def _cityscapes_files_to_dict(files, from_json, to_polygons):
    """
    Parse cityscapes annotation files to a instance segmentation dataset dict.

    Args:
        files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file)
        from_json (bool): whether to read annotations from the raw json file or the png files.
        to_polygons (bool): whether to represent the segmentation as polygons
            (COCO's format) instead of masks (cityscapes's format).

    Returns:
        A dict in Detectron2 Dataset format.
    """
    from cityscapesscripts.helpers.labels import id2label, name2label

    image_file, instance_id_file, _, json_file = files

    annos = []

    if from_json:
        from shapely.geometry import MultiPolygon, Polygon

        with PathManager.open(json_file, "r") as f:
            jsonobj = json.load(f)
        ret = {
            "file_name": image_file,
            "image_id": os.path.basename(image_file),
            "height": jsonobj["imgHeight"],
            "width": jsonobj["imgWidth"],
        }

        # `polygons_union` contains the union of all valid polygons.
        polygons_union = Polygon()

        # CityscapesScripts draw the polygons in sequential order
        # and each polygon *overwrites* existing ones. See
        # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa
        # We use reverse order, and each polygon *avoids* early ones.
        # This will resolve the ploygon overlaps in the same way as CityscapesScripts.
        for obj in jsonobj["objects"][::-1]:
            if "deleted" in obj:  # cityscapes data format specific
                continue
            label_name = obj["label"]

            try:
                label = name2label[label_name]
            except KeyError:
                if label_name.endswith("group"):  # crowd area
                    label = name2label[label_name[: -len("group")]]
                else:
                    raise
            if label.id < 0:  # cityscapes data format
                continue

            # Cityscapes's raw annotations uses integer coordinates
            # Therefore +0.5 here
            poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5
            # CityscapesScript uses PIL.ImageDraw.polygon to rasterize
            # polygons for evaluation. This function operates in integer space
            # and draws each pixel whose center falls into the polygon.
            # Therefore it draws a polygon which is 0.5 "fatter" in expectation.
            # We therefore dilate the input polygon by 0.5 as our input.
            poly = Polygon(poly_coord).buffer(0.5, resolution=4)

            if not label.hasInstances or label.ignoreInEval:
                # even if we won't store the polygon it still contributes to overlaps resolution
                polygons_union = polygons_union.union(poly)
                continue

            # Take non-overlapping part of the polygon
            poly_wo_overlaps = poly.difference(polygons_union)
            if poly_wo_overlaps.is_empty:
                continue
            polygons_union = polygons_union.union(poly)

            anno = {}
            anno["iscrowd"] = label_name.endswith("group")
            anno["category_id"] = label.id

            if isinstance(poly_wo_overlaps, Polygon):
                poly_list = [poly_wo_overlaps]
            elif isinstance(poly_wo_overlaps, MultiPolygon):
                poly_list = poly_wo_overlaps.geoms
            else:
                raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps))

            poly_coord = []
            for poly_el in poly_list:
                # COCO API can work only with exterior boundaries now, hence we store only them.
                # TODO: store both exterior and interior boundaries once other parts of the
                # codebase support holes in polygons.
                poly_coord.append(list(chain(*poly_el.exterior.coords)))
            anno["segmentation"] = poly_coord
            (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds

            anno["bbox"] = (xmin, ymin, xmax, ymax)
            anno["bbox_mode"] = BoxMode.XYXY_ABS

            annos.append(anno)
    else:
        # See also the official annotation parsing scripts at
        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py  # noqa
        with PathManager.open(instance_id_file, "rb") as f:
            inst_image = np.asarray(Image.open(f), order="F")
        # ids < 24 are stuff labels (filtering them first is about 5% faster)
        flattened_ids = np.unique(inst_image[inst_image >= 24])

        ret = {
            "file_name": image_file,
            "image_id": os.path.basename(image_file),
            "height": inst_image.shape[0],
            "width": inst_image.shape[1],
        }

        for instance_id in flattened_ids:
            # For non-crowd annotations, instance_id // 1000 is the label_id
            # Crowd annotations have <1000 instance ids
            label_id = instance_id // 1000 if instance_id >= 1000 else instance_id
            label = id2label[label_id]
            if not label.hasInstances or label.ignoreInEval:
                continue

            anno = {}
            anno["iscrowd"] = instance_id < 1000
            anno["category_id"] = label.id

            mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F")

            inds = np.nonzero(mask)
            ymin, ymax = inds[0].min(), inds[0].max()
            xmin, xmax = inds[1].min(), inds[1].max()
            anno["bbox"] = (xmin, ymin, xmax, ymax)
            if xmax <= xmin or ymax <= ymin:
                continue
            anno["bbox_mode"] = BoxMode.XYXY_ABS
            if to_polygons:
                # This conversion comes from D4809743 and D5171122,
                # when Mask-RCNN was first developed.
                contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[
                    -2
                ]
                polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3]
                # opencv's can produce invalid polygons
                if len(polygons) == 0:
                    continue
                anno["segmentation"] = polygons
            else:
                anno["segmentation"] = mask_util.encode(mask[:, :, None])[0]
            annos.append(anno)
    ret["annotations"] = annos
    return ret

    Parse cityscapes annotation files to a instance segmentation dataset dict.

    Args:
        files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file)
        from_json (bool): whether to read annotations from the raw json file or the png files.
        to_polygons (bool): whether to represent the segmentation as polygons
            (COCO's format) instead of masks (cityscapes's format).

    Returns:
        A dict in Detectron2 Dataset format.
    
video_chat_text/video_chat_with_ChatGPT/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py