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@ -1,8 +1,28 @@ |
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#include "cv.h" // include standard OpenCV headers, same as before |
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#include "cv.h" // include standard OpenCV headers, same as before |
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#include "highgui.h" |
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#include "highgui.h" |
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#include "ml.h" |
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#include <stdio.h> |
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#include <stdio.h> |
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#include <iostream> |
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#include <opencv2/flann/flann.hpp> |
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using namespace cv; // all the new API is put into "cv" namespace. Export its content
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using namespace cv; // all the new API is put into "cv" namespace. Export its content
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using namespace std; |
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using namespace cv::flann; |
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#define RAD2DEG 57.295779513082321 |
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void print32FMat(const CvMat&mat) |
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{ |
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float *data = mat.data.fl; |
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for (int r = 0; r < mat.rows; ++r) |
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{ |
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for (int c = 0; c < mat.cols; ++c) |
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{ |
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printf("%+1.9f\t", data[r*mat.cols+c]); |
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} |
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printf("\n"); |
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} |
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} |
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#if 0 |
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#if 0 |
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@ -114,26 +134,610 @@ int main( int argc, char** argv ) |
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int main(int argc, char *argv[]) |
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int main(int argc, char *argv[]) |
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{ |
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{ |
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Mat im(160, 160, CV_32F); |
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/*double a = 56004.409155979447;
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randu(im, Scalar(0.0), Scalar(1.0)); |
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double b = -15158.994132169822; |
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Mat dd = Mat::zeros(17,1,CV_32F); |
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double c = 215540.83745481662; |
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Mat lp = Mat::zeros(17,1,CV_32F); |
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dd.at<float>(0) = 0.5; |
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{ |
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dd.at<float>(16) = -0.5; |
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double A[4]; |
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lp.at<float>(0) = 0.5; |
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double InvA[4]; |
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lp.at<float>(16) = 0.5; |
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CvMat matA, matInvA; |
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int p = 16; |
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Mat H = dd*lp.t(); |
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A[0] = a; |
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Mat imcrop(im, Rect(17, 17, im.cols-2*p, im.rows-2*p)); |
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A[1] = A[2] = b; |
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Mat out1, out2; |
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A[3] = c; |
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filter2D(imcrop, out1, CV_32F, H, Point(-1,-1)); |
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sepFilter2D(imcrop, out2, CV_32F, lp, dd, Point(-1,-1)); |
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cvInitMatHeader( &matA, 2, 2, CV_64F, A ); |
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Mat temp; |
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cvInitMatHeader( &matInvA, 2, 2, CV_64FC1, InvA ); |
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out1.convertTo(temp, CV_16U, 65535.0, 32768.0); |
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imshow("filtered1.png", temp); |
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cvInvert( &matA, &matInvA, CV_SVD ); |
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out2.convertTo(temp, CV_16U, 65535.0, 32768.0); |
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imshow("filtered2.png", temp); |
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printf("%g\t%g\n%g\t%g\n", InvA[0], InvA[1], InvA[2], InvA[3]); |
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waitKey(); |
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}*/ |
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//Mat img = imread("/Users/vp/work/ocv/opencv/samples/c/left04.jpg", 1);
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//Vec<string, 4> v;
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/*Mat img = Mat::zeros(20, 20, CV_8U);
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img(Range(0,10),Range(0,10)) = Scalar(255); |
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img.at<uchar>(10,10)=255; |
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img(Range(11,20),Range(11,20)) = Scalar(255); |
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vector<Point2f> corner(1, Point2f(9.5,9.5)); |
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cornerSubPix(img, corner, Size(5,5), Size(-1,-1), TermCriteria(3, 30, 0.001)); |
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printf("Corner at (%g, %g)", corner[0].x, corner[0].y);*/ |
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/*Mat large, large2, gray;
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resize(img, large, img.size()*3, 0, 0, CV_INTER_LANCZOS4); |
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cvtColor(large, gray, CV_BGR2GRAY); |
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vector<Point2f> corners; |
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bool found = findChessboardCorners(gray, Size(9,6), corners); |
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cornerSubPix(gray, corners, Size(11,11), Size(-1,-1), |
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01)); |
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drawChessboardCorners(large, Size(9,6), Mat(corners), false); |
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//rectangle(img, Point(150,100), Point(250,200), Scalar(0,255,0), 1);
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resize(large(Rect(150*3,100*3,100*3,100*3)), large2, Size(), 4, 4, CV_INTER_CUBIC); |
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imshow("test", large2); |
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waitKey(0);*/ |
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/*int N=4;
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CvEM em_model; |
