Adding new/updated blue_team_unet2d-zerocostdL4mic_0.1.0

solutions/blue_team/unet2d-zerocostdL4mic/CHANGELOG.md

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+# Changelog
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.1.0] - 2024-03-21
+

solutions/blue_team/unet2d-zerocostdL4mic/requirements.txt

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+absl-py==2.1.0
+aiofiles==22.1.0
+aiosqlite==0.20.0
+album-runner @ file:///home/conda/feedstock_root/build_artifacts/album-runner_1678835528483/work
+anyio==4.3.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asciitree==0.3.3
+asttokens==2.4.1
+astunparse==1.6.3
+attrs==23.2.0
+Babel==2.14.0
+beautifulsoup4==4.12.3
+bioimageio.core==0.5.9
+bioimageio.spec==0.4.9.post5
+bleach==6.1.0
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.16.0
+charset-normalizer==2.0.12
+click==8.1.7
+colorama==0.4.6
+comm==0.2.2
+contourpy==1.2.0
+cycler==0.12.1
+data==0.4
+debugpy==1.8.1
+decorator==5.1.1
+defusedxml==0.7.1
+entrypoints==0.4
+exceptiongroup==1.2.0
+executing==2.0.1
+fasteners==0.19
+fastjsonschema==2.19.1
+flatbuffers==24.3.7
+fonttools==4.50.0
+fpdf2==2.7.4
+fqdn==1.5.1
+funcsigs==1.0.2
+future==0.18.3
+gast==0.4.0
+google==2.0.3
+google-auth==2.28.2
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+grpcio==1.62.1
+h5py==3.10.0
+idna==3.6
+imageio==2.34.0
+ipykernel==6.29.3
+ipython==8.22.2
+ipython-genutils==0.2.0
+ipywidgets==8.1.0
+isoduration==20.11.0
+jax==0.4.25
+jedi==0.19.1
+Jinja2==3.1.3
+joblib==1.3.2
+json5==0.9.24
+jsonpointer==2.4
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+jupyter-events==0.10.0
+jupyter-ydoc==0.2.5
+jupyter_client==7.4.9
+jupyter_core==5.7.2
+jupyter_server==2.13.0
+jupyter_server_fileid==0.9.1
+jupyter_server_terminals==0.5.3
+jupyter_server_ydoc==0.6.1
+jupyterlab==3.6.0
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.24.0
+jupyterlab_widgets==3.0.10
+keras==2.12.0
+kiwisolver==1.4.5
+libclang==18.1.1
+Markdown==3.6
+MarkupSafe==2.1.5
+marshmallow==3.21.1
+marshmallow-jsonschema==0.13.0
+marshmallow-union==0.1.15.post1
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+mistune==3.0.2
+ml-dtypes==0.3.2
+nbclassic==1.0.0
+nbclient==0.10.0
+nbconvert==7.16.2
+nbformat==5.9.2
+nest-asyncio==1.6.0
+networkx==3.2.1
+notebook==6.5.6
+notebook_shim==0.2.4
+numcodecs==0.12.1
+numexpr==2.8.4
+numpy==1.22.4
+oauthlib==3.2.2
+opt-einsum==3.3.0
+overrides==7.7.0
+packaging==24.0
+pandas==1.5.3
+pandocfilters==1.5.1
+parso==0.8.3
+pathlib==1.0.1
+Pillow==8.4.0
+platformdirs==4.2.0
+prometheus_client==0.20.0
+prompt-toolkit==3.0.43
+protobuf==4.25.3
+psutil==5.9.8
+PTable==0.9.2
+pure-eval==0.2.2
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pycparser==2.21
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-json-logger==2.0.7
+pytz==2024.1
+PyWavelets==1.5.0
+pywin32==306
+pywinpty==2.0.13
+PyYAML==6.0.1
+pyzmq==24.0.1
+referencing==0.34.0
+requests==2.28.0
+requests-oauthlib==1.4.0
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.18.0
+rsa==4.9
+ruamel.yaml==0.18.6
+ruamel.yaml.clib==0.2.8
+scikit-image==0.19.3
+scikit-learn==1.2.2
+scipy==1.10.1
+Send2Trash==1.8.2
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.5
+stack-data==0.6.3
+tensorboard==2.12.3
+tensorboard-data-server==0.7.2
+tensorflow==2.12.0
+tensorflow-estimator==2.12.0
+tensorflow-intel==2.12.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.4.0
+terminado==0.18.1
+threadpoolctl==3.3.0
+tifffile==2023.7.4
+tinycss2==1.2.1
+tomli==2.0.1
+tornado==6.4
+tqdm==4.65.0
+traitlets==5.14.2
+typer==0.9.0
+types-python-dateutil==2.9.0.20240316
+typing_extensions==4.10.0
+unzip==1.0.0
+uri-template==1.3.0
+urllib3==1.26.18
+wcwidth==0.2.13
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.7.0
+Werkzeug==3.0.1
+wget==3.2
+widgetsnbextension==4.0.10
+wrapt==1.14.1
+xarray==2023.12.0
+y-py==0.6.2
+ypy-websocket==0.8.4
+zarr==2.15.0

