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)