s_multimae/visualizer.py

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

_LARGE_MASK_AREA_THRESH = 120000
_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)
)


def random_color(rgb=False, maximum=255):
    """
    Args:
        rgb (bool): whether to return RGB colors or BGR colors.
        maximum (int): either 255 or 1

    Returns:
        ndarray: a vector of 3 numbers
    """
    idx = np.random.randint(0, len(_COLORS))
    ret = _COLORS[idx] * maximum
    if not rgb:
        ret = ret[::-1]
    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 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 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 Visualizer:
    """
    Visualizer that draws data about detection/segmentation on images.

    It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
    that draw primitive objects to images, as well as high-level wrappers like
    `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
    that draw composite data in some pre-defined style.

    Note that the exact visualization style for the high-level wrappers are subject to change.
    Style such as color, opacity, label contents, visibility of labels, or even the visibility
    of objects themselves (e.g. when the object is too small) may change according
    to different heuristics, as long as the results still look visually reasonable.

    To obtain a consistent style, you can implement custom drawing functions with the
    abovementioned primitive methods instead. If you need more customized visualization
    styles, you can process the data yourself following their format documented in
    tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
    intend to satisfy everyone's preference on drawing styles.

    This visualizer focuses on high rendering quality rather than performance. It is not
    designed to be used for real-time applications.
    """

    # TODO implement a fast, rasterized version using OpenCV

    def __init__(
        self,
        img_rgb: Union[Image.Image, np.ndarray],
        scale=1.0,
        instance_mode=ColorMode.IMAGE,
    ):
        """
        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].
            instance_mode (ColorMode): defines one of the pre-defined style for drawing
                instances on an image.
        """
        if type(img_rgb) == np.ndarray:
            img_rgb = img_rgb[:, :, ::-1]
        else:
            img_rgb = np.array(img_rgb)[:, :, ::-1]
        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
        self.output = VisImage(self.img, scale=scale)

        # 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._instance_mode = instance_mode

    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

    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

    """
    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

    def _draw_text_in_mask(self, binary_mask, text, color):
        """
        Find proper places to draw text given a binary mask.
        """
        # TODO sometimes drawn on wrong objects. the heuristics here can improve.
        _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(
            binary_mask, 8
        )
        if stats[1:, -1].size == 0:
            return
        largest_component_id = np.argmax(stats[1:, -1]) + 1

        # draw text on the largest component, as well as other very large components.
        for cid in range(1, _num_cc):
            if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
                # median is more stable than centroid
                # center = centroids[largest_component_id]
                center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
                self.draw_text(text, center, color=color)

    def get_output(self):
        """
        Returns:
            output (VisImage): the image output containing the visualizations added
            to the image.
        """
        return self.output


def apply_threshold(pred: np.ndarray) -> np.ndarray:
    """Apply threshold to a salient map

    Args:
        pred (np.ndarray): each pixel is in range [0, 255]

    Returns:
        np.ndarray: each pixel is only 0.0 or 1.0
    """
    binary_mask = pred / 255.0
    binary_mask[binary_mask >= 0.5] = 1.0
    binary_mask[binary_mask < 0.5] = 0.0
    return binary_mask


def normalize(data: np.ndarray) -> np.ndarray:
    return (data - data.min()) / (data.max() - data.min() + 1e-8)


def post_processing_depth(depth: np.ndarray) -> np.ndarray:
    depth = (normalize(depth) * 255).astype(np.uint8)
    return cv2.applyColorMap(depth, cv2.COLORMAP_OCEAN)


def apply_vis_to_image(
    rgb: np.ndarray, binary_mask: np.ndarray, color: np.ndarray
) -> np.ndarray:
    if rgb.shape[:2] != binary_mask.shape[:2]:
        print(rgb.shape, binary_mask.shape)
        binary_mask = cv2.resize(binary_mask, [rgb.shape[1], rgb.shape[0]])
    visualizer = Visualizer(rgb)
    vis_image: VisImage = visualizer.draw_binary_mask(binary_mask, color)
    vis_image = vis_image.get_image()[:, :, ::-1]
    return vis_image