{"id":26239646,"url":"https://github.com/ccomkhj/datumaro-gui","last_synced_at":"2025-04-23T02:12:18.268Z","repository":{"id":255852291,"uuid":"853747738","full_name":"ccomkhj/datumaro-gui","owner":"ccomkhj","description":"Simplify the process of managing datasets in computer vision tasks using Datumaro,  by offering a GUI. 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This project aims to provide an easy-to-use interface for registering, merging, and filtering annotations.\n\n## Features\n\n- **Register new datasets**: Upload and process images and annotations to create new datasets.\n- **Merge datasets**: Combine existing datasets with new data and split them into training and validation sets.\n- **Filter annotations**: Apply custom filters to datasets to extract specific data based on conditions.\n- **Validate annotations**: Validate dataset and spot the potential issues.\n- **AWS S3 Integration**: Load existing datasets from S3 and upload the processed datasets back to S3.\n\n## Project Structure\n\n```\ndatumaro-gui/\n├── app.py\n├── utils.py\n├── pages/\n│   ├── merge.py\n│   ├── new.py\n│   ├── filter.py\n|   |── validate.py\n├── requirements.txt\n├── README.md\n└── credentials/\n    └── aws.yaml\n```\n\n- **app.py**: The main entry point for the Streamlit application.\n- **utils.py**: Utility functions for handling file uploads, AWS S3 interactions, and other helper functions.\n- **pages/**: Contains the different pages for the Streamlit app.\n    - **merge.py**: Page for merging datasets.\n    - **new.py**: Page for registering new annotations.\n    - **filter.py**: Page for applying filters to datasets.\n    - **validate.py**: Page for validating datasets.\n- **credentials/aws.yaml**: Stores the AWS credentials for interacting with S3.\n\n## Installation\n\n1. Clone the repository:\n    ```bash\n    git clone https://github.com/ccomkhj/datumaro-gui.git\n    cd datumaro-gui/\n    ```\n\n2. Create a virtual environment and activate it:\n    ```bash\n    conda create -n datumaro-gui python=3.11 -y\n    conda activate datumaro-gui\n    ```\n    [Note] tested with python =\u003c 3.11\n    OR\n    ```bash\n    python3 -m venv env\n    source env/bin/activate  # On Windows, use `env\\Scripts\\activate`\n    ```\n\n3. Install the dependencies:\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n4. (if you want to use S3) Set up AWS credentials in `credentials/aws.yaml`:\n    ```yaml\n    aws_access_key_id: \u003cYOUR_AWS_ACCESS_KEY_ID\u003e\n    aws_secret_access_key: \u003cYOUR_AWS_SECRET_ACCESS_KEY\u003e\n    ```\n\n## Usage\n\n1. Run the Streamlit application:\n    ```bash\n    streamlit run app.py\n    ```\n\n2. Open your browser and go to `http://localhost:8501` to interact with the Datumaro-GUI.\n\n\n## To read new dataset\n```\n-- annotations\n    |- instances_train.json\n    |- instances_val.json\n    # subsets are train and val\n-- images\n    |- train\n    |- val\n\n```\nor\n```\n-- annotations\n    |- instance_default.json\n    # subsets are only default\n-- images\n    |- train\n```\n\n## Recommended workflow\n1. use `new` if it's your first dataset\n2. use `merge` if your new dataset needs to be merged\n3. use `filter` if you want to create the dataset selectively (after `merge` or `new`)\n4. use `validate` if you want to validate the dataset selectively (after `merge` or `new`)\n\n\n## Contributing\n\nContributions are welcome! Feel free to submit a pull request or open an issue.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fccomkhj%2Fdatumaro-gui","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fccomkhj%2Fdatumaro-gui","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fccomkhj%2Fdatumaro-gui/lists"}