import math
import numbers
import os.path as osp
import random
import cv2
import mmcv
import numpy as np
from ..registry import PIPELINES
[docs]@PIPELINES.register_module()
class Resize(object):
"""Resize data to a specific size for training or resize the images to fit
the network input regulation for testing.
When used for resizing images to fit network input regulation, the case is
that a network may have several downsample and then upsample operation,
then the input height and width should be divisible by the downsample
factor of the network.
For example, the network would downsample the input for 5 times with
stride 2, then the downsample factor is 2^5 = 32 and the height
and width should be divisible by 32.
Required keys are the keys in attribute "keys", added or modified keys are
"keep_ratio", "scale_factor", "interpolation" and the
keys in attribute "keys".
All keys in "keys" should have the same shape. "test_trans" is used to
record the test transformation to align the input's shape.
Args:
keys (list[str]): The images to be resized.
scale (float | Tuple[int]): If scale is Tuple(int), target spatial
size (h, w). Otherwise, target spatial size is scaled by input
size. If any of scale is -1, we will rescale short edge.
Note that when it is used, `size_factor` and `max_size` are
useless. Default: None
keep_ratio (bool): If set to True, images will be resized without
changing the aspect ratio. Otherwise, it will resize images to a
given size. Default: False.
Note that it is used togher with `scale`.
size_factor (int): Let the output shape be a multiple of size_factor.
Default:None.
Note that when it is used, `scale` should be set to None and
`keep_ratio` should be set to False.
max_size (int): The maximum size of the longest side of the output.
Default:None.
Note that it is used togher with `size_factor`.
interpolation (str): Algorithm used for interpolation:
"nearest" | "bilinear" | "bicubic" | "area" | "lanczos".
Default: "bilinear".
"""
def __init__(self,
keys,
scale=None,
keep_ratio=False,
size_factor=None,
max_size=None,
interpolation='bilinear'):
assert keys, 'Keys should not be empty.'
if size_factor:
assert scale is None, ('When size_factor is used, scale should ',
f'be None. But received {scale}.')
assert keep_ratio is False, ('When size_factor is used, '
'keep_ratio should be False.')
if max_size:
assert size_factor is not None, (
'When max_size is used, '
f'size_factor should also be set. But received {size_factor}.')
if isinstance(scale, float):
if scale <= 0:
raise ValueError(f'Invalid scale {scale}, must be positive.')
elif mmcv.is_tuple_of(scale, int):
max_long_edge = max(scale)
max_short_edge = min(scale)
if max_short_edge == -1:
# assign np.inf to long edge for rescaling short edge later.
scale = (np.inf, max_long_edge)
elif scale is not None:
raise TypeError(
f'Scale must be None, float or tuple of int, but got '
f'{type(scale)}.')
self.keys = keys
self.scale = scale
self.size_factor = size_factor
self.max_size = max_size
self.keep_ratio = keep_ratio
self.interpolation = interpolation
def _resize(self, img):
if self.keep_ratio:
img, self.scale_factor = mmcv.imrescale(
img,
self.scale,
return_scale=True,
interpolation=self.interpolation)
else:
img, w_scale, h_scale = mmcv.imresize(
img,
self.scale,
return_scale=True,
interpolation=self.interpolation)
self.scale_factor = np.array((w_scale, h_scale), dtype=np.float32)
return img
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
if self.size_factor:
h, w = results[self.keys[0]].shape[:2]
new_h = h - (h % self.size_factor)
new_w = w - (w % self.size_factor)
if self.max_size:
new_h = min(self.max_size - (self.max_size % self.size_factor),
new_h)
new_w = min(self.max_size - (self.max_size % self.size_factor),
new_w)
self.scale = (new_w, new_h)
for key in self.keys:
results[key] = self._resize(results[key])
if len(results[key].shape) == 2:
results[key] = np.expand_dims(results[key], axis=2)
results['scale_factor'] = self.scale_factor
results['keep_ratio'] = self.keep_ratio
results['interpolation'] = self.interpolation
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (
f'(keys={self.keys}, scale={self.scale}, '
f'keep_ratio={self.keep_ratio}, size_factor={self.size_factor}, '
f'max_size={self.max_size},interpolation={self.interpolation})')
return repr_str
[docs]@PIPELINES.register_module()
class Flip(object):
"""Flip the input data with a probability.
