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transforms_video.py
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transforms_video.py
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import torch
import cv2
import numpy as np
import numbers
import collections
import random
class ComposeMix(object):
r"""Composes several transforms together. It takes a list of
transformations, where each element odf transform is a list with 2
elements. First being the transform function itself, second being a string
indicating whether it's an "img" or "vid" transform
Args:
transforms (List[Transform, "<type>"]): list of transforms to compose.
<type> = "img" | "vid"
Example:
>>> transforms.ComposeMix([
[RandomCropVideo(84), "vid"],
[torchvision.transforms.ToTensor(), "img"],
[torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], # default values for imagenet
std=[0.229, 0.224, 0.225]), "img"]
])
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, imgs):
for t in self.transforms:
if t[1] == "img":
for idx, img in enumerate(imgs):
imgs[idx] = t[0](img)
elif t[1] == "vid":
imgs = t[0](imgs)
else:
print("Please specify the transform type")
raise ValueError
return imgs
class RandomCropVideo(object):
r"""Crop the given video frames at a random location. Crop location is the
same for all the frames.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (w, h), a square crop (size, size) is
made.
padding (int or sequence, optional): Optional padding on each border
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
pad_method (cv2 constant): Method to be used for padding.
"""
def __init__(self, size, padding=0, pad_method=cv2.BORDER_CONSTANT):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
self.pad_method = pad_method
def __call__(self, imgs):
"""
Args:
img (numpy.array): Video to be cropped.
Returns:
numpy.array: Cropped video.
"""
th, tw = self.size
h, w = imgs[0].shape[:2]
x1 = np.random.randint(0, w - tw)
y1 = np.random.randint(0, h - th)
for idx, img in enumerate(imgs):
if self.padding > 0:
img = cv2.copyMakeBorder(img, self.padding, self.padding,
self.padding, self.padding,
self.pad_method)
# sample crop locations if not given
# it is necessary to keep cropping same in a video
img_crop = img[y1:y1 + th, x1:x1 + tw]
imgs[idx] = img_crop
return imgs
class RandomHorizontalFlipVideo(object):
"""Horizontally flip the given video frames randomly with a given probability.
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, imgs):
"""
Args:
imgs (numpy.array): Video to be flipped.
Returns:
numpy.array: Randomly flipped video.
"""
if random.random() < self.p:
for idx, img in enumerate(imgs):
imgs[idx] = cv2.flip(img, 1)
return imgs
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
class RandomReverseTimeVideo(object):
"""Reverse the given video frames in time randomly with a given probability.
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, imgs):
"""
Args:
imgs (numpy.array): Video to be flipped.
Returns:
numpy.array: Randomly flipped video.
"""
if random.random() < self.p:
imgs = imgs[::-1]
return imgs
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
class RandomRotationVideo(object):
"""Rotate the given video frames randomly with a given degree.
Args:
degree (float): degrees used to rotate the video
"""
def __init__(self, degree=10):
self.degree = degree
def __call__(self, imgs):
"""
Args:
imgs (numpy.array): Video to be rotated.
Returns:
numpy.array: Randomly rotated video.
"""
h, w = imgs[0].shape[:2]
degree_sampled = np.random.choice(
np.arange(-self.degree, self.degree, 0.5))
M = cv2.getRotationMatrix2D((w / 2, h / 2), degree_sampled, 1)
for idx, img in enumerate(imgs):
imgs[idx] = cv2.warpAffine(img, M, (w, h))
return imgs
def __repr__(self):
return self.__class__.__name__ + '(degree={})'.format(self.degree_sampled)
class IdentityTransform(object):
"""
Returns same video back
"""
def __init__(self,):
pass
def __call__(self, imgs):
return imgs
class Scale(object):
r"""Rescale the input image to the given size.
Args:
size (sequence or int): Desired output size. If size is a sequence like
(w, h), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size)
interpolation (int, optional): Desired interpolation. Default is
``cv2.INTER_LINEAR``
"""
def __init__(self, size, interpolation=cv2.INTER_LINEAR):
assert isinstance(size, int) or (isinstance(
size, collections.Iterable) and len(size) == 2)
self.size = size
self.interpolation = interpolation
def __call__(self, img):
"""
Args:
img (numpy.array): Image to be scaled.
Returns:
numpy.array: Rescaled image.
"""
if isinstance(self.size, int):
h, w = img.shape[:2]
if (w <= h and w == self.size) or (h <= w and h == self.size):
return img
if w < h:
ow = self.size
oh = int(self.size * h / w)
if ow < w:
return cv2.resize(img, (ow, oh), cv2.INTER_AREA)
else:
return cv2.resize(img, (ow, oh))
else:
oh = self.size
ow = int(self.size * w / h)
if oh < h:
return cv2.resize(img, (ow, oh), cv2.INTER_AREA)
else:
return cv2.resize(img, (ow, oh))
else:
return cv2.resize(img, tuple(self.size))
class UnNormalize(object):
"""Unnormalize an tensor image with mean and standard deviation.
Given mean: (R, G, B) and std: (R, G, B),
will normalize each channel of the torch.*Tensor, i.e.
channel = (channel x std) + mean
Args:
mean (sequence): Sequence of means for R, G, B channels respecitvely.
std (sequence): Sequence of standard deviations for R, G, B channels
respecitvely.
"""
def __init__(self, mean, std):
self.mean = np.array(mean).astype('float32')
self.std = np.array(std).astype('float32')
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized image.
"""
if isinstance(tensor, torch.Tensor):
self.mean = torch.FloatTensor(self.mean)
self.std = torch.FloatTensor(self.std)
if (self.std.dim() != tensor.dim() or
self.mean.dim() != tensor.dim()):
for i in range(tensor.dim() - self.std.dim()):
self.std = self.std.unsqueeze(-1)
self.mean = self.mean.unsqueeze(-1)
tensor = torch.add(torch.mul(tensor, self.std), self.mean)
else:
# Relying on Numpy broadcasting abilities
tensor = tensor * self.std + self.mean
return tensor