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Notebook","funding_links":[],"categories":["🔬 Domain-Specific Applications"],"sub_categories":["🧬 Biology \u0026 Medicine"],"readme":"![License](https://img.shields.io/github/license/bioimage-io/core-bioimage-io-python.svg)\n[![PyPI](https://img.shields.io/pypi/v/bioimageio-core.svg?style=popout)](https://pypi.org/project/bioimageio.core/)\n[![conda-version](https://anaconda.org/conda-forge/bioimageio.core/badges/version.svg)](https://anaconda.org/conda-forge/bioimageio.core/)\n[![downloads](https://static.pepy.tech/badge/bioimageio.core)](https://pepy.tech/project/bioimageio.core)\n[![conda-forge downloads](https://img.shields.io/conda/dn/conda-forge/bioimageio.core.svg?label=conda-forge)](https://anaconda.org/conda-forge/bioimageio.core/)\n![code style](https://img.shields.io/badge/code%20style-black-000000.svg)\n[![coverage](https://bioimage-io.github.io/core-bioimage-io-python/coverage/coverage-badge.svg)](https://bioimage-io.github.io/core-bioimage-io-python/coverage/index.html)\n\n# bioimageio.core\n\nPython specific core utilities for bioimage.io resources (in particular DL models).\n\n## Get started\n\nTo get started we recommend installing bioimageio.core with conda together with a deep\nlearning framework, e.g. pytorch, and run a few `bioimageio` commands to see what\nbioimage.core has to offer:\n\n1. install with conda (for more details on conda environments, [checkout the conda docs](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html))\n\n    ```console\n    conda install -c conda-forge bioimageio.core pytorch\n    ```\n\n1. test a model\n\n    ```console\n    $ bioimageio test powerful-chipmunk\n    ...\n    ```\n\n    \u003cdetails\u003e\n    \u003csummary\u003e(Click to expand output)\u003c/summary\u003e\n\n    ```console\n\n\n      ✔️                 bioimageio validation passed\n    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n      source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files/rdf.yaml\n      format version    model 0.4.10\n      bioimageio.spec   0.5.3post4\n      bioimageio.core   0.6.8\n\n\n\n      ❓   location                                     detail\n    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n      ✔️                                                 initialized ModelDescr to describe model 0.4.10\n\n      ✔️                                                 bioimageio.spec format validation model 0.4.10\n      🔍   context.perform_io_checks                    True\n      🔍   context.root                                 https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files\n      🔍   context.known_files.weights.pt               3bd9c518c8473f1e35abb7624f82f3aa92f1015e66fb1f6a9d08444e1f2f5698\n      🔍   context.known_files.weights-torchscript.pt   4e568fd81c0ffa06ce13061327c3f673e1bac808891135badd3b0fcdacee086b\n      🔍   context.warning_level                        error\n\n      ✔️                                                 Reproduce test outputs from test inputs\n\n      ✔️                                                 Reproduce test outputs from test inputs\n    ```\n\n    \u003c/details\u003e\n\n    or\n\n    ```console\n    $ bioimageio test impartial-shrimp\n    ...\n    ```\n\n    \u003cdetails\u003e\u003csummary\u003e(Click to expand output)\u003c/summary\u003e\n\n    ```console\n      ✔️                 bioimageio validation passed\n    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n      source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/impartial-shrimp/1.1/files/rdf.yaml\n      format version    model 0.5.3\n      bioimageio.spec   0.5.3.2\n      bioimageio.core   0.6.9\n\n\n      ❓   location                    detail\n    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n      ✔️                                initialized ModelDescr to describe model 0.5.3\n\n\n      ✔️                                bioimageio.spec format validation model 0.5.3\n\n      🔍   context.perform_io_checks   False\n      🔍   context.warning_level       error\n\n      ✔️                                Reproduce test outputs from test inputs (pytorch_state_dict)\n\n\n      ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:\n\n                                      0\n\n      ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:\n\n                                      0\n\n      ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:\n\n                                      1\n\n      ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:\n\n                                      1\n\n      ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:\n\n                                      2\n\n      ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:\n\n                                      2\n\n      ✔️                                Reproduce test outputs from test inputs (torchscript)\n\n\n      ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 0\n\n\n      ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 0\n\n\n      ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 1\n\n\n      ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 1\n\n\n      ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 2\n\n\n      ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 2\n    ```\n\n    \u003c/details\u003e\n1. run prediction on your data\n\n- display the `bioimageio-predict` command help to get an overview:\n\n    ```console\n    $ bioimageio predict --help\n    ...