monoarti/visualizer.py

from enum import Enum, unique
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg
import random
import colorsys
import pycocotools.mask as mask_util
import cv2


_SMALL_OBJECT_AREA_THRESH = 1000
_LARGE_MASK_AREA_THRESH = 120000
_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
_BLACK = (0, 0, 0)
_RED = (1.0, 0, 0)

_KEYPOINT_THRESHOLD = 0.05


# fmt: off
# RGB:
_COLORS = np.array(
    [
        0.000, 0.447, 0.741,
        0.850, 0.325, 0.098,
        0.929, 0.694, 0.125,
        0.494, 0.184, 0.556,
        0.466, 0.674, 0.188,
        0.301, 0.745, 0.933,
        0.635, 0.078, 0.184,
        0.300, 0.300, 0.300,
        0.600, 0.600, 0.600,
        1.000, 0.000, 0.000,
        1.000, 0.500, 0.000,
        0.749, 0.749, 0.000,
        0.000, 1.000, 0.000,
        0.000, 0.000, 1.000,
        0.667, 0.000, 1.000,
        0.333, 0.333, 0.000,
        0.333, 0.667, 0.000,
        0.333, 1.000, 0.000,
        0.667, 0.333, 0.000,
        0.667, 0.667, 0.000,
        0.667, 1.000, 0.000,
        1.000, 0.333, 0.000,
        1.000, 0.667, 0.000,
        1.000, 1.000, 0.000,
        0.000, 0.333, 0.500,
        0.000, 0.667, 0.500,
        0.000, 1.000, 0.500,
        0.333, 0.000, 0.500,
        0.333, 0.333, 0.500,
        0.333, 0.667, 0.500,
        0.333, 1.000, 0.500,
        0.667, 0.000, 0.500,
        0.667, 0.333, 0.500,
        0.667, 0.667, 0.500,
        0.667, 1.000, 0.500,
        1.000, 0.000, 0.500,
        1.000, 0.333, 0.500,
        1.000, 0.667, 0.500,
        1.000, 1.000, 0.500,
        0.000, 0.333, 1.000,
        0.000, 0.667, 1.000,
        0.000, 1.000, 1.000,
        0.333, 0.000, 1.000,
        0.333, 0.333, 1.000,
        0.333, 0.667, 1.000,
        0.333, 1.000, 1.000,
        0.667, 0.000, 1.000,
        0.667, 0.333, 1.000,
        0.667, 0.667, 1.000,
        0.667, 1.000, 1.000,
        1.000, 0.000, 1.000,
        1.000, 0.333, 1.000,
        1.000, 0.667, 1.000,
        0.333, 0.000, 0.000,
        0.500, 0.000, 0.000,
        0.667, 0.000, 0.000,
        0.833, 0.000, 0.000,
        1.000, 0.000, 0.000,
        0.000, 0.167, 0.000,
        0.000, 0.333, 0.000,
        0.000, 0.500, 0.000,
        0.000, 0.667, 0.000,
        0.000, 0.833, 0.000,
        0.000, 1.000, 0.000,
        0.000, 0.000, 0.167,
        0.000, 0.000, 0.333,
        0.000, 0.000, 0.500,
        0.000, 0.000, 0.667,
        0.000, 0.000, 0.833,
        0.000, 0.000, 1.000,
        0.000, 0.000, 0.000,
        0.143, 0.143, 0.143,
        0.857, 0.857, 0.857,
        1.000, 1.000, 1.000
    ]
).astype(np.float32).reshape(-1, 3)
# fmt: on

def random_colors(N, rgb=False, maximum=255):
    """
    Args:
        N (int): number of unique colors needed
        rgb (bool): whether to return RGB colors or BGR colors.
        maximum (int): either 255 or 1
    Returns:
        ndarray: a list of random_color
    """
    indices = random.sample(range(len(_COLORS)), N)
    ret = [_COLORS[i] * maximum for i in indices]
    if not rgb:
        ret = [x[::-1] for x in ret]
    return ret


@unique
class ColorMode(Enum):
    """
    Enum of different color modes to use for instance visualizations.
    """

