"}, "Blocks.Cropper.main": {"fullname": "Blocks.Cropper.main", "modulename": "Blocks.Cropper", "qualname": "main", "type": "function", "doc": "

Crops an Image

\n\n

The image which is to be cropped is read through the inputs using the inputs.read_image() function.\nThe parameters ask for x, y, w, h

\n\n
x: x co-ordinate of where the crop should start\n\ny: y co-ordinate of where the crop should start\n\nw: width of the crop\n\nh: height of the crop\n\n
\n\n

Image is cropped by simple list slicing.

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.\nOutput is shared via share_image()

\n\n

Inputs: BGR Image

\n\n

Outputs: Resized BGR Image

\n\n

Parameters: x, y, width, height

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Dilation": {"fullname": "Blocks.Dilation", "modulename": "Blocks.Dilation", "type": "module", "doc": "

\n"}, "Blocks.Dilation.main": {"fullname": "Blocks.Dilation.main", "modulename": "Blocks.Dilation", "qualname": "main", "type": "function", "doc": "

Dilates an Image

\n\n

You can specify the kernel dimensions and number of iterations in the parameters.

\n\n

We first convert the colour of the image from BGR to GRAY then we apply dilation on it \nusing the cv2.dilate() function.

\n\n

Finaly we convert from GRAY back to BGR and output the image through the share_image() function.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: Kernel, Iterations

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.EdgeDetector": {"fullname": "Blocks.EdgeDetector", "modulename": "Blocks.EdgeDetector", "type": "module", "doc": "

\n"}, "Blocks.EdgeDetector.main": {"fullname": "Blocks.EdgeDetector.main", "modulename": "Blocks.EdgeDetector", "qualname": "main", "type": "function", "doc": "

Detects Edges in an Image

\n\n

It takes in two parameters Lower and Upper. These parameters are used as the limits in Canny Edge \nDetection. First we convert the input BGR image to GRAY. Next we apply Canny Edge Detection via the \ncv2.Canny() function. The resulting image is then converted back to BGR.

\n\n

This image is then shared to the wire via the share_image() function.

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: Lower, Upper (Threshold values)

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Erosion": {"fullname": "Blocks.Erosion", "modulename": "Blocks.Erosion", "type": "module", "doc": "

\n"}, "Blocks.Erosion.main": {"fullname": "Blocks.Erosion.main", "modulename": "Blocks.Erosion", "qualname": "main", "type": "function", "doc": "

Erodes an Image

\n\n

You can specify the kernel dimensions and number of iterations in the parameters.

\n\n

We first convert the colour of the image from BGR to GRAY then we apply erosion on it \nusing the cv2.erode() function.

\n\n

Finaly we convert from GRAY back to BGR and output the image through the share_image() function.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: Kernel, Iterations

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.FaceDetector": {"fullname": "Blocks.FaceDetector", "modulename": "Blocks.FaceDetector", "type": "module", "doc": "

\n"}, "Blocks.FaceDetector.main": {"fullname": "Blocks.FaceDetector.main", "modulename": "Blocks.FaceDetector", "qualname": "main", "type": "function", "doc": "

Detects Faces in the Image

\n\n

This block applies a Harr Cascade based model on the input image. \nIt takes as an input the parameter BoxOrImage. This parameter has two possible values:\nBoxOrImage: image / box

\n\n

If image is given: The output is the image passed in with a bounding box around the area where \na face is detected. Image is shared through the share_image() function.

\n\n

Else if box is given, the output is the co-ordinates of the bounding box in the form of an array. It \nis chared through the share_array() function.

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image with Bounding Boxes

\n\n

Parameters: BoxOrImage ('box' for Bounding Boxes, 'image' for Image with Detections)

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.IMU": {"fullname": "Blocks.IMU", "modulename": "Blocks.IMU", "type": "module", "doc": "

\n"}, "Blocks.IMU.callback": {"fullname": "Blocks.IMU.callback", "modulename": "Blocks.IMU", "qualname": "callback", "type": "function", "doc": "

The callback function is required by the Subscriber to the ROSTopic. This callback function reads the orientation list from the IMU\nIt then converts the quaternion angles to euler ones. This gives us the roll, pitch and yaw of the body.\nWe convert these radian values to degrees to get the orientation of the body.

\n\n

Aside from these values the IMU also gives us the angular velocity of the body.

