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python/cucim/README.md

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# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuCIM</div>
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[RAPIDS](https://rapids.ai) cuCIM is an open-source, accelerated computer vision and image processing software library for multidimensional images used in biomedical, geospatial, material and life science, and remote sensing use cases.
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cuCIM offers:
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- Enhanced Image Processing Capabilities for large and n-dimensional tag image file format (TIFF) files
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- Accelerated performance through Graphics Processing Unit (GPU)-based image processing and computer vision primitives
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- A Straightforward Pythonic Interface with Matching Application Programming Interface (API) for Openslide
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cuCIM supports the following formats:
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- Aperio ScanScope Virtual Slide (SVS)
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- Philips TIFF
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- Generic Tiled, Multi-resolution RGB TIFF files with the following compression schemes:
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- No Compression
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- JPEG
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- JPEG2000
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- Lempel-Ziv-Welch (LZW)
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- Deflate
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**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cucim/blob/main/README.md) ensure you are on the `main` branch.
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- [GTC 2022 Accelerating Storage IO to GPUs with Magnum IO [S41347]](https://events.rainfocus.com/widget/nvidia/gtcspring2022/sessioncatalog/session/1634960000577001Etxp)
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- cuCIM's GDS API examples: <https://github.com/NVIDIA/MagnumIO/tree/main/gds/readers/cucim-gds>
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- [SciPy 2021 cuCIM - A GPU image I/O and processing library](https://www.scipy2021.scipy.org/)
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- [video](https://youtu.be/G46kOOM9xbQ)
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- [GTC 2021 cuCIM: A GPU Image I/O and Processing Toolkit [S32194]](https://www.nvidia.com/en-us/on-demand/search/?facet.mimetype[]=event%20session&layout=list&page=1&q=cucim&sort=date)
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- [video](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s32194/)
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**[Developer Page](https://developer.nvidia.com/multidimensional-image-processing)**
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**Blogs**
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- [Enhanced Image Analysis with Multidimensional Image Processing](https://developer.nvidia.com/blog/enhanced-image-analysis-with-multidimensional-image-processing/)
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- [Accelerating Scikit-Image API with cuCIM: n-Dimensional Image Processing and IO on GPUs](https://developer.nvidia.com/blog/cucim-rapid-n-dimensional-image-processing-and-i-o-on-gpus/)
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- [Accelerating Digital Pathology Pipelines with NVIDIA Clara™ Deploy](https://developer.nvidia.com/blog/accelerating-digital-pathology-pipelines-with-nvidia-clara-deploy-2/)
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**Webinars**
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- [cuCIM: a GPU Image IO and Processing Library](https://www.youtube.com/watch?v=G46kOOM9xbQ)
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**[Documentation](https://docs.rapids.ai/api/cucim/stable)**
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**Release notes** are available on our [wiki page](https://github.com/rapidsai/cucim/wiki/Release-Notes).
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## Install cuCIM
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### Conda
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#### [Conda (stable)](https://anaconda.org/rapidsai/cucim)
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> conda create -n cucim -c rapidsai -c conda-forge cucim cudatoolkit=`<CUDA version>`
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`<CUDA version>` should be 11.0+ (e.g., `11.0`, `11.2`, etc.)
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#### [Conda (nightlies)](https://anaconda.org/rapidsai-nightly/cucim)
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> conda create -n cucim -c rapidsai-nightly -c conda-forge cucim cudatoolkit=`<CUDA version>`
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`<CUDA version>` should be 11.0+ (e.g., `11.0`, `11.2`, etc)
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### [PyPI](https://pypi.org/project/cucim/)
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```bash
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pip install cucim
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# Install dependencies for `cucim.skimage` (assuming that CUDA 11.0 is used for CuPy)
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pip install scipy scikit-image cupy-cuda110
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```
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### Notebooks
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Please check out our [Welcome](notebooks/Welcome.ipynb) notebook ([NBViewer](https://nbviewer.jupyter.org/github/rapidsai/cucim/blob/branch-22.10/notebooks/Welcome.ipynb))
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#### Downloading sample images
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To download images used in the notebooks, please execute the following commands from the repository root folder to copy sample input images into `notebooks/input` folder:
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(You will need [Docker](https://www.docker.com/) installed in your system)
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```bash
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./run download_testdata
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```
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or
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```bash
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mkdir -p notebooks/input
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tmp_id=$(docker create gigony/svs-testdata:little-big)
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docker cp $tmp_id:/input notebooks
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docker rm -v ${tmp_id}
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```
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## Build/Install from Source
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See build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).
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## Contributing Guide
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Contributions to cuCIM are more than welcome!
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Please review the [CONTRIBUTING.md](https://github.com/rapidsai/cucim/blob/main/CONTRIBUTING.md) file for information on how to contribute code and issues to the project.
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## Acknowledgments
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Without awesome third-party open source software, this project wouldn't exist.
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Please find [LICENSE-3rdparty.md](LICENSE-3rdparty.md) to see which third-party open source software
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is used in this project.
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## License
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Apache-2.0 License (see [LICENSE](LICENSE) file).
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Copyright (c) 2020-2022, NVIDIA CORPORATION.

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