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Open Source Computer Vision Library
https://opencv.org/
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688 lines
23 KiB
688 lines
23 KiB
#include "opencv2/core/core.hpp" |
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#include "opencv2/imgproc/imgproc.hpp" |
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#include "opencv2/calib3d/calib3d.hpp" |
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#include "opencv2/features2d/features2d.hpp" |
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#include "opencv2/highgui/highgui.hpp" |
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#include <map> |
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#include <ctype.h> |
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#include <stdio.h> |
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#include <stdlib.h> |
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using namespace cv; |
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using namespace std; |
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void myhelp() |
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{ |
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printf("\nSigh: This program is not complete/will be replaced. \n" |
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"So: Use this just to see hints of how to use things like Rodrigues\n" |
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" conversions, finding the fundamental matrix, using descriptor\n" |
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" finding and matching in features2d and using camera parameters\n" |
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); |
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} |
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static bool readCameraMatrix(const string& filename, |
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Mat& cameraMatrix, Mat& distCoeffs, |
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Size& calibratedImageSize ) |
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{ |
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FileStorage fs(filename, FileStorage::READ); |
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fs["image_width"] >> calibratedImageSize.width; |
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fs["image_height"] >> calibratedImageSize.height; |
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fs["distortion_coefficients"] >> distCoeffs; |
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fs["camera_matrix"] >> cameraMatrix; |
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if( distCoeffs.type() != CV_64F ) |
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distCoeffs = Mat_<double>(distCoeffs); |
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if( cameraMatrix.type() != CV_64F ) |
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cameraMatrix = Mat_<double>(cameraMatrix); |
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return true; |
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} |
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static bool readModelViews( const string& filename, vector<Point3f>& box, |
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vector<string>& imagelist, |
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vector<Rect>& roiList, vector<Vec6f>& poseList ) |
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{ |
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imagelist.resize(0); |
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roiList.resize(0); |
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poseList.resize(0); |
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box.resize(0); |
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FileStorage fs(filename, FileStorage::READ); |
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if( !fs.isOpened() ) |
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return false; |
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fs["box"] >> box; |
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FileNode all = fs["views"]; |
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if( all.type() != FileNode::SEQ ) |
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return false; |
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FileNodeIterator it = all.begin(), it_end = all.end(); |
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for(; it != it_end; ++it) |
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{ |
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FileNode n = *it; |
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imagelist.push_back((string)n["image"]); |
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FileNode nr = n["roi"]; |
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roiList.push_back(Rect((int)nr[0], (int)nr[1], (int)nr[2], (int)nr[3])); |
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FileNode np = n["pose"]; |
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poseList.push_back(Vec6f((float)np[0], (float)np[1], (float)np[2], |
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(float)np[3], (float)np[4], (float)np[5])); |
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} |
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return true; |
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} |
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struct PointModel |
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{ |
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vector<Point3f> points; |
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vector<vector<int> > didx; |
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Mat descriptors; |
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string name; |
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}; |