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CvEMParams params; |
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int nsamples=500; |
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for (int D=2; D < 15; D++) |
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{ |
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CvMat* samples = cvCreateMat( nsamples, D, CV_32FC1 ); |
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for (int s=0; s < nsamples;s++) |
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for (int d=0; d <D;d++) |
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cvmSet(samples, s, d, (double)s+d); |
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// initialize model's parameters
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params.covs = NULL; |
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params.means = NULL; |
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params.weights = NULL; |
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params.probs = NULL; |
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params.nclusters = N; |
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params.cov_mat_type = CvEM::COV_MAT_DIAGONAL; |
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params.start_step = CvEM::START_AUTO_STEP; |
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params.term_crit.max_iter = 100; |
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params.term_crit.epsilon = 0.01; |
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params.term_crit.type = CV_TERMCRIT_ITER|CV_TERMCRIT_EPS; |
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em_model.train( samples, 0, params); |
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const CvMat* w = em_model.get_weights(); |
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double sum=0; |
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for (int i=0; i < N; i++) |
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sum+=cvmGet(w, 0, i); |
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printf("D = %d, sum = %f\n", D, sum); |
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cvReleaseMat( &samples ); |
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}*/ |
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/*Mat a(1, 0, CV_32F);
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cout << " empty=" << a.empty() << " data=" << (size_t)a.data << endl;*/ |
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//XOR input
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/*double in[]={ 0 ,0,
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1, 0, |
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0, 1, |
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1, 1}; |
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double out[]={ 0, |
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1, |
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1, |
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0}; |
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const int IVS = 2; // Input Vector Size
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const int OVS = 1; // Output Vector Size
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const int HN = 10; // Number of Hidden nodes
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const int NV= 4; //Number of Training Vector
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int layer[] = { IVS, HN, OVS}; |
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CvMat *input =cvCreateMat( NV, IVS, CV_64FC1); |
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CvMat *output =cvCreateMat( NV, OVS, CV_64FC1); |
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CvMat *p_output =cvCreateMat( NV, OVS, CV_64FC1); |
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CvMat *layersize =cvCreateMat( 1 , 3 , CV_32SC1); |
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cvInitMatHeader(input, NV, IVS, CV_64FC1, in); |
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cvInitMatHeader(output, NV, OVS, CV_64FC1, out); |
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cvInitMatHeader(layersize, 1, 3, CV_32SC1, layer); |
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CvANN_MLP train_model(layersize, CvANN_MLP::SIGMOID_SYM,1,1); |
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std::cout<< " =========== =========== =========== =========== ==========="<<std::endl; |
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std::cout<< " * First Iteration with initialzation of weights"<<std::endl; |
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std::cout<< " =========== =========== =========== =========== ==========="<<std::endl; |
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int iter = train_model.train( input, |
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output,
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NULL,
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0, |
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CvANN_MLP_TrainParams( cvTermCriteria ( CV_TERMCRIT_ITER | |
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CV_TERMCRIT_EPS, |
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5000,0.000001), |
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CvANN_MLP_TrainParams::BACKPROP, |
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0.1,0.1), |
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0 |
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//+ CvANN_MLP::NO_OUTPUT_SCALE
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); |
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std::cout << " * iteration :"<<iter<<std::endl; |
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train_model.predict( input, p_output ); |
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for(int i=0; i<NV;i++){ |
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std::cout<< CV_MAT_ELEM(*input,double,i,0) << " ," << CV_MAT_ELEM(*input,double,i,1) |
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<< " : " << CV_MAT_ELEM(*p_output,double,i,0) <<std::endl; |
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} |
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train_model.save( "firstModel.xml"); |
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std::cout<< " =========== =========== =========== =========== ==========="<<std::endl; |
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std::cout<< " * Second Iteration without initialzation of weights"<<std::endl; |
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std::cout<< " =========== =========== =========== =========== ==========="<<std::endl; |
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int iter2; |
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for(int i=0;i<5;i++) |
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{ |
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iter2 = train_model.train( input, |
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output,
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NULL,
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0, |
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CvANN_MLP_TrainParams( cvTermCriteria ( CV_TERMCRIT_ITER | |
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CV_TERMCRIT_EPS, |
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5000,0.0000001), |
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CvANN_MLP_TrainParams::BACKPROP, |
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0.1,0.1), |
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0 |
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+CvANN_MLP::UPDATE_WEIGHTS |
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//+ CvANN_MLP::NO_OUTPUT_SCALE
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); |
