基于OpenCV实现视频中人脸定位

直接贴代码

#include "opencv2/objdetect.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/core/utility.hpp"

#include "opencv2/video.hpp"

#include <iostream>
#include <iterator>
#include <stdio.h>

using namespace std;
using namespace cv;

void detectAndDraw(Mat& img, CascadeClassifier& cascade,
double scale, bool tryflip);
void processVideo2(char* videoFilename);

string cascadeName = "D:\\tmp\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt.xml";
string aviFilename = "C:\\Users\\wengjie\\Downloads\\test.avi";

int main(int argc, char** argv) {
double scale = 1;
bool tryflip = false;

CascadeClassifier cascade, nestedCascade;

if (!cascade.load(cascadeName)) {
cerr << "ERROR: Could not load classifier cascade" << endl;
return -1;
}

VideoCapture cap(aviFilename);
if (!cap.isOpened()) {
cerr << "Can not open a camera or file." << endl;
return -1;
}

for (;;) {
Mat frame;
cap >> frame;
if (frame.empty())
break;
detectAndDraw(frame, cascade, scale, tryflip);
if (waitKey(30) >= 0)
break;

}
return 0;
}

void detectAndDraw(Mat& img, CascadeClassifier& cascade,
double scale, bool tryflip)
{
int i = 0;
double t = 0;
vector<Rect> faces, faces2;
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) };
Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);

cvtColor(img, gray, COLOR_BGR2GRAY);
resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);
equalizeHist(smallImg, smallImg);

t = (double)cvGetTickCount();
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)cvGetTickCount() - t;
printf("detection time = %g ms\n", t / ((double)cvGetTickFrequency()*1000.));
for (vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, 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);
}
else
rectangle(img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
cvPoint(cvRound((r->x + r->width - 1)*scale), cvRound((r->y + r->height - 1)*scale)),
color, 3, 8, 0);
}
cv::imshow("result", img);
}

结果截图，借用下马总的视频，识别一帧的时间是0.3s左右，需要改进下方法。