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static void writeModel(const string& modelFileName, const string& modelname, |
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const PointModel& model) |
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{ |
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FileStorage fs(modelFileName, FileStorage::WRITE); |
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fs << modelname << "{" << |
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"points" << "[:" << model.points << "]" << |
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"idx" << "[:"; |
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for( size_t i = 0; i < model.didx.size(); i++ ) |
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fs << "[:" << model.didx[i] << "]"; |
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fs << "]" << "descriptors" << model.descriptors; |
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} |
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static void unpackPose(const Vec6f& pose, Mat& R, Mat& t) |
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{ |
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Mat rvec = (Mat_<double>(3,1) << pose[0], pose[1], pose[2]); |
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t = (Mat_<double>(3,1) << pose[3], pose[4], pose[5]); |
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Rodrigues(rvec, R); |
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} |
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static Mat getFundamentalMat( const Mat& R1, const Mat& t1, |
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const Mat& R2, const Mat& t2, |
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const Mat& cameraMatrix ) |
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{ |
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Mat_<double> R = R2*R1.t(), t = t2 - R*t1; |
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double tx = t.at<double>(0,0), ty = t.at<double>(1,0), tz = t.at<double>(2,0); |
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Mat E = (Mat_<double>(3,3) << 0, -tz, ty, tz, 0, -tx, -ty, tx, 0)*R; |
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Mat iK = cameraMatrix.inv(); |
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Mat F = iK.t()*E*iK; |
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#if 0 |
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static bool checked = false; |
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if(!checked) |
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{ |
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vector<Point3f> objpoints(100); |
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Mat O(objpoints); |
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randu(O, Scalar::all(-10), Scalar::all(10)); |
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vector<Point2f> imgpoints1, imgpoints2; |
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projectPoints(Mat(objpoints), R1, t1, cameraMatrix, Mat(), imgpoints1); |
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projectPoints(Mat(objpoints), R2, t2, cameraMatrix, Mat(), imgpoints2); |
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double* f = (double*)F.data; |
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for( size_t i = 0; i < objpoints.size(); i++ ) |
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{ |
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Point2f p1 = imgpoints1[i], p2 = imgpoints2[i]; |
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double diff = p2.x*(f[0]*p1.x + f[1]*p1.y + f[2]) + |
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p2.y*(f[3]*p1.x + f[4]*p1.y + f[5]) + |
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f[6]*p1.x + f[7]*p1.y + f[8]; |
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CV_Assert(fabs(diff) < 1e-3); |
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} |
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checked = true; |
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} |
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#endif |
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return F; |
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} |
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static void findConstrainedCorrespondences(const Mat& _F, |
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const vector<KeyPoint>& keypoints1, |
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const vector<KeyPoint>& keypoints2, |
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const Mat& descriptors1, |
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const Mat& descriptors2, |
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vector<Vec2i>& matches, |
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double eps, double ratio) |
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{ |
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float F[9]={0}; |
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int dsize = descriptors1.cols; |
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Mat Fhdr = Mat(3, 3, CV_32F, F); |
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_F.convertTo(Fhdr, CV_32F); |
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matches.clear(); |
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for( size_t i = 0; i < keypoints1.size(); i++ ) |
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{ |
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Point2f p1 = keypoints1[i].pt; |
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double bestDist1 = DBL_MAX, bestDist2 = DBL_MAX; |
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int bestIdx1 = -1, bestIdx2 = -1; |
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const float* d1 = descriptors1.ptr<float>(i); |