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} |
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std::cout << " * iteration :"<<iter2<<std::endl; |
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train_model.save( "secondModel.xml"); |
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train_model.predict( input, p_output ); |
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for(int i=0; i<NV;i++){ |
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std::cout<< CV_MAT_ELEM(*input,double,i,0) << " ," << CV_MAT_ELEM(*input,double,i,1) |
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<< " : " << CV_MAT_ELEM(*p_output,double,i,0) <<std::endl; |
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}*/ |
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/*cv::Size imageSize;
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int Nimg, Npts; |
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vector<vector<cv::Point3f> > objectPoints; |
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vector<vector<cv::Point2f> >imagePoints; |
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cv::FileStorage f("/Users/vp/Downloads/calib_debug.2.yml",cv::FileStorage::READ); |
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cv::FileNodeIterator it = f["img_sz"].begin(); it >> imageSize.width >> imageSize.height; |
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Nimg = (int) f ["NofImages"]; |
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Npts = (int) f["NofPoints"]; |
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for (int i=0; i<Nimg;i++) { |
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std::stringstream imagename; imagename << "image" << i; |
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cv::FileNode img = f[imagename.str()]; |
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vector <cv::Point3f> ov; |
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vector <cv::Point2f> iv; |
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for (int j=0; j<Npts; j++) { |
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std::stringstream nodename; nodename << "node" << j; |
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cv::FileNode pnt = img[nodename.str()]; |
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cv::Point3f op; |
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cv::Point2f ip; |
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cv::FileNodeIterator ot = pnt["objPnt"].begin(); ot >> op.x >> op.y >> op.z; |
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cv::FileNodeIterator it = pnt["imgPnt"].begin(); it >> ip.x >> ip.y; |
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iv.push_back(ip); |
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ov.push_back(op); |
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} |
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imagePoints.push_back(iv); |
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objectPoints.push_back(ov); |
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} |
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cv::Mat M,D; |
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vector<cv::Mat> R,T; |
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cv::calibrateCamera(objectPoints, imagePoints, imageSize, M, D,R,T, |
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CV_CALIB_FIX_ASPECT_RATIO + 1*CV_CALIB_FIX_K3 + 1*CV_CALIB_ZERO_TANGENT_DIST); |
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cv::FileStorage fo("calib_output.yml",cv::FileStorage::WRITE); |
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//fo << "M" << M;
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cout << "M: " << M;*/ |
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/*Mat img = imread("/Users/vp/Downloads/test5.tif", CV_LOAD_IMAGE_ANYDEPTH|CV_LOAD_IMAGE_COLOR), img16;
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cout << "img.depth()=" << img.depth() << endl; |
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if(img.depth() == CV_16U) |
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img16 = img; |
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else |
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img.convertTo(img16, CV_16U, 256, 0); |
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imshow("test", img16); |
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imwrite("/Users/vp/tmp/test16_2.png", img16); |
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waitKey();*/ |
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/*Mat img(600,800,CV_8UC3);
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img = Scalar::all(0); |
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IplImage _img = img; |
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CvFont font = cvFont(2,1); |
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cvPutText(&_img, "Test", cvPoint(100, 100), &font, cvScalar(0,0,255)); |
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imshow("test", img); |
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waitKey();*/ |
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/*IplImage* img = cvCreateImage(cvSize(800,600), 8, 3);
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cvZero(img); |
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CvFont font = cvFont(2,1); |
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cvPutText(img, "Test", cvPoint(100, 100), &font, cvScalar(0,0,255));
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cvNamedWindow("test", 1); |
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cvShowImage("test", img); |
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cvWaitKey(0);*/ |
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/*int sz[] = {1, 5, 5};
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CvMatND* src = cvCreateMatND(3, sz, CV_64F); |
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CvMatND* dst = cvCreateMatND(3, sz, CV_64F); |
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CvRNG rng = cvRNG(-1); |
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cvRandArr(&rng, src, CV_RAND_UNI, cvScalarAll(-100), cvScalarAll(100)); |
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cvAddS(src, cvScalar(100), dst, 0); |
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cvSave("_input.xml", src); |
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cvSave("_output.xml", dst);*/ |
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/*
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/// random data generation :
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Mat data(100,10,CV_32FC1); |
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randn(data, 0.0, 1.0); |
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/// Creating the ANN engine
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AutotunedIndexParams autoParams(0.9,0.5,0.2,1); |
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Index index(data,autoParams); |
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/// Creating a query
|
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SearchParams searchParams(5); |
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vector<float> query, dist; |
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vector<int> foundIndice; |
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foundIndice.push_back(0); |
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dist.push_back(0); |
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for(int i = 0 ; i < 10 ; i++) |
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{ |
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query.push_back(data.at<float>(2,i)); |
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} |
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/// Do a reaserch : result must be equal to 2.
