Code cleanup

debian/postinst

@@ -38,6 +38,7 @@ def handleStatus(status):
 		sys.exit(1)
 
 
+# Create shorthand for subprocess creation
 sc = subprocess.call
 
 # We're not in fresh configuration mode so don't continue the setup
@@ -98,79 +99,88 @@ handleStatus(sc(["pip3", "install", "--no-cache-dir", "--upgrade", "pip"]))
 
 log("Downloading and unpacking data files")
 
+# Run the bash script to download and unpack the .dat files needed
 handleStatus(subprocess.call(["./install.sh"], shell=True, cwd="/lib/security/howdy/dlib-data"))
 
-log('Downloading dlib')
+log("Downloading dlib")
 
-dlib_archive = '/tmp/v19.16.tar.gz'
+dlib_archive = "/tmp/v19.16.tar.gz"
-
+loader = which("curl")
-loader = which('curl')
 LOADER_CMD = None
+
+# If curl is installed, use that as the downloader
 if loader:
-	LOADER_CMD = [loader, '--retry', '5', '--location', '--output']
+	LOADER_CMD = [loader, "--retry", "5", "--location", "--output"]
+# Otherwise, fall back on wget
 else:
-	loader = which('wget')
+	loader = which("wget")
-	LOADER_CMD = [loader, '--tries', '5', '--output-document']
+	LOADER_CMD = [loader, "--tries", "5", "--output-document"]
-
-cmd = LOADER_CMD + [dlib_archive, 'https://github.com/davisking/dlib/archive/v19.16.tar.gz']
 
+# Assemble and execute the download command
+cmd = LOADER_CMD + [dlib_archive, "https://github.com/davisking/dlib/archive/v19.16.tar.gz"]
 handleStatus(sc(cmd))
 
+# The folder containing the dlib source
 DLIB_DIR = None
-
+# A regex of all files to ignore while unpacking the archive
 excludes = re.compile(
-	'davisking-dlib-\w+/(dlib/(http_client|java|matlab|test/)|'
+	"davisking-dlib-\w+/(dlib/(http_client|java|matlab|test/)|"
-	'(docs|examples|python_examples)|'
+	"(docs|examples|python_examples)|"
-	'tools/(archive|convert_dlib_nets_to_caffe|htmlify|imglab|python/test|visual_studio_natvis))'
+	"tools/(archive|convert_dlib_nets_to_caffe|htmlify|imglab|python/test|visual_studio_natvis))"
 )
 
+# Open the archive
 with tarfile.open(dlib_archive) as tf:
 	for item in tf:
-		# tarball contains directory davisking-dlib-<commit id>, so peek into archive for the name
+		# Set the destenation dir if unset
 		if not DLIB_DIR:
 			DLIB_DIR = "/tmp/" + item.name
 
 		# extract only files sufficient for building
 		if not excludes.match(item.name):
-			tf.extract(item, '/tmp')
+			tf.extract(item, "/tmp")
 
+# Delete the downloaded archive
 os.unlink(dlib_archive)
 
 log("Building dlib")
 
-# Start the build
 cmd = ["sudo", "python3", "setup.py", "install"]
 cuda_used = False
+flags = ""
 
-flags = ''
+# Get the CPU details
-with open('/proc/cpuinfo') as info:
+with open("/proc/cpuinfo") as info:
 	for line in info:
-		if 'flags' in line:
+		if "flags" in line:
 			flags = line
 			break
 
-if 'avx' in flags:
+# Use the most efficient instruction set the CPU supports
+if "avx" in flags:
 	cmd += ["--yes", "USE_AVX_INSTRUCTIONS"]
-elif 'sse4' in flags:
+elif "sse4" in flags:
 	cmd += ["--yes", "USE_SSE4_INSTRUCTIONS"]
-elif 'sse3' in flags:
+elif "sse3" in flags:
 	cmd += ["--yes", "USE_SSE3_INSTRUCTIONS"]
-elif 'sse2' in flags:
+elif "sse2" in flags:
 	cmd += ["--yes", "USE_SSE2_INSTRUCTIONS"]
 
+# Compile and link dlib
 try:
 	sp = subprocess.Popen(cmd, cwd=DLIB_DIR, stdout=subprocess.PIPE)
 except subprocess.CalledProcessError:
 	print("Error while building dlib")
 	raise
 
+# Go through each line from stdout
 while sp.poll() is None:
 	line = sp.stdout.readline().decode("utf-8")
 
-	if 'DLIB WILL USE CUDA' in line:
+	if "DLIB WILL USE CUDA" in line:
 		cuda_used = True
 
-	print(line, end='')
+	print(line, end="")
 
 log("Cleaning up dlib")
 
@@ -188,20 +198,19 @@ log("Configuring howdy")
 
