using namespace std;
using namespace cv::flann;
-#define RAD2DEG 57.295779513082321
-void print32FMat(const CvMat&mat)
-{
- float *data = mat.data.fl;
-
- for (int r = 0; r < mat.rows; ++r)
- {
- for (int c = 0; c < mat.cols; ++c)
- {
- printf("%+1.9f\t", data[r*mat.cols+c]);
- }
- printf("\n");
- }
-}
-
-#if 0
-
// enable/disable use of mixed API in the code below.
#define DEMO_MIXED_API_USE 1
return 0;
// all the memory will automatically be released by Vector<>, Mat and Ptr<> destructors.
}
-
-#else
-
-int main(int argc, char *argv[])
-{
- /*double a = 56004.409155979447;
- double b = -15158.994132169822;
- double c = 215540.83745481662;
-
- {
- double A[4];
- double InvA[4];
- CvMat matA, matInvA;
-
- A[0] = a;
- A[1] = A[2] = b;
- A[3] = c;
-
- cvInitMatHeader( &matA, 2, 2, CV_64F, A );
- cvInitMatHeader( &matInvA, 2, 2, CV_64FC1, InvA );
-
- cvInvert( &matA, &matInvA, CV_SVD );
-
- printf("%g\t%g\n%g\t%g\n", InvA[0], InvA[1], InvA[2], InvA[3]);
- }*/
-
- //Mat img = imread("/Users/vp/work/ocv/opencv/samples/c/left04.jpg", 1);
- //Vec<string, 4> v;
-
- /*Mat img = Mat::zeros(20, 20, CV_8U);
- img(Range(0,10),Range(0,10)) = Scalar(255);
- img.at<uchar>(10,10)=255;
- img(Range(11,20),Range(11,20)) = Scalar(255);
- vector<Point2f> corner(1, Point2f(9.5,9.5));
- cornerSubPix(img, corner, Size(5,5), Size(-1,-1), TermCriteria(3, 30, 0.001));
- printf("Corner at (%g, %g)", corner[0].x, corner[0].y);*/
-
- /*Mat large, large2, gray;
- resize(img, large, img.size()*3, 0, 0, CV_INTER_LANCZOS4);
- cvtColor(large, gray, CV_BGR2GRAY);
- vector<Point2f> corners;
- bool found = findChessboardCorners(gray, Size(9,6), corners);
- cornerSubPix(gray, corners, Size(11,11), Size(-1,-1),
- TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01));
-
- drawChessboardCorners(large, Size(9,6), Mat(corners), false);
- //rectangle(img, Point(150,100), Point(250,200), Scalar(0,255,0), 1);
- resize(large(Rect(150*3,100*3,100*3,100*3)), large2, Size(), 4, 4, CV_INTER_CUBIC);
- imshow("test", large2);
- waitKey(0);*/
-
- /*int N=4;
- CvEM em_model;
- CvEMParams params;
- int nsamples=500;
- for (int D=2; D < 15; D++)
- {
- CvMat* samples = cvCreateMat( nsamples, D, CV_32FC1 );
- for (int s=0; s < nsamples;s++)
- for (int d=0; d <D;d++)
- cvmSet(samples, s, d, (double)s+d);
- // initialize model's parameters
- params.covs = NULL;
- params.means = NULL;
- params.weights = NULL;
- params.probs = NULL;
- params.nclusters = N;
- params.cov_mat_type = CvEM::COV_MAT_DIAGONAL;
- params.start_step = CvEM::START_AUTO_STEP;
- params.term_crit.max_iter = 100;
- params.term_crit.epsilon = 0.01;
- params.term_crit.type = CV_TERMCRIT_ITER|CV_TERMCRIT_EPS;
- em_model.train( samples, 0, params);
- const CvMat* w = em_model.get_weights();
- double sum=0;
- for (int i=0; i < N; i++)
- sum+=cvmGet(w, 0, i);
- printf("D = %d, sum = %f\n", D, sum);
- cvReleaseMat( &samples );
- }*/
- /*Mat a(1, 0, CV_32F);
- cout << " empty=" << a.empty() << " data=" << (size_t)a.data << endl;*/
-
- //XOR input
- /*double in[]={ 0 ,0,
- 1, 0,
- 0, 1,
- 1, 1};
- double out[]={ 0,
- 1,
- 1,
- 0};
-
- const int IVS = 2; // Input Vector Size
- const int OVS = 1; // Output Vector Size
- const int HN = 10; // Number of Hidden nodes
- const int NV= 4; //Number of Training Vector
-
- int layer[] = { IVS, HN, OVS};
-
- CvMat *input =cvCreateMat( NV, IVS, CV_64FC1);
- CvMat *output =cvCreateMat( NV, OVS, CV_64FC1);
- CvMat *p_output =cvCreateMat( NV, OVS, CV_64FC1);
- CvMat *layersize =cvCreateMat( 1 , 3 , CV_32SC1);
-
- cvInitMatHeader(input, NV, IVS, CV_64FC1, in);
