By RN Software

Generate artificial images from a small amount of input images with advanced annotation and augmentation capabilities.

• Bounding Box Annotation: Draw rectangles around objects
• Labeling Annotation: Add text labels to objects
• Segmentation Annotation: Freestyle pen drawing for precise object boundaries

• YOLO format
• COCO format
• CNN format
• RNN format

• Geometric Variations: Rotation, flipping, shearing, translation, scaling
• Visual Effects: Brightness, contrast adjustments
• Image Effects: Blurring, shadows, lighting, glare, reflections
• Background Noise: Custom backgrounds, random textures, salt & pepper noise, Gaussian noise, scribbled/cluttered backgrounds

• Randomly mix objects from different input images
• Intelligent positioning to ensure visibility
• Configurable object density and spacing

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

1. Run the application:

python main.py

2. Follow the pipeline:
   • Select input images
   • Choose annotation types
   • Annotate objects in each image
   • Select augmentation options
   • Configure dataset size and export format
   • Generate your dataset!

Clean and intuitive main interface showing the pipeline stages and annotation type selection

Browse and select images from your local system with thumbnail preview

Professional annotation interface with bounding box, labeling, and segmentation tools for precise object marking

Real-time progress tracking during dataset generation with configuration options and augmentation settings

Gallery view of the generated augmented dataset showing various transformations and object combinations

1. Image Selection: Choose images from your local system
2. Annotation: Draw bounding boxes, segments, and add labels
3. Augmentation Configuration: Select desired variations
4. Dataset Generation: Specify output size and format

• Python 3.8+
• OpenCV
• scikit-image

GENxCRY/
├── main.py                 # Application entry point
├── requirements.txt        # Python dependencies
├── run.bat                # Windows startup script
├── run.sh                 # Linux/Mac startup script
├── gui/                   # User interface modules
│   ├── main_window.py     # Main application window
│   ├── image_selection.py # Image selection interface
│   ├── annotation_window.py # Annotation tools
│   ├── augmentation_config.py # Augmentation settings
│   └── dataset_generation.py # Generation interface
├── core/                  # Core functionality
│   ├── dataset_generator.py # Main generation engine
│   ├── augmentation_engine.py # Image augmentation
│   └── export_formats.py # Format exporters
├── examples/              # Documentation and examples
│   └── sample_workflow.md # Step-by-step guide
└── LICENSE               # MIT License

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

For issues and questions:

• Create an issue on GitHub
• Contact: RN Software

MIT License - see LICENSE file for details.

Created by RN with ❤️ for the Dev community.