\n    ```\n\n    \u003cdetails\u003e\n    \u003csummary\u003e(Click to expand output)\u003c/summary\u003e\n\n    ```console\n    usage: bioimageio predict [-h] [--inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLenmin_length=1]]]]\n                              [--outputs {str,Tuple[str,...]}] [--overwrite bool] [--blockwise bool] [--stats Path]\n                              [--preview bool]\n                              [--weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}]\n                              [--example bool]\n                              SOURCE\n\n    bioimageio-predict - Run inference on your data with a bioimage.io model.\n\n    positional arguments:\n      SOURCE                Url/path to a bioimageio.yaml/rdf.yaml file\n                            or a bioimage.io resource identifier, e.g. 'affable-shark'\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      --inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLen(min_length=1)]]]\n                            Model input sample paths (for each input tensor)\n\n                            The input paths are expected to have shape...\n                            - (n_samples,) or (n_samples,1) for models expecting a single input tensor\n                            - (n_samples,) containing the substring '{input_id}', or\n                            - (n_samples, n_model_inputs) to provide each input tensor path explicitly.\n\n                            All substrings that are replaced by metadata from the model description:\n                            - '{model_id}'\n                            - '{input_id}'\n\n                            Example inputs to process sample 'a' and 'b'\n                            for a model expecting a 'raw' and a 'mask' input tensor:\n                            --inputs=\"[[\"a_raw.tif\",\"a_mask.tif\"],[\"b_raw.tif\",\"b_mask.tif\"]]\"\n                            (Note that JSON double quotes need to be escaped.)\n\n                            Alternatively a `bioimageio-cli.yaml` (or `bioimageio-cli.json`) file\n                            may provide the arguments, e.g.:\n                            ```yaml\n                            inputs:\n                            - [a_raw.tif, a_mask.tif]\n                            - [b_raw.tif, b_mask.tif]\n                            ```\n\n                            `.npy` and any file extension supported by imageio are supported.\n                            Aavailable formats are listed at\n                            https://imageio.readthedocs.io/en/stable/formats/index.html#all-formats.\n                            Some formats have additional dependencies.\n\n                              (default: ('{input_id}/001.tif',))\n      --outputs {str,Tuple[str,...]}\n                            Model output path pattern (per output tensor)\n\n                            All substrings that are replaced:\n                            - '{model_id}' (from model description)\n                            - '{output_id}' (from model description)\n                            - '{sample_id}' (extracted from input paths)\n\n                              (default: outputs_{model_id}/{output_id}/{sample_id}.tif)\n      --overwrite bool      allow overwriting existing output files (default: False)\n      --blockwise bool      process inputs blockwise (default: False)\n      --stats Path          path to dataset statistics\n                            (will be written if it does not exist,\n                            but the model requires statistical dataset measures)\n                              (default: dataset_statistics.json)\n      --preview bool        preview which files would be processed\n                            and what outputs would be generated. (default: False)\n      --weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}\n                            The weight format to use. (default: any)\n      --example bool        generate and run an example\n\n                            1. downloads example model inputs\n                            2. creates a `{model_id}_example` folder\n                            3. writes input arguments to `{model_id}_example/bioimageio-cli.yaml`\n                            4. executes a preview dry-run\n                            5. executes prediction with example input\n\n                              (default: False)\n    ```\n\n    \u003c/details\u003e\n\n- create an example and run prediction locally!\n\n    ```console\n    $ bioimageio predict impartial-shrimp --example=True\n    ...\n    ```\n\n    \u003cdetails\u003e\n    \u003csummary\u003e(Click to expand output)\u003c/summary\u003e\n\n    ```console\n    🛈 bioimageio prediction preview structure:\n    {'{sample_id}': {'inputs': {'{input_id}': '\u003cinput path\u003e'},\n                    'outputs': {'{output_id}': '\u003coutput path\u003e'}}}\n    🔎 bioimageio prediction preview output:\n    {'1': {'inputs': {'input0': 'impartial-shrimp_example/input0/001.tif'},\n          'outputs': {'output0': 'impartial-shrimp_example/outputs/output0/1.tif'}}}\n    predict with impartial-shrimp: 100%|███████████████████████████████████████████████████| 1/1 [00:21\u003c00:00, 21.76s/sample]\n    🎉 Sucessfully ran example prediction!