def get_mask_type(mask_type, predicted_available):
""" Return the requested mask type. Return default mask if
predicted requested but not available """
logger.debug("Requested mask_type: %s", mask_type)
if mask_type == "predicted" and not predicted_available:
mask_type = model_masks.get_default_mask()
logger.warning("Predicted selected, but the model was not trained with a mask. "
"Switching to '%s'", mask_type)
logger.debug("Returning mask_type: %s", mask_type)
return mask_type
def get_mask_type(mask_type, predicted_available):
""" Return the requested mask type. Return default mask if
predicted requested but not available """
logger.debug("Requested mask_type: %s", mask_type)
if mask_type == "predicted" and not predicted_available:
mask_type = model_masks.get_default_mask()
logger.warning("Predicted selected, but the model was not trained with a mask. "
"Switching to '%s'", mask_type)
logger.debug("Returning mask_type: %s", mask_type)
return mask_type
def add_alpha_mask(self, frame, predicted):
""" Adding a 4th channel should happen after all other channel operations
Add the default mask as 4th channel for saving as png with alpha channel """
if not self.draw_transparent:
return frame
logger.trace("Creating transparent image: '%s'", predicted["filename"])
mask_type = getattr(model_masks, model_masks.get_default_mask())
final_mask = np.zeros(frame.shape[:2] + (1, ), dtype="float32")
for detected_face in predicted["detected_faces"]:
landmarks = detected_face.landmarks_as_xy
final_mask = cv2.bitwise_or(
final_mask, # pylint: disable=no-member
mask_type(landmarks, frame, channels=1).mask)
final_mask = np.expand_dims(
final_mask, axis=-1) if final_mask.ndim == 2 else final_mask
frame = np.concatenate((frame, final_mask), axis=-1)
logger.trace("Created transparent image: '%s'", predicted["filename"])
return frame