\n\n

All of these values are stored in the global data variable of the block.

\n", "signature": "(msg)", "funcdef": "def"}, "Blocks.IMU.main": {"fullname": "Blocks.IMU.main", "modulename": "Blocks.IMU", "qualname": "main", "type": "function", "doc": "

Reads IMU sensor data

\n\n

This is a specialized block used to read IMU sensor data.

\n\n

It reads the ROSTopic name from the ROSTopic parameter. Default is mavros/imu/data.

\n\n

This data is sent to the callback function which converts the orientation list obtained into roll, pitch and yaw for the\nrobot that the IMU is present on. Alongwith orientation, it also gives the angular velocity of the robot.\nThis data is shared in the form of an array using the share_array() function.

\n\n

Inputs: None

\n\n

Outputs: Array [Roll, Pitch , Yaw, Angular Velocity in X, Angular Velocity in Y, Angular Velocity in Z]

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ImageRead": {"fullname": "Blocks.ImageRead", "modulename": "Blocks.ImageRead", "type": "module", "doc": "

\n"}, "Blocks.ImageRead.main": {"fullname": "Blocks.ImageRead.main", "modulename": "Blocks.ImageRead", "qualname": "main", "type": "function", "doc": "

Reads an Image from a Specified Path

\n\n

This box reads an image from a given file path. The path to be specified is written in the parameter\nImagePath.

\n\n

It is read through the cv2.imread() function and shared through the share_image() function.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: ImagePath

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.MotorDriver": {"fullname": "Blocks.MotorDriver", "modulename": "Blocks.MotorDriver", "type": "module", "doc": "

\n"}, "Blocks.MotorDriver.callback": {"fullname": "Blocks.MotorDriver.callback", "modulename": "Blocks.MotorDriver", "qualname": "callback", "type": "function", "doc": "

\n", "signature": "(inp)", "funcdef": "def"}, "Blocks.MotorDriver.main": {"fullname": "Blocks.MotorDriver.main", "modulename": "Blocks.MotorDriver", "qualname": "main", "type": "function", "doc": "

Publishes Twist Command to drive Motors

\n\n

It publishes to the ROSTopic name from the ROSTopic parameter. Default is /robot/cmd_vel.

\n\n

It reads an array as an input by the read_array() function.

\n\n

This is assumed to be of the format [ linear_velocity, angular_velocity ].

\n\n

This data is then converted into a Twist() message with the linear.x = linear_velocity and angular.z = angular_velocity

\n\n

The data is then published continuously

\n\n

Inputs: cmd_vel (Linear Velocity, Angular Velocity)

\n\n

Outputs: None

\n\n

Parameters: ROSTopic

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Reads Data from An Odometer

\n\n

It reads the ROSTopic name from the ROSTopic parameter.\nIt then initializes a Subscriber to subscribe to that ROSTopic, once the data is obtained through the callback\nfunction, it is formatted into an array with the format: [ x, y, yaw ]

\n\n

This data is then shared to the wire using the share_array() function.

\n\n

Inputs: None

\n\n

Outputs: Array [X, Y, Yaw]

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.PID": {"fullname": "Blocks.PID", "modulename": "Blocks.PID", "type": "module", "doc": "

\n"}, "Blocks.PID.main": {"fullname": "Blocks.PID.main", "modulename": "Blocks.PID", "qualname": "main", "type": "function", "doc": "

Applies PID for a Given Error Value

\n\n

The error is read as an input from the inputs wire.

\n\n

The Kp, Ki, and Kd parameters are read from the parameters of the same name.\nOnce there it applies the PID technique to the error variable in order to minimize it.

\n\n

The resulting values are shared through the share_array() function.

\n\n

Inputs: Error

\n\n

Outputs: cmd_vel (Linear Velocity, Angular Velocity)

\n\n

Parameters: Kp, Ki, Kd

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ROSCamera": {"fullname": "Blocks.ROSCamera", "modulename": "Blocks.ROSCamera", "type": "module", "doc": "

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Gets Image from a ROSCamera

\n\n

The camera topic is read from the ROSTopic parameter, by default it is /robot/camera

\n\n

The image message is converted to OpenCV compatible format via the imgmsg_to_cv2() function.

