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Python3利用Dlib19.7實(shí)現(xiàn)攝像頭人臉識(shí)別的方法

更新時(shí)間：2018年05月11日 14:53:41 作者：TimeStamp

這篇文章主要介紹了Python 3 利用 Dlib 19.7 實(shí)現(xiàn)攝像頭人臉識(shí)別，利用python開(kāi)發(fā)，借助Dlib庫(kù)捕獲攝像頭中的人臉，提取人臉特征，通過(guò)計(jì)算歐氏距離來(lái)和預(yù)存的人臉特征進(jìn)行對(duì)比，達(dá)到人臉識(shí)別的目的，感興趣的小伙伴們可以參考一下

0.引言

利用python開(kāi)發(fā)，借助Dlib庫(kù)捕獲攝像頭中的人臉，提取人臉特征，通過(guò)計(jì)算歐氏距離來(lái)和預(yù)存的人臉特征進(jìn)行對(duì)比，達(dá)到人臉識(shí)別的目的；

可以自動(dòng)從攝像頭中摳取人臉圖片存儲(chǔ)到本地，然后提取構(gòu)建預(yù)設(shè)人臉特征；

根據(jù)摳取的 / 已有的同一個(gè)人多張人臉圖片提取128D特征值，然后計(jì)算該人的128D特征均值；

然后和攝像頭中實(shí)時(shí)獲取到的人臉提取出的特征值，計(jì)算歐氏距離，判定是否為同一張人臉；　　

人臉識(shí)別 / face recognition的說(shuō)明：

wikipedia 關(guān)于人臉識(shí)別系統(tǒng) / face recognition system 的描述：theywork by comparing selected facial featuresfrom given image with faces within a database.

本項(xiàng)目中就是比較預(yù)設(shè)的人臉的特征和攝像頭實(shí)時(shí)獲取到的人臉的特征；

核心就是提取128D人臉特征，然后計(jì)算攝像頭人臉特征和預(yù)設(shè)的特征臉的歐式距離，進(jìn)行比對(duì)；

效果如下（攝像頭認(rèn)出來(lái)我是default_person預(yù)設(shè)的人臉 / 另一個(gè)人不是預(yù)設(shè)人臉顯示diff）：

圖1 攝像頭人臉識(shí)別效果gif

1.總體流程

先說(shuō)下人臉檢測(cè) (face detection) 和人臉識(shí)別 (face recognition) ，前者是達(dá)到檢測(cè)出場(chǎng)景中人臉的目的就可以了，而后者不僅需要檢測(cè)出人臉，還要和已有人臉數(shù)據(jù)進(jìn)行比對(duì)，識(shí)別出是否在數(shù)據(jù)庫(kù)中，或者進(jìn)行身份標(biāo)注之類處理，人臉檢測(cè)和人臉識(shí)別兩者有時(shí)候可能會(huì)被理解混淆；

我的之前一些項(xiàng)目都是用dlib做人臉檢測(cè)這塊，這個(gè)項(xiàng)目想要實(shí)現(xiàn)的功能是人臉識(shí)別功能，借助的是 dlib官網(wǎng)中 face_recognition.py這個(gè)例程（link：http://dlib.net/face_recognition.py.html）；

核心在于利用 “dlib_face_recognition_resnet_model_v1.dat” 這個(gè)model，提取人臉圖像的128D特征，然后比對(duì)不同人臉圖片的128D特征，設(shè)定閾值計(jì)算歐氏距離來(lái)判斷是否為同一張臉；

# face recognition model, the object maps human faces into 128D vectors
facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
 
shape = predictor(img, dets[0])
face_descriptor = facerec.compute_face_descriptor(img, shape)

圖2 總體設(shè)計(jì)流程

2.源碼介紹

主要有

get_face_from_camera.py ,
get_features_into_CSV.py，
face_reco_from_camera.py

這三個(gè)py文件；

2.1get_face_from_camera.py / 采集構(gòu)建XXX人臉數(shù)據(jù)

