基于OpenCV读取摄像头进行人脸检测和人脸识别

淡淡的烟草味﹌ 2022-08-27 03:39 254阅读 0赞

前段时间使用OpenCV的库函数实现了人脸检测和人脸识别，笔者的实验环境为VS2010+OpenCV2.4.4，OpenCV的环境配置网上有很多，不再赘述。检测的代码网上很多，记不清楚从哪儿copy的了，识别的代码是从OpenCV官网上找到的：http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec\_api.html

需要注意的是，opencv的FaceRecogizer目前有三个类实现了它，特征脸和fisherface方法最少训练图像为两张，而LBP可以单张图像训练。本人的实验采用的图片是100x100大小的，所以如果要添加自己的图像进行识别的话务必调整为100x100，不然会报错。当然在recog\_and\_draw这个函数里，笔者也将每次检测到的人脸进行了保存，拖出来重命名就可以，路径自己找吧。使用不同的方法识别时，其阈值设置也不同，LBP大概在100，其他两种方法大概在1000。本人的代码已共享，下载链接：[http://download.csdn.net/detail/u010944555/6749725][http_download.csdn.net_detail_u010944555_6749725]

ps：有人说代码的检测率不高，其实可以归结为两方面的原因，第一人脸检测率不高，这个可以通过嵌套检测嘴角、眼睛等来降低，或者背景、光照固定的话可以通过图像差分来解决；第二是识别方法本身的问题，如果想提高识别率，可以添加多张不同姿态、光照下的人脸作为训练的样本，如果有时间的话可以在采集图像时给出一个人脸框，引导用户对齐人脸进行采集，三星手机解除锁屏就有这么一个功能。

效果图：![SouthEast][]

废话不多说，上传代码。

main:

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detect\_recog.cpp:

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1.  \#include "stdafx.h"
2.  \#include "cv.h"
3.  \#include "highgui.h"
4.  \#include <stdio.h>
5.  \#include <stdlib.h>
6.  \#include <string.h>
7.  \#include <assert.h>
8.  \#include <math.h>
9.  \#include <float.h>
10. \#include <limits.h>
11. \#include <time.h>
12. \#include <ctype.h>
13. \#include "detect\_recog.h"
14. \#include <opencv2\\contrib\\contrib.hpp> 
15. \#include <opencv2\\core\\core.hpp> 
16. \#include <opencv2\\highgui\\highgui.hpp> 
17. \#include <iostream>
18. \#include <fstream>
19. \#include <sstream>
20. \#include <stdio.h>
21. \#include <io.h> 
22. \#include <direct.h> 
23. 
24. **using****namespace** std; 
25. **using****namespace** cv; 
26. 
27. //检测并圈出人脸，并将检测到的人脸进行判断属于训练图像中的哪一类
28. **void** recog\_and\_draw( IplImage\* img ) 
29. \{ 
30. **static** CvScalar colors\[\] = 
31.  \{ 
32.  \{ \{0,0,255\}\}, 
33.  \{ \{0,128,255\}\}, 
34.  \{ \{0,255,255\}\}, 
35.  \{ \{0,255,0\}\}, 
36.  \{ \{255,128,0\}\}, 
37.  \{ \{255,255,0\}\}, 
38.  \{ \{255,0,0\}\}, 
39.  \{ \{255,0,255\}\} 
40.  \}; 
41.  IplImage \*gray, \*small\_img; 
42. **int** i, j; 
43.  gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 ); 
44.  small\_img = cvCreateImage( cvSize( cvRound (img->width/scale), 
45.  cvRound (img->height/scale)), 8, 1 ); 
46.  cvCvtColor( img, gray, CV\_BGR2GRAY ); // 彩色RGB图像转为灰度图像 
47.  cvResize( gray, small\_img, CV\_INTER\_LINEAR ); 
48.  cvEqualizeHist( small\_img, small\_img ); // 直方图均衡化 
49.  cvClearMemStorage( storage ); 
50. **if**( cascade ) 
51.  \{ 
52. **double** t = (**double**)cvGetTickCount(); 
53.  CvSeq\* faces = cvHaarDetectObjects( small\_img, cascade, storage, 
54.  1.1, 2, 0 
55. //|CV\_HAAR\_FIND\_BIGGEST\_OBJECT
56. //|CV\_HAAR\_DO\_ROUGH\_SEARCH
57.  |CV\_HAAR\_DO\_CANNY\_PRUNING 
58. //|CV\_HAAR\_SCALE\_IMAGE
59.  , 
60.  cvSize(30, 30) ); 
61.  t = (**double**)cvGetTickCount() - t; // 统计检测使用时间 
62. //printf( "detection time = %gms\\n", t/((double)cvGetTickFrequency()\*1000.) );
63. **for**( i = 0; i < (faces ? faces->total : 0); i++ ) 
64.  \{ 
65.  CvRect\* r = (CvRect\*)cvGetSeqElem( faces, i ); // 将faces数据从CvSeq转为CvRect 
66.  CvMat small\_img\_roi; 
67.  CvSeq\* nested\_objects; 
68.  CvPoint center; 
69.  CvScalar color = colors\[i%8\]; // 使用不同颜色绘制各个face，共八种色 
70. **int** radius; 
71.  center.x = cvRound((r->x + r->width\*0.5)\*scale); // 找出faces中心 
72.  center.y = cvRound((r->y + r->height\*0.5)\*scale); 
73.  radius = cvRound((r->width + r->height)\*0.25\*scale); 
74. 
75.  cvGetSubRect( small\_img, &small\_img\_roi, \*r ); 
76. 
77. //截取检测到的人脸区域作为识别的图像
78.  IplImage \*result; 
79.  CvRect roi; 
80.  roi = \*r; 
81.  result = cvCreateImage( cvSize(r->width, r->height), img->depth, img->nChannels ); 
82.  cvSetImageROI(img,roi); 
83. // 创建子图像
84.  cvCopy(img,result); 
85.  cvResetImageROI(img); 
86. 
87.  IplImage \*resizeRes; 
88.  CvSize dst\_cvsize; 
89.  dst\_cvsize.width=(**int**)(100); 
90.  dst\_cvsize.height=(**int**)(100); 
91.  resizeRes=cvCreateImage(dst\_cvsize,result->depth,result->nChannels); 
92. //检测到的区域可能不是100x100大小，所以需要插值处理到统一大小，图像的大小可以自己指定的
93.  cvResize(result,resizeRes,CV\_INTER\_NN); 
94.  IplImage\* img1 = cvCreateImage(cvGetSize(resizeRes), IPL\_DEPTH\_8U, 1);//创建目标图像 
95.  cvCvtColor(resizeRes,img1,CV\_BGR2GRAY);//cvCvtColor(src,des,CV\_BGR2GRAY)
96.  cvShowImage( "resize", resizeRes ); 
97.  cvCircle( img, center, radius, color, 3, 8, 0 ); // 从中心位置画圆，圈出脸部区域
