import random from .functional import to_tensor, center_crop __all__ = ["CenterCrop", "ToTensor", "RandomBool", "ConditionApply"] class CenterCrop(object): def __init__(self, output_size): self.output_size = output_size def __call__(self, tensor): return center_crop(tensor, self.output_size) def __repr__(self): return self.__class__.__name__ + '()' class ToTensor(object): """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor without typecasting and rescaling. Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.ByteTensor of shape (C x H x W) in the range [0, 255] if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8 """ def __call__(self, pic): """ Args: pic (PIL Image or numpy.ndarray): Image to be converted to tensor. Returns: Tensor: Converted image. """ return to_tensor(pic) def __repr__(self): return self.__class__.__name__ + '()' class RandomBool(object): """Produce a random boolean with p probability for it to be True Args: p (float): probability """ def __init__(self, p=0.5): self.p = p def __call__(self): return random.random() < self.p class ConditionApply(object): """Apply a transformation if condition is met Args: transform: """ def __init__(self, transform): self.transform = transform def __call__(self, tensor, condition): """ :param tensor: :param condition (bool): True: apply, False: do not apply :return: """ if condition: tensor = self.transform(tensor) return tensor def __repr__(self): format_string = self.__class__.__name__ + '(' format_string += '\n' format_string += ' {0}'.format(self.transform) format_string += '\n)' return format_string