Class PolygonMasks

detectron2/structures/masks.py:239–438

This class stores the segmentation masks for all objects in one image, in the form of polygons. Attributes: polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.

class PolygonMasks:
    """
    This class stores the segmentation masks for all objects in one image, in the form of polygons.

    Attributes:
        polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.
    """

    def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]):
        """
        Arguments:
            polygons (list[list[np.ndarray]]): The first
                level of the list correspond to individual instances,
                the second level to all the polygons that compose the
                instance, and the third level to the polygon coordinates.
                The third level array should have the format of
                [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
        """
        assert isinstance(polygons, list), (
            "Cannot create PolygonMasks: Expect a list of list of polygons per image. "
            "Got '{}' instead.".format(type(polygons))
        )

        def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray:
            # Use float64 for higher precision, because why not?
            # Always put polygons on CPU (self.to is a no-op) since they
            # are supposed to be small tensors.
            # May need to change this assumption if GPU placement becomes useful
            if isinstance(t, torch.Tensor):
                t = t.cpu().numpy()
            return np.asarray(t).astype("float64")

        def process_polygons(
            polygons_per_instance: List[Union[torch.Tensor, np.ndarray]]
        ) -> List[np.ndarray]:
            assert isinstance(polygons_per_instance, list), (
                "Cannot create polygons: Expect a list of polygons per instance. "
                "Got '{}' instead.".format(type(polygons_per_instance))
            )
            # transform the polygon to a tensor
            polygons_per_instance = [_make_array(p) for p in polygons_per_instance]
            for polygon in polygons_per_instance:
                assert len(polygon) % 2 == 0 and len(polygon) >= 6
            return polygons_per_instance

        self.polygons: List[List[np.ndarray]] = [
            process_polygons(polygons_per_instance) for polygons_per_instance in polygons
        ]

    def to(self, *args: Any, **kwargs: Any) -> "PolygonMasks":
        return self

    @property
    def device(self) -> torch.device:
        return torch.device("cpu")

    def get_bounding_boxes(self) -> Boxes:
        """