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https://github.com/rapidsai/cucim
cuCIM - RAPIDS GPU-accelerated image processing library
https://github.com/rapidsai/cucim
computer-vision cuda digital-pathology gpu image-analysis image-data image-processing medical-imaging microscopy multidimensional-image-processing nvidia segmentation
Last synced: 5 days ago
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cuCIM - RAPIDS GPU-accelerated image processing library
- Host: GitHub
- URL: https://github.com/rapidsai/cucim
- Owner: rapidsai
- License: apache-2.0
- Created: 2021-04-09T17:54:18.000Z (over 3 years ago)
- Default Branch: branch-25.02
- Last Pushed: 2024-12-16T15:39:06.000Z (14 days ago)
- Last Synced: 2024-12-19T06:06:50.129Z (12 days ago)
- Topics: computer-vision, cuda, digital-pathology, gpu, image-analysis, image-data, image-processing, medical-imaging, microscopy, multidimensional-image-processing, nvidia, segmentation
- Language: Jupyter Notebook
- Homepage: https://docs.rapids.ai/api/cucim/stable/
- Size: 24 MB
- Stars: 362
- Watchers: 17
- Forks: 61
- Open Issues: 113
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-pathology - cuCIM - NVIDIA's accelerated computer vision and image processing software library for multidimensional images. (Software / Image IO)
README
#
cuCIM
[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:
- [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=``
```
`` 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=``
```
`` 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.