Yolo Marker HSV

This project implements a GUI application using PyQt5 and OpenCV for processing images. The user inputs a class_number, uses an hsv.cfg configuration file for HSV thresholding, processes images in the source folder, saves results to text files, and displays processed images.

Project Structure

project_directory/
│
├── source/
│   ├── example1.jpg
│   ├── example2.png
│   └── ...
│
├── hsv.cfg
├── yolo_marker.py
└── README.md

• source/: Folder containing the image files.
• hsv.cfg: Configuration file for HSV range settings.
• yolo_marker.py: Main script for image processing and GUI.
• README.md: Documentation for the project.

Configuration File (hsv.cfg)

The hsv.cfg file defines the HSV color range settings. It should have the following format:

H Min: 0  # Minimum Hue value
H Max: 180  # Maximum Hue value
S Min: 0  # Minimum Saturation value
S Max: 255  # Maximum Saturation value
V Min: 0  # Minimum Value (Brightness) value
V Max: 255  # Maximum Value (Brightness) value

Example hsv.cfg:

H Min: 0 
H Max: 180
S Min: 0 
S Max: 255 
V Min: 0 
V Max: 255

Installation

To run this project, you need to install the required Python packages. You can use pip to install the dependencies listed in the requirements.txt file. Also you can manually install the dependencies as follows:

Using requirements.txt

$ pip install -r requirements.txt

Without requirements.txt

$ pip install opencv-python
$ pip install numpy
$ pip install PyQt5

Running the Application

1. Ensure the hsv.cfg file is properly configured.
2. Place image files in the source folder.
3. Run the yolo_marker.py script to start the application.

python yolo_marker.py

4. Enter the class_number in the GUI and click Start to begin processing.

Contributing

Feel free to submit issues, feature requests, and pull requests to improve the package.

License

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

Contact

For any questions or feedback, please contact [email protected].