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|
index.knnSearch(query, foundIndice, dist, 1, searchParams); |
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|
cout << "Found indice (must be 2) : " << foundIndice[0] << endl; |
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|
/// save params
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|
index.save(string("test")); |
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*/ |
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/*namedWindow("orig", CV_WINDOW_AUTOSIZE);
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namedWindow("canny", CV_WINDOW_AUTOSIZE); |
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namedWindow("hough", CV_WINDOW_AUTOSIZE); |
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|
Mat orig = cv::imread("/Users/vp/Downloads/1.jpg", 0); |
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|
//equalizeHist(orig, orig);
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|
|
Mat hough; |
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|
cvtColor(orig, hough, CV_GRAY2BGR); |
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|
Mat canny; |
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|
Canny(orig, canny, 100, 50); // reproduce Canny-Filtering as in Hough-Circles
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int bestRad = 20; |
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|
int minRad = bestRad / 1.3; |
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|
int maxRad = bestRad * 1.3; |
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|
|
vector<Vec3f> circles; // detect circles
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|
HoughCircles(orig, circles, CV_HOUGH_GRADIENT, |
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|
1, // accu-scaling
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|
20, // minDist
|
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|
100, // CannyParam
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|
10, // minAccuCount
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minRad, |
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|
maxRad); |
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|
// Draw Circles into image in gray
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|
for( size_t i = 0; i < circles.size(); i++ ) |
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|
|
{ |
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|
Point center(cvRound(circles[i][0]), cvRound(circles[i][1])); |
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|
int radius = cvRound(circles[i][2]); |
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|
// draw the circle center
|
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|
circle( hough, center, 3, Scalar(0,0,255), -1, 8, 0 ); |
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|
// draw the circle outline
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|
circle( hough, center, radius, Scalar(0,255,255), 1, 8, 0 ); |
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|
} |
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|
// Draw reference circles
|
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|
Point c(bestRad * 3, bestRad * 3); |
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|
circle(hough, c, bestRad, 255); |
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|
circle(hough, c, minRad, 255); |
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|
circle(hough, c, maxRad, 255); |
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|
cv::imshow("orig", orig); |
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|
|
cv::imshow("canny", canny); |
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|
|
cv::imshow("hough", hough); |
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|
|
cv::waitKey();*/ |
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|
|
|
/*int npoints = 4;
|
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|
|
CvMat *OP = cvCreateMat(1, npoints, CV_32FC3); |
|
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|
|
CvPoint3D32f *op = (CvPoint3D32f *)OP->data.fl; |
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|
|
CvMat *OP2 = cvCreateMat(1, npoints, CV_32FC3); |
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|
|
CvPoint3D32f *op2 = (CvPoint3D32f *)OP2->data.fl; |
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|
|
CvMat *IP = cvCreateMat(1, npoints, CV_32FC2); |
|
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|
|
CvPoint2D32f *ip = (CvPoint2D32f *)IP->data.fl; |
|
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|
|
CvMat *IP2 = cvCreateMat(1, npoints, CV_32FC2); |
|
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|
|
CvPoint2D32f *ip2 = (CvPoint2D32f *)IP2->data.fl; |
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|
|
CvMat *IP0 = cvCreateMat(1, npoints, CV_32FC2); |
|
|
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|
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|
|
|
|
|
|
|
|
float rv[3], rv2[3]; |
|
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|
|
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|
|
float rotMat[9]; |
|
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|
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|
|
float t[3], t2[3]; |
|
|
|
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|
|
float tRotMat[16]; |
|
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|
|
|
|
|
double kMat[9]; |
|
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|
|
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|