\n\n

This is then shared ahead using the share_image() function.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Screen": {"fullname": "Blocks.Screen", "modulename": "Blocks.Screen", "type": "module", "doc": "

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Displays the given Image

\n\n

Takes an image as an input and displays it on the user's screen.\nThe cv2.imshow() function is used in order to display the image.

\n\n

Inputs: BGR Image

\n\n

Outputs: None

\n\n

Parameters: None

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Used to Imitate the Movements of the Operator

\n\n

It takes in an array as input, depending on the array variables, it will output another array\ncontaining the velocity it deems appropriate.\nThe linear_velocity can be given via the Linear parameter.

\n\n

The output data is a list of the format: [ linear_velocity, angular_velocity ]

\n\n

This is then shared to the output wire using the share_array() function.

\n\n

Inputs: Bounding Box (x, y, width, height)

\n\n

Outputs: cmd_vel (linear velocity, angular velocity)

\n\n

Parameters: Linear(Linear Velocity)

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Threshold": {"fullname": "Blocks.Threshold", "modulename": "Blocks.Threshold", "type": "module", "doc": "

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Thresholds an Image

\n\n

THis block reads the parameters LowerThreshold and UpperThreshold.

\n\n

Based on these values it converts the input image form BGR into GRAY and applies the cv2.threshold() function on it.

\n\n

The image is then converted back into BGR and shared to the output wire using the\nshare_image() function.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: LowerThreshold, UpperThreshold

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.VideoStreamer": {"fullname": "Blocks.VideoStreamer", "modulename": "Blocks.VideoStreamer", "type": "module", "doc": "

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Streams Video from File

\n\n

The filepath of your video is given in the PathToFile parameter.\nNote: that this file path is relative to the modules folder of the final built application.

\n\n

Capturing begins using the cv2.VideoCapture() function. \nThe video is then read frame by frame and each frame is shared to the output wire using the\nshare_image() function.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: PathToFile

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1.4142135623730951}}, "df": 1}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"Blocks.ColorFilter.main": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"Blocks.ContourDetector.main": {"tf": 1}, "Blocks.Cropper.main": {"tf": 1.4142135623730951}, "Blocks.Teleoperator.main": {"tf": 1}}, "df": 3}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"Blocks": {"tf": 2}}, "df": 1}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"Blocks.IMU.callback": {"tf": 1}}, "df": 1}}}}}}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"Blocks": {"tf": 1}}, "df": 1}, "v": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"Blocks.IMU.main": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"Blocks": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"Blocks.PID.main": {"tf": 1}}, "df": 1}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"Blocks": {"tf": 1}, "Blocks.Blur.main": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"Blocks.Blur.main": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"Blocks.ColorFilter.main": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"Blocks.MotorDriver.main": {"tf": 1}, "Blocks.ROSCamera.main": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"Blocks.ContourDetector.main": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"Blocks.FaceDetector.main": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"Blocks.VideoStreamer.main": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"Blocks.MotorDriver.main": {"tf": 1}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"Blocks.Teleoperator.main": {"tf": 1}}, "df": 1}}}}}}}}}, "x": {"docs": {"Blocks": {"tf": 1}, "Blocks.ContourDetector.main": {"tf": 1}, "Blocks.Cropper.main": {"tf": 2}, "Blocks.IMU.main": {"tf": 1}, "Blocks.MotorDriver.main": {"tf": 1}, "Blocks.Odometer.main": {"tf": 1.4142135623730951}, "Blocks.Teleoperator.main": {"tf": 1}}, "df": 7}, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"Blocks": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"Blocks.Dilation.main": {"tf": 1.4142135623730951}, "Blocks.Erosion.main": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "p": {"docs": {"Blocks.PID.main": {"tf": 1.4142135623730951}}, "df": 1}, "i": {"docs": {"Blocks.PID.main": {"tf": 1.4142135623730951}}, "df": 1}, "d": {"docs": {"Blocks.PID.main": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"Blocks": {"fullname": "Blocks", "modulename": "Blocks", "type": "module", "doc": "
\n\n

\n\n

Visual Circuit

\n\n

\"Contributors\"\n\"Forks\"\n\"Stargazers\"\n\"Issues\"\n\"License\"

\n\n

Visual Circuit is an open source tool to develop robotic applications. It aims to make developing applications for ROS and Gazebo simple and user friendly by its intuitive block-based interface. Users have the ablity to drag and drop blocks to develop their logic. Users are also able to build completely custom blocks as well as edit code in the existing blocks, this makes Visual Circuit a robust and powerful tool to develop even complicated applications.

