mirror of
https://github.com/opencv/opencv.git
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removed garbage from the image sample
This commit is contained in:
Vadim Pisarevsky
parent
cbe132cabe
commit
91d8b2aaac
@ -9,23 +9,6 @@ using namespace cv; // all the new API is put into "cv" namespace. Export its co
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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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// enable/disable use of mixed API in the code below.
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#define DEMO_MIXED_API_USE 1
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@ -129,615 +112,3 @@ int main( int argc, char** argv )
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return 0;
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// all the memory will automatically be released by Vector<>, Mat and Ptr<> destructors.
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}
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#else
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int main(int argc, char *argv[])
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{
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/*double a = 56004.409155979447;
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double b = -15158.994132169822;
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double c = 215540.83745481662;
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{
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double A[4];
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double InvA[4];
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CvMat matA, matInvA;
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A[0] = a;
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A[1] = A[2] = b;
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A[3] = c;
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cvInitMatHeader( &matA, 2, 2, CV_64F, A );
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cvInitMatHeader( &matInvA, 2, 2, CV_64FC1, InvA );
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cvInvert( &matA, &matInvA, CV_SVD );
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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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}*/
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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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float rv[3], rv2[3];
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float rotMat[9];
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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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CvMat K = cvMat(3, 3, CV_64F, kMat);
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CvMat T = cvMat(3, 1, CV_32F, t);
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CvMat RV = cvMat(3, 1, CV_32F, rv);
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CvMat T2 = cvMat(3, 1, CV_32F, t2);
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CvMat RV2 = cvMat(3, 1, CV_32F, rv2);
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CvMat R = cvMat(3, 3, CV_32F, rotMat);
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float r0, r1, r2;
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kMat[0] = 659.88;
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kMat[1] = 0.00;
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kMat[2] = 320.40;
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kMat[3] = 0.00;
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kMat[4] = 657.53;
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kMat[5] = 240.98;
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kMat[6] = 0.00;
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kMat[7] = 0.00;
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kMat[8] = 1.00;
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ip[0].x = 277.56; ip[0].y = 184.03;
