Method __init__

utils/visualizer.py:160–194

(self, panoptic_seg, segments_info, metadata=None)

    """

    def __init__(self, panoptic_seg, segments_info, metadata=None):
        if segments_info is None:
            assert metadata is not None
            # If "segments_info" is None, we assume "panoptic_img" is a
            # H*W int32 image storing the panoptic_id in the format of
            # category_id * label_divisor + instance_id. We reserve -1 for
            # VOID label.
            label_divisor = metadata.label_divisor
            segments_info = []
            for panoptic_label in np.unique(panoptic_seg.numpy()):
                if panoptic_label == -1:
                    # VOID region.
                    continue
                pred_class = panoptic_label // label_divisor
                isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
                segments_info.append(
                    {
                        "id": int(panoptic_label),
                        "category_id": int(pred_class),
                        "isthing": bool(isthing),
                    }
                )
        del metadata

        self._seg = panoptic_seg

        self._sinfo = {s["id"]: s for s in segments_info}  # seg id -> seg info
        segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
        areas = areas.numpy()
        sorted_idxs = np.argsort(-areas)
        self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
        self._seg_ids = self._seg_ids.tolist()
        for sid, area in zip(self._seg_ids, self._seg_areas):
            if sid in self._sinfo:
                self._sinfo[sid]["area"] = float(area)

    def non_empty_mask(self):
        """