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openCV的人臉識別主要通過Haar分類器實現，當然，這是在已有訓練數據的基礎上。openCV安裝在 opencv/opencv/sources/data/haarcascades_cuda（或haarcascades）中存在預先訓練好的物體檢測器（xml格式），包括正臉、側臉、眼睛、微笑、上半身、下半身、全身等。
openCV的的Haar分類器是一個監督分類器，首先對圖像進行直方圖均衡化并歸一化到同樣大小，然后標記里面是否包含要監測的物體。它首先由Paul Viola和Michael Jones設計，稱為Viola Jones檢測器。Viola Jones分類器在級聯的每個節點中使用AdaBoost來學習一個高檢測率低拒絕率的多層樹分類器。它使用了以下一些新的特征：
1. 使用類Haar輸入特征：對矩形圖像區域的和或者差進行閾值化。
2. 積分圖像技術加速了矩形區域的45°旋轉的值的計算，用來加速類Haar輸入特征的計算。
3. 使用統計boosting來創建兩類問題（人臉和非人臉）的分類器節點（高通過率，低拒絕率）
4. 把弱分類器節點組成篩選式級聯。即，第一組分類器最優，能通過包含物體的圖像區域，同時允許一些不包含物體通過的圖像通過；第二組分
類器次優分類器，也是有較低的拒絕率；以此類推。也就是說，對于每個boosting分類器，只要有人臉都能檢測到，同時拒絕一小部分非人臉，并將其傳給下一個分類器，是為低拒絕率。以此類推，最后一個分類器將幾乎所有的非人臉都拒絕掉，只剩下人臉區域。只要圖像區域通過了整個級聯，則認為里面有物體。
此技術雖然適用于人臉檢測，但不限于人臉檢測，還可用于其他物體的檢測，如汽車、飛機等的正面、側面、后面檢測。在檢測時，先導入訓練好的參數文件，其中haarcascade_frontalface_alt2.xml對正面臉的識別效果較好haarcascade_profileface.xml對側臉的檢測效果較好。當然，如果要達到更高的分類精度，可以收集更多的數據進行訓練，這是后話。
以下代碼基本實現了正臉、眼睛、微笑、側臉的識別，若要添加其他功能，可以自行調整。
// faceDetector.h
// This is just the face, eye, smile, profile detector from OpenCV's samples/c directory
//
/* *************** License:**************************
Jul. 18, 2016
Author: Liuph
Right to use this code in any way you want without warranty, support or any guarantee of it working.
OTHER OPENCV SITES:
* The source code is on sourceforge at:
http://sourceforge.net/projects/opencvlibrary/
* The OpenCV wiki page (As of Oct 1, 2008 this is down for changing over servers, but should come back):
http://opencvlibrary.sourceforge.net/
* An active user group is at:
http://tech.groups.yahoo.com/group/OpenCV/
* The minutes of weekly OpenCV development meetings are at:
http://pr.willowgarage.com/wiki/OpenCV
************************************************** */
#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <math.h>
#include <float.h>
#include <limits.h>
#include <time.h>
#include <ctype.h>
#include <iostream>
using namespace std;
static CvMemStorage* storage = 0;
static CvHaarClassifierCascade* cascade = 0;
static CvHaarClassifierCascade* nested_cascade = 0;
static CvHaarClassifierCascade* smile_cascade = 0;
static CvHaarClassifierCascade* profile = 0;
int use_nested_cascade = 0;
void detect_and_draw( IplImage* image );
/* The path that stores the trained parameter files.