 # Manually change the camera id to the one picked
 for line in fileinput.input(["/lib/security/howdy/config.ini"], inplace=1):
-	print(
+	line = line.replace("device_path = none", "device_path = " + picked)
-		line
+	line = line.replace("use_cnn = false", "use_cnn = " + str(cuda_used).lower())
-			.replace("device_path = none", "device_path = " + picked)
+
-			.replace("use_cnn = false", "use_cnn = " + str(cuda_used).lower()),
+	print(line, end="")
-		end=""
+
-	)
 print("Camera ID saved")
 
 # Secure the howdy folder
 handleStatus(sc(["chmod 744 -R /lib/security/howdy/"], shell=True))
 
 # Allow anyone to execute the python CLI
-os.chmod('/lib/security/howdy', 0o755)
+os.chmod("/lib/security/howdy", 0o755)
-os.chmod('/lib/security/howdy/cli.py', 0o755)
+os.chmod("/lib/security/howdy/cli.py", 0o755)
 handleStatus(sc(["chmod 755 -R /lib/security/howdy/cli"], shell=True))
 print("Permissions set")
 

debian/preinst

@@ -9,6 +9,7 @@ def col(id):
 	if id == 3: return "\033[31m"
 	return "\033[0m"
 
+
 import subprocess
 import time
 import sys
@@ -24,7 +25,7 @@ if "upgrade" in sys.argv:
 
 		# Let the user know so he knows where to look on a failed install
 		print("Backup of Howdy config file created in /tmp/howdy_config_backup_v" + sys.argv[2] + ".ini")
-	except e:
+	except subprocess.CalledProcessError:
 		print("Could not make an backup of old Howdy config file")
 
 	# Don't continue setup when we're just upgrading

src/cli.py

@@ -11,7 +11,7 @@ import builtins
 # Try to get the original username (not "root") from shell
 try:
 	user = os.getlogin()
-except:
+except Exception:
 	user = os.environ.get("SUDO_USER")
 
 # If that fails, try to get the direct user

src/compare.py

@@ -6,7 +6,7 @@ import time
 
 # Start timing
 timings = {
-	'st': time.time()
+	"st": time.time()
 }
 
 # Import required modules
@@ -21,23 +21,21 @@ import _thread as thread
 
 
 def init_detector(lock):
+	"""Start face detector, encoder and predictor in a new thread"""
 	global face_detector, pose_predictor, face_encoder
+
+	# Use the CNN detector if enabled
 	if use_cnn:
-		face_detector = dlib.cnn_face_detection_model_v1(
+		face_detector = dlib.cnn_face_detection_model_v1(PATH + "/dlib-data/mmod_human_face_detector.dat")
-			PATH + '/dlib-data/mmod_human_face_detector.dat'
-		)
 	else:
 		face_detector = dlib.get_frontal_face_detector()
 
-	pose_predictor = dlib.shape_predictor(
+	# Start the others regardless
-		PATH + '/dlib-data/shape_predictor_5_face_landmarks.dat'
+	pose_predictor = dlib.shape_predictor(PATH + "/dlib-data/shape_predictor_5_face_landmarks.dat")
-	)
+	face_encoder = dlib.face_recognition_model_v1(PATH + "/dlib-data/dlib_face_recognition_resnet_model_v1.dat")
 
-	face_encoder = dlib.face_recognition_model_v1(
-		PATH + '/dlib-data/dlib_face_recognition_resnet_model_v1.dat'
-	)
 	# Note the time it took to initialize detectors
-	timings['ll'] = time.time() - timings['ll']
+	timings["ll"] = time.time() - timings["ll"]
 	lock.release()
 
 
@@ -52,7 +50,7 @@ if len(sys.argv) < 2:
 	sys.exit(12)
 
 # Get the absolute path to the current directory
-PATH = os.path.abspath(__file__ + '/..')
+PATH = os.path.abspath(__file__ + "/..")
 
 # The username of the user being authenticated
 user = sys.argv[1]
@@ -86,25 +84,26 @@ if len(models) < 1:
 config = configparser.ConfigParser()
 config.read(PATH + "/config.ini")
 
-# CNN usage flag
+# Get all config values needed
-use_cnn = config.getboolean('core', 'use_cnn', fallback=False)
+use_cnn = config.getboolean("core", "use_cnn", fallback=False)
 timeout = config.getint("video", "timout", fallback=5)
 dark_threshold = config.getfloat("video", "dark_threshold", fallback=50.0)
 video_certainty = config.getfloat("video", "certainty", fallback=3.5) / 10
 end_report = config.getboolean("debug", "end_report", fallback=False)
 
 # Save the time needed to start the script
-timings['in'] = time.time() - timings['st']
+timings["in"] = time.time() - timings["st"]
 
