def imread(img_or_path, flag='color', channel_order='bgr'):
"""Read an image.
Args:
img_or_path (ndarray or str): Either a numpy array or image path.
If it is a numpy array (loaded image), then it will be returned
as is.
flag (str): Flags specifying the color type of a loaded image,
candidates are `color`, `grayscale` and `unchanged`.
Note that the `turbojpeg` backened does not support `unchanged`.
channel_order (str): Order of channel, candidates are `bgr` and `rgb`.
Returns:
ndarray: Loaded image array.
"""
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
check_file_exist(img_or_path,
'img file does not exist: {}'.format(img_or_path))
if imread_backend == 'turbojpeg':
with open(img_or_path, 'rb') as in_file:
img = jpeg.decode(in_file.read(),
_jpegflag(flag, channel_order))
if img.shape[-1] == 1:
img = img[:, :, 0]
return img
else:
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imread(img_or_path, flag)
if flag == IMREAD_COLOR and channel_order == 'rgb':
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
return img
else:
raise TypeError('"img" must be a numpy array or a filename')
def philly_imread(self, img_or_path, flag='color'):
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
flag = imread_flags[flag] if is_str(flag) else flag
# check_file_exist(img_or_path, 'img file does not exist: {}'.format(img_or_path))
return phillyzip_imread(img_or_path, flag)
else:
raise TypeError('"img" must be a numpy array or a filename')
def imread(img_or_path, flag='color', channel_order='bgr', backend=None):
"""Read an image.
Args:
img_or_path (ndarray or str or Path): Either a numpy array or str or
pathlib.Path. If it is a numpy array (loaded image), then
it will be returned as is.
flag (str): Flags specifying the color type of a loaded image,
candidates are `color`, `grayscale` and `unchanged`.
Note that the `turbojpeg` backened does not support `unchanged`.
channel_order (str): Order of channel, candidates are `bgr` and `rgb`.
backend (str | None): The image decoding backend type. Options are
`cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`.
If backend is None, the global imread_backend specified by
``mmcv.use_backend()`` will be used. Default: None.
Returns:
ndarray: Loaded image array.
"""
if backend is None:
backend = imread_backend
if backend not in supported_backends:
raise ValueError(f'backend: {backend} is not supported. Supported '
"backends are 'cv2', 'turbojpeg', 'pillow'")
if isinstance(img_or_path, Path):
img_or_path = str(img_or_path)
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
check_file_exist(img_or_path,
f'img file does not exist: {img_or_path}')
if backend == 'turbojpeg':
with open(img_or_path, 'rb') as in_file:
img = jpeg.decode(in_file.read(),
_jpegflag(flag, channel_order))
if img.shape[-1] == 1:
img = img[:, :, 0]
return img
elif backend == 'pillow':
img = Image.open(img_or_path)
img = _pillow2array(img, flag, channel_order)
return img
elif backend == 'tifffile':
img = tifffile.imread(img_or_path)
return img
else:
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imread(img_or_path, flag)
if flag == IMREAD_COLOR and channel_order == 'rgb':
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
return img
else:
raise TypeError('"img" must be a numpy array or a str or '
'a pathlib.Path object')
def imread(path, flag='color'):
zip_path, path_img = ZipReader.split_zip_style_path(path)
zfile = ZipReader.get_zipfile(zip_path)
data = zfile.read(path_img)
flag = imread_flags[flag] if is_str(flag) else flag
im = cv2.imdecode(np.frombuffer(data, np.uint8), flag)
return im
def color_val(color):
"""Convert various input to color tuples.
Args:
color (:obj:`Color`/str/tuple/int/ndarray): Color inputs
Returns:
tuple[int]: A tuple of 3 integers indicating BGR channels.