After openCv is installed, the file path is
"opencv/opencv/sources/data/haarcascades_cuda" or "opencv/opencv/sources/data/haarcascades" */
const char* cascade_name =
"../faceDetect/haarcascade_frontalface_alt2.xml";
const char* nested_cascade_name =
"../faceDetect/haarcascade_eye_tree_eyeglasses.xml";
const char* smile_cascade_name =
"../faceDetect/haarcascade_smile.xml";
const char* profile_name =
"../faceDetect/haarcascade_profileface.xml";
double scale = 1;
int faceDetector(const char* imageName, int nNested, int nSmile, int nProfile)
{
CvCapture* capture = 0;
IplImage *frame, *frame_copy = 0;
IplImage *image = 0;
const char* scale_opt = "--scale=";
int scale_opt_len = (int)strlen(scale_opt);
const char* cascade_opt = "--cascade=";
int cascade_opt_len = (int)strlen(cascade_opt);
const char* nested_cascade_opt = "--nested-cascade";
int nested_cascade_opt_len = (int)strlen(nested_cascade_opt);
const char* smile_cascade_opt = "--smile-cascade";
int smile_cascade_opt_len = (int)strlen(smile_cascade_opt);
const char* profile_opt = "--profile";
int profile_opt_len = (int)strlen(profile_opt);
int i;
const char* input_name = 0;
int opt_num = 7;
char** opts = new char*[7];
opts[0] = "compile_opencv.exe";
opts[1] = "--scale=1";
opts[2] = "--cascade=1";
if (nNested == 1)
opts[3] = "--nested-cascade=1";
else
opts[3] = "--nested-cascade=0";
if (nSmile == 1)
opts[4] = "--smile-cascade=1";
else
opts[4] = "--smile-cascade=0";
if (nProfile == 1)
opts[5] = "--profile=1";
else
opts[5] = "--profile=0";
opts[6] = (char*)imageName;
for( i = 1; i < opt_num; i++ )
{
if( strncmp( opts[i], cascade_opt, cascade_opt_len) == 0)
{
cout<<"cascade: "<<cascade_name<<endl;
}
else if( strncmp( opts[i], nested_cascade_opt, nested_cascade_opt_len ) == 0)
{
if( opts[i][nested_cascade_opt_len + 1] == '1')
{
cout<<"nested: "<<nested_cascade_name<<endl;
nested_cascade = (CvHaarClassifierCascade*)cvLoad( nested_cascade_name, 0, 0, 0 );
}
if( !nested_cascade )
fprintf( stderr, "WARNING: Could not load classifier cascade for nested objects\n" );
}
else if( strncmp( opts[i], scale_opt, scale_opt_len ) == 0 )
{
cout<< "scale: "<< scale<<endl;
if( !sscanf( opts[i] + scale_opt_len, "%lf", &scale ) || scale < 1 )
scale = 1;
}
else if (strncmp( opts[i], smile_cascade_opt, smile_cascade_opt_len ) == 0)
{
if( opts[i][smile_cascade_opt_len + 1] == '1')
{
cout<<"smile: "<<smile_cascade_name<<endl;
smile_cascade = (CvHaarClassifierCascade*)cvLoad( smile_cascade_name, 0, 0, 0 );
}
if( !smile_cascade )
fprintf( stderr, "WARNING: Could not load classifier cascade for smile objects\n" );
}
else if (strncmp( opts[i], profile_opt, profile_opt_len ) == 0)
{
if( opts[i][profile_opt_len + 1] == '1')
{
cout<<"profile: "<<profile_name<<endl;
profile = (CvHaarClassifierCascade*)cvLoad( profile_name, 0, 0, 0 );
}
if( !profile )
fprintf( stderr, "WARNING: Could not load classifier cascade for profile objects\n" );
}
else if( opts[i][0] == '-' )
{
fprintf( stderr, "WARNING: Unknown option %s\n", opts[i] );
}
else
{
input_name = imageName;
printf("input_name: %s\n", imageName);
}
}
cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );
if( !cascade )
{
fprintf( stderr, "ERROR: Could not load classifier cascade\n" );
fprintf( stderr,
"Usage: facedetect [--cascade=\"<cascade_path>\"]\n"
