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import numpy as np
import numbers
import random
class RandomCrop(object):
"""Crop the given video sequences (t x h x w) at a random location.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
@staticmethod
def get_params(img, output_size):
"""Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
"""
t, h, w, c = img.shape
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = random.randint(0, h - th) if h!=th else 0
j = random.randint(0, w - tw) if w!=tw else 0
return i, j, th, tw
def __call__(self, imgs):
i, j, h, w = self.get_params(imgs, self.size)
imgs = imgs[:, i:i+h, j:j+w, :]
return imgs
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class CenterCrop(object):
"""Crops the given seq Images at the center.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, imgs):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
t, h, w, c = imgs.shape
th, tw = self.size
i = int(np.round((h - th) / 2.))
j = int(np.round((w - tw) / 2.))
return imgs[:, i:i+th, j:j+tw, :]
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class RandomHorizontalFlip(object):
"""Horizontally flip the given seq Images 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:
img (seq Images): seq Images to be flipped.
Returns:
seq Images: Randomly flipped seq images.
"""
if random.random() < self.p:
# t x h x w
return np.flip(imgs, axis=2).copy()
return imgs
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
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