人臉識(shí)別需要將提取到的圖像數(shù)據(jù) 和已有圖像數(shù)據(jù)進(jìn)行比對(duì)分析，所以這個(gè)py文件實(shí)現(xiàn)的功能就是采集構(gòu)建XXX的人臉數(shù)據(jù)；

程序會(huì)生成一個(gè)窗口，顯示調(diào)用的攝像頭實(shí)時(shí)獲取的圖像（關(guān)于攝像頭的調(diào)用方式可以參考我的另一博客http://www.dbjr.com.cn/article/135512.htm）；

按s鍵可以保存當(dāng)前視頻流中的人臉圖像，保存的路徑由 path_save = “xxxx/get_from_camera/” 規(guī)定；

按q鍵退出窗口；

攝像頭的調(diào)用是利用opencv庫(kù)的cv2.VideoCapture(0), 此處參數(shù)為0代表調(diào)用的是筆記本的默認(rèn)攝像頭，你也可以讓它調(diào)用傳入已有視頻文件；

圖3get_face_from_camera.py 的界面

這樣的話，你就可以在 path_save指定的目錄下得到一組捕獲到的人臉；

圖4 捕獲到的一組人臉

源碼如下：

# 2018-5-11
# By TimeStamp
# cnblogs: http://www.cnblogs.com/AdaminXie

import dlib # 人臉識(shí)別的庫(kù)dlib
import numpy as np # 數(shù)據(jù)處理的庫(kù)numpy
import cv2 # 圖像處理的庫(kù)OpenCv

# dlib預(yù)測(cè)器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# 創(chuàng)建cv2攝像頭對(duì)象
cap = cv2.VideoCapture(0)

# cap.set(propId, value)
# 設(shè)置視頻參數(shù)，propId設(shè)置的視頻參數(shù)，value設(shè)置的參數(shù)值
cap.set(3, 480)

# 截圖screenshoot的計(jì)數(shù)器
cnt_ss = 0

# 人臉截圖的計(jì)數(shù)器
cnt_p = 0

# 保存
path_save = "F:/code/python/P_dlib_face_reco/data/get_from_camera/"

# cap.isOpened（） 返回true/false 檢查初始化是否成功
while cap.isOpened():

 # cap.read()
 # 返回兩個(gè)值：
 # 一個(gè)布爾值true/false，用來(lái)判斷讀取視頻是否成功/是否到視頻末尾
 # 圖像對(duì)象，圖像的三維矩陣q
 flag, im_rd = cap.read()

 # 每幀數(shù)據(jù)延時(shí)1ms，延時(shí)為0讀取的是靜態(tài)幀
 kk = cv2.waitKey(1)

 # 取灰度
 img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)

 # 人臉數(shù)rects
 rects = detector(img_gray, 0)

 # print(len(rects))

 # 待會(huì)要寫(xiě)的字體
 font = cv2.FONT_HERSHEY_SIMPLEX

 if (len(rects) != 0):
 # 檢測(cè)到人臉

 # 矩形框
 for k, d in enumerate(rects):

 # 計(jì)算矩形大小
 # (x,y), (寬度width, 高度height)
 pos_start = tuple([d.left(), d.top()])
 pos_end = tuple([d.right(), d.bottom()])

 # 計(jì)算矩形框大小
 height = d.bottom() - d.top()
 width = d.right() - d.left()

 # 根據(jù)人臉大小生成空的圖像
 im_blank = np.zeros((height, width, 3), np.uint8)

 im_rd = cv2.rectangle(im_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
 im_blank = np.zeros((height, width, 3), np.uint8)

 # 保存人臉到本地
 if (kk == ord('s')):
 cnt_p += 1
 for ii in range(height):
  for jj in range(width):
  im_blank[ii][jj] = im_rd[d.top() + ii][d.left() + jj]
 print(path_save + "img_face_" + str(cnt_p) + ".jpg")
 cv2.imwrite(path_save + "img_face_" + str(cnt_p) + ".jpg", im_blank)
 cv2.putText(im_rd, "faces: " + str(len(rects)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

 else:
 # 沒(méi)有檢測(cè)到人臉
 cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

