external/engine/mmengine/visualization/visualizer.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import inspect
import os.path as osp
import warnings
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union

if TYPE_CHECKING:
    from matplotlib.font_manager import FontProperties

import cv2
import numpy as np
import torch
import torch.nn.functional as F

from mmengine.config import Config
from mmengine.dist import master_only
from mmengine.registry import VISBACKENDS, VISUALIZERS
from mmengine.structures import BaseDataElement
from mmengine.utils import ManagerMixin, is_seq_of
from mmengine.visualization.utils import (check_type, check_type_and_length,
                                          color_str2rgb, color_val_matplotlib,
                                          convert_overlay_heatmap,
                                          img_from_canvas, tensor2ndarray,
                                          value2list, wait_continue)
from mmengine.visualization.vis_backend import BaseVisBackend

VisBackendsType = Union[List[Union[List, BaseDataElement]], BaseDataElement,
                        dict, None]


@VISUALIZERS.register_module()
class Visualizer(ManagerMixin):
    """MMEngine provides a Visualizer class that uses the ``Matplotlib``
    library as the backend. It has the following functions:

    - Basic drawing methods

      - draw_bboxes: draw single or multiple bounding boxes
      - draw_texts: draw single or multiple text boxes
      - draw_points: draw single or multiple points
      - draw_lines: draw single or multiple line segments
      - draw_circles: draw single or multiple circles
      - draw_polygons: draw single or multiple polygons
      - draw_binary_masks: draw single or multiple binary masks
      - draw_featmap: draw feature map

    - Basic visualizer backend methods

      - add_configs: write config to all vis storage backends
      - add_graph: write model graph to all vis storage backends
      - add_image: write image to all vis storage backends
      - add_scalar: write scalar to all vis storage backends
      - add_scalars: write scalars to all vis storage backends
      - add_datasample: write datasample to all vis storage \
         backends. The abstract drawing interface used by the user

    - Basic info methods

      - set_image: sets the original image data
      - get_image: get the image data in Numpy format after drawing
      - show: visualization
      - close: close all resources that have been opened
      - get_backend: get the specified vis backend


    All the basic drawing methods support chain calls, which is convenient for
    overlaydrawing and display. Each downstream algorithm library can inherit
    ``Visualizer`` and implement the add_datasample logic. For example,
    ``DetLocalVisualizer`` in MMDetection inherits from ``Visualizer``
    and implements functions, such as visual detection boxes, instance masks,
    and semantic segmentation maps in the add_datasample interface.

    Args:
        name (str): Name of the instance. Defaults to 'visualizer'.
        image (np.ndarray, optional): the origin image to draw. The format
            should be RGB. Defaults to None.
        vis_backends (list, optional): Visual backend config list.
            Defaults to None.
        save_dir (str, optional): Save file dir for all storage backends.
            If it is None, the backend storage will not save any data.
        fig_save_cfg (dict): Keyword parameters of figure for saving.
            Defaults to empty dict.
        fig_show_cfg (dict): Keyword parameters of figure for showing.
            Defaults to empty dict.

    Examples:
        >>> # Basic info methods
        >>> vis = Visualizer()
        >>> vis.set_image(image)
        >>> vis.get_image()
        >>> vis.show()

        >>> # Basic drawing methods
        >>> vis = Visualizer(image=image)
        >>> vis.draw_bboxes(np.array([0, 0, 1, 1]), edge_colors='g')
        >>> vis.draw_bboxes(bbox=np.array([[1, 1, 2, 2], [2, 2, 3, 3]]),
        >>>                    edge_colors=['g', 'r'])
        >>> vis.draw_lines(x_datas=np.array([1, 3]),
        >>>                y_datas=np.array([1, 3]),
        >>>                colors='r', line_widths=1)
        >>> vis.draw_lines(x_datas=np.array([[1, 3], [2, 4]]),
        >>>                y_datas=np.array([[1, 3], [2, 4]]),
        >>>                colors=['r', 'r'], line_widths=[1, 2])
        >>> vis.draw_texts(text='MMEngine',
        >>>               position=np.array([2, 2]),
        >>>               colors='b')
        >>> vis.draw_texts(text=['MMEngine','OpenMMLab'],
        >>>                position=np.array([[2, 2], [5, 5]]),
        >>>                colors=['b', 'b'])
        >>> vis.draw_circles(circle_coord=np.array([2, 2]), radius=np.array[1])
        >>> vis.draw_circles(circle_coord=np.array([[2, 2], [3, 5]),
        >>>                  radius=np.array[1, 2], colors=['g', 'r'])
        >>> square = np.array([[0, 0], [100, 0], [100, 100], [0, 100]])
        >>> vis.draw_polygons(polygons=square, edge_colors='g')
        >>> squares = [np.array([[0, 0], [100, 0], [100, 100], [0, 100]]),
        >>>            np.array([[0, 0], [50, 0], [50, 50], [0, 50]])]
        >>> vis.draw_polygons(polygons=squares, edge_colors=['g', 'r'])
        >>> vis.draw_binary_masks(binary_mask, alpha=0.6)
        >>> heatmap = vis.draw_featmap(featmap, img,
        >>>                            channel_reduction='select_max')
        >>> heatmap = vis.draw_featmap(featmap, img, channel_reduction=None,
        >>>                            topk=8, arrangement=(4, 2))
        >>> heatmap = vis.draw_featmap(featmap, img, channel_reduction=None,
        >>>                            topk=-1)

        >>> # chain calls
        >>> vis.draw_bboxes().draw_texts().draw_circle().draw_binary_masks()

        >>> # Backend related methods
        >>> vis = Visualizer(vis_backends=[dict(type='LocalVisBackend')],
        >>>                                save_dir='temp_dir')
        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
        >>> vis.add_config(cfg)
        >>> image=np.random.randint(0, 256, size=(10, 10, 3)).astype(np.uint8)
        >>> vis.add_image('image',image)
        >>> vis.add_scaler('mAP', 0.6)
        >>> vis.add_scalars({'loss': 0.1,'acc':0.8})

