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