solutions/blue_team/unet2d-zerocostdL4mic/solution.py

@@ -0,0 +1,149 @@
+from album.runner.api import setup
+from pathlib import Path
+
+
+env_file = """channels:
+  - conda-forge
+  - nvidia
+  - anaconda
+  - defaults
+dependencies:
+  - python=3.10
+  - cudatoolkit=11.2.2
+  - cudnn=8.1.0
+  - pip
+  - pkg-config
+  - pip:
+    - unzip
+    - GitPython
+    - PTable==0.9.2
+    - Pillow==8.4.0
+    - bioimageio.core==0.5.9
+    - data==0.4
+    - fpdf2==2.7.4
+    - future==0.18.3
+    - google==2.0.3
+    - matplotlib==3.7.1
+    - numexpr==2.8.4
+    - numpy==1.22.4
+    - pandas==1.5.3
+    - pathlib==1.0.1
+    - pip==23.1.2
+    - requests==2.28.0
+    - scikit-image==0.19.3
+    - scikit-learn==1.2.2
+    - scipy==1.10.1
+    - tensorflow==2.12.0
+    - tifffile==2023.7.4
+    - tqdm==4.65.0
+    - wget==3.2
+    - zarr==2.15.0
+    - nbformat==5.9.2
+    - ipywidgets==8.1.0
+    - jupyterlab==3.6.0
+name: dl4miceverywhere
+"""
+
+
+def install():
+    from album.runner.api import get_app_path
+    from git import Repo
+    import subprocess
+    import requests
+    import shutil
+    import os
+
+    # Clone the DL4MicEverywhere repository
+    clone_url = "https://github.com/HenriquesLab/DL4MicEverywhere"
+    to = get_app_path().joinpath("DL4MicEverywhere")
+    Repo.clone_from(clone_url, to)
+    assert (to.exists())
+
+    # Download the notebook that will execute the solution
+    notebook_name = "U-Net_2D_ZeroCostDL4Mic.ipynb"
+    notebook_url = f"https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/{notebook_name}"
+
+    notebook_path = get_app_path().joinpath(notebook_name)
+    notebook_path.parent.mkdir(parents=True, exist_ok=True)
+
+    response = requests.get(notebook_url)
+    response.raise_for_status()
+
+    with open(notebook_path, 'wb') as notebook_file:
+        notebook_file.write(response.content)
+
+    assert notebook_path.exists(), "Notebook download failed"
+
+    # Convert the notebook to its colabless form
+    subprocess.run(["python", ".tools/notebook_autoconversion/transform.py", "-p", f"{get_app_path()}", "-n", f"{notebook_name}", "-s", "1.1.", "1.2.", "2.", "6.3."], cwd=to)
+    subprocess.run(["mv", get_app_path().joinpath(f"colabless_{notebook_name}"), get_app_path().joinpath(f"{notebook_name}")])
+
+    # Remove the cloned DL4MicEverywhere repository
+    if os.name == 'nt':
+        os.system(f'rmdir /s /q "{to}"')
+    else:
+        shutil.rmtree(to) # rmtree has no permission to do this on Windows
+
+def run():
+    from album.runner.api import get_args, get_app_path
+    import subprocess
+    import os
+
+    # Fetch arguments and paths
+    args = get_args()
+    app_path = get_app_path()
+
+    # Path to the downloaded notebook
+    notebook_path = app_path.joinpath("U-Net_2D_ZeroCostDL4Mic.ipynb")
+
+    # Ensure the notebook exists
+    assert notebook_path.exists(), f"Notebook {notebook_path} does not exist"
+
+    # Output path for running the notebook
+    output_path = args.path
+    os.makedirs(output_path, exist_ok=True)
+    print(f"Saving output to {output_path}")
+
+    # Set the LD_LIBRARY_PATH to allow TensorFlow to find the CUDA libraries
+    os.environ["LD_LIBRARY_PATH"] = f"{os.environ['LD_LIBRARY_PATH']}:{os.environ['CONDA_PREFIX']}/lib"
+
+    # Optionally, launch the Jupyter notebook to show the results
+    subprocess.run(["jupyter", "lab", str(notebook_path)], cwd=str(output_path))
+
+
+setup(
+    group="blue_team",
+    name="unet2d-zerocostdL4mic",
+    version="0.1.0",
+    album_api_version="0.5.1",
+    title="U-Net 2D",
+    description="2D binary segmentation. U-Net is an encoder-decoder architecture originally used for image segmentation. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.",
+    documentation="https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md",
+    covers=[],
+    cite=[{
+        "text": "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021)",
+        "doi": "https://doi.org/10.1038/s41467-021-22518-0"
+    }, {
+        "text": "Iván Hidalgo-Cenalmor, Joanna W Pylvänäinen, Mariana G Ferreira, Craig T Russell, Ignacio Arganda-Carreras, AI4Life Consortium, Guillaume Jacquemet, Ricardo Henriques, Estibaliz Gómez-de-Mariscal. DL4MicEverywhere: Deep learning for microscopy made flexible, shareable, and reproducible. bioRxiv 2023",
+        "doi": "https://doi.org/10.1101/2023.11.19.567606"
+    }, {
+        "text": "Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer International Publishing, 2015.",
+        "doi": "https://doi.org/10.48550/arXiv.1505.04597"
+    }, {
+        "text": "Falk, T., Mai, D., Bensch, R. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 16, 67–70 (2019).",
+        "doi": "https://doi.org/10.1038/s41592-018-0261-2"
+    }],
+    solution_creators=["DL4MicEverywhere team", "album team"],
+    dependencies={"environment_file": env_file},
+    tags=["unet", "segmentation", "colab", "notebook", "U-Net", "ZeroCostDL4Mic", "2D", "dl4miceverywhere"],
+    args=[
+        {
+            "name": "path",
+            "type": "string",
+            "default": ".",
+            "description": "What is your working path?"
+        }
+    ],
+    run=run,
+    install=install,
+)

