我正在尝试使用OpenCV的人脸识别模块从视频中识别出2个主体。我从视频中剪切了30张第一个主体和20张第二个主体的人脸图像,并将它们用作我的训练集。
我已经测试了所有三种方法(特征脸、费舍尔脸和LBP直方图),但是在这些方法中都没有得到好的结果。有时第一个主体被分类为第二个主体,反之亦然;有时虚假检测被归类为两个主题中的一个,有时视频中的其他人被归类为两个主题中的一个。
如何提高性能?扩大训练集是否有助于改善结果?是否有其他可以考虑在C ++中执行人脸识别的软件包?我认为这应该是一个简单的任务,因为我只想识别两个不同的主体。
以下是我的代码(我正在使用Windows 8上的OpenCV 2.4.7和VS2012):
我已经测试了所有三种方法(特征脸、费舍尔脸和LBP直方图),但是在这些方法中都没有得到好的结果。有时第一个主体被分类为第二个主体,反之亦然;有时虚假检测被归类为两个主题中的一个,有时视频中的其他人被归类为两个主题中的一个。
如何提高性能?扩大训练集是否有助于改善结果?是否有其他可以考虑在C ++中执行人脸识别的软件包?我认为这应该是一个简单的任务,因为我只想识别两个不同的主体。
以下是我的代码(我正在使用Windows 8上的OpenCV 2.4.7和VS2012):
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/contrib/contrib.hpp"
#include <iostream>
#include <stdio.h>
#include <fstream>
#include <sstream>
#define EIGEN 0
#define FISHER 0
#define LBPH 1;
using namespace std;
using namespace cv;
/** Function Headers */
void detectAndDisplay( Mat frame , int i,Ptr<FaceRecognizer> model);
static Mat toGrayscale(InputArray _src) {
Mat src = _src.getMat();
// only allow one channel
if(src.channels() != 1) {
CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
}
// create and return normalized image
Mat dst;
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
/** Global variables */
String face_cascade_name = "C:\\OIM\\code\\OIM2 - face detection\\Debug\\haarcascade_frontalface_alt.xml";
//String face_cascade_name = "C:\\OIM\\code\\OIM2 - face detection\\Debug\\NewCascade.xml";
//String face_cascade_name = "C:\\OIM\\code\\OIM2 - face detection\\Debug\\haarcascade_eye_tree_eyeglasses.xml";
String eyes_cascade_name = "C:\\OIM\\code\\OIM2 - face detection\\Debug\\haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
string window_name = "Capture - Face detection";
RNG rng(12345);
/** @function main */
int main( int argc, const char** argv )
{
string fn_csv = "C:\\OIM\\faces_org.csv";
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines create an Eigenfaces model for
// face recognition and train it with the images and
// labels read from the given CSV file.
// This here is a full PCA, if you just want to keep
// 10 principal components (read Eigenfaces), then call
// the factory method like this:
//
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidennce threshold, call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
//Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
#if EIGEN
Ptr<FaceRecognizer> model = createEigenFaceRecognizer(10,2000000000);
#elif FISHER
Ptr<FaceRecognizer> model = createFisherFaceRecognizer(0, 200000000);
#elif LBPH
Ptr<FaceRecognizer> model =createLBPHFaceRecognizer(1,8,8,8,200000000);
#endif
model->train(images, labels);
Mat frame;
//-- 1. Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
// Get the frame rate
bool stop(false);
int count=1;
char filename[512];
for (int i=1;i<=517;i++){
sprintf(filename,"C:\\OIM\\original_frames2\\image%d.jpg",i);
Mat frame=imread(filename);
detectAndDisplay(frame,i,model);
waitKey(0);
}
return 0;
}
/** @function detectAndDisplay */
void detectAndDisplay( Mat frame ,int i, Ptr<FaceRecognizer> model)
{
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, CV_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
//face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 1, 0|CV_HAAR_SCALE_IMAGE, Size(10, 10) );
for( size_t i = 0; i < faces.size(); i++ )
{
Rect roi = Rect(faces[i].x,faces[i].y,faces[i].width,faces[i].height);
Mat face=frame_gray(roi);
resize(face,face,Size(200,200));
int predictedLabel = -1;
double confidence = 0.0;
model->predict(face, predictedLabel, confidence);
//imshow("gil",face);
//waitKey(0);
#if EIGEN
int M=10000;
#elif FISHER
int M=500;
#elif LBPH
int M=300;
#endif
Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
if ((predictedLabel==1)&& (confidence<M))
ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 0, 0, 255 ), 4, 8, 0 );
if ((predictedLabel==0)&& (confidence<M))
ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 0), 4, 8, 0 );
if (confidence>M)
ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 0, 255, 0), 4, 8, 0 );
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
for( size_t j = 0; j < eyes.size(); j++ )
{
Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
//circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
}
}
//-- Show what you got
//imshow( window_name, frame );
char filename[512];
sprintf(filename,"C:\\OIM\\FaceRecognitionResults\\image%d.jpg",i);
imwrite(filename,frame);
}
提前致谢,
Gil。