Code cleanup
This commit is contained in:
boltgolt
parent
e38422c8a0
commit
2917e8ada4
4 changed files with 81 additions and 70 deletions
75
debian/postinst
vendored
75
debian/postinst
vendored
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@ -38,6 +38,7 @@ def handleStatus(status):
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sys.exit(1)
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# Create shorthand for subprocess creation
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sc = subprocess.call
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# We're not in fresh configuration mode so don't continue the setup
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@ -98,79 +99,88 @@ handleStatus(sc(["pip3", "install", "--no-cache-dir", "--upgrade", "pip"]))
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log("Downloading and unpacking data files")
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# Run the bash script to download and unpack the .dat files needed
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handleStatus(subprocess.call(["./install.sh"], shell=True, cwd="/lib/security/howdy/dlib-data"))
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log('Downloading dlib')
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log("Downloading dlib")
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dlib_archive = '/tmp/v19.16.tar.gz'
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loader = which('curl')
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dlib_archive = "/tmp/v19.16.tar.gz"
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loader = which("curl")
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LOADER_CMD = None
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# If curl is installed, use that as the downloader
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if loader:
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LOADER_CMD = [loader, '--retry', '5', '--location', '--output']
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LOADER_CMD = [loader, "--retry", "5", "--location", "--output"]
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# Otherwise, fall back on wget
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else:
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loader = which('wget')
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LOADER_CMD = [loader, '--tries', '5', '--output-document']
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cmd = LOADER_CMD + [dlib_archive, 'https://github.com/davisking/dlib/archive/v19.16.tar.gz']
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loader = which("wget")
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LOADER_CMD = [loader, "--tries", "5", "--output-document"]
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# Assemble and execute the download command
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cmd = LOADER_CMD + [dlib_archive, "https://github.com/davisking/dlib/archive/v19.16.tar.gz"]
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handleStatus(sc(cmd))
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# The folder containing the dlib source
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DLIB_DIR = None
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# A regex of all files to ignore while unpacking the archive
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excludes = re.compile(
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'davisking-dlib-\w+/(dlib/(http_client|java|matlab|test/)|'
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'(docs|examples|python_examples)|'
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'tools/(archive|convert_dlib_nets_to_caffe|htmlify|imglab|python/test|visual_studio_natvis))'
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"davisking-dlib-\w+/(dlib/(http_client|java|matlab|test/)|"
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"(docs|examples|python_examples)|"
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"tools/(archive|convert_dlib_nets_to_caffe|htmlify|imglab|python/test|visual_studio_natvis))"
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)
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# Open the archive
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with tarfile.open(dlib_archive) as tf:
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for item in tf:
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# tarball contains directory davisking-dlib-<commit id>, so peek into archive for the name
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# Set the destenation dir if unset
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if not DLIB_DIR:
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DLIB_DIR = "/tmp/" + item.name
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# extract only files sufficient for building
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if not excludes.match(item.name):
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tf.extract(item, '/tmp')
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tf.extract(item, "/tmp")
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# Delete the downloaded archive
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os.unlink(dlib_archive)
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log("Building dlib")
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# Start the build
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cmd = ["sudo", "python3", "setup.py", "install"]
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cuda_used = False
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flags = ""
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flags = ''
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with open('/proc/cpuinfo') as info:
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# Get the CPU details
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with open("/proc/cpuinfo") as info:
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for line in info:
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if 'flags' in line:
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if "flags" in line:
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flags = line
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break
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if 'avx' in flags:
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# Use the most efficient instruction set the CPU supports
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if "avx" in flags:
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cmd += ["--yes", "USE_AVX_INSTRUCTIONS"]
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elif 'sse4' in flags:
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elif "sse4" in flags:
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cmd += ["--yes", "USE_SSE4_INSTRUCTIONS"]
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elif 'sse3' in flags:
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elif "sse3" in flags:
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cmd += ["--yes", "USE_SSE3_INSTRUCTIONS"]
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elif 'sse2' in flags:
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elif "sse2" in flags:
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cmd += ["--yes", "USE_SSE2_INSTRUCTIONS"]
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# Compile and link dlib
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try:
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sp = subprocess.Popen(cmd, cwd=DLIB_DIR, stdout=subprocess.PIPE)
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except subprocess.CalledProcessError:
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print("Error while building dlib")
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raise
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# Go through each line from stdout
