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779 lines
25 KiB
C++
779 lines
25 KiB
C++
#include "opencv2/core.hpp"
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#include <opencv2/core/utility.hpp>
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#include "opencv2/imgproc.hpp"
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#include "opencv2/calib3d.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/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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static void help()
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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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"Usage: build3dmodel -i <intrinsics_filename>\n"
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"\t[-d <detector>] [-de <descriptor_extractor>] -m <model_name>\n\n");
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return;
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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( int i = 0; i < (int)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( int j = 0; j < (int)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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int i1 = 0;
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for( ; i1 < (int)keypoints1.size(); i1++ )
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{
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if( i1 == i )
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continue;
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Point2f pt1 = 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]*pt1.x + F[1]*pt1.y + F[2]) +
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p2.y*(F[3]*pt1.x + F[4]*pt1.y + F[5]) +
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F[6]*pt1.x + F[7]*pt1.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 == (int)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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static 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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template<typename _Tp, class _EqPredicate> static
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int partition( const std::vector<_Tp>& _vec, std::vector<int>& labels,
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_EqPredicate predicate=_EqPredicate())
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{
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int i, j, N = (int)_vec.size();
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const _Tp* vec = &_vec[0];
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const int PARENT=0;
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const int RANK=1;
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std::vector<int> _nodes(N*2);
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int (*nodes)[2] = (int(*)[2])&_nodes[0];
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// The first O(N) pass: create N single-vertex trees
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for(i = 0; i < N; i++)
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{
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nodes[i][PARENT]=-1;
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nodes[i][RANK] = 0;
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}
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// The main O(N^2) pass: merge connected components
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for( i = 0; i < N; i++ )
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{
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int root = i;
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// find root
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while( nodes[root][PARENT] >= 0 )
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root = nodes[root][PARENT];
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for( j = 0; j < N; j++ )
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{
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if( i == j || !predicate(vec[i], vec[j]))
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continue;
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int root2 = j;
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while( nodes[root2][PARENT] >= 0 )
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root2 = nodes[root2][PARENT];
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if( root2 != root )
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{
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// unite both trees
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int rank = nodes[root][RANK], rank2 = nodes[root2][RANK];
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if( rank > rank2 )
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nodes[root2][PARENT] = root;
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else
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{
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nodes[root][PARENT] = root2;
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nodes[root2][RANK] += rank == rank2;
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root = root2;
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}
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CV_Assert( nodes[root][PARENT] < 0 );
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int k = j, parent;
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// compress the path from node2 to root
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while( (parent = nodes[k][PARENT]) >= 0 )
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{
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nodes[k][PARENT] = root;
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k = parent;
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}
