# phenocv
## Introduction
**phenocv** is a toolkits for rice high-throught phenotyping using computer vision.
**phenocv** is still in early development stage, and more features will be added in the future.
For label-studio semi-automatic annotation, please refer to [playground](https://github.com/open-mmlab/playground).
For mmdetection training, please refer to [mmdetection](https://github.com/open-mmlab/mmdetection).
For yolo training, please refer to [Ultralytics](https://github.com/ultralytics/ultralytics).
Support for mmdetection and label-studio will be added in the future.
## Installation
Before install the package, make sure you have installed [pytorch](https://pytorch.org/get-started/locally/) and install in the python environment with python>=3.8.
### Install with pip:
```shell
pip install phenocv
```
### Install in editable mode, allow changes to the source code to be immediately available:
```shell
git clone https://github.com/r1cheu/phenocv.git
cd phenocv
pip install -e .
```
## Tutorial
| Getting Start | [](https://github.com/r1cheu/phenocv/blob/main/tutorial/getting_start.ipynb) | [](https://colab.research.google.com/github/r1cheu/phenocv/blob/main/tutorial/getting_start.ipynb) |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
## License
This project is released under the [AGPL 3.0 license](LICENSE).
## Citation
If you find this project useful in your research, please consider cite:
```Bibtex
@misc{2023phenocv,
title={Rice high-throught phenotyping computer vision toolkits},
author={RuLei Chen},
howpublished = {\url{https://github.com/r1cheu/phenocv}},
year={2023}
}
```
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"description": "# phenocv\n\n## Introduction\n\n**phenocv** is a toolkits for rice high-throught phenotyping using computer vision.\n\n**phenocv** is still in early development stage, and more features will be added in the future.\n\nFor label-studio semi-automatic annotation, please refer to [playground](https://github.com/open-mmlab/playground).\n\nFor mmdetection training, please refer to [mmdetection](https://github.com/open-mmlab/mmdetection).\n\nFor yolo training, please refer to [Ultralytics](https://github.com/ultralytics/ultralytics).\n\nSupport for mmdetection and label-studio will be added in the future.\n\n## Installation\n\nBefore install the package, make sure you have installed [pytorch](https://pytorch.org/get-started/locally/) and install in the python environment with python>=3.8.\n\n### Install with pip:\n\n```shell\npip install phenocv\n```\n\n### Install in editable mode, allow changes to the source code to be immediately available:\n\n```shell\ngit clone https://github.com/r1cheu/phenocv.git\ncd phenocv\npip install -e .\n```\n\n## Tutorial\n\n| Getting Start | [](https://github.com/r1cheu/phenocv/blob/main/tutorial/getting_start.ipynb) | [](https://colab.research.google.com/github/r1cheu/phenocv/blob/main/tutorial/getting_start.ipynb) |\n| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n\n## License\n\nThis project is released under the [AGPL 3.0 license](LICENSE).\n\n## Citation\n\nIf you find this project useful in your research, please consider cite:\n\n```Bibtex\n@misc{2023phenocv,\n title={Rice high-throught phenotyping computer vision toolkits},\n author={RuLei Chen},\n howpublished = {\\url{https://github.com/r1cheu/phenocv}},\n year={2023}\n}\n```\n",
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