def match_distribute()

in evaluation/tiny_benchmark/maskrcnn_benchmark/data/transforms/scale_match.py [0:0]

• 41 lines of code
• 21 McCabe index (conditional complexity)

    def match_distribute(src_annotations, dst_distri_cumsum, use_size_in_image=True):
        def get_json_sizes(annos):
            annos = [anno for anno in annos if not anno['iscrowd']]
            if len(annos) > 0 and 'ignore' in annos[0]:
                annos = [anno for anno in annos if not anno['ignore']]
            sizes = np.sqrt(np.array([anno['bbox'][2] * anno['bbox'][3] for anno in annos]))
            sizes = sizes[sizes > 0]
            return sizes

        def get_im2annos(annotations):
            im2annos = {}
            for anno in annotations:
                iid = anno['image_id']
                if iid in im2annos:
                    im2annos[iid].append(anno)
                else:
                    im2annos[iid] = [anno]
            return im2annos

        def get_json_sizes_in_image(annotations):
            im2annos = get_im2annos(annotations)
            _sizes = []
            for iid, annos in im2annos.items():
                annos = [anno for anno in annos if not anno['iscrowd']]
                if len(annos) > 0 and 'ignore' in annos[0]:
                    annos = [anno for anno in annos if not anno['ignore']]
                sizes = np.sqrt(np.array([anno['bbox'][2] * anno['bbox'][3] for anno in annos]))
                sizes = sizes[sizes > 0]
                size = np.exp(np.log(sizes).mean())
                _sizes.append(size)
            return _sizes
        if use_size_in_image:
            sizes = get_json_sizes_in_image(src_annotations)
        else:
            sizes = get_json_sizes(src_annotations)
        sizes = np.sort(sizes)
        N = len(sizes)
        src_sizes = [sizes[0]]
        for p_sum in dst_distri_cumsum:
            src_sizes.append(sizes[min(int(p_sum * N), N-1)])
        if src_sizes[-1] < sizes[-1]:
            src_sizes[-1] = sizes[-1]
        return np.array(src_sizes)