Python 3 利用 Dlib 19.7 实现摄像头人脸识别

0.引言

利用python开发，借助Dlib库捕获摄像头中的人脸，提取人脸特征，通过计算欧氏距离来和预存的人脸特征进行对比，达到人脸识别的目的；

可以自动从摄像头中抠取人脸图片存储到本地；

根据抠取的 / 已有的同一个人多张人脸图片提取128D特征值，然后计算该人的128D特征均值；

然后和摄像头中实时获取到的人脸提取出的特征值，计算欧氏距离，判定是否为同一张人脸；

效果如下（摄像头认出来我是default_person预设的人脸 / 另一个人不是预设人脸显示diff）：

图1 摄像头人脸识别效果gif

1.总体流程

先说下 人脸检测 (face detection) 和 人脸识别 (face recognition) ，前者是达到检测出场景中人脸的目的就可以了，而后者不仅需要检测出人脸，还要和 已有人脸数据进行比对 ，识别出是否在数据库中，或者进行身份标注之类处理，人脸检测和人脸识别两者有时候可能会被理解混淆；

我的之前一些项目都是用dlib做人脸检测这块，这个项目想要实现的功能是人脸识别功能，借助的是 dlib官网中 face_recognition.py这个例程 （link： http://dlib.net/face_recognition.py.html）；

核心在于 利用 “dlib_face_recognition_resnet_model_v1.dat” 这个model，提取 人脸图像的128D特征 ，然后比对不同人脸图片的128D特征，设定阈值 计算欧氏距离 来判断是否为同一张脸；

1 # face recognition model, the object maps human faces into 128D vectors
2 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
3 
4 shape = predictor(img, dets[0])
5 face_descriptor = facerec.compute_face_descriptor(img, shape)
 # face recognition model, the object maps human faces into 128D vectors
2 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
3 
4 shape = predictor(img, dets[0])
5 face_descriptor = facerec.compute_face_descriptor(img, shape)

图2 总体设计流程

2.源码介绍

主要有 get_face_from_camera.py ,   get_features_into_CSV.py 和 face_reco_from_camera.py 这三个py文件；

2.1 get_face_from_camera.py / 采集构建XXX人脸数据

人脸识别需要将 提取到的图像数据 和  已有图像数据  进行比对分析，所以这个py文件实现的功能就是 采集构建XXX的人脸数据 ；

程序会生成一个窗口，显示调用的摄像头实时获取的图像（关于摄像头的调用方式可以参考我的另一博客 http://www.cnblogs.com/AdaminXie/p/8472743.html ）；

