Metadata-Version: 2.1
Name: cucim
Version: 24.6.0
Summary: cuCIM - an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.
Author: NVIDIA Corporation
License: Apache 2.0
Project-URL: Homepage, https://developer.nvidia.com/multidimensional-image-processing
Project-URL: Documentation, https://docs.rapids.ai/api/cucim/stable/
Project-URL: Changelog, https://github.com/rapidsai/cucim/blob/main/CHANGELOG.md
Project-URL: Source, https://github.com/rapidsai/cucim
Project-URL: Tracker, https://github.com/rapidsai/cucim/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: POSIX :: Linux
Classifier: Environment :: Console
Classifier: Environment :: GPU :: NVIDIA CUDA :: 11
Classifier: Environment :: GPU :: NVIDIA CUDA :: 12
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
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Description-Content-Type: text/markdown
License-File: LICENSE
License-File: LICENSE-3rdparty.md
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# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuCIM</div>

[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.

cuCIM offers:

- Enhanced Image Processing Capabilities for large and n-dimensional tag image file format (TIFF) files
- Accelerated performance through Graphics Processing Unit (GPU)-based image processing and computer vision primitives
- A Straightforward Pythonic Interface with Matching Application Programming Interface (API) for Openslide

cuCIM supports the following formats:

- Aperio ScanScope Virtual Slide (SVS)
- Philips TIFF
- Generic Tiled, Multi-resolution RGB TIFF files with the following compression schemes:
  - No Compression
  - JPEG
  - JPEG2000
  - Lempel-Ziv-Welch (LZW)
  - Deflate

**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cucim/blob/main/README.md) ensure you are on the `main` branch.

- [GTC 2022 Accelerating Storage IO to GPUs with Magnum IO [S41347]](https://events.rainfocus.com/widget/nvidia/gtcspring2022/sessioncatalog/session/1634960000577001Etxp)
  - cuCIM's GDS API examples: <https://github.com/NVIDIA/MagnumIO/tree/main/gds/readers/cucim-gds>
- [SciPy 2021 cuCIM - A GPU image I/O and processing library](https://www.scipy2021.scipy.org/)
  - [video](https://youtu.be/G46kOOM9xbQ)
- [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)
  - [video](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s32194/)

**[Developer Page](https://developer.nvidia.com/multidimensional-image-processing)**

**Blogs**
- [Enhanced Image Analysis with Multidimensional Image Processing](https://developer.nvidia.com/blog/enhanced-image-analysis-with-multidimensional-image-processing/)
- [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/)
- [Accelerating Digital Pathology Pipelines with NVIDIA Clara™ Deploy](https://developer.nvidia.com/blog/accelerating-digital-pathology-pipelines-with-nvidia-clara-deploy-2/)

**Webinars**

- [cuCIM: a GPU Image IO and Processing Library](https://www.youtube.com/watch?v=G46kOOM9xbQ)

**[Documentation](https://docs.rapids.ai/api/cucim/stable)**

**Release notes** are available on our [wiki page](https://github.com/rapidsai/cucim/wiki/Release-Notes).

## Install cuCIM

### Conda

#### [Conda (stable)](https://anaconda.org/rapidsai/cucim)

```bash
conda create -n cucim -c rapidsai -c conda-forge cucim cuda-version=`<CUDA version>`
```

`<CUDA version>` should be 11.2+ (e.g., `11.2`, `12.0`, etc.)

#### [Conda (nightlies)](https://anaconda.org/rapidsai-nightly/cucim)

```bash
conda create -n cucim -c rapidsai-nightly -c conda-forge cucim cuda-version=`<CUDA version>`
```

`<CUDA version>` should be 11.2+ (e.g., `11.2`, `12.0`, etc.)

### [PyPI](https://pypi.org/project/cucim/)

Install for CUDA 12:

```bash
pip install cucim-cu12
```

Alternatively install for CUDA 11:

```bash
pip install cucim-cu11
```

### Notebooks

Please check out our [Welcome](notebooks/Welcome.ipynb) notebook ([NBViewer](https://nbviewer.org/github/rapidsai/cucim/blob/main/notebooks/Welcome.ipynb))

#### Downloading sample images

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:

(You will need [Docker](https://www.docker.com/) installed in your system)

```bash
./run download_testdata
```
or

```bash
mkdir -p notebooks/input
tmp_id=$(docker create gigony/svs-testdata:little-big)
docker cp $tmp_id:/input notebooks
docker rm -v ${tmp_id}
```

## Build/Install from Source

See build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).

## Contributing Guide

Contributions to cuCIM are more than welcome!
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.

## Acknowledgments

Without awesome third-party open source software, this project wouldn't exist.

Please find [LICENSE-3rdparty.md](LICENSE-3rdparty.md) to see which third-party open source software
is used in this project.

## License

Apache-2.0 License (see [LICENSE](LICENSE) file).

Copyright (c) 2020-2022, NVIDIA CORPORATION.