Reverse the order of elements in the given data with a specific direction.
The shape of the data is preserved, but the elements are reordered.
Required keys are the keys in attributes "keys", added or modified keys are
"flip", "flip_direction" and the keys in attributes "keys".
It also supports flipping a list of images with the same flip.
Args:
keys (list[str]): The images to be flipped.
flip_ratio (float): The propability to flip the images.
direction (str): Flip images horizontally or vertically. Options are
"horizontal" | "vertical". Default: "horizontal".
"""
_directions = ['horizontal', 'vertical']
def __init__(self, keys, flip_ratio=0.5, direction='horizontal'):
if direction not in self._directions:
raise ValueError(f'Direction {direction} is not supported.'
f'Currently support ones are {self._directions}')
self.keys = keys
self.flip_ratio = flip_ratio
self.direction = direction
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
flip = np.random.random() < self.flip_ratio
if flip:
for key in self.keys:
if isinstance(results[key], list):
for v in results[key]:
mmcv.imflip_(v, self.direction)
else:
mmcv.imflip_(results[key], self.direction)
results['flip'] = flip
results['flip_direction'] = self.direction
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(keys={self.keys}, flip_ratio={self.flip_ratio}, '
f'direction={self.direction})')
return repr_str
[docs]@PIPELINES.register_module()
class Pad(object):
"""Pad the images to align with network downsample factor for testing.
See `Reshape` for more explanation. `numpy.pad` is used for the pad
operation.
Required keys are the keys in attribute "keys", added or
modified keys are "test_trans" and the keys in attribute
"keys". All keys in "keys" should have the same shape. "test_trans" is used
to record the test transformation to align the input's shape.
Args:
keys (list[str]): The images to be padded.
ds_factor (int): Downsample factor of the network. The height and
weight will be padded to a multiple of ds_factor. Default: 32.
kwargs (option): any keyword arguments to be passed to `numpy.pad`.
"""
def __init__(self, keys, ds_factor=32, **kwargs):
self.keys = keys
self.ds_factor = ds_factor
self.kwargs = kwargs
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
h, w = results[self.keys[0]].shape[:2]
new_h = self.ds_factor * ((h - 1) // self.ds_factor + 1)
new_w = self.ds_factor * ((w - 1) // self.ds_factor + 1)
pad_h = new_h - h
pad_w = new_w - w
if new_h != h or new_w != w:
pad_width = ((0, pad_h), (0, pad_w), (0, 0))
for key in self.keys:
results[key] = np.pad(results[key],
pad_width[:results[key].ndim],
**self.kwargs)
results['pad'] = (pad_h, pad_w)
return results
def __repr__(self):
repr_str = self.__class__.__name__
kwargs_str = ', '.join(
[f'{key}={val}' for key, val in self.kwargs.items()])
repr_str += (f'(keys={self.keys}, ds_factor={self.ds_factor}, '
f'{kwargs_str})')
return repr_str
[docs]@PIPELINES.register_module()
class RandomAffine(object):
"""Apply random affine to input images.
This class is adopted from
https://github.com/pytorch/vision/blob/v0.5.0/torchvision/transforms/transforms.py#L1015 # noqa
It should be noted that in
https://github.com/Yaoyi-Li/GCA-Matting/blob/master/dataloader/data_generator.py#L70 # noqa
random flip is added. See explanation of `flip_ratio` below.
Required keys are the keys in attribute "keys", modified keys
are keys in attribute "keys".
Args:
keys (Sequence[str]): The images to be affined.
degrees (float | tuple[float]): Range of degrees to select from. If it
is a float instead of a tuple like (min, max), the range of degrees
will be (-degrees, +degrees). Set to 0 to deactivate rotations.
translate (tuple, optional): Tuple of maximum absolute fraction for
horizontal and vertical translations. For example translate=(a, b),
then horizontal shift is randomly sampled in the range
-img_width * a < dx < img_width * a and vertical shift is randomly
sampled in the range -img_height * b < dy < img_height * b.
Default: None.
scale (tuple, optional): Scaling factor interval, e.g (a, b), then
scale is randomly sampled from the range a <= scale <= b.