按s键可以保存当前视频流中的人脸图像，保存的路径由 path_save = “xxxx/get_from_camera/” 规定；

按q键退出窗口；

摄像头的调用是利用opencv库的 cv2.VideoCapture(0), 此处参数为0代表调用的是笔记本的默认摄像头，你也可以让它调用传入已有视频文件；

图3  get_face_from_camera.py 的界面

这样的话，你就可以在 path_save指定的目录下得到一组捕获到的人脸；

图4 捕获到的一组人脸

源码如下：

  1 # 2018-5-11
  2 # By TimeStamp
  3 # cnblogs: http://www.cnblogs.com/AdaminXie
  4 
  5 import dlib  # 人脸识别的库dlib
  6 import numpy as np  # 数据处理的库numpy
  7 import cv2  # 图像处理的库OpenCv
  8 
  9 # dlib预测器
 10 detector = dlib.get_frontal_face_detector()
 11 predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
 12 
 13 # 创建cv2摄像头对象
 14 cap = cv2.VideoCapture(0)
 15 
 16 # cap.set(propId, value)
 17 # 设置视频参数，propId设置的视频参数，value设置的参数值
 18 cap.set(3, 480)
 19 
 20 # 截图screenshoot的计数器
 21 cnt_ss = 0
 22 
 23 # 人脸截图的计数器
 24 cnt_p = 0
 25 
 26 # 保存
 27 path_save = "F:/code/python/P_dlib_face_reco/data/get_from_camera/"
 28 
 29 # cap.isOpened（） 返回true/false 检查初始化是否成功
 30 while cap.isOpened():
 31 
 32     # cap.read()
 33     # 返回两个值：
 34     #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
 35     #    图像对象，图像的三维矩阵q
 36     flag, im_rd = cap.read()
 37 
 38     # 每帧数据延时1ms，延时为0读取的是静态帧
 39     kk = cv2.waitKey(1)
 40 
 41     # 取灰度
 42     img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
 43 
 44     # 人脸数rects
 45     rects = detector(img_gray, 0)
 46 
 47     # print(len(rects))
 48 
 49     # 待会要写的字体
 50     font = cv2.FONT_HERSHEY_SIMPLEX
 51 
 52     if (len(rects) != 0):
 53         # 检测到人脸
 54 
 55         # 矩形框
 56         for k, d in enumerate(rects):
 57 
 58             # 计算矩形大小
 59             # (x,y), (宽度width, 高度height)
 60             pos_start = tuple([d.left(), d.top()])
 61             pos_end = tuple([d.right(), d.bottom()])
 62 
 63             # 计算矩形框大小
 64             height = d.bottom() - d.top()
 65             width = d.right() - d.left()
 66 
 67             # 根据人脸大小生成空的图像
 68             im_blank = np.zeros((height, width, 3), np.uint8)
 69 
 70             im_rd = cv2.rectangle(im_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
 71             im_blank = np.zeros((height, width, 3), np.uint8)
 72 
 73             # 保存人脸到本地
 74             if (kk == ord('s')):
 75                 cnt_p += 1
 76                 for ii in range(height):
 77                     for jj in range(width):
 78                         im_blank[ii][jj] = im_rd[d.top() + ii][d.left() + jj]
 79                 print(path_save + "img_face_" + str(cnt_p) + ".jpg")
 80                 cv2.imwrite(path_save + "img_face_" + str(cnt_p) + ".jpg", im_blank)
 81         cv2.putText(im_rd, "faces: " + str(len(rects)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 82 
 83     else:
 84         # 没有检测到人脸
 85         cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 86 
 87     # 添加说明
 88     im_rd = cv2.putText(im_rd, "s: save face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 89     im_rd = cv2.putText(im_rd, "q: quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 90   
 91     # 按下q键退出
 92     if (kk == ord('q')):
 93         break
 94 
 95     # 窗口显示
 96     cv2.imshow("camera", im_rd)
 97 
 98 # 释放摄像头
 99 cap.release()
100 
101 # 删除建立的窗口
102 cv2.destroyAllWindows()
1 # 2018-5-11
  2 # By TimeStamp
  3 # cnblogs: http://www.cnblogs.com/AdaminXie
  4 
  5 import dlib  # 人脸识别的库dlib
  6 import numpy as np  # 数据处理的库numpy
  7 import cv2  # 图像处理的库OpenCv
  8 
  9 # dlib预测器
 10 detector = dlib.get_frontal_face_detector()
 11 predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
 12 
 13 # 创建cv2摄像头对象
 14 cap = cv2.VideoCapture(0)
 15 
 16 # cap.set(propId, value)
 17 # 设置视频参数，propId设置的视频参数，value设置的参数值
 18 cap.set(3, 480)
 19 
 20 # 截图screenshoot的计数器
 21 cnt_ss = 0
 22 
 23 # 人脸截图的计数器
 24 cnt_p = 0
 25 
 26 # 保存
 27 path_save = "F:/code/python/P_dlib_face_reco/data/get_from_camera/"
 28 
 29 # cap.isOpened（） 返回true/false 检查初始化是否成功
 30 while cap.isOpened():
 31 
 32     # cap.read()
 33     # 返回两个值：
 34     #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
 35     #    图像对象，图像的三维矩阵q
 36     flag, im_rd = cap.read()
 37 
 38     # 每帧数据延时1ms，延时为0读取的是静态帧
 39     kk = cv2.waitKey(1)
 40 
 41     # 取灰度
 42     img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
 43 
 44     # 人脸数rects
 45     rects = detector(img_gray, 0)
 46 
 47     # print(len(rects))
 48 
 49     # 待会要写的字体
 50     font = cv2.FONT_HERSHEY_SIMPLEX
 51 
 52     if (len(rects) != 0):
 53         # 检测到人脸
 54 
 55         # 矩形框
 56         for k, d in enumerate(rects):
 57 
 58             # 计算矩形大小
 59             # (x,y), (宽度width, 高度height)
 60             pos_start = tuple([d.left(), d.top()])
 61             pos_end = tuple([d.right(), d.bottom()])
 62 
 63             # 计算矩形框大小
 64             height = d.bottom() - d.top()
 65             width = d.right() - d.left()
 66 
 67             # 根据人脸大小生成空的图像
 68             im_blank = np.zeros((height, width, 3), np.uint8)
 69 
 70             im_rd = cv2.rectangle(im_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
 71             im_blank = np.zeros((height, width, 3), np.uint8)
 72 
 73             # 保存人脸到本地
 74             if (kk == ord('s')):
 75                 cnt_p += 1
 76                 for ii in range(height):
 77                     for jj in range(width):
 78                         im_blank[ii][jj] = im_rd[d.top() + ii][d.left() + jj]
 79                 print(path_save + "img_face_" + str(cnt_p) + ".jpg")
 80                 cv2.imwrite(path_save + "img_face_" + str(cnt_p) + ".jpg", im_blank)
 81         cv2.putText(im_rd, "faces: " + str(len(rects)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 82 
 83     else:
 84         # 没有检测到人脸
 85         cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 86 
 87     # 添加说明
 88     im_rd = cv2.putText(im_rd, "s: save face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 89     im_rd = cv2.putText(im_rd, "q: quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
 90   
 91     # 按下q键退出
 92     if (kk == ord('q')):
 93         break
 94 
 95     # 窗口显示
 96     cv2.imshow("camera", im_rd)
 97 
 98 # 释放摄像头
 99 cap.release()
100 
101 # 删除建立的窗口
102 cv2.destroyAllWindows()