\n\n

For more information visit our site VisualCircuit

\n\n

Setup

\n\n

Front-end

\n\n

For more specific instructions check the frontend readme

\n\n
    \n
  1. Clone the repository https://github.com/JdeRobot/VisualCircuit.git
    2. \n
  2. Change directory to frontend
    3. \n
  3. Run npm install
    4. \n
  4. Run npm start
    5. \n
  5. Open http://localhost:3000/ in browser.
    6. \n
\n\n

Back-end

\n\n

For more specific instructions check the backend readme

\n\n
    \n
  1. Clone the repository https://github.com/JdeRobot/VisualCircuit.git
    2. \n
  2. Change directory to backend
    3. \n
  3. Create a Python3 virtual environment using venv. \nFor eg. python -m venv .venv
    4. \n
  4. After activating the virtual environment, install the dependencies by running\npip install -r requirements.txt
    5. \n
  5. Add .env file to the backend folder. And add the variables as defined in .env.template
    6. \n
  6. Create the static files to serve during execution by python manage.py collectstatic
    7. \n
  7. Start the server by running python manage.py runserver 8000
    8. \n
\n\n\n\n

Contributing

\n\n

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. For more info on how to design a block, refer to this link

\n\n

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag \"enhancement\".\nDon't forget to give the project a star! Thanks again!

\n\n
    \n
  1. Fork the Project
    2. \n
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
    3. \n
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
    4. \n
  4. Push to the Branch (git push origin feature/AmazingFeature)
    5. \n
  5. Open a Pull Request
    6. \n
\n\n

\n

\n"}, "Blocks.Blur": {"fullname": "Blocks.Blur", "modulename": "Blocks.Blur", "type": "module", "doc": "

\n"}, "Blocks.Blur.main": {"fullname": "Blocks.Blur.main", "modulename": "Blocks.Blur", "qualname": "main", "type": "function", "doc": "

Blurs an Object

\n\n

The object to be blurred is read through the inputs.\nWe have multiple available blurs including Gaussian, Averaging and Median Blur. \nWe can change these blurs by changing the name given in the parameter block

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.

\n\n

Outputs the blurred image through the share_image() function

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: BlurType

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Camera": {"fullname": "Blocks.Camera", "modulename": "Blocks.Camera", "type": "module", "doc": "

\n"}, "Blocks.Camera.main": {"fullname": "Blocks.Camera.main", "modulename": "Blocks.Camera", "qualname": "main", "type": "function", "doc": "

Opens your Camera using OpenCV

\n\n

The Camera block opens your webcam using OpenCV and begins capturing the video feed.\nThis video feed is then propagated forward through the share_image() function

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: None

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ColorFilter": {"fullname": "Blocks.ColorFilter", "modulename": "Blocks.ColorFilter", "type": "module", "doc": "

\n"}, "Blocks.ColorFilter.main": {"fullname": "Blocks.ColorFilter.main", "modulename": "Blocks.ColorFilter", "qualname": "main", "type": "function", "doc": "

Filters Colour according to given parameters

\n\n

The image to be filtered is read through the inputs.\nWe can give a Filter between any HSV range by changing the range of the parameters LowerRGB and UpperRGB.

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire .

\n\n

Here the image is tranformed from BGR to HSV and then the filter is applied through the cv2.inRange()\nfunction. Finally the filtered image is overlayed on the orignal by the means of the\ncv2.bitwise_and() function. This filtered image is then shared through the share_image() function.

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: LowerHSV, UpperHSV

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ContourDetector": {"fullname": "Blocks.ContourDetector", "modulename": "Blocks.ContourDetector", "type": "module", "doc": "

\n"}, "Blocks.ContourDetector.main": {"fullname": "Blocks.ContourDetector.main", "modulename": "Blocks.ContourDetector", "qualname": "main", "type": "function", "doc": "

Detects Contours in an Image

\n\n

The image in which contours are to be detected is read through the inputs.\nFirst the image is converted from BGR to Grayscale, the thresholding values are 60, 255.\nThe function used is cv2.threshold().\nOnce it is thersholded, the contours are detected in the image using cv2.findContours()

\n\n

The program then detects the biggest contour present in the image and finds the co-ordinates of its center\nusing the cv2.moments() function.