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for( size_t j = 0; j < keypoints2.size(); j++ ) |
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{ |
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Point2f p2 = keypoints2[j].pt; |
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double e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) + |
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p2.y*(F[3]*p1.x + F[4]*p1.y + F[5]) + |
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F[6]*p1.x + F[7]*p1.y + F[8]; |
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if( fabs(e) > eps ) |
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continue; |
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const float* d2 = descriptors2.ptr<float>(j); |
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double dist = 0; |
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int k = 0; |
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for( ; k <= dsize - 8; k += 8 ) |
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{ |
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float t0 = d1[k] - d2[k], t1 = d1[k+1] - d2[k+1]; |
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float t2 = d1[k+2] - d2[k+2], t3 = d1[k+3] - d2[k+3]; |
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float t4 = d1[k+4] - d2[k+4], t5 = d1[k+5] - d2[k+5]; |
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float t6 = d1[k+6] - d2[k+6], t7 = d1[k+7] - d2[k+7]; |
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dist += t0*t0 + t1*t1 + t2*t2 + t3*t3 + |
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t4*t4 + t5*t5 + t6*t6 + t7*t7; |
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if( dist >= bestDist2 ) |
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break; |
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} |
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if( dist < bestDist2 ) |
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{ |
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for( ; k < dsize; k++ ) |
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{ |
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float t = d1[k] - d2[k]; |
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dist += t*t; |
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} |
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if( dist < bestDist1 ) |
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{ |
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bestDist2 = bestDist1; |
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bestIdx2 = bestIdx1; |
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bestDist1 = dist; |
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bestIdx1 = (int)j; |
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} |
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else if( dist < bestDist2 ) |
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{ |
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bestDist2 = dist; |
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bestIdx2 = (int)j; |
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} |
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} |
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} |
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if( bestIdx1 >= 0 && bestDist1 < bestDist2*ratio ) |
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{ |
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Point2f p2 = keypoints1[bestIdx1].pt; |
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double e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) + |
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p2.y*(F[3]*p1.x + F[4]*p1.y + F[5]) + |
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F[6]*p1.x + F[7]*p1.y + F[8]; |
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if( e > eps*0.25 ) |
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continue; |
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double threshold = bestDist1/ratio; |
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const float* d22 = descriptors2.ptr<float>(bestIdx1); |
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size_t i1 = 0; |
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for( ; i1 < keypoints1.size(); i1++ ) |
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{ |
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if( i1 == i ) |
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continue; |
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Point2f p1 = keypoints1[i1].pt; |
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const float* d11 = descriptors1.ptr<float>(i1); |
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double dist = 0; |
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e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) + |
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p2.y*(F[3]*p1.x + F[4]*p1.y + F[5]) + |
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F[6]*p1.x + F[7]*p1.y + F[8]; |
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if( fabs(e) > eps ) |
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continue; |
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for( int k = 0; k < dsize; k++ ) |
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{ |
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float t = d11[k] - d22[k]; |
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dist += t*t; |
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if( dist >= threshold ) |
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break; |
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} |
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if( dist < threshold ) |
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break; |
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} |
