👁️ Welcome to the Computer Vision Compendium!👋🛒

1-Introduction

🚀 Explore the vast landscape of computer vision through our comprehensive repository, It include resource about deep learning for vision, image processing tutorials, OpenCV projects, YOLO object detection, CNN tutorials, vision transformers, serving as your A-Z guide to this captivating field. Whether you're delving into image processing, object detection, or deep learning, you'll find a treasure trove of resources here to deepen your understanding and hone your skills.

computer vision course, computer vision with Python, AI in image analysis, edge detection, computer vision GitHub repository, free computer vision resources

📚 Table of Contents

• Introduction
• Why Join This Course?
• How to Get Involved
• Chapters Overview
• Computer Vision Resources

🎯 Why Join This Course?

1. 📸 End-to-End Learning: Master the full spectrum of computer vision — from image basics and filters to deep learning, object detection, and segmentation.

2. 🛠 Practical Implementation: Each topic includes hands-on coding exercises, Jupyter notebooks, and real-world projects.

3. 🌍 Collaborative Development: Join a global community of learners, developers, and researchers. Contribute on GitHub through pull requests, discussions, and issue tracking..

4. 🤖 Cutting-Edge Tech Stack: Stay at the forefront with tools like CNNs, YOLO, OpenCV, Vision Transformers, and more — all integrated with AI-powered workflows.

💡 How to Get Involved in the Computer Vision Project?

🚀 Fork & Star the Repo:Show your support and stay updated — fork the repository and give it a ⭐ on GitHub!

👩‍💻 Dive Into Structured Lessons: Start learning with well-organized, beginner-to-advanced tutorials curated to help you build real skills step by step.

🛠️ Contribute to Code & Content:Enhance existing blogs, refine code, fix bugs, or write new tutorials on exciting computer vision topics.

🧪 Experiment & Innovate:Use the provided codebase as your playground — tweak, test, and explore to discover something new.

🤝 Collaborate with the Community:Join discussions, review PRs, and team up with fellow developers, students, and AI enthusiasts around the world.

📌 Share Your Knowledge:Submit your own implementations, mini-projects, or useful resources like blogs, website, videos, GitHub repos, and research papers etc.

Also please subscribe to my youtube channel!

Star this repo if you find it useful ⭐

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📕Beginner → Course 01 - 👁️ Introduction of Computer Vision

👁️ Chapter1: - Foundations of Computer Vision

Topic Name/Tutorial	Video	Code
✅1- What is computer Vision-Substack Link	1
✅2-Computer Vision Tasks and Applications	1-2
✅Best Free Resources to Computer Vision	---	---

🔹Chapter2: - Image As Function

Topic Name/Tutorial	Video	Notbook
✅1-Images as Functions Part 1?	1
✅2-Images as Functions Part 2?	1
✅3-Define an Image as a Function (Quiz)	1-2
✅4-Color Planes and Color Image as a Function(Quiz)	1-2-3
✅5- Digital Images	1-2
✅6-Compute Image Size Quiz-s	1
✅7-Read image in Matlab and Python-S	---
✅8-Image Size and Data Type Quiz/Solution-S	1
✅9-Crop an Image-s	1
✅10-Add 2 Images-s	1-2-3
✅11-Multiply image by a scaler and Blend 2 Images⭐️	1-2-3
✅12-Common Types of Noise⭐️	1
✅13-Image Difference⭐️	1-2-3
✅14-Generate Gaussian Noise⭐️	1
✅15-Effect of Sigma on Gaussian Noise⭐️	1-2-3
🌐16-Apply Gaussian Noise⭐️	1-2
🌐17-Displaying Images in Matlab and Python⭐️	1

🔹Chapter3: - Filtering

Topic Name/Tutorial	Video	NoteBook
🌐1- What is Filtering?	1
🌐2- What is Gaussian Noise?	1-2
🌐3- Weighted Moving Average?	1-2
🌐4- Correlation Filtering?	1
🌐5- Averaging Filter?	1
🌐6- Gaussian Filter?	1-2
🌐7- Gaussian Filter with Matlab and Python?	1
🌐8- Remove Noise?(r)	1-2

