def intensity_postprocessing_mr_random(self, image):
"""
Intensity postprocessing for MR input. Random augmentation version.
:param image: The np input image.
:return: The processed image.
"""
image = change_gamma_unnormalized(image, float_uniform(0.5, 1.5))
image = normalize_robust(image, consideration_factors=(0.1, 0.1))
return ShiftScaleClamp(random_shift=0.6,
random_scale=0.6,
clamp_min=-1.0)(image)
def intensity_postprocessing_ct_random(self, image):
"""
Intensity postprocessing for CT input. Random augmentation version.
:param image: The np input image.
:return: The processed image.
"""
if not self.normalize_zero_mean_unit_variance:
random_lambda = float_uniform(0.9, 1.1)
image = change_gamma_unnormalized(image, random_lambda)
output = ShiftScaleClamp(shift=0,
scale=1 / 2048,
random_shift=self.random_intensity_shift,
random_scale=self.random_intensity_scale,
clamp_min=-1.0,
clamp_max=1.0)(image)
else:
random_lambda = float_uniform(0.9, 1.1)
image = change_gamma_unnormalized(image, random_lambda)
output = normalize_zero_mean_unit_variance(image)
return output
def postprocessing_random(self, image):
"""
Performs random augmentations of a grayscale image. Augmentation consists of random gamma correction,
random intensity shift/scale per video and per frame.
:param image: The grayscale image to augment.
:return: The augmented grayscale image.
"""
random_lambda = float_uniform(0.6, 1.4)
image = change_gamma_unnormalized(image, random_lambda)
image = ShiftScaleClamp(random_shift=0.65, random_scale=0.65)(image)
if len(image.shape) == 4:
for i in range(image.shape[self.video_frame_stack_axis]):
current_slice = [slice(None), slice(None)]
current_slice.insert(self.video_frame_stack_axis, slice(i, i + 1))
image[tuple(current_slice)] = ShiftScaleClamp(random_shift=0.1, random_scale=0.1)(image[tuple(current_slice)])
return image