 # 添加說(shuō)明
 im_rd = cv2.putText(im_rd, "s: save face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 im_rd = cv2.putText(im_rd, "q: quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 
 # 按下q鍵退出
 if (kk == ord('q')):
 break

 # 窗口顯示
 cv2.imshow("camera", im_rd)

# 釋放攝像頭
cap.release()

# 刪除建立的窗口
cv2.destroyAllWindows()

2.2get_features_into_CSV.py / 提取特征存入CSV

已經(jīng)得到了XXX的一組人臉圖像，現(xiàn)在就需要把他的面部特征提取出來(lái)；

這里借助 dlib 庫(kù)的 face recognition model 人臉識(shí)別模型；

# face recognition model, the object maps human faces into 128D vectors
facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")

# detector to find the faces
detector = dlib.get_frontal_face_detector()

# shape predictor to find the face landmarks
predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")

# 讀取圖片
img = io.imread(path_img)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

dets = detector(img_gray, 1)
shape = predictor(img_gray, dets[0])
face_descriptor = facerec.compute_face_descriptor(img_gray, shape)

我們可以看下對(duì)于某張圖片，face_descriptor的輸出結(jié)果：

綠色框內(nèi)是我們的返回128D特征的函數(shù)；

在紅色框內(nèi)調(diào)用該函數(shù)來(lái)計(jì)算img_face_13.jpg；

可以看到黃色框中的輸出為128D的向量；

圖5 返回單張圖像的128D特征的計(jì)算結(jié)果

所以我們就可以把path_save中的圖像，進(jìn)行批量的特征計(jì)算，然后寫(xiě)入CSV中（利用 write_into_CSV函數(shù)），我這邊csv的命名為default_person.csv；

就可以得到行數(shù)（人臉數(shù)）*128列的一個(gè)特征CSV；

這是某個(gè)人的人臉特征，然后計(jì)算128D特征的均值，求mean（利用 compute_the_mean函數(shù)）

運(yùn)行的輸出結(jié)果，這個(gè)128D的特征值，就是default_person的特征；

也就是我們內(nèi)置/預(yù)設(shè)的人臉，之后攝像頭捕獲的人臉將要拿過(guò)來(lái)和這個(gè)特征值進(jìn)行比對(duì)，進(jìn)行人臉識(shí)別的處理；

復(fù)制代碼代碼如下:

