Adding new/updated DL4MicEverywhere_u-net-2d-multilabel-zerocostdl4mi…

…c_2.1.2

solutions/DL4MicEverywhere/u-net-2d-multilabel-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).
+
+## [2.1.2] - 2024-10-15
+../CHANGELOG.md

solutions/DL4MicEverywhere/u-net-2d-multilabel-zerocostdl4mic/solution.py

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+###album catalog: cellcanvas
+
+# Based on https://github.com/HenriquesLab/DL4MicEverywhere/blob/main/notebooks/ZeroCostDL4Mic_notebooks/U-Net_2D_Multilabel_DL4Mic/configuration.yaml
+# and https://github.com/betaseg/solutions/blob/main/solutions/io.github.betaseg/cellsketch-plot/solution.py
+
+from album.runner.api import setup
+import subprocess
+
+try:
+    subprocess.check_output('nvidia-smi')
+    gpu_access = True
+except Exception: 
+    gpu_access = False
+
+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"
+    repo_path = get_app_path().joinpath("DL4MicEverywhere")
+    Repo.clone_from(clone_url, repo_path)
+    assert (repo_path.exists())
+
+    # URL of the notebook you want to download
+    notebook_url = "https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb"
+
+    notebook_path = get_app_path().joinpath("U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb")
+    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
+    section_to_remove = "1.1. 1.2. 2. 6.2."
+    section_to_remove = section_to_remove.split(' ')
+
+    python_command = ["python", ".tools/notebook_autoconversion/transform.py", "-p", f"{get_app_path()}", "-n", "U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb", "-s"]
+    python_command += section_to_remove
+
+    subprocess.run(python_command, cwd=to)
+    subprocess.run(["mv", get_app_path().joinpath("colabless_U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb"), get_app_path().joinpath("U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb")])
+
+    # Remove the cloned DL4MicEverywhere repository
+    if os.name == 'nt':
+        os.system(f'rmdir /s /q "{to}"')
+    else:
+        # rmtree has no permission to do this on Windows
+        shutil.rmtree(to) 
+
+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_Multilabel_ZeroCostDL4Mic.ipynb")
+
+    # Ensure the notebook exists
+    assert notebook_path.exists(), "Notebook 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
+    global gpu_access
+    if gpu_access:
+        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))
+
+if gpu_access:
+    channels = """
+- conda-forge
+- nvidia
+- anaconda
+- defaults
+"""
+    dependencies = """
+- python=3.10
+- cudatoolkit=11.8.0
+- cudnn=8.6.0
+- pip
+- pkg-config
+"""
+else:
+    channels = """
+- conda-forge
+- defaults
+"""
+    dependencies = f"""
+- python=3.10
+- pip
+- pkg-config
+"""
+
+env_file = f"""
+channels:
+{channels}
+dependencies:
+{dependencies}
+- pip:
+    - GitPython==3.1.43 
+    - 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
+    - h5py==3.10.0
+    - ipywidgets==8.0.7
+    - 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==2.27.1
+    - 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
+"""
+
+setup(
+    group="DL4MicEverywhere",
+    name="u-net-2d-multilabel-zerocostdl4mic",
+    version="2.1.2",
+    solution_creators=["DL4Mic team", "album team"],
+    title="u-net-2d-multilabel-zerocostdl4mic implementation.",
+    description="2D semantic 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",
+    tags=['colab', 'notebook', 'u-net', 'segmentation', 'semantic-segmentation', 'multilabel', 'ZeroCostDL4Mic', '2D', 'dl4miceverywhere'],
+    args=[{
+        "name": "path",
+        "type": "string",
+        "default": ".",
+        "description": "What is your working path?"
+    }],
+    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). https://doi.org/10.1038/s41467-021-22518-0'}, {'text': 'Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597', 'url': 'https://arxiv.org/abs/1505.04597'}],
+    album_api_version="0.5.1",
+    covers=[],
+    run=run,
+    install=install,
+    dependencies={"environment_file": env_file},
+)

solutions/DL4MicEverywhere/u-net-2d-multilabel-zerocostdl4mic/solution.yml

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+album_api_version: 0.5.1
+args:
+- default: .
+  description: What is your working path?
+  name: path
+  type: string
+changelog: ../CHANGELOG.md
+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). https://doi.org/10.1038/s41467-021-22518-0
+- text: 'Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks
+    for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597'
+  url: https://arxiv.org/abs/1505.04597
+covers: []
+description: 2D semantic 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: DL4MicEverywhere
+name: u-net-2d-multilabel-zerocostdl4mic
+solution_creators:
+- DL4Mic team
+- album team
+tags:
+- colab
+- notebook
+- u-net
+- segmentation
+- semantic-segmentation
+- multilabel
+- ZeroCostDL4Mic
+- 2D
+- dl4miceverywhere
+timestamp: '2024-10-15T17:50:33.770193'
+title: u-net-2d-multilabel-zerocostdl4mic implementation.
+version: 2.1.2