Panorama

Overview

This project implements Harris Corner Detector and an image stitching pipeline using SIFT (Scale-Invariant Feature Transform) for feature detection and matching, followed by homography estimation using RANSAC (Random Sample Consensus). The final result is a seamless combination of two images into a single panorama.

Features

Feature Detection and Matching: Uses SIFT to detect keypoints and match them between two images.
Homography Estimation: Implements RANSAC to compute the best homography matrix that aligns the two images based on the matched keypoints.
Image Stitching: Warps one image onto another using the estimated homography matrix to create a panorama.
Visualization: Provides functions to visualize the matched keypoints and the final stitched image using matplotlib.

Requirements

Python >= 3.8
OpenCV (cv2)
NumPy
Matplotlib

You can install the required libraries using pip:

pip install opencv-python-headless numpy matplotlib

How It Works

1. Feature Detection and Matching

The SIFT algorithm detects keypoints in both images and extracts descriptors. These descriptors are then matched using a Brute Force matcher. A ratio test is applied to filter out poor matches.

2. Homography Estimation

RANSAC is used to compute the best homography matrix by iteratively selecting random sets of points and calculating the transformation matrix that maximizes the number of inliers.

3. Image Stitching

The homography matrix is used to warp one image onto the coordinate space of the other, effectively stitching them together into a single panorama.

4. Visualization

The matched keypoints and the final stitched image are displayed using matplotlib.

License

This project is licensed under the CC0 License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue to discuss any changes.

AmirMohamadBabaee/Panorama