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ip[1].x = 329.00; ip[1].y = 199.04;
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ip[2].x = 405.96; ip[2].y = 205.96;
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ip[3].x = 364.00; ip[3].y = 187.97;
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op[0].x = -189.00; op[0].y = 171.00;
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op[1].x = -280.00; op[1].y = 265.00;
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op[2].x = -436.00; op[2].y = 316.00;
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op[3].x = -376.00; op[3].y = 209.00;
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ip2[0].x = 277.56; ip2[0].y = 184.03;
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ip2[1].x = 328.00; ip2[1].y = 199.11;
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ip2[2].x = 405.89; ip2[2].y = 206.89;
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ip2[3].x = 366.00; ip2[3].y = 187.93;
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op2[0].x = -194.00; op2[0].y = 168.00;
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op2[1].x = -281.00; op2[1].y = 267.00;
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op2[2].x = -433.00; op2[2].y = 321.00;
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op2[3].x = -372.00; op2[3].y = 208.00;
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//ip[4].x = 405.89; ip[4].y = 206.89;
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//op[4].x = -433.00; op[4].y = 321.00;
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//ip2[4].x = 364.00; ip2[4].y = 187.97;
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//op2[4].x = -376.00; op2[4].y = 209.00;
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cvFindExtrinsicCameraParams2(OP, IP, &K,
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NULL, //&D,
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&RV, &T, 0);
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cvRodrigues2(&RV, &R, 0);
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printf("--first--\n");
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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());
|
||||
|
||||
Mat imgc;
|
||||
cvtColor(img, imgc, CV_GRAY2BGR);
|
||||
|
||||
for( size_t i = 0; i < corners.size(); i++ )
|
||||
{
|
||||
circle(imgc, corners[i], 3, Scalar(0,255,0), -1);
|
||||
}
|
||||
imshow("corners", imgc);*/
|
||||
/*Mat imgf, imgf2, img2;
|
||||
img.convertTo(imgf, CV_64F, 1./255);
|
||||
resize(imgf, imgf2, Size(), 0.7, 0.7, CV_INTER_LANCZOS4);
|
||||
imgf2.convertTo(img2, CV_8U, 255);
|
||||
imshow("test", img2);
|
||||
|
||||
waitKey();*/
|
||||
|
||||
/*Mat src = imread("/Users/vp/work/ocv/opencv/samples/c/fruits.jpg", 1);
|
||||
//if( argc != 2 || !(src=imread(argv[1], 1)).data )
|
||||
// return -1;
|
||||
|
||||
Mat hsv;
|
||||
cvtColor(src, hsv, CV_BGR2HSV);
|
||||
|
||||
// let's quantize the hue to 30 levels
|
||||
// and the saturation to 32 levels
|
||||
int hbins = 30, sbins = 32;
|
||||
int histSize[] = {hbins, sbins};
|
||||
// hue varies from 0 to 179, see cvtColor
|
||||
float hranges[] = { 0, 180 };
|
||||
// saturation varies from 0 (black-gray-white) to
|
||||
// 255 (pure spectrum color)
|
||||
float sranges[] = { 0, 256 };
|
||||
const float* ranges[] = { hranges, sranges };
|
||||
MatND hist;
|
||||
// we compute the histogram from the 0-th and 1-st channels
|
||||
int channels[] = {0, 1};
|
||||
|
||||
calcHist( &hsv, 1, channels, Mat(), // do not use mask
|
||||
hist, 2, histSize, ranges,
|
||||
true, // the histogram is uniform
|
||||
false );
|
||||
double maxVal=0;
|
||||
minMaxLoc(hist, 0, &maxVal, 0, 0);
|
||||
|
||||
int scale = 10;
|
||||
Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
|
||||
|
||||
for( int h = 0; h < hbins; h++ )
|
||||
for( int s = 0; s < sbins; s++ )
|
||||
{
|
||||
float binVal = hist.at<float>(h, s);
|
||||
int intensity = cvRound(binVal*255/maxVal);
