// This file is part of the OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
//[1] David S. Bolme et al. "Visual Object Tracking using Adaptive Correlation Filters"
// http://www.cs.colostate.edu/~draper/papers/bolme_cvpr10.pdf
//
//
// credits:
// Kun-Hsin Chen: for initial c++ code
// Cracki: for the idea of only converting the used patch to gray
//
#include "opencv2/tracking.hpp"
namespace cv {
namespace tracking {
struct DummyModel : TrackerModel
{
virtual void modelUpdateImpl() CV_OVERRIDE {}
virtual void modelEstimationImpl( const std::vector<Mat>& ) CV_OVERRIDE {}
};
const double eps=0.00001; // for normalization
const double rate=0.2; // learning rate
const double psrThreshold=5.7; // no detection, if PSR is smaller than this
struct MosseImpl CV_FINAL : TrackerMOSSE
{
protected:
Point2d center; //center of the bounding box
Size size; //size of the bounding box
Mat hanWin;
Mat G; //goal
Mat H, A, B; //state
// Element-wise division of complex numbers in src1 and src2
Mat divDFTs( const Mat &src1, const Mat &src2 ) const
{
Mat c1[2],c2[2],a1,a2,s1,s2,denom,re,im;
// split into re and im per src
cv::split(src1, c1);
cv::split(src2, c2);
// (Re2*Re2 + Im2*Im2) = denom
// denom is same for both channels
cv::multiply(c2[0], c2[0], s1);
cv::multiply(c2[1], c2[1], s2);
cv::add(s1, s2, denom);
// (Re1*Re2 + Im1*Im1)/(Re2*Re2 + Im2*Im2) = Re
cv::multiply(c1[0], c2[0], a1);
cv::multiply(c1[1], c2[1], a2);
cv::divide(a1+a2, denom, re, 1.0 );
// (Im1*Re2 - Re1*Im2)/(Re2*Re2 + Im2*Im2) = Im
cv::multiply(c1[1], c2[0], a1);
cv::multiply(c1[0], c2[1], a2);
cv::divide(a1+a2, denom, im, -1.0);
// Merge Re and Im back into a complex matrix
Mat dst, chn[] = {re,im};
cv::merge(chn, 2, dst);
return dst;
}
void preProcess( Mat &window ) const
{
window.convertTo(window, CV_32F);
log(window + 1.0f, window);
//normalize
Scalar mean,StdDev;
meanStdDev(window, mean, StdDev);
window = (window-mean[0]) / (StdDev[0]+eps);
//Gaussain weighting
window = window.mul(hanWin);
}
double correlate( const Mat &image_sub, Point &delta_xy ) const
{
Mat IMAGE_SUB, RESPONSE, response;
// filter in dft space
dft(image_sub, IMAGE_SUB, DFT_COMPLEX_OUTPUT);
mulSpectrums(IMAGE_SUB, H, RESPONSE, 0, true );
idft(RESPONSE, response, DFT_SCALE|DFT_REAL_OUTPUT);
// update center position
double maxVal; Point maxLoc;
minMaxLoc(response, 0, &maxVal, 0, &maxLoc);
delta_xy.x = maxLoc.x - int(response.size().width/2);
delta_xy.y = maxLoc.y - int(response.size().height/2);
// normalize response
Scalar mean,std;
meanStdDev(response, mean, std);
return (maxVal-mean[0]) / (std[0]+eps); // PSR
}
Mat randWarp( const Mat& a ) const
{
cv::RNG rng(8031965);
// random rotation
double C=0.1;
double ang = rng.uniform(-C,C);
double c=cos(ang), s=sin(ang);
// affine warp matrix
Mat_<float> W(2,3);
W << c + rng.uniform(-C,C), -s + rng.uniform(-C,C), 0,
s + rng.uniform(-C,C), c + rng.uniform(-C,C), 0;
// random translation
Mat_<float> center_warp(2, 1);
center_warp << a.cols/2, a.rows/2;
W.col(2) = center_warp - (W.colRange(0, 2))*center_warp;
Mat warped;
warpAffine(a, warped, W, a.size(), BORDER_REFLECT);
return warped;
}
virtual bool initImpl( const Mat& image, const Rect2d& boundingBox ) CV_OVERRIDE
{
model = makePtr<DummyModel>();
Mat img;
if (image.channels() == 1)
img = image;
else
cvtColor(image, img, COLOR_BGR2GRAY);
int w = getOptimalDFTSize(int(boundingBox.width));
int h = getOptimalDFTSize(int(boundingBox.height));
//Get the center position
int x1 = int(floor((2*boundingBox.x+boundingBox.width-w)/2));
int y1 = int(floor((2*boundingBox.y+boundingBox.height-h)/2));
center.x = x1 + (w)/2;
center.y = y1 + (h)/2;
size.width = w;
size.height = h;
Mat window;
getRectSubPix(img, size, center, window);
createHanningWindow(hanWin, size, CV_32F);
// goal
Mat g=Mat::zeros(size,CV_32F);
g.at<float>(h/2, w/2) = 1;
GaussianBlur(g, g, Size(-1,-1), 2.0);
double maxVal;
minMaxLoc(g, 0, &maxVal);
g = g / maxVal;
dft(g, G, DFT_COMPLEX_OUTPUT);
// initial A,B and H
A = Mat::zeros(G.size(), G.type());
B = Mat::zeros(G.size(), G.type());
for(int i=0; i<8; i++)
{
Mat window_warp = randWarp(window);
preProcess(window_warp);
Mat WINDOW_WARP, A_i, B_i;
dft(window_warp, WINDOW_WARP, DFT_COMPLEX_OUTPUT);
mulSpectrums(G , WINDOW_WARP, A_i, 0, true);
mulSpectrums(WINDOW_WARP, WINDOW_WARP, B_i, 0, true);
A+=A_i;
B+=B_i;
}
H = divDFTs(A,B);
return true;
}
virtual bool updateImpl( const Mat& image, Rect2d& boundingBox ) CV_OVERRIDE
{
if (H.empty()) // not initialized
return false;
Mat image_sub;
getRectSubPix(image, size, center, image_sub);
if (image_sub.channels() != 1)
cvtColor(image_sub, image_sub, COLOR_BGR2GRAY);
preProcess(image_sub);
Point delta_xy;
double PSR = correlate(image_sub, delta_xy);
if (PSR < psrThreshold)
return false;
//update location
center.x += delta_xy.x;
center.y += delta_xy.y;
Mat img_sub_new;
getRectSubPix(image, size, center, img_sub_new);
if (img_sub_new.channels() != 1)
cvtColor(img_sub_new, img_sub_new, COLOR_BGR2GRAY);
preProcess(img_sub_new);
// new state for A and B
Mat F, A_new, B_new;
dft(img_sub_new, F, DFT_COMPLEX_OUTPUT);
mulSpectrums(G, F, A_new, 0, true );
mulSpectrums(F, F, B_new, 0, true );
// update A ,B, and H
A = A*(1-rate) + A_new*rate;
B = B*(1-rate) + B_new*rate;
H = divDFTs(A, B);
// return tracked rect
double x=center.x, y=center.y;
int w = size.width, h=size.height;
boundingBox = Rect2d(Point2d(x-0.5*w, y-0.5*h), Point2d(x+0.5*w, y+0.5*h));
return true;
}
public:
MosseImpl() { isInit = 0; }
// dummy implementation.
virtual void read( const FileNode& ) CV_OVERRIDE {}
virtual void write( FileStorage& ) const CV_OVERRIDE {}
}; // MosseImpl
} // tracking
Ptr<TrackerMOSSE> TrackerMOSSE::create()
{
return makePtr<tracking::MosseImpl>();
}
} // cv