def get(self, landmarks, transformation): """ Return generated heatmaps :param landmarks: list of landmarks :param transformation: transformation to transform landmarks :return: generated heatmaps """ flip = self.is_flipped(transformation) preprocessed_landmarks = self.preprocess_landmarks(landmarks, transformation, flip) heatmap_image_generator = HeatmapImageGenerator(image_size=self.output_size_np, sigma=self.sigma, scale_factor=self.scale_factor, normalize_center=self.normalize_center) heatmaps = heatmap_image_generator.generate_heatmaps(preprocessed_landmarks, self.stack_axis) return heatmaps
def get(self, landmarks, transformation, **kwargs): """ Return generated heatmaps :param landmarks: list of landmarks :param transformation: transformation to transform landmarks :return: generated heatmaps """ output_size = kwargs.get('output_size', self.output_size) output_spacing = kwargs.get('output_spacing', self.output_spacing) preprocessed_landmarks = self.preprocess_landmarks(landmarks, transformation, output_size, output_spacing) heatmap_image_generator = HeatmapImageGenerator(image_size=list(reversed(output_size)), sigma=self.sigma, scale_factor=self.scale_factor, normalize_center=self.normalize_center) heatmaps = heatmap_image_generator.generate_heatmaps(preprocessed_landmarks, self.stack_axis, dtype=self.np_pixel_type) return heatmaps
def get(self, landmarks_multiple, transformation): """ Return generated heatmaps :param landmarks_multiple: list of list of landmarks :param transformation: transformation to transform landmarks :return: generated heatmaps """ flip = self.is_flipped(transformation) heatmaps = None for i, landmarks in enumerate(landmarks_multiple): preprocessed_landmarks = self.preprocess_landmarks(landmarks, transformation, flip) heatmap_image_generator = HeatmapImageGenerator(image_size=self.output_size_np, sigma=self.sigma, scale_factor=self.scale_factor, normalize_center=self.normalize_center) current_heatmaps = heatmap_image_generator.generate_heatmaps(preprocessed_landmarks, self.stack_axis) if heatmaps is None: heatmaps = current_heatmaps else: heatmaps = np.maximum(heatmaps, current_heatmaps) return heatmaps
def get(self, landmarks_multiple, transformation, **kwargs): """ Return generated heatmaps :param landmarks_multiple: list of list of landmarks :param transformation: transformation to transform landmarks :return: generated heatmaps """ output_size = kwargs.get('output_size', self.output_size) output_spacing = kwargs.get('output_spacing', self.output_spacing) heatmaps = None for i, landmarks in enumerate(landmarks_multiple): preprocessed_landmarks = self.preprocess_landmarks(landmarks, transformation, output_size, output_spacing) heatmap_image_generator = HeatmapImageGenerator(image_size=list(reversed(output_size)), sigma=self.sigma, scale_factor=self.scale_factor, normalize_center=self.normalize_center) current_heatmaps = heatmap_image_generator.generate_heatmaps(preprocessed_landmarks, self.stack_axis) if heatmaps is None: heatmaps = current_heatmaps else: heatmaps = np.maximum(heatmaps, current_heatmaps) return heatmaps