WebSVN – seema-scanner – Diff – /src/cvtools.cpp


#include "cvtools.h"
 
#ifdef _WIN32
#include <cstdint>
#endif
 
#include <stdio.h>
 
namespace cvtools{
 
// Lightly modified OpenCV function which accepts a line width argument
void drawChessboardCorners(cv::InputOutputArray _image, cv::Size patternSize, cv::InputArray _corners, bool patternWasFound, int line_width){
    cv::Mat corners = _corners.getMat();
    if( corners.empty() )
        return;
    cv::Mat image = _image.getMat(); CvMat c_image = _image.getMat();
    int nelems = corners.checkVector(2, CV_32F, true);
    CV_Assert(nelems >= 0);
    cvDrawChessboardCorners( &c_image, patternSize, (CvPoint2D32f*)corners.data,
                             nelems, patternWasFound, line_width);
}
 
void cvDrawChessboardCorners(CvArr* _image, CvSize pattern_size, CvPoint2D32f* corners, int count, int found, int line_width){
    const int shift = 0;
    const int radius = 12;
    const int r = radius*(1 << shift);
    int i;
    CvMat stub, *image;
    double scale = 1;
    int type, cn, line_type;
 
    image = cvGetMat( _image, &stub );
 
    type = CV_MAT_TYPE(image->type);
    cn = CV_MAT_CN(type);
    if( cn != 1 && cn != 3 && cn != 4 )
        CV_Error( CV_StsUnsupportedFormat, "Number of channels must be 1, 3 or 4" );
 
    switch( CV_MAT_DEPTH(image->type) )
    {
    case CV_8U:
        scale = 1;
        break;
    case CV_16U:
        scale = 256;
        break;
    case CV_32F:
        scale = 1./255;
        break;
    default:
        CV_Error( CV_StsUnsupportedFormat,
            "Only 8-bit, 16-bit or floating-point 32-bit images are supported" );
    }
 
    line_type = type == CV_8UC1 || type == CV_8UC3 ? CV_AA : 8;
 
    if( !found )
    {
        CvScalar color = {{0,0,255}};
        if( cn == 1 )
            color = cvScalarAll(200);
        color.val[0] *= scale;
        color.val[1] *= scale;
        color.val[2] *= scale;
        color.val[3] *= scale;
 
        for( i = 0; i < count; i++ )
        {
            CvPoint pt;
            pt.x = cvRound(corners[i].x*(1 << shift));
            pt.y = cvRound(corners[i].y*(1 << shift));
            cvLine( image, cvPoint( pt.x - r, pt.y - r ),
                    cvPoint( pt.x + r, pt.y + r ), color, line_width, line_type, shift );
            cvLine( image, cvPoint( pt.x - r, pt.y + r),
                    cvPoint( pt.x + r, pt.y - r), color, line_width, line_type, shift );
            cvCircle( image, pt, r+(1<<shift), color, line_width, line_type, shift );
        }
    }
    else
    {
        int x, y;
        CvPoint prev_pt = {0, 0};
        const int line_max = 7;
        static const CvScalar line_colors[line_max] =
        {
            {{0,0,255}},
            {{0,128,255}},
            {{0,200,200}},
            {{0,255,0}},
            {{200,200,0}},
            {{255,0,0}},
            {{255,0,255}}
        };
 
        for( y = 0, i = 0; y < pattern_size.height; y++ )
        {
            CvScalar color = line_colors[y % line_max];
            if( cn == 1 )
                color = cvScalarAll(200);
            color.val[0] *= scale;
            color.val[1] *= scale;
            color.val[2] *= scale;
            color.val[3] *= scale;
 
            for( x = 0; x < pattern_size.width; x++, i++ )
            {
                CvPoint pt;
                pt.x = cvRound(corners[i].x*(1 << shift));
                pt.y = cvRound(corners[i].y*(1 << shift));
 
                if( i != 0 )
                    cvLine( image, prev_pt, pt, color, 1, line_type, shift );
 
                cvLine( image, cvPoint(pt.x - r, pt.y - r),
                        cvPoint(pt.x + r, pt.y + r), color, line_width, line_type, shift );
                cvLine( image, cvPoint(pt.x - r, pt.y + r),
                        cvPoint(pt.x + r, pt.y - r), color, line_width, line_type, shift );
                cvCircle( image, pt, r+(1<<shift), color, line_width, line_type, shift );
                prev_pt = pt;
            }
        }
    }
}
 
// Returns the result of mod(mat(x,y), moduli) for each matrix element
cv::Mat modulo(const cv::Mat &mat, float n){
 
    cv::Mat ret(mat.rows, mat.cols, mat.type());
 
    for(int row=0; row<ret.rows; row++){
        for(int col=0; col<ret.cols; col++){
            float val = mat.at<float>(row, col);
            // note: std::fmod calculates the remainder, not arithmetic modulo
            ret.at<float>(row, col) = val - n * std::floor(val / n);
        }
    }
 
    return ret;
}
 
// Convert a 3xN matrix to a vector of Point3fs.
void matToPoints3f(const cv::Mat &mat, std::vector<cv::Point3f> &points){
 
    unsigned int nPoints = mat.cols;
    points.resize(nPoints);
 
    for(unsigned int i=0; i<nPoints; i++)
        points[i] = cv::Point3f(mat.at<float>(0, i), mat.at<float>(1, i), mat.at<float>(2, i));
}
 
// Convert a (Dim+1)xN matrix of homogenous points to a DimxN matrix of points in non-homogenous coordinates.
void convertMatFromHomogeneous(cv::Mat &src, cv::Mat &dst){
    unsigned int N = src.cols;
    unsigned int Dim = src.rows-1;
    dst.create(Dim, N, src.type());
    for(unsigned int i=0; i<N; i++){
        for(unsigned int j=0; j<Dim; j++)
            dst.at<float>(j,i) = src.at<float>(j,i)/src.at<float>(Dim,i);
    }
 
}
 
// Function to solve the hand-eye (or eye-in-hand) calibration problem.
// Finds [Omega | tau], to minimize ||[R_mark | t_mark][Omega | tau] - [Omega | tau][R | t]||^2
// Algorithm according to Tsai, Lenz, A new technique for fully autonomous and efficient 3d robotics hand-eye calibration
// DTU, 2014, Jakob Wilm
void handEyeCalibrationTsai(const std::vector<cv::Matx33f> R, const std::vector<cv::Vec3f> t, const std::vector<cv::Matx33f> R_mark, const std::vector<cv::Vec3f> t_mark, cv::Matx33f &Omega, cv::Vec3f &tau){
 
    int N = R.size();
    assert(N == R_mark.size());
    assert(N == t.size());
    assert(N == t_mark.size());
 
    // construct equations for rotation
    cv::Mat A(3*N, 3, CV_32F);
    cv::Mat b(3*N, 1, CV_32F);
    for(int i=0; i<N; i++){
        // angle axis representations
        cv::Vec3f rot;
        cv::Vec3f rot_mark;
        cv::Rodrigues(R[i], rot);
        cv::Rodrigues(R_mark[i], rot_mark);
 
        cv::Vec3f P = 2.0*sin(cv::norm(rot)/2.0)*cv::normalize(rot);
//std::cout << "P: " << std::endl << P << std::endl;
        cv::Vec3f P_mark = 2.0*sin(cv::norm(rot_mark)/2.0)*cv::normalize(rot_mark);
//std::cout << "P_mark: " << std::endl << P_mark << std::endl;
        cv::Vec3f sum = P+P_mark;
        cv::Mat crossProduct = (cv::Mat_<float>(3,3) << 0.0, -sum(2), sum(1), sum(2), 0.0, -sum(0), -sum(1), sum(0), 0.0);
//std::cout << "crossProduct: " << std::endl << crossProduct << std::endl;
        crossProduct.copyTo(A.rowRange(i*3, i*3+3));
 
        cv::Mat(P-P_mark).copyTo(b.rowRange(i*3, i*3+3));
    }
 
    // solve for rotation
    cv::Vec3f P_prime;
    cv::solve(A, b, P_prime, cv::DECOMP_SVD);
    cv::Vec3f P = 2.0*P_prime/(cv::sqrt(1.0 + cv::norm(P_prime)*cv::norm(P_prime)));
    float nP = cv::norm(P);
    cv::Mat crossProduct = (cv::Mat_<float>(3,3) << 0.0, -P(2), P(1), P(2), 0.0, -P(0), -P(1), P(0), 0.0);
    cv::Mat OmegaMat = (1.0-nP*nP/2.0)*cv::Mat::eye(3,3,CV_32F) + 0.5*(cv::Mat(P)*cv::Mat(P).t() + cv::sqrt(4.0 - nP*nP)*crossProduct);
    Omega = cv::Matx33f(OmegaMat);
 
    // construct equations for translation
    A.setTo(0.0);
    b.setTo(0.0);
    for(int i=0; i<N; i++){
 
        cv::Mat diff = cv::Mat(R_mark[i]) - cv::Mat::eye(3, 3, CV_32F);
        diff.copyTo(A.rowRange(i*3, i*3+3));
 
        cv::Mat diff_mark = cv::Mat(Omega*t[i] - t_mark[i]);
        diff_mark.copyTo(b.rowRange(i*3, i*3+3));
    }
 