- cvInitMatHeader(output, NV, OVS, CV_64FC1, out);
- cvInitMatHeader(layersize, 1, 3, CV_32SC1, layer);
-
- CvANN_MLP train_model(layersize, CvANN_MLP::SIGMOID_SYM,1,1);
- std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
- std::cout<< " * First Iteration with initialzation of weights"<<std::endl;
- std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
- int iter = train_model.train( input,
- output,
- NULL,
- 0,
- CvANN_MLP_TrainParams( cvTermCriteria ( CV_TERMCRIT_ITER |
- CV_TERMCRIT_EPS,
- 5000,0.000001),
- CvANN_MLP_TrainParams::BACKPROP,
- 0.1,0.1),
- 0
- //+ CvANN_MLP::NO_OUTPUT_SCALE
- );
-
- std::cout << " * iteration :"<<iter<<std::endl;
- train_model.predict( input, p_output );
- for(int i=0; i<NV;i++){
- std::cout<< CV_MAT_ELEM(*input,double,i,0) << " ," << CV_MAT_ELEM(*input,double,i,1)
- << " : " << CV_MAT_ELEM(*p_output,double,i,0) <<std::endl;
-
- }
- train_model.save( "firstModel.xml");
- std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
- std::cout<< " * Second Iteration without initialzation of weights"<<std::endl;
- std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
-
- int iter2;
- for(int i=0;i<5;i++)
- {
- iter2 = train_model.train( input,
- output,
- NULL,
- 0,
- CvANN_MLP_TrainParams( cvTermCriteria ( CV_TERMCRIT_ITER |
- CV_TERMCRIT_EPS,
- 5000,0.0000001),
- CvANN_MLP_TrainParams::BACKPROP,
- 0.1,0.1),
- 0
- +CvANN_MLP::UPDATE_WEIGHTS
- //+ CvANN_MLP::NO_OUTPUT_SCALE
- );
- }
- std::cout << " * iteration :"<<iter2<<std::endl;
- train_model.save( "secondModel.xml");
-
- train_model.predict( input, p_output );
- for(int i=0; i<NV;i++){
- std::cout<< CV_MAT_ELEM(*input,double,i,0) << " ," << CV_MAT_ELEM(*input,double,i,1)
- << " : " << CV_MAT_ELEM(*p_output,double,i,0) <<std::endl;
-
- }*/
-
- /*cv::Size imageSize;
- int Nimg, Npts;
- vector<vector<cv::Point3f> > objectPoints;
- vector<vector<cv::Point2f> >imagePoints;
- cv::FileStorage f("/Users/vp/Downloads/calib_debug.2.yml",cv::FileStorage::READ);
- cv::FileNodeIterator it = f["img_sz"].begin(); it >> imageSize.width >> imageSize.height;
- Nimg = (int) f ["NofImages"];
- Npts = (int) f["NofPoints"];
- for (int i=0; i<Nimg;i++) {
- std::stringstream imagename; imagename << "image" << i;
- cv::FileNode img = f[imagename.str()];
- vector <cv::Point3f> ov;
- vector <cv::Point2f> iv;
- for (int j=0; j<Npts; j++) {
- std::stringstream nodename; nodename << "node" << j;
- cv::FileNode pnt = img[nodename.str()];
- cv::Point3f op;
- cv::Point2f ip;
- cv::FileNodeIterator ot = pnt["objPnt"].begin(); ot >> op.x >> op.y >> op.z;
- cv::FileNodeIterator it = pnt["imgPnt"].begin(); it >> ip.x >> ip.y;
- iv.push_back(ip);
- ov.push_back(op);
- }
- imagePoints.push_back(iv);
- objectPoints.push_back(ov);
- }
- cv::Mat M,D;
- vector<cv::Mat> R,T;
- cv::calibrateCamera(objectPoints, imagePoints, imageSize, M, D,R,T,
- CV_CALIB_FIX_ASPECT_RATIO + 1*CV_CALIB_FIX_K3 + 1*CV_CALIB_ZERO_TANGENT_DIST);
- cv::FileStorage fo("calib_output.yml",cv::FileStorage::WRITE);
- //fo << "M" << M;
- cout << "M: " << M;*/
-
- /*Mat img = imread("/Users/vp/Downloads/test5.tif", CV_LOAD_IMAGE_ANYDEPTH|CV_LOAD_IMAGE_COLOR), img16;
- cout << "img.depth()=" << img.depth() << endl;
- if(img.depth() == CV_16U)
- img16 = img;
- else
- img.convertTo(img16, CV_16U, 256, 0);
- imshow("test", img16);
- imwrite("/Users/vp/tmp/test16_2.png", img16);
- waitKey();*/
-
- /*Mat img(600,800,CV_8UC3);
- img = Scalar::all(0);
- IplImage _img = img;
-
- CvFont font = cvFont(2,1);
- cvPutText(&_img, "Test", cvPoint(100, 100), &font, cvScalar(0,0,255));
-
- imshow("test", img);
