#define_CRT_SECURE_NO_WARNINGS#include<iostream>#include<opencv2/opencv.hpp>usingnamespace std;usingnamespace cv;staticvoidhelp(constchar** argv){
cout <<"\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n""This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n""It's most known use is for faces.\n""Usage:\n"<< argv[0]<<" [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"" [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n"" [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n"" [--try-flip]\n"" [filename|camera_index]\n\n""example:\n"<< argv[0]<<" --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n""During execution:\n\tHit any key to quit.\n""\tUsing OpenCV version "<< CV_VERSION <<"\n"<< endl;}voiddetectAndDraw( Mat& img, CascadeClassifier& cascade,
CascadeClassifier& nestedCascade,double scale,bool tryflip );
string cascadeName;
string nestedCascadeName;intmain(int argc,constchar** argv ){
VideoCapture capture;
Mat frame, image;
string inputName;bool tryflip;
CascadeClassifier cascade, nestedCascade;double scale;
cv::CommandLineParser parser(argc, argv,"{help h""{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}""{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}""{scale|1|}{try-flip||}{@filename||}");if(parser.has("help")){help(argv);return0;}
cascadeName = parser.get<string>("cascade");
nestedCascadeName = parser.get<string>("nested-cascade");
scale = parser.get<double>("scale");if(scale <1)
scale =1;
tryflip = parser.has("try-flip");
inputName = parser.get<string>("@filename");if(!parser.check()){
parser.printErrors();return0;}if(!nestedCascade.load(samples::findFileOrKeep(nestedCascadeName)))
cerr <<"WARNING: Could not load classifier cascade for nested objects"<< endl;if(!cascade.load(samples::findFile(cascadeName))){
cerr <<"ERROR: Could not load classifier cascade"<< endl;help(argv);return-1;}if( inputName.empty()||(isdigit(inputName[0])&& inputName.size()==1)){int camera = inputName.empty()?0: inputName[0]-'0';if(!capture.open(camera)){
cout <<"Capture from camera #"<< camera <<" didn't work"<< endl;return1;}}elseif(!inputName.empty()){
image =imread(samples::findFileOrKeep(inputName), IMREAD_COLOR);if(image.empty()){if(!capture.open(samples::findFileOrKeep(inputName))){
cout <<"Could not read "<< inputName << endl;return1;}}}else{
image =imread(samples::findFile("lena.jpg"), IMREAD_COLOR);if(image.empty()){
cout <<"Couldn't read lena.jpg"<< endl;return1;}}if( capture.isOpened()){
cout <<"Video capturing has been started ..."<< endl;for(;;){
capture >> frame;if( frame.empty())break;
Mat frame1 = frame.clone();detectAndDraw( frame1, cascade, nestedCascade, scale, tryflip );char c =(char)waitKey(10);if( c ==27|| c =='q'|| c =='Q')break;}}else{
cout <<"Detecting face(s) in "<< inputName << endl;if(!image.empty()){detectAndDraw( image, cascade, nestedCascade, scale, tryflip );waitKey(0);}elseif(!inputName.empty()){/* assume it is a text file containing the
list of the image filenames to be processed - one per line */
FILE* f =fopen( inputName.c_str(),"rt");if( f ){char buf[1000+1];while(fgets( buf,1000, f )){int len =(int)strlen(buf);while( len >0&&isspace(buf[len-1]))
len--;
buf[len]='\0';
cout <<"file "<< buf << endl;
image =imread( buf,1);if(!image.empty()){detectAndDraw( image, cascade, nestedCascade, scale, tryflip );char c =(char)waitKey(0);if( c ==27|| c =='q'|| c =='Q')break;}else{
cerr <<"Aw snap, couldn't read image "<< buf << endl;}}fclose(f);}}}return0;}voiddetectAndDraw( Mat& img, CascadeClassifier& cascade,
CascadeClassifier& nestedCascade,double scale,bool tryflip ){double t =0;
vector<Rect> faces, faces2;conststatic Scalar colors[]={Scalar(255,0,0),Scalar(255,128,0),Scalar(255,255,0),Scalar(0,255,0),Scalar(0,128,255),Scalar(0,255,255),Scalar(0,0,255),Scalar(255,0,255)};
Mat gray, smallImg;cvtColor( img, gray, COLOR_BGR2GRAY );double fx =1/ scale;resize( gray, smallImg,Size(), fx, fx, INTER_LINEAR_EXACT );equalizeHist( smallImg, smallImg );
t =(double)getTickCount();
cascade.detectMultiScale( smallImg, faces,1.1,2,0//|CASCADE_FIND_BIGGEST_OBJECT//|CASCADE_DO_ROUGH_SEARCH|CASCADE_SCALE_IMAGE,Size(30,30));if( tryflip ){flip(smallImg, smallImg,1);
cascade.detectMultiScale( smallImg, faces2,1.1,2,0//|CASCADE_FIND_BIGGEST_OBJECT//|CASCADE_DO_ROUGH_SEARCH|CASCADE_SCALE_IMAGE,Size(30,30));for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end();++r ){
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));}}
t =(double)getTickCount()- t;printf("detection time = %g ms\n", t*1000/getTickFrequency());for( size_t i =0; i < faces.size(); i++){
Rect r = faces[i];
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
Scalar color = colors[i%8];int radius;double aspect_ratio =(double)r.width/r.height;if(0.75< aspect_ratio && aspect_ratio <1.3){
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);circle( img, center, radius, color,3,8,0);}elserectangle( img,Point(cvRound(r.x*scale),cvRound(r.y*scale)),Point(cvRound((r.x + r.width-1)*scale),cvRound((r.y + r.height-1)*scale)),
color,3,8,0);if( nestedCascade.empty())continue;
smallImgROI =smallImg( r );
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,1.1,2,0//|CASCADE_FIND_BIGGEST_OBJECT//|CASCADE_DO_ROUGH_SEARCH//|CASCADE_DO_CANNY_PRUNING|CASCADE_SCALE_IMAGE,Size(30,30));for( size_t j =0; j < nestedObjects.size(); j++){
Rect nr = nestedObjects[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);circle( img, center, radius, color,3,8,0);}}imshow("result", img );}
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