|
|
|
|
CvMat K = cvMat(3, 3, CV_64F, kMat); |
|
|
|
|
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|
|
CvMat T = cvMat(3, 1, CV_32F, t); |
|
|
|
|
|
|
|
CvMat RV = cvMat(3, 1, CV_32F, rv); |
|
|
|
|
|
|
|
CvMat T2 = cvMat(3, 1, CV_32F, t2); |
|
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|
|
|
|
|
CvMat RV2 = cvMat(3, 1, CV_32F, rv2); |
|
|
|
|
|
|
|
CvMat R = cvMat(3, 3, CV_32F, rotMat); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
float r0, r1, r2; |
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
kMat[0] = 659.88; |
|
|
|
|
|
|
|
kMat[1] = 0.00;
|
|
|
|
|
|
|
|
kMat[2] = 320.40;
|
|
|
|
|
|
|
|
kMat[3] = 0.00;
|
|
|
|
|
|
|
|
kMat[4] = 657.53;
|
|
|
|
|
|
|
|
kMat[5] = 240.98;
|
|
|
|
|
|
|
|
kMat[6] = 0.00;
|
|
|
|
|
|
|
|
kMat[7] = 0.00; |
|
|
|
|
|
|
|
kMat[8] = 1.00; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ip[0].x = 277.56; ip[0].y = 184.03;
|
|
|
|
|
|
|
|
ip[1].x = 329.00; ip[1].y = 199.04;
|
|
|
|
|
|
|
|
ip[2].x = 405.96; ip[2].y = 205.96;
|
|
|
|
|
|
|
|
ip[3].x = 364.00; ip[3].y = 187.97; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
op[0].x = -189.00; op[0].y = 171.00;
|
|
|
|
|
|
|
|
op[1].x = -280.00; op[1].y = 265.00;
|
|
|
|
|
|
|
|
op[2].x = -436.00; op[2].y = 316.00;
|
|
|
|
|
|
|
|
op[3].x = -376.00; op[3].y = 209.00; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ip2[0].x = 277.56; ip2[0].y = 184.03;
|
|
|
|
|
|
|
|
ip2[1].x = 328.00; ip2[1].y = 199.11;
|
|
|
|
|
|
|
|
ip2[2].x = 405.89; ip2[2].y = 206.89;
|
|
|
|
|
|
|
|
ip2[3].x = 366.00; ip2[3].y = 187.93; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
op2[0].x = -194.00; op2[0].y = 168.00;
|
|
|
|
|
|
|
|
op2[1].x = -281.00; op2[1].y = 267.00;
|
|
|
|
|
|
|
|
op2[2].x = -433.00; op2[2].y = 321.00;
|
|
|
|
|
|
|
|
op2[3].x = -372.00; op2[3].y = 208.00; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
//ip[4].x = 405.89; ip[4].y = 206.89;
|
|
|
|
|
|
|
|
//op[4].x = -433.00; op[4].y = 321.00;
|
|
|
|
|
|
|
|
//ip2[4].x = 364.00; ip2[4].y = 187.97;
|
|
|
|
|
|
|
|
//op2[4].x = -376.00; op2[4].y = 209.00;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvFindExtrinsicCameraParams2(OP, IP, &K,
|
|
|
|
|
|
|
|
NULL, //&D,
|
|
|
|
|
|
|
|
&RV, &T, 0); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvRodrigues2(&RV, &R, 0); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
printf("--first--\n"); |
|
|
|
|
|
|
|
print32FMat(R); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvFindExtrinsicCameraParams2(OP2, IP2, &K,
|
|
|
|
|
|
|
|
NULL, //&D,
|
|
|
|
|
|
|
|
&RV2, &T2, 0); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvRodrigues2(&RV2, &R, 0); |
|
|
|
|
|
|
|
printf("---second---\n"); |
|
|
|
|
|
|
|
print32FMat(R); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
double err; |
|
|
|
|
|
|
|
cvProjectPoints2(OP, &RV, &T, &K, NULL, IP0); |
|
|
|
|
|
|
|
err = cvNorm(IP, IP0, CV_L2); |
|
|
|
|
|
|
|
printf("\n\nfirst avg reprojection error = %g\n", sqrt(err*err/npoints)); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvProjectPoints2(OP2, &RV2, &T2, &K, NULL, IP0); |
|
|
|
|
|
|
|
err = cvNorm(IP2, IP0, CV_L2); |
|
|
|
|
|
|
|
printf("second avg reprojection error = %g\n", sqrt(err*err/npoints)); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvProjectPoints2(OP, &RV2, &T2, &K, NULL, IP0); |
|
|
|
|
|
|
|
err = cvNorm(IP, IP0, CV_L2); |
|
|
|
|
|
|
|
printf("\n\nsecond->first cross reprojection error = %g\n", sqrt(err*err/npoints)); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
cvProjectPoints2(OP2, &RV, &T, &K, NULL, IP0); |
|
|
|
|
|
|
|
err = cvNorm(IP2, IP0, CV_L2); |
|
|
|
|
|
|
|
printf("first->second cross reprojection error = %g\n", sqrt(err*err/npoints)); |
|
|
|
|
|
|
|
*/ |
|
|
|
|
|
|
|
/*Mat img = imread("/Users/vp/work/ocv/opencv/samples/c/baboon.jpg", 1);
|
|
|
|
|
|
|
|
vector<Point2f> corners; |
|
|
|
|
|
|
|
double t0 = 0, t; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for( size_t i = 0; i < 50; i++ ) |
|
|
|
|
|
|
|
{ |
|
|
|
|
|
|
|
corners.clear(); |
|
|
|
|
|
|
|
t = (double)getTickCount(); |
|
|
|
|
|
|
|
goodFeaturesToTrack(img, corners, 1000, 0.01, 10); |
|
|
|
|
|
|
|
t = (double)getTickCount() - t; |
|
|
|
|
|
|
|
if( i == 0 || t0 > t ) |
|
|
|
|
|
|
|
t0 = t; |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
printf("minimum running time = %gms\n", t0*1000./getTickFrequency()); |
|
|
|
|
|
|
|
|
|
|
|
|
|
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Mat imgc; |
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cvtColor(img, imgc, CV_GRAY2BGR); |
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for( size_t i = 0; i < corners.size(); i++ ) |
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{ |
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circle(imgc, corners[i], 3, Scalar(0,255,0), -1); |
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} |
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imshow("corners", imgc);*/ |
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/*Mat imgf, imgf2, img2;
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img.convertTo(imgf, CV_64F, 1./255); |
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resize(imgf, imgf2, Size(), 0.7, 0.7, CV_INTER_LANCZOS4); |
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imgf2.convertTo(img2, CV_8U, 255); |
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imshow("test", img2); |
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waitKey();*/ |
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/*Mat src = imread("/Users/vp/work/ocv/opencv/samples/c/fruits.jpg", 1);