    IMAGE = 0
    """
    Picks a random color for every instance and overlay segmentations with low opacity.
    """
    SEGMENTATION = 1
    """
    Let instances of the same category have similar colors
    (from metadata.thing_colors), and overlay them with
    high opacity. This provides more attention on the quality of segmentation.
    """
    IMAGE_BW = 2
    """
    Same as IMAGE, but convert all areas without masks to gray-scale.
    Only available for drawing per-instance mask predictions.
    """


class GenericMask:
    """
    Attribute:
        polygons (list[ndarray]): list[ndarray]: polygons for this mask.
            Each ndarray has format [x, y, x, y, ...]
        mask (ndarray): a binary mask
    """

    def __init__(self, mask_or_polygons, height, width):
        self._mask = self._polygons = self._has_holes = None
        self.height = height
        self.width = width

        m = mask_or_polygons
        if isinstance(m, dict):
            # RLEs
            assert "counts" in m and "size" in m
            if isinstance(m["counts"], list):  # uncompressed RLEs
                h, w = m["size"]
                assert h == height and w == width
                m = mask_util.frPyObjects(m, h, w)
            self._mask = mask_util.decode(m)[:, :]
            return

        if isinstance(m, list):  # list[ndarray]
            self._polygons = [np.asarray(x).reshape(-1) for x in m]
            return

        if isinstance(m, np.ndarray):  # assumed to be a binary mask
            assert m.shape[1] != 2, m.shape
            assert m.shape == (
                height,
                width,
            ), f"mask shape: {m.shape}, target dims: {height}, {width}"
            self._mask = m.astype("uint8")
            return

        raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))

    @property
    def mask(self):
        if self._mask is None:
            self._mask = self.polygons_to_mask(self._polygons)
        return self._mask

    @property
    def polygons(self):
        if self._polygons is None:
            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
        return self._polygons

    @property
    def has_holes(self):
        if self._has_holes is None:
            if self._mask is not None:
                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
            else:
                self._has_holes = False  # if original format is polygon, does not have holes
        return self._has_holes

    def mask_to_polygons(self, mask):
        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
        # Internal contours (holes) are placed in hierarchy-2.
        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
        mask = np.ascontiguousarray(mask)  # some versions of cv2 does not support incontiguous arr
        res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
        hierarchy = res[-1]
        if hierarchy is None:  # empty mask
            return [], False
        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
        res = res[-2]
        res = [x.flatten() for x in res]
        # These coordinates from OpenCV are integers in range [0, W-1 or H-1].
        # We add 0.5 to turn them into real-value coordinate space. A better solution
        # would be to first +0.5 and then dilate the returned polygon by 0.5.
        res = [x + 0.5 for x in res if len(x) >= 6]
        return res, has_holes

    def polygons_to_mask(self, polygons):
        rle = mask_util.frPyObjects(polygons, self.height, self.width)
        rle = mask_util.merge(rle)
        return mask_util.decode(rle)[:, :]

    def area(self):
        return self.mask.sum()

    def bbox(self):
        p = mask_util.frPyObjects(self.polygons, self.height, self.width)
        p = mask_util.merge(p)
        bbox = mask_util.toBbox(p)
        bbox[2] += bbox[0]
        bbox[3] += bbox[1]
        return bbox




class VisImage:
    def __init__(self, img, scale=1.0):
        """
        Args:
            img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
            scale (float): scale the input image
        """
        self.img = img
        self.scale = scale
        self.width, self.height = img.shape[1], img.shape[0]
        self._setup_figure(img)

    def _setup_figure(self, img):
        """
        Args:
            Same as in :meth:`__init__()`.
        Returns:
            fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
            ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
        """
        fig = mplfigure.Figure(frameon=False)
        self.dpi = fig.get_dpi()
        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation
        # (https://github.com/matplotlib/matplotlib/issues/15363)
        fig.set_size_inches(
            (self.width * self.scale + 1e-2) / self.dpi,
            (self.height * self.scale + 1e-2) / self.dpi,
        )
        self.canvas = FigureCanvasAgg(fig)
        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
        ax.axis("off")
        self.fig = fig
        self.ax = ax
        self.reset_image(img)

    def reset_image(self, img):
        """
        Args:
            img: same as in __init__
        """
        img = img.astype("uint8")
        self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")