2.2 get_features_into_CSV.py / 提取特征存入CSV

已经得到了XXX的一组人脸图像，现在就需要把他的面部特征提取出来；

这里借助 dlib 库的 face recognition model 人脸识别模型；

 1 # face recognition model, the object maps human faces into 128D vectors
 2 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
 3 
 4 # detector to find the faces
 5 detector = dlib.get_frontal_face_detector()
 6 
 7 # shape predictor to find the face landmarks
 8 predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
 9 
10 # 读取图片
11 img = io.imread(path_img)
12 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
13 
14 dets = detector(img_gray, 1)
15 shape = predictor(img_gray, dets[0])
16 face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
1 # face recognition model, the object maps human faces into 128D vectors
 2 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
 3 
 4 # detector to find the faces
 5 detector = dlib.get_frontal_face_detector()
 6 
 7 # shape predictor to find the face landmarks
 8 predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
 9 
10 # 读取图片
11 img = io.imread(path_img)
12 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
13 
14 dets = detector(img_gray, 1)
15 shape = predictor(img_gray, dets[0])
16 face_descriptor = facerec.compute_face_descriptor(img_gray, shape)

 

我们可以看下对于某张图片，face_descriptor的输出结果：

绿色框内是我们的返回128D特征的函数 ， 在红色框内调用该函数来计算img_face_13.jpg ， 可以看到黄色框中的输出为128D的向量 ；

图5 返回单张图像的128D特征的计算结果

所以我们就可以把path_save中的图像，进行批量的特征计算，然后写入CSV中（利用 write_into_CSV  函数），我这边csv的命名为default_person.csv；

就可以得到行数（人脸数）*128列的一个特征CSV；

这是某个人的人脸特征，然后计算128D特征的均值，求mean（利用 compute_the_mean  函数）

运行的输出结果，这个128D的特征值，就是default_person的特征；

也就是我们 内置/预设的人脸 ，之后  摄像头捕获的人脸将要拿过来和这个特征值进行比对，进行人脸识别的处理 ；

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源码:

 1 # 2018-5-11
 2 # By TimeStamp
 3 # cnblogs: http://www.cnblogs.com/AdaminXie
 4 
 5 #   return_128d_features()          获取某张图像的128d特征
 6 #   write_into_csv()                将某个文件夹中的图像读取特征兵写入csv
 7 #   compute_the_mean()              从csv中读取128d特征，并计算特征均值
 8 
 9 import cv2
10 import os
11 import dlib
12 from skimage import io
13 import csv
14 import numpy as np
15 import pandas as pd
16 
17 path_pics = "F:/code/python/P_dlib_face_reco/data/get_from_camera/"
18 path_csv = "F:/code/python/P_dlib_face_reco/data/csvs/"
19 
20 # detector to find the faces
21 detector = dlib.get_frontal_face_detector()
22 
23 # shape predictor to find the face landmarks
24 predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
25 
26 # face recognition model, the object maps human faces into 128D vectors
27 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
28 
29 # 返回单张图像的128D特征
30 def return_128d_features(path_img):
31     img = io.imread(path_img)
32     img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
33     dets = detector(img_gray, 1)
34 
35     if(len(dets)!=0):
36         shape = predictor(img_gray, dets[0])
37         face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
38     else:
39         face_descriptor = 0
40         print("no face")
41 
42    # print(face_descriptor)
43     return face_descriptor
44 
45 #return_128d_features(path_pics+"img_face_13.jpg")
46 
47 # 将文件夹中照片特征提取出来，写入csv
48 # 输入input:
49 #   path_pics:  图像文件夹的路径
50 #   path_csv:   要生成的csv路径
51 
52 def write_into_csv(path_pics ,path_csv):
53     dir_pics = os.listdir(path_pics)
54 
55     with open(path_csv, "w", newline="") as csvfile:
56         writer = csv.writer(csvfile)
57         for i in range(len(dir_pics)):
58             # 调用return_128d_features()得到128d特征
59             print(path_pics+dir_pics[i])
60             features_128d = return_128d_features(path_pics+dir_pics[i])
61           #  print(features_128d)
62             # 遇到没有检测出人脸的图片跳过
63             if features_128d==0:
64                 i += 1
65             else:
66                 writer.writerow(features_128d)
67 
68 #write_into_csv(path_pics, path_csv+"default_person.csv")
69 
70 path_csv_rd = "F:/code/python/P_dlib_face_reco/data/csvs/default_person.csv"
71 
72 # 从csv中读取数据，计算128d特征的均值
73 def compute_the_mean(path_csv_rd):
74     column_names = []
75 
76     for i in range(128):
77         column_names.append("features_" + str(i + 1))
78 
79     rd = pd.read_csv(path_csv_rd, names=column_names)
80 
81     # 存放128维特征的均值
82     feature_mean = []
83 
84     for i in range(128):
85         tmp_arr = rd["features_"+str(i+1)]
86         tmp_arr = np.array(tmp_arr)
87 
88         # 计算某一个特征的均值
89         tmp_mean = np.mean(tmp_arr)
90 
91         feature_mean.append(tmp_mean)
92 
93     print(feature_mean)
94     return feature_mean
95 
96 compute_the_mean(path_csv_rd)
1 # 2018-5-11
 2 # By TimeStamp
 3 # cnblogs: http://www.cnblogs.com/AdaminXie
 4 
 5 #   return_128d_features()          获取某张图像的128d特征
 6 #   write_into_csv()                将某个文件夹中的图像读取特征兵写入csv
 7 #   compute_the_mean()              从csv中读取128d特征，并计算特征均值
 8 
 9 import cv2
10 import os
11 import dlib
12 from skimage import io
13 import csv
14 import numpy as np
15 import pandas as pd
16 
17 path_pics = "F:/code/python/P_dlib_face_reco/data/get_from_camera/"
18 path_csv = "F:/code/python/P_dlib_face_reco/data/csvs/"
19 
20 # detector to find the faces
21 detector = dlib.get_frontal_face_detector()
22 
23 # shape predictor to find the face landmarks
24 predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
25 
26 # face recognition model, the object maps human faces into 128D vectors
27 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
28 
29 # 返回单张图像的128D特征
30 def return_128d_features(path_img):
31     img = io.imread(path_img)
32     img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
33     dets = detector(img_gray, 1)
34 
35     if(len(dets)!=0):
36         shape = predictor(img_gray, dets[0])
37         face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
38     else:
39         face_descriptor = 0
40         print("no face")
41 
42    # print(face_descriptor)
43     return face_descriptor
44 
45 #return_128d_features(path_pics+"img_face_13.jpg")
46 
47 # 将文件夹中照片特征提取出来，写入csv
48 # 输入input:
49 #   path_pics:  图像文件夹的路径
50 #   path_csv:   要生成的csv路径
51 
52 def write_into_csv(path_pics ,path_csv):
53     dir_pics = os.listdir(path_pics)
54 
55     with open(path_csv, "w", newline="") as csvfile:
56         writer = csv.writer(csvfile)
57         for i in range(len(dir_pics)):
58             # 调用return_128d_features()得到128d特征
59             print(path_pics+dir_pics[i])
60             features_128d = return_128d_features(path_pics+dir_pics[i])
61           #  print(features_128d)
62             # 遇到没有检测出人脸的图片跳过
63             if features_128d==0:
64                 i += 1
65             else:
66                 writer.writerow(features_128d)
67 
68 #write_into_csv(path_pics, path_csv+"default_person.csv")
69 
70 path_csv_rd = "F:/code/python/P_dlib_face_reco/data/csvs/default_person.csv"
71 
72 # 从csv中读取数据，计算128d特征的均值
73 def compute_the_mean(path_csv_rd):
74     column_names = []
75 
76     for i in range(128):
77         column_names.append("features_" + str(i + 1))
78 
79     rd = pd.read_csv(path_csv_rd, names=column_names)
80 
81     # 存放128维特征的均值
82     feature_mean = []
83 
84     for i in range(128):
85         tmp_arr = rd["features_"+str(i+1)]
86         tmp_arr = np.array(tmp_arr)
87 
88         # 计算某一个特征的均值
89         tmp_mean = np.mean(tmp_arr)
90 
91         feature_mean.append(tmp_mean)
92 
93     print(feature_mean)
94     return feature_mean
95 
96 compute_the_mean(path_csv_rd)

