因此,对于以下OpenCV教程,我能够编写此代码:
#include <windows.h>
#include <mmsystem.h>
#pragma comment(lib, "winmm.lib")
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;
int main(int argc, const char** argv)
{
//create the cascade classifier object used for the face detection
CascadeClassifier face_cascade;
//use the haarcascade_frontalface_alt.xml library
face_cascade.load("C:/cascades/haarcascade_frontalface_alt_tree.xml");
//setup video capture device and link it to the first capture device
VideoCapture captureDevice;
captureDevice.open(3);
//setup image files used in the capture process
Mat captureFrame;
Mat grayscaleFrame;
//create a window to present the results
namedWindow("outputCapture", 1);
//create a loop to capture and find faces
while(true)
{
//capture a new image frame
captureDevice>>captureFrame;
//convert captured image to gray scale and equalize
cvtColor(captureFrame, grayscaleFrame, CV_BGR2GRAY);
equalizeHist(grayscaleFrame, grayscaleFrame);
//create a vector array to store the face found
std::vector<Rect> faces;
//find faces and store them in the vector array
face_cascade.detectMultiScale(grayscaleFrame, faces);
//draw a rectangle for all found faces in the vector array on the original image
for(int i = 0; i < (int)faces.size(); i++)
{
Scalar color(0, 0, 255);
Point pt1(faces[i].x + faces[i].width, faces[i].y + faces[i].height);
Point pt2(faces[i].x, faces[i].y);
rectangle(captureFrame, pt1, pt2, color, 1, 8, 0);
string text = "Adam yuzi";
int fontFace = FONT_HERSHEY_TRIPLEX;
double fontScale = 1;
int thickness = 2;
putText(captureFrame, text, pt2, fontFace, fontScale, color, thickness);
//PlaySound(TEXT("C:/cascades/adam.wav"), NULL, SND_FILENAME | SND_SYNC);// - the correct code
//Sleep(1000);
//break;
//cout<<char(7);
}
//print the output
imshow("outputCapture", captureFrame);
//pause for 33ms
waitKey(33);
}
return 0;
}
现在我想用GPU改进这段代码的性能。我怎样才能做到这一点?任何代码示例?