    def save(self, filepath):
        """
        Args:
            filepath (str): a string that contains the absolute path, including the file name, where
                the visualized image will be saved.
        """
        self.fig.savefig(filepath)

    def get_image(self):
        """
        Returns:
            ndarray:
                the visualized image of shape (H, W, 3) (RGB) in uint8 type.
                The shape is scaled w.r.t the input image using the given `scale` argument.
        """
        canvas = self.canvas
        s, (width, height) = canvas.print_to_buffer()
        # buf = io.BytesIO()  # works for cairo backend
        # canvas.print_rgba(buf)
        # width, height = self.width, self.height
        # s = buf.getvalue()

        buffer = np.frombuffer(s, dtype="uint8")

        img_rgba = buffer.reshape(height, width, 4)
        rgb, alpha = np.split(img_rgba, [3], axis=2)
        return rgb.astype("uint8")


class Visualizer:
    def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
        """
        Monoarti Visualizer

        Args:
            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
                the height and width of the image respectively. C is the number of
                color channels. The image is required to be in RGB format since that
                is a requirement of the Matplotlib library. The image is also expected
                to be in the range [0, 255].
            metadata (Metadata): dataset metadata (e.g. class names and colors)
            instance_mode (ColorMode): defines one of the pre-defined style for drawing
                instances on an image.
        """
        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
        #if metadata is None:
        #    metadata = MetadataCatalog.get("__nonexist__")
        #self.metadata = metadata
        self.output = VisImage(self.img, scale=scale)
        #self.cpu_device = torch.device("cpu")

        # too small texts are useless, therefore clamp to 9
        self._default_font_size = max(
            np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
        )
        self._default_font_size = self._default_font_size * 2

        self._instance_mode = instance_mode
        self.keypoint_threshold = 0.05

    def overlay_instances(
        self,
        instances,
        assigned_colors=None,
        alpha=0.5,
    ):
        if assigned_colors is None:
            assigned_colors = random_colors(len(instances), maximum=1)

        for idx, bbox in enumerate(instances):
            is_fixture = bbox['move'] == 'fixture'

            if bbox['rigid'] == 'yes':
                if bbox['kinematic'] == 'freeform':
                    text = '_'.join([bbox['move'][:3] + 'hand', 'rigid', 'free'])
                elif bbox['kinematic'] == 'rotation':
                    text = '_'.join([bbox['move'][:3] + 'hand', 'rigid', 'rot', bbox['pull_or_push']])
                elif bbox['kinematic'] == 'translation':
                    text = '_'.join([bbox['move'][:3] + 'hand', 'rigid', 'trans', bbox['pull_or_push']])
                else:
                    raise ValueError
            elif bbox['rigid'] == 'no':
                text = '_'.join([bbox['move'][:3] + 'hand', 'nonrigid'])
            else:
                #text = 'object'
                text = ''

            if is_fixture:
                text = ''

            text_pos = None
            if bbox['bbox'] is not None and not is_fixture:
                text_box = bbox['bbox']
                x0, y0, x1, y1 = text_box
                text_pos = (x0, y0)
                instance_area = (y1 - y0) * (x1 - x0)
                if (
                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
                    or y1 - y0 < 40 * self.output.scale
                ):
                    if y1 >= self.output.height - 5:
                        text_pos = (x1, y0)
                    else:
                        text_pos = (x0, y1)

                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
                font_size = (
                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
                    * 0.5
                    * self._default_font_size
                )

                self.draw_box(bbox['bbox'], edge_color=assigned_colors[idx], alpha=alpha)

            if text_pos is None:
                text_pos = bbox['keypoint']

            if len(text) > 0:
                # adjust color
                font_size = self._default_font_size
                lighter_color = self._change_color_brightness(assigned_colors[idx], brightness_factor=0.7)
                self.draw_text(text, text_pos, color=lighter_color, horizontal_alignment='left', font_size=font_size)

            if bbox['keypoint'] is not None:
                self.draw_circle(bbox['keypoint'], color=assigned_colors[idx])
            
            if bbox['affordance'] is not None and not is_fixture:
                self.draw_regular_polygon(bbox['affordance'], color=assigned_colors[idx], num_vertices=3)