        >>> # inherit
        >>> class DetLocalVisualizer(Visualizer):
        >>>      def add_datasample(self,
        >>>                         name,
        >>>                         image: np.ndarray,
        >>>                         gt_sample:
        >>>                             Optional['BaseDataElement'] = None,
        >>>                         pred_sample:
        >>>                             Optional['BaseDataElement'] = None,
        >>>                         draw_gt: bool = True,
        >>>                         draw_pred: bool = True,
        >>>                         show: bool = False,
        >>>                         wait_time: int = 0,
        >>>                         step: int = 0) -> None:
        >>>         pass
    """

    def __init__(
        self,
        name='visualizer',
        image: Optional[np.ndarray] = None,
        vis_backends: VisBackendsType = None,
        save_dir: Optional[str] = None,
        fig_save_cfg=dict(frameon=False),
        fig_show_cfg=dict(frameon=False)
    ) -> None:
        super().__init__(name)
        self._dataset_meta: Optional[dict] = None
        self._vis_backends: Dict[str, BaseVisBackend] = {}

        if vis_backends is None:
            vis_backends = []

        if isinstance(vis_backends, (dict, BaseVisBackend)):
            vis_backends = [vis_backends]  # type: ignore

        if not is_seq_of(vis_backends, (dict, BaseVisBackend)):
            raise TypeError('vis_backends must be a list of dicts or a list '
                            'of BaseBackend instances')
        if save_dir is not None:
            save_dir = osp.join(save_dir, 'vis_data')

        for vis_backend in vis_backends:  # type: ignore
            name = None
            if isinstance(vis_backend, dict):
                name = vis_backend.pop('name', None)
                vis_backend.setdefault('save_dir', save_dir)
                vis_backend = VISBACKENDS.build(vis_backend)

            # If vis_backend requires `save_dir` (with no default value)
            # but is initialized with None, then don't add this
            # vis_backend to the visualizer.
            save_dir_arg = inspect.signature(
                vis_backend.__class__.__init__).parameters.get('save_dir')
            if (save_dir_arg is not None
                    and save_dir_arg.default is save_dir_arg.empty
                    and getattr(vis_backend, '_save_dir') is None):
                # warnings.warn(f'Failed to add {vis_backend.__class__}, please provide the `save_dir` argument.')
                continue

            type_name = vis_backend.__class__.__name__
            name = name or type_name

            if name in self._vis_backends:
                raise RuntimeError(f'vis_backend name {name} already exists')
            self._vis_backends[name] = vis_backend  # type: ignore

        self.fig_save = None
        self.fig_save_cfg = fig_save_cfg
        self.fig_show_cfg = fig_show_cfg

        (self.fig_save_canvas, self.fig_save,
         self.ax_save) = self._initialize_fig(fig_save_cfg)
        self.dpi = self.fig_save.get_dpi()

        if image is not None:
            self.set_image(image)

    @property  # type: ignore
    @master_only
    def dataset_meta(self) -> Optional[dict]:
        """Optional[dict]: Meta info of the dataset."""
        return self._dataset_meta

    @dataset_meta.setter  # type: ignore
    @master_only
    def dataset_meta(self, dataset_meta: dict) -> None:
        """Set the dataset meta info to the Visualizer."""
        self._dataset_meta = dataset_meta

    @master_only
    def show(self,
             drawn_img: Optional[np.ndarray] = None,
             win_name: str = 'image',
             wait_time: float = 0.,
             continue_key: str = ' ',
             backend: str = 'matplotlib') -> None:
        """Show the drawn image.

        Args:
            drawn_img (np.ndarray, optional): The image to show. If drawn_img
                is None, it will show the image got by Visualizer. Defaults
                to None.
            win_name (str):  The image title. Defaults to 'image'.
            wait_time (float): Delay in seconds. 0 is the special
                value that means "forever". Defaults to 0.
            continue_key (str): The key for users to continue. Defaults to
                the space key.
            backend (str): The backend to show the image. Defaults to
                'matplotlib'. `New in version 0.7.3.`
        """
        if backend == 'matplotlib':
            import matplotlib.pyplot as plt
            is_inline = 'inline' in plt.get_backend()
            img = self.get_image() if drawn_img is None else drawn_img
            self._init_manager(win_name)
            fig = self.manager.canvas.figure
            # remove white edges by set subplot margin
            fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
            fig.clear()
            ax = fig.add_subplot()
            ax.axis(False)
            ax.imshow(img)
            self.manager.canvas.draw()

            # Find a better way for inline to show the image
            if is_inline:
                return fig
            wait_continue(fig, timeout=wait_time, continue_key=continue_key)
        elif backend == 'cv2':
            # Keep images are shown in the same window, and the title of window
            # will be updated with `win_name`.
            cv2.namedWindow(winname=f'{id(self)}')
            cv2.setWindowTitle(f'{id(self)}', win_name)
            cv2.imshow(
                str(id(self)),
                self.get_image() if drawn_img is None else drawn_img)
            cv2.waitKey(int(np.ceil(wait_time * 1000)))
        else:
            raise ValueError('backend should be "matplotlib" or "cv2", '
                             f'but got {backend} instead')

    @master_only
    def set_image(self, image: np.ndarray) -> None:
        """Set the image to draw.

