refactor: add path factory to not recreate paths

howdy/src/cli/add.py

@@ -9,6 +9,7 @@ import configparser
 import builtins
 import numpy as np
 import paths
+import paths_factory
 
 from recorders.video_capture import VideoCapture
 from i18n import _
@@ -28,7 +29,7 @@ except ImportError as err:
 import cv2
 
 # Test if at lest 1 of the data files is there and abort if it's not
-if not os.path.isfile(paths.dlib_data_dir / "shape_predictor_5_face_landmarks.dat"):
+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.dlib_data_dir)
 	print("\tsudo ./install.sh\n")
@@ -36,20 +37,20 @@ if not os.path.isfile(paths.dlib_data_dir / "shape_predictor_5_face_landmarks.da
 
 # Read config from disk
 config = configparser.ConfigParser()
-config.read(paths.config_dir / "config.ini")
+config.read(paths_factory.config_file_path())
 
 use_cnn = config.getboolean("core", "use_cnn", fallback=False)
 if use_cnn:
-	face_detector = dlib.cnn_face_detection_model_v1(paths.dlib_data_dir / "mmod_human_face_detector.dat")
+	face_detector = dlib.cnn_face_detection_model_v1(str(paths_factory.mmod_human_face_detector_path()))
 else:
 	face_detector = dlib.get_frontal_face_detector()
 
-pose_predictor = dlib.shape_predictor(paths.dlib_data_dir / "shape_predictor_5_face_landmarks.dat")
-face_encoder = dlib.face_recognition_model_v1(paths.dlib_data_dir / "dlib_face_recognition_resnet_model_v1.dat")
+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()))
 
 user = builtins.howdy_user
 # The permanent file to store the encoded model in
-enc_file = paths.user_models_dir / f"{user}.dat"
+enc_file = paths_factory.user_model_path(user)
 # Known encodings
 encodings = []
 

howdy/src/cli/clear.py

@@ -5,6 +5,7 @@ import os
 import sys
 import builtins
 import paths
+import paths_factory
 
 from i18n import _
 
@@ -17,7 +18,7 @@ if not os.path.exists(paths.user_models_dir):
 	sys.exit(1)
 
 # Check if the user has a models file to delete
-if not os.path.isfile(paths.user_models_dir / f"{user}.dat"):
+if not os.path.isfile(paths_factory.user_model_path(user)):
 	print(_("{} has no models or they have been cleared already").format(user))
 	sys.exit(1)
 
@@ -33,5 +34,5 @@ if not builtins.howdy_args.y:
 		sys.exit(1)
 
 # Delete otherwise
-os.remove(paths.user_models_dir / f"{user}.dat")
+os.remove(paths_factory.user_model_path(user))
 print(_("\nModels cleared"))

howdy/src/cli/config.py

@@ -4,6 +4,7 @@
 import os
 import subprocess
 import paths
+import paths_factory
 
 from i18n import _
 
@@ -20,4 +21,4 @@ elif os.path.isfile("/etc/alternatives/editor"):
 	editor = "/etc/alternatives/editor"
 
 # Open the editor as a subprocess and fork it
-subprocess.call([editor, paths.config_dir / "config.ini"])
+subprocess.call([editor, paths_factory.config_file_path()])

howdy/src/cli/disable.py

@@ -7,11 +7,12 @@ import builtins
 import fileinput
 import configparser
 import paths
+import paths_factory
 
 from i18n import _
 
 # Get the absolute filepath
-config_path = paths.config_dir.parent / "config.ini"
+config_path = paths_factory.config_file_path()
 
 # Read config from disk
 config = configparser.ConfigParser()

howdy/src/cli/list.py

@@ -7,6 +7,7 @@ import json
 import time
 import builtins
 import paths
+import paths_factory
 
 from i18n import _
 
@@ -19,7 +20,7 @@ if not os.path.exists(paths.user_models_dir):
 	sys.exit(1)
 
 # Path to the models file
-enc_file = paths.user_models_dir / f"{user}.dat"
+enc_file = paths_factory.user_model_path(user)
 
 # Try to load the models file and abort if the user does not have it yet
 try:

howdy/src/cli/remove.py

@@ -6,6 +6,7 @@ import os
 import json
 import builtins
 import paths
+import paths_factory
 
 from i18n import _
 
@@ -27,7 +28,7 @@ if not os.path.exists(paths.user_models_dir):
 	sys.exit(1)
 
 # Path to the models file
-enc_file = paths.user_models_dir / f"{user}.dat"
+enc_file = paths_factory.user_model_path(user)
 
 # Try to load the models file and abort if the user does not have it yet
 try:

howdy/src/cli/set.py

@@ -6,11 +6,12 @@ import os
 import builtins
 import fileinput
 import paths
+import paths_factory
 
 from i18n import _
 
 # Get the absolute filepath
-config_path = paths.config_dir / "/config.ini"
+config_path = paths_factory.config_file_path()
 
 # Check if enough arguments have been passed
 if len(builtins.howdy_args.arguments) < 2:

howdy/src/cli/snap.py

@@ -6,13 +6,14 @@ import configparser
 import datetime
 import snapshot
 import paths
+import paths_factory
 from recorders.video_capture import VideoCapture
 
 from i18n import _
 
 # Read the config
 config = configparser.ConfigParser()
-config.read(paths.config_dir / "config.ini")
+config.read(paths_factory.config_file_path())
 
