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import numpy as np |
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import numbers |
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import random |
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class RandomCrop(object): |
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"""Crop the given video sequences (t x h x w) at a random location. |
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Args: |
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size (sequence or int): Desired output size of the crop. If size is an |
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int instead of sequence like (h, w), a square crop (size, size) is |
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made. |
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""" |
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def __init__(self, size): |
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if isinstance(size, numbers.Number): |
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self.size = (int(size), int(size)) |
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else: |
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self.size = size |
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@staticmethod |
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def get_params(img, output_size): |
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"""Get parameters for ``crop`` for a random crop. |
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Args: |
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img (PIL Image): Image to be cropped. |
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output_size (tuple): Expected output size of the crop. |
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Returns: |
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tuple: params (i, j, h, w) to be passed to ``crop`` for random crop. |
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""" |
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t, h, w, c = img.shape |
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th, tw = output_size |
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if w == tw and h == th: |
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return 0, 0, h, w |
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i = random.randint(0, h - th) if h!=th else 0 |
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j = random.randint(0, w - tw) if w!=tw else 0 |
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return i, j, th, tw |
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def __call__(self, imgs): |
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i, j, h, w = self.get_params(imgs, self.size) |
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imgs = imgs[:, i:i+h, j:j+w, :] |
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return imgs |
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def __repr__(self): |
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return self.__class__.__name__ + '(size={0})'.format(self.size) |
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class CenterCrop(object): |
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"""Crops the given seq Images at the center. |
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Args: |
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size (sequence or int): Desired output size of the crop. If size is an |
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int instead of sequence like (h, w), a square crop (size, size) is |
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made. |
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""" |
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def __init__(self, size): |
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if isinstance(size, numbers.Number): |
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self.size = (int(size), int(size)) |
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else: |
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self.size = size |
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def __call__(self, imgs): |
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""" |
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Args: |
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img (PIL Image): Image to be cropped. |
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Returns: |
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PIL Image: Cropped image. |
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""" |
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t, h, w, c = imgs.shape |
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th, tw = self.size |
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i = int(np.round((h - th) / 2.)) |
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j = int(np.round((w - tw) / 2.)) |
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return imgs[:, i:i+th, j:j+tw, :] |
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def __repr__(self): |
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return self.__class__.__name__ + '(size={0})'.format(self.size) |
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class RandomHorizontalFlip(object): |
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"""Horizontally flip the given seq Images randomly with a given probability. |
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Args: |
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p (float): probability of the image being flipped. Default value is 0.5 |
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""" |
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def __init__(self, p=0.5): |
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self.p = p |
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def __call__(self, imgs): |
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""" |
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Args: |
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img (seq Images): seq Images to be flipped. |
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Returns: |
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seq Images: Randomly flipped seq images. |
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""" |
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if random.random() < self.p: |
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return np.flip(imgs, axis=2).copy() |
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return imgs |
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def __repr__(self): |
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return self.__class__.__name__ + '(p={})'.format(self.p) |
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