{"id":14977362,"url":"https://github.com/biomedical-imaging-group/image-access","last_synced_at":"2025-07-27T16:34:43.738Z","repository":{"id":57271939,"uuid":"290152818","full_name":"Biomedical-Imaging-Group/image-access","owner":"Biomedical-Imaging-Group","description":"A class for the didactic implementation of image-processing algorithms in JavaScript.","archived":false,"fork":false,"pushed_at":"2021-09-27T10:39:24.000Z","size":1569,"stargazers_count":9,"open_issues_count":2,"forks_count":4,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-02-01T10:51:12.283Z","etag":null,"topics":["education","epfl","image-processing","javascript","jupyter-notebook"],"latest_commit_sha":null,"homepage":"","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Biomedical-Imaging-Group.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-08-25T08:01:30.000Z","updated_at":"2024-11-18T20:49:30.000Z","dependencies_parsed_at":"2022-09-11T13:04:53.607Z","dependency_job_id":null,"html_url":"https://github.com/Biomedical-Imaging-Group/image-access","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Biomedical-Imaging-Group%2Fimage-access","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Biomedical-Imaging-Group%2Fimage-access/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Biomedical-Imaging-Group%2Fimage-access/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Biomedical-Imaging-Group%2Fimage-access/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Biomedical-Imaging-Group","download_url":"https://codeload.github.com/Biomedical-Imaging-Group/image-access/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238590593,"owners_count":19497351,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["education","epfl","image-processing","javascript","jupyter-notebook"],"created_at":"2024-09-24T13:55:31.331Z","updated_at":"2025-02-13T03:32:17.265Z","avatar_url":"https://github.com/Biomedical-Imaging-Group.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ImageAccess\nA class for the didactic implementation of image-processing algorithms in JavaScript.\n\n## Overview\nThe `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. \n\nThe 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.\n\nThe [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.\n\n## Main Features\nWith the purpose of simplifying image-processing programming in JavaScript, the `ImageAccess` class implements, among others, the following key features\n* dedicated verbose error handling and image comparison,\n* simple read and write access to single pixels, rows, and columns, \n* simple read access to pixel neighborhoods, supporting the simplified implementation of IP workflows using the neighborhood concept,\n* simple write access to subimages, simplifying the creation of composite images, \n* and automatic handling of boundary conditions in all of the above.\n\n## Installation\n### npm\nThe `ImageAccess` class can easily be installed from [npm](https://www.npmjs.com/) with\n```\nnpm install image-access\n```\nand then be imported using\n```javascript\nvar ImageAccess = require('image-access')\n```\n\n### GitHub\nAlternatively, the [github repository](https://github.com/Biomedical-Imaging-Group/image-access) can be cloned and the `ImageAccess` class can be imported with\n```javascript\nvar ImageAccess = require('./ImageAccess.js')\n```\n\n## Contributors\nThe class was developed at the [EPFL's Biomedical Imaging Group](http://bigwww.epfl.ch), mainly by\n\n* Kay Lächler (kay.lachler@epfl.ch, [TheUser0571](https://github.com/TheUser0571))\n\nwith contributions from\n\n* Alejandro Noguerón Aramburu (alejandro.nogueronaramburu@epfl.ch, [Alejandro-1996](https://github.com/Alejandro-1996)),\n* [Pol del Aguila Pla](https://poldap.github.io), (pol.delaguilapla@epfl.ch, [poldap](https://github.com/poldap)),\n* [Daniel Sage](http://bigwww.epfl.ch/sage/index.html), (daniel.sage@epfl.ch, [dasv74](https://github.com/dasv74)).\n\nThe 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. \n\n[![Image Processing Labs with Jupyter video on YouTube](http://img.youtube.com/vi/AF18wN37B6Q/0.jpg)](http://www.youtube.com/watch?v=AF18wN37B6Q \"Image Processing Labs with Jupyter\")\n\n## Documentation\nA detailed documentation can be found [here](https://biomedical-imaging-group.github.io/image-access/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiomedical-imaging-group%2Fimage-access","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiomedical-imaging-group%2Fimage-access","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiomedical-imaging-group%2Fimage-access/lists"}