        Args:
            image (np.ndarray): The image to draw.
        """
        assert image is not None
        image = image.astype('uint8')
        self._image = image
        self.width, self.height = image.shape[1], image.shape[0]
        self._default_font_size = max(
            np.sqrt(self.height * self.width) // 90, 10)

        # add a small 1e-2 to avoid precision lost due to matplotlib's
        # truncation (https://github.com/matplotlib/matplotlib/issues/15363)
        self.fig_save.set_size_inches(  # type: ignore
            (self.width + 1e-2) / self.dpi, (self.height + 1e-2) / self.dpi)
        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
        self.ax_save.cla()
        self.ax_save.axis(False)
        self.ax_save.imshow(
            image,
            extent=(0, self.width, self.height, 0),
            interpolation='none')

    @master_only
    def get_image(self) -> np.ndarray:
        """Get the drawn image. The format is RGB.

        Returns:
            np.ndarray: the drawn image which channel is RGB.
        """
        assert self._image is not None, 'Please set image using `set_image`'
        return img_from_canvas(self.fig_save_canvas)  # type: ignore

    def _initialize_fig(self, fig_cfg) -> tuple:
        """Build figure according to fig_cfg.

        Args:
            fig_cfg (dict): The config to build figure.

        Returns:
             tuple: build canvas figure and axes.
        """
        from matplotlib.backends.backend_agg import FigureCanvasAgg
        from matplotlib.figure import Figure
        fig = Figure(**fig_cfg)
        ax = fig.add_subplot()
        ax.axis(False)

        # remove white edges by set subplot margin
        fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
        canvas = FigureCanvasAgg(fig)
        return canvas, fig, ax

    def _init_manager(self, win_name: str) -> None:
        """Initialize the matplot manager.

        Args:
            win_name (str): The window name.
        """
        from matplotlib.figure import Figure
        from matplotlib.pyplot import new_figure_manager
        if getattr(self, 'manager', None) is None:
            self.manager = new_figure_manager(
                num=1, FigureClass=Figure, **self.fig_show_cfg)

        try:
            self.manager.set_window_title(win_name)
        except Exception:
            self.manager = new_figure_manager(
                num=1, FigureClass=Figure, **self.fig_show_cfg)
            self.manager.set_window_title(win_name)

    @master_only
    def get_backend(self, name) -> 'BaseVisBackend':
        """get vis backend by name.

        Args:
            name (str): The name of vis backend

        Returns:
             BaseVisBackend: The vis backend.
        """
        return self._vis_backends.get(name)  # type: ignore

    def _is_posion_valid(self, position: np.ndarray) -> bool:
        """Judge whether the position is in image.

        Args:
            position (np.ndarray): The position to judge which last dim must
                be two and the format is [x, y].

        Returns:
            bool: Whether the position is in image.
        """
        flag = (position[..., 0] < self.width).all() and \
               (position[..., 0] >= 0).all() and \
               (position[..., 1] < self.height).all() and \
               (position[..., 1] >= 0).all()
        return flag

    @master_only
    def draw_points(self,
                    positions: Union[np.ndarray, torch.Tensor],
                    colors: Union[str, tuple, List[str], List[tuple]] = 'g',
                    marker: Optional[str] = None,
                    sizes: Optional[Union[np.ndarray, torch.Tensor]] = None):
        """Draw single or multiple points.

        Args:
            positions (Union[np.ndarray, torch.Tensor]): Positions to draw.
            colors (Union[str, tuple, List[str], List[tuple]]): The colors
                of points. ``colors`` can have the same length with points or
                just single value. If ``colors`` is single value, all the
                points will have the same colors. Reference to
                https://matplotlib.org/stable/gallery/color/named_colors.html
                for more details. Defaults to 'g.
            marker (str, optional): The marker style.
                See :mod:`matplotlib.markers` for more information about
                marker styles. Defaults to None.
            sizes (Optional[Union[np.ndarray, torch.Tensor]]): The marker size.
                Defaults to None.
        """
        check_type('positions', positions, (np.ndarray, torch.Tensor))
        positions = tensor2ndarray(positions)

        if len(positions.shape) == 1:
            positions = positions[None]
        assert positions.shape[-1] == 2, (
            'The shape of `positions` should be (N, 2), '
            f'but got {positions.shape}')
        colors = color_val_matplotlib(colors)  # type: ignore
        self.ax_save.scatter(
            positions[:, 0], positions[:, 1], c=colors, s=sizes, marker=marker)
        return self

    @master_only
    def draw_texts(
        self,
        texts: Union[str, List[str]],
        positions: Union[np.ndarray, torch.Tensor],
        font_sizes: Optional[Union[int, List[int]]] = None,
        colors: Union[str, tuple, List[str], List[tuple]] = 'g',
        vertical_alignments: Union[str, List[str]] = 'top',
        horizontal_alignments: Union[str, List[str]] = 'left',
        font_families: Union[str, List[str]] = 'sans-serif',
        bboxes: Optional[Union[dict, List[dict]]] = None,
        font_properties: Optional[Union['FontProperties',
                                        List['FontProperties']]] = None
    ) -> 'Visualizer':
        """Draw single or multiple text boxes.

