基于openCV实现人脸检测

openCV的人脸识别主要通过Haar分类器实现，当然，这是在已有训练数据的基础上。openCV安装在 opencv/opencv/sources/data/haarcascades_cuda（或haarcascades）中存在预先训练好的物体检测器（xml格式），包括正脸、侧脸、眼睛、微笑、上半身、下半身、全身等。

openCV的的Haar分类器是一个监督分类器，首先对图像进行直方图均衡化并归一化到同样大小，然后标记里面是否包含要监测的物体。它首先由Paul Viola和Michael Jones设计，称为Viola Jones检测器。Viola Jones分类器在级联的每个节点中使用AdaBoost来学习一个高检测率低拒绝率的多层树分类器。它使用了以下一些新的特征：

1. 使用类Haar输入特征：对矩形图像区域的和或者差进行阈值化。 
2. 积分图像技术加速了矩形区域的45°旋转的值的计算，用来加速类Haar输入特征的计算。
3. 使用统计boosting来创建两类问题（人脸和非人脸）的分类器节点（高通过率，低拒绝率）
4. 把弱分类器节点组成筛选式级联。即，第一组分类器最优，能通过包含物体的图像区域，同时允许一些不包含物体通过的图像通过；第二组分

类器次优分类器，也是有较低的拒绝率；以此类推。也就是说，对于每个boosting分类器，只要有人脸都能检测到，同时拒绝一小部分非人脸，并将其传给下一个分类器，是为低拒绝率。以此类推，最后一个分类器将几乎所有的非人脸都拒绝掉，只剩下人脸区域。只要图像区域通过了整个级联，则认为里面有物体。

此技术虽然适用于人脸检测，但不限于人脸检测，还可用于其他物体的检测，如汽车、飞机等的正面、侧面、后面检测。在检测时，先导入训练好的参数文件，其中haarcascade_frontalface_alt2.xml对正面脸的识别效果较好haarcascade_profileface.xml对侧脸的检测效果较好。当然，如果要达到更高的分类精度，可以收集更多的数据进行训练，这是后话。

以下代码基本实现了正脸、眼睛、微笑、侧脸的识别，若要添加其他功能，可以自行调整。

// 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
//附加包含目录: include, include/opencv, include/opencv2
//附加库目录: lib
//附加依赖项: 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;
}

调整主函数中faceDetect(const char* imageName, int nNested, int nSmile, int nProfile)函数中的参数，分别表示图像文件名，是否检测眼睛，是否检测微笑，是否检测侧脸。以检测正脸、眼睛为例：

再来看一张合影。

========华丽丽的分割线==========

如果对分类器的参数不满意，或者说想识别其他的物体例如车、人、飞机、苹果等等等等，只需要选择适当的样本训练，获取该物体的各个方面的参数，训练过程可以通过openCV的haartraining实现（参考haartraining参考文档，opencv/apps/traincascade），主要包括个步骤：

1. 收集打算学习的物体数据集（如正面人脸图，侧面汽车图等， 1000~10000个正样本为宜），把它们存储在一个或多个目录下面。
2. 使用createsamples来建立正样本的向量输出文件，通过这个文件可以重复训练过程，使用同一个向量输出文件尝试各种参数。
3. 获取负样本，即不包含该物体的图像。
4. 训练。命令行实现。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。

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