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.ipynb_checkpoints
.ipynb_checkpoints
 
 
Haarcascades
Haarcascades
 
 
images
images
 
 
pyfundamentals
pyfundamentals
 
 
.DS_Store
.DS_Store
 
 
Beginning Tips.ipynb
Beginning Tips.ipynb
 
 
Lecture 2.4 - Reading, writing and displaying images.ipynb
Lecture 2.4 - Reading, writing and displaying images.ipynb
 
 
Lecture 2.5 - Grayscaling.ipynb
Lecture 2.5 - Grayscaling.ipynb
 
 
Lecture 2.6 - Color Spaces.ipynb
Lecture 2.6 - Color Spaces.ipynb
 
 
Lecture 2.7 - Histograms.ipynb
Lecture 2.7 - Histograms.ipynb
 
 
Lecture 2.8 - Drawing Images.ipynb
Lecture 2.8 - Drawing Images.ipynb
 
 
Lecture 3.10 - Sharpening.ipynb
Lecture 3.10 - Sharpening.ipynb
 
 
Lecture 3.11 - Thresholding, Binarization & Adaptive Thresholding.ipynb
Lecture 3.11 - Thresholding, Binarization & Adaptive Thresholding.ipynb
 
 
Lecture 3.12 - Dilation, Erosion, Opening and Closing.ipynb
Lecture 3.12 - Dilation, Erosion, Opening and Closing.ipynb
 
 
Lecture 3.13 - Edge Detection & Image Gradients.ipynb
Lecture 3.13 - Edge Detection & Image Gradients.ipynb
 
 
Lecture 3.14 - Perspective & Affine Transforms.ipynb
Lecture 3.14 - Perspective & Affine Transforms.ipynb
 
 
Lecture 3.15 - Mini Project # 1 - Live Sketch Using Webcam.ipynb
Lecture 3.15 - Mini Project # 1 - Live Sketch Using Webcam.ipynb
 
 
Lecture 3.2 - Translations.ipynb
Lecture 3.2 - Translations.ipynb
 
 
Lecture 3.3 - Rotations.ipynb
Lecture 3.3 - Rotations.ipynb
 
 
Lecture 3.4 - Scaling, re-sizing and interpolations.ipynb
Lecture 3.4 - Scaling, re-sizing and interpolations.ipynb
 
 
Lecture 3.5 - Image Pyramids.ipynb
Lecture 3.5 - Image Pyramids.ipynb
 
 
Lecture 3.6 - Cropping.ipynb
Lecture 3.6 - Cropping.ipynb
 
 
Lecture 3.7 - Arithmetic Operations.ipynb
Lecture 3.7 - Arithmetic Operations.ipynb
 
 
Lecture 3.8 - Bitwise Operations and Masking.ipynb
Lecture 3.8 - Bitwise Operations and Masking.ipynb
 
 
Lecture 3.9 - Convolutions and Blurring.ipynb
Lecture 3.9 - Convolutions and Blurring.ipynb
 
 
Lecture 4.1 - Understanding Contours.ipynb
Lecture 4.1 - Understanding Contours.ipynb
 
 
Lecture 4.2 - Sorting Contours.ipynb
Lecture 4.2 - Sorting Contours.ipynb
 
 
Lecture 4.3 - Approximating Contours and Convex Hull .ipynb
Lecture 4.3 - Approximating Contours and Convex Hull .ipynb
 
 
Lecture 4.4 - Matching Contours Shape.ipynb
Lecture 4.4 - Matching Contours Shape.ipynb
 
 
Lecture 4.5 - Mini Project # 2 - Identifying Contours by Shape.ipynb
Lecture 4.5 - Mini Project # 2 - Identifying Contours by Shape.ipynb
 
 
Lecture 4.6 - Line Detection using Hough Lines.ipynb
Lecture 4.6 - Line Detection using Hough Lines.ipynb
 
 
Lecture 4.7 - Circle Detection using Hough Circles.ipynb
Lecture 4.7 - Circle Detection using Hough Circles.ipynb
 
 
Lecture 4.8 - Blob Detection.ipynb
Lecture 4.8 - Blob Detection.ipynb
 
 
Lecture 4.9 - Mini Project # 3 - Counting Circles and Ellipses .ipynb
Lecture 4.9 - Mini Project # 3 - Counting Circles and Ellipses .ipynb
 
 
Lecture 5.2 - Mini Project # 4 - Finding Waldo.ipynb
Lecture 5.2 - Mini Project # 4 - Finding Waldo.ipynb
 