        Args:
            texts (Union[str, List[str]]): Texts to draw.
            positions (Union[np.ndarray, torch.Tensor]): The position to draw
                the texts, which should have the same length with texts and
                each dim contain x and y.
            font_sizes (Union[int, List[int]], optional): The font size of
                texts. ``font_sizes`` can have the same length with texts or
                just single value. If ``font_sizes`` is single value, all the
                texts will have the same font size. Defaults to None.
            colors (Union[str, tuple, List[str], List[tuple]]): The colors
                of texts. ``colors`` can have the same length with texts or
                just single value. If ``colors`` is single value, all the
                texts will have the same colors. Reference to
                https://matplotlib.org/stable/gallery/color/named_colors.html
                for more details. Defaults to 'g.
            vertical_alignments (Union[str, List[str]]): The verticalalignment
                of texts. verticalalignment controls whether the y positional
                argument for the text indicates the bottom, center or top side
                of the text bounding box.
                ``vertical_alignments`` can have the same length with
                texts or just single value. If ``vertical_alignments`` is
                single value, all the texts will have the same
                verticalalignment. verticalalignment can be 'center' or
                'top', 'bottom' or 'baseline'. Defaults to 'top'.
            horizontal_alignments (Union[str, List[str]]): The
                horizontalalignment of texts. Horizontalalignment controls
                whether the x positional argument for the text indicates the
                left, center or right side of the text bounding box.
                ``horizontal_alignments`` can have
                the same length with texts or just single value.
                If ``horizontal_alignments`` is single value, all the texts
                will have the same horizontalalignment. Horizontalalignment
                can be 'center','right' or 'left'. Defaults to 'left'.
            font_families (Union[str, List[str]]): The font family of
                texts. ``font_families`` can have the same length with texts or
                just single value. If ``font_families`` is single value, all
                the texts will have the same font family.
                font_familiy can be 'serif', 'sans-serif', 'cursive', 'fantasy'
                or 'monospace'.  Defaults to 'sans-serif'.
            bboxes (Union[dict, List[dict]], optional): The bounding box of the
                texts. If bboxes is None, there are no bounding box around
                texts. ``bboxes`` can have the same length with texts or
                just single value. If ``bboxes`` is single value, all
                the texts will have the same bbox. Reference to
                https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch
                for more details. Defaults to None.
            font_properties (Union[FontProperties, List[FontProperties]], optional):
                The font properties of texts. FontProperties is
                a ``font_manager.FontProperties()`` object.
                If you want to draw Chinese texts, you need to prepare
                a font file that can show Chinese characters properly.
                For example: `simhei.ttf`, `simsun.ttc`, `simkai.ttf` and so on.
                Then set ``font_properties=matplotlib.font_manager.FontProperties(fname='path/to/font_file')``
                ``font_properties`` can have the same length with texts or
                just single value. If ``font_properties`` is single value,
                all the texts will have the same font properties.
                Defaults to None.
                `New in version 0.6.0.`
        """  # noqa: E501
        from matplotlib.font_manager import FontProperties
        check_type('texts', texts, (str, list))
        if isinstance(texts, str):
            texts = [texts]
        num_text = len(texts)
        check_type('positions', positions, (np.ndarray, torch.Tensor))
        positions = tensor2ndarray(positions)
        if len(positions.shape) == 1:
            positions = positions[None]
        assert positions.shape == (num_text, 2), (
            '`positions` should have the shape of '
            f'({num_text}, 2), but got {positions.shape}')
        if not self._is_posion_valid(positions):
            warnings.warn(
                'Warning: The text is out of bounds,'
                ' the drawn text may not be in the image', UserWarning)
        positions = positions.tolist()

        if font_sizes is None:
            font_sizes = self._default_font_size
        check_type_and_length('font_sizes', font_sizes, (int, float, list),
                              num_text)
        font_sizes = value2list(font_sizes, (int, float), num_text)

        check_type_and_length('colors', colors, (str, tuple, list), num_text)
        colors = value2list(colors, (str, tuple), num_text)
        colors = color_val_matplotlib(colors)  # type: ignore

        check_type_and_length('vertical_alignments', vertical_alignments,
                              (str, list), num_text)
        vertical_alignments = value2list(vertical_alignments, str, num_text)

        check_type_and_length('horizontal_alignments', horizontal_alignments,
                              (str, list), num_text)
        horizontal_alignments = value2list(horizontal_alignments, str,
                                           num_text)

        check_type_and_length('font_families', font_families, (str, list),
                              num_text)
        font_families = value2list(font_families, str, num_text)

        if font_properties is None:
            font_properties = [None for _ in range(num_text)]  # type: ignore
        else:
            check_type_and_length('font_properties', font_properties,
                                  (FontProperties, list), num_text)
            font_properties = value2list(font_properties, FontProperties,
                                         num_text)

        if bboxes is None:
            bboxes = [None for _ in range(num_text)]  # type: ignore
        else:
            check_type_and_length('bboxes', bboxes, (dict, list), num_text)
            bboxes = value2list(bboxes, dict, num_text)

        for i in range(num_text):
            self.ax_save.text(
                positions[i][0],
                positions[i][1],
                texts[i],
                size=font_sizes[i],  # type: ignore
                bbox=bboxes[i],  # type: ignore
                verticalalignment=vertical_alignments[i],
                horizontalalignment=horizontal_alignments[i],
                family=font_families[i],
                fontproperties=font_properties[i],
                color=colors[i])
        return self

    @master_only
    def draw_lines(
        self,
        x_datas: Union[np.ndarray, torch.Tensor],
        y_datas: Union[np.ndarray, torch.Tensor],
        colors: Union[str, tuple, List[str], List[tuple]] = 'g',
        line_styles: Union[str, List[str]] = '-',
        line_widths: Union[Union[int, float], List[Union[int, float]]] = 2
    ) -> 'Visualizer':
        """Draw single or multiple line segments.

