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Source code for mmdet.utils.mot_error_visualize

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Union

try:
    import seaborn as sns
except ImportError:
    sns = None
import cv2
import matplotlib.pyplot as plt
import mmcv
import numpy as np
from matplotlib.patches import Rectangle
from mmengine.utils import mkdir_or_exist


[docs]def imshow_mot_errors(*args, backend: str = 'cv2', **kwargs): """Show the wrong tracks on the input image. Args: backend (str, optional): Backend of visualization. Defaults to 'cv2'. """ if backend == 'cv2': return _cv2_show_wrong_tracks(*args, **kwargs) elif backend == 'plt': return _plt_show_wrong_tracks(*args, **kwargs) else: raise NotImplementedError()
def _cv2_show_wrong_tracks(img: Union[str, np.ndarray], bboxes: np.ndarray, ids: np.ndarray, error_types: np.ndarray, thickness: int = 2, font_scale: float = 0.4, text_width: int = 10, text_height: int = 15, show: bool = False, wait_time: int = 100, out_file: str = None) -> np.ndarray: """Show the wrong tracks with opencv. Args: img (str or ndarray): The image to be displayed. bboxes (ndarray): A ndarray of shape (k, 5). ids (ndarray): A ndarray of shape (k, ). error_types (ndarray): A ndarray of shape (k, ), where 0 denotes false positives, 1 denotes false negative and 2 denotes ID switch. thickness (int, optional): Thickness of lines. Defaults to 2. font_scale (float, optional): Font scale to draw id and score. Defaults to 0.4. text_width (int, optional): Width to draw id and score. Defaults to 10. text_height (int, optional): Height to draw id and score. Defaults to 15. show (bool, optional): Whether to show the image on the fly. Defaults to False. wait_time (int, optional): Value of waitKey param. Defaults to 100. out_file (str, optional): The filename to write the image. Defaults to None. Returns: ndarray: Visualized image. """ if sns is None: raise ImportError('please run pip install seaborn') assert bboxes.ndim == 2, \ f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.' assert ids.ndim == 1, \ f' ids ndim should be 1, but its ndim is {ids.ndim}.' assert error_types.ndim == 1, \ f' error_types ndim should be 1, but its ndim is {error_types.ndim}.' assert bboxes.shape[0] == ids.shape[0], \ 'bboxes.shape[0] and ids.shape[0] should have the same length.' assert bboxes.shape[1] == 5, \ f' bboxes.shape[1] should be 5, but its {bboxes.shape[1]}.' bbox_colors = sns.color_palette() # red, yellow, blue bbox_colors = [bbox_colors[3], bbox_colors[1], bbox_colors[0]] bbox_colors = [[int(255 * _c) for _c in bbox_color][::-1] for bbox_color in bbox_colors] if isinstance(img, str): img = mmcv.imread(img) else: assert img.ndim == 3 img_shape = img.shape bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) for bbox, error_type, id in zip(bboxes, error_types, ids): x1, y1, x2, y2 = bbox[:4].astype(np.int32) score = float(bbox[-1]) # bbox bbox_color = bbox_colors[error_type] cv2.rectangle(img, (x1, y1), (x2, y2), bbox_color, thickness=thickness) # FN does not have id and score if error_type == 1: continue # score text = '{:.02f}'.format(score) width = (len(text) - 1) * text_width img[y1:y1 + text_height, x1:x1 + width, :] = bbox_color cv2.putText( img, text, (x1, y1 + text_height - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, color=(0, 0, 0)) # id text = str(id) width = len(text) * text_width img[y1 + text_height:y1 + text_height * 2, x1:x1 + width, :] = bbox_color cv2.putText( img, str(id), (x1, y1 + text_height * 2 - 2), cv2.FONT_HERSHEY_COMPLEX, font_scale, color=(0, 0, 0)) if show: mmcv.imshow(img, wait_time=wait_time) if out_file is not None: mmcv.imwrite(img, out_file) return img def _plt_show_wrong_tracks(img: Union[str, np.ndarray], bboxes: np.ndarray, ids: np.ndarray, error_types: np.ndarray, thickness: float = 0.1, font_scale: float = 3.0, text_width: int = 8, text_height: int = 13, show: bool = False, wait_time: int = 100, out_file: str = None) -> np.ndarray: """Show the wrong tracks with matplotlib. Args: img (str or ndarray): The image to be displayed. bboxes (ndarray): A ndarray of shape (k, 5). ids (ndarray): A ndarray of shape (k, ). error_types (ndarray): A ndarray of shape (k, ), where 0 denotes false positives, 1 denotes false negative and 2 denotes ID switch. thickness (float, optional): Thickness of lines. Defaults to 0.1. font_scale (float, optional): Font scale to draw id and score. Defaults to 3.0. text_width (int, optional): Width to draw id and score. Defaults to 8. text_height (int, optional): Height to draw id and score. Defaults to 13. show (bool, optional): Whether to show the image on the fly. Defaults to False. wait_time (int, optional): Value of waitKey param. Defaults to 100. out_file (str, optional): The filename to write the image. Defaults to None. Returns: ndarray: Original image. """ assert bboxes.ndim == 2, \ f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.' assert ids.ndim == 1, \ f' ids ndim should be 1, but its ndim is {ids.ndim}.' assert error_types.ndim == 1, \ f' error_types ndim should be 1, but its ndim is {error_types.ndim}.' assert bboxes.shape[0] == ids.shape[0], \ 'bboxes.shape[0] and ids.shape[0] should have the same length.' assert bboxes.shape[1] == 5, \ f' bboxes.shape[1] should be 5, but its {bboxes.shape[1]}.' bbox_colors = sns.color_palette() # red, yellow, blue bbox_colors = [bbox_colors[3], bbox_colors[1], bbox_colors[0]] if isinstance(img, str): img = plt.imread(img) else: assert img.ndim == 3 img = mmcv.bgr2rgb(img) img_shape = img.shape bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) plt.imshow(img) plt.gca().set_axis_off() plt.autoscale(False) plt.subplots_adjust( top=1, bottom=0, right=1, left=0, hspace=None, wspace=None) plt.margins(0, 0) plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) plt.rcParams['figure.figsize'] = img_shape[1], img_shape[0] for bbox, error_type, id in zip(bboxes, error_types, ids): x1, y1, x2, y2, score = bbox w, h = int(x2 - x1), int(y2 - y1) left_top = (int(x1), int(y1)) # bbox plt.gca().add_patch( Rectangle( left_top, w, h, thickness, edgecolor=bbox_colors[error_type], facecolor='none')) # FN does not have id and score if error_type == 1: continue # score text = '{:.02f}'.format(score) width = len(text) * text_width plt.gca().add_patch( Rectangle((left_top[0], left_top[1]), width, text_height, thickness, edgecolor=bbox_colors[error_type], facecolor=bbox_colors[error_type])) plt.text( left_top[0], left_top[1] + text_height + 2, text, fontsize=font_scale) # id text = str(id) width = len(text) * text_width plt.gca().add_patch( Rectangle((left_top[0], left_top[1] + text_height + 1), width, text_height, thickness, edgecolor=bbox_colors[error_type], facecolor=bbox_colors[error_type])) plt.text( left_top[0], left_top[1] + 2 * (text_height + 1), text, fontsize=font_scale) if out_file is not None: mkdir_or_exist(osp.abspath(osp.dirname(out_file))) plt.savefig(out_file, dpi=300, bbox_inches='tight', pad_inches=0.0) if show: plt.draw() plt.pause(wait_time / 1000.) plt.clf() return img
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