 
Lecture 5.4 - Finding Corners.ipynb
Lecture 5.4 - Finding Corners.ipynb
 
 
Lecture 5.5 - SIFT, SURF, FAST, BRIEF & ORB.ipynb
Lecture 5.5 - SIFT, SURF, FAST, BRIEF & ORB.ipynb
 
 
Lecture 5.6 - Mini Project # 5 - Object Detection using SIFT & ORB.ipynb
Lecture 5.6 - Mini Project # 5 - Object Detection using SIFT & ORB.ipynb
 
 
Lecture 5.7 Histogram of Oriented Gradients.ipynb
Lecture 5.7 Histogram of Oriented Gradients.ipynb
 
 
Lecture 6.2 - Face & Eye Detection.ipynb
Lecture 6.2 - Face & Eye Detection.ipynb
 
 
Lecture 6.3 - Mini Project # 6 - Car & Pedestrian Detection.ipynb
Lecture 6.3 - Mini Project # 6 - Car & Pedestrian Detection.ipynb
 
 
README.md
README.md
 
 

Repository files navigation

👁️ Computer Vision using OpenCV (Full Course + Mini Projects)

📌 Overview

This repository contains a complete Computer Vision learning module implemented using Python and OpenCV.
It includes lecture-wise Jupyter notebooks, mini projects, and datasets covering fundamental to advanced topics.

🎯 Objectives

  • Learn core image processing techniques
  • Understand feature detection and transformations
  • Build real-time computer vision applications
  • Implement mini projects using OpenCV

🛠️ Tech Stack

  • Python
  • OpenCV
  • NumPy
  • Matplotlib
  • Jupyter Notebook

📂 Project Structure

ComputerVision/ │ ├── notebooks/ # All lecture notebooks ├── images/ # Input images ├── Haarcascades/ # Pre-trained XML models ├── pyfundamentals/ # Python basics ├── .ipynb_checkpoints/ # Auto-generated files (ignore) │ ├── Mini Projects: │ ├── Live Sketch Using Webcam │ ├── Identifying Shapes │ ├── Counting Circles & Ellipses │ ├── Finding Waldo │ ├── Object Detection (SIFT & ORB) │ ├── Car & Pedestrian Detection

📘 Topics Covered

🔹 Basic Image Processing

  • Reading, writing, displaying images
  • Grayscaling
  • Color spaces
  • Histograms
  • Drawing on images

🔹 Image Transformations

  • Translation, Rotation
  • Scaling and resizing
  • Cropping
  • Image pyramids

🔹 Image Processing Techniques

  • Blurring and convolution
  • Sharpening
  • Thresholding
  • Morphological operations (Dilation, Erosion)

🔹 Advanced Techniques

  • Edge detection
  • Perspective & affine transformations
  • Bitwise operations and masking

🔹 Feature Detection

  • Contours and shape detection
  • Convex hull
  • Corner detection
  • Blob detection

🔹 Object Detection

  • Hough Transform (lines & circles)
  • Haar Cascade classifiers
  • SIFT, SURF, ORB, FAST, BRIEF
  • Histogram of Oriented Gradients (HOG)

🚀 Mini Projects

  1. 🎨 Live Sketch using Webcam
  2. 🔺 Shape Identification
  3. 🔵 Counting Circles & Ellipses
  4. 🔍 Finding Waldo
  5. 🧠 Object Detection using SIFT & ORB
  6. 🚗 Car & Pedestrian Detection

📊 Datasets Used

  • Custom images (included in /images)
  • Video files (cars.avi, walking.avi)
  • Haar Cascade XML files

▶️ How to Run

  1. Clone the repository: git clone https://github.com/your-username/ComputerVision.git

  2. Navigate to project: cd ComputerVision

  3. Install dependencies: pip install opencv-python numpy matplotlib

  4. Run notebooks: jupyter notebook

📚 Base Papers / References

  • "Digital Image Processing" – Gonzalez & Woods
  • "Feature Detection and Description" – David Lowe (SIFT)
  • Viola-Jones Face Detection Framework
  • "Histogram of Oriented Gradients for Human Detection" – Dalal & Triggs
  • OpenCV Official Documentation

🔮 Future Improvements

  • Add deep learning models (CNNs)
  • Integrate real-time AI applications
  • Deploy as web app using Flask/Streamlit
  • Optimize performance for large datasets

⚠️ Notes

  • .ipynb_checkpoints and .DS_Store are auto-generated files
  • Ensure correct file paths for images and videos before running

🤝 Contributing

Feel free to fork this repository and contribute improvements or new projects.

📜 License

This project is for educational purposes.

👤 Author

Kishore B

⭐ Acknowledgement

Thanks to the OpenCV community and research papers for providing foundational knowledge in computer vision.

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