\n    To predict the example input using the CLI example config file impartial-shrimp_example\\bioimageio-cli.yaml, execute `bioimageio predict` from impartial-shrimp_example:\n    $ cd impartial-shrimp_example\n    $ bioimageio predict \"impartial-shrimp\"\n\n    Alternatively run the following command in the current workind directory, not the example folder:\n    $ bioimageio predict --preview=False --overwrite=True --stats=\"impartial-shrimp_example/dataset_statistics.json\" --inputs=\"[[\\\"impartial-shrimp_example/input0/001.tif\\\"]]\" --outputs=\"impartial-shrimp_example/outputs/{output_id}/{sample_id}.tif\" \"impartial-shrimp\"\n    (note that a local 'bioimageio-cli.json' or 'bioimageio-cli.yaml' may interfere with this)\n    ```\n\n    \u003c/details\u003e\n\n## Installation\n\n### Via Conda\n\nThe `bioimageio.core` package can be installed from conda-forge via\n\n```console\nconda install -c conda-forge bioimageio.core\n```\n\nIf you do not install any additional deep learning libraries, you will only be able to use general convenience\nfunctionality, but not any functionality depending on model prediction.\nTo install additional deep learning libraries add `pytorch`, `onnxruntime`, `keras` or `tensorflow`.\n\nDeeplearning frameworks to consider installing alongside `bioimageio.core`:\n\n- [Pytorch/Torchscript](https://pytorch.org/get-started/locally/)\n- [TensorFlow](https://www.tensorflow.org/install)\n- [ONNXRuntime](https://onnxruntime.ai/docs/install/#python-installs)\n\n### Via pip\n\nThe package is also available via pip\n(e.g. with recommended extras `onnx` and `pytorch`):\n\n```console\npip install \"bioimageio.core[onnx,pytorch]\"\n```\n\n## 🐍 Use in Python\n\n`bioimageio.core` is a python package that implements prediction with bioimageio models\nincluding standardized pre- and postprocessing operations.\nThese models are described by---and can be loaded with---the bioimageio.spec package.\n\nIn addition bioimageio.core provides functionality to convert model weight formats.\n\n### Documentation\n\n[Here you find the bioimageio.core documentation.](https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core.html)\n\n#### Presentations\n\n- [Create a model from scratch](https://bioimage-io.github.io/core-bioimage-io-python/presentations/create_ambitious_sloth.slides.html) ([source](https://github.com/bioimage-io/core-bioimage-io-python/tree/main/presentations))\n\n#### Examples\n\n\u003cdl\u003e\n  \u003cdt\u003eNotebooks that save and load resource descriptions and validate their format (using \u003ca href=\"https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/spec.html\"\u003ebioimageio.spec\u003c/a\u003e, a dependency of bioimageio.core)\u003c/dt\u003e\n  \u003cdd\u003e\u003ca href=\"https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb\"\u003eload_model_and_create_your_own.ipynb\u003c/a\u003e \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\u003c/dd\u003e\n\u003cdd\u003e\u003ca href=\"https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/dataset_creation.ipynb\"\u003edataset_creation.ipynb\u003c/a\u003e \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/bioimage-io/spec-bioimage-io/blob/main/example/dataset_creation.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\u003c/dd\u003e\n\u003cdt\u003eUse the described resources in Python with \u003ca href=\"https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core.html\"\u003ebioimageio.core\u003c/a\u003e\u003c/dt\u003e\n \u003cdd\u003e\u003ca href=\"https://github.com/bioimage-io/core-bioimage-io-python/blob/main/example/model_usage.ipynb\"\u003emodel_usage.ipynb\u003c/a\u003e\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/bioimage-io/core-bioimage-io-python/blob/main/example/model_usage.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/dl\u003e\n\n## 💻 Use the Command Line Interface\n\n`bioimageio.core` installs a command line interface (CLI) for testing models and other functionality.\nYou can list all the available commands via:\n\n```console\nbioimageio\n```\n\nFor examples see [Get started](#get-started).\n\n### CLI inputs from file\n\nFor convenience the command line options (not arguments) may be given in a `bioimageio-cli.json`\nor `bioimageio-cli.yaml` file, e.g.:\n\n```yaml\n# bioimageio-cli.yaml\ninputs: inputs/*_{tensor_id}.h5\noutputs: outputs_{model_id}/{sample_id}_{tensor_id}.h5\noverwrite: true\nblockwise: true\nstats: inputs/dataset_statistics.json\n```\n\n## Set up Development Environment\n\nTo set up a development environment run the following commands:\n\n```console\nconda create -n core python=$(grep -E '^requires-python' pyproject.toml | grep -oE '[0-9]+\\.[0-9]+')\nconda activate core\npip install -e .[dev,partners]\n```\n\n### Joint development of bioimageio.spec and bioimageio.core\n\nAssuming [spec-bioimage-io](https://github.com/bioimage-io/spec-bioimage-io) is cloned to the parent folder\na joint development environment can be created with the following commands:\n\n```console\nconda create -n core python=$(grep -E '^requires-python' pyproject.toml | grep -oE '[0-9]+\\.[0-9]+')\nconda activate core\npip install -e .[dev,partners] -e ../spec-bioimage-io[dev]\n```\n\n## Logging level\n\n`bioimageio.spec` and `bioimageio.core` use [loguru](https://github.com/Delgan/loguru) for logging, hence the logging level\nmay be controlled with the `LOGURU_LEVEL` environment variable.\nThe `bioimageio` CLI has logging enabled by default.\nTo activate logging when using bioimageio.spec/bioimageio.core as a library, add\n\n```python\nfrom loguru import logger\n\nlogger.enable(\"bioimageio\")\n```\n\n## Changelog\n\nSee [changelog.md](changelog.md)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbioimage-io%2Fcore-bioimage-io-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbioimage-io%2Fcore-bioimage-io-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbioimage-io%2Fcore-bioimage-io-python/lists"}