{"id":15030859,"url":"https://github.com/jasmcaus/opencv-course","last_synced_at":"2025-05-15T09:05:55.861Z","repository":{"id":37544470,"uuid":"300270936","full_name":"jasmcaus/opencv-course","owner":"jasmcaus","description":"Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.","archived":false,"fork":false,"pushed_at":"2024-07-29T17:21:19.000Z","size":9224,"stargazers_count":1205,"open_issues_count":10,"forks_count":1008,"subscribers_count":31,"default_branch":"master","last_synced_at":"2025-04-11T19:55:32.309Z","etag":null,"topics":["caer","concepts","face-detection","face-recognition","faces","freecodecamp","opencv","opencv-course","opencv-python","python","recognition","video"],"latest_commit_sha":null,"homepage":"https://youtu.be/oXlwWbU8l2o","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jasmcaus.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-10-01T12:26:54.000Z","updated_at":"2025-04-08T02:59:21.000Z","dependencies_parsed_at":"2024-07-29T22:26:56.817Z","dependency_job_id":null,"html_url":"https://github.com/jasmcaus/opencv-course","commit_stats":{"total_commits":79,"total_committers":10,"mean_commits":7.9,"dds":"0.21518987341772156","last_synced_commit":"c028d3624fd77f34ae9f1a57a13106551a018c4c"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasmcaus%2Fopencv-course","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasmcaus%2Fopencv-course/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasmcaus%2Fopencv-course/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasmcaus%2Fopencv-course/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jasmcaus","download_url":"https://codeload.github.com/jasmcaus/opencv-course/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254310513,"owners_count":22049468,"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":["caer","concepts","face-detection","face-recognition","faces","freecodecamp","opencv","opencv-course","opencv-python","python","recognition","video"],"created_at":"2024-09-24T20:14:24.921Z","updated_at":"2025-05-15T09:05:55.844Z","avatar_url":"https://github.com/jasmcaus.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# OpenCV with Python in 4 Hours\nNotes and code used in my [**Python and OpenCV course**](https://youtu.be/oXlwWbU8l2o) on [freeCodeCamp.org](http://freecodecamp.org). You can find me on [Twitter](https://twitter.com/jasmcaus) for more info on courses I'm working on currently.\n\n\n## Important Updates:\n`caer.train_val_split()` is a deprecated feature in [`caer`](https://github.com/jasmcaus/caer/). Use `sklearn.model_selection.train_test_split()` instead. See [#9](https://github.com/jasmcaus/opencv-course/issues/9) for more details.\n\n\n# Course Outline (with timestamps)\n## 1. Installation\nBesides installing OpenCV, we cover the installation of the following package:\n\n[**`Caer`**](https://github.com/jasmcaus/caer/) is a *lightweight, high-performance* Vision library for high-performance AI research. It simplifies your approach towards Computer Vision by abstracting away unnecessary boilerplate code giving you the **flexibility** to quickly prototype deep learning models and research ideas. \n```bash\n$ pip install caer\n```\n\n\n## 2. Basic Concepts:\n- Reading Images and Video ([0:04:12](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=252s))\n- Resizing and Rescaling Images and Video Frames ([0:12:57](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=777s))\n- Drawing Shapes and Placing text on images ([0:20:21](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=1221s))\n- 5 Essential Methods in OpenCV ([0:31:55](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=1915s))\n- Image Transformations ([0:44:13](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=2653s))\n- Contour Detection ([0:57:06](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=3426s))\n    \n## 3. Advanced Concepts:\n- Switching between Colour Spaces (RGB, BGR, Grayscale, HSV and L*a*b) ([1:12:53](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=4373s))\n- Splitting and Merging Colour Channels ([1:23:10](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=4990s))\n- Blurring ([1:31:03](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=5463s))\n- BITWISE operations ([1:44:27](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=6267s))\n- Masking ([1:53:06](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=6786s))\n- Histogram Computation ([2:01:43](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=7303s))\n- Thresholding/Binarizing Images ([2:15:22](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=8122s))\n- Advanced Edge Detection ([2:26:27](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=8787s))\n    \n## 4. Face Detection and Recognition\n- Face Detection using Haar Cascades ([2:35:25](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=9325s))\n- Face Recognition using OpenCV's LBPHFaceRecognizer algorithm ([2:49:05](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=10145s))\n    \n## 5. Capstone: Deep Computer Vision\n- Building a Deep Computer Vision model to classify between the characters in the popular TV series The Simpsons ([3:11:57](https://www.youtube.com/watch?v=x3c8w2ruhjs\u0026t=11517s))\n\n# Credits\nThe images in the [Photos](https://github.com/jasmcaus/opencv-course/tree/master/Resources/Photos) and [Videos](https://github.com/jasmcaus/opencv-course/tree/master/Resources/Videos) folders were downloaded from [Unsplash](http://unsplash.com) and [Pixabay](http://pixabay.com), unless otherwise mentioned.\n\n\nThe images in the [Faces](https://github.com/jasmcaus/opencv-course/tree/master/Resources/Faces) folder were procurred from a [repo](https://www.kaggle.com/dansbecker/5-celebrity-faces-dataset) on Kaggle.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjasmcaus%2Fopencv-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjasmcaus%2Fopencv-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjasmcaus%2Fopencv-course/lists"}