98. **int** predictedLabel = -1; 
99.  Mat test = img1; 
100. //images\[images.size() - 1\] = test;
101.  model->train(images, labels); 
102. 
103. //如果调用read\_img函数时 chdir将默认目录做了更改，所以output.jpg自己找一下吧
104.  imwrite("../ouput.jpg",test); 
105. 
106. //在这里对人脸进行判别
107. **double** predicted\_confidence = 0.0; 
108.  model->predict(test,predictedLabel,predicted\_confidence); 
109. **if**(predictedLabel == 0) 
110.  cvText(img, "yes", r->x+r->width\*0.5, r->y); 
111. **else**
112.  cvText(img, "no", r->x+r->width\*0.5, r->y); 
113. //cout << "predict:"<<model->predict(test) << endl;
114.  cout << "predict:"<< predictedLabel << "\\nconfidence:" << predicted\_confidence << endl; 
115. 
116. **if**( !nested\_cascade ) 
117. **continue**; 
118. 
119.  nested\_objects = cvHaarDetectObjects( &small\_img\_roi, nested\_cascade, storage, 
120.  1.1, 2, 0 
121. //|CV\_HAAR\_FIND\_BIGGEST\_OBJECT
122. //|CV\_HAAR\_DO\_ROUGH\_SEARCH
123. //|CV\_HAAR\_DO\_CANNY\_PRUNING
124. //|CV\_HAAR\_SCALE\_IMAGE
125.  , 
126.  cvSize(0, 0) ); 
127. **for**( j = 0; j < (nested\_objects ? nested\_objects->total : 0); j++ ) 
128.  \{ 
129.  CvRect\* nr = (CvRect\*)cvGetSeqElem( nested\_objects, j ); 
130.  center.x = cvRound((r->x + nr->x + nr->width\*0.5)\*scale); 
131.  center.y = cvRound((r->y + nr->y + nr->height\*0.5)\*scale); 
132.  radius = cvRound((nr->width + nr->height)\*0.25\*scale); 
133.  cvCircle( img, center, radius, color, 3, 8, 0 ); 
134.  \} 
135.  \} 
136.  \} 
137.  cvShowImage( "result", img ); 
138.  cvReleaseImage( &gray ); 
139.  cvReleaseImage( &small\_img ); 
140. \} 
141. **void** cvText(IplImage\* img, **const****char**\* text, **int** x, **int** y) 
142. \{ 
143.  CvFont font; 
144. **double** hscale = 1.0; 
145. **double** vscale = 1.0; 
146. **int** linewidth = 2; 
147.  cvInitFont(&font,CV\_FONT\_HERSHEY\_SIMPLEX | CV\_FONT\_ITALIC,hscale,vscale,0,linewidth); 
148.  CvScalar textColor =cvScalar(0,255,255); 
149.  CvPoint textPos =cvPoint(x, y); 
150.  cvPutText(img, text, textPos, &font,textColor); 
151. \} 
152. 
153. Mat norm\_0\_255(cv::InputArray \_src) 
154. \{ 
155.  Mat src = \_src.getMat(); 
156.  Mat dst; 
157. 
158. **switch**(src.channels()) 
159.  \{ 
160. **case** 1: 
161.  cv::normalize(\_src, dst, 0, 255, cv::NORM\_MINMAX, CV\_8UC1); 
162. **break**; 
163. **case** 3: 
164.  cv::normalize(\_src, dst, 0, 255, cv::NORM\_MINMAX, CV\_8UC3); 
165. **break**; 
166. **default**: 
167.  src.copyTo(dst); 
168. **break**; 
169.  \} 
170. 
171. **return** dst; 
172. \} 
173. //读取文件中的图像数据和类别，存入images和labels这两个容器
174. **void** read\_csv(**const** string &filename, vector<Mat> &images, vector<**int**> &labels, **char** separator) 
175. \{ 
176.  std::ifstream file(filename.c\_str(), ifstream::in); 
177. **if**(!file) 
178.  \{ 
179.  string error\_message = "No valid input file was given."; 
180.  CV\_Error(CV\_StsBadArg, error\_message); 
181.  \} 
182. 
183.  string line, path, classlabel; 
184. **while**(getline(file, line)) 
185.  \{ 
186.  stringstream liness(line); 
187.  getline(liness, path, separator); //遇到分号就结束
188.  getline(liness, classlabel); //继续从分号后面开始，遇到换行结束
189. **if**(!path.empty() && !classlabel.empty()) 
190.  \{ 
191.  images.push\_back(imread(path, 0)); 
192.  labels.push\_back(atoi(classlabel.c\_str())); 
193.  \} 
194.  \} 
195. \} 
196. **bool** read\_img(vector<Mat> &images, vector<**int**> &labels) 
197. \{ 
198. 
199. **long** file; 
200. **struct****\_finddata\_t** find; 
201. 