        Args:
            x_datas (Union[np.ndarray, torch.Tensor]): The x coordinate of
                each line' start and end points.
            y_datas (Union[np.ndarray, torch.Tensor]): The y coordinate of
                each line' start and end points.
            colors (Union[str, tuple, List[str], List[tuple]]): The colors of
                lines. ``colors`` can have the same length with lines or just
                single value. If ``colors`` is single value, all the lines
                will have the same colors. Reference to
                https://matplotlib.org/stable/gallery/color/named_colors.html
                for more details. Defaults to 'g'.
            line_styles (Union[str, List[str]]): The linestyle
                of lines. ``line_styles`` can have the same length with
                texts or just single value. If ``line_styles`` is single
                value, all the lines will have the same linestyle.
                Reference to
                https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle
                for more details. Defaults to '-'.
            line_widths (Union[Union[int, float], List[Union[int, float]]]):
                The linewidth of lines. ``line_widths`` can have
                the same length with lines or just single value.
                If ``line_widths`` is single value, all the lines will
                have the same linewidth. Defaults to 2.
        """
        from matplotlib.collections import LineCollection
        check_type('x_datas', x_datas, (np.ndarray, torch.Tensor))
        x_datas = tensor2ndarray(x_datas)
        check_type('y_datas', y_datas, (np.ndarray, torch.Tensor))
        y_datas = tensor2ndarray(y_datas)
        assert x_datas.shape == y_datas.shape, (
            '`x_datas` and `y_datas` should have the same shape')
        assert x_datas.shape[-1] == 2, (
            f'The shape of `x_datas` should be (N, 2), but got {x_datas.shape}'
        )
        if len(x_datas.shape) == 1:
            x_datas = x_datas[None]
            y_datas = y_datas[None]
        colors = color_val_matplotlib(colors)  # type: ignore
        lines = np.concatenate(
            (x_datas.reshape(-1, 2, 1), y_datas.reshape(-1, 2, 1)), axis=-1)
        if not self._is_posion_valid(lines):
            warnings.warn(
                'Warning: The line is out of bounds,'
                ' the drawn line may not be in the image', UserWarning)
        line_collect = LineCollection(
            lines.tolist(),
            colors=colors,
            linestyles=line_styles,
            linewidths=line_widths)
        self.ax_save.add_collection(line_collect)
        return self

    @master_only
    def draw_circles(
        self,
        center: Union[np.ndarray, torch.Tensor],
        radius: Union[np.ndarray, torch.Tensor],
        edge_colors: Union[str, tuple, List[str], List[tuple]] = 'g',
        line_styles: Union[str, List[str]] = '-',
        line_widths: Union[Union[int, float], List[Union[int, float]]] = 2,
        face_colors: Union[str, tuple, List[str], List[tuple]] = 'none',
        alpha: Union[float, int] = 0.8,
    ) -> 'Visualizer':
        """Draw single or multiple circles.

        Args:
            center (Union[np.ndarray, torch.Tensor]): The x coordinate of
                each line' start and end points.
            radius (Union[np.ndarray, torch.Tensor]): The y coordinate of
                each line' start and end points.
            edge_colors (Union[str, tuple, List[str], List[tuple]]): The
                colors of circles. ``colors`` can have the same length with
                lines or just single value. If ``colors`` is single value,
                all the lines will have the same colors. Reference to
                https://matplotlib.org/stable/gallery/color/named_colors.html
                for more details. Defaults to 'g.
            line_styles (Union[str, List[str]]): The linestyle
                of lines. ``line_styles`` can have the same length with
                texts or just single value. If ``line_styles`` is single
                value, all the lines will have the same linestyle.
                Reference to
                https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle
                for more details. Defaults to '-'.
            line_widths (Union[Union[int, float], List[Union[int, float]]]):
                The linewidth of lines. ``line_widths`` can have
                the same length with lines or just single value.
                If ``line_widths`` is single value, all the lines will
                have the same linewidth. Defaults to 2.
            face_colors (Union[str, tuple, List[str], List[tuple]]):
                The face colors. Defaults to None.
            alpha (Union[int, float]): The transparency of circles.
                Defaults to 0.8.
        """
        from matplotlib.collections import PatchCollection
        from matplotlib.patches import Circle
        check_type('center', center, (np.ndarray, torch.Tensor))
        center = tensor2ndarray(center)
        check_type('radius', radius, (np.ndarray, torch.Tensor))
        radius = tensor2ndarray(radius)
        if len(center.shape) == 1:
            center = center[None]
        assert center.shape == (radius.shape[0], 2), (
            'The shape of `center` should be (radius.shape, 2), '
            f'but got {center.shape}')
        if not (self._is_posion_valid(center -
                                      np.tile(radius.reshape((-1, 1)), (1, 2)))
                and self._is_posion_valid(
                    center + np.tile(radius.reshape((-1, 1)), (1, 2)))):
            warnings.warn(
                'Warning: The circle is out of bounds,'
                ' the drawn circle may not be in the image', UserWarning)

        center = center.tolist()
        radius = radius.tolist()
        edge_colors = color_val_matplotlib(edge_colors)  # type: ignore
        face_colors = color_val_matplotlib(face_colors)  # type: ignore
        circles = []
        for i in range(len(center)):
            circles.append(Circle(tuple(center[i]), radius[i]))

        if isinstance(line_widths, (int, float)):
            line_widths = [line_widths] * len(circles)
        line_widths = [
            min(max(linewidth, 1), self._default_font_size / 4)
            for linewidth in line_widths
        ]
        p = PatchCollection(
            circles,
            alpha=alpha,
            facecolors=face_colors,
            edgecolors=edge_colors,
            linewidths=line_widths,
            linestyles=line_styles)
        self.ax_save.add_collection(p)
        return self

    @master_only
    def draw_bboxes(
        self,
        bboxes: Union[np.ndarray, torch.Tensor],
        edge_colors: Union[str, tuple, List[str], List[tuple]] = 'g',
        line_styles: Union[str, List[str]] = '-',
        line_widths: Union[Union[int, float], List[Union[int, float]]] = 2,
        face_colors: Union[str, tuple, List[str], List[tuple]] = 'none',
        alpha: Union[int, float] = 0.8,
    ) -> 'Visualizer':
        """Draw single or multiple bboxes.

