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https://github.com/biomedical-imaging-group/image-access
A class for the didactic implementation of image-processing algorithms in JavaScript.
https://github.com/biomedical-imaging-group/image-access
education epfl image-processing javascript jupyter-notebook
Last synced: about 1 month ago
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A class for the didactic implementation of image-processing algorithms in JavaScript.
- Host: GitHub
- URL: https://github.com/biomedical-imaging-group/image-access
- Owner: Biomedical-Imaging-Group
- License: cc0-1.0
- Created: 2020-08-25T08:01:30.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-09-27T10:39:24.000Z (about 3 years ago)
- Last Synced: 2024-10-11T15:44:12.363Z (about 1 month ago)
- Topics: education, epfl, image-processing, javascript, jupyter-notebook
- Language: JavaScript
- Homepage:
- Size: 1.5 MB
- Stars: 8
- Watchers: 5
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ImageAccess
A class for the didactic implementation of image-processing algorithms in JavaScript.
## Overview
The `ImageAccess` class has been developed to teach image-processing programming at the pixel level in engineering university courses. The class is written in JavaScript and is intended to be used in [Jupyter Notebook](https://jupyter.org/) laboratories run by the [IJavascript](http://n-riesco.github.io/ijavascript/) kernel. However, it can also be used in a native JavaScript environment.
The aim of the `ImageAccess` class is to facilitate the creation and modification of images in JavaScript. As such, it provides an easy-to-use representation of graylevel images, offering methods for pixel, neighborgood and subimage access automatically taking care of boundary conditions.
The [ImageAccess example notebook.ipynb](https://nbviewer.jupyter.org/github/Biomedical-Imaging-Group/image-access/blob/master/ImageAccess%20example%20notebook.ipynb) showcases the basic functionalities of the `ImageAccess` class. Students and employees at the [École polytechnique fédérale de Lausanne (EPFL)](https://epfl.ch/en) can click [here](https://bit.ly/2FGVRzn) to run it on the EPFL's JupyterLab instance, [Noto](https://www.epfl.ch/education/educational-initiatives/cede/digitaltools/noto/). Others may click on the Binder tag for this repository above and run it there instead.
## Main Features
With the purpose of simplifying image-processing programming in JavaScript, the `ImageAccess` class implements, among others, the following key features
* dedicated verbose error handling and image comparison,
* simple read and write access to single pixels, rows, and columns,
* simple read access to pixel neighborhoods, supporting the simplified implementation of IP workflows using the neighborhood concept,
* simple write access to subimages, simplifying the creation of composite images,
* and automatic handling of boundary conditions in all of the above.
## Installation
### npm
The `ImageAccess` class can easily be installed from [npm](https://www.npmjs.com/) with
```
npm install image-access
```
and then be imported using
```javascript
var ImageAccess = require('image-access')
```
### GitHub
Alternatively, the [github repository](https://github.com/Biomedical-Imaging-Group/image-access) can be cloned and the `ImageAccess` class can be imported with
```javascript
var ImageAccess = require('./ImageAccess.js')
```
## Contributors
The class was developed at the [EPFL's Biomedical Imaging Group](http://bigwww.epfl.ch), mainly by
* Kay Lächler ([email protected], [TheUser0571](https://github.com/TheUser0571))
with contributions from
* Alejandro Noguerón Aramburu ([email protected], [Alejandro-1996](https://github.com/Alejandro-1996)),
* [Pol del Aguila Pla](https://poldap.github.io), ([email protected], [poldap](https://github.com/poldap)),
* [Daniel Sage](http://bigwww.epfl.ch/sage/index.html), ([email protected], [dasv74](https://github.com/dasv74)).
The development of this class was supported by the [Digital Resources for Instruction and Learning (DRIL) Fund](https://www.epfl.ch/education/educational-initiatives/cede/digitaltools/dril/) at EPFL, which supported the projects _IPLAB – Image Processing Laboratories on Noto_ and _FeedbackNow – Automatic grading and formative feedback for image processing laboratories_ by Pol del Aguila Pla and Daniel Sage in the sprint and fall semesters of 2020, respectively. See the video below for more information.
[](http://www.youtube.com/watch?v=AF18wN37B6Q "Image Processing Labs with Jupyter")
## Documentation
A detailed documentation can be found [here](https://biomedical-imaging-group.github.io/image-access/).