OpenCV Algorithm Implementations

A comprehensive collection of computer vision algorithms and image processing techniques implemented using OpenCV and Python. From basic image processing to advanced computer vision applications.

🌟 Key Features

• Image fundamentals and transformations
• Image segmentation and filtering
• Object detection and tracking
• Face and feature detection
• Background removal and inpainting
• Style transfer and colorization
• QR/Barcode handling
• Motion tracking
• Advanced image effects

📚 Implementations

Core Image Processing

• Image Fundamentals

  • Color space manipulations (RGB, HSV)
  • Basic transformations and rotations
  • Scaling and interpolation
  • Arithmetic operations
  • Bitwise operations
  • Histogram equalization

• Filtering & Enhancement

  • Color filtering
  • Blur detection and quantification
  • Noise handling
  • Image denoising
  • Convolutions and kernels

Detection & Recognition

• Object Detection

  • YOLO implementation
  • Haar Cascade Classifiers
  • Face detection
  • Eye detection
  • Car detection
  • Pedestrian detection

• Pattern Recognition

  • QR code generation and detection
  • Barcode generation and detection
  • Corner detection
  • Line detection
  • Circle detection
  • Blob detection

Advanced Techniques

• Segmentation

  • Watershed segmentation
  • Grabcut background removal
  • Contour detection and analysis
  • Edge detection
  • Shape detection
  • Color clustering

• Motion Analysis

  • Optical flow tracking
  • Dense optical flow
  • Mean shift tracking
  • CAMShift tracking
  • Background subtraction
  • Motion detection

Artistic Effects

• Style Transfer
  • Neural style transfer
  • ECCV16 implementation
  • Caffe colorization
  • Tilt-shift effects
  • Image blending
  • Inpainting

🔧 Core Components

Image Processing Tools

# Color filtering example
def filter_colors(image, lower_bound, upper_bound):
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower_bound, upper_bound)
    return cv2.bitwise_and(image, image, mask=mask)

Motion Tracking

# Basic optical flow setup
def setup_optical_flow():
    feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
    lucas_kanade_params = dict(winSize=(15,15), maxLevel=2)
    return feature_params, lucas_kanade_params

🚀 Getting Started

Prerequisites

python 3.6+
opencv-python
numpy
matplotlib
dlib
pyzbar
python-barcode
scikit-image
tensorflow (for neural networks)

Installation

1. Clone the repository:

git clone https://github.com/Btzel/opencv-algorithm-implementations.git
cd opencv-algorithm-implementations

2. Install dependencies:

pip install -r requirements.txt

📁 Project Structure

opencv-algorithm-implementations/
└── opencv/
    ├── add_remove_noise.py          # Noise handling and reduction
    ├── Barcode_detecting.py         # Barcode detection
    ├── Barcode_generation.py        # Barcode creation
    ├── blur.py                      # Blur effects and detection
    ├── caffe_colorize_image.py      # Image colorization using Caffe
    ├── cam_shift_motion_tracking.py # CAMShift tracking algorithm
    ├── color_clustering.py          # K-means color clustering
    ├── color_object_tracking.py     # Color-based object tracking
    ├── comparing_images.py          # Image comparison techniques
    ├── create_mask.py              # Mask creation for image processing
    ├── dense_optical_flow_object_tracking.py  # Dense optical flow
    ├── detect_blur.py              # Blur detection
    ├── dlib_facial_landmark_detection.py     # Facial landmarks
    ├── eccv16_nst.py               # Neural style transfer
    ├── facial_recognition.py       # Face recognition
    ├── fg_subtraction1.py          # Background subtraction v1
    ├── fg_subtraction2.py          # Background subtraction v2
    ├── filtering_colors.py         # Color filtering
    ├── fundamentals.py             # OpenCV basics
    ├── grabcut_bg_removal.py       # Background removal
    ├── haar_cascade_classifiers.py # Object detection
    ├── histogram_equalization.py   # Histogram processing
    ├── image_segmentation.py       # Image segmentation
    ├── inpainting_images.py        # Image restoration
    ├── mean_shift_motion_tracking.py # Mean-shift tracking
    ├── neural_style_transfer.py    # Style transfer
    ├── OCR_text_detection.py       # Text detection
    ├── optical_flow_object_tracking.py # Optical flow
    ├── QR_detecting.py             # QR code detection
    ├── QR_generation.py            # QR code creation
    ├── tilt_shift_effects.py       # Tilt-shift effects
    ├── transform_perspectives.py    # Perspective transformation
    ├── watershed_image_segmentation.py # Watershed algorithm
    └── yolov3.py                   # YOLO object detection

🤝 Contributing

Contributions are welcome! Feel free to:

1. Fork the repository
2. Create a feature branch
3. Submit a Pull Request

📖 Documentation

Each implementation includes:

• Detailed comments explaining the algorithm
• Usage examples
• Parameter explanations
• Visual output examples where applicable

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

⚡ Future Improvements

• Deep learning integration
• Real-time video processing
• GPU acceleration
• Additional styling effects
• More detection models
• Performance optimizations

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Note: This repository is actively maintained and new implementations are being added regularly.