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https://github.com/mjahmadee/surf

Image Matching Using SURF in Google Colab
https://github.com/mjahmadee/surf

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Image Matching Using SURF in Google Colab

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README 

        
# SURF Feature Matching and Image Stitching with OpenCV 🖼️🔍



[![Python](https://img.shields.io/badge/Python-3.7%2B-blue.svg)](https://www.python.org/)

[![OpenCV](https://img.shields.io/badge/OpenCV-4.5.1-green.svg)](https://opencv.org/)

[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1j1LKky_TTLX3UXZ6HJVLllwyMZOQdGNZ?usp=sharing)



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](#installation)

- [Usage](#usage)

- [Example Output](#example-output)

- [Contributing](#contributing)

- [License](#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 Detectors1](https://raw.githubusercontent.com/MJAHMADEE/SURF/main/Results/Key%20Point%20Detectors1.png)



### Key Point Detectors 2

![Key Point Detectors2](https://raw.githubusercontent.com/MJAHMADEE/SURF/main/Results/Key%20Point%20Detectors2.png)



### Matching Features with All the Detectors

![Matching Features with all the Detectors](https://raw.githubusercontent.com/MJAHMADEE/SURF/main/Results/Matching%20Features%20with%20all%20the%20Detectors.png)



### Matching the Features

![Matching the Features](https://raw.githubusercontent.com/MJAHMADEE/SURF/main/Results/Matching%20the%20Features.png)



### Naive Warping

![Naive Warping](https://raw.githubusercontent.com/MJAHMADEE/SURF/main/Results/naive%20warping.png)



### Better Warping

![Better Warping](https://raw.githubusercontent.com/MJAHMADEE/SURF/main/Results/better%20warping.png)



## 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](LICENSE) file for details.



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🌟 **Happy Coding!** 🌟