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if( i1 == keypoints1.size() ) |
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matches.push_back(Vec2i(i,bestIdx1)); |
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} |
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} |
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} |
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static Point3f findRayIntersection(Point3f k1, Point3f b1, Point3f k2, Point3f b2) |
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{ |
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float a[4], b[2], x[2]; |
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a[0] = k1.dot(k1); |
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a[1] = a[2] = -k1.dot(k2); |
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a[3] = k2.dot(k2); |
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b[0] = k1.dot(b2 - b1); |
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b[1] = k2.dot(b1 - b2); |
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Mat_<float> A(2, 2, a), B(2, 1, b), X(2, 1, x); |
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solve(A, B, X); |
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float s1 = X.at<float>(0, 0); |
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float s2 = X.at<float>(1, 0); |
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return (k1*s1 + b1 + k2*s2 + b2)*0.5f; |
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} |
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static Point3f triangulatePoint(const vector<Point2f>& ps, |
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const vector<Mat>& Rs, |
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const vector<Mat>& ts, |
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const Mat& cameraMatrix) |
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{ |
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Mat_<double> K(cameraMatrix); |
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/*if( ps.size() > 2 ) |
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{ |
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Mat_<double> L(ps.size()*3, 4), U, evalues; |
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Mat_<double> P(3,4), Rt(3,4), Rt_part1=Rt.colRange(0,3), Rt_part2=Rt.colRange(3,4); |
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for( size_t i = 0; i < ps.size(); i++ ) |
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{ |
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double x = ps[i].x, y = ps[i].y; |
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Rs[i].convertTo(Rt_part1, Rt_part1.type()); |
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ts[i].convertTo(Rt_part2, Rt_part2.type()); |
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P = K*Rt; |
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for( int k = 0; k < 4; k++ ) |
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{ |
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L(i*3, k) = x*P(2,k) - P(0,k); |
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L(i*3+1, k) = y*P(2,k) - P(1,k); |
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L(i*3+2, k) = x*P(1,k) - y*P(0,k); |
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} |
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} |
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eigen(L.t()*L, evalues, U); |
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CV_Assert(evalues(0,0) >= evalues(3,0)); |
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double W = fabs(U(3,3)) > FLT_EPSILON ? 1./U(3,3) : 0; |
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return Point3f((float)(U(3,0)*W), (float)(U(3,1)*W), (float)(U(3,2)*W)); |
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} |
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else*/ |
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{ |
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Mat_<float> iK = K.inv(); |
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Mat_<float> R1t = Mat_<float>(Rs[0]).t(); |
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Mat_<float> R2t = Mat_<float>(Rs[1]).t(); |
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Mat_<float> m1 = (Mat_<float>(3,1) << ps[0].x, ps[0].y, 1); |
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Mat_<float> m2 = (Mat_<float>(3,1) << ps[1].x, ps[1].y, 1); |
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Mat_<float> K1 = R1t*(iK*m1), K2 = R2t*(iK*m2); |
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Mat_<float> B1 = -R1t*Mat_<float>(ts[0]); |
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Mat_<float> B2 = -R2t*Mat_<float>(ts[1]); |
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return findRayIntersection(*K1.ptr<Point3f>(), *B1.ptr<Point3f>(), |
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*K2.ptr<Point3f>(), *B2.ptr<Point3f>()); |
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} |
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} |
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void triangulatePoint_test(void) |
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{ |
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int i, n = 100; |
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vector<Point3f> objpt(n), delta1(n), delta2(n); |
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Mat rvec1(3,1,CV_32F), tvec1(3,1,CV_64F); |
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Mat rvec2(3,1,CV_32F), tvec2(3,1,CV_64F); |
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Mat objptmat(objpt), deltamat1(delta1), deltamat2(delta2); |
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randu(rvec1, Scalar::all(-10), Scalar::all(10)); |
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randu(tvec1, Scalar::all(-10), Scalar::all(10)); |
