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#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/ocl/ocl.hpp"
#include <iostream>
#include <stdio.h>
#if defined(_MSC_VER) && (_MSC_VER >= 1700)
# include <thread>
#endif
using namespace std;
using namespace cv;
#define LOOP_NUM 10
#define MAX_THREADS 10
///////////////////////////single-threading faces detecting///////////////////////////////
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)
} ;
int64 work_begin[MAX_THREADS] = {0};
int64 work_total[MAX_THREADS] = {0};
string inputName, outputName, cascadeName;
static void workBegin(int i = 0)
{
work_begin[i] = getTickCount();
}
static void workEnd(int i = 0)
{
work_total[i] += (getTickCount() - work_begin[i]);
}
static double getTotalTime(int i = 0)
{
return work_total[i] /getTickFrequency() * 1000.;
}
static void detect( Mat& img, vector<Rect>& faces,
ocl::OclCascadeClassifier& cascade,
double scale, bool calTime);
static void detectCPU( Mat& img, vector<Rect>& faces,
CascadeClassifier& cascade,
double scale, bool calTime);
static void Draw(Mat& img, vector<Rect>& faces, double scale);
// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
// Else if will return (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
double checkRectSimilarity(Size sz, vector<Rect>& cpu_rst, vector<Rect>& gpu_rst);
static int facedetect_one_thread(bool useCPU, double scale )
{
CvCapture* capture = 0;
Mat frame, frameCopy, image;
ocl::OclCascadeClassifier cascade;
CascadeClassifier cpu_cascade;
if( !cascade.load( cascadeName ) || !cpu_cascade.load(cascadeName) )
{
cout << "ERROR: Could not load classifier cascade: " << cascadeName << endl;
return EXIT_FAILURE;
}
if( inputName.empty() )
{
capture = cvCaptureFromCAM(0);
if(!capture)
cout << "Capture from CAM 0 didn't work" << endl;
}
else
{
image = imread( inputName, CV_LOAD_IMAGE_COLOR );
if( image.empty() )
{
capture = cvCaptureFromAVI( inputName.c_str() );
if(!capture){
cout << "Capture from AVI didn't work" << endl;
return EXIT_FAILURE;
}
}
}
if( capture )
{
cout << "In capture ..." << endl;
for(;;)
{
IplImage* iplImg = cvQueryFrame( capture );
frame = iplImg;
vector<Rect> faces;
if( frame.empty() )
break;
if( iplImg->origin == IPL_ORIGIN_TL )
frame.copyTo( frameCopy );
else
flip( frame, frameCopy, 0 );
if(useCPU)
detectCPU(frameCopy, faces, cpu_cascade, scale, false);
else
detect(frameCopy, faces, cascade, scale, false);
Draw(frameCopy, faces, scale);
if( waitKey( 10 ) >= 0 )
break;
}
cvReleaseCapture( &capture );
}
else
{
cout << "In image read " << image.size() << endl;
vector<Rect> faces;
vector<Rect> ref_rst;
double accuracy = 0.;
cout << "loops: ";
for(int i = 0; i <= LOOP_NUM; i++)
{
cout << i << ", ";
if(useCPU)
detectCPU(image, faces, cpu_cascade, scale, i!=0);
else
{
detect(image, faces, cascade, scale, i!=0);
if(i == 0)
{
detectCPU(image, ref_rst, cpu_cascade, scale, false);
accuracy = checkRectSimilarity(image.size(), ref_rst, faces);
}
}
}
cout << "done!" << endl;
if (useCPU)
cout << "average CPU time (noCamera) : ";
else
cout << "average GPU time (noCamera) : ";
cout << getTotalTime() / LOOP_NUM << " ms" << endl;
cout << "accuracy value: " << accuracy <<endl;
Draw(image, faces, scale);
waitKey(0);
}
cvDestroyWindow("result");
std::cout<< "single-threaded sample has finished" <<std::endl;
return 0;
}
///////////////////////////////////////detectfaces with multithreading////////////////////////////////////////////
#if defined(_MSC_VER) && (_MSC_VER >= 1700)
static void detectFaces(std::string fileName, int threadNum)
{
ocl::OclCascadeClassifier cascade;
if(!cascade.load(cascadeName))
{
std::cout << "ERROR: Could not load classifier cascade: " << cascadeName << std::endl;
return;
}
Mat img = imread(fileName, CV_LOAD_IMAGE_COLOR);
if (img.empty())
{
std::cout << '[' << threadNum << "] " << "can't open file " + fileName <<std::endl;
return;
}
ocl::oclMat d_img;
d_img.upload(img);
std::vector<Rect> oclfaces;
std::thread::id tid = std::this_thread::get_id();
std::cout << '[' << threadNum << "] "
<< "ThreadID = " << tid
<< ", CommandQueue = " << *(void**)ocl::getClCommandQueuePtr()
<< endl;
for(int i = 0; i <= LOOP_NUM; i++)
{
if(i>0) workBegin(threadNum);
cascade.detectMultiScale(d_img, oclfaces, 1.1, 3, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30), Size(0, 0));
if(i>0) workEnd(threadNum);
}
std::cout << '[' << threadNum << "] " << "Average time = " << getTotalTime(threadNum) / LOOP_NUM << " ms" << endl;
for(unsigned int i = 0; i<oclfaces.size(); i++)
rectangle(img, Point(oclfaces[i].x, oclfaces[i].y), Point(oclfaces[i].x + oclfaces[i].width, oclfaces[i].y + oclfaces[i].height), colors[i%8], 3);
