FasterRCNNDetection/data/coco_camera_traps_dataset.py [18:42]:
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def load_taxonomy(ann_data, tax_levels, classes):
    """ Loads taxonomy data and converts to ints """

    taxonomy = {}

    if 'categories' in ann_data.keys():
        num_classes = len(ann_data['categories'])
        for tt in tax_levels:
            tax_data = [aa[tt] for aa in ann_data['categories']]
            _, tax_id = np.unique(tax_data, return_inverse=True)
            taxonomy[tt] = dict(zip(range(num_classes), list(tax_id)))
    else:
        # set up dummy data
        for tt in tax_levels:
            taxonomy[tt] = dict(zip([0], [0]))

    # create a dictionary of lists containing taxonomic labels
    classes_taxonomic = {}
    for cc in classes:
        tax_ids = [0]*len(tax_levels)
        for ii, tt in enumerate(tax_levels):
            tax_ids[ii] = taxonomy[tt][cc]
        classes_taxonomic[cc] = tax_ids

    return taxonomy, classes_taxonomic
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FasterRCNNDetection/data/iwildcam_dataset.py [18:42]:
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def load_taxonomy(ann_data, tax_levels, classes):

    # loads the taxonomy data and converts to ints
    taxonomy = {}

    if 'categories' in ann_data.keys():
        num_classes = len(ann_data['categories'])
        for tt in tax_levels:
            tax_data = [aa[tt] for aa in ann_data['categories']]
            _, tax_id = np.unique(tax_data, return_inverse=True)
            taxonomy[tt] = dict(zip(range(num_classes), list(tax_id)))
    else:
        # set up dummy data
        for tt in tax_levels:
            taxonomy[tt] = dict(zip([0], [0]))

    # create a dictionary of lists containing taxonomic labels
    classes_taxonomic = {}
    for cc in classes:
        tax_ids = [0]*len(tax_levels)
        for ii, tt in enumerate(tax_levels):
            tax_ids[ii] = taxonomy[tt][cc]
        classes_taxonomic[cc] = tax_ids

    return taxonomy, classes_taxonomic
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