//---------------------------------------- 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; } }