Default: None.
shear (float | tuple[float], optional): Range of shear degrees to
select from. If shear is a float, a shear parallel to the x axis
and a shear parallel to the y axis in the range (-shear, +shear)
will be applied. Else if shear is a tuple of 2 values, a x-axis
shear and a y-axis shear in (shear[0], shear[1]) will be applied.
Default: None.
flip_ratio (float, optional): Probability of the image being flipped.
The flips in horizontal direction and vertical direction are
independent. The image may be flipped in both directions.
Default: None.
"""
def __init__(self,
keys,
degrees,
translate=None,
scale=None,
shear=None,
flip_ratio=None):
self.keys = keys
if isinstance(degrees, numbers.Number):
assert degrees >= 0, ('If degrees is a single number, '
'it must be positive.')
self.degrees = (-degrees, degrees)
else:
assert isinstance(degrees, tuple) and len(degrees) == 2, \
'degrees should be a tuple and it must be of length 2.'
self.degrees = degrees
if translate is not None:
assert isinstance(translate, tuple) and len(translate) == 2, \
'translate should be a tuple and it must be of length 2.'
for t in translate:
assert 0.0 <= t <= 1.0, ('translation values should be '
'between 0 and 1.')
self.translate = translate
if scale is not None:
assert isinstance(scale, tuple) and len(scale) == 2, \
'scale should be a tuple and it must be of length 2.'
for s in scale:
assert s > 0, 'scale values should be positive.'
self.scale = scale
if shear is not None:
if isinstance(shear, numbers.Number):
assert shear >= 0, ('If shear is a single number, '
'it must be positive.')
self.shear = (-shear, shear)
else:
assert isinstance(shear, tuple) and len(shear) == 2, \
'shear should be a tuple and it must be of length 2.'
# X-Axis and Y-Axis shear with (min, max)
self.shear = shear
else:
self.shear = shear
if flip_ratio is not None:
assert isinstance(flip_ratio,
float), 'flip_ratio should be a float.'
self.flip_ratio = flip_ratio
else:
self.flip_ratio = 0
@staticmethod
def _get_params(degrees, translate, scale_ranges, shears, flip_ratio,
img_size):
"""Get parameters for affine transformation.
Returns:
paras (tuple): Params to be passed to the affine transformation.
"""
angle = np.random.uniform(degrees[0], degrees[1])
if translate is not None:
max_dx = translate[0] * img_size[0]
max_dy = translate[1] * img_size[1]
translations = (np.round(np.random.uniform(-max_dx, max_dx)),
np.round(np.random.uniform(-max_dy, max_dy)))
else:
translations = (0, 0)
if scale_ranges is not None:
scale = (np.random.uniform(scale_ranges[0], scale_ranges[1]),
np.random.uniform(scale_ranges[0], scale_ranges[1]))
else:
scale = (1.0, 1.0)
if shears is not None:
shear = np.random.uniform(shears[0], shears[1])
else:
shear = 0.0
flip = (np.random.rand(2) < flip_ratio).astype(np.int) * 2 - 1
return angle, translations, scale, shear, flip
@staticmethod
def _get_inverse_affine_matrix(center, angle, translate, scale, shear,
flip):
"""Helper method to compute inverse matrix for affine transformation.
As it is explained in PIL.Image.rotate, we need compute INVERSE of
affine transformation matrix: M = T * C * RSS * C^-1 where
T is translation matrix:
[1, 0, tx | 0, 1, ty | 0, 0, 1];
C is translation matrix to keep center:
[1, 0, cx | 0, 1, cy | 0, 0, 1];
RSS is rotation with scale and shear matrix.
It is different from the original function in torchvision.
1. The order are changed to flip -> scale -> rotation -> shear.
2. x and y have different scale factors.
RSS(shear, a, scale, f) =
[ cos(a + shear)*scale_x*f -sin(a + shear)*scale_y 0]
[ sin(a)*scale_x*f cos(a)*scale_y 0]
[ 0 0 1]
Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1.
