def setUp(self):
np.random.seed(1234)
self.data_3D = np.random.random((2, 64, 56, 48))
self.data_2D = np.random.random((2, 64, 56))
self.factor = (0.75, 1.25)
self.d_3D = augment_contrast(self.data_3D, contrast_range=self.factor, preserve_range=False, per_channel=False)
self.d_2D = augment_contrast(self.data_2D, contrast_range=self.factor, preserve_range=False, per_channel=False)
def constrast_augment(data):
'''
对比度增广
:return:
'''
result = augment_contrast(data)
return result
def __call__(self, **data_dict):
data_dict['data'] = augment_contrast(
data_dict['data'],
contrast_range=self.contrast_range,
preserve_range=self.preserve_range,
per_channel=self.per_channel)
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_contrast(data_dict[self.data_key][b],
contrast_range=self.contrast_range,
preserve_range=self.preserve_range,
per_channel=self.per_channel)
return data_dict
def contrast_augmentation_generator(generator, contrast_range=(0.75, 1.25), preserve_range=True, per_channel=True):
warn("using deprecated generator contrast_augmentation_generator", Warning)
# if contrast_range[0] is <1 and contrast_range[2] is >1 then it will do 50:50 (reduce, increase contrast)
# preserve_range will cut off values that due to contrast enhancement get over/below the original minimum/maximum of the data
# per_channel does the contrast enhancement separately for each channel
for data_dict in generator:
data_dict['data'] = augment_contrast(data_dict['data'], contrast_range=contrast_range,
preserve_range=preserve_range, per_channel=per_channel)
yield data_dict
def __call__(self, **data_dict):
data_dict['data'] = augment_contrast(data_dict[self.data_key], contrast_range=self.contrast_range,
preserve_range=self.preserve_range, per_channel=self.per_channel)
return data_dict