- waitKey();*/
-
- /*IplImage* img = cvCreateImage(cvSize(800,600), 8, 3);
- cvZero(img);
- CvFont font = cvFont(2,1);
- cvPutText(img, "Test", cvPoint(100, 100), &font, cvScalar(0,0,255));
- cvNamedWindow("test", 1);
- cvShowImage("test", img);
- cvWaitKey(0);*/
- /*int sz[] = {1, 5, 5};
- CvMatND* src = cvCreateMatND(3, sz, CV_64F);
- CvMatND* dst = cvCreateMatND(3, sz, CV_64F);
- CvRNG rng = cvRNG(-1);
- cvRandArr(&rng, src, CV_RAND_UNI, cvScalarAll(-100), cvScalarAll(100));
- cvAddS(src, cvScalar(100), dst, 0);
- cvSave("_input.xml", src);
- cvSave("_output.xml", dst);*/
-
- /*
- /// random data generation :
- Mat data(100,10,CV_32FC1);
- randn(data, 0.0, 1.0);
- /// Creating the ANN engine
- AutotunedIndexParams autoParams(0.9,0.5,0.2,1);
- Index index(data,autoParams);
- /// Creating a query
- SearchParams searchParams(5);
- vector<float> query, dist;
- vector<int> foundIndice;
- foundIndice.push_back(0);
- dist.push_back(0);
- for(int i = 0 ; i < 10 ; i++)
- {
- query.push_back(data.at<float>(2,i));
- }
- /// Do a reaserch : result must be equal to 2.
- index.knnSearch(query, foundIndice, dist, 1, searchParams);
- cout << "Found indice (must be 2) : " << foundIndice[0] << endl;
- /// save params
- index.save(string("test"));
- */
-
- /*namedWindow("orig", CV_WINDOW_AUTOSIZE);
- namedWindow("canny", CV_WINDOW_AUTOSIZE);
- namedWindow("hough", CV_WINDOW_AUTOSIZE);
-
- Mat orig = cv::imread("/Users/vp/Downloads/1.jpg", 0);
- //equalizeHist(orig, orig);
- Mat hough;
- cvtColor(orig, hough, CV_GRAY2BGR);
-
- Mat canny;
- Canny(orig, canny, 100, 50); // reproduce Canny-Filtering as in Hough-Circles
-
- int bestRad = 20;
- int minRad = bestRad / 1.3;
- int maxRad = bestRad * 1.3;
-
- vector<Vec3f> circles; // detect circles
- HoughCircles(orig, circles, CV_HOUGH_GRADIENT,
- 1, // accu-scaling
- 20, // minDist
- 100, // CannyParam
- 10, // minAccuCount
- minRad,
- maxRad);
- // Draw Circles into image in gray
- for( size_t i = 0; i < circles.size(); i++ )
- {
- Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
- int radius = cvRound(circles[i][2]);
- // draw the circle center
- circle( hough, center, 3, Scalar(0,0,255), -1, 8, 0 );
- // draw the circle outline
- circle( hough, center, radius, Scalar(0,255,255), 1, 8, 0 );
- }
-
- // Draw reference circles
- Point c(bestRad * 3, bestRad * 3);
- circle(hough, c, bestRad, 255);
- circle(hough, c, minRad, 255);
- circle(hough, c, maxRad, 255);
-
-
-
- cv::imshow("orig", orig);
- cv::imshow("canny", canny);
- cv::imshow("hough", hough);
-
- cv::waitKey();*/
-
- /*int npoints = 4;
- CvMat *OP = cvCreateMat(1, npoints, CV_32FC3);
- CvPoint3D32f *op = (CvPoint3D32f *)OP->data.fl;
-
- CvMat *OP2 = cvCreateMat(1, npoints, CV_32FC3);
- CvPoint3D32f *op2 = (CvPoint3D32f *)OP2->data.fl;
-
- CvMat *IP = cvCreateMat(1, npoints, CV_32FC2);
- CvPoint2D32f *ip = (CvPoint2D32f *)IP->data.fl;
-
- CvMat *IP2 = cvCreateMat(1, npoints, CV_32FC2);
- CvPoint2D32f *ip2 = (CvPoint2D32f *)IP2->data.fl;
-
- CvMat *IP0 = cvCreateMat(1, npoints, CV_32FC2);
-
- float rv[3], rv2[3];
- float rotMat[9];
- float t[3], t2[3];
- float tRotMat[16];
-
- double kMat[9];
-
- CvMat K = cvMat(3, 3, CV_64F, kMat);
- CvMat T = cvMat(3, 1, CV_32F, t);
- CvMat RV = cvMat(3, 1, CV_32F, rv);
- CvMat T2 = cvMat(3, 1, CV_32F, t2);
- CvMat RV2 = cvMat(3, 1, CV_32F, rv2);
- CvMat R = cvMat(3, 3, CV_32F, rotMat);
-
- float r0, r1, r2;
-
- 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());
-
- 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
projects / profile / ivi / opencv.git / commitdiff