"""
angle = math.radians(angle)
shear = math.radians(shear)
scale_x = 1.0 / scale[0] * flip[0]
scale_y = 1.0 / scale[1] * flip[1]
# Inverted rotation matrix with scale and shear
d = math.cos(angle + shear) * math.cos(angle) + math.sin(
angle + shear) * math.sin(angle)
matrix = [
math.cos(angle) * scale_x,
math.sin(angle + shear) * scale_x, 0, -math.sin(angle) * scale_y,
math.cos(angle + shear) * scale_y, 0
]
matrix = [m / d for m in matrix]
# Apply inverse of translation and of center translation:
# RSS^-1 * C^-1 * T^-1
matrix[2] += matrix[0] * (-center[0] - translate[0]) + matrix[1] * (
-center[1] - translate[1])
matrix[5] += matrix[3] * (-center[0] - translate[0]) + matrix[4] * (
-center[1] - translate[1])
# Apply center translation: C * RSS^-1 * C^-1 * T^-1
matrix[2] += center[0]
matrix[5] += center[1]
return matrix
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
h, w = results[self.keys[0]].shape[:2]
# if image is too small, set degree to 0 to reduce introduced dark area
if np.maximum(h, w) < 1024:
params = self._get_params((0, 0), self.translate, self.scale,
self.shear, self.flip_ratio, (h, w))
else:
params = self._get_params(self.degrees, self.translate, self.scale,
self.shear, self.flip_ratio, (h, w))
center = (w * 0.5 + 0.5, h * 0.5 + 0.5)
M = self._get_inverse_affine_matrix(center, *params)
M = np.array(M).reshape((2, 3))
for key in self.keys:
results[key] = cv2.warpAffine(
results[key],
M, (w, h),
flags=cv2.INTER_NEAREST + cv2.WARP_INVERSE_MAP)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(keys={self.keys}, degrees={self.degrees}, '
f'translate={self.translate}, scale={self.scale}, '
f'shear={self.shear}, flip_ratio={self.flip_ratio})')
return repr_str
[docs]@PIPELINES.register_module()
class RandomJitter(object):
"""Randomly jitter the foreground in hsv space.
The jitter range of hue is adjustable while the jitter ranges of saturation
and value are adaptive to the images. Side effect: the "fg" image will be
converted to `np.float32`.
Required keys are "fg" and "alpha", modified key is "fg".
Args:
hue_range (float | tuple[float]): Range of hue jittering. If it is a
float instead of a tuple like (min, max), the range of hue
jittering will be (-hue_range, +hue_range). Default: 40.
"""
def __init__(self, hue_range=40):
if isinstance(hue_range, numbers.Number):
assert hue_range >= 0, ('If hue_range is a single number, '
'it must be positive.')
self.hue_range = (-hue_range, hue_range)
else:
assert isinstance(hue_range, tuple) and len(hue_range) == 2, \
'hue_range should be a tuple and it must be of length 2.'
self.hue_range = hue_range
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
fg, alpha = results['fg'], results['alpha']
# convert to HSV space;
# convert to float32 image to keep precision during space conversion.
fg = mmcv.bgr2hsv(fg.astype(np.float32) / 255)
# Hue noise
hue_jitter = np.random.randint(self.hue_range[0], self.hue_range[1])
fg[:, :, 0] = np.remainder(fg[:, :, 0] + hue_jitter, 360)
# Saturation noise
sat_mean = fg[:, :, 1][alpha > 0].mean()
# jitter saturation within range (1.1 - sat_mean) * [-0.1, 0.1]
sat_jitter = (1.1 - sat_mean) * (np.random.rand() * 0.2 - 0.1)
sat = fg[:, :, 1]
sat = np.abs(sat + sat_jitter)
sat[sat > 1] = 2 - sat[sat > 1]
fg[:, :, 1] = sat
# Value noise
val_mean = fg[:, :, 2][alpha > 0].mean()
# jitter value within range (1.1 - val_mean) * [-0.1, 0.1]
val_jitter = (1.1 - val_mean) * (np.random.rand() * 0.2 - 0.1)
val = fg[:, :, 2]
val = np.abs(val + val_jitter)
val[val > 1] = 2 - val[val > 1]
fg[:, :, 2] = val
# convert back to BGR space
fg = mmcv.hsv2bgr(fg)
results['fg'] = fg * 255
return results
def __repr__(self):
return self.__class__.__name__ + f'hue_range={self.hue_range}'
[docs]class BinarizeImage(object):
"""Binarize image.
