Interactive-Automatic-Image-Labeling-Platform-Development

This repository is an extended version of ScribblePrompt, providing an interactive image labeling platform with advanced features like auto-inference, 3D segmentation, and batch processing.

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Features

• Interactive annotation with points, bounding boxes, and scribbles.
• Automatic labeling with guide-free inference.
• Support for 3D images (e.g., NIfTI) and videos.
• Batch processing with progress tracking.
• Post-processing mask editor for refinement.
• Model fine-tuning using custom training data.

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Environment Setup

You can choose between two environment management options: venv (lightweight Python-native) or conda (for multi-language and scientific projects).

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Option 1: Using venv

1. Clone the Repository

git clone https://github.com/YYYhan/Interactive-Automatic-Image-Labeling-Platform-Development.git
cd Interactive-Automatic-Image-Labeling-Platform-Development

2. Create and Activate Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

pip install -r requirements.txt

4. Download Pretrained Models

• Download pretrained models from the Releases section.
• Place them in the checkpoints/ directory:

  checkpoints/
  ├── scribbleprompt_unet.pth
  ├── scribbleprompt_sam.pth
  

5. Run the Application

python app.py

Open the provided URL in your browser (e.g., http://127.0.0.1:7860).

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Option 2: Using conda

1. Clone the Repository

git clone https://github.com/YYYhan/Interactive-Automatic-Image-Labeling-Platform-Development.git
cd Interactive-Automatic-Image-Labeling-Platform-Development

2. Create and Activate Conda Environment

conda create --name img_label_env python=3.9
conda activate img_label_env

3. Install Dependencies

pip install -r requirements.txt

4. Download Pretrained Models

• Download pretrained models from the Releases section.
• Place them in the checkpoints/ directory:

  checkpoints/
  ├── scribbleprompt_unet.pth
  ├── scribbleprompt_sam.pth
  

5. Run the Application

python app.py

Open the provided URL in your browser (e.g., http://127.0.0.1:7860).

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Usage

1. Interactive Annotation

• Use bounding boxes, points, or scribbles to annotate images interactively.
• Adjust the mask in the post-processing editor.

2. Automatic Labeling

• Enable "Auto-Inference Mode" for guide-free labeling.

3. 3D Image and Video Segmentation

• Upload NIfTI files or videos.
• Use sliders to select slices or frames for segmentation.

4. Batch Processing

• Upload multiple images, select from the dropdown menu, and track progress using the "Done" button.

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Project Structure

Interactive-Automatic-Image-Labeling-Platform-Development/
├── checkpoints/           # Pretrained models directory
├── test_examples/         # Example images and test data
├── app.py                 # Main application script
├── network.py             # Network definitions (e.g., UNet, SAM)
├── predictor.py           # Model inference logic
├── requirements.txt       # Python dependencies
├── LICENSE                # License file
└── README.md              # Project documentation

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Contributing

1. Fork the repository.
2. Create a new branch for your feature:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature-name"

4. Push the branch:

   git push origin feature-name

5. Open a Pull Request.

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License

This project is licensed under the MIT License.

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Acknowledgments

This project builds on the ScribblePrompt repository, extending its functionality and usability.