refactor: remove useless conversion

howdy/src/cli/add.py

@@ -40,16 +40,16 @@
 
 use_cnn = config.getboolean("core", "use_cnn", fallback=False)
 if use_cnn:
- face_detector = dlib.cnn_face_detection_model_v1(str(paths_factory.mmod_human_face_detector_path()))
+ face_detector = dlib.cnn_face_detection_model_v1(paths_factory.mmod_human_face_detector_path())
 else:
  face_detector = dlib.get_frontal_face_detector()
 
-pose_predictor = dlib.shape_predictor(str(paths_factory.shape_predictor_5_face_landmarks_path()))
-face_encoder = dlib.face_recognition_model_v1(str(paths_factory.dlib_face_recognition_resnet_model_v1_path()))
+pose_predictor = dlib.shape_predictor(paths_factory.shape_predictor_5_face_landmarks_path())
+face_encoder = dlib.face_recognition_model_v1(paths_factory.dlib_face_recognition_resnet_model_v1_path())
 
 user = builtins.howdy_user
 # The permanent file to store the encoded model in
-enc_file = str(paths_factory.user_model_path(user))
+enc_file = paths_factory.user_model_path(user)
 # Known encodings
 encodings = []
 

howdy/src/cli/test.py

@@ -57,13 +57,13 @@ def print_text(line_number, text):
 
 if use_cnn:
  face_detector = dlib.cnn_face_detection_model_v1(
- str(paths_factory.mmod_human_face_detector_path())
+ paths_factory.mmod_human_face_detector_path()
  )
 else:
  face_detector = dlib.get_frontal_face_detector()
 
-pose_predictor = dlib.shape_predictor(str(paths_factory.shape_predictor_5_face_landmarks_path()))
-face_encoder = dlib.face_recognition_model_v1(str(paths_factory.dlib_face_recognition_resnet_model_v1_path()))
+pose_predictor = dlib.shape_predictor(paths_factory.shape_predictor_5_face_landmarks_path())
+face_encoder = dlib.face_recognition_model_v1(paths_factory.dlib_face_recognition_resnet_model_v1_path())
 
 encodings = []
 models = None

howdy/src/compare.py

@@ -45,7 +45,7 @@ def init_detector(lock):
  global face_detector, pose_predictor, face_encoder
 
  # Test if at lest 1 of the data files is there and abort if it's not
- if not os.path.isfile(str(paths_factory.shape_predictor_5_face_landmarks_path())):
+ if not os.path.isfile(paths_factory.shape_predictor_5_face_landmarks_path()):
  print(_("Data files have not been downloaded, please run the following commands:"))
  print("\n\tcd " + paths_factory.dlib_data_dir_path())
  print("\tsudo ./install.sh\n")
@@ -54,13 +54,13 @@ def init_detector(lock):
 
  # Use the CNN detector if enabled
  if use_cnn:
- face_detector = dlib.cnn_face_detection_model_v1(str(paths_factory.mmod_human_face_detector_path()))
+ face_detector = dlib.cnn_face_detection_model_v1(paths_factory.mmod_human_face_detector_path())
  else:
  face_detector = dlib.get_frontal_face_detector()
 
  # Start the others regardless
- pose_predictor = dlib.shape_predictor(str(paths_factory.shape_predictor_5_face_landmarks_path()))
- face_encoder = dlib.face_recognition_model_v1(str(paths_factory.dlib_face_recognition_resnet_model_v1_path()))
+ pose_predictor = dlib.shape_predictor(paths_factory.shape_predictor_5_face_landmarks_path())
+ face_encoder = dlib.face_recognition_model_v1(paths_factory.dlib_face_recognition_resnet_model_v1_path())
 
  # Note the time it took to initialize detectors
  timings["ll"] = time.time() - timings["ll"]

howdy/src/pam/meson.build

@@ -10,8 +10,7 @@ subdir('po')
 paths_h = configure_file(
  input: 'paths.hh.in',
  output: 'paths.hh',
- configuration: pam_module_conf_data,
- install_dir: get_option('pam_dir')
+ configuration: pam_module_conf_data
 )
 
 pamdir = get_option('pam_dir') != '' ? get_option('pam_dir') : join_paths(get_option('prefix'), get_option('libdir'), 'security')

howdy/src/snapshot.py

@@ -30,7 +30,7 @@ def generate(frames, text_lines):
  # Add the Howdy logo if there's space to do so
  if len(frames) > 1:
  # Load the logo from file
- logo = cv2.imread(str(paths_factory.logo_path()))
+ logo = cv2.imread(paths_factory.logo_path())
  # Calculate the position of the logo
  logo_y = frame_height + 20
  logo_x = frame_width * len(frames) - 210
@@ -54,7 +54,7 @@ def generate(frames, text_lines):
 
  # Generate a filename based on the current time
  filename = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%S.jpg")
- filepath = str(paths_factory.snapshot_path(filename))
+ filepath = paths_factory.snapshot_path(filename)
  # Write the image to that file
  cv2.imwrite(filepath, snap)