Args:
keys (Sequence[str]): The images to be binarized.
binary_thr (float): Threshold for binarization.
to_int (bool): If True, return image as int32, otherwise
return image as float32.
"""
def __init__(self, keys, binary_thr, to_int=False):
self.keys = keys
self.binary_thr = binary_thr
self.to_int = to_int
def _binarize(self, img):
type_ = np.float32 if not self.to_int else np.int32
img = (img[..., :] > self.binary_thr).astype(type_)
return img
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
for k in self.keys:
results[k] = self._binarize(results[k])
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(keys={self.keys}, binary_thr={self.binary_thr}, '
f'to_int={self.to_int})')
return repr_str
[docs]@PIPELINES.register_module()
class RandomMaskDilation(object):
"""Randomly dilate binary masks.
Args:
keys (Sequence[str]): The images to be resized.
get_binary (bool): If True, according to binary_thr, reset final
output as binary mask. Otherwise, return masks directly.
binary_thr (float): Threshold for obtaining binary mask.
kernel_min (int): Min size of dilation kernel.
kernel_max (int): Max size of dilation kernel.
"""
def __init__(self, keys, binary_thr=0., kernel_min=9, kernel_max=49):
self.keys = keys
self.kernel_min = kernel_min
self.kernel_max = kernel_max
self.binary_thr = binary_thr
def _random_dilate(self, img):
kernel_size = random.randint(self.kernel_min, self.kernel_max)
kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
dilate_kernel_size = kernel_size
img_ = cv2.dilate(img, kernel, iterations=1)
h, w = img_.shape[:2]
img_ = (img_ > self.binary_thr).astype(np.float32)
return img_, dilate_kernel_size
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
for k in self.keys:
results[k], d_kernel = self._random_dilate(results[k])
if len(results[k].shape) == 2:
results[k] = np.expand_dims(results[k], axis=2)
results[k + '_dilate_kernel_size'] = d_kernel
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(keys={self.keys}, kernel_min={self.kernel_min}, '
f'kernel_max={self.kernel_max})')
return repr_str
[docs]@PIPELINES.register_module()
class RandomTransposeHW(object):
"""Randomly transpose images in H and W dimensions with a probability.
(TransposeHW = horizontal flip + anti-clockwise rotatation by 90 degrees)
When used with horizontal/vertical flips, it serves as a way of rotation
augmentation.
It also supports randomly transposing a list of images.
Required keys are the keys in attributes "keys", added or modified keys are
"transpose" and the keys in attributes "keys".
Args:
keys (list[str]): The images to be transposed.
transpose_ratio (float): The propability to transpose the images.
"""
def __init__(self, keys, transpose_ratio=0.5):
self.keys = keys
self.transpose_ratio = transpose_ratio
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
transpose = np.random.random() < self.transpose_ratio
if transpose:
for key in self.keys:
if isinstance(results[key], list):
results[key] = [v.transpose(1, 0, 2) for v in results[key]]
else:
results[key] = results[key].transpose(1, 0, 2)
results['transpose'] = transpose
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (
f'(keys={self.keys}, transpose_ratio={self.transpose_ratio})')
return repr_str
[docs]@PIPELINES.register_module()
class GenerateFrameIndiceswithPadding(object):
"""Generate frame index with padding for REDS dataset and Vid4 dataset
during testing.
Required keys: lq_path, gt_path, key, num_input_frames, max_frame_num
Added or modified keys: lq_path, gt_path
Args:
padding (str): padding mode, one of
'replicate' | 'reflection' | 'reflection_circle' | 'circle'.
Examples: current_idx = 0, num_input_frames = 5
The generated frame indices under different padding mode:
replicate: [0, 0, 0, 1, 2]
reflection: [2, 1, 0, 1, 2]
reflection_circle: [4, 3, 0, 1, 2]
circle: [3, 4, 0, 1, 2]
filename_tmpl (str): Template for file name. Default: '{:08d}'.
"""
def __init__(self, padding, filename_tmpl='{:08d}'):
if padding not in ('replicate', 'reflection', 'reflection_circle',
'circle'):
raise ValueError(f'Wrong padding mode {padding}.'