            if bbox['axis'][0] > -1e-3 and not is_fixture:
                try:                
                    line_x = [
                        int(bbox['axis'][0] * self.output.width),
                        int(bbox['axis'][2] * self.output.width),
                    ] 
                    line_y = [
                        int(bbox['axis'][1] * self.output.height),
                        int(bbox['axis'][3] * self.output.height),
                    ] 
                    self.draw_line(line_x, line_y, color=assigned_colors[idx])
                except OverflowError:
                    print("overflow {}".format(bbox['axis']))
                    pass
                #axis_colors = np.array([[31, 73, 125], ]) / 255.0
                #self.draw_arrow(line_x, line_y, color=axis_colors[idx])
                
            if bbox['mask'] is not None and not is_fixture:
                self.draw_binary_mask(bbox['mask'], color=assigned_colors[idx], edge_color=_OFF_WHITE, alpha=alpha)


        return self.output


    def overlay_annotations(
        self,
        bboxes,
        assigned_colors=None,
    ):
        if assigned_colors is None:
            assigned_colors = random_colors(len(bboxes), maximum=1)

        for idx, bbox in enumerate(bboxes):
            if bbox['rigid'] == 'yes':
                if bbox['kinematic'] == 'freeform':
                    text = '_'.join([bbox['move'][:3], 'rigid', 'free'])
                elif bbox['kinematic'] == 'rotation':
                    text = '_'.join([bbox['move'][:3], 'rigid', 'rot', bbox['pull_or_push']])
                elif bbox['kinematic'] == 'translation':
                    text = '_'.join([bbox['move'][:3], 'rigid', 'trans', bbox['pull_or_push']])
                else:
                    raise ValueError
            else:
                text = '_'.join([bbox['move'][:3], 'nonrigid'])

            text_box = bbox['bbox']
            x0, y0, x1, y1 = text_box
            text_pos = (x0, y0)
            instance_area = (y1 - y0) * (x1 - x0)
            #_SMALL_OBJECT_AREA_THRESH = 1000
            if (
                instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
                or y1 - y0 < 40 * self.output.scale
            ):
                if y1 >= self.output.height - 5:
                    text_pos = (x1, y0)
                else:
                    text_pos = (x0, y1)

            height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
            font_size = (
                np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
                * 0.5
                * self._default_font_size
            )

            # adjust color
            lighter_color = self._change_color_brightness(assigned_colors[idx], brightness_factor=0.7)
            self.draw_text(text, text_pos, color=lighter_color, horizontal_alignment='left', font_size=font_size)

            self.draw_box(bbox['bbox'], edge_color=assigned_colors[idx])

            self.draw_circle(bbox['keypoint'], color=assigned_colors[idx])
            self.draw_regular_polygon(bbox['affordance'], color=assigned_colors[idx], num_vertices=3)
            

            

        return self.output


    def overlay_instances_old(
        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 = 0
        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:
            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
        if num_instances == 0:
            return self.output
        if boxes is not None and boxes.shape[1] == 5:
            return self.overlay_rotated_instances(
                boxes=boxes, labels=labels, assigned_colors=assigned_colors
            )

        # Display in largest to smallest order to reduce occlusion.
        areas = None
        if boxes is not None:
            areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
        elif masks is not None:
            areas = np.asarray([x.area() for x in masks])

        if areas is not None:
            sorted_idxs = np.argsort(-areas).tolist()
            # Re-order overlapped instances in descending order.
            boxes = boxes[sorted_idxs] if boxes is not None else None
            labels = [labels[k] for k in sorted_idxs] if labels is not None else None
            masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
            assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
            keypoints = keypoints[sorted_idxs] if keypoints is not None else None

        for i in range(num_instances):
            color = assigned_colors[i]
            if boxes is not None:
                self.draw_box(boxes[i], edge_color=color)

            # if masks is not None:
            #     for segment in masks[i].polygons:
            #         self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)

            if labels is not None:
                # first get a box
                if boxes is not None:
                    x0, y0, x1, y1 = boxes[i]
                    text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.
                    horiz_align = "left"
                elif masks is not None:
                    # skip small mask without polygon
                    if len(masks[i].polygons) == 0:
                        continue

                    x0, y0, x1, y1 = masks[i].bbox()

