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/src/cell_classification/prepare_data_script_tonic.py
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import os
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from cell_classification.segmentation_data_prep import SegmentationTFRecords




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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/TONIC/raw/segmentation_data/deepcell_output",










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        fname + "_feature_0.tif"










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    )










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data_prep = SegmentationTFRecords(




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    data_dir=os.path.normpath(










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        "E:/angelo_lab/data/TONIC/raw/image_data/samples"










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    ),










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    cell_table_path=os.path.normpath(










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        "E:/angelo_lab/data/TONIC/raw/" +










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        "combined_cell_table_normalized_cell_labels_updated.csv"










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    ),










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    conversion_matrix_path=os.path.normpath(










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        "E:/angelo_lab/data/TONIC/raw/TONIC_conversion_matrix.csv"










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    ),










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    imaging_platform="MIBI",










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    dataset="TONIC",










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    tissue_type="TNBC",










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    nuclei_channels=["H3K27me3", "H3K9ac"],










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    membrane_channels=["CD45", "ECAD", "CD14", "CD38", "CK17"],










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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/TONIC"),










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    # normalization_dict_path=os.path.normpath(










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










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    # ),










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    normalization_quantile=0.999,










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    cell_type_key="cell_meta_cluster",










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    sample_key="fov",










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    segmentation_fname="cell_segmentation",










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    segmentation_naming_convention=naming_convention,










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    segment_label_key="label",










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    exclude_background_tiles=True,










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)










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data_prep.make_tf_record()




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