        Args:
            bboxes (Union[np.ndarray, torch.Tensor]): The bboxes to draw with
                the format of(x1,y1,x2,y2).
            edge_colors (Union[str, tuple, List[str], List[tuple]]): The
                colors of bboxes. ``colors`` can have the same length with
                lines or just single value. If ``colors`` is single value, all
                the lines will have the same colors. Refer to `matplotlib.
                colors` for full list of formats that are accepted.
                Defaults to 'g'.
            line_styles (Union[str, List[str]]): The linestyle
                of lines. ``line_styles`` can have the same length with
                texts or just single value. If ``line_styles`` is single
                value, all the lines will have the same linestyle.
                Reference to
                https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle
                for more details. Defaults to '-'.
            line_widths (Union[Union[int, float], List[Union[int, float]]]):
                The linewidth of lines. ``line_widths`` can have
                the same length with lines or just single value.
                If ``line_widths`` is single value, all the lines will
                have the same linewidth. Defaults to 2.
            face_colors (Union[str, tuple, List[str], List[tuple]]):
                The face colors. Defaults to None.
            alpha (Union[int, float]): The transparency of bboxes.
                Defaults to 0.8.
        """
        check_type('bboxes', bboxes, (np.ndarray, torch.Tensor))
        bboxes = tensor2ndarray(bboxes)

        if len(bboxes.shape) == 1:
            bboxes = bboxes[None]
        assert bboxes.shape[-1] == 4, (
            f'The shape of `bboxes` should be (N, 4), but got {bboxes.shape}')

        assert (bboxes[:, 0] <= bboxes[:, 2]).all() and (bboxes[:, 1] <=
                                                         bboxes[:, 3]).all()
        if not self._is_posion_valid(bboxes.reshape((-1, 2, 2))):
            warnings.warn(
                'Warning: The bbox is out of bounds,'
                ' the drawn bbox may not be in the image', UserWarning)
        poly = np.stack(
            (bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 1],
             bboxes[:, 2], bboxes[:, 3], bboxes[:, 0], bboxes[:, 3]),
            axis=-1).reshape(-1, 4, 2)
        poly = [p for p in poly]
        return self.draw_polygons(
            poly,
            alpha=alpha,
            edge_colors=edge_colors,
            line_styles=line_styles,
            line_widths=line_widths,
            face_colors=face_colors)

    @master_only
    def draw_polygons(
        self,
        polygons: Union[Union[np.ndarray, torch.Tensor],
                        List[Union[np.ndarray, torch.Tensor]]],
        edge_colors: Union[str, tuple, List[str], List[tuple]] = 'g',
        line_styles: Union[str, List[str]] = '-',
        line_widths: Union[Union[int, float], List[Union[int, float]]] = 2,
        face_colors: Union[str, tuple, List[str], List[tuple]] = 'none',
        alpha: Union[int, float] = 0.8,
    ) -> 'Visualizer':
        """Draw single or multiple bboxes.

        Args:
            polygons (Union[Union[np.ndarray, torch.Tensor],\
                List[Union[np.ndarray, torch.Tensor]]]): The polygons to draw
                with the format of (x1,y1,x2,y2,...,xn,yn).
            edge_colors (Union[str, tuple, List[str], List[tuple]]): The
                colors of polygons. ``colors`` can have the same length with
                lines or just single value. If ``colors`` is single value,
                all the lines will have the same colors. Refer to
                `matplotlib.colors` for full list of formats that are accepted.
                Defaults to 'g.
            line_styles (Union[str, List[str]]): The linestyle
                of lines. ``line_styles`` can have the same length with
                texts or just single value. If ``line_styles`` is single
                value, all the lines will have the same linestyle.
                Reference to
                https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle
                for more details. Defaults to '-'.
            line_widths (Union[Union[int, float], List[Union[int, float]]]):
                The linewidth of lines. ``line_widths`` can have
                the same length with lines or just single value.
                If ``line_widths`` is single value, all the lines will
                have the same linewidth. Defaults to 2.
            face_colors (Union[str, tuple, List[str], List[tuple]]):
                The face colors. Defaults to None.
            alpha (Union[int, float]): The transparency of polygons.
                Defaults to 0.8.
        """
        from matplotlib.collections import PolyCollection
        check_type('polygons', polygons, (list, np.ndarray, torch.Tensor))
        edge_colors = color_val_matplotlib(edge_colors)  # type: ignore
        face_colors = color_val_matplotlib(face_colors)  # type: ignore

        if isinstance(polygons, (np.ndarray, torch.Tensor)):
            polygons = [polygons]
        if isinstance(polygons, list):
            for polygon in polygons:
                assert polygon.shape[1] == 2, (
                    'The shape of each polygon in `polygons` should be (M, 2),'
                    f' but got {polygon.shape}')
        polygons = [tensor2ndarray(polygon) for polygon in polygons]
        for polygon in polygons:
            if not self._is_posion_valid(polygon):
                warnings.warn(
                    'Warning: The polygon is out of bounds,'
                    ' the drawn polygon may not be in the image', UserWarning)
        if isinstance(line_widths, (int, float)):
            line_widths = [line_widths] * len(polygons)
        line_widths = [
            min(max(linewidth, 1), self._default_font_size / 4)
            for linewidth in line_widths
        ]
        polygon_collection = PolyCollection(
            polygons,
            alpha=alpha,
            facecolor=face_colors,
            linestyles=line_styles,
            edgecolors=edge_colors,
            linewidths=line_widths)

        self.ax_save.add_collection(polygon_collection)
        return self

    @master_only
    def draw_binary_masks(
            self,
            binary_masks: Union[np.ndarray, torch.Tensor],
            colors: Union[str, tuple, List[str], List[tuple]] = 'g',
            alphas: Union[float, List[float]] = 0.8) -> 'Visualizer':
        """Draw single or multiple binary masks.