|
||||
rectangle( histImg, Point(h*scale, s*scale),
|
||||
Point( (h+1)*scale - 1, (s+1)*scale - 1),
|
||||
Scalar::all(intensity),
|
||||
CV_FILLED );
|
||||
}
|
||||
|
||||
namedWindow( "Source", 1 );
|
||||
imshow( "Source", src );
|
||||
|
||||
namedWindow( "H-S Histogram", 1 );
|
||||
imshow( "H-S Histogram", histImg );
|
||||
waitKey();*/
|
||||
|
||||
/*Mat_<double> a(3, 3);
|
||||
a << 1, 0, 0, 0, 1, 0, 0, 0, 1;
|
||||
Mat_<double> b(3, 1);
|
||||
b << 1, 2, 3;
|
||||
Mat c;
|
||||
hconcat(a, b, c);
|
||||
cout << c;*/
|
||||
|
||||
/*Mat img = imread("/Users/vp/work/ocv/opencv/samples/c/lena.jpg", 1), img2;
|
||||
cv::imshow( "Original Image D", img );
|
||||
|
||||
if( img.channels()==3 )
|
||||
{
|
||||
Mat L,u,v;
|
||||
Mat luv;
|
||||
vector<Mat> splitted;
|
||||
Mat img_float0, img_float;
|
||||
|
||||
img.convertTo( img_float0, CV_32F, 1./255, 0 );
|
||||
cvtColor( img_float0, luv, CV_BGR2Luv);
|
||||
|
||||
cv::split( luv, splitted );
|
||||
|
||||
L = (Mat)splitted[0];
|
||||
u = (Mat)splitted[1];
|
||||
v = (Mat)splitted[2];
|
||||
|
||||
vector<Mat> res;
|
||||
|
||||
res.push_back( L );
|
||||
res.push_back( u );
|
||||
res.push_back( v );
|
||||
|
||||
cv::merge( res, luv );
|
||||
|
||||
cvtColor( luv, img_float, CV_Luv2BGR );
|
||||
|
||||
printf("diff = %g\n", cv::norm(img_float0, img_float, CV_C));
|
||||
|
||||
img_float.convertTo( img2, CV_8U, 255. );
|
||||
}
|
||||
|
||||
cv::imshow( "After Darken", img2 );
|
||||
cv::absdiff(img, img2, img2);
|
||||
img2 *= 255;
|
||||
cv::imshow("Magnified difference", img2);
|
||||
|
||||
waitKey();*/
|
||||
|
||||
/*const char* imgFilename = "/Users/vp/Downloads/tsukuba.png";
|
||||
|
||||
Mat bgr = imread( imgFilename );
|
||||
Mat gray = imread( imgFilename, 0 ), gray_;
|
||||
cvtColor( bgr, gray_, CV_BGR2GRAY );
|
||||
|
||||
int N = countNonZero( gray != gray_ );
|
||||
printf( "Count non zero = %d / %d\n", N, gray.cols * gray.rows );
|
||||
|
||||
Mat diff = abs( gray-gray_ );
|
||||
double maxVal = 0;
|
||||
minMaxLoc( diff, 0, &maxVal, 0, 0);
|
||||
printf( "Max abs diff = %f\n", maxVal);*/
|
||||
/*Mat img = imread("/Users/vp/Downloads/r_forearm_cam_rect_crop.png", 1);
|
||||
vector<Point2f> corners;
|
||||
Mat big;
|
||||
resize(img, big, Size(), 1, 1);
|
||||
bool found = findChessboardCorners(big, Size(5,4), corners);
|
||||
drawChessboardCorners(big, Size(5,4), Mat(corners), found);
|
||||
imshow("test", big);
|
||||
waitKey();*/
|
||||
|
||||
/*float x[] = {0, 1};
|
||||
float y[] = {0, 1};
|
||||
CvMat mx = cvMat(2, 1, CV_32F, x);
|
||||
CvMat my = cvMat(2, 1, CV_32F, y);
|
||||
CvNormalBayesClassifier b;
|
||||
bool s = b.train(&mx, &my, 0, 0, false);*/
|
||||
|
||||
/*float responseData[] = {1, 1, 1, 0, 0, 0};
|
||||
float intdata[] = { 1, 0, 0, 1,
|
||||
1, 0, 1, 0,
|
||||
|
||||
1, 1, 0, 0,
|
||||
|
||||
0, 0, 0, 1,
|
||||
|
||||
0, 0, 1, 0,
|
||||
|
||||
0, 1, 0, 0};
|
||||
|
||||
CvMat data = cvMat(6, 4, CV_32FC1, intdata);
|
||||
|
||||
CvMat responses = cvMat(6, 1, CV_32FC1, responseData);
|
||||
|
||||
CvNormalBayesClassifier bc;
|
||||
|
||||
bool succ = bc.train(&data, &responses, 0, 0, false);
|
||||
float testData[] = {1.0, 1, 0, 0};
|
||||
float dummy[] = {0};
|
||||
CvMat test = cvMat(1, 4, CV_32FC1, testData);
|
||||
|
||||
CvMat testResults = cvMat(1, 6, CV_32FC1, 0);
|
||||
|
||||
float whatsthis = bc.predict(&test, &testResults);*/
|
||||
|
||||
int sz[] = {10, 20, 30};
|
||||
Mat m(3, sz, CV_32F);
|
||||
randu(m, Scalar::all(-10), Scalar::all(10));
|
||||
double maxVal0, maxVal = -FLT_MAX;
|
||||
minMaxIdx(m, 0, &maxVal0, 0, 0);
|
||||
|
||||
MatConstIterator_<float> it = m.begin<float>(), it_end = m.end<float>();
|
||||
|
||||
for( ; it != it_end; ++it )
|
||||
{
|
||||
if( maxVal < *it )
|
||||
maxVal = *it;
|
||||
}
|
||||
|
||||
printf("maxval(minmaxloc) = %g, maxval(iterator) = %g\n", maxVal0, maxVal);
|
||||
return 0;
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
Loading…
Reference in New Issue
Block a user