" [--nested-cascade[=\"nested_cascade_path\"]]\n"
" [--scale[=<image scale>\n"
" [filename|camera_index]\n" );
return -1;
}
storage = cvCreateMemStorage(0);
if( !input_name || (isdigit(input_name[0]) && input_name[1] == '\0') )
capture = cvCaptureFromCAM( !input_name ? 0 : input_name[0] - '0' );
else if( input_name )
{
image = cvLoadImage( input_name, 1 );
if( !image )
capture = cvCaptureFromAVI( input_name );
}
else
image = cvLoadImage( "../lena.jpg", 1 );
cvNamedWindow( "result", 1 );
if( capture )
{
for(;;)
{
if( !cvGrabFrame( capture ))
break;
frame = cvRetrieveFrame( capture );
if( !frame )
break;
if( !frame_copy )
frame_copy = cvCreateImage( cvSize(frame->width,frame->height),
IPL_DEPTH_8U, frame->nChannels );
if( frame->origin == IPL_ORIGIN_TL )
cvCopy( frame, frame_copy, 0 );
else
cvFlip( frame, frame_copy, 0 );
detect_and_draw( frame_copy );
if( cvWaitKey( 10 ) >= 0 )
goto _cleanup_;
}
cvWaitKey(0);
_cleanup_:
cvReleaseImage( &frame_copy );
cvReleaseCapture( &capture );
}
else
{
if( image )
{
detect_and_draw( image );
cvWaitKey(0);
cvReleaseImage( &image );
}
else if( input_name )
{
/* assume it is a text file containing the
list of the image filenames to be processed - one per line */
FILE* f = fopen( input_name, "rt" );
if( f )
{
char buf[1000+1];
while( fgets( buf, 1000, f ) )
{
int len = (int)strlen(buf), c;
while( len > 0 && isspace(buf[len-1]) )
len--;
buf[len] = '\0';
printf( "file %s\n", buf );
image = cvLoadImage( buf, 1 );
if( image )
{
detect_and_draw( image );
c = cvWaitKey(0);
if( c == 27 || c == 'q' || c == 'Q' )
break;
cvReleaseImage( &image );
}
}
fclose(f);
}
}
}
cvDestroyWindow("result");
return 0;
}
void detect_and_draw( IplImage* img )
{
static CvScalar colors[] =
{
{{0,0,255}},
{{0,128,255}},
{{0,255,255}},
{{0,255,0}},
{{255,128,0}},
{{255,255,0}},
{{255,0,0}},
{{255,0,255}}
};
IplImage *gray, *small_img;
int i, j;
gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 );
small_img = cvCreateImage( cvSize( cvRound (img->width/scale),
cvRound (img->height/scale)), 8, 1 );
cvCvtColor( img, gray, CV_BGR2GRAY );
cvResize( gray, small_img, CV_INTER_LINEAR );
cvEqualizeHist( small_img, small_img );
cvClearMemStorage( storage );
if( cascade )
{
double t = (double)cvGetTickCount();
CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_DO_CANNY_PRUNING
//|CV_HAAR_SCALE_IMAGE
,
cvSize(30, 30) );
t = (double)cvGetTickCount() - t;
printf( "faces detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) );
for( i = 0; i < (faces ? faces->total : 0); i++ )
{
CvRect* r = (CvRect*)cvGetSeqElem( faces, i );
CvMat small_img_roi;
CvSeq* nested_objects;
CvSeq* smile_objects;
CvPoint center;
CvScalar color = colors[i%8];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
cvCircle( img, center, radius, color, 3, 8, 0 );
//eye
if( nested_cascade != 0)
{
cvGetSubRect( small_img, &small_img_roi, *r );
nested_objects = cvHaarDetectObjects( &small_img_roi, nested_cascade, storage,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
//|CV_HAAR_SCALE_IMAGE
,
cvSize(0, 0) );
for( j = 0; j < (nested_objects ? nested_objects->total : 0); j++ )
{
CvRect* nr = (CvRect*)cvGetSeqElem( nested_objects, j );
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
cvCircle( img, center, radius, color, 3, 8, 0 );
