ubantu / linux mint安裝howdy人臉識別

1、首先要自備好梯子

期間需要安裝一些東西，鏡像在國外，下載得很慢，甚至安裝失敗

2、終端命令

sudo add-apt-repository ppa:boltgolt/howdy sudo apt update sudo apt install howdy

安裝時會出現f/b/s三個選項，fast/balance/security分別表示識別快、平衡、安全，根據需要選擇

3、配置文件

#安裝v4l-utils，用于檢測攝像頭設備 sudo apt-get install v4l-utils #查看攝像頭設備，一般/dev/video0就是攝像頭設備 v4l2-ctl --list-devices #修改howdy的配置文件 sudo vim /lib/security/howdy/config.ini #將device_path修改為/dev/video0 device_path = /dev/video0

4、添加人臉

sudo howdy add # 輸入一個24字符以內的標簽用于區分，然后會自動打開攝像頭進行人臉掃描，完成之后提示Scan complete

5、設置登錄解鎖

完成第四步就已經可以人臉使用sudo了，但鎖屏還不能使用

5.1 終端命令提升howdy權限

sudo chmod 777 -R /lib/security/howdy
若不能解決

5.2 終端命令行

cd /lib/security/howdy sudo vim compare.py

將compare.py內容替換為

# Compare incomming video with known faces # Running in a local python instance to get around PATH issues# Import time so we can start timing asap import time# Start timing timings = {"st": time.time() }# Import required modules import sys # Some user libraries will make howdy dosn't work sys.path.append("/lib/security/howdy/") for path in range(len(sys.path)): if len(sys.path[path])>5 and sys.path[path][:5]=="/home":sys.path[path]="" import os import json import configparser import dlib import cv2 import datetime import atexit import subprocess import snapshot import numpy as np import _thread as threadfrom i18n import _ from recorders.video_capture import VideoCapturedef exit(code=None):"""Exit while closeing howdy-gtk properly"""global gtk_proc# Exit the auth ui process if there is oneif "gtk_proc" in globals():gtk_proc.terminate()# Exit compareif code is not None:sys.exit(code)def init_detector(lock):"""Start face detector, encoder and predictor in a new thread"""global face_detector, pose_predictor, face_encoder# Test if at lest 1 of the data files is there and abort if it's notif not os.path.isfile(PATH + "/dlib-data/shape_predictor_5_face_landmarks.dat"):print(_("Data files have not been downloaded, please run the following commands:"))print("\n\tcd " + PATH + "/dlib-data")print("\tsudo ./install.sh\n")lock.release()exit(1)# Use the CNN detector if enabledif use_cnn:face_detector = dlib.cnn_face_detection_model_v1(PATH + "/dlib-data/mmod_human_face_detector.dat")else:face_detector = dlib.get_frontal_face_detector()# Start the others regardlesspose_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")# Note the time it took to initialize detectorstimings["ll"] = time.time() - timings["ll"]lock.release()def make_snapshot(type):"""Generate snapshot after detection"""snapshot.generate(snapframes, [type + _(" LOGIN"),_("Date: ") + datetime.datetime.utcnow().strftime("%Y/%m/%d %H:%M:%S UTC"),_("Scan time: ") + str(round(time.time() - timings["fr"], 2)) + "s",_("Frames: ") + str(frames) + " (" + str(round(frames / (time.time() - timings["fr"]), 2)) + "FPS)",_("Hostname: ") + os.uname().nodename,_("Best certainty value: ") + str(round(lowest_certainty * 10, 1))])def send_to_ui(type, message):"""Send message to the auth ui"""global gtk_proc# Only execute of the proccess startedif "gtk_proc" in globals():# Format message so the ui can parse itmessage = type + "=" + message + " \n"# Try to send the message to the auth ui, but it's okay if that failstry:gtk_proc.stdin.write(bytearray(message.encode("utf-8")))gtk_proc.stdin.flush()except IOError:pass# Make sure we were given an username to tast against if len(sys.argv) < 2:exit(12)# Get the absolute path to the current directory PATH = os.path.abspath(__file__ + "/..")