def split_labels(self, image):
"""
Splits a groundtruth label image into a stack of one-hot encoded images.
:param image: The groundtruth label image.
:return: The one-hot encoded image.
"""
split = split_label_image(np.squeeze(image, 0), list(range(8)),
np.uint8)
split_smoothed = [
gaussian(i, self.label_gaussian_sigma) for i in split
]
smoothed = np.stack(split_smoothed, 0)
image_smoothed = np.argmax(smoothed, axis=0)
split = split_label_image(image_smoothed, list(range(8)), np.uint8)
return np.stack(split, 0)
def binary_labels(self, image):
"""
Converts an instance label image into a binary label. All instances will be set to 1.
:param image: The instance label image.
:return: A list of np arrays. First, is the background. Second is the foreground.
"""
all_seg = ShiftScaleClamp(clamp_min=0, clamp_max=1)(image)
return split_label_image(all_seg, [0, 1])
def split_labels(self, image):
"""
Splits a groundtruth label image into a stack of one-hot encoded images.
:param image: The groundtruth label image.
:return: The one-hot encoded image.
"""
channel_axis = 0 if self.data_format == 'channels_first' else -1
label_images = split_label_image(np.squeeze(image, channel_axis), [0] + self.landmark_labels, np.uint8)
label_images_smoothed = smooth_label_images(label_images, sigma=self.label_gaussian_sigma)
return np.stack(label_images_smoothed, channel_axis)
def split_labels(self, image):
"""
Splits a groundtruth label image into a stack of one-hot encoded images.
:param image: The groundtruth label image.
:return: The one-hot encoded image.
"""
label_images = split_label_image(np.squeeze(image, 0),
list(range(self.num_labels)),
np.uint8)
label_images_smoothed = smooth_label_images(
label_images, sigma=self.label_gaussian_sigma)
return np.stack(label_images_smoothed, 0)