}
}
//smile
if (smile_cascade != 0)
{
cvGetSubRect( small_img, &small_img_roi, *r );
smile_objects = cvHaarDetectObjects( &small_img_roi, smile_cascade, storage,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
//|CV_HAAR_SCALE_IMAGE
,
cvSize(0, 0) );
for( j = 0; j < (smile_objects ? smile_objects->total : 0); j++ )
{
CvRect* nr = (CvRect*)cvGetSeqElem( smile_objects, j );
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
cvCircle( img, center, radius, color, 3, 8, 0 );
}
}
}
}
if( profile )
{
double t = (double)cvGetTickCount();
CvSeq* faces = cvHaarDetectObjects( small_img, profile, storage,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_DO_CANNY_PRUNING
//|CV_HAAR_SCALE_IMAGE
,
cvSize(30, 30) );
t = (double)cvGetTickCount() - t;
printf( "profile faces detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) );
for( i = 0; i < (faces ? faces->total : 0); i++ )
{
CvRect* r = (CvRect*)cvGetSeqElem( faces, i );
CvMat small_img_roi;
CvSeq* nested_objects;
CvSeq* smile_objects;
CvPoint center;
CvScalar color = colors[(7-i)%8];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
cvCircle( img, center, radius, color, 3, 8, 0 );
//eye
if( nested_cascade != 0)
{
cvGetSubRect( small_img, &small_img_roi, *r );
nested_objects = cvHaarDetectObjects( &small_img_roi, nested_cascade, storage,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
//|CV_HAAR_SCALE_IMAGE
,
cvSize(0, 0) );
for( j = 0; j < (nested_objects ? nested_objects->total : 0); j++ )
{
CvRect* nr = (CvRect*)cvGetSeqElem( nested_objects, j );
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
cvCircle( img, center, radius, color, 3, 8, 0 );
}
}
//smile
if (smile_cascade != 0)
{
cvGetSubRect( small_img, &small_img_roi, *r );
smile_objects = cvHaarDetectObjects( &small_img_roi, smile_cascade, storage,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
//|CV_HAAR_SCALE_IMAGE
,
cvSize(0, 0) );
for( j = 0; j < (smile_objects ? smile_objects->total : 0); j++ )
{
CvRect* nr = (CvRect*)cvGetSeqElem( smile_objects, j );
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
cvCircle( img, center, radius, color, 3, 8, 0 );
}
}
}
}
cvShowImage( "result", img );
cvReleaseImage( &gray );
cvReleaseImage( &small_img );
}
//main.cpp
//openCV配置
//附加包含目錄: include, include/opencv, include/opencv2
//附加庫目錄: lib
//附加依賴項: debug:--> opencv_calib3d243d.lib;...;
// release:--> opencv_calib3d243.lib;...;
#include<string>
#include <opencv2\opencv.hpp>
#include "CV2_compile.h"
#include "CV_compile.h"
#include "face_detector.h"
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
const char* imagename = "../lena.jpg";
faceDetector(imagename,1,0,0);
return 0;
}
調整主函數中faceDetect(const char* imageName, int nNested, int nSmile, int nProfile)函數中的參數，分別表示圖像文件名，是否檢測眼睛，是否檢測微笑，是否檢測側臉。以檢測正臉、眼睛為例：
再來看一張合影。
========華麗麗的分割線==========
如果對分類器的參數不滿意，或者說想識別其他的物體例如車、人、飛機、蘋果等等等等，只需要選擇適當的樣本訓練，獲取該物體的各個方面的參數，訓練過程可以通過openCV的haartraining實現（參考haartraining參考文檔，opencv/apps/traincascade），主要包括個步驟：
1. 收集打算學習的物體數據集（如正面人臉圖，側面汽車圖等， 1000~10000個正樣本為宜），把它們存儲在一個或多個目錄下面。
2. 使用createsamples來建立正樣本的向量輸出文件，通過這個文件可以重復訓練過程，使用同一個向量輸出文件嘗試各種參數。
3. 獲取負樣本，即不包含該物體的圖像。
4. 訓練。命令行實現。
以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支持億速云。
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