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while sp.poll() is None:
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line = sp.stdout.readline().decode("utf-8")
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if 'DLIB WILL USE CUDA' in line:
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if "DLIB WILL USE CUDA" in line:
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cuda_used = True
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print(line, end='')
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print(line, end="")
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log("Cleaning up dlib")
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@ -188,20 +198,19 @@ log("Configuring howdy")
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# Manually change the camera id to the one picked
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for line in fileinput.input(["/lib/security/howdy/config.ini"], inplace=1):
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print(
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line
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.replace("device_path = none", "device_path = " + picked)
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.replace("use_cnn = false", "use_cnn = " + str(cuda_used).lower()),
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end=""
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)
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line = line.replace("device_path = none", "device_path = " + picked)
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line = line.replace("use_cnn = false", "use_cnn = " + str(cuda_used).lower())
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print(line, end="")
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print("Camera ID saved")
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# Secure the howdy folder
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handleStatus(sc(["chmod 744 -R /lib/security/howdy/"], shell=True))
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# Allow anyone to execute the python CLI
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os.chmod('/lib/security/howdy', 0o755)
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os.chmod('/lib/security/howdy/cli.py', 0o755)
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os.chmod("/lib/security/howdy", 0o755)
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os.chmod("/lib/security/howdy/cli.py", 0o755)
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handleStatus(sc(["chmod 755 -R /lib/security/howdy/cli"], shell=True))
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print("Permissions set")
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3
debian/preinst
vendored
3
debian/preinst
vendored
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@ -9,6 +9,7 @@ def col(id):
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if id == 3: return "\033[31m"
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return "\033[0m"
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import subprocess
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import time
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import sys
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@ -24,7 +25,7 @@ if "upgrade" in sys.argv:
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# Let the user know so he knows where to look on a failed install
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print("Backup of Howdy config file created in /tmp/howdy_config_backup_v" + sys.argv[2] + ".ini")
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except e:
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except subprocess.CalledProcessError:
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print("Could not make an backup of old Howdy config file")
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# Don't continue setup when we're just upgrading
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@ -11,7 +11,7 @@ import builtins
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# Try to get the original username (not "root") from shell
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try:
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user = os.getlogin()
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except:
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except Exception:
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user = os.environ.get("SUDO_USER")
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# If that fails, try to get the direct user
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@ -6,7 +6,7 @@ import time
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# Start timing
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timings = {
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'st': time.time()
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"st": time.time()
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}
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# Import required modules
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@ -21,23 +21,21 @@ import _thread as thread
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def init_detector(lock):
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"""Start face detector, encoder and predictor in a new thread"""
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global face_detector, pose_predictor, face_encoder
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# Use the CNN detector if enabled
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if use_cnn:
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face_detector = dlib.cnn_face_detection_model_v1(
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PATH + '/dlib-data/mmod_human_face_detector.dat'
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)
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face_detector = dlib.cnn_face_detection_model_v1(PATH + "/dlib-data/mmod_human_face_detector.dat")
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else:
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face_detector = dlib.get_frontal_face_detector()
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pose_predictor = dlib.shape_predictor(
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PATH + '/dlib-data/shape_predictor_5_face_landmarks.dat'
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)
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# Start the others regardless
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pose_predictor = dlib.shape_predictor(PATH + "/dlib-data/shape_predictor_5_face_landmarks.dat")
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face_encoder = dlib.face_recognition_model_v1(PATH + "/dlib-data/dlib_face_recognition_resnet_model_v1.dat")
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face_encoder = dlib.face_recognition_model_v1(
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PATH + '/dlib-data/dlib_face_recognition_resnet_model_v1.dat'
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)
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# Note the time it took to initialize detectors
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timings['ll'] = time.time() - timings['ll']
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timings["ll"] = time.time() - timings["ll"]
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lock.release()
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@ -52,7 +50,7 @@ if len(sys.argv) < 2:
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sys.exit(12)
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# Get the absolute path to the current directory
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PATH = os.path.abspath(__file__ + '/..')
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PATH = os.path.abspath(__file__ + "/..")