std::string::size_type pos = outputName.rfind('.');
std::string strTid = std::to_string(_threadid);
if( !outputName.empty() )
{
if(pos == std::string::npos)
{
std::cout << "Invalid output file name: " << outputName << std::endl;
}
else
{
std::string outputNameTid = outputName.substr(0, pos) + "_" + strTid + outputName.substr(pos);
imwrite(outputNameTid, img);
}
}
imshow(strTid, img);
waitKey(0);
}
static void facedetect_multithreading(int nthreads)
{
int thread_number = MAX_THREADS < nthreads ? MAX_THREADS : nthreads;
std::vector<std::thread> threads;
for(int i = 0; i<thread_number; i++)
threads.push_back(std::thread(detectFaces, inputName, i));
for(int i = 0; i<thread_number; i++)
threads[i].join();
}
#endif
int main( int argc, const char** argv )
{
const char* keys =
"{ h | help | false | print help message }"
"{ i | input | | specify input image }"
"{ t | template | haarcascade_frontalface_alt.xml |"
" specify template file path }"
"{ c | scale | 1.0 | scale image }"
"{ s | use_cpu | false | use cpu or gpu to process the image }"
"{ o | output | | specify output image save path(only works when input is images) }"
"{ n | thread_num | 1 | set number of threads >= 1 }";
CommandLineParser cmd(argc, argv, keys);
if (cmd.get<bool>("help"))
{
cout << "Usage : facedetect [options]" << endl;
cout << "Available options:" << endl;
cmd.printParams();
return EXIT_SUCCESS;
}
bool useCPU = cmd.get<bool>("s");
inputName = cmd.get<string>("i");
outputName = cmd.get<string>("o");
cascadeName = cmd.get<string>("t");
double scale = cmd.get<double>("c");
int n = cmd.get<int>("n");
if(n > 1)
{
#if defined(_MSC_VER) && (_MSC_VER >= 1700)
std::cout<<"multi-threaded sample is running" <<std::endl;
facedetect_multithreading(n);
std::cout<<"multi-threaded sample has finished" <<std::endl;
return 0;
#else
std::cout << "std::thread is not supported, running a single-threaded version" << std::endl;
#endif
}
if (n<0)
std::cout<<"incorrect number of threads:" << n << ", running a single-threaded version" <<std::endl;
else
std::cout<<"single-threaded sample is running" <<std::endl;
return facedetect_one_thread(useCPU, scale);
}
void detect( Mat& img, vector<Rect>& faces,
ocl::OclCascadeClassifier& cascade,
double scale, bool calTime)
{
ocl::oclMat image(img);
ocl::oclMat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
if(calTime) workBegin();
ocl::cvtColor( image, gray, CV_BGR2GRAY );
ocl::resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
ocl::equalizeHist( smallImg, smallImg );
cascade.detectMultiScale( smallImg, faces, 1.1,
3, 0
|CV_HAAR_SCALE_IMAGE
, Size(30,30), Size(0, 0) );
if(calTime) workEnd();
}
void detectCPU( Mat& img, vector<Rect>& faces,
CascadeClassifier& cascade,
double scale, bool calTime)
{
if(calTime) workBegin();
Mat cpu_gray, cpu_smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
cvtColor(img, cpu_gray, CV_BGR2GRAY);
resize(cpu_gray, cpu_smallImg, cpu_smallImg.size(), 0, 0, INTER_LINEAR);
equalizeHist(cpu_smallImg, cpu_smallImg);
cascade.detectMultiScale(cpu_smallImg, faces, 1.1,
3, 0 | CV_HAAR_SCALE_IMAGE,
Size(30, 30), Size(0, 0));
if(calTime) workEnd();
}
void Draw(Mat& img, vector<Rect>& faces, double scale)
{
int i = 0;
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Point center;
Scalar 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);
circle( img, center, radius, color, 3, 8, 0 );
}
if( !outputName.empty() ) imwrite( outputName, img );
if( abs(scale-1.0)>.001 )
{
resize(img, img, Size((int)(img.cols/scale), (int)(img.rows/scale)));
}
imshow( "result", img );
}
double checkRectSimilarity(Size sz, vector<Rect>& ob1, vector<Rect>& ob2)
{
double final_test_result = 0.0;
size_t sz1 = ob1.size();
size_t sz2 = ob2.size();
if(sz1 != sz2)
{
return sz1 > sz2 ? (double)(sz1 - sz2) : (double)(sz2 - sz1);
}
else
{
if(sz1==0 && sz2==0)
return 0;
Mat cpu_result(sz, CV_8UC1);
cpu_result.setTo(0);
for(vector<Rect>::const_iterator r = ob1.begin(); r != ob1.end(); r++)
{
Mat cpu_result_roi(cpu_result, *r);
cpu_result_roi.setTo(1);
cpu_result.copyTo(cpu_result);
}
int cpu_area = countNonZero(cpu_result > 0);
Mat gpu_result(sz, CV_8UC1);
gpu_result.setTo(0);
for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
{
cv::Mat gpu_result_roi(gpu_result, *r2);
gpu_result_roi.setTo(1);
gpu_result.copyTo(gpu_result);
}
Mat result_;
multiply(cpu_result, gpu_result, result_);
int result = countNonZero(result_ > 0);
if(cpu_area!=0 && result!=0)
final_test_result = 1.0 - (double)result/(double)cpu_area;
else if(cpu_area==0 && result!=0)
final_test_result = -1;
}
return final_test_result;
}