snapstitch 0.1.1

An open-source python library to generate synthetic datasets for computer vision tasks

SnapStitch

An open-source Python library for generating synthetic datasets for computer vision tasks. SnapStitch aims to simplify the process of creating realistic synthetic images to help improve computer vision model training.

Features

Current Focus:

• Support YOLO model training by generating synthetic images by superimposing smaller objects onto a background and creating YOLO dataset

Future Plans:

• Supports various image transformations and augmentations to be applied on synthetic data.
• Support other computer vision tasks such as Segmentation and Pose Estimation
• Easy integration with existing computer vision pipelines.

Current Status

This library is currently under development. Key features and functionalities are being implemented step-by-step. Below is the planned architecture and the class diagram representing the intended design.

Class Diagram

Note: The above class diagram illustrates the core components and their relationships within the library. It is a work in progress and will evolve as development continues.

Getting Started

Prerequisites

pdm install

Usage

The library is envisioned to be used in this way.

from snapstitch import Stitcher, PartsLoader, BackgroundLoader, YOLOv8Generator

# Initialise path to backgrounds
background = BackgroundLoader("examples/supermarket/background")

# Initialise all your classes
bread = PartsLoader("examples/supermarket/parts/bread")
canned_beans = PartsLoader("examples/supermarket/parts/canned_beans")
jam = PartsLoader("examples/supermarket/parts/jam")

# Generate YOLOv8 data 
generator = YOLOv8Generator()

# Main class that handles generation
stitcher = Stitcher(generator, background, {"bread": bread, "canned_beans": canned_beans, "jam":jam}, 30, ["bread", "canned_beans", "jam"])

# Generate as many times as needed
stitcher.execute(10, "examples/supermarket/output", "train_data1")
stitcher.execute(10, "examples/supermarket/output", "train_data2")
stitcher.execute(10, "examples/supermarket/output", "train_data3")

Contributing

Contributions are welcome! If you'd like to contribute to SnapStitch, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

Please make sure to update the documentation and tests as needed.

License

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

Acknowledgments

• Inspiration and guidance from the open-source community.
• Tools and libraries that facilitate synthetic data generation.