SURF Feature Matching and Image Stitching with OpenCV 🖼️🔍

Welcome to the SURF Feature Matching and Image Stitching repository! This project demonstrates the use of OpenCV's SURF (Speeded-Up Robust Features) algorithm to detect keypoints, match features, and stitch images together. 🌟

Table of Contents

• Installation
• Usage
• Example Output
• Contributing
• License

Installation 📥

To get started, you'll need to install the necessary packages and clone the required repositories.

1. Install necessary packages:

   • cmake
   • libopencv-dev

2. Clone the OpenCV repository and its extra modules from GitHub.

3. Create a build directory and configure OpenCV with CMake, enabling non-free modules.

4. Build and install OpenCV.

Usage 🚀

Here's a step-by-step guide to use the provided script:

1. Import necessary libraries: Ensure you have the following libraries imported:

   • cv2
   • matplotlib.pyplot
   • numpy
   • os

2. Load and process images: Write a function to check if the image is loaded correctly, and then load and process the images for keypoint detection.

3. Detect keypoints using SURF and match features: Use the SURF algorithm to detect keypoints and compute descriptors for the images.

4. Draw keypoints and match them: Utilize the Brute Force Matcher to find the best matches between the images and apply ratio tests to filter good matches.

5. Warp images and display the stitched image: Define a function to warp the images based on homography and display the final stitched image.

Example Output 📸

Key Point Detectors 1

Key Point Detectors 2

Matching Features with All the Detectors

Matching the Features

Naive Warping

Better Warping

Contributing 🤝

Contributions are welcome! Feel free to open an issue or submit a pull request.

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

License 📜

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

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🌟 Happy Coding! 🌟