\n\n

This co-ords of the center alongwith the contour characteristics are part of the output array.\nTHis array is shared through share_array()

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: Array [x, y, width, height, angle of rotation], BGR Image

\n\n

Parameters: None

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Cropper": {"fullname": "Blocks.Cropper", "modulename": "Blocks.Cropper", "type": "module", "doc": "

\n"}, "Blocks.Cropper.main": {"fullname": "Blocks.Cropper.main", "modulename": "Blocks.Cropper", "qualname": "main", "type": "function", "doc": "

Crops an Image

\n\n

The image which is to be cropped is read through the inputs using the inputs.read_image() function.\nThe parameters ask for x, y, w, h

\n\n
x: x co-ordinate of where the crop should start\n\ny: y co-ordinate of where the crop should start\n\nw: width of the crop\n\nh: height of the crop\n\n
\n\n

Image is cropped by simple list slicing.

\n\n

while loop is the part of the program that is executed continuously.\nIt is enabled by default but can be disabled by passing in 0 through the enable wire.\nOutput is shared via share_image()

\n\n

Inputs: BGR Image

\n\n

Outputs: Resized BGR Image

\n\n

Parameters: x, y, width, height

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Dilation": {"fullname": "Blocks.Dilation", "modulename": "Blocks.Dilation", "type": "module", "doc": "

\n"}, "Blocks.Dilation.main": {"fullname": "Blocks.Dilation.main", "modulename": "Blocks.Dilation", "qualname": "main", "type": "function", "doc": "

Dilates an Image

\n\n

You can specify the kernel dimensions and number of iterations in the parameters.

\n\n

We first convert the colour of the image from BGR to GRAY then we apply dilation on it \nusing the cv2.dilate() function.

\n\n

Finaly we convert from GRAY back to BGR and output the image through the share_image() function.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: Kernel, Iterations

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.EdgeDetector": {"fullname": "Blocks.EdgeDetector", "modulename": "Blocks.EdgeDetector", "type": "module", "doc": "

\n"}, "Blocks.EdgeDetector.main": {"fullname": "Blocks.EdgeDetector.main", "modulename": "Blocks.EdgeDetector", "qualname": "main", "type": "function", "doc": "

Detects Edges in an Image

\n\n

It takes in two parameters Lower and Upper. These parameters are used as the limits in Canny Edge \nDetection. First we convert the input BGR image to GRAY. Next we apply Canny Edge Detection via the \ncv2.Canny() function. The resulting image is then converted back to BGR.

\n\n

This image is then shared to the wire via the share_image() function.

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: Lower, Upper (Threshold values)

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Erosion": {"fullname": "Blocks.Erosion", "modulename": "Blocks.Erosion", "type": "module", "doc": "

\n"}, "Blocks.Erosion.main": {"fullname": "Blocks.Erosion.main", "modulename": "Blocks.Erosion", "qualname": "main", "type": "function", "doc": "

Erodes an Image

\n\n

You can specify the kernel dimensions and number of iterations in the parameters.

\n\n

We first convert the colour of the image from BGR to GRAY then we apply erosion on it \nusing the cv2.erode() function.

\n\n

Finaly we convert from GRAY back to BGR and output the image through the share_image() function.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: Kernel, Iterations

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.FaceDetector": {"fullname": "Blocks.FaceDetector", "modulename": "Blocks.FaceDetector", "type": "module", "doc": "

\n"}, "Blocks.FaceDetector.main": {"fullname": "Blocks.FaceDetector.main", "modulename": "Blocks.FaceDetector", "qualname": "main", "type": "function", "doc": "

Detects Faces in the Image

\n\n

This block applies a Harr Cascade based model on the input image. \nIt takes as an input the parameter BoxOrImage. This parameter has two possible values:\nBoxOrImage: image / box

\n\n

If image is given: The output is the image passed in with a bounding box around the area where \na face is detected. Image is shared through the share_image() function.

\n\n

Else if box is given, the output is the co-ordinates of the bounding box in the form of an array. It \nis chared through the share_array() function.