        Args:
            binary_masks (np.ndarray, torch.Tensor): The binary_masks to draw
                with of shape (N, 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.
            colors (np.ndarray): The colors which binary_masks will convert to.
                ``colors`` can have the same length with binary_masks or just
                single value. If ``colors`` is single value, all the
                binary_masks will convert to the same colors. The colors format
                is RGB. Defaults to np.array([0, 255, 0]).
            alphas (Union[int, List[int]]): The transparency of masks.
                Defaults to 0.8.
        """
        check_type('binary_masks', binary_masks, (np.ndarray, torch.Tensor))
        binary_masks = tensor2ndarray(binary_masks)
        assert binary_masks.dtype == np.bool_, (
            'The dtype of binary_masks should be np.bool_, '
            f'but got {binary_masks.dtype}')
        binary_masks = binary_masks.astype('uint8') * 255
        img = self.get_image()
        if binary_masks.ndim == 2:
            binary_masks = binary_masks[None]
        assert img.shape[:2] == binary_masks.shape[
                                1:], '`binary_marks` must have ' \
                                     'the same shape with image'
        binary_mask_len = binary_masks.shape[0]

        check_type_and_length('colors', colors, (str, tuple, list),
                              binary_mask_len)
        colors = value2list(colors, (str, tuple), binary_mask_len)
        colors = [
            color_str2rgb(color) if isinstance(color, str) else color
            for color in colors
        ]
        for color in colors:
            assert len(color) == 3
            for channel in color:
                assert 0 <= channel <= 255  # type: ignore

        if isinstance(alphas, float):
            alphas = [alphas] * binary_mask_len

        for binary_mask, color, alpha in zip(binary_masks, colors, alphas):
            binary_mask_complement = cv2.bitwise_not(binary_mask)
            rgb = np.zeros_like(img)
            rgb[...] = color
            rgb = cv2.bitwise_and(rgb, rgb, mask=binary_mask)
            img_complement = cv2.bitwise_and(
                img, img, mask=binary_mask_complement)
            rgb = rgb + img_complement
            img = cv2.addWeighted(img, 1 - alpha, rgb, alpha, 0)
        self.ax_save.imshow(
            img,
            extent=(0, self.width, self.height, 0),
            interpolation='nearest')
        return self

    @staticmethod
    @master_only
    def draw_featmap(featmap: torch.Tensor,
                     overlaid_image: Optional[np.ndarray] = None,
                     channel_reduction: Optional[str] = 'squeeze_mean',
                     topk: int = 20,
                     arrangement: Tuple[int, int] = (4, 5),
                     resize_shape: Optional[tuple] = None,
                     alpha: float = 0.5) -> np.ndarray:
        """Draw featmap.

        - If `overlaid_image` is not None, the final output image will be the
          weighted sum of img and featmap.

        - If `resize_shape` is specified, `featmap` and `overlaid_image`
          are interpolated.

        - If `resize_shape` is None and `overlaid_image` is not None,
          the feature map will be interpolated to the spatial size of the image
          in the case where the spatial dimensions of `overlaid_image` and
          `featmap` are different.

        - If `channel_reduction` is "squeeze_mean" and "select_max",
          it will compress featmap to single channel image and weighted
          sum to `overlaid_image`.

        - If `channel_reduction` is None

          - If topk <= 0, featmap is assert to be one or three
            channel and treated as image and will be weighted sum
            to ``overlaid_image``.
          - If topk > 0, it will select topk channel to show by the sum of
            each channel. At the same time, you can specify the `arrangement`
            to set the window layout.

        Args:
            featmap (torch.Tensor): The featmap to draw which format is
                (C, H, W).
            overlaid_image (np.ndarray, optional): The overlaid image.
                Defaults to None.
            channel_reduction (str, optional): Reduce multiple channels to a
                single channel. The optional value is 'squeeze_mean'
                or 'select_max'. Defaults to 'squeeze_mean'.
            topk (int): If channel_reduction is not None and topk > 0,
                it will select topk channel to show by the sum of each channel.
                if topk <= 0, tensor_chw is assert to be one or three.
                Defaults to 20.
            arrangement (Tuple[int, int]): The arrangement of featmap when
                channel_reduction is not None and topk > 0. Defaults to (4, 5).
            resize_shape (tuple, optional): The shape to scale the feature map.
                Defaults to None.
            alpha (Union[int, List[int]]): The transparency of featmap.
                Defaults to 0.5.

        Returns:
            np.ndarray: RGB image.
        """
        import matplotlib.pyplot as plt
        assert isinstance(featmap,
                          torch.Tensor), (f'`featmap` should be torch.Tensor,'
                                          f' but got {type(featmap)}')
        assert featmap.ndim == 3, f'Input dimension must be 3, ' \
                                  f'but got {featmap.ndim}'
        featmap = featmap.detach().cpu()

        if overlaid_image is not None:
            if overlaid_image.ndim == 2:
                overlaid_image = cv2.cvtColor(overlaid_image,
                                              cv2.COLOR_GRAY2RGB)

            if overlaid_image.shape[:2] != featmap.shape[1:]:
                warnings.warn(
                    f'Since the spatial dimensions of '
                    f'overlaid_image: {overlaid_image.shape[:2]} and '
                    f'featmap: {featmap.shape[1:]} are not same, '
                    f'the feature map will be interpolated. '
                    f'This may cause mismatch problems ！')
                if resize_shape is None:
                    featmap = F.interpolate(
                        featmap[None],
                        overlaid_image.shape[:2],
                        mode='bilinear',
                        align_corners=False)[0]

        if resize_shape is not None:
            featmap = F.interpolate(
                featmap[None],
                resize_shape,
                mode='bilinear',
                align_corners=False)[0]
            if overlaid_image is not None:
                overlaid_image = cv2.resize(overlaid_image, resize_shape[::-1])

