Edge Detection using Canny and Marr-Hildreth

This repository contains a Python script that performs edge detection on an input image using two different techniques: Canny Edge Detection and Marr-Hildreth Edge Detection. The results of both methods are visualized alongside the original image for easy comparison.

Features

• Loads an image from a file and converts it to grayscale.
• Applies Canny Edge Detection to highlight edges in the image.
• Applies Marr-Hildreth Edge Detection using the Laplacian filter.
• Displays the original image, the edge-detection results, and the final output images in a 2x3 grid format for comparison.
• Uses Matplotlib to visualize and compare the results.

Requirements

Before running the script, ensure you have the following Python libraries installed:

• OpenCV: For image processing.
• NumPy: For numerical operations.
• Matplotlib: For visualization.

You can install the required libraries using pip:

pip install opencv-python numpy matplotlib

Usage

1. Clone this repository to your local machine:

   git clone https://github.com/yourusername/edge-detection.git

2. Place the image you want to process (e.g., img.jpg) in the project directory.

3. Run the script edge_detection.py:

   python edge_detection.py

4. The script will process the image and display the results in a grid, including:

   • The original image.
   • The Canny edge detection result.
   • The output of the Canny method overlaid on the original image.
   • The Marr-Hildreth edge detection result.
   • The output of the Marr-Hildreth method overlaid on the original image.

How It Works

1. Image Loading & Preprocessing:

   • The input image is read using OpenCV’s cv2.imread().
   • The image is converted to grayscale using cv2.cvtColor() to simplify edge detection.

2. Canny Edge Detection:

   • The Canny algorithm is applied using cv2.Canny(). It detects edges by finding areas of rapid intensity changes in the image.

3. Marr-Hildreth Edge Detection:

   • The Laplacian of the grayscale image is calculated using cv2.Laplacian().
   • The absolute values of the Laplacian are taken to highlight edges and then scaled to 8-bit values with cv2.convertScaleAbs().

4. Visualization:

   • The images are displayed using Matplotlib. A 2x3 grid is used to show:
     • The original image.
     • The edge detection results (Canny and Marr-Hildreth).
     • The original image with the detected edges overlaid.