"""
if is_str(color):
return Color[color].value
elif isinstance(color, Color):
return color.value
elif isinstance(color, tuple):
assert len(color) == 3
for channel in color:
assert channel >= 0 and channel <= 255
return color
elif isinstance(color, int):
assert color >= 0 and color <= 255
return color, color, color
elif isinstance(color, np.ndarray):
assert color.ndim == 1 and color.size == 3
assert np.all((color >= 0) & (color <= 255))
color = color.astype(np.uint8)
return tuple(color)
else:
raise TypeError('Invalid type for color: {}'.format(type(color)))
def color_val(color=None):
if is_str(color):
color = color[0].upper() + color[1:].lower()
return list(COLORS[color])[::-1]
elif color == None:
color_name = random.choice(list(COLORS.keys()))
return list(COLORS[color_name])[::-1]
elif type(color) == int:
return list(COLORS[list(COLORS.keys())[color]])[::-1]
else:
return list(COLORS['Red'])[::-1]
def color_val(color = None):
# COLORS = {k: v for k, v in sorted(COLORS.items(), key=lambda item: item[0])}
if is_str(color):
color = color[0].upper() + color[1:].lower()
return list(COLORS[color])[::-1]
elif color == None:
color_name = random.choice(list(COLORS.keys()))
return list(COLORS[color_name])[::-1]
elif type(color) == int:
return list(COLORS[list(COLORS.keys())[color]])[::-1]
else:
return list(COLORS['Red'])[::-1]
def imfrombytes(content, flag='color', channel_order='bgr', backend=None):
"""Read an image from bytes.
Args:
content (bytes): Image bytes got from files or other streams.
flag (str): Same as :func:`imread`.
channel_order (str): The channel order of the output, candidates
are 'bgr' and 'rgb'. Default to 'bgr'.
backend (str | None): The image decoding backend type. Options are
`cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`. If backend is
None, the global imread_backend specified by ``mmcv.use_backend()``
will be used. Default: None.
Returns:
ndarray: Loaded image array.
Examples:
>>> img_path = '/path/to/img.jpg'
>>> with open(img_path, 'rb') as f:
>>> img_buff = f.read()
>>> img = mmcv.imfrombytes(img_buff)
>>> img = mmcv.imfrombytes(img_buff, flag='color', channel_order='rgb')
>>> img = mmcv.imfrombytes(img_buff, backend='pillow')
>>> img = mmcv.imfrombytes(img_buff, backend='cv2')
"""
if backend is None:
backend = imread_backend
if backend not in supported_backends:
raise ValueError(
f'backend: {backend} is not supported. Supported '
"backends are 'cv2', 'turbojpeg', 'pillow', 'tifffile'")
if backend == 'turbojpeg':
img = jpeg.decode(content, _jpegflag(flag, channel_order))
if img.shape[-1] == 1:
img = img[:, :, 0]
return img
elif backend == 'pillow':
with io.BytesIO(content) as buff:
img = Image.open(buff)
img = _pillow2array(img, flag, channel_order)
return img
elif backend == 'tifffile':
with io.BytesIO(content) as buff:
img = tifffile.imread(buff)
return img
else:
img_np = np.frombuffer(content, np.uint8)
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imdecode(img_np, flag)
if flag == IMREAD_COLOR and channel_order == 'rgb':
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
return img
def imread(img_or_path, flag='color'):
"""Read an image.
Args:
img_or_path (ndarray or str): Either a numpy array or image path.
If it is a numpy array (loaded image), then it will be returned
as is.
flag (str): Flags specifying the color type of a loaded image,
candidates are `color`, `grayscale` and `unchanged`.
Returns:
ndarray: Loaded image array.
"""
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
flag = imread_flags[flag] if is_str(flag) else flag
check_file_exist(img_or_path,
'img file does not exist: {}'.format(img_or_path))
return cv2.imread(img_or_path, flag)
else:
raise TypeError('"img" must be a numpy array or a filename')
def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
"""Read an optical flow map.
Args:
flow_or_path (ndarray or str): A flow map or filepath.
quantize (bool): whether to read quantized pair, if set to True,
remaining args will be passed to :func:`dequantize_flow`.
concat_axis (int): The axis that dx and dy are concatenated,
can be either 0 or 1. Ignored if quantize is False.
