{"id":13708525,"url":"https://github.com/rapidsai/cucim","last_synced_at":"2025-04-11T03:28:56.900Z","repository":{"id":37639725,"uuid":"356356336","full_name":"rapidsai/cucim","owner":"rapidsai","description":"cuCIM - RAPIDS GPU-accelerated image processing library","archived":false,"fork":false,"pushed_at":"2025-04-03T19:58:51.000Z","size":25458,"stargazers_count":391,"open_issues_count":127,"forks_count":66,"subscribers_count":15,"default_branch":"branch-25.06","last_synced_at":"2025-04-03T20:29:06.843Z","etag":null,"topics":["computer-vision","cuda","digital-pathology","gpu","image-analysis","image-data","image-processing","medical-imaging","microscopy","multidimensional-image-processing","nvidia","segmentation"],"latest_commit_sha":null,"homepage":"https://docs.rapids.ai/api/cucim/stable/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rapidsai.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-04-09T17:54:18.000Z","updated_at":"2025-04-03T15:06:56.000Z","dependencies_parsed_at":"2023-09-21T21:25:32.532Z","dependency_job_id":"2e06fcfa-f242-4993-907f-6c3364657986","html_url":"https://github.com/rapidsai/cucim","commit_stats":{"total_commits":381,"total_committers":28,"mean_commits":"13.607142857142858","dds":0.6929133858267716,"last_synced_commit":"cc1b25f31de1d917cfb8162e62e58ed55eeffc23"},"previous_names":[],"tags_count":55,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcucim","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcucim/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcucim/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcucim/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rapidsai","download_url":"https://codeload.github.com/rapidsai/cucim/tar.gz/refs/heads/branch-25.06","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248264670,"owners_count":21074797,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","cuda","digital-pathology","gpu","image-analysis","image-data","image-processing","medical-imaging","microscopy","multidimensional-image-processing","nvidia","segmentation"],"created_at":"2024-08-02T23:00:25.097Z","updated_at":"2025-04-11T03:28:56.879Z","avatar_url":"https://github.com/rapidsai.png","language":"Jupyter Notebook","funding_links":[],"categories":["Software","🔋 Performance"],"sub_categories":["Image IO","Software tools"],"readme":"# \u003cdiv align=\"left\"\u003e\u003cimg src=\"https://rapids.ai/assets/images/rapids_logo.png\" width=\"90px\"/\u003e\u0026nbsp;cuCIM\u003c/div\u003e\n\n[RAPIDS](https://rapids.ai) cuCIM (pronounced \"koo-sim\", see [here]( https://ipa-reader.com/?text=ku%CB%90%CB%88s%C9%AAm\u0026voice=Joey )) 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: \u003chttps://github.com/NVIDIA/MagnumIO/tree/main/gds/readers/cucim-gds\u003e\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\u0026layout=list\u0026page=1\u0026q=cucim\u0026sort=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™ 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=`\u003cCUDA version\u003e`\n```\n\n`\u003cCUDA version\u003e` 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=`\u003cCUDA version\u003e`\n```\n\n`\u003cCUDA version\u003e` 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frapidsai%2Fcucim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frapidsai%2Fcucim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frapidsai%2Fcucim/lists"}