 # Import face recognition, takes some time
-timings['ll'] = time.time()
+timings["ll"] = time.time()
 
+# Start threading and wait for init to finish
 lock = thread.allocate_lock()
 lock.acquire()
 thread.start_new_thread(init_detector, (lock, ))
 
 # Start video capture on the IR camera
-timings['ic'] = time.time()
+timings["ic"] = time.time()
 
 # Check if the user explicitly set ffmpeg as recorder
 if config.get("video", "recording_plugin") == "ffmpeg":
@@ -120,7 +119,8 @@ else:
 # Force MJPEG decoding if true
 if config.getboolean("video", "force_mjpeg", fallback=False):
 	# Set a magic number, will enable MJPEG but is badly documented
-	video_capture.set(cv2.CAP_PROP_FOURCC, 1196444237) # 1196444237 is 'GPJM' in ASCII
+	# 1196444237 is "GPJM" in ASCII
+	video_capture.set(cv2.CAP_PROP_FOURCC, 1196444237)
 
 # Set the frame width and height if requested
 fw = config.getint("video", "frame_width", fallback=-1)
@@ -135,14 +135,13 @@ if fh != -1:
 video_capture.grab()
 
 # Note the time it took to open the camera
-timings['ic'] = time.time() - timings['ic']
+timings["ic"] = time.time() - timings["ic"]
 
 # wait for thread to finish
 lock.acquire()
 lock.release()
 del lock
 
-
 # Fetch the max frame height
 max_height = config.getfloat("video", "max_height", fallback=0.0)
 # Get the height of the image
@@ -158,14 +157,14 @@ end_report = config.getboolean("debug", "end_report")
 
 # Start the read loop
 frames = 0
-timings['fr'] = time.time()
+timings["fr"] = time.time()
 
 while True:
 	# Increment the frame count every loop
 	frames += 1
 
 	# Stop if we've exceded the time limit
-	if time.time() - timings['fr'] > timeout:
+	if time.time() - timings["fr"] > timeout:
 		stop(11)
 
 	# Grab a single frame of video
@@ -190,17 +189,18 @@ while True:
 		gsframe = cv2.resize(gsframe, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
 
 	# Get all faces from that frame as encodings
-	face_locations = face_detector(gsframe, 1) # upsample 1 time
+	# Upsamples 1 time
+	face_locations = face_detector(gsframe, 1)
 
 	# Loop through each face
 	for fl in face_locations:
 		if use_cnn:
 			fl = fl.rect
 
+		# Fetch the faces in the image
 		face_landmark = pose_predictor(frame, fl)
-		face_encoding = np.array(
+		face_encoding = np.array(face_encoder.compute_face_descriptor(frame, face_landmark, 1))
-			face_encoder.compute_face_descriptor(frame, face_landmark, 1) # num_jitters=1
+
-		)
 		# Match this found face against a known face
 		matches = np.linalg.norm(encodings - face_encoding, axis=1)
 
@@ -210,8 +210,8 @@ while True:
 
 		# Check if a match that's confident enough
 		if 0 < match < video_certainty:
-			timings['tt'] = time.time() - timings['st']
+			timings["tt"] = time.time() - timings["st"]
-			timings['fr'] = time.time() - timings['fr']
+			timings["fr"] = time.time() - timings["fr"]
 
 			# If set to true in the config, print debug text
 			if end_report:
@@ -219,13 +219,14 @@ while True:
 					"""Helper function to print a timing from the list"""
 					print("  %s: %dms" % (label, round(timings[k] * 1000)))
 
+				# Print a nice timing report
 				print("Time spent")
-				print_timing("Starting up", 'in')
+				print_timing("Starting up", "in")
-				print("  Open cam + load libs: %dms" % (round(max(timings['ll'], timings['ic']) * 1000, )))
+				print("  Open cam + load libs: %dms" % (round(max(timings["ll"], timings["ic"]) * 1000, )))
-				print_timing("  Opening the camera", 'ic')
+				print_timing("  Opening the camera", "ic")
-				print_timing("  Importing recognition libs", 'll')
+				print_timing("  Importing recognition libs", "ll")
-				print_timing("Searching for known face", 'fr')
+				print_timing("Searching for known face", "fr")
-				print_timing("Total time", 'tt')
+				print_timing("Total time", "tt")
 
 				print("\nResolution")
 				width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) or 1
@@ -235,7 +236,7 @@ while True:
 				print("  Used: %dx%d" % (scale_height, scale_width))
 
 				# Show the total number of frames and calculate the FPS by deviding it by the total scan time
-				print("\nFrames searched: %d (%.2f fps)" % (frames, frames / timings['fr']))
+				print("\nFrames searched: %d (%.2f fps)" % (frames, frames / timings["fr"]))
 				print("Dark frames ignored: %d " % (dark_tries, ))
 				print("Certainty of winning frame: %.3f" % (match * 10, ))