Haris-corner-detection-algorithm

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Harris Corner Detection in Images

This repository contains a Python implementation for applying Harris Corner Detection to images, detecting corners, and highlighting them with a visual effect. The project uses OpenCV for image processing and is intended to be run in a Python environment, such as Google Colab.

Features

Corner Detection: Detects corners in images using the Harris Corner Detection method.

Corner Highlighting: Highlights the detected corners by marking them in red on the original image.

Edge Detection Preprocessing: Applies Gaussian blur to reduce noise and enhance corner detection accuracy.

Corner Counting: Counts the number of corners detected in each image.

Visualization: Displays the images with the highlighted corners in Google Colab using matplotlib.

Flexible Image Input: Works with a list of images by providing file paths.

Usage

1 Clone the repository or copy the code.

2 Provide the paths to your images by replacing the image_paths list:

image_paths = '/path/to/image1.jpg', # Replace with your image file path '/path/to/image2.jpg', # Replace with your image file path '/path/to/image3.jpg' # Replace with your image file path

3 Run the code in your Python environment (e.g., Google Colab). 4 The script will output: 5 Displayed images with highlighted corners. 6 Printed number of corners detected in each image.

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Features

Corner Detection:

1 Detects corners in images using the Harris Corner Detection method.

2 Enhances corner points by applying dilation.

Corner Matching:

1 Compares the detected corners between two images.

2 Computes a match score based on the intersection of detected corners.

3 Match Thresholding:

Allows setting a threshold to categorize images based on their match score.

4 Visualization:

Displays images with detected corners highlighted.

5 Console Output:

Prints the match score and determines whether images are good matches or not based on the threshold.

5 Edge Highlighting (Optional):

Enhances corner visibility by coloring the detected edges distinctly.

File Output:

Currently outputs match scores in the console.

Set the image paths:

Replace the paths in the script below with your image paths:

original_image_path = '/path/to/original/image.png' image_paths = '/path/to/rotated/image1.jpg', '/path/to/rotated/image2.jpg', '/path/to/another/image.jpg'

Requirements

Python 3.x

OpenCV

NumPy

Matplotlib

Google Colab (optional, but preferred for displaying images)