ransac.rar_RANSAC_RANSAC算法_ransac c++

#include <math.h> #include "LineParamEstimator.h" LineParamEstimator::LineParamEstimator(double delta) : m_deltaSquared(delta*delta) {} /*****************************************************************************/ /* * Compute the line parameters [n_x,n_y,a_x,a_y] */ void LineParamEstimator::estimate(std::vector<Point2D *> &data, std::vector<double> &parameters) { parameters.clear(); if(data.size()<2) return; double nx = data[1]->y - data[0]->y; double ny = data[0]->x - data[1]->x; double norm = sqrt(nx*nx + ny*ny); parameters.push_back(nx/norm); parameters.push_back(ny/norm); parameters.push_back(data[0]->x); parameters.push_back(data[0]->y); } /*****************************************************************************/ /* * Compute the line parameters [n_x,n_y,a_x,a_y] */ void LineParamEstimator::leastSquaresEstimate(std::vector<Point2D *> &data, std::vector<double> &parameters) { double meanX, meanY, nx, ny, norm; double covMat11, covMat12, covMat21, covMat22; // The entries of the symmetric covarinace matrix int i, dataSize = data.size(); parameters.clear(); if(data.size()<2) return; meanX = meanY = 0.0; covMat11 = covMat12 = covMat21 = covMat22 = 0; for(i=0; i<dataSize; i++) { meanX +=data[i]->x; meanY +=data[i]->y; covMat11 +=data[i]->x * data[i]->x; covMat12 +=data[i]->x * data[i]->y; covMat22 +=data[i]->y * data[i]->y; } meanX/=dataSize; meanY/=dataSize; covMat11 -= dataSize*meanX*meanX; covMat12 -= dataSize*meanX*meanY; covMat22 -= dataSize*meanY*meanY; covMat21 = covMat12; if(covMat11<1e-12) { nx = 1.0; ny = 0.0; } else { //lamda1 is the largest eigen-value of the covariance matrix //and is used to compute the eigne-vector corresponding to the smallest //eigenvalue, which isn't computed explicitly. double lamda1 = (covMat11 + covMat22 + sqrt((covMat11-covMat22)*(covMat11-covMat22) + 4*covMat12*covMat12)) / 2.0; nx = -covMat12; ny = lamda1 - covMat22; norm = sqrt(nx*nx + ny*ny); nx/=norm; ny/=norm; } parameters.push_back(nx); parameters.push_back(ny); parameters.push_back(meanX); parameters.push_back(meanY); } /*****************************************************************************/ /* * Given the line parameters [n_x,n_y,a_x,a_y] check if * [n_x, n_y] dot [data.x-a_x, data.y-a_y] < m_delta */ bool LineParamEstimator::agree(std::vector<double> &parameters, Point2D &data) { double signedDistance = parameters[0]*(data.x-parameters[2]) + parameters[1]*(data.y-parameters[3]); return ((signedDistance*signedDistance) < m_deltaSquared); } /*****************************************************************************/ void LineParamEstimator::debugTest(ostream &out) { std::vector<double> lineParameters; LineParamEstimator lpEstimator(0.5); std::vector<Point2D *> pointData; pointData.push_back(new Point2D(7,7)); pointData.push_back(new Point2D(-1,-1)); lpEstimator.estimate(pointData,lineParameters); out<<"[n_x,n_y,a_x,a_y] [ "<<lineParameters[0]<<", "<<lineParameters[1]<<", "; out<<lineParameters[2]<<", "<<lineParameters[3]<<" ]"<<endl; lpEstimator.leastSquaresEstimate(pointData,lineParameters); out<<"[n_x,n_y,a_x,a_y] [ "<<lineParameters[0]<<", "<<lineParameters[1]<<", "; out<<lineParameters[2]<<", "<<lineParameters[3]<<" ]"<<endl; delete pointData[0]; delete pointData[1]; pointData.clear(); pointData.push_back(new Point2D(6,12)); pointData.push_back(new Point2D(6,6)); lpEstimator.estimate(pointData,lineParameters); out<<"[n_x,n_y,a_x,a_y] [ "<<lineParameters[0]<<", "<<lineParameters[1]<<", "; out<<lineParameters[2]<<", "<<lineParameters[3]<<" ]"<<endl; lpEstimator.leastSquaresEstimate(pointData,lineParameters); out<<"[n_x,n_y,a_x,a_y] [ "<<lineParameters[0]<<", "<<lineParameters[1]<<", "; out<<lineParameters[2]<<", "<<lineParameters[3]<<" ]"<<endl; delete pointData[0]; delete pointData[1]; pointData.clear(); pointData.push_back(new Point2D(7,9)); pointData.push_back(new Point2D(10,9)); lpEstimator.estimate(pointData,lineParameters); out<<"[n_x,n_y,a_x,a_y] [ "<<lineParameters[0]<<", "<<lineParameters[1]<<", "; out<<lineParameters[2]<<", "<<lineParameters[3]<<" ]"<<endl; lpEstimator.leastSquaresEstimate(pointData,lineParameters); out<<"[n_x,n_y,a_x,a_y] [ "<<lineParameters[0]<<", "<<lineParameters[1]<<", "; out<<lineParameters[2]<<", "<<lineParameters[3]<<" ]"<<endl; delete pointData[0]; delete pointData[1]; pointData.clear(); }