This project is developed as part of the coursework for II-Industrial Vision Technology. It features an advanced image stitcher that uses state-of-the-art computer vision techniques for feature detection and extraction. The image stitcher is equipped with a user-friendly graphical user interface (GUI), enabling users to effortlessly stitch multiple images to produce a seamless high-resolution panorama.
- Feature Detection: Utilizes SIFT/ORB/AKAZE/BRISK for robust feature detection.
- Feature Extraction: Implements SIFT/ORB/AKAZE/BRISK to accurately extract relevant features from images.
- Image Stitching: Efficiently stitches multiple images by aligning and blending them seamlessly.
- Graphical User Interface: Simple and intuitive GUI for easy operation by users of all skill levels.
- Python 3.x
- Please refer to
requirements.txtfor a list of required libraries.
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Clone the Repository
git clone https://github.com/ahmadshakleya/FeatureExtraction.git
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Create a Virtual Environment Navigate to the project directory and create a virtual environment:
python -m venv venv
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Activate the Virtual Environment
- On Windows:
venv\Scripts\activate
- On macOS and Linux:
source venv/bin/activate
- On Windows:
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Install Dependencies Install the required libraries with pip:
pip install -r requirements.txt
You have two options to run the application:
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Using Python Script: After installing dependencies, run the following command within the activated virtual environment:
python gui.py
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Using Executable: If you don't have python installed, simply navigate to the directory containing
gui.exeand run it:./gui.exe
- Launch the application using one of the methods described above.
- Use the GUI to upload the images you wish to stitch.
- Adjust settings as necessary and click the 'Stitch Images' button.
- Save or view the resulting panoramic image.
- Ahmad Shakleya
- Ken Van Laer
- Toon Smets
- Thanks to Prof. dr. Steve Vanlanduit for guidance and course materials.
- Gratitude to anyone whose code was used.