🔹Chapter4: - Linearity and Convolution

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction of linear intuition of filtering	1
🌐2- Impulse Function and Response	1
🌐4- Filtering an Impulse Signal	1
🌐5- Correlation vs Convolution	1-2
🌐5-Properties of Convolution	1
🌐6-Computational Complexity and Separability	1
🌐7-Boundary Issues	1
🌐8-Methods	1
🌐9-Explore Edge Options	1
🌐10-Practicing with Linear Filters	1-2
🌐11-Different Kinds of Noise	1-2-3

🔹Chapter5: - Filters as Templates

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction of Filters as templates, 1D correlation and 2D Correlations	1-2 -3
🌐2- Find Tempalte ID	1-2
🌐3- Template Matching⭐️	1-2-3-4-5

🔹Chapter6: - Edge detection: Gradients

Topic Name/Tutorial	Video	NoteBook
🌐1- Pattern Finding and Feature Detection	1
🌐2- Understanding Edges in Images: Why They Matter in Visual Perception	1-2
🌐3- Edge Detection⭐️	1
🌐4-Derivatives and Edges⭐️	1
🌐5-What is Gradients⭐️	1
🌐6-Finite Differences⭐️	1
🌐7-Partial Derivatives of an Image⭐️	1
🌐8-The Discrete Gradient⭐️	1-2
🌐9-Sobel Operator⭐️	1-2-3
🌐10-Well Known Gradients⭐️	1
🌐11-Gradients direction⭐️	1
🌐12-But in the Real World⭐️	1

🔹Chapter7: - Edge detection: 2D operators

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction	1
🌐2-Derivative of Gaussian Filter 2D	1
🌐3- Effect of Sigma on Derivatives	1
**🌐4-Canny Edge Operator P1 **	1
🌐5-Canny Edge Operator P2	1
🌐6- For Your Eyes Only Demo	1-2
🌐7-Canny Results	1
🌐8-Single 2D Edge Detection Filter	1

🔹Chapter8: - L1 Hough transform: Lines

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction	1
🌐2-Parametric Model	1
🌐3-Line Fitting	1
🌐4-Voting	1-2

📕 Computer Vision Resources

🔹Chapter1: - Free Courses

Title/link	Description	Reading Status
✅1- Deep Learning for Computer Vision	by Michigan Online,Youtube	Pending
✅2- Introduction of Computer Science	It is free course and it contain notes and video	Inprogress

🔹Chapter2: - Important Website

Title/link	Description	Code
🌐1- Computer Science courses with video lectures	It is Videos and github	---

🔹Chapter3: - Important Social medica Groups

Title/link	Description	Code
✅1- Jeff Heaton	It is Videos and github	---
✅2- First Principles of Computer Vision	It is Videos and github	---

🔹Chapter4: - Free Books

Title/link	Description	Code
✅1- Foundations of Computer Vision	Antonio Torralba, Phillip Isola, and William Freeman	---

🔹Chapter5: - Github Repository

Title/link	Description	Status
✅1- Computer Science courses with video lectures	It is Videos and github	Pending
✅2-courses & resources	It is course of all AI domain	Pending
✅3-AIBauchi-Computer-Vision-Bootcamp	It is course of all AI domain	Inprogress
✅4-Awesome Computer Vision	It is course of all AI domain	Inprogress

👁️ Chapter1: - Important Library and Packages

Title/link	Description	Code
🌐1- Computer Science courses with video lectures	It is Videos and github	---

👁️ Chapter1: - Importatant tutorial

Title/link	Description	Status
✅1- Multimodal Data Analysis with Deep Learning	It is Videos and github	pending

💻 Workflow:

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Computer Vision")

⚙️ Things to Note

• Anybody interested in learning and contributing to computer Vision repository
• There are no hard prerequisites other than a dedication to learning
• Some experience with the following will be beneficial:,C++ Programming, Basic of Computer
• You can only work on issues that have been assigned to you.
• If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
• If you have modified/added code work, make sure the code compiles before submitting.
• Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
• Do not update the README.md.

🔍 Explore more

Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait — enroll now and unleash your Computer Vision potential!”

• Computer Vision Basic

✨Top Contributors

We would love your help in making this repository even better! If you know of an amazing Computer Vision course or you know intrested Computer Vision related tutorial/Video that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.

                   Together, let's make this the best AI learning hub website! 🚀

Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