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image with Bounding Boxes

\n\n

Parameters: BoxOrImage ('box' for Bounding Boxes, 'image' for Image with Detections)

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.IMU": {"fullname": "Blocks.IMU", "modulename": "Blocks.IMU", "type": "module", "doc": "

\n"}, "Blocks.IMU.callback": {"fullname": "Blocks.IMU.callback", "modulename": "Blocks.IMU", "qualname": "callback", "type": "function", "doc": "

The callback function is required by the Subscriber to the ROSTopic. This callback function reads the orientation list from the IMU\nIt then converts the quaternion angles to euler ones. This gives us the roll, pitch and yaw of the body.\nWe convert these radian values to degrees to get the orientation of the body.

\n\n

Aside from these values the IMU also gives us the angular velocity of the body.

\n\n

All of these values are stored in the global data variable of the block.

\n", "signature": "(msg)", "funcdef": "def"}, "Blocks.IMU.main": {"fullname": "Blocks.IMU.main", "modulename": "Blocks.IMU", "qualname": "main", "type": "function", "doc": "

Reads IMU sensor data

\n\n

This is a specialized block used to read IMU sensor data.

\n\n

It reads the ROSTopic name from the ROSTopic parameter. Default is mavros/imu/data.

\n\n

This data is sent to the callback function which converts the orientation list obtained into roll, pitch and yaw for the\nrobot that the IMU is present on. Alongwith orientation, it also gives the angular velocity of the robot.\nThis data is shared in the form of an array using the share_array() function.

\n\n

Inputs: None

\n\n

Outputs: Array [Roll, Pitch , Yaw, Angular Velocity in X, Angular Velocity in Y, Angular Velocity in Z]

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ImageRead": {"fullname": "Blocks.ImageRead", "modulename": "Blocks.ImageRead", "type": "module", "doc": "

\n"}, "Blocks.ImageRead.main": {"fullname": "Blocks.ImageRead.main", "modulename": "Blocks.ImageRead", "qualname": "main", "type": "function", "doc": "

Reads an Image from a Specified Path

\n\n

This box reads an image from a given file path. The path to be specified is written in the parameter\nImagePath.

\n\n

It is read through the cv2.imread() function and shared through the share_image() function.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: ImagePath

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.MotorDriver": {"fullname": "Blocks.MotorDriver", "modulename": "Blocks.MotorDriver", "type": "module", "doc": "

\n"}, "Blocks.MotorDriver.callback": {"fullname": "Blocks.MotorDriver.callback", "modulename": "Blocks.MotorDriver", "qualname": "callback", "type": "function", "doc": "

\n", "signature": "(inp)", "funcdef": "def"}, "Blocks.MotorDriver.main": {"fullname": "Blocks.MotorDriver.main", "modulename": "Blocks.MotorDriver", "qualname": "main", "type": "function", "doc": "

Publishes Twist Command to drive Motors

\n\n

It publishes to the ROSTopic name from the ROSTopic parameter. Default is /robot/cmd_vel.

\n\n

It reads an array as an input by the read_array() function.

\n\n

This is assumed to be of the format [ linear_velocity, angular_velocity ].

\n\n

This data is then converted into a Twist() message with the linear.x = linear_velocity and angular.z = angular_velocity

\n\n

The data is then published continuously

\n\n

Inputs: cmd_vel (Linear Velocity, Angular Velocity)

\n\n

Outputs: None

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Odometer": {"fullname": "Blocks.Odometer", "modulename": "Blocks.Odometer", "type": "module", "doc": "

\n"}, "Blocks.Odometer.callback": {"fullname": "Blocks.Odometer.callback", "modulename": "Blocks.Odometer", "qualname": "callback", "type": "function", "doc": "

\n", "signature": "(msg)", "funcdef": "def"}, "Blocks.Odometer.main": {"fullname": "Blocks.Odometer.main", "modulename": "Blocks.Odometer", "qualname": "main", "type": "function", "doc": "

Reads Data from An Odometer

\n\n

It reads the ROSTopic name from the ROSTopic parameter.\nIt then initializes a Subscriber to subscribe to that ROSTopic, once the data is obtained through the callback\nfunction, it is formatted into an array with the format: [ x, y, yaw ]

\n\n

This data is then shared to the wire using the share_array() function.

\n\n

Inputs: None

\n\n

Outputs: Array [X, Y, Yaw]

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.PID": {"fullname": "Blocks.PID", "modulename": "Blocks.PID", "type": "module", "doc": "

\n"}, "Blocks.PID.main": {"fullname": "Blocks.PID.main", "modulename": "Blocks.PID", "qualname": "main", "type": "function", "doc": "

Applies PID for a Given Error Value

\n\n

The error is read as an input from the inputs wire.

