def draw_dataset_dict(self, dic):
        """
        Draw annotations/segmentaions in Detectron2 Dataset format.
        Args:
            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
        Returns:
            output (VisImage): image object with visualizations.
        """
        annos = dic.get("annotations", None)
        if annos:
            if "segmentation" in annos[0]:
                masks = [x["segmentation"] for x in annos]
            else:
                masks = None
            if "keypoints" in annos[0]:
                keypts = [x["keypoints"] for x in annos]
                keypts = np.array(keypts).reshape(len(annos), -1, 3)
            else:
                keypts = None

            boxes = [
                BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
                if len(x["bbox"]) == 4
                else x["bbox"]
                for x in annos
            ]

            colors = None
            category_ids = [x["category_id"] for x in annos]
            if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"):
                colors = [
                    self._jitter([x / 255 for x in self.metadata.stuff_colors[c]])
                    for c in category_ids
                ]
            names = self.metadata.get("stuff_classes", None)
            labels = _create_text_labels(
                category_ids,
                scores=None,
                class_names=names,
                is_crowd=[x.get("iscrowd", 0) for x in annos],
            )
            self.overlay_instances(
                labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
            )

        sem_seg = dic.get("sem_seg", None)
        if sem_seg is None and "sem_seg_file_name" in dic:
            with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
                sem_seg = Image.open(f)
                sem_seg = np.asarray(sem_seg, dtype="uint8")
        if sem_seg is not None:
            self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)

        pan_seg = dic.get("pan_seg", None)
        # if pan_seg is None and "pan_seg_file_name" in dic:
        #     with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
        #         pan_seg = Image.open(f)
        #         pan_seg = np.asarray(pan_seg)
        #         from panopticapi.utils import rgb2id
        #
        #         pan_seg = rgb2id(pan_seg)
        if pan_seg is not None:
            segments_info = dic["segments_info"]
            pan_seg = torch.tensor(pan_seg)
            self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
        return self.output

        Draw annotations/segmentaions in Detectron2 Dataset format.
        Args:
            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
        Returns:
            output (VisImage): image object with visualizations.
        
ControlNet-v1-1-nightly/annotator/oneformer/oneformer/demo/visualizer.py