Merge pull request #1423 from berak:mosse_tracker
Vadim Pisarevsky
7 years ago
commit
188ca4a5bd
6 changed files with 295 additions and 1 deletions
@ -0,0 +1,249 @@ |
||||
// 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(){} |
||||
virtual void modelEstimationImpl( const std::vector<Mat>& ){} |
||||
}; |
||||
|
||||
|
||||
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 : 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 ) |
||||
{ |
||||
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 ) |
||||
{ |
||||
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& ){} |
||||
virtual void write( FileStorage& ) const{} |
||||
|
||||
}; // MosseImpl
|
||||
|
||||
} // tracking
|
||||
|
||||
|
||||
Ptr<TrackerMOSSE> TrackerMOSSE::create() |
||||
{ |
||||
return makePtr<tracking::MosseImpl>(); |
||||
} |
||||
|
||||
|
||||
} // cv
|
||||
Loading…
Reference in new issue