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randu(rvec2, Scalar::all(-10), Scalar::all(10)); |
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randu(tvec2, Scalar::all(-10), Scalar::all(10)); |
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randu(objptmat, Scalar::all(-10), Scalar::all(10)); |
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double eps = 1e-2; |
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randu(deltamat1, Scalar::all(-eps), Scalar::all(eps)); |
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randu(deltamat2, Scalar::all(-eps), Scalar::all(eps)); |
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vector<Point2f> imgpt1, imgpt2; |
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Mat_<float> cameraMatrix(3,3); |
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double fx = 1000., fy = 1010., cx = 400.5, cy = 300.5; |
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cameraMatrix << fx, 0, cx, 0, fy, cy, 0, 0, 1; |
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projectPoints(Mat(objpt)+Mat(delta1), rvec1, tvec1, cameraMatrix, Mat(), imgpt1); |
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projectPoints(Mat(objpt)+Mat(delta2), rvec2, tvec2, cameraMatrix, Mat(), imgpt2); |
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vector<Point3f> objptt(n); |
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vector<Point2f> pts(2); |
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vector<Mat> Rv(2), tv(2); |
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Rodrigues(rvec1, Rv[0]); |
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Rodrigues(rvec2, Rv[1]); |
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tv[0] = tvec1; tv[1] = tvec2; |
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for( i = 0; i < n; i++ ) |
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{ |
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pts[0] = imgpt1[i]; pts[1] = imgpt2[i]; |
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objptt[i] = triangulatePoint(pts, Rv, tv, cameraMatrix); |
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} |
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double err = norm(Mat(objpt), Mat(objptt), CV_C); |
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CV_Assert(err < 1e-1); |
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} |
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typedef pair<int, int> Pair2i; |
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typedef map<Pair2i, int> Set2i; |
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struct EqKeypoints |
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{ |
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EqKeypoints(const vector<int>* _dstart, const Set2i* _pairs) |
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: dstart(_dstart), pairs(_pairs) {} |
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bool operator()(const Pair2i& a, const Pair2i& b) const |
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{ |
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return pairs->find(Pair2i(dstart->at(a.first) + a.second, |
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dstart->at(b.first) + b.second)) != pairs->end(); |
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} |
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const vector<int>* dstart; |
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const Set2i* pairs; |
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}; |
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static void build3dmodel( const Ptr<FeatureDetector>& detector, |
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const Ptr<DescriptorExtractor>& descriptorExtractor, |
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const vector<Point3f>& /*modelBox*/, |
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const vector<string>& imageList, |
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const vector<Rect>& roiList, |
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const vector<Vec6f>& poseList, |
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const Mat& cameraMatrix, |
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PointModel& model ) |
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{ |
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int progressBarSize = 10; |
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const double Feps = 5; |
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const double DescriptorRatio = 0.7; |
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vector<vector<KeyPoint> > allkeypoints; |
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vector<int> dstart; |
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vector<float> alldescriptorsVec; |
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vector<Vec2i> pairwiseMatches; |
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vector<Mat> Rs, ts; |
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int descriptorSize = 0; |
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Mat descriptorbuf; |
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Set2i pairs, keypointsIdxMap; |
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model.points.clear(); |
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model.didx.clear(); |
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dstart.push_back(0); |
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size_t nimages = imageList.size(); |
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size_t nimagePairs = (nimages - 1)*nimages/2 - nimages; |
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printf("\nComputing descriptors "); |
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// 1. find all the keypoints and all the descriptors |