    // solve for translation
    cv::solve(A, b, tau, cv::DECOMP_SVD);
 
    cv::Mat err_tau = b - (A*cv::Mat(tau));
    std::cout << err_tau << std::endl;
}
 
// Function to solve for the rotation axis from sets of 3D point coordinates of flat pattern feature points
// Algorithm according to Chen et al., Rotation axis calibration of a turntable using constrained global optimization, Optik 2014
// DTU, 2014, Jakob Wilm
void rotationAxisCalibration(const std::vector< std::vector<cv::Point3f> > Qcam, const std::vector<cv::Point3f> Qobj, cv::Vec3f &axis, cv::Vec3f &point){
 
    // number of frames (points on each arch)
    int l = Qcam.size();
 
    // number of points in each frame
    int mn = Qobj.size();
 
    assert(mn == Qcam[0].size());
 
    // construct matrix for axis determination
    cv::Mat M(6, 6, CV_32F, cv::Scalar(0));
 
    for(int k=0; k<l; k++){
        for(int idx=0; idx<mn; idx++){
 
//            float i = Qobj[idx].x+4;
//            float j = Qobj[idx].y+4;
            float i = Qobj[idx].x;
            float j = Qobj[idx].y;
 
            float x = Qcam[k][idx].x;
            float y = Qcam[k][idx].y;
            float z = Qcam[k][idx].z;
 
            M += (cv::Mat_<float>(6,6) << x*x, x*y, x*z, x, i*x, j*x,
                                            0, y*y, y*z, y, i*y, j*y,
                                            0,   0, z*z, z, i*z, j*z,
                                            0,   0,   0, 1,   i,   j,
                                            0,   0,   0, 0, i*i, i*j,
                                            0,   0,   0, 0,   0, j*j);
 
        }
    }
//std::cout << M << std::endl;
    //M *= 1.0/(l*mn);
    cv::completeSymm(M, false);
//std::cout << M << std::endl;
    // solve for axis
    std::vector<float> lambda;
    cv::Mat u;
    cv::eigen(M, lambda, u);
 
    float minLambda = abs(lambda[0]);
    int idx = 0;
    for(int i=1; i<lambda.size(); i++){
        if(abs(lambda[i]) < minLambda){
            minLambda = lambda[i];
            idx = i;
        }
    }
 
//std::cout << "u:" <<  u << std::endl;
 
    axis = u.row(idx).colRange(0, 3);
    axis /= cv::norm(axis);
 
    float nx = u.at<float>(idx, 0);
    float ny = u.at<float>(idx, 1);
    float nz = u.at<float>(idx, 2);
    float d  = u.at<float>(idx, 3);
    float dh = u.at<float>(idx, 4);
    float dv = u.at<float>(idx, 5);
 
    cv::Mat A(l*mn, mn+2, CV_32F, cv::Scalar(0.0));
    cv::Mat bb(l*mn, 1, CV_32F);
 
    for(int k=0; k<l; k++){
        for(int idx=0; idx<mn; idx++){
 
            float i = Qobj[idx].x;
            float j = Qobj[idx].y;
 
            float x = Qcam[k][idx].x;
            float y = Qcam[k][idx].y;
            float z = Qcam[k][idx].z;
 
            float f = x*x + y*y + z*z + (2*x*nx + 2*y*ny + 2*z*nz)*(i*dh + j*dv);
 
            int row = k*mn+idx;
            A.at<float>(row, 0) = 2*x - (2*z*nx)/nz;
            A.at<float>(row, 1) = 2*y - (2*z*ny)/nz;
            A.at<float>(row, idx+2) = 1.0;
 
            bb.at<float>(row, 0) = f + (2*z*d)/nz;
        }
    }
 
    // solve for point
    cv::Mat abe;
    cv::solve(A, bb, abe, cv::DECOMP_SVD);
 
    float a = abe.at<float>(0, 0);
    float b = abe.at<float>(1, 0);
    float c = -(nx*a+ny*b)/nz;
 
    point[0] = a;
    point[1] = b;
    point[2] = c;
 
}
 
// Function to fit two sets of corresponding pose data.
// Finds [Omega | tau], to minimize ||[R_mark | t_mark] - [Omega | tau][R | t]||^2
// Algorithm and notation according to Mili Shah, Comparing two sets of corresponding six degree of freedom data, CVIU 2011.
// DTU, 2013, Oline V. Olesen, Jakob Wilm
void fitSixDofData(const std::vector<cv::Matx33f> R, const std::vector<cv::Vec3f> t, const std::vector<cv::Matx33f> R_mark, const std::vector<cv::Vec3f> t_mark, cv::Matx33f &Omega, cv::Vec3f &tau){
 
    int N = R.size();
    assert(N == R_mark.size());
    assert(N == t.size());
    assert(N == t_mark.size());
 
    // Mean translations
    cv::Vec3f t_mean;
    cv::Vec3f t_mark_mean;
    for(int i=0; i<N; i++){
        t_mean += 1.0/N * t[i];
        t_mark_mean += 1.0/N * t_mark[i];
    }
 
    // Data with mean adjusted translations
    cv::Mat X_bar(3, 4*N, CV_32F);
    cv::Mat X_mark_bar(3, 4*N, CV_32F);
    for(int i=0; i<N; i++){
        cv::Mat(R[i]).copyTo(X_bar.colRange(i*4,i*4+3));
        cv::Mat(t[i] - t_mean).copyTo(X_bar.col(i*4+3));
        cv::Mat(R_mark[i]).copyTo(X_mark_bar.colRange(i*4,i*4+3));
        cv::Mat(t_mark[i] - t_mark_mean).copyTo(X_mark_bar.col(i*4+3));
    }
    //std::cout << X_bar << std::endl;
    // SVD
    cv::Mat W, U, VT;
    cv::SVDecomp(X_bar*X_mark_bar.t(), W, U, VT);
 
    cv::Matx33f D = cv::Matx33f::eye();
    if(cv::determinant(VT*U) < 0)
        D(3,3) = -1;
 
    // Best rotation
    Omega = cv::Matx33f(cv::Mat(VT.t()))*D*cv::Matx33f(cv::Mat(U.t()));
 
    // Best translation
    tau = t_mark_mean - Omega*t_mean;
 
}
 
// Forward distortion of points. The inverse of the undistortion in cv::initUndistortRectifyMap().
// Inspired by Pascal Thomet, http://code.opencv.org/issues/1387#note-11
// Convention for distortion parameters: http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/parameters.html
void initDistortMap(const cv::Matx33f cameraMatrix, const cv::Vec<float, 5> distCoeffs, const cv::Size size, cv::Mat &map1, cv::Mat &map2){
 
    float fx = cameraMatrix(0,0);
    float fy = cameraMatrix(1,1);
    float ux = cameraMatrix(0,2);
    float uy = cameraMatrix(1,2);
 
    float k1 = distCoeffs[0];
    float k2 = distCoeffs[1];
    float p1 = distCoeffs[2];
    float p2 = distCoeffs[3];
    float k3 = distCoeffs[4];
 
    map1.create(size, CV_32F);
    map2.create(size, CV_32F);
 
    for(int col = 0; col < size.width; col++){
        for(int row = 0; row < size.height; row++){
 
            // move origo to principal point and convert using focal length
            float x = (col-ux)/fx;
            float y = (row-uy)/fy;
 
            float xCorrected, yCorrected;
 
            //Step 1 : correct distortion
            float r2 = x*x + y*y;
            //radial
            xCorrected = x * (1. + k1*r2 + k2*r2*r2 + k3*r2*r2*r2);
            yCorrected = y * (1. + k1*r2 + k2*r2*r2 + k3*r2*r2*r2);
            //tangential
            xCorrected = xCorrected + (2.*p1*x*y + p2*(r2+2.*x*x));
            yCorrected = yCorrected + (p1*(r2+2.*y*y) + 2.*p2*x*y);
 
            //convert back to pixel coordinates
            float col_displaced = xCorrected * fx + ux;
            float row_displaced = yCorrected * fy + uy;
 
            // correct the vector in the opposite direction
            map1.at<float>(row,col) = col+(col-col_displaced);
            map2.at<float>(row,col) = row +(row-row_displaced);
        }
    }
}
 
// Downsample a texture which was created in virtual column/row space for a diamond pixel array projector
cv::Mat diamondDownsample(cv::Mat &pattern){
 
    cv::Mat pattern_diamond(pattern.rows,pattern.cols/2,CV_8UC3);
 
    for(unsigned int col = 0; col < pattern_diamond.cols; col++){
        for(unsigned int row = 0; row < pattern_diamond.rows; row++){
 
            pattern_diamond.at<cv::Vec3b>(row,col)=pattern.at<cv::Vec3b>(row,col*2+row%2);
        }
    }
 
    return pattern_diamond;
 
