| Name | cucim-cu11 JSON |
| Version |
24.12.0
JSON |
| download |
| home_page | None |
| 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. |
| upload_time | 2024-12-12 23:52:14 |
| maintainer | None |
| docs_url | None |
| author | NVIDIA Corporation |
| requires_python | >=3.10 |
| license | Apache 2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/> 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.
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"description": "# <div align=\"left\"><img src=\"https://rapids.ai/assets/images/rapids_logo.png\" width=\"90px\"/> cuCIM</div>\n\n[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.\n\ncuCIM offers:\n\n- Enhanced Image Processing Capabilities for large and n-dimensional tag image file format (TIFF) files\n- Accelerated performance through Graphics Processing Unit (GPU)-based image processing and computer vision primitives\n- A Straightforward Pythonic Interface with Matching Application Programming Interface (API) for Openslide\n\ncuCIM supports the following formats:\n\n- Aperio ScanScope Virtual Slide (SVS)\n- Philips TIFF\n- Generic Tiled, Multi-resolution RGB TIFF files with the following compression schemes:\n - No Compression\n - JPEG\n - JPEG2000\n - Lempel-Ziv-Welch (LZW)\n - Deflate\n\n**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cucim/blob/main/README.md) ensure you are on the `main` branch.\n\n- [GTC 2022 Accelerating Storage IO to GPUs with Magnum IO [S41347]](https://events.rainfocus.com/widget/nvidia/gtcspring2022/sessioncatalog/session/1634960000577001Etxp)\n - cuCIM's GDS API examples: <https://github.com/NVIDIA/MagnumIO/tree/main/gds/readers/cucim-gds>\n- [SciPy 2021 cuCIM - A GPU image I/O and processing library](https://www.scipy2021.scipy.org/)\n - [video](https://youtu.be/G46kOOM9xbQ)\n- [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)\n - [video](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s32194/)\n\n**[Developer Page](https://developer.nvidia.com/multidimensional-image-processing)**\n\n**Blogs**\n- [Enhanced Image Analysis with Multidimensional Image Processing](https://developer.nvidia.com/blog/enhanced-image-analysis-with-multidimensional-image-processing/)\n- [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/)\n- [Accelerating Digital Pathology Pipelines with NVIDIA Clara\u2122 Deploy](https://developer.nvidia.com/blog/accelerating-digital-pathology-pipelines-with-nvidia-clara-deploy-2/)\n\n**Webinars**\n\n- [cuCIM: a GPU Image IO and Processing Library](https://www.youtube.com/watch?v=G46kOOM9xbQ)\n\n**[Documentation](https://docs.rapids.ai/api/cucim/stable)**\n\n**Release notes** are available on our [wiki page](https://github.com/rapidsai/cucim/wiki/Release-Notes).\n\n## Install cuCIM\n\n### Conda\n\n#### [Conda (stable)](https://anaconda.org/rapidsai/cucim)\n\n```bash\nconda create -n cucim -c rapidsai -c conda-forge cucim cuda-version=`<CUDA version>`\n```\n\n`<CUDA version>` should be 11.2+ (e.g., `11.2`, `12.0`, etc.)\n\n#### [Conda (nightlies)](https://anaconda.org/rapidsai-nightly/cucim)\n\n```bash\nconda create -n cucim -c rapidsai-nightly -c conda-forge cucim cuda-version=`<CUDA version>`\n```\n\n`<CUDA version>` should be 11.2+ (e.g., `11.2`, `12.0`, etc.)\n\n### [PyPI](https://pypi.org/project/cucim/)\n\nInstall for CUDA 12:\n\n```bash\npip install cucim-cu12\n```\n\nAlternatively install for CUDA 11:\n\n```bash\npip install cucim-cu11\n```\n\n### Notebooks\n\nPlease check out our [Welcome](notebooks/Welcome.ipynb) notebook ([NBViewer](https://nbviewer.org/github/rapidsai/cucim/blob/main/notebooks/Welcome.ipynb))\n\n#### Downloading sample images\n\nTo 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:\n\n(You will need [Docker](https://www.docker.com/) installed in your system)\n\n```bash\n./run download_testdata\n```\nor\n\n```bash\nmkdir -p notebooks/input\ntmp_id=$(docker create gigony/svs-testdata:little-big)\ndocker cp $tmp_id:/input notebooks\ndocker rm -v ${tmp_id}\n```\n\n## Build/Install from Source\n\nSee build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).\n\n## Contributing Guide\n\nContributions to cuCIM are more than welcome!\nPlease 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.\n\n## Acknowledgments\n\nWithout awesome third-party open source software, this project wouldn't exist.\n\nPlease find [LICENSE-3rdparty.md](LICENSE-3rdparty.md) to see which third-party open source software\nis used in this project.\n\n## License\n\nApache-2.0 License (see [LICENSE](LICENSE) file).\n\nCopyright (c) 2020-2022, NVIDIA CORPORATION.\n",
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