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)