OpenCV Learning Project

This repository contains code and resources for learning and experimenting with OpenCV, a popular computer vision library. The project includes comprehensive functions for video and image processing, deep learning tutorials with CNNs, and sample data for testing and learning.

Project Structure

data/
    imgs/
        woman.jpg                          # Original sample image
        grayscaled_woman.jpg              # Processed grayscale version
        HARRISSED_IMAGES/                 # Harris corner detection results
        MERGED_IMAGES/                    # Image merging examples
            horizontal/
            vertical/
        NOISED_IMAGES/                    # Salt and pepper noise examples
        RECTANGLED_IMAGES/                # Rectangle drawing examples
        ROTATED_IMAGES/                   # Image rotation examples
            opencv-301/, X/, XY/, Y/
        SHARPENED_IMAGES/                 # Image sharpening results
        SHIFTED_IMAGES/                   # Image shifting examples
    videos/                               # Sample video files
src/
    image_processing_funcs.py             # Core image processing functions
    video_processing_funcs.py             # Video processing and analysis
    computer-vision/
        introduction/
            advanced_python_concepts.py   # Python concepts for CV
        deep-learning/
            introduction.ipynb            # Deep learning introduction
            CNN/
                CNN.ipynb                 # Convolutional Neural Networks
                main.ipynb               # CNN main examples
                30-38+CNN.pdf            # CNN reference material
        pytorch-playground/               # PyTorch experiments
    images/                              # Additional image resources
    logs/                                # Processing logs
requirements.txt                         # Python dependencies

Features

Image Processing

• Basic Operations: Grayscale conversion, image normalization, display and saving
• Geometric Transformations: Image rotation, shifting, merging (horizontal/vertical)
• Drawing Operations: Rectangle drawing and shape overlay
• Filtering & Enhancement: Bilateral filtering, image sharpening
• Noise Processing: Salt and pepper noise addition and removal
• Corner Detection: Harris corner detection, Shi-Tomasi corner detection, sub-pixel corner refinement
• Statistical Analysis: Image histograms, min/max value analysis, mean and standard deviation

Video Processing

• Basic Video Operations: Video information display, frame processing
• Real-time Processing: Live video feed processing with multiple filter options
• Motion Analysis: Background/foreground extraction, KLT (Kanade-Lucas-Tomasi) tracking
• Optical Flow: Dense optical flow computation and visualization

Deep Learning & Computer Vision

• CNN Tutorials: Comprehensive Convolutional Neural Network examples and theory
• PyTorch Playground: Experimental deep learning implementations
• Educational Materials: Advanced Python concepts for computer vision
• Jupyter Notebooks: Interactive learning environment with step-by-step tutorials

Sample Data & Examples

• Sample Images: Test images with various processed versions (rotated, filtered, etc.)
• Video Examples: Sample videos for testing processing algorithms
• Generated Results: Pre-processed example outputs for reference

Prerequisites

• Python 3.8 or higher
• Virtual environment (recommended)
• Jupyter Notebook or JupyterLab (for interactive tutorials)

Dependencies

The project uses the following main libraries:

• OpenCV (4.12.0.88): Computer vision and image processing
• NumPy (2.2.6): Numerical computing and array operations
• Matplotlib (3.10.5): Plotting and visualization
• Pillow (11.3.0): Additional image processing capabilities

See requirements.txt for the complete list of dependencies with specific versions.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/opencv-learning.git
   cd opencv-learning

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Mac/Linux
   # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Install Jupyter (if not included in requirements):

   pip install jupyter

Usage

Basic Image Processing

1. Add your images to the data/imgs/ directory

2. Run individual image processing functions:

   from src.image_processing_funcs import *
   
   # Display image information
   show_image_info('data/imgs/woman.jpg')
   
   # Convert to grayscale
   gray_img = grayscale_img('data/imgs/woman.jpg')
   
   # Apply corner detection
   harris_corner_detection('data/imgs/woman.jpg', is_save=True)
   
   # Add noise and sharpen
   add_salt_and_pepper_noise('data/imgs/woman.jpg', is_save=True)
   sharpen_image('data/imgs/woman.jpg', is_save=True)

3. Run the main processing script:

   python src/image_processing_funcs.py

Video Processing

1. Add video files to the data/videos/ directory

2. Process videos:

   from src.video_processing_funcs import *
   
   # Show video information
   show_video_info('data/videos/your_video.mp4')
   
   # Process live camera feed
   live_video_processing(cam_option=1)
   
   # Extract background/foreground
   background_foreground_extraction('data/videos/your_video.mp4', threshold=50)

3. Run video processing script:

   python src/video_processing_funcs.py

Interactive Learning with Jupyter Notebooks

1. Start Jupyter:

   jupyter notebook

2. Open the learning notebooks:

   • src/computer-vision/deep-learning/introduction.ipynb - Deep learning fundamentals
   • src/computer-vision/deep-learning/CNN/CNN.ipynb - Convolutional Neural Networks
   • src/computer-vision/deep-learning/CNN/main.ipynb - CNN examples

3. Explore advanced concepts:

   • Check src/computer-vision/introduction/advanced_python_concepts.py for Python CV techniques
   • Review src/computer-vision/deep-learning/CNN/30-38+CNN.pdf for theoretical background

Example Function Usage

# Image merging
merge_images(
    img1_path='data/imgs/woman.jpg',
    img2_path='data/imgs/grayscaled_woman.jpg',
    direction='horizontal',
    is_save=True
)

# Image rotation with different axes
rotate_img('data/imgs/woman.jpg', axis='X', angle=45, is_save=True)

# Bilateral filtering for noise reduction
bilateral_filter('data/imgs/woman.jpg')

Deep Learning & CNN Tutorial Content

This project includes comprehensive educational materials for learning deep learning and computer vision:

📚 Learning Path

1. Start with Python Fundamentals:

   • src/computer-vision/introduction/advanced_python_concepts.py
   • Essential Python concepts for computer vision work

2. Deep Learning Introduction:

   • src/computer-vision/deep-learning/introduction.ipynb
   • Fundamentals of deep learning for computer vision

3. Convolutional Neural Networks:

   • src/computer-vision/deep-learning/CNN/CNN.ipynb - Comprehensive CNN tutorial
   • src/computer-vision/deep-learning/CNN/main.ipynb - Practical CNN examples
   • src/computer-vision/deep-learning/CNN/30-38+CNN.pdf - Theoretical reference material

4. Experimental Playground:

   • src/computer-vision/pytorch-playground/ - PyTorch experiments and advanced implementations

🎯 Learning Objectives

• Understand core computer vision concepts with OpenCV
• Learn image processing techniques and their applications
• Master CNN architecture and implementation
• Apply deep learning to computer vision problems
• Gain hands-on experience with real-world examples

💡 Getting Started with Learning

1. Begin with basic image processing functions in src/image_processing_funcs.py
2. Experiment with video processing in src/video_processing_funcs.py
3. Work through the Jupyter notebooks in order
4. Try the example functions with your own images
5. Explore the deep learning materials for advanced topics

Acknowledgments

• Turkcell Geleceği Yazanlar Opencv Course
• OpenCV Documentation
• Tutorials and resources from the OpenCV community.