[-0.030892765492592986, 0.13333227054068916, 0.054221574805284799, -0.050820438289328626, -0.056331159841073189, 0.0039378538311116004, -0.044465327145237675, -0.13096490031794497, 0.14215188983239627, -0.084465635842398593, 0.34389359700052363, -0.062936659118062566, -0.24372901571424385, -0.13270603316394905, -0.0472818422866495, 0.15475224742763921, -0.24415240554433121, -0.11213862150907516, 0.032288033417180964, 0.023676671577911628, 0.098508275653186594, -0.010117797634417289, 0.0048202000815715448, -0.014808513420192819, -0.060100053486071135, -0.34934839135722112, -0.095795629448012301, -0.050788544706608117, 0.032316677762489567, -0.099673464894294739, -0.080181991975558434, 0.096361607705291952, -0.1823408101734362, -0.045472671817007815, -0.0066827326326778062, 0.047393877549391041, -0.038414973079373964, -0.039067085930391363, 0.15961966781239761, 0.0092458106136243598, -0.16182226570029007, 0.026322136191945327, -0.0039144184832510193, 0.2492692768573761, 0.19180528427425184, 0.022950534855848866, -0.019220497949342979, -0.15331173021542399, 0.047744840089427795, -0.17038608616904208, 0.026140184680882254, 0.19366614363695445, 0.066497623724372762, 0.07038829416820877, -0.0549700813073861, -0.11961311768544347, -0.032121153940495695, 0.083507449611237169, -0.14934051350543373, 0.011458799806668571, 0.10686114273573223, -0.10744074888919529, -0.04377919611962218, -0.11030520381111848, 0.20804878441910996, 0.093076545941202266, -0.11621182490336268, -0.1991656830436305, 0.10751579348978244, -0.11251544991606161, -0.12237925866716787, 0.058218707869711672, -0.15829276019021085, -0.17670038891466042, -0.2718416170070046, 0.034569320955166689, 0.30443575821424784, 0.061833358712886512, -0.19622498672259481, 0.011373612000361868, -0.050225612756453063, -0.036157087079788507, 0.12961127491373764, 0.13962576616751521, -0.0074232793168017737, 0.020964263007044792, -0.11185114399382942, 0.012502493042694894, 0.17834208513561048, -0.072658227462517586, -0.041312719401168194, 0.25095899873658228, -0.056628625839948654, 0.10285118379090961, 0.046701753217923012, 0.042323612264896691, 0.0036216247826814651, 0.066720707440062574, -0.16388990533979317, -0.0193739396421925, 0.027835704435251261, -0.086023958105789985, -0.05472404568603164, 0.14802298341926776, -0.10644183582381199, 0.098863413851512108, 0.00061285014778963834, 0.062096107555063146, 0.051960245755157973, -0.099548895108072383, -0.058173993112225285, -0.065454461562790375, 0.14721672511414477, -0.25363486848379435, 0.20384312381869868, 0.16890435312923632, 0.097537552447695477, 0.087824966562421697, 0.091438713434495431, 0.093809676797766431, -0.034379941362299417, -0.085149037210564868, -0.24900743130006289, 0.021165960517368819, 0.076710369830068792, -0.0061752907196549996, 0.028413473285342519, -0.029983982541843465]

源碼:

# 2018-5-11
# By TimeStamp
# cnblogs: http://www.cnblogs.com/AdaminXie

# return_128d_features() 獲取某張圖像的128d特征
# write_into_csv() 將某個(gè)文件夾中的圖像讀取特征兵寫(xiě)入csv
# compute_the_mean() 從csv中讀取128d特征，并計(jì)算特征均值

import cv2
import os
import dlib
from skimage import io
import csv
import numpy as np
import pandas as pd

path_pics = "F:/code/python/P_dlib_face_reco/data/get_from_camera/"
path_csv = "F:/code/python/P_dlib_face_reco/data/csvs/"

# detector to find the faces
detector = dlib.get_frontal_face_detector()

# shape predictor to find the face landmarks
predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")

# face recognition model, the object maps human faces into 128D vectors
facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")

# 返回單張圖像的128D特征
def return_128d_features(path_img):
 img = io.imread(path_img)
 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 dets = detector(img_gray, 1)

 if(len(dets)!=0):
 shape = predictor(img_gray, dets[0])
 face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
 else:
 face_descriptor = 0
 print("no face")

 # print(face_descriptor)
 return face_descriptor

#return_128d_features(path_pics+"img_face_13.jpg")

# 將文件夾中照片特征提取出來(lái)，寫(xiě)入csv
# 輸入input:
# path_pics: 圖像文件夾的路徑
# path_csv: 要生成的csv路徑

def write_into_csv(path_pics ,path_csv):
 dir_pics = os.listdir(path_pics)

 with open(path_csv, "w", newline="") as csvfile:
 writer = csv.writer(csvfile)
 for i in range(len(dir_pics)):
 # 調(diào)用return_128d_features()得到128d特征
 print(path_pics+dir_pics[i])
 features_128d = return_128d_features(path_pics+dir_pics[i])
 # print(features_128d)
 # 遇到?jīng)]有檢測(cè)出人臉的圖片跳過(guò)
 if features_128d==0:
 i += 1
 else:
 writer.writerow(features_128d)

#write_into_csv(path_pics, path_csv+"default_person.csv")

path_csv_rd = "F:/code/python/P_dlib_face_reco/data/csvs/default_person.csv"