}
 
 
void mouseCallback(int evt, int x, int y, int flags, void* param){
    cv::Mat *im = (cv::Mat*) param;
    if (evt == CV_EVENT_LBUTTONDOWN) {
        if(im->type() == CV_8UC3){
            printf("%d %d: %d, %d, %d\n",
                   x, y,
                   (int)(*im).at<cv::Vec3b>(y, x)[0],
                    (int)(*im).at<cv::Vec3b>(y, x)[1],
                    (int)(*im).at<cv::Vec3b>(y, x)[2]);
        } else if (im->type() == CV_32F) {
            printf("%d %d: %f\n",
                   x, y,
                   im->at<float>(y, x));
        }
    }
}
 
void imshow(const char *windowName, cv::Mat im, unsigned int x, unsigned int y){
 
    // Imshow
    if(!cvGetWindowHandle(windowName)){
        int windowFlags = CV_GUI_EXPANDED | CV_WINDOW_KEEPRATIO;
        cv::namedWindow(windowName, windowFlags);
        cv::moveWindow(windowName, x, y);
    }
    cv::imshow(windowName, im);
}
 
void imagesc(const char *windowName, cv::Mat im){
 
    // Imshow with scaled image
 
 
}
 
cv::Mat histimage(cv::Mat histogram){
 
    cv::Mat histImage(512, 640, CV_8UC3, cv::Scalar(0));
 
    // Normalize the result to [ 2, histImage.rows-2 ]
    cv::normalize(histogram, histogram, 2, histImage.rows-2, cv::NORM_MINMAX, -1, cv::Mat());
 
    float bin_w = (float)histImage.cols/(float)histogram.rows;
 
    // Draw main histogram
    for(int i = 1; i < histogram.rows-10; i++){
        cv::line(histImage, cv::Point( bin_w*(i-1), histImage.rows - cvRound(histogram.at<float>(i-1)) ),
                 cv::Point( bin_w*(i), histImage.rows - cvRound(histogram.at<float>(i)) ),
                 cv::Scalar(255, 255, 255), 2, 4);
    }
 
    // Draw red max
    for(int i = histogram.rows-10; i < histogram.rows; i++){
        cv::line(histImage, cv::Point( bin_w*(i-1), histImage.rows - cvRound(histogram.at<float>(i-1)) ),
                 cv::Point( bin_w*(i), histImage.rows - cvRound(histogram.at<float>(i)) ),
                 cv::Scalar(0, 0, 255), 2, 4);
    }
 
    return histImage;
}
 
void hist(const char *windowName, cv::Mat histogram, unsigned int x, unsigned int y){
 
    // Display
    imshow(windowName, histimage(histogram), x, y);
    cv::Point(1,2);
}
 
 
void writeMat(cv::Mat const& mat, const char* filename, const char* varName, bool bgr2rgb){
    /*!
         *  \author Philip G. Lee <rocketman768@gmail.com>
         *  Write \b mat into \b filename
         *  in uncompressed .mat format (Level 5 MATLAB) for Matlab.
         *  The variable name in matlab will be \b varName. If
         *  \b bgr2rgb is true and there are 3 channels, swaps 1st and 3rd
         *  channels in the output. This is needed because OpenCV matrices
         *  are bgr, while Matlab is rgb. This has been tested to work with
         *  3-channel single-precision floating point matrices, and I hope
         *  it works on other types/channels, but not exactly sure.
         *  Documentation at <http://www.mathworks.com/help/pdf_doc/matlab/matfile_format.pdf>
         */
    int textLen = 116;
    char* text;
    int subsysOffsetLen = 8;
    char* subsysOffset;
    int verLen = 2;
    char* ver;
    char flags;
    int bytes;
    int padBytes;
    int bytesPerElement;
    int i,j,k,k2;
    bool doBgrSwap;
    char mxClass;
    int32_t miClass;
    uchar const* rowPtr;
    uint32_t tmp32;
    float tmp;
    FILE* fp;
 
    // Matlab constants.
    const uint16_t MI = 0x4d49; // Contains "MI" in ascii.
    const int32_t miINT8 = 1;
    const int32_t miUINT8 = 2;
    const int32_t miINT16 = 3;
    const int32_t miUINT16 = 4;
    const int32_t miINT32 = 5;
    const int32_t miUINT32 = 6;
    const int32_t miSINGLE = 7;
    const int32_t miDOUBLE = 9;
    const int32_t miMATRIX = 14;
    const char mxDOUBLE_CLASS = 6;
    const char mxSINGLE_CLASS = 7;
    const char mxINT8_CLASS = 8;
    const char mxUINT8_CLASS = 9;
    const char mxINT16_CLASS = 10;
    const char mxUINT16_CLASS = 11;
    const char mxINT32_CLASS = 12;
    const char mxUINT32_CLASS = 13;
    const uint64_t zero = 0; // Used for padding.
 
    fp = fopen( filename, "wb" );
 
    if( fp == 0 )
        return;
 
    const int rows = mat.rows;
    const int cols = mat.cols;
    const int chans = mat.channels();
 
    doBgrSwap = (chans==3) && bgr2rgb;
 
    // I hope this mapping is right :-/
    switch( mat.depth() ){
    case CV_8U:
        mxClass = mxUINT8_CLASS;
        miClass = miUINT8;
        bytesPerElement = 1;
        break;
    case CV_8S:
        mxClass = mxINT8_CLASS;
        miClass = miINT8;
        bytesPerElement = 1;
        break;
    case CV_16U:
        mxClass = mxUINT16_CLASS;
        miClass = miUINT16;
        bytesPerElement = 2;
        break;
    case CV_16S:
        mxClass = mxINT16_CLASS;
        miClass = miINT16;
        bytesPerElement = 2;
        break;
    case CV_32S:
        mxClass = mxINT32_CLASS;
        miClass = miINT32;
        bytesPerElement = 4;
        break;
    case CV_32F:
        mxClass = mxSINGLE_CLASS;
        miClass = miSINGLE;
        bytesPerElement = 4;
        break;
    case CV_64F:
        mxClass = mxDOUBLE_CLASS;
        miClass = miDOUBLE;
        bytesPerElement = 8;
        break;
    default:
        return;
    }
 
    //==================Mat-file header (128 bytes, page 1-5)==================
    text = new char[textLen]; // Human-readable text.
    memset( text, ' ', textLen );
    text[textLen-1] = '\0';
    const char* t = "MATLAB 5.0 MAT-file, Platform: PCWIN";
    memcpy( text, t, strlen(t) );
 
    subsysOffset = new char[subsysOffsetLen]; // Zeros for us.
    memset( subsysOffset, 0x00, subsysOffsetLen );
    ver = new char[verLen];
    ver[0] = 0x00;
    ver[1] = 0x01;
 
    fwrite( text, 1, textLen, fp );
    fwrite( subsysOffset, 1, subsysOffsetLen, fp );
    fwrite( ver, 1, verLen, fp );
    // Endian indicator. MI will show up as "MI" on big-endian
    // systems and "IM" on little-endian systems.
    fwrite( &MI, 2, 1, fp );
    //+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
    //===================Data element tag (8 bytes, page 1-8)==================
    bytes = 16 + 24 + (8 + strlen(varName) + (8-(strlen(varName)%8))%8)
            + (8 + rows*cols*chans*bytesPerElement);
    fwrite( &miMATRIX, 4, 1, fp ); // Data type.
    fwrite( &bytes, 4, 1, fp); // Data size in bytes.
    //+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
    //====================Array flags (16 bytes, page 1-15)====================
    bytes = 8;
    fwrite( &miUINT32, 4, 1, fp );
    fwrite( &bytes, 4, 1, fp );
    flags = 0x00; // Complex, logical, and global flags all off.
 
    tmp32 = 0;
    tmp32 = (flags << 8 ) | (mxClass);
    fwrite( &tmp32, 4, 1, fp );
 
    fwrite( &zero, 4, 1, fp ); // Padding to 64-bit boundary.
    //+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
    //===============Dimensions subelement (24 bytes, page 1-17)===============
    bytes = 12;
    fwrite( &miINT32, 4, 1, fp );
    fwrite( &bytes, 4, 1, fp );
 
    fwrite( &rows, 4, 1, fp );
    fwrite( &cols, 4, 1, fp );
    fwrite( &chans, 4, 1, fp );
    fwrite( &zero, 4, 1, fp ); // Padding to 64-bit boundary.
    //+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
    //==Array name (8 + strlen(varName) + (8-(strlen(varName)%8))%8 bytes, page 1-17)==
    bytes = strlen(varName);
 
    fwrite( &miINT8, 4, 1, fp );
    fwrite( &bytes, 4, 1, fp );
    fwrite( varName, 1, bytes, fp );
 
    // Pad to nearest 64-bit boundary.
    padBytes = (8-(bytes%8))%8;
    fwrite( &zero, 1, padBytes, fp );
    //+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
    //====Matrix data (rows*cols*chans*bytesPerElement+8 bytes, page 1-20)=====
    bytes = rows*cols*chans*bytesPerElement;
    fwrite( &miClass, 4, 1, fp );
    fwrite( &bytes, 4, 1, fp );
 
    for( k = 0; k < chans; ++k )
    {
        if( doBgrSwap )
        {
            k2 = (k==0)? 2 : ((k==2)? 0 : 1);
        }
        else
            k2 = k;
 
        for( j = 0; j < cols; ++j )
        {
            for( i = 0; i < rows; ++i )
            {
                rowPtr = mat.data + mat.step*i;
                fwrite( rowPtr + (chans*j + k2)*bytesPerElement, bytesPerElement, 1, fp );
            }
        }
    }
 
    // Pad to 64-bit boundary.
    padBytes = (8-(bytes%8))%8;
    fwrite( &zero, 1, padBytes, fp );
    //+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
    fclose(fp);
    delete[] text;
    delete[] subsysOffset;
    delete[] ver;
}
 
 
 
 
 