Returns:
ndarray: Optical flow represented as a (h, w, 2) numpy array
"""
if isinstance(flow_or_path, np.ndarray):
if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2):
raise ValueError('Invalid flow with shape {}'.format(
flow_or_path.shape))
return flow_or_path
elif not is_str(flow_or_path):
raise TypeError(
'"flow_or_path" must be a filename or numpy array, not {}'.format(
type(flow_or_path)))
if not quantize:
with open(flow_or_path, 'rb') as f:
try:
header = f.read(4).decode('utf-8')
except Exception:
raise IOError('Invalid flow file: {}'.format(flow_or_path))
else:
if header != 'PIEH':
raise IOError(
'Invalid flow file: {}, header does not contain PIEH'.
format(flow_or_path))
w = np.fromfile(f, np.int32, 1).squeeze()
h = np.fromfile(f, np.int32, 1).squeeze()
flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
else:
assert concat_axis in [0, 1]
cat_flow = imread(flow_or_path, flag='unchanged')
if cat_flow.ndim != 2:
raise IOError(
'{} is not a valid quantized flow file, its dimension is {}.'.
format(flow_or_path, cat_flow.ndim))
assert cat_flow.shape[concat_axis] % 2 == 0
dx, dy = np.split(cat_flow, 2, axis=concat_axis)
flow = dequantize_flow(dx, dy, *args, **kwargs)
return flow.astype(np.float32)
def imfrombytes(content, flag='color'):
"""Read an image from bytes.
Args:
content (bytes): Image bytes got from files or other streams.
flag (str): Same as :func:`imread`.
Returns:
ndarray: Loaded image array.
"""
img_np = np.frombuffer(content, np.uint8)
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imdecode(img_np, flag)
return img
def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
'Read an optical flow map.\n\n Args:\n flow_or_path (ndarray or str): A flow map or filepath.\n quantize (bool): whether to read quantized pair, if set to True,\n remaining args will be passed to :func:`dequantize_flow`.\n concat_axis (int): The axis that dx and dy are concatenated,\n can be either 0 or 1. Ignored if quantize is False.\n\n Returns:\n ndarray: Optical flow represented as a (h, w, 2) numpy array\n '
if isinstance(flow_or_path, np.ndarray):
if ((flow_or_path.ndim != 3) or (flow_or_path.shape[(-1)] != 2)):
raise ValueError(''.join(
['Invalid flow with shape ', '{}'.format(flow_or_path.shape)]))
return flow_or_path
elif (not is_str(flow_or_path)):
raise TypeError(''.join([
'"flow_or_path" must be a filename or numpy array, not ',
'{}'.format(type(flow_or_path))
]))
if (not quantize):
with open(flow_or_path, 'rb') as f:
try:
header = f.read(4).decode('utf-8')
except Exception:
raise IOError(''.join(
['Invalid flow file: ', '{}'.format(flow_or_path)]))
else:
if (header != 'PIEH'):
raise IOError(''.join([
'Invalid flow file: ', '{}'.format(flow_or_path),
', header does not contain PIEH'
]))
w = np.fromfile(f, np.int32, 1).squeeze()
h = np.fromfile(f, np.int32, 1).squeeze()
flow = np.fromfile(f, np.float32, ((w * h) * 2)).reshape((h, w, 2))
else:
assert (concat_axis in [0, 1])
cat_flow = imread(flow_or_path, flag='unchanged')
if (cat_flow.ndim != 2):
raise IOError(''.join([
'{}'.format(flow_or_path),
' is not a valid quantized flow file, its dimension is ',
'{}'.format(cat_flow.ndim), '.'
]))
assert ((cat_flow.shape[concat_axis] % 2) == 0)
(dx, dy) = np.split(cat_flow, 2, axis=concat_axis)
flow = dequantize_flow(dx, dy, *args, **kwargs)
return flow.astype(np.float32)
def imfrombytes(content, flag='color', channel_order='bgr'):
"""Read an image from bytes.
Args:
content (bytes): Image bytes got from files or other streams.
flag (str): Same as :func:`imread`.
Returns:
ndarray: Loaded image array.
