基于感知哈希的视觉目标跟踪算法代码

#include <opencv2/opencv.hpp>

using namespace cv;
using namesp
4000
ace std;

static Rect box;
static bool drawing_box = false;
static bool gotBB = false;

static void mouseHandler(int event, int x, int y, int flags, void *param){
switch( event ){
case CV_EVENT_MOUSEMOVE:
if (drawing_box){
box.width = x-box.x;
box.height = y-box.y;
}
break;
case CV_EVENT_LBUTTONDOWN:
drawing_box = true;
box = Rect( x, y, 0, 0 );
break;
case CV_EVENT_LBUTTONUP:
drawing_box = false;
if( box.width < 0 ){
box.x += box.width;
box.width *= -1;
}
if( box.height < 0 ){
box.y += box.height;
box.height *= -1;
}
gotBB = true;
break;
}
}

// calculate the hash code of image
static Mat calHashCode(Mat image)
{
resize(image, image, Size(8, 8));
Scalar imageMean = mean(image);
return (image > imageMean[0]);
}

// calculate the pHash code of image
static Mat calPHashCode(Mat image)
{
Mat floatImage, imageDct;
resize(image, image, Size(32, 32));
image.convertTo(floatImage, CV_32FC1);
dct(floatImage, imageDct);
Rect roi(0, 0, 8, 8);
Scalar imageMean = mean(imageDct(roi));
return (imageDct(roi) > imageMean[0]);
}

// get hamming distance of two hash code
static int calHammingDistance(Mat modelHashCode, Mat testHashCode)
{
return countNonZero(modelHashCode != testHashCode);
}

// tracker: get search patches around the last tracking box,
// and find the most similar one using hamming distance
static void hashTrack(Mat frame, Mat &model, Rect &trackBox, int flag = 0)
{
Mat gray;
cvtColor(frame, gray, CV_RGB2GRAY);

Rect searchWindow;
searchWindow.width = trackBox.width * 3;
searchWindow.height = trackBox.height * 3;
searchWindow.x = trackBox.x + trackBox.width * 0.5 - searchWindow.width * 0.5;
searchWindow.y = trackBox.y + trackBox.height * 0.5 - searchWindow.height * 0.5;
searchWindow &= Rect(0, 0, frame.cols, frame.rows);

Mat modelHashCode, testHashCode;
if (flag)
modelHashCode = calHashCode(model);
else
modelHashCode = calPHashCode(model);
int step = 2;
int min = 1000;
Rect window = trackBox;
for (int i = 0; i * step < searchWindow.height - trackBox.height; i++)
{
window.y = searchWindow.y + i * step;
for (int j = 0; j * step < searchWindow.width - trackBox.width; j++)
{
window.x = searchWindow.x + j * step;
if (flag)
testHashCode = calHashCode(gray(window));
else
testHashCode = calPHashCode(gray(window));
int distance = calHammingDistance(modelHashCode, testHashCode);
if (distance < min)
{
trackBox = window;
min = distance;
}
}
}
model = gray(trackBox);
cout << "The min hanming distance is: " << min << endl;
}

int main(int argc, const char * argv[])
{
VideoCapture capture;
// from video
capture.open("optical_flow_input.avi");
bool fromfile = true;

// from camera
//capture.open(0);
//bool fromfile = false;

//Init camera
if (!capture.isOpened())
{
cout << "capture device failed to open!" << endl;
return -1;
}
//Register mouse callback to draw the bounding box
cvNamedWindow("hashTracker", CV_WINDOW_AUTOSIZE);
cvSetMouseCallback("hashTracker", mouseHandler, NULL );

Mat frame, model;
capture >> frame;
while(!gotBB)
{
if (!fromfile)
capture >> frame;

imshow("hashTracker", frame);
if (cvWaitKey(20) == 'q')
return 1;
}
//Remove callback
cvSetMouseCallback("hashTracker", NULL, NULL );

Mat gray;
cvtColor(frame, gray, CV_RGB2GRAY);
model = gray(box);

int frameCount = 0;
while (1)
{
capture >> frame;
if (frame.empty())
return -1;
double t = (double)cvGetTickCount();
frameCount++;

// tracking
hashTrack(frame, model, box, 0);

// show
stringstream buf;
buf << frameCount;
string num = buf.str();
putText(frame, num, Point(20, 30), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 0, 255), 3);
rectangle(frame, box, Scalar(0, 0, 255), 3);
imshow("hashTracker", frame);

t = (double)cvGetTickCount() - t;
cout << "cost time: " << t / ((double)cvGetTickFrequency()*1000.) << endl;

if ( cvWaitKey(1) == 27 )
break;
}

return 0;
}

转载：http://blog.csdn.net/zouxy09