        if channel_reduction is not None:
            assert channel_reduction in [
                'squeeze_mean', 'select_max'], \
                f'Mode only support "squeeze_mean", "select_max", ' \
                f'but got {channel_reduction}'
            if channel_reduction == 'select_max':
                sum_channel_featmap = torch.sum(featmap, dim=(1, 2))
                _, indices = torch.topk(sum_channel_featmap, 1)
                feat_map = featmap[indices]
            else:
                feat_map = torch.mean(featmap, dim=0)
            return convert_overlay_heatmap(feat_map, overlaid_image, alpha)
        elif topk <= 0:
            featmap_channel = featmap.shape[0]
            assert featmap_channel in [
                1, 3
            ], ('The input tensor channel dimension must be 1 or 3 '
                'when topk is less than 1, but the channel '
                f'dimension you input is {featmap_channel}, you can use the'
                ' channel_reduction parameter or set topk greater than '
                '0 to solve the error')
            return convert_overlay_heatmap(featmap, overlaid_image, alpha)
        else:
            row, col = arrangement
            channel, height, width = featmap.shape
            assert row * col >= topk, 'The product of row and col in ' \
                                      'the `arrangement` is less than ' \
                                      'topk, please set the ' \
                                      '`arrangement` correctly'

            # Extract the feature map of topk
            topk = min(channel, topk)
            sum_channel_featmap = torch.sum(featmap, dim=(1, 2))
            _, indices = torch.topk(sum_channel_featmap, topk)
            topk_featmap = featmap[indices]

            fig = plt.figure(frameon=False)
            # Set the window layout
            fig.subplots_adjust(
                left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
            dpi = fig.get_dpi()
            fig.set_size_inches((width * col + 1e-2) / dpi,
                                (height * row + 1e-2) / dpi)
            for i in range(topk):
                axes = fig.add_subplot(row, col, i + 1)
                axes.axis('off')
                axes.text(2, 15, f'channel: {indices[i]}', fontsize=10)
                axes.imshow(
                    convert_overlay_heatmap(topk_featmap[i], overlaid_image,
                                            alpha))
            image = img_from_canvas(fig.canvas)
            plt.close(fig)
            return image

    @master_only
    def add_config(self, config: Config, **kwargs):
        """Record the config.

        Args:
            config (Config): The Config object.
        """
        for vis_backend in self._vis_backends.values():
            vis_backend.add_config(config, **kwargs)

    @master_only
    def add_graph(self, model: torch.nn.Module, data_batch: Sequence[dict],
                  **kwargs) -> None:
        """Record the model graph.

        Args:
            model (torch.nn.Module): Model to draw.
            data_batch (Sequence[dict]): Batch of data from dataloader.
        """
        for vis_backend in self._vis_backends.values():
            vis_backend.add_graph(model, data_batch, **kwargs)

    @master_only
    def add_image(self, name: str, image: np.ndarray, step: int = 0) -> None:
        """Record the image.

        Args:
            name (str): The image identifier.
            image (np.ndarray, optional): The image to be saved. The format
                should be RGB. Defaults to None.
            step (int): Global step value to record. Defaults to 0.
        """
        for vis_backend in self._vis_backends.values():
            vis_backend.add_image(name, image, step)  # type: ignore

    @master_only
    def add_scalar(self,
                   name: str,
                   value: Union[int, float],
                   step: int = 0,
                   **kwargs) -> None:
        """Record the scalar data.

        Args:
            name (str): The scalar identifier.
            value (float, int): Value to save.
            step (int): Global step value to record. Defaults to 0.
        """
        for vis_backend in self._vis_backends.values():
            vis_backend.add_scalar(name, value, step, **kwargs)  # type: ignore

    @master_only
    def add_scalars(self,
                    scalar_dict: dict,
                    step: int = 0,
                    file_path: Optional[str] = None,
                    **kwargs) -> None:
        """Record the scalars' data.

        Args:
            scalar_dict (dict): Key-value pair storing the tag and
                corresponding values.
            step (int): Global step value to record. Defaults to 0.
            file_path (str, optional): The scalar's data will be
                saved to the `file_path` file at the same time
                if the `file_path` parameter is specified.
                Defaults to None.
        """
        for vis_backend in self._vis_backends.values():
            vis_backend.add_scalars(scalar_dict, step, file_path, **kwargs)

    @master_only
    def add_datasample(self,
                       name,
                       image: np.ndarray,
                       data_sample: Optional['BaseDataElement'] = None,
                       draw_gt: bool = True,
                       draw_pred: bool = True,
                       show: bool = False,
                       wait_time: int = 0,
                       step: int = 0) -> None:
        """Draw datasample."""
        pass

    def close(self) -> None:
        """close an opened object."""
        for vis_backend in self._vis_backends.values():
            vis_backend.close()

    @classmethod
    def get_instance(cls, name: str, **kwargs) -> 'Visualizer':
        """Make subclass can get latest created instance by
        ``Visualizer.get_current_instance()``.

        Downstream codebase may need to get the latest created instance
        without knowing the specific Visualizer type. For example, mmdetection
        builds visualizer in runner and some component which cannot access
        runner wants to get latest created visualizer. In this case,
        the component does not know which type of visualizer has been built
        and cannot get target instance. Therefore, :class:`Visualizer`
        overrides the :meth:`get_instance` and its subclass will register
        the created instance to :attr:`_instance_dict` additionally.
        :meth:`get_current_instance` will return the latest created subclass
        instance.

        Examples:
            >>> class DetLocalVisualizer(Visualizer):
            >>>     def __init__(self, name):
            >>>         super().__init__(name)
            >>>
            >>> visualizer1 = DetLocalVisualizer.get_instance('name1')
            >>> visualizer2 = Visualizer.get_current_instance()
            >>> visualizer3 = DetLocalVisualizer.get_current_instance()
            >>> assert id(visualizer1) == id(visualizer2) == id(visualizer3)

        Args:
            name (str): Name of instance.

        Returns:
            object: Corresponding name instance.
        """
        instance = super().get_instance(name, **kwargs)
        Visualizer._instance_dict[name] = instance
        return instance