OpenCV Basics - Computer Vision Learning Scripts

A comprehensive collection of Python scripts for learning OpenCV and computer vision fundamentals. Perfect for beginners getting started with image processing, transformations, and basic computer vision techniques.

🎯 Description

This repository contains well-commented Python scripts demonstrating essential OpenCV functionalities:

• Basic Operations: Image loading, display, and basic manipulations
• Drawing & Graphics: Creating shapes, lines, and text on images
• Image Transformations: Scaling, rotation, translation, flipping, and cropping
• Image Processing: Blurring, edge detection, dilation, and erosion
• Video Processing: Video capture, frame processing, and real-time manipulation
• Advanced Features: Contour detection and face recognition

📁 Project Structure

opencv-basics/
├── images/                     # Sample images for testing
│   ├── Cat.jpg
│   ├── elder-man.jpg
│   ├── man.jpg
│   ├── squirrel.jpg
│   └── woman.jpg
├── videos/                     # Sample video files
│   ├── clip1.mp4
│   └── clip2.mp4
├── output/                     # Generated output images
│   ├── Cat_gray.jpg
│   ├── elder-man_cropped.jpg
│   ├── elder-man_flipped-horizontal.jpg
│   ├── elder-man_resized-500x500.jpg
│   ├── elder-man_rotated-45.jpg
│   ├── elder-man_rotated-90.jpg
│   └── elder-man_translated.jpg
├── basic_functions.py          # Core image processing operations
├── draw.py                     # Drawing shapes and text
├── image_transformations.py    # Geometric transformations
├── rescale_display.py          # Image/video scaling and display
├── identify_countours.py       # Contour detection (in development)
├── requirements.txt            # Python dependencies
└── README.md                   # This file

🚀 Setup and Installation

Prerequisites

• Python 3.7+ (recommended: Python 3.8 or higher)
• pip package manager

Installation Steps

1. Clone the repository

   git clone <repository-url>
   cd opencv-basics

2. Install dependencies

   pip install -r requirements.txt

3. Verify installation

   python -c "import cv2; print(cv2.__version__)"

🎮 Usage Examples

Run Individual Scripts

# Basic image processing operations
python basic_functions.py

# Drawing shapes and text
python draw.py

# Image transformations (rotate, scale, translate)
python image_transformations.py

# Image and video rescaling
python rescale_display.py

# Contour detection
python identify_countours.py

Key Features Demonstrated

Basic Functions (basic_functions.py)

• Gaussian blur for noise reduction
• Canny edge detection
• Morphological operations (dilation/erosion)
• Image resizing and cropping

Drawing Operations (draw.py)

• Creating blank canvases
• Drawing rectangles, circles, and lines
• Adding text with different fonts
• Color manipulation (BGR format)

Image Transformations (image_transformations.py)

• Translation (shifting images)
• Rotation around custom points
• Scaling with different interpolation methods
• Horizontal/vertical flipping
• Cropping specific regions

Video Processing (rescale_display.py)

• Video file reading and display
• Real-time frame processing
• Resolution changing for live video
• Frame-by-frame manipulation

📚 Learning Path

1. Start with draw.py - Learn basic OpenCV window management and drawing
2. Move to basic_functions.py - Understand core image processing operations
3. Explore rescale_display.py - Work with images and videos at different scales
4. Try image_transformations.py - Master geometric transformations
5. Experiment with identify_countours.py - Advanced shape detection

🛠 Dependencies

• opencv-contrib-python - Complete OpenCV package with extra modules
• numpy - Numerical operations and array handling
• caer - Additional computer vision utilities
• matplotlib - For advanced plotting and visualization
• imutils - Convenience functions for common OpenCV operations

💡 Tips for Beginners

• Press any key to close image windows when using cv.waitKey(0)
• Press 'd' to exit video playback loops
• Images are stored as NumPy arrays in BGR format (Blue, Green, Red)
• Coordinate system: (0,0) is top-left corner
• Always check if images loaded successfully before processing

🔄 Common Operations Reference

# Load image
img = cv.imread('path/to/image.jpg')

# Display image
cv.imshow('Window Name', img)

# Wait for keypress
cv.waitKey(0)

# Close all windows
cv.destroyAllWindows()

# Save image
cv.imwrite('output/image.jpg', processed_img)

🤝 Contributing

Feel free to add more examples, improve documentation, or suggest enhancements. This project is designed for learning, so clarity and educational value are priorities.

📝 Notes

• All output images are automatically saved to the output/ folder
• Sample images and videos are included for immediate testing
• Scripts include detailed comments explaining each operation
• Error handling is implemented for file loading operations

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Happy Learning! 🎉 Start with any script that interests you and experiment with different parameters to see how they affect the results.