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/src/cell_classification/prepare_data_script_MSK_colon.py
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import os
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from cell_classification.simple_data_prep import SimpleTFRecords




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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"




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def naming_convention(fname):




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    return os.path.join(




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        "E:/angelo_lab/data/MSKCC_colon/segmentation",










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        fname + "feature_0.ome.tif"










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    )










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data_prep = SimpleTFRecords(




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    data_dir=os.path.normpath(










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        "E:/angelo_lab/data/MSKCC_colon/raw_structured"










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    ),










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    cell_table_path=os.path.normpath(










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        "E:/angelo_lab/data/MSKCC_colon/cell_table.csv"










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    ),










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    imaging_platform="Vectra",










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    dataset="MSK_colon",










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    tissue_type="colon",










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    nuclei_channels=["DAPI"],










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    membrane_channels=["CD3", "CD8", "ICOS", "panCK+CK7+CAM5.2"],










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    tile_size=[256, 256],










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    stride=[240, 240],










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    tf_record_path=os.path.normpath("E:/angelo_lab/data/MSKCC_colon"),










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    normalization_quantile=0.999,










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    selected_markers=["CD3", "CD8", "Foxp3", "ICOS", "panCK+CK7+CAM5.2", "PD-L1"],










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    sample_key="fov",










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    segment_label_key="labels",










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    segmentation_naming_convention=naming_convention,










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    exclude_background_tiles=True,










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    img_suffix=".ome.tif",










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    # normalization_dict_path=os.path.normpath(










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    #     "E:/angelo_lab/data/MSKCC_colon/normalization_dict.json"










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    # ),










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)










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data_prep.make_tf_record()




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