}
 

Rev 82	Rev 83
1	`#include "cvtools.h"`	1	`#include "cvtools.h"`
2		2
3	`#ifdef _WIN32`	3	`#ifdef _WIN32`
4	`#include <cstdint>`	4	`#include <cstdint>`
5	`#endif`	5	`#endif`
6		6
7	`#include <stdio.h>`	7	`#include <stdio.h>`
8		8
9	`namespace cvtools{`	9	`namespace cvtools{`
10		10
11	`// Lightly modified OpenCV function which accepts a line width argument`	11	`// Lightly modified OpenCV function which accepts a line width argument`
12	`void drawChessboardCorners(cv::InputOutputArray _image, cv::Size patternSize, cv::InputArray _corners, bool patternWasFound, int line_width){`	12	`void drawChessboardCorners(cv::InputOutputArray _image, cv::Size patternSize, cv::InputArray _corners, bool patternWasFound, int line_width){`
13	`cv::Mat corners = _corners.getMat();`	13	`cv::Mat corners = _corners.getMat();`
14	`if( corners.empty() )`	14	`if( corners.empty() )`
15	`return;`	15	`return;`
16	`cv::Mat image = _image.getMat(); CvMat c_image = _image.getMat();`	16	`cv::Mat image = _image.getMat(); CvMat c_image = _image.getMat();`
17	`int nelems = corners.checkVector(2, CV_32F, true);`	17	`int nelems = corners.checkVector(2, CV_32F, true);`
18	`CV_Assert(nelems >= 0);`	18	`CV_Assert(nelems >= 0);`
19	`cvDrawChessboardCorners( &c_image, patternSize, (CvPoint2D32f*)corners.data,`	19	`cvDrawChessboardCorners( &c_image, patternSize, (CvPoint2D32f*)corners.data,`
20	`nelems, patternWasFound, line_width);`	20	`nelems, patternWasFound, line_width);`
21	`}`	21	`}`
22		22
23	`void cvDrawChessboardCorners(CvArr* _image, CvSize pattern_size, CvPoint2D32f* corners, int count, int found, int line_width){`	23	`void cvDrawChessboardCorners(CvArr* _image, CvSize pattern_size, CvPoint2D32f* corners, int count, int found, int line_width){`
24	`const int shift = 0;`	24	`const int shift = 0;`
25	`const int radius = 12;`	25	`const int radius = 12;`
26	`const int r = radius*(1 << shift);`	26	`const int r = radius*(1 << shift);`
27	`int i;`	27	`int i;`
28	`CvMat stub, *image;`	28	`CvMat stub, *image;`
29	`double scale = 1;`	29	`double scale = 1;`
30	`int type, cn, line_type;`	30	`int type, cn, line_type;`
31		31
32	`image = cvGetMat( _image, &stub );`	32	`image = cvGetMat( _image, &stub );`
33		33
34	`type = CV_MAT_TYPE(image->type);`	34	`type = CV_MAT_TYPE(image->type);`
35	`cn = CV_MAT_CN(type);`	35	`cn = CV_MAT_CN(type);`
36	`if( cn != 1 && cn != 3 && cn != 4 )`	36	`if( cn != 1 && cn != 3 && cn != 4 )`
37	`CV_Error( CV_StsUnsupportedFormat, "Number of channels must be 1, 3 or 4" );`	37	`CV_Error( CV_StsUnsupportedFormat, "Number of channels must be 1, 3 or 4" );`
38		38
39	`switch( CV_MAT_DEPTH(image->type) )`	39	`switch( CV_MAT_DEPTH(image->type) )`
40	`{`	40	`{`
41	`case CV_8U:`	41	`case CV_8U:`
42	`scale = 1;`	42	`scale = 1;`
43	`break;`	43	`break;`
44	`case CV_16U:`	44	`case CV_16U:`
45	`scale = 256;`	45	`scale = 256;`
46	`break;`	46	`break;`
47	`case CV_32F:`	47	`case CV_32F:`
48	`scale = 1./255;`	48	`scale = 1./255;`
49	`break;`	49	`break;`
50	`default:`	50	`default:`
51	`CV_Error( CV_StsUnsupportedFormat,`	51	`CV_Error( CV_StsUnsupportedFormat,`
52	`"Only 8-bit, 16-bit or floating-point 32-bit images are supported" );`	52	`"Only 8-bit, 16-bit or floating-point 32-bit images are supported" );`
53	`}`	53	`}`
54		54
55	`line_type = type == CV_8UC1 \|\| type == CV_8UC3 ? CV_AA : 8;`	55	`line_type = type == CV_8UC1 \|\| type == CV_8UC3 ? CV_AA : 8;`
56		56
57	`if( !found )`	57	`if( !found )`
58	`{`	58	`{`
59	`CvScalar color = {{0,0,255}};`	59	`CvScalar color = {{0,0,255}};`
60	`if( cn == 1 )`	60	`if( cn == 1 )`
61	`color = cvScalarAll(200);`	61	`color = cvScalarAll(200);`
62	`color.val[0] *= scale;`	62	`color.val[0] *= scale;`
63	`color.val[1] *= scale;`	63	`color.val[1] *= scale;`
64	`color.val[2] *= scale;`	64	`color.val[2] *= scale;`
65	`color.val[3] *= scale;`	65	`color.val[3] *= scale;`
66		66
67	`for( i = 0; i < count; i++ )`	67	`for( i = 0; i < count; i++ )`
68	`{`	68	`{`
69	`CvPoint pt;`	69	`CvPoint pt;`
70	`pt.x = cvRound(corners[i].x*(1 << shift));`	70	`pt.x = cvRound(corners[i].x*(1 << shift));`
71	`pt.y = cvRound(corners[i].y*(1 << shift));`	71	`pt.y = cvRound(corners[i].y*(1 << shift));`
72	`cvLine( image, cvPoint( pt.x - r, pt.y - r ),`	72	`cvLine( image, cvPoint( pt.x - r, pt.y - r ),`
73	`cvPoint( pt.x + r, pt.y + r ), color, line_width, line_type, shift );`	73	`cvPoint( pt.x + r, pt.y + r ), color, line_width, line_type, shift );`
74	`cvLine( image, cvPoint( pt.x - r, pt.y + r),`	74	`cvLine( image, cvPoint( pt.x - r, pt.y + r),`
75	`cvPoint( pt.x + r, pt.y - r), color, line_width, line_type, shift );`	75	`cvPoint( pt.x + r, pt.y - r), color, line_width, line_type, shift );`
76	`cvCircle( image, pt, r+(1<<shift), color, line_width, line_type, shift );`	76	`cvCircle( image, pt, r+(1<<shift), color, line_width, line_type, shift );`
77	`}`	77	`}`
78	`}`	78	`}`
79	`else`	79	`else`
80	`{`	80	`{`
81	`int x, y;`	81	`int x, y;`
82	`CvPoint prev_pt = {0, 0};`	82	`CvPoint prev_pt = {0, 0};`
83	`const int line_max = 7;`	83	`const int line_max = 7;`
84	`static const CvScalar line_colors[line_max] =`	84	`static const CvScalar line_colors[line_max] =`
85	`{`	85	`{`
86	`{{0,0,255}},`	86	`{{0,0,255}},`
87	`{{0,128,255}},`	87	`{{0,128,255}},`
88	`{{0,200,200}},`	88	`{{0,200,200}},`
89	`{{0,255,0}},`	89	`{{0,255,0}},`
90	`{{200,200,0}},`	90	`{{200,200,0}},`
91	`{{255,0,0}},`	91	`{{255,0,0}},`
92	`{{255,0,255}}`	92	`{{255,0,255}}`
93	`};`	93	`};`
94		94
95	`for( y = 0, i = 0; y < pattern_size.height; y++ )`	95	`for( y = 0, i = 0; y < pattern_size.height; y++ )`
96	`{`	96	`{`
97	`CvScalar color = line_colors[y % line_max];`	97	`CvScalar color = line_colors[y % line_max];`
98	`if( cn == 1 )`	98	`if( cn == 1 )`
99	`color = cvScalarAll(200);`	99	`color = cvScalarAll(200);`
100	`color.val[0] *= scale;`	100	`color.val[0] *= scale;`
101	`color.val[1] *= scale;`	101	`color.val[1] *= scale;`
102	`color.val[2] *= scale;`	102	`color.val[2] *= scale;`
103	`color.val[3] *= scale;`	103	`color.val[3] *= scale;`
104		104
105	`for( x = 0; x < pattern_size.width; x++, i++ )`	105	`for( x = 0; x < pattern_size.width; x++, i++ )`
106	`{`	106	`{`
107	`CvPoint pt;`	107	`CvPoint pt;`
108	`pt.x = cvRound(corners[i].x*(1 << shift));`	108	`pt.x = cvRound(corners[i].x*(1 << shift));`
109	`pt.y = cvRound(corners[i].y*(1 << shift));`	109	`pt.y = cvRound(corners[i].y*(1 << shift));`
110		110
111	`if( i != 0 )`	111	`if( i != 0 )`
112	`cvLine( image, prev_pt, pt, color, 1, line_type, shift );`	112	`cvLine( image, prev_pt, pt, color, 1, line_type, shift );`
113		113
114	`cvLine( image, cvPoint(pt.x - r, pt.y - r),`	114	`cvLine( image, cvPoint(pt.x - r, pt.y - r),`
115	`cvPoint(pt.x + r, pt.y + r), color, line_width, line_type, shift );`	115	`cvPoint(pt.x + r, pt.y + r), color, line_width, line_type, shift );`
116	`cvLine( image, cvPoint(pt.x - r, pt.y + r),`	116	`cvLine( image, cvPoint(pt.x - r, pt.y + r),`
117	`cvPoint(pt.x + r, pt.y - r), color, line_width, line_type, shift );`	117	`cvPoint(pt.x + r, pt.y - r), color, line_width, line_type, shift );`
118	`cvCircle( image, pt, r+(1<<shift), color, line_width, line_type, shift );`	118	`cvCircle( image, pt, r+(1<<shift), color, line_width, line_type, shift );`
119	`prev_pt = pt;`	119	`prev_pt = pt;`
120	`}`	120	`}`
121	`}`	121	`}`
122	`}`	122	`}`
123	`}`	123	`}`
124		124
125	`// Returns the result of mod(mat(x,y), moduli) for each matrix element`	125	`// Returns the result of mod(mat(x,y), moduli) for each matrix element`
126	`cv::Mat modulo(const cv::Mat &mat, float n){`	126	`cv::Mat modulo(const cv::Mat &mat, float n){`
127		127
128	`cv::Mat ret(mat.rows, mat.cols, mat.type());`	128	`cv::Mat ret(mat.rows, mat.cols, mat.type());`
129		129
130	`for(int row=0; row<ret.rows; row++){`	130	`for(int row=0; row<ret.rows; row++){`
131	`for(int col=0; col<ret.cols; col++){`	131	`for(int col=0; col<ret.cols; col++){`
132	`float val = mat.at<float>(row, col);`	132	`float val = mat.at<float>(row, col);`
133	`// note: std::fmod calculates the remainder, not arithmetic modulo`	133	`// note: std::fmod calculates the remainder, not arithmetic modulo`
134	`ret.at<float>(row, col) = val - n * std::floor(val / n);`	134	`ret.at<float>(row, col) = val - n * std::floor(val / n);`
135	`}`	135	`}`
136	`}`	136	`}`
137		137
138	`return ret;`	138	`return ret;`
139	`}`	139	`}`
140		140
141	`// Convert a 3xN matrix to a vector of Point3fs.`	141	`// Convert a 3xN matrix to a vector of Point3fs.`
142	`void matToPoints3f(const cv::Mat &mat, std::vector<cv::Point3f> &points){`	142	`void matToPoints3f(const cv::Mat &mat, std::vector<cv::Point3f> &points){`
143		143
144	`unsigned int nPoints = mat.cols;`	144	`unsigned int nPoints = mat.cols;`
145	`points.resize(nPoints);`	145	`points.resize(nPoints);`
146		146
147	`for(unsigned int i=0; i<nPoints; i++)`	147	`for(unsigned int i=0; i<nPoints; i++)`
148	`points[i] = cv::Point3f(mat.at<float>(0, i), mat.at<float>(1, i), mat.at<float>(2, i));`	148	`points[i] = cv::Point3f(mat.at<float>(0, i), mat.at<float>(1, i), mat.at<float>(2, i));`
149	`}`	149	`}`
150		150
151	`// Convert a (Dim+1)xN matrix of homogenous points to a DimxN matrix of points in non-homogenous coordinates.`	151	`// Convert a (Dim+1)xN matrix of homogenous points to a DimxN matrix of points in non-homogenous coordinates.`
152	`void convertMatFromHomogeneous(cv::Mat &src, cv::Mat &dst){`	152	`void convertMatFromHomogeneous(cv::Mat &src, cv::Mat &dst){`
153	`unsigned int N = src.cols;`	153	`unsigned int N = src.cols;`
154	`unsigned int Dim = src.rows-1;`	154	`unsigned int Dim = src.rows-1;`
155	`dst.create(Dim, N, src.type());`	155	`dst.create(Dim, N, src.type());`
156	`for(unsigned int i=0; i<N; i++){`	156	`for(unsigned int i=0; i<N; i++){`
157	`for(unsigned int j=0; j<Dim; j++)`	157	`for(unsigned int j=0; j<Dim; j++)`
158	`dst.at<float>(j,i) = src.at<float>(j,i)/src.at<float>(Dim,i);`	158	`dst.at<float>(j,i) = src.at<float>(j,i)/src.at<float>(Dim,i);`
159	`}`	159	`}`
160		160
161	`}`	161	`}`
162		162
163	`// Function to solve the hand-eye (or eye-in-hand) calibration problem.`	163	`// Function to solve the hand-eye (or eye-in-hand) calibration problem.`
164	`// Finds [Omega \| tau], to minimize \|\|[R_mark \| t_mark][Omega \| tau] - [Omega \| tau][R \| t]\|\|^2`	164	`// Finds [Omega \| tau], to minimize \|\|[R_mark \| t_mark][Omega \| tau] - [Omega \| tau][R \| t]\|\|^2`
165	`// Algorithm according to Tsai, Lenz, A new technique for fully autonomous and efficient 3d robotics hand-eye calibration`	165	`// Algorithm according to Tsai, Lenz, A new technique for fully autonomous and efficient 3d robotics hand-eye calibration`
166	`// DTU, 2014, Jakob Wilm`	166	`// DTU, 2014, Jakob Wilm`
167	`void handEyeCalibrationTsai(const std::vector<cv::Matx33f> R, const std::vector<cv::Vec3f> t, const std::vector<cv::Matx33f> R_mark, const std::vector<cv::Vec3f> t_mark, cv::Matx33f &Omega, cv::Vec3f &tau){`	167	`void handEyeCalibrationTsai(const std::vector<cv::Matx33f> R, const std::vector<cv::Vec3f> t, const std::vector<cv::Matx33f> R_mark, const std::vector<cv::Vec3f> t_mark, cv::Matx33f &Omega, cv::Vec3f &tau){`
168		168
169	`int N = R.size();`	169	`int N = R.size();`
170	`assert(N == R_mark.size());`	170	`assert(N == R_mark.size());`
171	`assert(N == t.size());`	171	`assert(N == t.size());`
172	`assert(N == t_mark.size());`	172	`assert(N == t_mark.size());`
173		173
174	`// construct equations for rotation`	174	`// construct equations for rotation`
175	`cv::Mat A(3*N, 3, CV_32F);`	175	`cv::Mat A(3*N, 3, CV_32F);`
176	`cv::Mat b(3*N, 1, CV_32F);`	176	`cv::Mat b(3*N, 1, CV_32F);`
177	`for(int i=0; i<N; i++){`	177	`for(int i=0; i<N; i++){`
178	`// angle axis representations`	178	`// angle axis representations`
179	`cv::Vec3f rot;`	179	`cv::Vec3f rot;`
180	`cv::Vec3f rot_mark;`	180	`cv::Vec3f rot_mark;`
181	`cv::Rodrigues(R[i], rot);`	181	`cv::Rodrigues(R[i], rot);`
182	`cv::Rodrigues(R_mark[i], rot_mark);`	182	`cv::Rodrigues(R_mark[i], rot_mark);`
183		183
184	`cv::Vec3f P = 2.0sin(cv::norm(rot)/2.0)cv::normalize(rot);`	184	`cv::Vec3f P = 2.0sin(cv::norm(rot)/2.0)cv::normalize(rot);`
185	`//std::cout << "P: " << std::endl << P << std::endl;`	185	`//std::cout << "P: " << std::endl << P << std::endl;`
186	`cv::Vec3f P_mark = 2.0sin(cv::norm(rot_mark)/2.0)cv::normalize(rot_mark);`	186	`cv::Vec3f P_mark = 2.0sin(cv::norm(rot_mark)/2.0)cv::normalize(rot_mark);`
187	`//std::cout << "P_mark: " << std::endl << P_mark << std::endl;`	187	`//std::cout << "P_mark: " << std::endl << P_mark << std::endl;`
188	`cv::Vec3f sum = P+P_mark;`	188	`cv::Vec3f sum = P+P_mark;`
189	`cv::Mat crossProduct = (cv::Mat_<float>(3,3) << 0.0, -sum(2), sum(1), sum(2), 0.0, -sum(0), -sum(1), sum(0), 0.0);`	189	`cv::Mat crossProduct = (cv::Mat_<float>(3,3) << 0.0, -sum(2), sum(1), sum(2), 0.0, -sum(0), -sum(1), sum(0), 0.0);`
190	`//std::cout << "crossProduct: " << std::endl << crossProduct << std::endl;`	190	`//std::cout << "crossProduct: " << std::endl << crossProduct << std::endl;`
191	`crossProduct.copyTo(A.rowRange(i3, i3+3));`	191	`crossProduct.copyTo(A.rowRange(i3, i3+3));`
192		192
193	`cv::Mat(P-P_mark).copyTo(b.rowRange(i3, i3+3));`	193	`cv::Mat(P-P_mark).copyTo(b.rowRange(i3, i3+3));`
194	`}`	194	`}`
195		195
196	`// solve for rotation`	196	`// solve for rotation`
197	`cv::Vec3f P_prime;`	197	`cv::Vec3f P_prime;`
198	`cv::solve(A, b, P_prime, cv::DECOMP_SVD);`	198	`cv::solve(A, b, P_prime, cv::DECOMP_SVD);`
199	`cv::Vec3f P = 2.0P_prime/(cv::sqrt(1.0 + cv::norm(P_prime)cv::norm(P_prime)));`	199	`cv::Vec3f P = 2.0P_prime/(cv::sqrt(1.0 + cv::norm(P_prime)cv::norm(P_prime)));`
200	`float nP = cv::norm(P);`	200	`float nP = cv::norm(P);`
201	`cv::Mat crossProduct = (cv::Mat_<float>(3,3) << 0.0, -P(2), P(1), P(2), 0.0, -P(0), -P(1), P(0), 0.0);`	201	`cv::Mat crossProduct = (cv::Mat_<float>(3,3) << 0.0, -P(2), P(1), P(2), 0.0, -P(0), -P(1), P(0), 0.0);`
202	`cv::Mat OmegaMat = (1.0-nPnP/2.0)cv::Mat::eye(3,3,CV_32F) + 0.5(cv::Mat(P)cv::Mat(P).t() + cv::sqrt(4.0 - nPnP)crossProduct);`	202	`cv::Mat OmegaMat = (1.0-nPnP/2.0)cv::Mat::eye(3,3,CV_32F) + 0.5(cv::Mat(P)cv::Mat(P).t() + cv::sqrt(4.0 - nPnP)crossProduct);`
203	`Omega = cv::Matx33f(OmegaMat);`	203	`Omega = cv::Matx33f(OmegaMat);`
204		204
205	`// construct equations for translation`	205	`// construct equations for translation`
206	`A.setTo(0.0);`	206	`A.setTo(0.0);`
207	`b.setTo(0.0);`	207	`b.setTo(0.0);`
208	`for(int i=0; i<N; i++){`	208	`for(int i=0; i<N; i++){`
209		209
210	`cv::Mat diff = cv::Mat(R_mark[i]) - cv::Mat::eye(3, 3, CV_32F);`	210	`cv::Mat diff = cv::Mat(R_mark[i]) - cv::Mat::eye(3, 3, CV_32F);`
211	`diff.copyTo(A.rowRange(i3, i3+3));`	211	`diff.copyTo(A.rowRange(i3, i3+3));`
212		212
213	`cv::Mat diff_mark = cv::Mat(Omega*t[i] - t_mark[i]);`	213	`cv::Mat diff_mark = cv::Mat(Omega*t[i] - t_mark[i]);`
214	`diff_mark.copyTo(b.rowRange(i3, i3+3));`	214	`diff_mark.copyTo(b.rowRange(i3, i3+3));`
215	`}`	215	`}`
216		216
217	`// solve for translation`	217	`// solve for translation`
218	`cv::solve(A, b, tau, cv::DECOMP_SVD);`	218	`cv::solve(A, b, tau, cv::DECOMP_SVD);`
219		219
220	`cv::Mat err_tau = b - (A*cv::Mat(tau));`	220	`cv::Mat err_tau = b - (A*cv::Mat(tau));`
221	`std::cout << err_tau << std::endl;`	221	`std::cout << err_tau << std::endl;`
222	`}`	222	`}`
223		223
224	`// Function to solve for the rotation axis from sets of 3D point coordinates of flat pattern feature points`	224	`// Function to solve for the rotation axis from sets of 3D point coordinates of flat pattern feature points`
225	`// Algorithm according to Chen et al., Rotation axis calibration of a turntable using constrained global optimization, Optik 2014`	225	`// Algorithm according to Chen et al., Rotation axis calibration of a turntable using constrained global optimization, Optik 2014`
226	`// DTU, 2014, Jakob Wilm`	226	`// DTU, 2014, Jakob Wilm`
227	`void rotationAxisCalibration(const std::vector< std::vector<cv::Point3f> > Qcam, const std::vector<cv::Point3f> Qobj, cv::Vec3f &axis, cv::Vec3f &point){`	227	`void rotationAxisCalibration(const std::vector< std::vector<cv::Point3f> > Qcam, const std::vector<cv::Point3f> Qobj, cv::Vec3f &axis, cv::Vec3f &point){`
228		228
229	`// number of frames (points on each arch)`	229	`// number of frames (points on each arch)`
230	`int l = Qcam.size();`	230	`int l = Qcam.size();`
231		231
232	`// number of points in each frame`	232	`// number of points in each frame`
233	`int mn = Qobj.size();`	233	`int mn = Qobj.size();`
234		234
235	`assert(mn == Qcam[0].size());`	235	`assert(mn == Qcam[0].size());`
236		236
237	`// construct matrix for axis determination`	237	`// construct matrix for axis determination`
238	`cv::Mat M(6, 6, CV_32F, cv::Scalar(0));`	238	`cv::Mat M(6, 6, CV_32F, cv::Scalar(0));`
239		239
240	`for(int k=0; k<l; k++){`	240	`for(int k=0; k<l; k++){`
241	`for(int idx=0; idx<mn; idx++){`	241	`for(int idx=0; idx<mn; idx++){`
242		242
243	`float i = Qobj[idx].x+4;`	243	`// float i = Qobj[idx].x+4;`
244	`float j = Qobj[idx].y+4;`	244	`// float j = Qobj[idx].y+4;`
-		245	`float i = Qobj[idx].x;`
-		246	`float j = Qobj[idx].y;`
245		247
246	`float x = Qcam[k][idx].x;`	248	`float x = Qcam[k][idx].x;`
247	`float y = Qcam[k][idx].y;`	249	`float y = Qcam[k][idx].y;`
248	`float z = Qcam[k][idx].z;`	250	`float z = Qcam[k][idx].z;`
249		251
250	`M += (cv::Mat_<float>(6,6) << xx, xy, xz, x, ix, j*x,`	252	`M += (cv::Mat_<float>(6,6) << xx, xy, xz, x, ix, j*x,`
251	`0, yy, yz, y, iy, jy,`	253	`0, yy, yz, y, iy, jy,`
252	`0, 0, zz, z, iz, j*z,`	254	`0, 0, zz, z, iz, j*z,`
253	`0, 0, 0, 1, i, j,`	255	`0, 0, 0, 1, i, j,`
254	`0, 0, 0, 0, ii, ij,`	256	`0, 0, 0, 0, ii, ij,`
255	`0, 0, 0, 0, 0, j*j);`	257	`0, 0, 0, 0, 0, j*j);`
256		258
257	`}`	259	`}`
258	`}`	260	`}`
259	`//std::cout << M << std::endl;`	261	`//std::cout << M << std::endl;`
260	`//M = 1.0/(lmn);`	262	`//M = 1.0/(lmn);`
261	`cv::completeSymm(M, false);`	263	`cv::completeSymm(M, false);`
262	`//std::cout << M << std::endl;`	264	`//std::cout << M << std::endl;`
263	`// solve for axis`	265	`// solve for axis`
264	`std::vector<float> lambda;`	266	`std::vector<float> lambda;`
265	`cv::Mat u;`	267	`cv::Mat u;`
266	`cv::eigen(M, lambda, u);`	268	`cv::eigen(M, lambda, u);`
267		269
268	`float minLambda = abs(lambda[0]);`	270	`float minLambda = abs(lambda[0]);`
269	`int idx = 0;`	271	`int idx = 0;`
270	`for(int i=1; i<lambda.size(); i++){`	272	`for(int i=1; i<lambda.size(); i++){`
271	`if(abs(lambda[i]) < minLambda){`	273	`if(abs(lambda[i]) < minLambda){`
272	`minLambda = lambda[i];`	274	`minLambda = lambda[i];`
273	`idx = i;`	275	`idx = i;`
274	`}`	276	`}`
275	`}`	277	`}`
276		278
277	`std::cout << "u:" << u << std::endl;`	279	`//std::cout << "u:" << u << std::endl;`
278		280
279	`axis = u.row(idx).colRange(0, 3);`	281	`axis = u.row(idx).colRange(0, 3);`
280	`axis /= cv::norm(axis);`	282	`axis /= cv::norm(axis);`
281		283
282	`float nx = u.at<float>(5, 0);`	284	`float nx = u.at<float>(idx, 0);`
283	`float ny = u.at<float>(5, 1);`	285	`float ny = u.at<float>(idx, 1);`
284	`float nz = u.at<float>(5, 2);`	286	`float nz = u.at<float>(idx, 2);`
285	`float d = u.at<float>(5, 3);`	287	`float d = u.at<float>(idx, 3);`
286	`float dh = u.at<float>(5, 4);`	288	`float dh = u.at<float>(idx, 4);`
287	`float dv = u.at<float>(5, 5);`	289	`float dv = u.at<float>(idx, 5);`
288		290
289	`cv::Mat A(l*mn, mn+2, CV_32F, cv::Scalar(0.0));`	291	`cv::Mat A(l*mn, mn+2, CV_32F, cv::Scalar(0.0));`
290	`cv::Mat b(l*mn, 1, CV_32F);`	292	`cv::Mat bb(l*mn, 1, CV_32F);`
291		293
292	`for(int k=0; k<l; k++){`	294	`for(int k=0; k<l; k++){`
293	`for(int idx=0; idx<mn; idx++){`	295	`for(int idx=0; idx<mn; idx++){`
294		296
295	`float i = Qobj[idx].x;`	297	`float i = Qobj[idx].x;`
296	`float j = Qobj[idx].y;`	298	`float j = Qobj[idx].y;`
297		299
298	`float x = Qcam[k][idx].x;`	300	`float x = Qcam[k][idx].x;`
299	`float y = Qcam[k][idx].y;`	301	`float y = Qcam[k][idx].y;`
300	`float z = Qcam[k][idx].z;`	302	`float z = Qcam[k][idx].z;`
301		303
302	`float f = xx + yy + zz + (2xnx + 2yny + 2znz)(idh + jdv);`	304	`float f = xx + yy + zz + (2xnx + 2yny + 2znz)(idh + jdv);`
303		305
304	`int row = k*mn+idx;`	306	`int row = k*mn+idx;`
305	`A.at<float>(row, 0) = 2x - (2z*nx)/nz;`	307	`A.at<float>(row, 0) = 2x - (2z*nx)/nz;`
306	`A.at<float>(row, 1) = 2y - (2z*ny)/nz;`	308	`A.at<float>(row, 1) = 2y - (2z*ny)/nz;`
307	`A.at<float>(row, idx+2) = 1.0;`	309	`A.at<float>(row, idx+2) = 1.0;`
308		310
309	`b.at<float>(row, 0) = f + (2zd)/nz;`	311	`bb.at<float>(row, 0) = f + (2zd)/nz;`
310	`}`	312	`}`
311	`}`	313	`}`
312		314
313	`// solve for point`	315	`// solve for point`
314	`cv::Mat abe;`	316	`cv::Mat abe;`
315	`cv::solve(A, b, abe, cv::DECOMP_SVD);`	317	`cv::solve(A, bb, abe, cv::DECOMP_SVD);`
316		318
317	`point[0] = abe.at<float>(0, 0);`	319	`float a = abe.at<float>(0, 0);`
318	`point[1] = abe.at<float>(1, 0);`	320	`float b = abe.at<float>(1, 0);`
-		321	`float c = -(nxa+nyb)/nz;`
-		322
-		323	`point[0] = a;`
-		324	`point[1] = b;`
319	`point[2] = abe.at<float>(2, 0);`	325	`point[2] = c;`
320		326
321	`}`	327	`}`
322		328
323	`// Function to fit two sets of corresponding pose data.`	329	`// Function to fit two sets of corresponding pose data.`
324	`// Finds [Omega \| tau], to minimize \|\|[R_mark \| t_mark] - [Omega \| tau][R \| t]\|\|^2`	330	`// Finds [Omega \| tau], to minimize \|\|[R_mark \| t_mark] - [Omega \| tau][R \| t]\|\|^2`
325	`// Algorithm and notation according to Mili Shah, Comparing two sets of corresponding six degree of freedom data, CVIU 2011.`	331	`// Algorithm and notation according to Mili Shah, Comparing two sets of corresponding six degree of freedom data, CVIU 2011.`
326	`// DTU, 2013, Oline V. Olesen, Jakob Wilm`	332	`// DTU, 2013, Oline V. Olesen, Jakob Wilm`
327	`void fitSixDofData(const std::vector<cv::Matx33f> R, const std::vector<cv::Vec3f> t, const std::vector<cv::Matx33f> R_mark, const std::vector<cv::Vec3f> t_mark, cv::Matx33f &Omega, cv::Vec3f &tau){`	333	`void fitSixDofData(const std::vector<cv::Matx33f> R, const std::vector<cv::Vec3f> t, const std::vector<cv::Matx33f> R_mark, const std::vector<cv::Vec3f> t_mark, cv::Matx33f &Omega, cv::Vec3f &tau){`
328		334
329	`int N = R.size();`	335	`int N = R.size();`
330	`assert(N == R_mark.size());`	336	`assert(N == R_mark.size());`
331	`assert(N == t.size());`	337	`assert(N == t.size());`
332	`assert(N == t_mark.size());`	338	`assert(N == t_mark.size());`
333		339
334	`// Mean translations`	340	`// Mean translations`
335	`cv::Vec3f t_mean;`	341	`cv::Vec3f t_mean;`
336	`cv::Vec3f t_mark_mean;`	342	`cv::Vec3f t_mark_mean;`
337	`for(int i=0; i<N; i++){`	343	`for(int i=0; i<N; i++){`
338	`t_mean += 1.0/N * t[i];`	344	`t_mean += 1.0/N * t[i];`
339	`t_mark_mean += 1.0/N * t_mark[i];`	345	`t_mark_mean += 1.0/N * t_mark[i];`
340	`}`	346	`}`
341		347
342	`// Data with mean adjusted translations`	348	`// Data with mean adjusted translations`
343	`cv::Mat X_bar(3, 4*N, CV_32F);`	349	`cv::Mat X_bar(3, 4*N, CV_32F);`
344	`cv::Mat X_mark_bar(3, 4*N, CV_32F);`	350	`cv::Mat X_mark_bar(3, 4*N, CV_32F);`
345	`for(int i=0; i<N; i++){`	351	`for(int i=0; i<N; i++){`
346	`cv::Mat(R[i]).copyTo(X_bar.colRange(i4,i4+3));`	352	`cv::Mat(R[i]).copyTo(X_bar.colRange(i4,i4+3));`
347	`cv::Mat(t[i] - t_mean).copyTo(X_bar.col(i*4+3));`	353	`cv::Mat(t[i] - t_mean).copyTo(X_bar.col(i*4+3));`
348	`cv::Mat(R_mark[i]).copyTo(X_mark_bar.colRange(i4,i4+3));`	354	`cv::Mat(R_mark[i]).copyTo(X_mark_bar.colRange(i4,i4+3));`
349	`cv::Mat(t_mark[i] - t_mark_mean).copyTo(X_mark_bar.col(i*4+3));`	355	`cv::Mat(t_mark[i] - t_mark_mean).copyTo(X_mark_bar.col(i*4+3));`
350	`}`	356	`}`
351	`//std::cout << X_bar << std::endl;`	357	`//std::cout << X_bar << std::endl;`
352	`// SVD`	358	`// SVD`
353	`cv::Mat W, U, VT;`	359	`cv::Mat W, U, VT;`
354	`cv::SVDecomp(X_bar*X_mark_bar.t(), W, U, VT);`	360	`cv::SVDecomp(X_bar*X_mark_bar.t(), W, U, VT);`
355		361
356	`cv::Matx33f D = cv::Matx33f::eye();`	362	`cv::Matx33f D = cv::Matx33f::eye();`
357	`if(cv::determinant(VT*U) < 0)`	363	`if(cv::determinant(VT*U) < 0)`
358	`D(3,3) = -1;`	364	`D(3,3) = -1;`
359		365
360	`// Best rotation`	366	`// Best rotation`
361	`Omega = cv::Matx33f(cv::Mat(VT.t()))Dcv::Matx33f(cv::Mat(U.t()));`	367	`Omega = cv::Matx33f(cv::Mat(VT.t()))Dcv::Matx33f(cv::Mat(U.t()));`
362		368
363	`// Best translation`	369	`// Best translation`
364	`tau = t_mark_mean - Omega*t_mean;`	370	`tau = t_mark_mean - Omega*t_mean;`
365		371
366	`}`	372	`}`
367		373
368	`// Forward distortion of points. The inverse of the undistortion in cv::initUndistortRectifyMap().`	374	`// Forward distortion of points. The inverse of the undistortion in cv::initUndistortRectifyMap().`
369	`// Inspired by Pascal Thomet, http://code.opencv.org/issues/1387#note-11`	375	`// Inspired by Pascal Thomet, http://code.opencv.org/issues/1387#note-11`
370	`// Convention for distortion parameters: http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/parameters.html`	376	`// Convention for distortion parameters: http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/parameters.html`
371	`void initDistortMap(const cv::Matx33f cameraMatrix, const cv::Vec<float, 5> distCoeffs, const cv::Size size, cv::Mat &map1, cv::Mat &map2){`	377	`void initDistortMap(const cv::Matx33f cameraMatrix, const cv::Vec<float, 5> distCoeffs, const cv::Size size, cv::Mat &map1, cv::Mat &map2){`
372		378
373	`float fx = cameraMatrix(0,0);`	379	`float fx = cameraMatrix(0,0);`
374	`float fy = cameraMatrix(1,1);`	380	`float fy = cameraMatrix(1,1);`
375	`float ux = cameraMatrix(0,2);`	381	`float ux = cameraMatrix(0,2);`
376	`float uy = cameraMatrix(1,2);`	382	`float uy = cameraMatrix(1,2);`
377		383
378	`float k1 = distCoeffs[0];`	384	`float k1 = distCoeffs[0];`
379	`float k2 = distCoeffs[1];`	385	`float k2 = distCoeffs[1];`
380	`float p1 = distCoeffs[2];`	386	`float p1 = distCoeffs[2];`
381	`float p2 = distCoeffs[3];`	387	`float p2 = distCoeffs[3];`
382	`float k3 = distCoeffs[4];`	388	`float k3 = distCoeffs[4];`
383		389
384	`map1.create(size, CV_32F);`	390	`map1.create(size, CV_32F);`
385	`map2.create(size, CV_32F);`	391	`map2.create(size, CV_32F);`
386		392
387	`for(int col = 0; col < size.width; col++){`	393	`for(int col = 0; col < size.width; col++){`
388	`for(int row = 0; row < size.height; row++){`	394	`for(int row = 0; row < size.height; row++){`
389		395
390	`// move origo to principal point and convert using focal length`	396	`// move origo to principal point and convert using focal length`
391	`float x = (col-ux)/fx;`	397	`float x = (col-ux)/fx;`
392	`float y = (row-uy)/fy;`	398	`float y = (row-uy)/fy;`
393		399
394	`float xCorrected, yCorrected;`	400	`float xCorrected, yCorrected;`
395		401
396	`//Step 1 : correct distortion`	402	`//Step 1 : correct distortion`
397	`float r2 = xx + yy;`	403	`float r2 = xx + yy;`
398	`//radial`	404	`//radial`
399	`xCorrected = x * (1. + k1r2 + k2r2r2 + k3r2r2r2);`	405	`xCorrected = x * (1. + k1r2 + k2r2r2 + k3r2r2r2);`
400	`yCorrected = y * (1. + k1r2 + k2r2r2 + k3r2r2r2);`	406	`yCorrected = y * (1. + k1r2 + k2r2r2 + k3r2r2r2);`
401	`//tangential`	407	`//tangential`
402	`xCorrected = xCorrected + (2.p1xy + p2(r2+2.xx));`	408	`xCorrected = xCorrected + (2.p1xy + p2(r2+2.xx));`
403	`yCorrected = yCorrected + (p1(r2+2.yy) + 2.p2xy);`	409	`yCorrected = yCorrected + (p1(r2+2.yy) + 2.p2xy);`
404		410
405	`//convert back to pixel coordinates`	411	`//convert back to pixel coordinates`
406	`float col_displaced = xCorrected * fx + ux;`	412	`float col_displaced = xCorrected * fx + ux;`
407	`float row_displaced = yCorrected * fy + uy;`	413	`float row_displaced = yCorrected * fy + uy;`
408		414
409	`// correct the vector in the opposite direction`	415	`// correct the vector in the opposite direction`
410	`map1.at<float>(row,col) = col+(col-col_displaced);`	416	`map1.at<float>(row,col) = col+(col-col_displaced);`
411	`map2.at<float>(row,col) = row +(row-row_displaced);`	417	`map2.at<float>(row,col) = row +(row-row_displaced);`
412	`}`	418	`}`
413	`}`	419	`}`
414	`}`	420	`}`
415		421
416	`// Downsample a texture which was created in virtual column/row space for a diamond pixel array projector`	422	`// Downsample a texture which was created in virtual column/row space for a diamond pixel array projector`
417	`cv::Mat diamondDownsample(cv::Mat &pattern){`	423	`cv::Mat diamondDownsample(cv::Mat &pattern){`
418		424
419	`cv::Mat pattern_diamond(pattern.rows,pattern.cols/2,CV_8UC3);`	425	`cv::Mat pattern_diamond(pattern.rows,pattern.cols/2,CV_8UC3);`
420		426
421	`for(unsigned int col = 0; col < pattern_diamond.cols; col++){`	427	`for(unsigned int col = 0; col < pattern_diamond.cols; col++){`
422	`for(unsigned int row = 0; row < pattern_diamond.rows; row++){`	428	`for(unsigned int row = 0; row < pattern_diamond.rows; row++){`
423		429
424	`pattern_diamond.at<cv::Vec3b>(row,col)=pattern.at<cv::Vec3b>(row,col*2+row%2);`	430	`pattern_diamond.at<cv::Vec3b>(row,col)=pattern.at<cv::Vec3b>(row,col*2+row%2);`
425	`}`	431	`}`
426	`}`	432	`}`
427		433
428	`return pattern_diamond;`	434	`return pattern_diamond;`
429		435
430	`}`	436	`}`
431		437
432		438
433	`void mouseCallback(int evt, int x, int y, int flags, void* param){`	439	`void mouseCallback(int evt, int x, int y, int flags, void* param){`
434	`cv::Mat im = (cv::Mat) param;`	440	`cv::Mat im = (cv::Mat) param;`
435	`if (evt == CV_EVENT_LBUTTONDOWN) {`	441	`if (evt == CV_EVENT_LBUTTONDOWN) {`
436	`if(im->type() == CV_8UC3){`	442	`if(im->type() == CV_8UC3){`
437	`printf("%d %d: %d, %d, %d\n",`	443	`printf("%d %d: %d, %d, %d\n",`
438	`x, y,`	444	`x, y,`
439	`(int)(*im).at<cv::Vec3b>(y, x)[0],`	445	`(int)(*im).at<cv::Vec3b>(y, x)[0],`
440	`(int)(*im).at<cv::Vec3b>(y, x)[1],`	446	`(int)(*im).at<cv::Vec3b>(y, x)[1],`
441	`(int)(*im).at<cv::Vec3b>(y, x)[2]);`	447	`(int)(*im).at<cv::Vec3b>(y, x)[2]);`
442	`} else if (im->type() == CV_32F) {`	448	`} else if (im->type() == CV_32F) {`
443	`printf("%d %d: %f\n",`	449	`printf("%d %d: %f\n",`
444	`x, y,`	450	`x, y,`
445	`im->at<float>(y, x));`	451	`im->at<float>(y, x));`
446	`}`	452	`}`
447	`}`	453	`}`
448	`}`	454	`}`
449		455
450	`void imshow(const char *windowName, cv::Mat im, unsigned int x, unsigned int y){`	456	`void imshow(const char *windowName, cv::Mat im, unsigned int x, unsigned int y){`
451		457
452	`// Imshow`	458	`// Imshow`
453	`if(!cvGetWindowHandle(windowName)){`	459	`if(!cvGetWindowHandle(windowName)){`
454	`int windowFlags = CV_GUI_EXPANDED \| CV_WINDOW_KEEPRATIO;`	460	`int windowFlags = CV_GUI_EXPANDED \| CV_WINDOW_KEEPRATIO;`
455	`cv::namedWindow(windowName, windowFlags);`	461	`cv::namedWindow(windowName, windowFlags);`
456	`cv::moveWindow(windowName, x, y);`	462	`cv::moveWindow(windowName, x, y);`
457	`}`	463	`}`
458	`cv::imshow(windowName, im);`	464	`cv::imshow(windowName, im);`
459	`}`	465	`}`
460		466
461	`void imagesc(const char *windowName, cv::Mat im){`	467	`void imagesc(const char *windowName, cv::Mat im){`
462		468
463	`// Imshow with scaled image`	469	`// Imshow with scaled image`
464		470
465		471
466	`}`	472	`}`
467		473
468	`cv::Mat histimage(cv::Mat histogram){`	474	`cv::Mat histimage(cv::Mat histogram){`
469		475
470	`cv::Mat histImage(512, 640, CV_8UC3, cv::Scalar(0));`	476	`cv::Mat histImage(512, 640, CV_8UC3, cv::Scalar(0));`
471		477
472	`// Normalize the result to [ 2, histImage.rows-2 ]`	478	`// Normalize the result to [ 2, histImage.rows-2 ]`
473	`cv::normalize(histogram, histogram, 2, histImage.rows-2, cv::NORM_MINMAX, -1, cv::Mat());`	479	`cv::normalize(histogram, histogram, 2, histImage.rows-2, cv::NORM_MINMAX, -1, cv::Mat());`
474		480
475	`float bin_w = (float)histImage.cols/(float)histogram.rows;`	481	`float bin_w = (float)histImage.cols/(float)histogram.rows;`
476		482
477	`// Draw main histogram`	483	`// Draw main histogram`
478	`for(int i = 1; i < histogram.rows-10; i++){`	484	`for(int i = 1; i < histogram.rows-10; i++){`
479	`cv::line(histImage, cv::Point( bin_w*(i-1), histImage.rows - cvRound(histogram.at<float>(i-1)) ),`	485	`cv::line(histImage, cv::Point( bin_w*(i-1), histImage.rows - cvRound(histogram.at<float>(i-1)) ),`
480	`cv::Point( bin_w*(i), histImage.rows - cvRound(histogram.at<float>(i)) ),`	486	`cv::Point( bin_w*(i), histImage.rows - cvRound(histogram.at<float>(i)) ),`
481	`cv::Scalar(255, 255, 255), 2, 4);`	487	`cv::Scalar(255, 255, 255), 2, 4);`
482	`}`	488	`}`
483		489
484	`// Draw red max`	490	`// Draw red max`
485	`for(int i = histogram.rows-10; i < histogram.rows; i++){`	491	`for(int i = histogram.rows-10; i < histogram.rows; i++){`
486	`cv::line(histImage, cv::Point( bin_w*(i-1), histImage.rows - cvRound(histogram.at<float>(i-1)) ),`	492	`cv::line(histImage, cv::Point( bin_w*(i-1), histImage.rows - cvRound(histogram.at<float>(i-1)) ),`
487	`cv::Point( bin_w*(i), histImage.rows - cvRound(histogram.at<float>(i)) ),`	493	`cv::Point( bin_w*(i), histImage.rows - cvRound(histogram.at<float>(i)) ),`
488	`cv::Scalar(0, 0, 255), 2, 4);`	494	`cv::Scalar(0, 0, 255), 2, 4);`
489	`}`	495	`}`
490		496
491	`return histImage;`	497	`return histImage;`
492	`}`	498	`}`
493		499
494	`void hist(const char *windowName, cv::Mat histogram, unsigned int x, unsigned int y){`	500	`void hist(const char *windowName, cv::Mat histogram, unsigned int x, unsigned int y){`

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(root)/src/cvtools.cpp – Rev 82 → 83