# The username of the user being authenticated user = sys.argv[1] # The model file contents models = [] # Encoded face models encodings = [] # Amount of ignored 100% black frames black_tries = 0 # Amount of ingnored dark frames dark_tries = 0 # Total amount of frames captured frames = 0 # Captured frames for snapshot capture snapframes = [] # Tracks the lowest certainty value in the loop lowest_certainty = 10 # Face recognition/detection instances face_detector = None pose_predictor = None face_encoder = None# Try to load the face model from the models folder try:models = json.load(open(PATH + "/models/" + user + ".dat"))for model in models:encodings += model["data"] except FileNotFoundError:exit(10)# Check if the file contains a model if len(models) < 1:exit(10)# Read config from disk config = configparser.ConfigParser() config.read(PATH + "/config.ini")# Get all config values needed use_cnn = config.getboolean("core", "use_cnn", fallback=False) timeout = config.getint("video", "timeout", 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) capture_failed = config.getboolean("snapshots", "capture_failed", fallback=False) capture_successful = config.getboolean("snapshots", "capture_successful", fallback=False) gtk_stdout = config.getboolean("debug", "gtk_stdout", fallback=False)# Send the gtk outupt to the terminal if enabled in the config gtk_pipe = sys.stdout if gtk_stdout else subprocess.DEVNULL# Start the auth ui, register it to be always be closed on exit try:gtk_proc = subprocess.Popen(["../howdy-gtk/src/init.py", "--start-auth-ui"], stdin=subprocess.PIPE, stdout=gtk_pipe, stderr=gtk_pipe)atexit.register(exit) except FileNotFoundError:pass# Write to the stdin to redraw ui send_to_ui("M", _("Starting up..."))# Save the time needed to start the script timings["in"] = time.time() - timings["st"]# Import face recognition, takes some 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()video_capture = VideoCapture(config)# Read exposure from config to use in the main loop exposure = config.getint("video", "exposure", fallback=-1)# Note the time it took to open the camera 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 height = video_capture.internal.get(cv2.CAP_PROP_FRAME_HEIGHT) or 1# Calculate the amount the image has to shrink scaling_factor = (max_height / height) or 1# Fetch config settings out of the loop timeout = config.getint("video", "timeout") dark_threshold = config.getfloat("video", "dark_threshold") end_report = config.getboolean("debug", "end_report")# Initiate histogram equalization clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))# Let the ui know that we're ready send_to_ui("M", _("Identifying you..."))# Start the read loop frames = 0 valid_frames = 0 timings["fr"] = time.time() dark_running_total = 0while True:# Increment the frame count every loopframes += 1# Form a string to let the user know we're real busyui_subtext = "Scanned " + str(valid_frames - dark_tries) + " frames"if (dark_tries > 1):ui_subtext += " (skipped " + str(dark_tries) + " dark frames)"# Show it in the ui as subtextsend_to_ui("S", ui_subtext)# Stop if we've exceded the time limitif time.time() - timings["fr"] > timeout:# Create a timeout snapshot if enabledif capture_failed:make_snapshot(_("FAILED"))if dark_tries == valid_frames:print(_("All frames were too dark, please check dark_threshold in config"))print(_("Average darkness: {avg}, Threshold: {threshold}").format(avg=str(dark_running_total / max(1, valid_frames)), threshold=str(dark_threshold)))exit(13)else:exit(11)# Grab a single frame of videoframe, gsframe = video_capture.read_frame()gsframe = clahe.apply(gsframe)# If snapshots have been turned onif capture_failed or capture_successful:# Start capturing frames for the snapshotif len(snapframes) < 3:snapframes.append(frame)# Create a histogram of the image with 8 valueshist = cv2.calcHist([gsframe], [0], None, [8], [0, 256])# All values combined for percentage calculationhist_total = np.sum(hist)# Calculate frame darknessdarkness = (hist[0] / hist_total * 100)# If the image is fully black due to a bad camera read,# skip to the next frameif (hist_total == 0) or (darkness == 100):black_tries += 1continuedark_running_total += darknessvalid_frames += 1# If the image exceeds darkness threshold due to subject distance,# skip to the next frameif (darkness > dark_threshold):dark_tries += 1continue# If the hight is too highif scaling_factor != 1:# Apply that factor to the frameframe = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)gsframe = cv2.resize(gsframe, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)# Get all faces from that frame as encodings# Upsamples 1 timeface_locations = face_detector(gsframe, 1)# Loop through each facefor fl in face_locations:if use_cnn:fl = fl.rect# Fetch the faces in the imageface_landmark = pose_predictor(frame, fl)face_encoding = np.array(face_encoder.compute_face_descriptor(frame, face_landmark, 1))# Match this found face against a known facematches = np.linalg.norm(encodings - face_encoding, axis=1)# Get best matchmatch_index = np.argmin(matches)match = matches[match_index]# Update certainty if we have a new lowif lowest_certainty > match:lowest_certainty = match# Check if a match that's confident enoughif 0 < match < video_certainty:timings["tt"] = time.time() - timings["st"]timings["fl"] = time.time() - timings["fr"]# If set to true in the config, print debug textif end_report:def print_timing(label, k):"""Helper function to print a timing from the list"""print(" %s: %dms" % (label, round(timings[k] * 1000)))# Print a nice timing reportprint(_("Time spent"))print_timing(_("Starting up"), "in")print(_(" Open cam + load libs: %dms") % (round(max(timings["ll"], timings["ic"]) * 1000, )))print_timing(_(" Opening the camera"), "ic")print_timing(_(" Importing recognition libs"), "ll")print_timing(_("Searching for known face"), "fl")print_timing(_("Total time"), "tt")print(_("\nResolution"))width = video_capture.fw or 1print(_(" Native: %dx%d") % (height, width))# Save the new size for diagnosticsscale_height, scale_width = frame.shape[:2]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 timeprint(_("\nFrames searched: %d (%.2f fps)") % (frames, frames / timings["fl"]))print(_("Black frames ignored: %d ") % (black_tries, ))print(_("Dark frames ignored: %d ") % (dark_tries, ))print(_("Certainty of winning frame: %.3f") % (match * 10, ))print(_("Winning model: %d (\"%s\")") % (match_index, models[match_index]["label"]))# Make snapshot if enabledif capture_successful:make_snapshot(_("SUCCESSFUL"))# Run rubberstamps if enabledif config.getboolean("rubberstamps", "enabled", fallback=False):import rubberstampssend_to_ui("S", "")if "gtk_proc" not in vars():gtk_proc = Nonerubberstamps.execute(config, gtk_proc, {"video_capture": video_capture,"face_detector": face_detector,"pose_predictor": pose_predictor,"clahe": clahe})# End peacefullyexit(0)if exposure != -1:# For a strange reason on some cameras (e.g. Lenoxo X1E) setting manual exposure works only after a couple frames# are captured and even after a delay it does not always work. Setting exposure at every frame is reliable though.video_capture.internal.set(cv2.CAP_PROP_AUTO_EXPOSURE, 1.0) # 1 = Manualvideo_capture.internal.set(cv2.CAP_PROP_EXPOSURE, float(exposure))

并新建i18n.py

# Support file for translations# Import modules import gettext import os# Get the right translation based on locale, falling back to base if none found translation = gettext.translation("core", localedir=os.path.join(os.path.dirname(__file__), 'locales'), fallback=True) translation.install()# Export translation function as _ _ = translation.gettext

6、啟動howdy

sudo howdy disable 1 sudo howdy disable 0

howdy常用命令：

sudo howdy list：面部模型記錄 sudo howdy remove face_ID：刪除指定ID的面部記錄 sudo howdy clear：清除所有面部模型記錄 sudo howdy disable 1：禁用howdy功能 sudo howdy disable 0：啟用howdy功能

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