//----------------------------------------
int FeatureFinderChessboard::find(cv::Mat& img) {
bool found = false;
for(int scale=1; scale<=settings.maxScale; scale++) {
cv::Mat timg;
if(scale==1) {
timg = img;
} else {
cv::resize(img, timg, cv::Size(), scale, scale);
}
found = cv::findChessboardCorners(timg, _boardSize, _features.imagePoints, cv::CALIB_CB_ADAPTIVE_THRESH + cv::CALIB_CB_NORMALIZE_IMAGE);
if(found) {
if(scale > 1) {
cv::Mat cornersMat(_features.imagePoints);
cornersMat *= 1./scale;
}
break;
}
}
if(found) {
cv::cornerSubPix(img, _features.imagePoints, cvSize(11, 11), cvSize(-1, -1), cv::TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 30, 0.1 ));
} else {
ofLog(OF_LOG_VERBOSE, "MSA::Cv::FeatureFinderChessboard::find - no features found");
return 0;
}
return _features.getNumPoints();
}
void stereoCalibThread::stereoCalibration(const vector<string>& imagelist, int boardWidth, int boardHeight,float sqsize)
{
Size boardSize;
boardSize.width=boardWidth;
boardSize.height=boardHeight;
if( imagelist.size() % 2 != 0 )
{
cout << "Error: the image list contains odd (non-even) number of elements\n";
return;
}
const int maxScale = 2;
// ARRAY AND VECTOR STORAGE:
std::vector<std::vector<Point2f> > imagePoints[2];
std::vector<std::vector<Point3f> > objectPoints;
Size imageSize;
int i, j, k, nimages = (int)imagelist.size()/2;
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
std::vector<string> goodImageList;
for( i = j = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
const string& filename = imagelist[i*2+k];
Mat img = cv::imread(filename, 0);
if(img.empty())
break;
if( imageSize == Size() )
imageSize = img.size();
else if( img.size() != imageSize )
{
cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
break;
}
bool found = false;
std::vector<Point2f>& corners = imagePoints[k][j];
for( int scale = 1; scale <= maxScale; scale++ )
{
Mat timg;
if( scale == 1 )
timg = img;
else
resize(img, timg, Size(), scale, scale);
if(boardType == "CIRCLES_GRID") {
found = findCirclesGridDefault(timg, boardSize, corners, CALIB_CB_SYMMETRIC_GRID | CALIB_CB_CLUSTERING);
} else if(boardType == "ASYMMETRIC_CIRCLES_GRID") {
found = findCirclesGridDefault(timg, boardSize, corners, CALIB_CB_ASYMMETRIC_GRID | CALIB_CB_CLUSTERING);
} else {
found = findChessboardCorners(timg, boardSize, corners,
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE);
}
if( found )
{
if( scale > 1 )
{
Mat cornersMat(corners);
cornersMat *= 1./scale;
}
break;
}
}
if( !found )
break;
}
if( k == 2 )
{
goodImageList.push_back(imagelist[i*2]);
goodImageList.push_back(imagelist[i*2+1]);
j++;
}
}
fprintf(stdout,"%i pairs have been successfully detected.\n",j);
nimages = j;
if( nimages < 2 )
{
fprintf(stdout,"Error: too few pairs detected \n");
return;
}
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
objectPoints.resize(nimages);
for( i = 0; i < nimages; i++ )
{
for( j = 0; j < boardSize.height; j++ )
for( k = 0; k < boardSize.width; k++ )
objectPoints[i].push_back(Point3f(j*squareSize, k*squareSize, 0));
}
fprintf(stdout,"Running stereo calibration ...\n");
Mat cameraMatrix[2], distCoeffs[2];
Mat E, F;
if(this->Kleft.empty() || this->Kright.empty())
{
double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
this->Kleft, this->DistL,
this->Kright, this->DistR,
imageSize, this->R, this->T, E, F,
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),
CV_CALIB_FIX_ASPECT_RATIO +
CV_CALIB_ZERO_TANGENT_DIST +
CV_CALIB_SAME_FOCAL_LENGTH +
CV_CALIB_FIX_K3);
fprintf(stdout,"done with RMS error= %f\n",rms);
} else
{
double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
this->Kleft, this->DistL,
this->Kright, this->DistR,
imageSize, this->R, this->T, E, F,
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),CV_CALIB_FIX_ASPECT_RATIO + CV_CALIB_FIX_INTRINSIC + CV_CALIB_FIX_K3);
fprintf(stdout,"done with RMS error= %f\n",rms);