# 從csv中讀取數(shù)據(jù)，計(jì)算128d特征的均值
def compute_the_mean(path_csv_rd):
 column_names = []

 for i in range(128):
 column_names.append("features_" + str(i + 1))

 rd = pd.read_csv(path_csv_rd, names=column_names)

 # 存放128維特征的均值
 feature_mean = []

 for i in range(128):
 tmp_arr = rd["features_"+str(i+1)]
 tmp_arr = np.array(tmp_arr)

 # 計(jì)算某一個(gè)特征的均值
 tmp_mean = np.mean(tmp_arr)

 feature_mean.append(tmp_mean)

 print(feature_mean)
 return feature_mean

compute_the_mean(path_csv_rd)

2.3 face_reco_from_camera.py / 實(shí)時(shí)人臉識(shí)別對(duì)比分析

這個(gè)py就是調(diào)用攝像頭，捕獲攝像頭中的人臉，然后如果檢測(cè)到人臉，將攝像頭中的人臉提取出128D的特征，然后和預(yù)設(shè)的default_person的128D特征進(jìn)行計(jì)算歐式距離，如果比較小，可以判定為一個(gè)人，否則不是一個(gè)人；

歐氏距離對(duì)比的閾值設(shè)定，是在 return_euclidean_distance函數(shù)的dist變量；

我這里程序里面指定的是0.4，具體閾值可以根據(jù)實(shí)際情況或者測(cè)得結(jié)果進(jìn)行修改；

源碼：

# 2018-5-11
# By TimeStamp
# cnblogs: http://www.cnblogs.com/AdaminXie

import dlib # 人臉識(shí)別的庫(kù)dlib
import numpy as np # 數(shù)據(jù)處理的庫(kù)numpy
import cv2 # 圖像處理的庫(kù)OpenCv

# face recognition model, the object maps human faces into 128D vectors
facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")

# 計(jì)算兩個(gè)向量間的歐式距離
def return_euclidean_distance(feature_1,feature_2):
 feature_1 = np.array(feature_1)
 feature_2 = np.array(feature_2)
 dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
 print(dist)

 if dist > 0.4:
 return "diff"
 else:
 return "same"


features_mean_default_person = [-0.030892765492592986, 0.13333227054068916, 0.054221574805284799, -0.050820438289328626, -0.056331159841073189, 0.0039378538311116004, -0.044465327145237675, -0.13096490031794497, 0.14215188983239627, -0.084465635842398593, 0.34389359700052363, -0.062936659118062566, -0.24372901571424385, -0.13270603316394905, -0.0472818422866495, 0.15475224742763921, -0.24415240554433121, -0.11213862150907516, 0.032288033417180964, 0.023676671577911628, 0.098508275653186594, -0.010117797634417289, 0.0048202000815715448, -0.014808513420192819, -0.060100053486071135, -0.34934839135722112, -0.095795629448012301, -0.050788544706608117, 0.032316677762489567, -0.099673464894294739, -0.080181991975558434, 0.096361607705291952, -0.1823408101734362, -0.045472671817007815, -0.0066827326326778062, 0.047393877549391041, -0.038414973079373964, -0.039067085930391363, 0.15961966781239761, 0.0092458106136243598, -0.16182226570029007, 0.026322136191945327, -0.0039144184832510193, 0.2492692768573761, 0.19180528427425184, 0.022950534855848866, -0.019220497949342979, -0.15331173021542399, 0.047744840089427795, -0.17038608616904208, 0.026140184680882254, 0.19366614363695445, 0.066497623724372762, 0.07038829416820877, -0.0549700813073861, -0.11961311768544347, -0.032121153940495695, 0.083507449611237169, -0.14934051350543373, 0.011458799806668571, 0.10686114273573223, -0.10744074888919529, -0.04377919611962218, -0.11030520381111848, 0.20804878441910996, 0.093076545941202266, -0.11621182490336268, -0.1991656830436305, 0.10751579348978244, -0.11251544991606161, -0.12237925866716787, 0.058218707869711672, -0.15829276019021085, -0.17670038891466042, -0.2718416170070046, 0.034569320955166689, 0.30443575821424784, 0.061833358712886512, -0.19622498672259481, 0.011373612000361868, -0.050225612756453063, -0.036157087079788507, 0.12961127491373764, 0.13962576616751521, -0.0074232793168017737, 0.020964263007044792, -0.11185114399382942, 0.012502493042694894, 0.17834208513561048, -0.072658227462517586, -0.041312719401168194, 0.25095899873658228, -0.056628625839948654, 0.10285118379090961, 0.046701753217923012, 0.042323612264896691, 0.0036216247826814651, 0.066720707440062574, -0.16388990533979317, -0.0193739396421925, 0.027835704435251261, -0.086023958105789985, -0.05472404568603164, 0.14802298341926776, -0.10644183582381199, 0.098863413851512108, 0.00061285014778963834, 0.062096107555063146, 0.051960245755157973, -0.099548895108072383, -0.058173993112225285, -0.065454461562790375, 0.14721672511414477, -0.25363486848379435, 0.20384312381869868, 0.16890435312923632, 0.097537552447695477, 0.087824966562421697, 0.091438713434495431, 0.093809676797766431, -0.034379941362299417, -0.085149037210564868, -0.24900743130006289, 0.021165960517368819, 0.076710369830068792, -0.0061752907196549996, 0.028413473285342519, -0.029983982541843465]