\n\n

The Kp, Ki, and Kd parameters are read from the parameters of the same name.\nOnce there it applies the PID technique to the error variable in order to minimize it.

\n\n

The resulting values are shared through the share_array() function.

\n\n

Inputs: Error

\n\n

Outputs: cmd_vel (Linear Velocity, Angular Velocity)

\n\n

Parameters: Kp, Ki, Kd

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.ROSCamera": {"fullname": "Blocks.ROSCamera", "modulename": "Blocks.ROSCamera", "type": "module", "doc": "

\n"}, "Blocks.ROSCamera.callback": {"fullname": "Blocks.ROSCamera.callback", "modulename": "Blocks.ROSCamera", "qualname": "callback", "type": "function", "doc": "

\n", "signature": "(msg)", "funcdef": "def"}, "Blocks.ROSCamera.main": {"fullname": "Blocks.ROSCamera.main", "modulename": "Blocks.ROSCamera", "qualname": "main", "type": "function", "doc": "

Gets Image from a ROSCamera

\n\n

The camera topic is read from the ROSTopic parameter, by default it is /robot/camera

\n\n

The image message is converted to OpenCV compatible format via the imgmsg_to_cv2() function.

\n\n

This is then shared ahead using the share_image() function.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: ROSTopic

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Screen": {"fullname": "Blocks.Screen", "modulename": "Blocks.Screen", "type": "module", "doc": "

\n"}, "Blocks.Screen.main": {"fullname": "Blocks.Screen.main", "modulename": "Blocks.Screen", "qualname": "main", "type": "function", "doc": "

Displays the given Image

\n\n

Takes an image as an input and displays it on the user's screen.\nThe cv2.imshow() function is used in order to display the image.

\n\n

Inputs: BGR Image

\n\n

Outputs: None

\n\n

Parameters: None

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Teleoperator": {"fullname": "Blocks.Teleoperator", "modulename": "Blocks.Teleoperator", "type": "module", "doc": "

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Used to Imitate the Movements of the Operator

\n\n

It takes in an array as input, depending on the array variables, it will output another array\ncontaining the velocity it deems appropriate.\nThe linear_velocity can be given via the Linear parameter.

\n\n

The output data is a list of the format: [ linear_velocity, angular_velocity ]

\n\n

This is then shared to the output wire using the share_array() function.

\n\n

Inputs: Bounding Box (x, y, width, height)

\n\n

Outputs: cmd_vel (linear velocity, angular velocity)

\n\n

Parameters: Linear(Linear Velocity)

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.Threshold": {"fullname": "Blocks.Threshold", "modulename": "Blocks.Threshold", "type": "module", "doc": "

\n"}, "Blocks.Threshold.main": {"fullname": "Blocks.Threshold.main", "modulename": "Blocks.Threshold", "qualname": "main", "type": "function", "doc": "

Thresholds an Image

\n\n

THis block reads the parameters LowerThreshold and UpperThreshold.

\n\n

Based on these values it converts the input image form BGR into GRAY and applies the cv2.threshold() function on it.

\n\n

The image is then converted back into BGR and shared to the output wire using the\nshare_image() function.

\n\n

Further reading

\n\n

Inputs: BGR Image

\n\n

Outputs: BGR Image

\n\n

Parameters: LowerThreshold, UpperThreshold

\n", "signature": "(inputs, outputs, parameters, synchronise)", "funcdef": "def"}, "Blocks.VideoStreamer": {"fullname": "Blocks.VideoStreamer", "modulename": "Blocks.VideoStreamer", "type": "module", "doc": "

\n"}, "Blocks.VideoStreamer.main": {"fullname": "Blocks.VideoStreamer.main", "modulename": "Blocks.VideoStreamer", "qualname": "main", "type": "function", "doc": "

Streams Video from File

\n\n

The filepath of your video is given in the PathToFile parameter.\nNote: that this file path is relative to the modules folder of the final built application.

\n\n

Capturing begins using the cv2.VideoCapture() function. \nThe video is then read frame by frame and each frame is shared to the output wire using the\nshare_image() function.

\n\n

Inputs: None

\n\n

Outputs: BGR Image

\n\n

Parameters: PathToFile

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