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// compress the path from node to root
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k = i;
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while( (parent = nodes[k][PARENT]) >= 0 )
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{
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nodes[k][PARENT] = root;
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k = parent;
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}
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}
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}
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}
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// Final O(N) pass: enumerate classes
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labels.resize(N);
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int nclasses = 0;
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for( i = 0; i < N; i++ )
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{
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int root = i;
|
|
while( nodes[root][PARENT] >= 0 )
|
|
root = nodes[root][PARENT];
|
|
// re-use the rank as the class label
|
|
if( nodes[root][RANK] >= 0 )
|
|
nodes[root][RANK] = ~nclasses++;
|
|
labels[i] = ~nodes[root][RANK];
|
|
}
|
|
|
|
return nclasses;
|
|
}
|
|
|
|
static void build3dmodel( const Ptr<FeatureDetector>& detector,
|
|
const Ptr<DescriptorExtractor>& descriptorExtractor,
|
|
const vector<Point3f>& /*modelBox*/,
|
|
const vector<string>& imageList,
|
|
const vector<Rect>& roiList,
|
|
const vector<Vec6f>& poseList,
|
|
const Mat& cameraMatrix,
|
|
PointModel& model )
|
|
{
|
|
int progressBarSize = 10;
|
|
|
|
const double Feps = 5;
|
|
const double DescriptorRatio = 0.7;
|
|
|
|
vector<vector<KeyPoint> > allkeypoints;
|
|
vector<int> dstart;
|
|
vector<float> alldescriptorsVec;
|
|
vector<Vec2i> pairwiseMatches;
|
|
vector<Mat> Rs, ts;
|
|
int descriptorSize = 0;
|
|
Mat descriptorbuf;
|
|
Set2i pairs, keypointsIdxMap;
|
|
|
|
model.points.clear();
|
|
model.didx.clear();
|
|
|
|
dstart.push_back(0);
|
|
|
|
size_t nimages = imageList.size();
|
|
size_t nimagePairs = (nimages - 1)*nimages/2 - nimages;
|
|
|
|
printf("\nComputing descriptors ");
|
|
|
|
// 1. find all the keypoints and all the descriptors
|
|
for( size_t i = 0; i < nimages; i++ )
|
|
{
|
|
Mat img = imread(imageList[i], 1), gray;
|
|
cvtColor(img, gray, CV_BGR2GRAY);
|
|
|
|
vector<KeyPoint> keypoints;
|
|
detector->detect(gray, keypoints);
|
|
descriptorExtractor->compute(gray, keypoints, descriptorbuf);
|
|
Point2f roiofs = roiList[i].tl();
|
|
for( size_t k = 0; k < keypoints.size(); k++ )
|
|
keypoints[k].pt += roiofs;
|
|
allkeypoints.push_back(keypoints);
|
|
|
|
Mat buf = descriptorbuf;
|
|
if( !buf.isContinuous() || buf.type() != CV_32F )
|
|
{
|
|
buf.release();
|
|
descriptorbuf.convertTo(buf, CV_32F);
|
|
}
|
|
descriptorSize = buf.cols;
|
|
|
|
size_t prev = alldescriptorsVec.size();
|
|
size_t delta = buf.rows*buf.cols;
|
|
alldescriptorsVec.resize(prev + delta);
|
|
std::copy(buf.ptr<float>(), buf.ptr<float>() + delta,
|
|
alldescriptorsVec.begin() + prev);
|
|
dstart.push_back(dstart.back() + (int)keypoints.size());
|
|
|
|
Mat R, t;
|
|
unpackPose(poseList[i], R, t);
|
|
Rs.push_back(R);
|
|
ts.push_back(t);
|
|
|
|
if( (i+1)*progressBarSize/nimages > i*progressBarSize/nimages )
|
|
{
|
|
putchar('.');
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
Mat alldescriptors((int)alldescriptorsVec.size()/descriptorSize, descriptorSize, CV_32F,
|
|
&alldescriptorsVec[0]);
|
|
|
|
printf("\nOk. total images = %d. total keypoints = %d\n",
|
|
(int)nimages, alldescriptors.rows);
|
|
|
|
printf("\nFinding correspondences ");
|
|
|
|
int pairsFound = 0;
|
|
|
|
vector<Point2f> pts_k(2);
|
|
vector<Mat> Rs_k(2), ts_k(2);
|
|
//namedWindow("img1", 1);
|
|
//namedWindow("img2", 1);
|
|
|
|
// 2. find pairwise correspondences
|
|
for( size_t i = 0; i < nimages; i++ )
|
|
for( size_t j = i+1; j < nimages; j++ )
|
|
{
|
|
const vector<KeyPoint>& keypoints1 = allkeypoints[i];
|
|
const vector<KeyPoint>& keypoints2 = allkeypoints[j];
|
|
Mat descriptors1 = alldescriptors.rowRange(dstart[i], dstart[i+1]);
|
|
Mat descriptors2 = alldescriptors.rowRange(dstart[j], dstart[j+1]);
|
|
|
|
Mat F = getFundamentalMat(Rs[i], ts[i], Rs[j], ts[j], cameraMatrix);
|
|
|
|
findConstrainedCorrespondences( F, keypoints1, keypoints2,
|
|
descriptors1, descriptors2,
|
|
pairwiseMatches, Feps, DescriptorRatio );
|
|
|
|
//pairsFound += (int)pairwiseMatches.size();
|
|
|
|
//Mat img1 = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)i), 1);
|
|
//Mat img2 = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)j), 1);
|
|
|
|
//double avg_err = 0;
|
|
for( size_t k = 0; k < pairwiseMatches.size(); k++ )
|
|
{
|
|
int i1 = pairwiseMatches[k][0], i2 = pairwiseMatches[k][1];
|
|
|
|
pts_k[0] = keypoints1[i1].pt;
|
|
pts_k[1] = keypoints2[i2].pt;
|
|
Rs_k[0] = Rs[i]; Rs_k[1] = Rs[j];
|
|
ts_k[0] = ts[i]; ts_k[1] = ts[j];
|
|
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((int)i,i1)] = 1;
|
|
keypointsIdxMap[Pair2i((int)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((int)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)
|
|
{
|
|
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;
|
|
|
|
if(argc < 3)
|
|
{
|
|
help();
|
|
return -1;
|
|
}
|
|
|
|
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
|
|
{
|
|
help();
|
|
printf("Incorrect option\n");
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
if( !intrinsicsFilename || !modelName )
|
|
{
|
|
printf("Some of the required parameters are missing\n");
|
|
help();
|
|
return -1;
|
|
}
|
|
|
|
triangulatePoint_test();
|
|
|
|
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");
|
|
help();
|
|
return -1;
|
|
}
|
|
|
|
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;
|
|
}
|