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