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for( size_t i = 0; i < nimages; i++ ) |
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{ |
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Mat img = imread(imageList[i], 1), gray; |
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cvtColor(img, gray, CV_BGR2GRAY); |
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vector<KeyPoint> keypoints; |
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detector->detect(gray, keypoints); |
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descriptorExtractor->compute(gray, keypoints, descriptorbuf); |
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Point2f roiofs = roiList[i].tl(); |
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for( size_t k = 0; k < keypoints.size(); k++ ) |
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keypoints[k].pt += roiofs; |
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allkeypoints.push_back(keypoints); |
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Mat buf = descriptorbuf; |
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if( !buf.isContinuous() || buf.type() != CV_32F ) |
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{ |
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buf.release(); |
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descriptorbuf.convertTo(buf, CV_32F); |
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} |
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descriptorSize = buf.cols; |
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size_t prev = alldescriptorsVec.size(); |
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size_t delta = buf.rows*buf.cols; |
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alldescriptorsVec.resize(prev + delta); |
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std::copy(buf.ptr<float>(), buf.ptr<float>() + delta, |
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alldescriptorsVec.begin() + prev); |
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dstart.push_back(dstart.back() + keypoints.size()); |
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Mat R, t; |
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unpackPose(poseList[i], R, t); |
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Rs.push_back(R); |
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ts.push_back(t); |
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if( (i+1)*progressBarSize/nimages > i*progressBarSize/nimages ) |
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{ |
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putchar('.'); |
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fflush(stdout); |
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} |
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} |
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Mat alldescriptors(alldescriptorsVec.size()/descriptorSize, descriptorSize, CV_32F, |
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&alldescriptorsVec[0]); |
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printf("\nOk. total images = %d. total keypoints = %d\n", |
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(int)nimages, alldescriptors.rows); |
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printf("\nFinding correspondences "); |
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int pairsFound = 0; |
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vector<Point2f> pts_k(2); |
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vector<Mat> Rs_k(2), ts_k(2); |
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//namedWindow("img1", 1); |
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//namedWindow("img2", 1); |
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// 2. find pairwise correspondences |
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for( size_t i = 0; i < nimages; i++ ) |
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for( size_t j = i+1; j < nimages; j++ ) |
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{ |
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const vector<KeyPoint>& keypoints1 = allkeypoints[i]; |
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const vector<KeyPoint>& keypoints2 = allkeypoints[j]; |
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Mat descriptors1 = alldescriptors.rowRange(dstart[i], dstart[i+1]); |
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Mat descriptors2 = alldescriptors.rowRange(dstart[j], dstart[j+1]); |
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Mat F = getFundamentalMat(Rs[i], ts[i], Rs[j], ts[j], cameraMatrix); |
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findConstrainedCorrespondences( F, keypoints1, keypoints2, |
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descriptors1, descriptors2, |
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pairwiseMatches, Feps, DescriptorRatio ); |
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//pairsFound += (int)pairwiseMatches.size(); |
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//Mat img1 = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)i), 1); |
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//Mat img2 = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)j), 1); |
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//double avg_err = 0; |
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for( size_t k = 0; k < pairwiseMatches.size(); k++ ) |
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{ |
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int i1 = pairwiseMatches[k][0], i2 = pairwiseMatches[k][1]; |
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pts_k[0] = keypoints1[i1].pt; |
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pts_k[1] = keypoints2[i2].pt; |
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Rs_k[0] = Rs[i]; Rs_k[1] = Rs[j]; |
