Computer Vision Practice

Welcome to the Computer Vision Practice repository! This repository is designed to help beginners learn and implement fundamental computer vision algorithms.

Contents

1. Edge Detection
   • Canny Edge Detector

Getting Started

Prerequisites

pip install numpy opencv-python matplotlib

Edge Detection

Canny Edge Detector

The Canny edge detector is a popular edge detection algorithm that follows these steps:

1. Gaussian smoothing
2. Gradient calculation
3. Non-maximum suppression
4. Double thresholding
5. Edge tracking by hysteresis

To try the Canny edge detector:

import cv2
import numpy as np
from edge_detection.canny import canny_edge_detector

# Read your image
image = cv2.imread('your_image.jpg', 0)  # Read as grayscale

# Apply Canny edge detection
edges = canny_edge_detector(image, 
                          low_threshold=50,
                          high_threshold=150,
                          sigma=1.0)

# Display results
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

Contributing

Feel free to contribute to this repository by:

1. Adding new computer vision algorithms
2. Improving existing implementations
3. Adding documentation and examples
4. Reporting issues

License

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