2.3 face_reco_from_camera.py / 实时人脸识别对比分析

这个py就是调用摄像头，捕获摄像头中的人脸，然后如果检测到人脸，将摄像头中的人脸提取出128D的特征，然后和预设的default_person的128D特征进行计算欧式距离，如果比较小，可以判定为一个人，否则不是一个人；

欧氏距离对比的阈值设定，是在 return_euclidean_distance  函数的  dist  变量；

我这里程序里面指定的是0.4，具体阈值可以根据实际情况或者测得结果进行修改；

源码：

  1 # 2018-5-11
  2 # By TimeStamp
  3 # cnblogs: http://www.cnblogs.com/AdaminXie
  4 
  5 import dlib         # 人脸识别的库dlib
  6 import numpy as np  # 数据处理的库numpy
  7 import cv2          # 图像处理的库OpenCv
  8 
  9 # face recognition model, the object maps human faces into 128D vectors
 10 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
 11 
 12 # 计算两个向量间的欧式距离
 13 def return_euclidean_distance(feature_1,feature_2):
 14     feature_1 = np.array(feature_1)
 15     feature_2 = np.array(feature_2)
 16     dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
 17     print(dist)
 18 
 19     if dist > 0.4:
 20         return "diff"
 21     else:
 22         return "same"
 23 
 24 
 25 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]
 26 
 27 
 28 # dlib预测器
 29 detector = dlib.get_frontal_face_detector()
 30 predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
 31 
 32 # 创建cv2摄像头对象
 33 cap = cv2.VideoCapture(0)
 34 
 35 # cap.set(propId, value)
 36 # 设置视频参数，propId设置的视频参数，value设置的参数值
 37 cap.set(3, 480)
 38 
 39 def get_128d_features(img_gray):
 40     dets = detector(img_gray, 1)
 41     if (len(dets) != 0):
 42         shape = predictor(img_gray, dets[0])
 43         face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
 44     else:
 45         face_descriptor=0
 46     return face_descriptor
 47 
 48 # cap.isOpened（） 返回true/false 检查初始化是否成功
 49 while (cap.isOpened()):
 50 
 51     # cap.read()
 52     # 返回两个值：
 53     #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
 54     #    图像对象，图像的三维矩阵
 55     flag, im_rd = cap.read()
 56 
 57     # 每帧数据延时1ms，延时为0读取的是静态帧
 58     kk = cv2.waitKey(1)
 59 
 60     # 取灰度
 61     img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
 62 
 63     # 人脸数rects
 64     rects = detector(img_gray, 0)
 65 
 66     # print(len(rects))
 67 
 68     # 待会要写的字体
 69     font = cv2.FONT_HERSHEY_SIMPLEX
 70 
 71     cv2.putText(im_rd, "q: quit", (20, 400), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
 72 
 73     if (len(rects) != 0):
 74         # 检测到人脸
 75 
 76         # 将捕获到的人脸提取特征和内置特征进行比对
 77         features_rd = get_128d_features(im_rd)
 78         compare = return_euclidean_distance(features_rd, features_mean_default_person)
 79 
 80         im_rd = cv2.putText(im_rd, compare.replace("same", "default_person"), (20, 350), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
 81 
 82         # 矩形框
 83         for k, d in enumerate(rects):
 84 
 85             # 绘制矩形框
 86             im_rd = cv2.rectangle(im_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
 87 
 88         cv2.putText(im_rd, "faces: " + str(len(rects)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 89 
 90     else:
 91         # 没有检测到人脸
 92         cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 93 
 94 
 95     # 按下q键退出
 96     if (kk == ord('q')):
 97         break
 98 
 99     # 窗口显示
100     cv2.imshow("camera", im_rd)
101 
102 # 释放摄像头
103 cap.release()
104 
105 # 删除建立的窗口
106 cv2.destroyAllWindows()
1 # 2018-5-11
  2 # By TimeStamp
  3 # cnblogs: http://www.cnblogs.com/AdaminXie
  4 
  5 import dlib         # 人脸识别的库dlib
  6 import numpy as np  # 数据处理的库numpy
  7 import cv2          # 图像处理的库OpenCv
