def __build_model(auth_params, func_params):
"""
This script has been defined in order to build (or update) a
face recognition model for a specific person based on a
set of instances previously extracted and saved.
The object representation of the model must already exist.
"""
token = auth_params.get('token', '1234')
model_id = func_params.get('id', 0)
# retrieve the entity model object if exists
model = get_model(model_id, token)
if model is None:
raise Exception('The provided model id is not valid!')
# retrieve all instances associated to the model
instances = get_instances_by_model(model_id, token)['results']
inst_paths = []
for inst in instances:
inst_paths.append(os.path.join(get_media_root(), inst['features']))
fm = FaceModels()
model_file_path = fm.create_model_from_image_list(aligned_faces_list)
tsm.set_model_file(model_id, model_file_path, token=token)
def update_face_model(auth_dict, param_dict):
"""
Function used to update global face models
used as training set for people recognition.
:param auth_dict: Input parameters provided by the trigger Action
:param param_dict: Output parameters returned by the trigger Action
"""
# Get instances associated to model
model_id = param_dict['id']
instances = tsm.get_instances_by_model(model_id, auth_params['token'])
# Get aligned faces from instances
aligned_faces_list = []
for instance in instances:
aligned_face_path = instance['features']
aligned_faces_list.append(aligned_face_path)
fm = FaceModels()
model_file_path = fm.create_model_from_image_list(aligned_faces_list)
tsm.set_model_file(model_id, model_file_path, auth_params['token'])