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ts_k[0] = ts[i]; ts_k[1] = ts[j]; |
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Point3f objpt = triangulatePoint(pts_k, Rs_k, ts_k, cameraMatrix); |
|
|
|
vector<Point3f> objpts; |
|
objpts.push_back(objpt); |
|
vector<Point2f> imgpts1, imgpts2; |
|
projectPoints(Mat(objpts), Rs_k[0], ts_k[0], cameraMatrix, Mat(), imgpts1); |
|
projectPoints(Mat(objpts), Rs_k[1], ts_k[1], cameraMatrix, Mat(), imgpts2); |
|
|
|
double e1 = norm(imgpts1[0] - keypoints1[i1].pt); |
|
double e2 = norm(imgpts2[0] - keypoints2[i2].pt); |
|
if( e1 + e2 > 5 ) |
|
continue; |
|
|
|
pairsFound++; |
|
|
|
//model.points.push_back(objpt); |
|
pairs[Pair2i(i1+dstart[i], i2+dstart[j])] = 1; |
|
pairs[Pair2i(i2+dstart[j], i1+dstart[i])] = 1; |
|
keypointsIdxMap[Pair2i(i,i1)] = 1; |
|
keypointsIdxMap[Pair2i(j,i2)] = 1; |
|
//CV_Assert(e1 < 5 && e2 < 5); |
|
//Scalar color(rand()%256,rand()%256, rand()%256); |
|
//circle(img1, keypoints1[i1].pt, 2, color, -1, CV_AA); |
|
//circle(img2, keypoints2[i2].pt, 2, color, -1, CV_AA); |
|
} |
|
//printf("avg err = %g\n", pairwiseMatches.size() ? avg_err/(2*pairwiseMatches.size()) : 0.); |
|
//imshow("img1", img1); |
|
//imshow("img2", img2); |
|
//waitKey(); |
|
|
|
if( (i+1)*progressBarSize/nimagePairs > i*progressBarSize/nimagePairs ) |
|
{ |
|
putchar('.'); |
|
fflush(stdout); |
|
} |
|
} |
|
|
|
printf("\nOk. Total pairs = %d\n", pairsFound ); |
|
|
|
// 3. build the keypoint clusters |
|
vector<Pair2i> keypointsIdx; |
|
Set2i::iterator kpidx_it = keypointsIdxMap.begin(), kpidx_end = keypointsIdxMap.end(); |
|
|
|
for( ; kpidx_it != kpidx_end; ++kpidx_it ) |
|
keypointsIdx.push_back(kpidx_it->first); |
|
|
|
printf("\nClustering correspondences "); |
|
|
|
vector<int> labels; |
|
int nclasses = partition( keypointsIdx, labels, EqKeypoints(&dstart, &pairs) ); |
|
|
|
printf("\nOk. Total classes (i.e. 3d points) = %d\n", nclasses ); |
|
|
|
model.descriptors.create(keypointsIdx.size(), descriptorSize, CV_32F); |
|
model.didx.resize(nclasses); |
|
model.points.resize(nclasses); |
|
|
|
vector<vector<Pair2i> > clusters(nclasses); |
|
for( size_t i = 0; i < keypointsIdx.size(); i++ ) |
|
clusters[labels[i]].push_back(keypointsIdx[i]); |
|
|
|
// 4. now compute 3D points corresponding to each cluster and fill in the model data |
|
printf("\nComputing 3D coordinates "); |
|
|
|
int globalDIdx = 0; |
|
for( int k = 0; k < nclasses; k++ ) |
|
{ |
|
int i, n = (int)clusters[k].size(); |
|
pts_k.resize(n); |
|
Rs_k.resize(n); |
|
ts_k.resize(n); |
|
model.didx[k].resize(n); |
|
for( i = 0; i < n; i++ ) |
|
{ |
|
int imgidx = clusters[k][i].first, ptidx = clusters[k][i].second; |
|
Mat dstrow = model.descriptors.row(globalDIdx); |
|
alldescriptors.row(dstart[imgidx] + ptidx).copyTo(dstrow); |
|
|
|
model.didx[k][i] = globalDIdx++; |
|
pts_k[i] = allkeypoints[imgidx][ptidx].pt; |
|
Rs_k[i] = Rs[imgidx]; |
|
ts_k[i] = ts[imgidx]; |
|
} |
|
Point3f objpt = triangulatePoint(pts_k, Rs_k, ts_k, cameraMatrix); |
|
model.points[k] = objpt; |
|
|
|
if( (i+1)*progressBarSize/nclasses > i*progressBarSize/nclasses ) |
|
{ |
|
putchar('.'); |
|
fflush(stdout); |
|
} |
|
} |
|
|
|
Mat img(768, 1024, CV_8UC3); |
|
vector<Point2f> imagePoints; |
|
namedWindow("Test", 1); |
|
|
|
// visualize the cloud |
|
for( size_t i = 0; i < nimages; i++ ) |
|
{ |
|
img = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)i), 1); |
|
projectPoints(Mat(model.points), Rs[i], ts[i], cameraMatrix, Mat(), imagePoints); |
|
|
|
for( int k = 0; k < (int)imagePoints.size(); k++ ) |
|
circle(img, imagePoints[k], 2, Scalar(0,255,0), -1, CV_AA, 0); |
|
|
|
imshow("Test", img); |
|
int c = waitKey(); |
|
if( c == 'q' || c == 'Q' ) |
|
break; |
|
} |
|
} |
|
|
|
|
|
int main(int argc, char** argv) |
|
{ |
|
triangulatePoint_test(); |
|
|
|
const char* help = "Usage: build3dmodel -i <intrinsics_filename>\n" |
|
"\t[-d <detector>] [-de <descriptor_extractor>] -m <model_name>\n\n"; |
|
|
|
if(argc < 3) |
|
{ |
|
puts(help); |
|
myhelp(); |
|
return 0; |
|
} |
|
const char* intrinsicsFilename = 0; |
|
const char* modelName = 0; |
|
const char* detectorName = "SURF"; |
|
const char* descriptorExtractorName = "SURF"; |
|
vector<Point3f> modelBox; |
|
vector<string> imageList; |
|
vector<Rect> roiList; |
|
vector<Vec6f> poseList; |
|
|
|
for( int i = 1; i < argc; i++ ) |
|
{ |
|
if( strcmp(argv[i], "-i") == 0 ) |
|
intrinsicsFilename = argv[++i]; |
|
else if( strcmp(argv[i], "-m") == 0 ) |
|
modelName = argv[++i]; |
|
else if( strcmp(argv[i], "-d") == 0 ) |
|
detectorName = argv[++i]; |
|
else if( strcmp(argv[i], "-de") == 0 ) |
|
descriptorExtractorName = argv[++i]; |
|
else |
|
{ |
|
printf("Incorrect option\n"); |
|
puts(help); |
|
return 0; |
|
} |
|
} |
|
|
|
if( !intrinsicsFilename || !modelName ) |
|
{ |
|
printf("Some of the required parameters are missing\n"); |
|
puts(help); |
|
return 0; |
|
} |
|
|
|
Mat cameraMatrix, distCoeffs; |
|
Size calibratedImageSize; |
|
readCameraMatrix(intrinsicsFilename, cameraMatrix, distCoeffs, calibratedImageSize); |
|
|
|
Ptr<FeatureDetector> detector = FeatureDetector::create(detectorName); |
|
Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create(descriptorExtractorName); |
|
|
|
string modelIndexFilename = format("%s_segm/frame_index.yml", modelName); |
|
if(!readModelViews( modelIndexFilename, modelBox, imageList, roiList, poseList)) |
|
{ |
|
printf("Can not read the model. Check the parameters and the working directory\n"); |
|
puts(help); |
|
return 0; |
|
} |
|
|
|
PointModel model; |
|
model.name = modelName; |
|
build3dmodel( detector, descriptorExtractor, modelBox, |
|
imageList, roiList, poseList, cameraMatrix, model ); |
|
string outputModelName = format("%s_model.yml.gz", modelName); |
|
|
|
|
|
printf("\nDone! Now saving the model ...\n"); |
|
writeModel(outputModelName, modelName, model); |
|
|
|
return 0; |
|
}
|
|
|