Adding new/updated DL4MicEverywhere_fnet-3d-zerocostdl4mic_1.13.1

solutions/DL4MicEverywhere/fnet-3d-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).
+
+## [1.13.1] - 2024-10-15
+../CHANGELOG.md

solutions/DL4MicEverywhere/fnet-3d-zerocostdl4mic/solution.py

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+###album catalog: cellcanvas
+
+# Based on https://github.com/HenriquesLab/DL4MicEverywhere/blob/main/notebooks/ZeroCostDL4Mic_notebooks/fnet_3D_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/fnet_3D_ZeroCostDL4Mic.ipynb"
+
+    notebook_path = get_app_path().joinpath("fnet_3D_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. 6.3."
+    section_to_remove = section_to_remove.split(' ')
+
+    python_command = ["python", ".tools/notebook_autoconversion/transform.py", "-p", f"{get_app_path()}", "-n", "fnet_3D_ZeroCostDL4Mic.ipynb", "-s"]
+    python_command += section_to_remove
+
+    subprocess.run(python_command, cwd=to)
+    subprocess.run(["mv", get_app_path().joinpath("colabless_fnet_3D_ZeroCostDL4Mic.ipynb"), get_app_path().joinpath("fnet_3D_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("fnet_3D_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=3.7
+- cudatoolkit=11.8.0
+- cudnn=8.6.0
+- pip
+- pkg-config
+"""
+else:
+    channels = """
+- conda-forge
+- defaults
+"""
+    dependencies = f"""
+- python=3.7
+- pip
+- pkg-config
+"""
+
+env_file = f"""
+channels:
+{channels}
+dependencies:
+{dependencies}
+- pip:
+    - GitPython==3.1.43 
+    - matplotlib==2.2.3
+    - numpy==1.18.0
+    - pandas>=0.21.1
+    - tifffile==2019.7.26
+    - tqdm==4.19.5
+    - scikit-image==0.18.0
+    - argschema
+    - scipy==1.4.1
+    - torch==1.4.0
+    - astropy==3.2.3
+    - fpdf2==2.7.4
+"""
+
+setup(
+    group="DL4MicEverywhere",
+    name="fnet-3d-zerocostdl4mic",
+    version="1.13.1",
+    solution_creators=["DL4Mic team", "album team"],
+    title="fnet-3d-zerocostdl4mic implementation.",
+    description="Paired image-to-image translation of 3D images. Label-free Prediction (fnet) is a neural network used to infer the features of cellular structures from brightfield or EM images without coloured labels. The network is trained using paired training images from the same field of view, imaged in a label-free (e.g. brightfield) and labelled condition (e.g. fluorescent protein). When trained, this allows the user to identify certain structures from brightfield images alone. The performance of fnet may depend significantly on the structure at hand. 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', 'fnet', 'labelling', 'ZeroCostDL4Mic', '3D', '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'}, {'doi': 'https://doi.org/10.1038/s41592-018-0111-2', 'text': 'Ounkomol, C., Seshamani, S., Maleckar, M.M. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat Methods 15, 917–920 (2018). https://doi.org/10.1038/s41592-018-0111-2'}],
+    album_api_version="0.5.1",
+    covers=[],
+    run=run,
+    install=install,
+    dependencies={"environment_file": env_file},
+)

solutions/DL4MicEverywhere/fnet-3d-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
+- doi: https://doi.org/10.1038/s41592-018-0111-2
+  text: "Ounkomol, C., Seshamani, S., Maleckar, M.M. et al. Label-free prediction\
+    \ of three-dimensional fluorescence images from transmitted-light microscopy.\
+    \ Nat Methods 15, 917\u2013920 (2018). https://doi.org/10.1038/s41592-018-0111-2"
+covers: []
+description: Paired image-to-image translation of 3D images. Label-free Prediction
+  (fnet) is a neural network used to infer the features of cellular structures from
+  brightfield or EM images without coloured labels. The network is trained using paired
+  training images from the same field of view, imaged in a label-free (e.g. brightfield)
+  and labelled condition (e.g. fluorescent protein). When trained, this allows the
+  user to identify certain structures from brightfield images alone. The performance
+  of fnet may depend significantly on the structure at hand. 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: fnet-3d-zerocostdl4mic
+solution_creators:
+- DL4Mic team
+- album team
+tags:
+- colab
+- notebook
+- fnet
+- labelling
+- ZeroCostDL4Mic
+- 3D
+- dl4miceverywhere
+timestamp: '2024-10-15T17:52:01.297896'
+title: fnet-3d-zerocostdl4mic implementation.
+version: 1.13.1