                    # draw text in the center (defined by median) when box is not drawn
                    # median is less sensitive to outliers.
                    text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
                    horiz_align = "center"
                else:
                    continue  # drawing the box confidence for keypoints isn't very useful.
                # for small objects, draw text at the side to avoid occlusion
                instance_area = (y1 - y0) * (x1 - x0)
                if (
                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
                    or y1 - y0 < 40 * self.output.scale
                ):
                    if y1 >= self.output.height - 5:
                        text_pos = (x1, y0)
                    else:
                        text_pos = (x0, y1)

                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
                font_size = (
                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
                    * 0.5
                    * self._default_font_size
                )
                self.draw_text(
                    labels[i],
                    text_pos,
                    color=lighter_color,
                    horizontal_alignment=horiz_align,
                    font_size=font_size,
                )

        return self.output
    

    def draw_arrow(self, x_data, y_data, color, linestyle="-", linewidth=None):
        if linewidth is None:
            linewidth = self._default_font_size / 3
        linewidth = max(linewidth, 1)

        self.output.ax.arrow(
            x=x_data[0],
            y=y_data[0],
            dx=(x_data[1] - x_data[0]) * 1.0,
            dy=(y_data[1] - y_data[0]) * 1.0,
            width=linewidth * self.output.scale,
            head_width=linewidth * self.output.scale * 5.0,
            length_includes_head=True,
            color=color,
            overhang=0.5,
            linestyle=linestyle,
        )

        return self.output

    def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
        """
        Args:
            box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
                are the coordinates of the image's top left corner. x1 and y1 are the
                coordinates of the image's bottom right corner.
            alpha (float): blending efficient. Smaller values lead to more transparent masks.
            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
                for full list of formats that are accepted.
            line_style (string): the string to use to create the outline of the boxes.
        Returns:
            output (VisImage): image object with box drawn.
        """
        x0, y0, x1, y1 = box_coord
        width = x1 - x0
        height = y1 - y0

        linewidth = max(self._default_font_size / 4, 1)

        self.output.ax.add_patch(
            mpl.patches.Rectangle(
                (x0, y0),
                width,
                height,
                fill=False,
                edgecolor=edge_color,
                linewidth=linewidth * self.output.scale,
                alpha=alpha,
                linestyle=line_style,
            )
        )
        return self.output

    def draw_circle(self, circle_coord, color, radius=3):
        """
        Args:
            circle_coord (list(int) or tuple(int)): contains the x and y coordinates
                of the center of the circle.
            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
                formats that are accepted.
            radius (int): radius of the circle.
        Returns:
            output (VisImage): image object with box drawn.
        """
        x, y = circle_coord
        self.output.ax.add_patch(
            mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
        )
        return self.output

    def draw_regular_polygon(self, polygon_coord, color, radius=3, num_vertices=5):
        """
        Args:
            polygon_coord (list(int) or tuple(int)): contains the x and y coordinates
                of the center of the polygon.
            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
                formats that are accepted.
            radius (int): radius of the polygon.
            num_vertices (int): number of vertices of the polygon.
        Returns:
            output (VisImage): image object with box drawn.
        """
        self.output.ax.add_patch(
            mpl.patches.RegularPolygon(polygon_coord, num_vertices, radius=radius, fill=True, color=color)
        )
        return self.output

    def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
        """
        Args:
            segment: numpy array of shape Nx2, containing all the points in the polygon.
            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
                formats that are accepted.
            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
                full list of formats that are accepted. If not provided, a darker shade
                of the polygon color will be used instead.
            alpha (float): blending efficient. Smaller values lead to more transparent masks.

