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Welcome to the3D Reconstruction and Plane Detection project! This repository contains a Python script that performs 3D reconstruction from stereo images using epipolar geometry. It detects keypoints, matches them between images, computes the Fundamental and Essential matrices, reconstructs a 3D point cloud, and fits planes to the reconstructed points using RANSAC.

This project demonstrates how to perform:

• 🔑Keypoint detection using SIFT
• 🔗Feature matching between two images
• 📐Computation of the Fundamental and Essential matrices
• 🧭Recovery of camera pose (rotation and translation)
• 🌐Triangulation of matched points to create a 3D point cloud
• ✈️Plane fitting using RANSAC
• 🎨Visualization of keypoints, matches, epipolar lines, and planes

• Keypoint Detection: Detects and computes descriptors using SIFT.
• Feature Matching: Matches features using BFMatcher with a ratio test.
• Epipolar Geometry: Calculates the Fundamental and Essential matrices.
• Pose Recovery: Determines the relative camera positions.
• 3D Reconstruction: Triangulates points to form a 3D point cloud.
• Plane Detection: Uses RANSAC to detect planes in the point cloud.
• Visualization: Provides comprehensive plotting functions for analysis.

• 🐍Python 3.6 or higher
• 📦NumPy
• 👁️OpenCV (cv2)
• 📊Matplotlib

1. Clone the Repository

   git clone https://github.com/danielbob32/ComputerVisonImageMatchingcd ComputerVisonImageMatching

2. Install Dependencies

Install the required Python packages:

pip install numpy opencv-python matplotlib

1. Prepare Your Data

• 📁 Create a directory for your data, e.g., data/example_3/.
• 🖼️ Place your stereo images I1.png and I2.png in this directory.
• 📄 Provide the camera intrinsic matrix K.txt in the same directory. The matrix should be in text format with comma-separated values.

2. Configure ParametersIn the main() function of the script, you can adjust the following parameters:

data_dir='data/example_3'# Path to your data directoryamount=70# Number of matches and epipolar lines to displayplanes_amount=2# Number of planes to detectRansac_iterations=1000# RANSAC iterations for plane fittingthreshold=0.5# Distance threshold for RANSAC

3. Run the Script

Execute the script using the command line:

python main.py

4. View the Results

The script will display various plots:

• 📷 Keypoints detected in both images
• 🔍 Matched features between images
• 📏 Matches with connecting lines
• 📐 Epipolar lines corresponding to the matches
• 🛰️ Planes fitted to the 3D point cloud
• 🧭 Normals of the detected planes projected onto the images

Note: The results directory should contain the generated images from running the script.

- main.py: Main Python script containing all functions and the main() function.- data/: Directory containing sample images and camera matrix.- results/: Directory where output images and plots are saved.- README.md: Project documentation.

Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create your feature branch: git checkout -b feature/YourFeature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature/YourFeature
5. Open a pull request.

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

For questions or suggestions, please contact Daniel Bobritski:

📧 Email:danielbob32@gmail.com💼 LinkedIn: Daniel Bobritski🎮 Discord: danielxp13#9709