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align=\"center\"\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/ova.png\" alt=\"Open Vision API\"\u003e\u003c/img\u003e\n\u003c/p\u003e\n\n![Static Badge](https://img.shields.io/badge/AGPLV3-License?style=for-the-badge\u0026label=LIcense)\n\n\u003c/div\u003e\n\n## 🌟 Project Description\n\nOpen Vision API is an open source computer vision API based on open source models.\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"https://openvisionapi.com/images/demo.jpeg\"  width=\"60%\" height=\"30%\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n## 🚀 Quick Start\n\nThe following instructions detail how to set up the ova-server.\n\nFor information regarding the official clients and a quick demo of the API functionality head to:\n\n- [ova-client](https://github.com/openvisionapi/ova-client): the official Python client.\n- [ova](https://github.com/openvisionapi/ova): the official Rust client.\n\n### Installation\n\nMake sure you have:\n\n- [just](https://github.com/casey/just)\n- [poetry](https://python-poetry.org/)\n\nSet up a local environment using TensorFlow Lite as the backend framework.\n\n```bash\n$ just setup-tensorflow-lite\n```\n\n\u003e See [documentation](https://github.com/openvisionapi/docs) for a list of supported deep learning frameworks.\n\nDownload the models.\n\n```bash\n$ poetry run ./cli.py download --model=yolov4 --framework=tensorflow_lite --hardware=cpu\n```\n\n### Usage\n\nRun the ova-server.\n\n```bash\n$ just run-with-tensorflow-lite\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\nINFO:     Started server process [3588600]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\nINFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n```\n\nGet the official client.\n\n#### Python client:\n\n```bash\n$ git clone https://github.com/openvisionapi/ova-client\n$ cd ova-client\n$ just setup\n$ OVA_DETECTION_URL=http://localhost:8000/api/v1/detection poetry run ./ova.py detection images/cat.jpeg\n```\n\n\u003e For more information about the python client, please visit https://github.com/openvisionapi/ova-client\n\n#### Rust client:\n\n```bash\n$ git clone https://github.com/openvisionapi/ova\n$ cd ova\n$ OVA_DETECTION_URL=http://localhost:8000/api/v1/detection cargo run -- detection -i assets/cat.jpeg\n```\n\n\u003e For more information about the rust client, please visit https://github.com/openvisionapi/ova\n\n\u003cbr\u003e\n\n## ⛏️ Built Using\n\n- [FastAPI](https://github.com/tiangolo/fastapi)\n- [Pillow](https://github.com/python-pillow/Pillow)\n- [Numpy](https://github.com/numpy/numpy)\n- [TensorFlow](https://github.com/tensorflow/tensorflow)\n- [TensorFlow Lite](https://github.com/tensorflow/tensorflow)\n\n\u003cbr\u003e\n\n## 🤝 Contributions\n\nAll contributions are welcome!\n\n### Setting up the Development Environment\n\nTo set up the development environment, simply run the command:\n\n```bash\n$ just dev\n```\n\n### Code Style Checks\n\nruff and mypy are used to ensure that contributions are stylized in a uniform manner.\n\n- [ruff](https://github.com/astral-sh/ruff) is used as a linter and a code formatter.\n- [mypy](https://github.com/python/mypy) is used for static typing.\n\n\u003cbr\u003e\n\n## 🔧 Tests\n\nTo run the tests, simply use the commands:\n\n```bash\n$ just dev\n$ just test\n```\n\n\u003cbr\u003e\n\n## 📄 Documentation\n\nThe complete documentation can be found here [documentation](https://github.com/openvisionapi/docs)\n\n\u003cbr\u003e\n\n## ⚖️ License\n\nAGPLv3\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenvisionapi%2Fova-server","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenvisionapi%2Fova-server","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenvisionapi%2Fova-server/lists"}