 # Start video capture
 video_capture = VideoCapture(config)

howdy/src/cli/test.py

@@ -11,16 +11,14 @@ import dlib
 import cv2
 import numpy as np
 import paths
+import paths_factory
 
 from i18n import _
 from recorders.video_capture import VideoCapture
 
-# The absolute path to the config directory
-path = "/etc/howdy"
-
 # Read config from disk
 config = configparser.ConfigParser()
-config.read(paths.config_dir / "config.ini")
+config.read(paths_factory.config_file_path())
 
 if config.get("video", "recording_plugin", fallback="opencv") != "opencv":
 	print(_("Howdy has been configured to use a recorder which doesn't support the test command yet, aborting"))
@@ -60,20 +58,20 @@ use_cnn = config.getboolean('core', 'use_cnn', fallback=False)
 
 if use_cnn:
 	face_detector = dlib.cnn_face_detection_model_v1(
-		paths.dlib_data_dir / "mmod_human_face_detector.dat"
+		paths_factory.mmod_human_face_detector_path()
 	)
 else:
 	face_detector = dlib.get_frontal_face_detector()
 
-pose_predictor = dlib.shape_predictor(paths.dlib_data_dir / "shape_predictor_5_face_landmarks.dat")
-face_encoder = dlib.face_recognition_model_v1(paths.dlib_data_dir / "dlib_face_recognition_resnet_model_v1.dat")
+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
 
 try:
 	user = builtins.howdy_user
-	models = json.load(open(paths.user_models_dir / f"{user}.dat"))
+	models = json.load(open(paths_factory.user_model_path(user)))
 
 	for model in models:
 		encodings += model["data"]

howdy/src/compare.py

@@ -24,6 +24,7 @@ import snapshot
 import numpy as np
 import _thread as thread
 import paths
+import paths_factory
 
 # Allow imports from the local howdy folder
 sys.path.append('/lib/security/howdy')
@@ -49,7 +50,7 @@ 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(paths.dlib_data_dir / "shape_predictor_5_face_landmarks.dat"):
+	if not os.path.isfile(str(paths_factory.shape_predictor_5_face_landmarks_path())):
 		print(_("Data files have not been downloaded, please run the following commands:"))
 		print("\n\tcd " + paths.dlib_data_dir)
 		print("\tsudo ./install.sh\n")
@@ -58,13 +59,13 @@ def init_detector(lock):
 
 	# Use the CNN detector if enabled
 	if use_cnn:
-		face_detector = dlib.cnn_face_detection_model_v1(paths.dlib_data_dir / "mmod_human_face_detector.dat")
+		face_detector = dlib.cnn_face_detection_model_v1(str(paths_factory.mmod_human_face_detector_path()))
 	else:
 		face_detector = dlib.get_frontal_face_detector()
 
 	# Start the others regardless
-	pose_predictor = dlib.shape_predictor(paths.dlib_data_dir / "shape_predictor_5_face_landmarks.dat")
-	face_encoder = dlib.face_recognition_model_v1(paths.dlib_data_dir / "dlib_face_recognition_resnet_model_v1.dat")
+	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()))
 
 	# Note the time it took to initialize detectors
 	timings["ll"] = time.time() - timings["ll"]
@@ -127,7 +128,7 @@ face_encoder = None
 
 # Try to load the face model from the models folder
 try:
-	models = json.load(open(paths.user_models_dir / f"{user}.dat"))
+	models = json.load(open(paths_factory.user_model_path(user)))
 
 	for model in models:
 		encodings += model["data"]
@@ -140,7 +141,7 @@ if len(models) < 1:
 
 # Read config from disk
 config = configparser.ConfigParser()
-config.read(paths.config_dir / "config.ini")
+config.read(paths_factory.config_file_path())
 
 # Get all config values needed
 use_cnn = config.getboolean("core", "use_cnn", fallback=False)

howdy/src/paths_factory.py

@@ -0,0 +1,36 @@
+from pathlib import PurePath
+import paths
+
+models = [
+    "shape_predictor_5_face_landmarks.dat",
+    "mmod_human_face_detector.dat",
+    "dlib_face_recognition_resnet_model_v1.dat",
+]
+
+
+def shape_predictor_5_face_landmarks_path() -> PurePath:
+    return paths.dlib_data_dir / models[0]
+
+
+def mmod_human_face_detector_path() -> PurePath:
+    return paths.dlib_data_dir / models[1]
+
+
+def dlib_face_recognition_resnet_model_v1_path() -> PurePath:
+    return paths.dlib_data_dir / models[2]
+
+
+def user_model_path(user: str) -> PurePath:
+    return paths.user_models_dir / f"{user}.dat"
+
+
+def config_file_path() -> PurePath:
+    return paths.config_dir / "config.ini"
+
+
+def snapshots_dir_path() -> PurePath:
+    return paths.log_path / "snapshots"
+
+
+def snapshot_path(snapshot: str) -> PurePath:
+    return snapshots_dir_path() / snapshot

howdy/src/snapshot.py

@@ -5,6 +5,7 @@ import cv2
 import os
 import datetime
 import numpy as np
+import paths_factory
 
 
 def generate(frames, text_lines):
@@ -50,15 +51,13 @@ def generate(frames, text_lines):
 		line_number += 1
 
 	# Made sure a snapshot folder exist
-	if not os.path.exists(paths.log_path):
-		os.makedirs(paths.log_path)
-	if not os.path.exists(paths.log_path / "snapshots"):
-		os.makedirs(paths.log_path / "snapshots")
+	if not os.path.exists(paths_factory.snapshots_dir_path()):
+		os.makedirs(paths_factory.snapshots_dir_path())
 
 	# Generate a filename based on the current time
 	filename = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%S.jpg")
 	# Write the image to that file
-	cv2.imwrite(paths.log_path / "snapshots" / filename, snap)
+	cv2.imwrite(paths_factory.snapshot_path(filename), snap)
 
 	# Return the saved file location
-	return paths.log_path / "/snapshots/" / filename
+	return paths_factory.snapshot_path(filename)