}
// CALIBRATION QUALITY CHECK
cameraMatrix[0] = this->Kleft;
cameraMatrix[1] = this->Kright;
distCoeffs[0]=this->DistL;
distCoeffs[1]=this->DistR;
Mat R, T;
T=this->T;
R=this->R;
double err = 0;
int npoints = 0;
std::vector<Vec3f> lines[2];
for( i = 0; i < nimages; i++ )
{
int npt = (int)imagePoints[0][i].size();
Mat imgpt[2];
for( k = 0; k < 2; k++ )
{
imgpt[k] = Mat(imagePoints[k][i]);
undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
}
for( j = 0; j < npt; j++ )
{
double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
fabs(imagePoints[1][i][j].x*lines[0][j][0] +
imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
err += errij;
}
npoints += npt;
}
fprintf(stdout,"average reprojection err = %f\n",err/npoints);
cout.flush();
}
void CalibrationDialog::processImages(const cv::Mat & imageLeft, const cv::Mat & imageRight, const QString & cameraName)
{
processingData_ = true;
if(cameraName_.isEmpty())
{
cameraName_ = "0000";
if(!cameraName.isEmpty())
{
cameraName_ = cameraName;
}
}
if(ui_->label_serial->text().isEmpty())
{
ui_->label_serial->setText(cameraName_);
}
std::vector<cv::Mat> inputRawImages(2);
if(ui_->checkBox_switchImages->isChecked())
{
inputRawImages[0] = imageRight;
inputRawImages[1] = imageLeft;
}
else
{
inputRawImages[0] = imageLeft;
inputRawImages[1] = imageRight;
}
std::vector<cv::Mat> images(2);
images[0] = inputRawImages[0];
images[1] = inputRawImages[1];
imageSize_[0] = images[0].size();
imageSize_[1] = images[1].size();
bool boardFound[2] = {false};
bool boardAccepted[2] = {false};
bool readyToCalibrate[2] = {false};
std::vector<std::vector<cv::Point2f> > pointBuf(2);
bool depthDetected = false;
for(int id=0; id<(stereo_?2:1); ++id)
{
cv::Mat viewGray;
if(!images[id].empty())
{
if(images[id].type() == CV_16UC1)
{
depthDetected = true;
//assume IR image: convert to gray scaled
const float factor = 255.0f / float((maxIrs_[id] - minIrs_[id]));
viewGray = cv::Mat(images[id].rows, images[id].cols, CV_8UC1);
for(int i=0; i<images[id].rows; ++i)
{
for(int j=0; j<images[id].cols; ++j)
{
viewGray.at<unsigned char>(i, j) = (unsigned char)std::min(float(std::max(images[id].at<unsigned short>(i,j) - minIrs_[id], 0)) * factor, 255.0f);
}
}
cvtColor(viewGray, images[id], cv::COLOR_GRAY2BGR); // convert to show detected points in color
}
else if(images[id].channels() == 3)
{
cvtColor(images[id], viewGray, cv::COLOR_BGR2GRAY);
}
else
{
viewGray = images[id];
cvtColor(viewGray, images[id], cv::COLOR_GRAY2BGR); // convert to show detected points in color
}
}
else
{
UERROR("Image %d is empty!! Should not!", id);
}
minIrs_[id] = 0;
maxIrs_[id] = 0x7FFF;
//Dot it only if not yet calibrated
if(!ui_->pushButton_save->isEnabled())
{
cv::Size boardSize(ui_->spinBox_boardWidth->value(), ui_->spinBox_boardHeight->value());
if(!viewGray.empty())
{
int flags = CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE;
if(!viewGray.empty())
{
int maxScale = viewGray.cols < 640?2:1;
for( int scale = 1; scale <= maxScale; scale++ )
{
cv::Mat timg;
if( scale == 1 )
timg = viewGray;
else
cv::resize(viewGray, timg, cv::Size(), scale, scale, CV_INTER_CUBIC);
boardFound[id] = cv::findChessboardCorners(timg, boardSize, pointBuf[id], flags);
if(boardFound[id])
{
if( scale > 1 )
{
cv::Mat cornersMat(pointBuf[id]);
cornersMat *= 1./scale;
}
break;
}
}
}
}
if(boardFound[id]) // If done with success,
{
// improve the found corners' coordinate accuracy for chessboard
float minSquareDistance = -1.0f;
for(unsigned int i=0; i<pointBuf[id].size()-1; ++i)
{
float d = cv::norm(pointBuf[id][i] - pointBuf[id][i+1]);
if(minSquareDistance == -1.0f || minSquareDistance > d)
{
minSquareDistance = d;
}
}
float radius = minSquareDistance/2.0f +0.5f;
cv::cornerSubPix( viewGray, pointBuf[id], cv::Size(radius, radius), cv::Size(-1,-1),
cv::TermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1 ));
// Draw the corners.
cv::drawChessboardCorners(images[id], boardSize, cv::Mat(pointBuf[id]), boardFound[id]);
std::vector<float> params(4,0);
getParams(pointBuf[id], boardSize, imageSize_[id], params[0], params[1], params[2], params[3]);
bool addSample = true;
for(unsigned int i=0; i<imageParams_[id].size(); ++i)
{
if(fabs(params[0] - imageParams_[id][i].at(0)) < 0.1 && // x
fabs(params[1] - imageParams_[id][i].at(1)) < 0.1 && // y
fabs(params[2] - imageParams_[id][i].at(2)) < 0.05 && // size
fabs(params[3] - imageParams_[id][i].at(3)) < 0.1) // skew
{
addSample = false;
}
}
if(addSample)
{
boardAccepted[id] = true;
imagePoints_[id].push_back(pointBuf[id]);
imageParams_[id].push_back(params);
UINFO("[%d] Added board, total=%d. (x=%f, y=%f, size=%f, skew=%f)", id, (int)imagePoints_[id].size(), params[0], params[1], params[2], params[3]);
}
// update statistics
std::vector<float> xRange(2, imageParams_[id][0].at(0));
std::vector<float> yRange(2, imageParams_[id][0].at(1));
std::vector<float> sizeRange(2, imageParams_[id][0].at(2));
std::vector<float> skewRange(2, imageParams_[id][0].at(3));
for(unsigned int i=1; i<imageParams_[id].size(); ++i)
{
xRange[0] = imageParams_[id][i].at(0) < xRange[0] ? imageParams_[id][i].at(0) : xRange[0];
xRange[1] = imageParams_[id][i].at(0) > xRange[1] ? imageParams_[id][i].at(0) : xRange[1];
yRange[0] = imageParams_[id][i].at(1) < yRange[0] ? imageParams_[id][i].at(1) : yRange[0];
yRange[1] = imageParams_[id][i].at(1) > yRange[1] ? imageParams_[id][i].at(1) : yRange[1];
sizeRange[0] = imageParams_[id][i].at(2) < sizeRange[0] ? imageParams_[id][i].at(2) : sizeRange[0];
sizeRange[1] = imageParams_[id][i].at(2) > sizeRange[1] ? imageParams_[id][i].at(2) : sizeRange[1];
skewRange[0] = imageParams_[id][i].at(3) < skewRange[0] ? imageParams_[id][i].at(3) : skewRange[0];
skewRange[1] = imageParams_[id][i].at(3) > skewRange[1] ? imageParams_[id][i].at(3) : skewRange[1];
}
//UINFO("Stats [%d]:", id);
//UINFO(" Count = %d", (int)imagePoints_[id].size());
//UINFO(" x = [%f -> %f]", xRange[0], xRange[1]);
//UINFO(" y = [%f -> %f]", yRange[0], yRange[1]);
//UINFO(" size = [%f -> %f]", sizeRange[0], sizeRange[1]);
//UINFO(" skew = [%f -> %f]", skewRange[0], skewRange[1]);
float xGood = xRange[1] - xRange[0];
float yGood = yRange[1] - yRange[0];
float sizeGood = sizeRange[1] - sizeRange[0];
float skewGood = skewRange[1] - skewRange[0];
if(id == 0)
{
ui_->progressBar_x->setValue(xGood*100);
ui_->progressBar_y->setValue(yGood*100);
ui_->progressBar_size->setValue(sizeGood*100);
ui_->progressBar_skew->setValue(skewGood*100);
if((int)imagePoints_[id].size() > ui_->progressBar_count->maximum())
{
ui_->progressBar_count->setMaximum((int)imagePoints_[id].size());
}
ui_->progressBar_count->setValue((int)imagePoints_[id].size());
}
else
{
ui_->progressBar_x_2->setValue(xGood*100);
ui_->progressBar_y_2->setValue(yGood*100);
ui_->progressBar_size_2->setValue(sizeGood*100);
ui_->progressBar_skew_2->setValue(skewGood*100);
if((int)imagePoints_[id].size() > ui_->progressBar_count_2->maximum())
{
ui_->progressBar_count_2->setMaximum((int)imagePoints_[id].size());
}
ui_->progressBar_count_2->setValue((int)imagePoints_[id].size());
}
if(imagePoints_[id].size() >= COUNT_MIN && xGood > 0.5 && yGood > 0.5 && sizeGood > 0.4 && skewGood > 0.5)
{
readyToCalibrate[id] = true;
}
//update IR values
if(inputRawImages[id].type() == CV_16UC1)
{
//update min max IR if the chessboard was found
minIrs_[id] = 0xFFFF;
maxIrs_[id] = 0;
for(size_t i = 0; i < pointBuf[id].size(); ++i)
{
const cv::Point2f &p = pointBuf[id][i];
cv::Rect roi(std::max(0, (int)p.x - 3), std::max(0, (int)p.y - 3), 6, 6);
roi.width = std::min(roi.width, inputRawImages[id].cols - roi.x);
roi.height = std::min(roi.height, inputRawImages[id].rows - roi.y);
//find minMax in the roi
double min, max;
cv::minMaxLoc(inputRawImages[id](roi), &min, &max);
if(min < minIrs_[id])
{
minIrs_[id] = min;
}
if(max > maxIrs_[id])
{
maxIrs_[id] = max;
}
}
}
}
}
}
ui_->label_baseline->setVisible(!depthDetected);
ui_->label_baseline_name->setVisible(!depthDetected);
if(stereo_ && ((boardAccepted[0] && boardFound[1]) || (boardAccepted[1] && boardFound[0])))
{
stereoImagePoints_[0].push_back(pointBuf[0]);
stereoImagePoints_[1].push_back(pointBuf[1]);
UINFO("Add stereo image points (size=%d)", (int)stereoImagePoints_[0].size());
}
if(!stereo_ && readyToCalibrate[0])
{
ui_->pushButton_calibrate->setEnabled(true);
}
else if(stereo_ && readyToCalibrate[0] && readyToCalibrate[1] && stereoImagePoints_[0].size())
{
ui_->pushButton_calibrate->setEnabled(true);
}
if(ui_->radioButton_rectified->isChecked())
{
if(models_[0].isValid())
{
images[0] = models_[0].rectifyImage(images[0]);
}
if(models_[1].isValid())
{
images[1] = models_[1].rectifyImage(images[1]);
}
}
else if(ui_->radioButton_stereoRectified->isChecked() &&
(stereoModel_.left().isValid() &&
stereoModel_.right().isValid()&&
(!ui_->label_baseline->isVisible() || stereoModel_.baseline() > 0.0)))
{
images[0] = stereoModel_.left().rectifyImage(images[0]);
images[1] = stereoModel_.right().rectifyImage(images[1]);
}
if(ui_->checkBox_showHorizontalLines->isChecked())
{
for(int id=0; id<(stereo_?2:1); ++id)
{
int step = imageSize_[id].height/16;
for(int i=step; i<imageSize_[id].height; i+=step)
{
cv::line(images[id], cv::Point(0, i), cv::Point(imageSize_[id].width, i), CV_RGB(0,255,0));
}
}
}
ui_->label_left->setText(tr("%1x%2").arg(images[0].cols).arg(images[0].rows));
//show frame
ui_->image_view->setImage(uCvMat2QImage(images[0]).mirrored(ui_->checkBox_mirror->isChecked(), false));
if(stereo_)
{
ui_->label_right->setText(tr("%1x%2").arg(images[1].cols).arg(images[1].rows));
ui_->image_view_2->setImage(uCvMat2QImage(images[1]).mirrored(ui_->checkBox_mirror->isChecked(), false));
}
processingData_ = false;
}
void StereoCalib(const vector<string>& imagelist, Size boardSize, bool useCalibrated=true, bool showRectified=true)
{
if( imagelist.size() % 2 != 0 )
{
cout << "Error: the image list contains odd (non-even) number of elements\n";
return;
}
printf("board size: %d x %d", boardSize.width, boardSize.height);
bool displayCorners = true;
const int maxScale = 2;
const float squareSize = 1.f; // Set this to your actual square size
// ARRAY AND VECTOR STORAGE:
vector<vector<Point2f> > imagePoints[2];
vector<vector<Point3f> > objectPoints;
Size imageSize;
int i, j, k, nimages = (int)imagelist.size()/2;
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
vector<string> goodImageList;
for( i = j = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
const string& filename = imagelist[i*2+k];
Mat img = imread(filename, 0);
if(img.empty())
break;
if( imageSize == Size() )
imageSize = img.size();
else if( img.size() != imageSize )
{
cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
break;
}
bool found = false;
vector<Point2f>& corners = imagePoints[k][j];
for( int scale = 1; scale <= maxScale; scale++ )
{
Mat timg;
if( scale == 1 )
timg = img;
else
resize(img, timg, Size(), scale, scale);
found = findChessboardCorners(timg, boardSize, corners,
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE);
if( found )
{
if( scale > 1 )
{
Mat cornersMat(corners);
cornersMat *= 1./scale;
}
break;
}
}
if( displayCorners )
{
cout << filename << endl;
Mat cimg, cimg1;
cvtColor(img, cimg, CV_GRAY2BGR);
drawChessboardCorners(cimg, boardSize, corners, found);
double sf = 640./MAX(img.rows, img.cols);
resize(cimg, cimg1, Size(), sf, sf);
imshow("corners", cimg1);
char c = (char)waitKey(500);
if( c == 27 || c == 'q' || c == 'Q' ) //Allow ESC to quit
exit(-1);
}
else
putchar('.');
if( !found )
break;
cornerSubPix(img, corners, Size(11,11), Size(-1,-1),
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
30, 0.01));
}
if( k == 2 )
{
goodImageList.push_back(imagelist[i*2]);
goodImageList.push_back(imagelist[i*2+1]);
j++;
}
}
cout << j << " pairs have been successfully detected.\n";
nimages = j;
if( nimages < 2 )
{
cout << "Error: too little pairs to run the calibration\n";
return;
}
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
objectPoints.resize(nimages);
for( i = 0; i < nimages; i++ )
{
for( j = 0; j < boardSize.height; j++ )
for( k = 0; k < boardSize.width; k++ )
objectPoints[i].push_back(Point3f(j*squareSize, k*squareSize, 0));
}
cout << "Running stereo calibration ...\n";
Mat cameraMatrix[2], distCoeffs[2];
cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
Mat R, T, E, F;
double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, E, F,
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),
CV_CALIB_FIX_ASPECT_RATIO +
CV_CALIB_ZERO_TANGENT_DIST +
//CV_CALIB_SAME_FOCAL_LENGTH +
CV_CALIB_RATIONAL_MODEL +
CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5);
cout << "done with RMS error=" << rms << endl;
// CALIBRATION QUALITY CHECK
// because the output fundamental matrix implicitly
// includes all the output information,
// we can check the quality of calibration using the
// epipolar geometry constraint: m2^t*F*m1=0
double err = 0;
int npoints = 0;
vector<Vec3f> lines[2];
for( i = 0; i < nimages; i++ )
{
int npt = (int)imagePoints[0][i].size();
Mat imgpt[2];
for( k = 0; k < 2; k++ )
{
imgpt[k] = Mat(imagePoints[k][i]);
undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
}
for( j = 0; j < npt; j++ )
{
double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
fabs(imagePoints[1][i][j].x*lines[0][j][0] +
imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
err += errij;
}
npoints += npt;
}
cout << "average reprojection err = " << err/npoints << endl;
// save intrinsic parameters
FileStorage fs("calib/intrinsics.yml", CV_STORAGE_WRITE);
if( fs.isOpened() )
{
fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
"M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
fs.release();
}
else
cout << "Error: can not save the intrinsic parameters\n";
Mat R1, R2, P1, P2, Q;
Rect validRoi[2];
stereoRectify(cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, R1, R2, P1, P2, Q,
CALIB_ZERO_DISPARITY, 1, imageSize, &validRoi[0], &validRoi[1]);
fs.open("calib/extrinsics.yml", CV_STORAGE_WRITE);
if( fs.isOpened() )
{
fs << "R" << R << "T" << T << "R1" << R1 << "R2" << R2 << "P1" << P1 << "P2" << P2 << "Q" << Q;
fs.release();
}
else
cout << "Error: can not save the intrinsic parameters\n";
// OpenCV can handle left-right
// or up-down camera arrangements
bool isVerticalStereo = fabs(P2.at<double>(1, 3)) > fabs(P2.at<double>(0, 3));
// COMPUTE AND DISPLAY RECTIFICATION
if( !showRectified )
return;
Mat rmap[2][2];
// IF BY CALIBRATED (BOUGUET'S METHOD)
if( useCalibrated )
{
// we already computed everything
}
// OR ELSE HARTLEY'S METHOD
else
// use intrinsic parameters of each camera, but
// compute the rectification transformation directly
// from the fundamental matrix
{
vector<Point2f> allimgpt[2];
for( k = 0; k < 2; k++ )
{
for( i = 0; i < nimages; i++ )
std::copy(imagePoints[k][i].begin(), imagePoints[k][i].end(), back_inserter(allimgpt[k]));
}
F = findFundamentalMat(Mat(allimgpt[0]), Mat(allimgpt[1]), FM_8POINT, 0, 0);
Mat H1, H2;
stereoRectifyUncalibrated(Mat(allimgpt[0]), Mat(allimgpt[1]), F, imageSize, H1, H2, 3);
R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
P1 = cameraMatrix[0];
P2 = cameraMatrix[1];
}
//Precompute maps for cv::remap()
initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);
Mat canvas;
double sf;
int w, h;
if( !isVerticalStereo )
{
sf = 600./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h, w*2, CV_8UC3);
}
else
{
sf = 300./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h*2, w, CV_8UC3);
}
for( i = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
Mat img = imread(goodImageList[i*2+k], 0), rimg, cimg;
remap(img, rimg, rmap[k][0], rmap[k][1], CV_INTER_LINEAR);
cvtColor(rimg, cimg, CV_GRAY2BGR);
Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
resize(cimg, canvasPart, canvasPart.size(), 0, 0, CV_INTER_AREA);
if( useCalibrated )
{
Rect vroi(cvRound(validRoi[k].x*sf), cvRound(validRoi[k].y*sf),
cvRound(validRoi[k].width*sf), cvRound(validRoi[k].height*sf));
rectangle(canvasPart, vroi, Scalar(0,0,255), 3, 8);
}
}
if( !isVerticalStereo )
for( j = 0; j < canvas.rows; j += 16 )
line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
else
for( j = 0; j < canvas.cols; j += 16 )
line(canvas, Point(j, 0), Point(j, canvas.rows), Scalar(0, 255, 0), 1, 8);
imshow("rectified", canvas);
char c = (char)waitKey();
if( c == 27 || c == 'q' || c == 'Q' )
break;
}
}