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//if( argc != 2 || !(src=imread(argv[1], 1)).data )
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// return -1;
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Mat hsv; |
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cvtColor(src, hsv, CV_BGR2HSV); |
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// let's quantize the hue to 30 levels
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// and the saturation to 32 levels
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int hbins = 30, sbins = 32; |
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int histSize[] = {hbins, sbins}; |
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// hue varies from 0 to 179, see cvtColor
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float hranges[] = { 0, 180 }; |
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// saturation varies from 0 (black-gray-white) to
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// 255 (pure spectrum color)
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float sranges[] = { 0, 256 }; |
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const float* ranges[] = { hranges, sranges }; |
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MatND hist; |
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// we compute the histogram from the 0-th and 1-st channels
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int channels[] = {0, 1}; |
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calcHist( &hsv, 1, channels, Mat(), // do not use mask
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hist, 2, histSize, ranges, |
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true, // the histogram is uniform
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false ); |
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double maxVal=0; |
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minMaxLoc(hist, 0, &maxVal, 0, 0); |
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int scale = 10; |
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Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3); |
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for( int h = 0; h < hbins; h++ ) |
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for( int s = 0; s < sbins; s++ ) |
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{ |
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float binVal = hist.at<float>(h, s); |
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int intensity = cvRound(binVal*255/maxVal); |
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rectangle( histImg, Point(h*scale, s*scale), |
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Point( (h+1)*scale - 1, (s+1)*scale - 1), |
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Scalar::all(intensity), |
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CV_FILLED ); |
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} |
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namedWindow( "Source", 1 ); |
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imshow( "Source", src ); |
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namedWindow( "H-S Histogram", 1 ); |
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imshow( "H-S Histogram", histImg ); |
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waitKey();*/ |
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/*Mat_<double> a(3, 3);
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a << 1, 0, 0, 0, 1, 0, 0, 0, 1; |
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Mat_<double> b(3, 1); |
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b << 1, 2, 3; |
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Mat c; |
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hconcat(a, b, c); |
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cout << c;*/ |
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/*Mat img = imread("/Users/vp/work/ocv/opencv/samples/c/lena.jpg", 1), img2;
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cv::imshow( "Original Image D", img ); |
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if( img.channels()==3 ) |
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{ |
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Mat L,u,v; |
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Mat luv; |
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vector<Mat> splitted; |
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Mat img_float0, img_float; |
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img.convertTo( img_float0, CV_32F, 1./255, 0 ); |
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cvtColor( img_float0, luv, CV_BGR2Luv); |
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cv::split( luv, splitted ); |
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L = (Mat)splitted[0]; |
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u = (Mat)splitted[1]; |
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v = (Mat)splitted[2];
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vector<Mat> res; |
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res.push_back( L ); |
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res.push_back( u ); |
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res.push_back( v ); |
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cv::merge( res, luv ); |
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cvtColor( luv, img_float, CV_Luv2BGR ); |
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printf("diff = %g\n", cv::norm(img_float0, img_float, CV_C)); |