"""
if imread_backend == 'turbojpeg':
img = jpeg.decode(content, _jpegflag(flag, channel_order))
if img.shape[-1] == 1:
img = img[:, :, 0]
return img
else:
img_np = np.frombuffer(content, np.uint8)
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imdecode(img_np, flag)
if flag == IMREAD_COLOR and channel_order == 'rgb':
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
return img
def imfrombytes(content, flag='color', channel_order='bgr', backend=None):
"""Read an image from bytes.
Args:
content (bytes): Image bytes got from files or other streams.
flag (str): Same as :func:`imread`.
backend (str | None): The image decoding backend type. Options are
`cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the
global imread_backend specified by ``mmcv.use_backend()`` will be
used. Default: None.
Returns:
ndarray: Loaded image array.
"""
if backend is None:
backend = imread_backend
if backend not in supported_backends:
raise ValueError("backend: %s is not supported. Supported backends are 'cv2', 'turbojpeg', 'pillow'"%backend)
if backend == 'turbojpeg':
img = jpeg.decode(content, _jpegflag(flag, channel_order))
if img.shape[-1] == 1:
img = img[:, :, 0]
return img
elif backend == 'pillow':
buff = io.BytesIO(content)
img = Image.open(buff)
img = _pillow2array(img, flag, channel_order)
return img
else:
img_np = np.frombuffer(content, np.uint8)
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imdecode(img_np, flag)
if flag == IMREAD_COLOR and channel_order == 'rgb':
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
return img
def imread(img_or_path,
flag='color',
channel_order='bgr',
backend=None,
file_client_args=None):
"""Read an image.
Note:
In v1.4.1 and later, add `file_client_args` parameters.
Args:
img_or_path (ndarray or str or Path): Either a numpy array or str or
pathlib.Path. If it is a numpy array (loaded image), then
it will be returned as is.
flag (str): Flags specifying the color type of a loaded image,
candidates are `color`, `grayscale`, `unchanged`,
`color_ignore_orientation` and `grayscale_ignore_orientation`.
By default, `cv2` and `pillow` backend would rotate the image
according to its EXIF info unless called with `unchanged` or
`*_ignore_orientation` flags. `turbojpeg` and `tifffile` backend
always ignore image's EXIF info regardless of the flag.
The `turbojpeg` backend only supports `color` and `grayscale`.
channel_order (str): Order of channel, candidates are `bgr` and `rgb`.
backend (str | None): The image decoding backend type. Options are
`cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`.
If backend is None, the global imread_backend specified by
``mmcv.use_backend()`` will be used. Default: None.
file_client_args (dict | None): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
Returns:
ndarray: Loaded image array.
Examples:
>>> import mmcv
>>> img_path = '/path/to/img.jpg'
>>> img = mmcv.imread(img_path)
>>> img = mmcv.imread(img_path, flag='color', channel_order='rgb',
... backend='cv2')
>>> img = mmcv.imread(img_path, flag='color', channel_order='bgr',
... backend='pillow')
>>> s3_img_path = 's3://bucket/img.jpg'
>>> # infer the file backend by the prefix s3
>>> img = mmcv.imread(s3_img_path)
>>> # manually set the file backend petrel
>>> img = mmcv.imread(s3_img_path, file_client_args={
... 'backend': 'petrel'})
>>> http_img_path = 'http://path/to/img.jpg'
>>> img = mmcv.imread(http_img_path)
>>> img = mmcv.imread(http_img_path, file_client_args={
... 'backend': 'http'})
"""
if isinstance(img_or_path, Path):
img_or_path = str(img_or_path)
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
file_client = FileClient.infer_client(file_client_args, img_or_path)
img_bytes = file_client.get(img_or_path)
return imfrombytes(img_bytes, flag, channel_order, backend)
else:
raise TypeError('"img" must be a numpy array or a str or '
'a pathlib.Path object')
def imshow_rbboxes(img_or_path,
rbboxes,
labels=None,
scores=None,
score_threshold=0.0,
colors='red',
show_label=False,
show_score=False,
thickness=3,
show=True,
win_name='',
wait_time=0,
out_file=None,
return_img=False):
""" Draw oriented bounding boxes on image
Args:
img (str or ndarray): The image to be displayed.