202.  \_chdir("./einfacedata/trainingdata/"); 
203. **if**((file=\_findfirst("\*.\*", &find))==-1L) \{ 
204. //printf("空白!/n"); 
205. **return****false**; 
206.  \} 
207. //fileNum = 0; 
208. //strcpy(fileName\[fileNum\], find.name);
209. **int** i = 0; 
210. **while**(\_findnext(file, &find)==0) 
211.  \{ 
212. **if**(i == 0) 
213.  \{ 
214.  i++; 
215. **continue**; 
216.  \} 
217.  images.push\_back(imread(find.name, 0)); 
218.  labels.push\_back(0); 
219.  cout << find.name << endl; 
220.  \} 
221.  \_findclose(file); 
222. **return****true**; 
223. \} 
224. // 只是检测人脸，并将人脸圈出 
225. **void** detect\_and\_draw( IplImage\* img ) 
226. \{ 
227. **static** CvScalar colors\[\] = 
228.  \{ 
229.  \{ \{0,0,255\}\}, 
230.  \{ \{0,128,255\}\}, 
231.  \{ \{0,255,255\}\}, 
232.  \{ \{0,255,0\}\}, 
233.  \{ \{255,128,0\}\}, 
234.  \{ \{255,255,0\}\}, 
235.  \{ \{255,0,0\}\}, 
236.  \{ \{255,0,255\}\} 
237.  \}; 
238.  IplImage \*gray, \*small\_img; 
239. **int** i, j; 
240.  gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 ); 
241.  small\_img = cvCreateImage( cvSize( cvRound (img->width/scale), 
242.  cvRound (img->height/scale)), 8, 1 ); 
243.  cvCvtColor( img, gray, CV\_BGR2GRAY ); // 彩色RGB图像转为灰度图像 
244.  cvResize( gray, small\_img, CV\_INTER\_LINEAR ); 
245.  cvEqualizeHist( small\_img, small\_img ); // 直方图均衡化 
246.  cvClearMemStorage( storage ); 
247. **if**( cascade ) 
248.  \{ 
249. **double** t = (**double**)cvGetTickCount(); 
250.  CvSeq\* faces = cvHaarDetectObjects( small\_img, cascade, storage, 
251.  1.1, 2, 0 
252. //|CV\_HAAR\_FIND\_BIGGEST\_OBJECT
253. //|CV\_HAAR\_DO\_ROUGH\_SEARCH
254.  |CV\_HAAR\_DO\_CANNY\_PRUNING 
255. //|CV\_HAAR\_SCALE\_IMAGE
256.  , 
257.  cvSize(30, 30) ); 
258.  t = (**double**)cvGetTickCount() - t; // 统计检测使用时间 
259.  printf( "detection time = %gms\\n", t/((**double**)cvGetTickFrequency()\*1000.) ); 
260. **for**( i = 0; i < (faces ? faces->total : 0); i++ ) 
261.  \{ 
262.  CvRect\* r = (CvRect\*)cvGetSeqElem( faces, i ); // 将faces数据从CvSeq转为CvRect 
263.  CvMat small\_img\_roi; 
264.  CvSeq\* nested\_objects; 
265.  CvPoint center; 
266.  CvScalar color = colors\[i%8\]; // 使用不同颜色绘制各个face，共八种色 
267. **int** radius; 
268.  center.x = cvRound((r->x + r->width\*0.5)\*scale); // 找出faces中心 
269.  center.y = cvRound((r->y + r->height\*0.5)\*scale); 
270.  radius = cvRound((r->width + r->height)\*0.25\*scale); 
271.  cvCircle( img, center, radius, color, 3, 8, 0 ); // 从中心位置画圆，圈出脸部区域 
272. **if**( !nested\_cascade ) 
273. **continue**; 
274.  cvGetSubRect( small\_img, &small\_img\_roi, \*r ); 
275.  nested\_objects = cvHaarDetectObjects( &small\_img\_roi, nested\_cascade, storage, 
276.  1.1, 2, 0 
277. //|CV\_HAAR\_FIND\_BIGGEST\_OBJECT
278. //|CV\_HAAR\_DO\_ROUGH\_SEARCH
279. //|CV\_HAAR\_DO\_CANNY\_PRUNING
280. //|CV\_HAAR\_SCALE\_IMAGE
281.  ,cvSize(0, 0) ); 
282. **for**( j = 0; j < (nested\_objects ? nested\_objects->total : 0); j++ ) 
283.  \{ 
284.  CvRect\* nr = (CvRect\*)cvGetSeqElem( nested\_objects, j ); 
285.  center.x = cvRound((r->x + nr->x + nr->width\*0.5)\*scale); 
286.  center.y = cvRound((r->y + nr->y + nr->height\*0.5)\*scale); 
287.  radius = cvRound((nr->width + nr->height)\*0.25\*scale); 
288.  cvCircle( img, center, radius, color, 3, 8, 0 ); 
289.  \} 
290.  \} 
291.  \} 
292.  cvShowImage( "result", img ); 
293.  cvReleaseImage( &gray ); 
294.  cvReleaseImage( &small\_img ); 
295. \}

detect\_recog.h:

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