Canny Edge Detector - Computer Vision Project

Table of Contents

1. Project Overview
2. Features
3. Prerequisites
4. Installation
5. Usage
6. Step-by-Step Guide
7. Parameters
8. Examples
9. Troubleshooting
10. Contributing
11. License

Project Overview

This project implements the Canny edge detection algorithm, a multi-stage process to detect a wide range of edges in images. The Canny edge detector is widely used in computer vision applications for feature detection and feature extraction.

Features

• Complete implementation of the Canny edge detection algorithm
• Customizable parameters for different edge detection needs
• Support for various image formats
• Visualization of intermediate processing steps
• Optimized for performance

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.7 or higher
• pip package manager
• Basic understanding of image processing concepts

Installation

Follow these steps to set up the project:

1. Clone the repository:

git clone https://github.com/shuvrajit-dey21/canny-edge-detector.git
cd canny-edge-detector

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

pip install -r requirements.txt

Usage

To run the Canny edge detector on an image:

python canny_edge_detector.py --input input_image.jpg --output output_image.png

Step-by-Step Guide

The Canny edge detection process consists of the following steps:

1. Noise Reduction

   • The image is smoothed using a Gaussian filter to reduce noise

2. Gradient Calculation

   • The intensity gradients of the image are calculated
   • Both magnitude and direction of gradients are computed

3. Non-Maximum Suppression

   • Only local maxima in gradient directions are kept
   • This thins the edges

4. Double Thresholding

   • Potential edges are determined by two threshold values
   • Strong edges, weak edges, and non-edges are classified

5. Edge Tracking by Hysteresis

   • Weak edges are either kept or discarded based on connectivity
   • Only edges connected to strong edges are preserved

Parameters

The script accepts the following parameters:

• --input: Path to input image (required)
• --output: Path to save output image (required)
• --low_threshold: Low threshold for hysteresis (default: 50)
• --high_threshold: High threshold for hysteresis (default: 150)
• --sigma: Sigma value for Gaussian blur (default: 1.4)
• --kernel_size: Size of Gaussian kernel (default: 5)

Examples

1. Basic edge detection:

python canny_edge_detector.py --input sample.jpg --output edges.png

2. Custom threshold values:

python canny_edge_detector.py --input sample.jpg --output edges.png --low_threshold 30 --high_threshold 100

3. Different blur settings:

python canny_edge_detector.py --input sample.jpg --output edges.png --sigma 2.0 --kernel_size 7

Troubleshooting

• Image not found: Ensure the input path is correct
• Blurry output: Try adjusting the sigma and kernel_size parameters
• Too many/too few edges: Adjust the threshold values
• Memory issues: Reduce image size before processing

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature-branch)
5. Open a Pull Request

License

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