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# 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
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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