# dlib預(yù)測(cè)器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# 創(chuàng)建cv2攝像頭對(duì)象
cap = cv2.VideoCapture(0)

# cap.set(propId, value)
# 設(shè)置視頻參數(shù)，propId設(shè)置的視頻參數(shù)，value設(shè)置的參數(shù)值
cap.set(3, 480)

def get_128d_features(img_gray):
 dets = detector(img_gray, 1)
 if (len(dets) != 0):
 shape = predictor(img_gray, dets[0])
 face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
 else:
 face_descriptor=0
 return face_descriptor

# cap.isOpened（） 返回true/false 檢查初始化是否成功
while (cap.isOpened()):

 # cap.read()
 # 返回兩個(gè)值：
 # 一個(gè)布爾值true/false，用來(lái)判斷讀取視頻是否成功/是否到視頻末尾
 # 圖像對(duì)象，圖像的三維矩陣
 flag, im_rd = cap.read()

 # 每幀數(shù)據(jù)延時(shí)1ms，延時(shí)為0讀取的是靜態(tài)幀
 kk = cv2.waitKey(1)

 # 取灰度
 img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)

 # 人臉數(shù)rects
 rects = detector(img_gray, 0)

 # print(len(rects))

 # 待會(huì)要寫(xiě)的字體
 font = cv2.FONT_HERSHEY_SIMPLEX

 cv2.putText(im_rd, "q: quit", (20, 400), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)

 if (len(rects) != 0):
 # 檢測(cè)到人臉

 # 將捕獲到的人臉提取特征和內(nèi)置特征進(jìn)行比對(duì)
 features_rd = get_128d_features(im_rd)
 compare = return_euclidean_distance(features_rd, features_mean_default_person)

 im_rd = cv2.putText(im_rd, compare.replace("same", "default_person"), (20, 350), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)

 # 矩形框
 for k, d in enumerate(rects):

 # 繪制矩形框
 im_rd = cv2.rectangle(im_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)

 cv2.putText(im_rd, "faces: " + str(len(rects)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

 else:
 # 沒(méi)有檢測(cè)到人臉
 cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)


 # 按下q鍵退出
 if (kk == ord('q')):
 break

 # 窗口顯示
 cv2.imshow("camera", im_rd)

# 釋放攝像頭
cap.release()

# 刪除建立的窗口
cv2.destroyAllWindows()

實(shí)時(shí)輸出結(jié)果：