Method overlay_instances

detectron2/utils/visualizer.py:577–718

Args: boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image, or a :class:`RotatedBoxes`, or an Nx5 numpy array of (x_center, y_center, width, h

(
        self,
        *,
        boxes=None,
        labels=None,
        masks=None,
        keypoints=None,
        assigned_colors=None,
        alpha=0.5
    )

        return self.output

    def overlay_instances(
        self,
        *,
        boxes=None,
        labels=None,
        masks=None,
        keypoints=None,
        assigned_colors=None,
        alpha=0.5
    ):
        """
        Args:
            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
                or a :class:`RotatedBoxes`,
                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
                for the N objects in a single image,
            labels (list[str]): the text to be displayed for each instance.
            masks (masks-like object): Supported types are:

                * :class:`detectron2.structures.PolygonMasks`,
                  :class:`detectron2.structures.BitMasks`.
                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.
                  The first level of the list corresponds to individual instances. The second
                  level to all the polygon that compose the instance, and the third level
                  to the polygon coordinates. The third level should have the format of
                  [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
                * list[ndarray]: each ndarray is a binary mask of shape (H, W).
                * list[dict]: each dict is a COCO-style RLE.
            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
                where the N is the number of instances and K is the number of keypoints.
                The last dimension corresponds to (x, y, visibility or score).
            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
                for full list of formats that the colors are accepted in.

        Returns:
            output (VisImage): image object with visualizations.
        """
        num_instances = None
        if boxes is not None:
            boxes = self._convert_boxes(boxes)
            num_instances = len(boxes)
        if masks is not None:
            masks = self._convert_masks(masks)
            if num_instances:
                assert len(masks) == num_instances
            else:
                num_instances = len(masks)
        if keypoints is not None:
            if num_instances:
                assert len(keypoints) == num_instances
            else:
                num_instances = len(keypoints)
            keypoints = self._convert_keypoints(keypoints)
        if labels is not None:
            assert len(labels) == num_instances
        if assigned_colors is None: