int main(int argc, char** argv){
// Initialize classes and configurations
ros::init(argc, argv, "findObject");
ObjectFinder f;
f.applyAction();
return 0;
}
//--------------------------------------------------------------
void ofApp::addObjectFinder(const string &cascadeFilename)
{
ObjectFinder finder;
finder.setup(cascadeFilename);
finder.setRescale(.25);
finder.setFindBiggestObject(true);
finder.getTracker().setSmoothingRate(.3);
finders.push_back(move(finder));
}
int main(void)
{
GetHistogram gh;
ObjectFinder of;
cv::Mat img1, img2;
cv::Mat imgROI;
cv::Mat histogram;
cv::Mat backProject;
img1 = cv::imread("..\\..\\..\\source\\1.jpg");
if(!img1.data)
return 0;
imgROI = img1(cv::Rect(80,300,35,40));
cv::rectangle(img1,cv::Rect(110,260,35,40),cv::Scalar(0,0,255));
cv::namedWindow("img1");
cv::imshow("img1", img1);
//获得imgROI的直方图
histogram = gh.getHueHistogram(imgROI, 65);
cv::normalize(histogram,histogram,1.0);
of.setHistogram(histogram);
//获取并处理第二幅图像
cv::Mat hsv;
std::vector<cv::Mat> hue;
img2 = cv::imread("..\\..\\..\\source\\2.jpg");
cv::cvtColor(img2, hsv, CV_BGR2HSV);
cv::split(hsv, hue);
cv::threshold(hue[1], hue[1], 65, 255, cv::THRESH_BINARY);//这个是为了将低饱和度的图像从反向投影结果中剔除
//对于第二幅图进行反向投影，使用第一幅图ROI的直方图
backProject = of.finder(img2);
cv::bitwise_and(backProject, hue[1], backProject);
cv::namedWindow("backProject");
cv::imshow("backProject",backProject);
//均值漂移
cv::Rect rect(110,260,35,40);
cv::rectangle(img2, rect, cv::Scalar(0,0,255));
cv::TermCriteria criteria(cv::TermCriteria::MAX_ITER,10,0.01);//最大迭代次数是10，移动距离阈值是0.01
cv::meanShift(backProject,rect,criteria);
cv::rectangle(img2, rect, cv::Scalar(0,255,0));
cv::namedWindow("img2");
cv::imshow("img2",img2);
cv::waitKey(0);
return 0;
}
int main(int argc, char *argv[])
{
Network yarp;
if (!yarp.checkNetwork())
return -1;
ResourceFinder rf;
rf.setVerbose(true);
rf.setDefault("name","objectFinder");
rf.setDefault("camera","left");
rf.setDefault("robot","icub");
rf.setDefaultContext("AffordancesProject");
rf.setDefaultConfigFile("objectFinder.ini");
rf.configure(argc,argv);
ObjectFinder objectFinder;
return objectFinder.runModule(rf);
}
int main()
{
// Read reference image
cv::Mat image= cv::imread("../images/baboon1.jpg");
if (!image.data)
return 0;
// Define ROI
cv::Mat imageROI= image(cv::Rect(110,260,35,40));
cv::rectangle(image, cv::Rect(110,260,35,40), cv::Scalar(0,0,255));
// Display image
cv::namedWindow("Image 1");
cv::imshow("Image 1",image);
// Get the Hue histogram
int minSat=65;
ColorHistogram hc;
cv::MatND colorhist= hc.getHueHistogram(imageROI, minSat);
ObjectFinder finder;
finder.setHistogram(colorhist);
finder.setThreshold(0.2f);
// Second image
image= cv::imread("../images/baboon3.jpg");
// Display image
cv::namedWindow("Image 2");
cv::imshow("Image 2",image);
// Convert to HSV space
cv::Mat hsv;
cv::cvtColor(image, hsv, CV_BGR2HSV);
// Split the image
vector<cv::Mat> v;
cv::split(hsv,v);
// Eliminate pixels with low saturation
cv::threshold(v[1],v[1],minSat,255,cv::THRESH_BINARY);
cv::namedWindow("Saturation");
cv::imshow("Saturation",v[1]);
// Get back-projection of hue histogram
int ch[1]={0};
cv::Mat result= finder.find(hsv,0.0f,180.0f,ch,1);
cv::namedWindow("Result Hue");
cv::imshow("Result Hue",result);
// Eliminate low stauration pixels
cv::bitwise_and(result,v[1],result);
cv::namedWindow("Result Hue and raw");
cv::imshow("Result Hue and raw",result);
cv::Rect rect(110,260,35,40);
cv::rectangle(image, rect, cv::Scalar(0,0,255));
cv::TermCriteria criteria(cv::TermCriteria::MAX_ITER,10,0.01);
cout << "meanshift= " << cv::meanShift(result,rect,criteria) << endl;
cv::rectangle(image, rect, cv::Scalar(0,255,0));
// Display image
cv::namedWindow("Image 2 result");
cv::imshow("Image 2 result",image);
cv::waitKey();
return 0;
}