datasets/prepare_ade20k_pan_seg.py [426:449]:
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                segment_id, color = id_generator.get_id_and_color(semantic_cat_id)
                pan_seg[mask] = color

                area = np.sum(mask)  # segment area computation
                # bbox computation for a segment
                hor = np.sum(mask, axis=0)
                hor_idx = np.nonzero(hor)[0]
                x = hor_idx[0]
                width = hor_idx[-1] - x + 1
                vert = np.sum(mask, axis=1)
                vert_idx = np.nonzero(vert)[0]
                y = vert_idx[0]
                height = vert_idx[-1] - y + 1
                bbox = [int(x), int(y), int(width), int(height)]

                segm_info.append(
                    {
                        "id": int(segment_id),
                        "category_id": int(semantic_cat_id),
                        "area": int(area),
                        "bbox": bbox,
                        "iscrowd": 0,
                    }
                )
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datasets/prepare_ade20k_pan_seg.py [461:484]:
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                segment_id, color = id_generator.get_id_and_color(semantic_cat_id)
                pan_seg[mask] = color

                area = np.sum(mask)  # segment area computation
                # bbox computation for a segment
                hor = np.sum(mask, axis=0)
                hor_idx = np.nonzero(hor)[0]
                x = hor_idx[0]
                width = hor_idx[-1] - x + 1
                vert = np.sum(mask, axis=1)
                vert_idx = np.nonzero(vert)[0]
                y = vert_idx[0]
                height = vert_idx[-1] - y + 1
                bbox = [int(x), int(y), int(width), int(height)]

                segm_info.append(
                    {
                        "id": int(segment_id),
                        "category_id": int(semantic_cat_id),
                        "area": int(area),
                        "bbox": bbox,
                        "iscrowd": 0,
                    }
                )
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