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# The username of the user being authenticated
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user = sys.argv[1]
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@ -86,25 +84,26 @@ if len(models) < 1:
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config = configparser.ConfigParser()
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config.read(PATH + "/config.ini")
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# CNN usage flag
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use_cnn = config.getboolean('core', 'use_cnn', fallback=False)
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# Get all config values needed
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use_cnn = config.getboolean("core", "use_cnn", fallback=False)
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timeout = config.getint("video", "timout", fallback=5)
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dark_threshold = config.getfloat("video", "dark_threshold", fallback=50.0)
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video_certainty = config.getfloat("video", "certainty", fallback=3.5) / 10
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end_report = config.getboolean("debug", "end_report", fallback=False)
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# Save the time needed to start the script
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timings['in'] = time.time() - timings['st']
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timings["in"] = time.time() - timings["st"]
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# Import face recognition, takes some time
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timings['ll'] = time.time()
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timings["ll"] = time.time()
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# Start threading and wait for init to finish
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lock = thread.allocate_lock()
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lock.acquire()
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thread.start_new_thread(init_detector, (lock, ))
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# Start video capture on the IR camera
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timings['ic'] = time.time()
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timings["ic"] = time.time()
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# Check if the user explicitly set ffmpeg as recorder
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if config.get("video", "recording_plugin") == "ffmpeg":
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@ -120,7 +119,8 @@ else:
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# Force MJPEG decoding if true
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if config.getboolean("video", "force_mjpeg", fallback=False):
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# Set a magic number, will enable MJPEG but is badly documented
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video_capture.set(cv2.CAP_PROP_FOURCC, 1196444237) # 1196444237 is 'GPJM' in ASCII
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# 1196444237 is "GPJM" in ASCII
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video_capture.set(cv2.CAP_PROP_FOURCC, 1196444237)
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# Set the frame width and height if requested
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fw = config.getint("video", "frame_width", fallback=-1)
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@ -135,14 +135,13 @@ if fh != -1:
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video_capture.grab()
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# Note the time it took to open the camera
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timings['ic'] = time.time() - timings['ic']
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timings["ic"] = time.time() - timings["ic"]
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# wait for thread to finish
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lock.acquire()
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lock.release()
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del lock
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# Fetch the max frame height
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max_height = config.getfloat("video", "max_height", fallback=0.0)
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# Get the height of the image
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@ -158,14 +157,14 @@ end_report = config.getboolean("debug", "end_report")
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# Start the read loop
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frames = 0
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timings['fr'] = time.time()
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timings["fr"] = time.time()
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while True:
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# Increment the frame count every loop
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frames += 1
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# Stop if we've exceded the time limit
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if time.time() - timings['fr'] > timeout:
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if time.time() - timings["fr"] > timeout:
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stop(11)
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# Grab a single frame of video
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@ -190,17 +189,18 @@ while True:
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gsframe = cv2.resize(gsframe, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
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# Get all faces from that frame as encodings
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face_locations = face_detector(gsframe, 1) # upsample 1 time
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# Upsamples 1 time
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face_locations = face_detector(gsframe, 1)
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# Loop through each face
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for fl in face_locations:
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if use_cnn:
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fl = fl.rect
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# Fetch the faces in the image
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face_landmark = pose_predictor(frame, fl)
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face_encoding = np.array(
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face_encoder.compute_face_descriptor(frame, face_landmark, 1) # num_jitters=1
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)
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face_encoding = np.array(face_encoder.compute_face_descriptor(frame, face_landmark, 1))
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# Match this found face against a known face
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matches = np.linalg.norm(encodings - face_encoding, axis=1)
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@ -210,8 +210,8 @@ while True:
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# Check if a match that's confident enough
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if 0 < match < video_certainty:
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timings['tt'] = time.time() - timings['st']
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timings['fr'] = time.time() - timings['fr']
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timings["tt"] = time.time() - timings["st"]
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timings["fr"] = time.time() - timings["fr"]
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# If set to true in the config, print debug text
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if end_report:
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@ -219,13 +219,14 @@ while True:
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"""Helper function to print a timing from the list"""
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print(" %s: %dms" % (label, round(timings[k] * 1000)))
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# Print a nice timing report
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print("Time spent")
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print_timing("Starting up", 'in')
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print(" Open cam + load libs: %dms" % (round(max(timings['ll'], timings['ic']) * 1000, )))
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print_timing(" Opening the camera", 'ic')
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print_timing(" Importing recognition libs", 'll')
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print_timing("Searching for known face", 'fr')
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print_timing("Total time", 'tt')
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print_timing("Starting up", "in")
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print(" Open cam + load libs: %dms" % (round(max(timings["ll"], timings["ic"]) * 1000, )))
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print_timing(" Opening the camera", "ic")
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print_timing(" Importing recognition libs", "ll")
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print_timing("Searching for known face", "fr")
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print_timing("Total time", "tt")
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print("\nResolution")
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width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) or 1
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@ -235,7 +236,7 @@ while True:
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print(" Used: %dx%d" % (scale_height, scale_width))
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# Show the total number of frames and calculate the FPS by deviding it by the total scan time
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print("\nFrames searched: %d (%.2f fps)" % (frames, frames / timings['fr']))
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print("\nFrames searched: %d (%.2f fps)" % (frames, frames / timings["fr"]))
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print("Dark frames ignored: %d " % (dark_tries, ))
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print("Certainty of winning frame: %.3f" % (match * 10, ))
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