  8 
  9 # face recognition model, the object maps human faces into 128D vectors
 10 facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
 11 
 12 # 计算两个向量间的欧式距离
 13 def return_euclidean_distance(feature_1,feature_2):
 14     feature_1 = np.array(feature_1)
 15     feature_2 = np.array(feature_2)
 16     dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
 17     print(dist)
 18 
 19     if dist > 0.4:
 20         return "diff"
 21     else:
 22         return "same"
 23 
 24 
 25 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]
 26 
 27 
 28 # dlib预测器
 29 detector = dlib.get_frontal_face_detector()
 30 predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
 31 
 32 # 创建cv2摄像头对象
 33 cap = cv2.VideoCapture(0)
 34 
 35 # cap.set(propId, value)
 36 # 设置视频参数，propId设置的视频参数，value设置的参数值
 37 cap.set(3, 480)
 38 
 39 def get_128d_features(img_gray):
 40     dets = detector(img_gray, 1)
 41     if (len(dets) != 0):
 42         shape = predictor(img_gray, dets[0])
 43         face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
 44     else:
 45         face_descriptor=0
 46     return face_descriptor
 47 
 48 # cap.isOpened（） 返回true/false 检查初始化是否成功
 49 while (cap.isOpened()):
 50 
 51     # cap.read()
 52     # 返回两个值：
 53     #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
 54     #    图像对象，图像的三维矩阵
 55     flag, im_rd = cap.read()
 56 
 57     # 每帧数据延时1ms，延时为0读取的是静态帧
 58     kk = cv2.waitKey(1)
 59 
 60     # 取灰度
 61     img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
 62 
 63     # 人脸数rects
 64     rects = detector(img_gray, 0)
 65 
 66     # print(len(rects))
 67 
 68     # 待会要写的字体
 69     font = cv2.FONT_HERSHEY_SIMPLEX
 70 
 71     cv2.putText(im_rd, "q: quit", (20, 400), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
 72 
 73     if (len(rects) != 0):
 74         # 检测到人脸
 75 
 76         # 将捕获到的人脸提取特征和内置特征进行比对
 77         features_rd = get_128d_features(im_rd)
 78         compare = return_euclidean_distance(features_rd, features_mean_default_person)
 79 
 80         im_rd = cv2.putText(im_rd, compare.replace("same", "default_person"), (20, 350), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
 81 
 82         # 矩形框
 83         for k, d in enumerate(rects):
 84 
 85             # 绘制矩形框
 86             im_rd = cv2.rectangle(im_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
 87 
 88         cv2.putText(im_rd, "faces: " + str(len(rects)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 89 
 90     else:
 91         # 没有检测到人脸
 92         cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 93 
 94 
 95     # 按下q键退出
 96     if (kk == ord('q')):
 97         break
 98 
 99     # 窗口显示
100     cv2.imshow("camera", im_rd)
101 
102 # 释放摄像头
103 cap.release()
104 
105 # 删除建立的窗口
106 cv2.destroyAllWindows()

实时输出结果：

图6 实时输出的欧氏距离结果

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通过实时的输出结果，看的比较明显；

当是我自己（即之前分析提取特征的 default_person ）时，计算出来的欧式距离基本都在  0.2X左右 ；

而换一张图片上去比如特朗普，明显看到欧式距离计算结果 涨到了0.8X ，此时就可以判定，后来这张人脸不是我们预设的人脸；

3.总结

之前接着那个摄像头人脸检测写的，不过拖到现在才更新，写的也比较粗糙，大家有具体需求和应用场景可以加以修改，有什么问题可以留言或者mail 我。。。不好意思

核心就是提取人脸特征，然后计算欧式距离和预设的特征脸进行比对；

不过我做的这个是实时获取摄像头人脸进行比对，要实时的进行计算摄像头脸的特征值，然后还要计算欧氏距离，，所以计算量比较大，可能摄像头视频流会出现卡顿；

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发邮件给我：

coneypo@foxmail.com

# 请尊重他人劳动成果，转载或者使用源码请注明出处： http://www.cnblogs.com/AdaminXie

# 代码已上传到了我的GitHub，如果对您有帮助欢迎star下： https://github.com/coneypo/Dlib_face_recognition_from_camera