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img_float.convertTo( img2, CV_8U, 255. ); |
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} |
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cv::imshow( "After Darken", img2 ); |
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cv::absdiff(img, img2, img2); |
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img2 *= 255; |
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cv::imshow("Magnified difference", img2); |
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waitKey();*/ |
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/*const char* imgFilename = "/Users/vp/Downloads/tsukuba.png";
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Mat bgr = imread( imgFilename ); |
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Mat gray = imread( imgFilename, 0 ), gray_; |
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cvtColor( bgr, gray_, CV_BGR2GRAY ); |
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int N = countNonZero( gray != gray_ ); |
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printf( "Count non zero = %d / %d\n", N, gray.cols * gray.rows ); |
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Mat diff = abs( gray-gray_ ); |
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double maxVal = 0; |
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minMaxLoc( diff, 0, &maxVal, 0, 0); |
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printf( "Max abs diff = %f\n", maxVal);*/ |
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/*Mat img = imread("/Users/vp/Downloads/r_forearm_cam_rect_crop.png", 1);
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vector<Point2f> corners; |
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Mat big; |
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resize(img, big, Size(), 1, 1); |
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bool found = findChessboardCorners(big, Size(5,4), corners); |
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drawChessboardCorners(big, Size(5,4), Mat(corners), found); |
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imshow("test", big); |
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waitKey();*/ |
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/*float x[] = {0, 1};
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float y[] = {0, 1}; |
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CvMat mx = cvMat(2, 1, CV_32F, x); |
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CvMat my = cvMat(2, 1, CV_32F, y); |
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CvNormalBayesClassifier b; |
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bool s = b.train(&mx, &my, 0, 0, false);*/ |
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/*float responseData[] = {1, 1, 1, 0, 0, 0};
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float intdata[] = { 1, 0, 0, 1, |
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1, 0, 1, 0, |
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1, 1, 0, 0, |
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0, 0, 0, 1, |
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0, 0, 1, 0, |
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0, 1, 0, 0}; |
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CvMat data = cvMat(6, 4, CV_32FC1, intdata); |
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CvMat responses = cvMat(6, 1, CV_32FC1, responseData); |
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CvNormalBayesClassifier bc; |
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bool succ = bc.train(&data, &responses, 0, 0, false); |
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float testData[] = {1.0, 1, 0, 0}; |
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float dummy[] = {0}; |
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CvMat test = cvMat(1, 4, CV_32FC1, testData); |
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CvMat testResults = cvMat(1, 6, CV_32FC1, 0); |
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float whatsthis = bc.predict(&test, &testResults);*/ |
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int sz[] = {10, 20, 30}; |
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Mat m(3, sz, CV_32F); |
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randu(m, Scalar::all(-10), Scalar::all(10)); |
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double maxVal0, maxVal = -FLT_MAX; |
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minMaxIdx(m, 0, &maxVal0, 0, 0); |
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MatConstIterator_<float> it = m.begin<float>(), it_end = m.end<float>(); |
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for( ; it != it_end; ++it ) |
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{ |
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if( maxVal < *it ) |
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maxVal = *it; |
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} |
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printf("maxval(minmaxloc) = %g, maxval(iterator) = %g\n", maxVal0, maxVal); |
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return 0; |
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} |
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} |
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#endif |
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#endif |
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Vadim Pisarevsky
14 years ago