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.factor = (0.75, 1.25) self.multiplier_range = [2,4] self.d_3D_per_channel = augment_brightness_additive(np.copy(self.data_input_3D), mu=100, sigma=10, per_channel=True) self.d_3D = augment_brightness_additive(np.copy(self.data_input_3D), mu=100, sigma=10, per_channel=False) self.d_2D_per_channel = augment_brightness_additive(np.copy(self.data_input_2D), mu=100, sigma=10, per_channel=True) self.d_2D = augment_brightness_additive(np.copy(self.data_input_2D), mu=100, sigma=10, per_channel=False) self.d_3D_per_channel_mult = augment_brightness_multiplicative(np.copy(self.data_input_3D), multiplier_range=self.multiplier_range, per_channel=True) self.d_3D_mult = augment_brightness_multiplicative(np.copy(self.data_input_3D), multiplier_range=self.multiplier_range, per_channel=False) self.d_2D_per_channel_mult = augment_brightness_multiplicative(np.copy(self.data_input_2D), multiplier_range=self.multiplier_range, per_channel=True) self.d_2D_mult = augment_brightness_multiplicative(np.copy(self.data_input_2D), multiplier_range=self.multiplier_range, per_channel=False)
def __call__(self, **data_dict): data = data_dict[self.data_key] for b in range(data.shape[0]): if np.random.uniform() < self.p_per_sample: data[b] = augment_brightness_additive(data[b], self.mu, self.sigma, self.per_channel) data_dict[self.data_key] = data return data_dict
def brightness_augmentation_generator(generator, mu, sigma, per_channel=True): warn("using deprecated generator brightness_augmentation_generator", Warning) ''' Adds a randomly sampled offset (gaussian with mean mu and std sigma). This is done separately for each channel if per_channel is set to True. ''' print( "Warning (for Fabian): This should no longer be used for brain tumor segmentation (brain mask support dropped)") for data_dict in generator: data_dict['data'] = augment_brightness_additive(data_dict['data'], mu, sigma, per_channel) yield data_dict
def __call__(self, **data_dict): data_dict['data'] = augment_brightness_additive( data_dict['data'], self.mu, self.sigma, self.per_channel) return data_dict
def __call__(self, **data_dict): data_dict[self.data_key] = augment_brightness_additive(data_dict[self.data_key], self.mu, self.sigma, self.per_channel) return data_dict