        Returns:
            output (VisImage): image object with polygon drawn.
        """
        if edge_color is None:
            # make edge color darker than the polygon color
            if alpha > 0.8:
                edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
            else:
                edge_color = color
        edge_color = mplc.to_rgb(edge_color) + (1,)

        polygon = mpl.patches.Polygon(
            segment,
            fill=True,
            facecolor=mplc.to_rgb(color) + (alpha,),
            edgecolor=edge_color,
            linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
        )
        self.output.ax.add_patch(polygon)
        return self.output

    def draw_text(
        self,
        text,
        position,
        *,
        font_size=None,
        color="g",
        horizontal_alignment="center",
        rotation=0,
    ):
        """
        Args:
            text (str): class label
            position (tuple): a tuple of the x and y coordinates to place text on image.
            font_size (int, optional): font of the text. If not provided, a font size
                proportional to the image width is calculated and used.
            color: color of the text. Refer to `matplotlib.colors` for full list
                of formats that are accepted.
            horizontal_alignment (str): see `matplotlib.text.Text`
            rotation: rotation angle in degrees CCW
        Returns:
            output (VisImage): image object with text drawn.
        """
        if not font_size:
            font_size = self._default_font_size

        # since the text background is dark, we don't want the text to be dark
        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
        color[np.argmax(color)] = max(0.8, np.max(color))

        x, y = position
        self.output.ax.text(
            x,
            y,
            text,
            size=font_size * self.output.scale,
            family="sans-serif",
            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
            verticalalignment="top",
            horizontalalignment=horizontal_alignment,
            color=color,
            zorder=10,
            rotation=rotation,
        )
        return self.output

    def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
        """
        Args:
            x_data (list[int]): a list containing x values of all the points being drawn.
                Length of list should match the length of y_data.
            y_data (list[int]): a list containing y values of all the points being drawn.
                Length of list should match the length of x_data.
            color: color of the line. Refer to `matplotlib.colors` for a full list of
                formats that are accepted.
            linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
                for a full list of formats that are accepted.
            linewidth (float or None): width of the line. When it's None,
                a default value will be computed and used.

        Returns:
            output (VisImage): image object with line drawn.
        """
        if linewidth is None:
            linewidth = self._default_font_size / 3
        linewidth = max(linewidth, 1)
        self.output.ax.add_line(
            mpl.lines.Line2D(
                x_data,
                y_data,
                linewidth=linewidth * self.output.scale,
                color=color,
                linestyle=linestyle,
            )
        )
        return self.output


    def draw_binary_mask(
        self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10
    ):
        """
        Args:
            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
                W is the image width. Each value in the array is either a 0 or 1 value of uint8
                type.
            color: color of the mask. Refer to `matplotlib.colors` for a full list of
                formats that are accepted. If None, will pick a random color.
            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
                full list of formats that are accepted.
            text (str): if None, will be drawn on the object
            alpha (float): blending efficient. Smaller values lead to more transparent masks.
            area_threshold (float): a connected component smaller than this area will not be shown.

        Returns:
            output (VisImage): image object with mask drawn.
        """
        if color is None:
            color = random_color(rgb=True, maximum=1)
        color = mplc.to_rgb(color)

        has_valid_segment = False
        binary_mask = binary_mask.astype("uint8")  # opencv needs uint8
        mask = GenericMask(binary_mask, self.output.height, self.output.width)
        shape2d = (binary_mask.shape[0], binary_mask.shape[1])

        if not mask.has_holes:
            # draw polygons for regular masks
            for segment in mask.polygons:
                area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
                if area < (area_threshold or 0):
                    continue
                has_valid_segment = True
                segment = segment.reshape(-1, 2)
                self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
        else:
            # TODO: Use Path/PathPatch to draw vector graphics:
            # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon
            rgba = np.zeros(shape2d + (4,), dtype="float32")
            rgba[:, :, :3] = color
            rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
            has_valid_segment = True
            self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))

        if text is not None and has_valid_segment:
            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
            self._draw_text_in_mask(binary_mask, text, lighter_color)
        return self.output


    """
    Internal methods
    """

    def _change_color_brightness(self, color, brightness_factor):
        """
        Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
        less or more saturation than the original color.
        Args:
            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
                formats that are accepted.
            brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
                0 will correspond to no change, a factor in [-1.0, 0) range will result in
                a darker color and a factor in (0, 1.0] range will result in a lighter color.
        Returns:
            modified_color (tuple[double]): a tuple containing the RGB values of the
                modified color. Each value in the tuple is in the [0.0, 1.0] range.
        """
        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
        color = mplc.to_rgb(color)
        polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
        modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
        modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
        modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
        modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
        return modified_color