vineethbabu / opencv-image-matching-techniques

OpenCV Image Matching

This project aims to demonstrate image matching techniques using OpenCV, specifically SIFT (Scale-Invariant Feature Transform), histogram comparison, and template matching.

Algorithms

• SIFT: This technique detects and describes distinctive keypoints in images, allowing for robust image matching even in the presence of scale, rotation, and viewpoint changes.

• Histogram Comparison: This technique compares the histograms of images to measure their similarity based on pixel intensity distributions.

• Template Matching: This technique searches for a template image within a larger target image by comparing pixel values at different positions.

Prerequisites

• Python 3.x
• OpenCV library (install using pip install opencv-python)

Installation

1. Clone the repository: git clone <repository_url>
2. Navigate to the project directory: cd opencv-image-matching-project

Usage

1. Place your query images in the query_images directory.
2. Place the target images in the target_images directory.
3. Open the algorithm file and customize the algorithm and parameters according to your requirements.
4. Run the script.

Results

The results with matching score for each query image will be printed with best match in database image.