'Should be "replicate", "reflection", '
'"reflection_circle", "circle"')
self.padding = padding
self.filename_tmpl = filename_tmpl
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
clip_name, frame_name = results['key'].split('/')
current_idx = int(frame_name)
max_frame_num = results['max_frame_num'] - 1 # start from 0
num_input_frames = results['num_input_frames']
num_pad = num_input_frames // 2
frame_list = []
for i in range(current_idx - num_pad, current_idx + num_pad + 1):
if i < 0:
if self.padding == 'replicate':
pad_idx = 0
elif self.padding == 'reflection':
pad_idx = -i
elif self.padding == 'reflection_circle':
pad_idx = current_idx + num_pad - i
else:
pad_idx = num_input_frames + i
elif i > max_frame_num:
if self.padding == 'replicate':
pad_idx = max_frame_num
elif self.padding == 'reflection':
pad_idx = max_frame_num * 2 - i
elif self.padding == 'reflection_circle':
pad_idx = (current_idx - num_pad) - (i - max_frame_num)
else:
pad_idx = i - num_input_frames
else:
pad_idx = i
frame_list.append(pad_idx)
lq_path_root = results['lq_path']
gt_path_root = results['gt_path']
lq_paths = [
osp.join(lq_path_root, clip_name,
f'{self.filename_tmpl.format(idx)}.png')
for idx in frame_list
]
gt_paths = [osp.join(gt_path_root, clip_name, f'{frame_name}.png')]
results['lq_path'] = lq_paths
results['gt_path'] = gt_paths
return results
def __repr__(self):
repr_str = self.__class__.__name__ + f"(padding='{self.padding}')"
return repr_str
[docs]@PIPELINES.register_module()
class GenerateFrameIndices(object):
"""Generate frame index for REDS datasets. It also performs
temporal augmention with random interval.
Required keys: lq_path, gt_path, key, num_input_frames
Added or modified keys: lq_path, gt_path, interval, reverse
Args:
interval_list (list[int]): Interval list for temporal augmentation.
It will randomly pick an interval from interval_list and sample
frame index with the interval.
frames_per_clip(int): Number of frames per clips. Default: 99 for
REDS dataset.
"""
def __init__(self, interval_list, frames_per_clip=99):
self.interval_list = interval_list
self.frames_per_clip = frames_per_clip
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
clip_name, frame_name = results['key'].split(
'/') # key example: 000/00000000
center_frame_idx = int(frame_name)
num_half_frames = results['num_input_frames'] // 2
interval = np.random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = center_frame_idx - num_half_frames * interval
end_frame_idx = center_frame_idx + num_half_frames * interval
while (start_frame_idx < 0) or (end_frame_idx > self.frames_per_clip):
center_frame_idx = np.random.randint(0, self.frames_per_clip + 1)
start_frame_idx = center_frame_idx - num_half_frames * interval
end_frame_idx = center_frame_idx + num_half_frames * interval
frame_name = f'{center_frame_idx:08d}'
neighbor_list = list(
range(center_frame_idx - num_half_frames * interval,
center_frame_idx + num_half_frames * interval + 1, interval))
lq_path_root = results['lq_path']
gt_path_root = results['gt_path']
lq_path = [
osp.join(lq_path_root, clip_name, f'{v:08d}.png')
for v in neighbor_list
]
gt_path = [osp.join(gt_path_root, clip_name, f'{frame_name}.png')]
results['lq_path'] = lq_path
results['gt_path'] = gt_path
results['interval'] = interval
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += (f'(interval_list={self.interval_list}, '
f'frames_per_clip={self.frames_per_clip})')
return repr_str
[docs]@PIPELINES.register_module()
class TemporalReverse(object):
"""Reverse frame lists for temporal augmentation.
Required keys are the keys in attributes "lq" and "gt",
added or modified keys are "lq", "gt" and "reverse".
Args:
keys (list[str]): The frame lists to be reversed.
reverse_ratio (float): The propability to reverse the frame lists.
Default: 0.5.
"""
def __init__(self, keys, reverse_ratio=0.5):
self.keys = keys
self.reverse_ratio = reverse_ratio
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
reverse = np.random.random() < self.reverse_ratio
if reverse:
for key in self.keys:
results[key].reverse()
results['reverse'] = reverse
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(keys={self.keys}, reverse_ratio={self.reverse_ratio})'
return repr_str