rbboxes (list or ndarray): A ndarray of shape (N, 8)
labels (list or ndarray): A ndarray of shape (N, 1)
scores (list or ndarray): A ndarray of shape (N, 1)
"""
if is_str(img_or_path):
img = cv2.imread(img_or_path)
else:
img = img_or_path
if rbboxes == []:
return
if isinstance(rbboxes, list):
rbboxes = np.array(rbboxes)
if rbboxes.shape[1] == 5:
rbboxes_ = []
for rbbox in rbboxes:
rbboxes_.append(thetaobb2pointobb(rbbox))
rbboxes = np.array(rbboxes_)
if rbboxes.ndim == 1:
rbboxes = np.array([rbboxes])
if labels is None:
labels_vis = np.array(['ins'] * rbboxes.shape[0])
else:
labels_vis = np.array(labels)
if labels_vis.ndim == 0:
labels_vis = np.array([labels_vis])
if scores is None:
scores_vis = np.array([1.0] * rbboxes.shape[0])
else:
scores_vis = np.array(scores)
if scores_vis.ndim == 0:
scores_vis = np.array([scores_vis])
if labels is None:
colors = dict()
colors[colors] = color_val(colors)
else:
max_label = max(labels)
colors = [color_val(_) for _ in range(max_label + 1)]
for rbbox, label, score in zip(rbboxes, labels_vis, scores_vis):
if score < score_threshold:
continue
if len(rbbox) == 5:
rbbox = np.array(thetaobb2pointobb(rbbox))
rbbox = rbbox.astype(np.int32)
cx = np.mean(rbbox[::2])
cy = np.mean(rbbox[1::2])
current_color = colors[label]
for idx in range(-1, 3, 1):
cv2.line(
img, (int(rbbox[idx * 2]), int(rbbox[idx * 2 + 1])),
(int(rbbox[(idx + 1) * 2]), int(rbbox[(idx + 1) * 2 + 1])),
current_color,
thickness=thickness)
if show_label:
cv2.putText(img,
label, (cx, cy),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
fontScale=1.0,
color=current_color,
thickness=2,
lineType=8)
if show_score:
cv2.putText(img,
"{:.2f}".format(score), (cx, cy),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
fontScale=1.0,
color=current_color,
thickness=2,
lineType=8)
if show:
cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
cv2.imshow(win_name, img)
cv2.waitKey(wait_time)
if out_file is not None:
dir_name = osp.abspath(osp.dirname(out_file))
mkdir_or_exist(dir_name)
cv2.imwrite(out_file, img)
if return_img:
return img
def imshow_bboxes(img_or_path,
bboxes,
labels=None,
scores=None,
score_threshold=0.0,
colors='red',
show_label=False,
show_score=False,
thickness=3,
show=True,
win_name='',
wait_time=0,
out_file=None,
origin_file=None,
return_img=False):
""" Draw horizontal bounding boxes on image
Args:
img (str or ndarray): The image to be displayed.
