def setUp(self): np.random.seed(1234) self.data_input_3D = np.random.random((2, 64, 56, 48)) self.data_input_2D = np.random.random((2, 64, 56)) self.d_3D = augment_gamma(np.copy(self.data_input_2D), gamma_range=(0.2, 1.2), per_channel=False)
def __call__(self, **data_dict): data_dict[self.data_key] = augment_gamma( data_dict[self.data_key], self.gamma_range, self.invert_image, per_channel=self.per_channel, retain_stats=self.retain_stats) return data_dict
def __call__(self, **data_dict): for b in range(len(data_dict[self.data_key])): if np.random.uniform() < self.p_per_sample: data_dict[self.data_key][b] = augment_gamma(data_dict[self.data_key][b], self.gamma_range, self.invert_image, per_channel=self.per_channel, retain_stats=self.retain_stats) return data_dict
def gamma_augmentation_generator(generator, gamma_range=(0.5, 2), invert_image=False): warn("using deprecated generator brightness_augmentation_by_multiplication_generator", Warning) # augments by shifting the gamma value as in gamma correction (https://en.wikipedia.org/wiki/Gamma_correction) for data_dict in generator: data_dict['data'] = augment_gamma(data_dict['data'], gamma_range, invert_image) yield data_dict
def __call__(self, **data_dict): data_dict['data'] = augment_gamma(data_dict['data'], self.gamma_range, self.invert_image) return data_dict
def __call__(self, **data_dict): data_dict[self.data_key] = augment_gamma(data_dict[self.data_key], self.gamma_range, self.invert_image, per_channel=self.per_channel, retain_stats=self.retain_stats) return data_dict