angelolab / cell_classification / 6002459992

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/src/cell_classification/prepare_data_script_decidua.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/decidua/segmentation_data/",










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        fname + "_segmentation_labels.tiff",










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    )










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data_prep = SegmentationTFRecords(




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    data_dir=os.path.normpath("E:/angelo_lab/data/decidua/image_data"),










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    cell_table_path=os.path.normpath(










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        "E:/angelo_lab/data/decidua/"










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        "Supplementary_table_3_single_cells_updated.csv"










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    ),










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    conversion_matrix_path=os.path.normpath(










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        "E:/angelo_lab/data/decidua/conversion_matrix.csv"










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    ),










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    imaging_platform="MIBI",










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    dataset="decidua_erin",










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    tissue_type="decidua",










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    nuclei_channels=["H3"],










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    membrane_channels=["VIM", "HLAG", "CD3", "CD14", "CD56"],










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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/decidua"),










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    normalization_dict_path=os.path.normpath(










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        "E:/angelo_lab/data/decidua/normalization_dict.json"










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    ),










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    selected_markers=[










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        "CD45", "CD14", "HLADR", "CD11c", "DCSIGN", "CD68", "CD206", "CD163", "CD3", "Ki67", "IDO",










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        "CD8", "CD4", "CD16", "CD56", "CD57", "SMA", "VIM", "CD31", "CK7", "HLAG", "FoxP3", "PDL1",










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    ],










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    normalization_quantile=0.999,










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    cell_type_key="lineage",










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    sample_key="Point",










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    segmentation_naming_convention=naming_convention,










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    segment_label_key="cell_ID_in_Point",










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    exclude_background_tiles=True,










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    img_suffix=".tif",










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)










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data_prep.make_tf_record()




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