bboxes (list or ndarray): A ndarray of shape (N, 4)
labels (list or ndarray): A ndarray of shape (N, 1)
scores (list or ndarray): A ndarray of shape (N, 1)
"""
if is_str(img_or_path):
img = cv2.imread(img_or_path)
img_origin = img.copy()
else:
img = img_or_path
img_origin = img.copy()
if len(bboxes) == []:
return
if isinstance(bboxes, list):
bboxes = np.array(bboxes)
if bboxes.ndim == 1:
bboxes = np.array([bboxes])
if labels is None:
labels_vis = np.array(['ins'] * bboxes.shape[0])
else:
labels_vis = np.array(labels)
if labels_vis.ndim == 0:
labels_vis = np.array([labels_vis])
if scores is None:
scores_vis = np.array([1.0] * bboxes.shape[0])
else:
scores_vis = np.array(scores)
if scores_vis.ndim == 0:
scores_vis = np.array([scores_vis])
if labels is None:
colors = dict()
colors[colors] = color_val(colors)
else:
max_label = max(labels)
if max_label > 20:
max_label = max_label % 20
colors = [color_val(_) for _ in range(max_label + 1)]
for bbox, label, score in zip(bboxes, labels_vis, scores_vis):
if score < score_threshold:
continue
bbox = bbox.astype(np.int32)
xmin, ymin, xmax, ymax = bbox
current_color = colors[label % 20]
cv2.rectangle(img, (xmin, ymin), (xmax, ymax),
color=current_color,
thickness=thickness)
if show_label:
cv2.putText(img,
label, (xmin, ymin - 5),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
fontScale=1.0,
color=current_color,
thickness=2,
lineType=8)
if show_score:
cv2.putText(img,
"{:.2f}".format(score), (xmin, ymin - 5),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
fontScale=1.0,
color=current_color,
thickness=2,
lineType=8)
if show:
cv2.imshow(win_name, img)
cv2.waitKey(wait_time)
if out_file is not None:
dir_name = osp.abspath(osp.dirname(out_file))
mkdir_or_exist(dir_name)
cv2.imwrite(out_file, img)
if origin_file is not None:
dir_name = osp.abspath(osp.dirname(origin_file))
mkdir_or_exist(dir_name)
cv2.imwrite(origin_file, img_origin)
if return_img:
return img
def imshow_bboxes(img_or_path,
bboxes,
labels=None,
scores=None,
score_threshold=0.0,
show_label=False,
show_score=False,
thickness=2,
show=False,
win_name='',
wait_time=0,
out_file=None,
origin_file=None,
return_img=False):
""" Draw horizontal bounding boxes on image
Args:
img (str or ndarray): The image to be displayed.
bboxes (list or ndarray): A ndarray of shape (N, 4)
labels (dict): {"category": idx}
scores (list or ndarray): A ndarray of shape (N, 1)
"""
if is_str(img_or_path):
img = cv2.imread(img_or_path)
img_origin = img.copy()
else:
img = img_or_path
img_origin = img.copy()
if len(bboxes) == 0:
return
if isinstance(bboxes, list):
bboxes = np.array(bboxes)
if bboxes.ndim == 1:
bboxes = np.array([bboxes])
if labels is None:
labels_vis = np.array(['ins'] * bboxes.shape[0])
else:
labels_vis = [list(label.keys())[0] for label in labels]
class_idxs = [list(label.values())[0] for label in labels]
max_label_idx = max(class_idxs)
if max_label_idx > 20:
max_label_idx = max_label_idx % 20
color_dict = {
list(label.keys())[0]: color_val(list(label.values())[0])
for label in labels
}
if scores is None:
scores_vis = np.array([1.0] * bboxes.shape[0])
else:
scores_vis = np.array(scores)
if scores_vis.ndim == 0:
scores_vis = np.array([scores_vis])
for bbox, label, score in zip(bboxes, labels_vis, scores_vis):
if score < score_threshold:
continue
bbox = bbox.astype(np.int32)
xmin, ymin, xmax, ymax = bbox
if labels is None:
current_color = color_val()
else:
current_color = color_dict[label]
cv2.rectangle(img, (xmin, ymin), (xmax, ymax),
color=current_color,
thickness=thickness)
if show_label:
cv2.putText(img,
label, (xmin, ymin - 5),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
fontScale=1.0,
color=current_color,
thickness=2,
lineType=8)
if show_score:
cv2.putText(img,
"{:.2f}".format(score), (xmin, ymin - 5),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
fontScale=1.0,
color=current_color,
thickness=2,
lineType=8)
if show:
# cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
# cv2.resizeWindow(win_name, 360, 360)
cv2.imshow(win_name, img)
cv2.waitKey(wait_time)
if out_file is not None:
dir_name = osp.abspath(osp.dirname(out_file))
mkdir_or_exist(dir_name)
cv2.imwrite(out_file, img)
if origin_file is not None:
dir_name = osp.abspath(osp.dirname(origin_file))
mkdir_or_exist(dir_name)
cv2.imwrite(origin_file, img_origin)
if return_img:
return img