solutions/blue_team/unet2d-zerocostdL4mic/solution.yml

@@ -0,0 +1,52 @@
+album_api_version: 0.5.1
+args:
+- default: .
+  description: What is your working path?
+  name: path
+  type: string
+changelog: null
+cite:
+- doi: https://doi.org/10.1038/s41467-021-22518-0
+  text: von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning
+    for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021)
+- doi: https://doi.org/10.1101/2023.11.19.567606
+  text: "Iv\xC3\xA1n Hidalgo-Cenalmor, Joanna W Pylv\xC3\xA4n\xC3\xA4inen, Mariana\
+    \ G Ferreira, Craig T Russell, Ignacio Arganda-Carreras, AI4Life Consortium, Guillaume\
+    \ Jacquemet, Ricardo Henriques, Estibaliz G\xC3\xB3mez-de-Mariscal. DL4MicEverywhere:\
+    \ Deep learning for microscopy made flexible, shareable, and reproducible. bioRxiv\
+    \ 2023"
+- doi: https://doi.org/10.48550/arXiv.1505.04597
+  text: "Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. U-net: Convolutional\
+    \ networks for biomedical image segmentation. Medical image computing and computer-assisted\
+    \ intervention\xE2\u20AC\u201CMICCAI 2015: 18th international conference, Munich,\
+    \ Germany, October 5-9, 2015, proceedings, part III 18. Springer International\
+    \ Publishing, 2015."
+- doi: https://doi.org/10.1038/s41592-018-0261-2
+  text: "Falk, T., Mai, D., Bensch, R. et al. U-Net: deep learning for cell counting,\
+    \ detection, and morphometry. Nat Methods 16, 67\xE2\u20AC\u201C70 (2019)."
+covers: []
+description: 2D binary segmentation. U-Net is an encoder-decoder architecture originally
+  used for image segmentation. The first half of the U-Net architecture is a downsampling
+  convolutional neural network which acts as a feature extractor from input images.
+  The other half upsamples these results and restores an image by combining results
+  from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki
+  to check the original publications this network is based on and make sure you cite
+  these.
+documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
+group: blue_team
+name: unet2d-zerocostdL4mic
+solution_creators:
+- DL4MicEverywhere team
+- album team
+tags:
+- unet
+- segmentation
+- colab
+- notebook
+- U-Net
+- ZeroCostDL4Mic
+- 2D
+- dl4miceverywhere
+timestamp: '2024-03-21T15:51:09.202091'
+title: U-Net 2D
+version: 0.1.0