From 6f855c4f297fd7bcca8253c3fe1a83118ff5a9d6 Mon Sep 17 00:00:00 2001 From: onlyliucat <362997191@qq.com> Date: Wed, 28 Aug 2019 15:41:46 +0800 Subject: [PATCH] feat:init add all file --- .gitignore | 73 ++++ 02.png | Bin 0 -> 687133 bytes CMakeLists.txt | 26 ++ ChessboradStruct.cpp | 785 ++++++++++++++++++++++++++++++++++++++++ ChessboradStruct.h | 32 ++ CornerDetAC.cpp | 826 +++++++++++++++++++++++++++++++++++++++++++ CornerDetAC.h | 65 ++++ HeaderCB.h | 75 ++++ corealgmatlab.cpp | 41 +++ corealgmatlab.h | 21 ++ main.cpp | 88 +++++ 11 files changed, 2032 insertions(+) create mode 100644 .gitignore create mode 100644 02.png create mode 100644 CMakeLists.txt create mode 100644 ChessboradStruct.cpp create mode 100644 ChessboradStruct.h create mode 100644 CornerDetAC.cpp create mode 100644 CornerDetAC.h create mode 100644 HeaderCB.h create mode 100644 corealgmatlab.cpp create mode 100644 corealgmatlab.h create mode 100644 main.cpp diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..fab7372 --- /dev/null +++ b/.gitignore @@ -0,0 +1,73 @@ +# This file is used to ignore files which are generated +# ---------------------------------------------------------------------------- + +*~ +*.autosave +*.a +*.core +*.moc +*.o +*.obj +*.orig +*.rej +*.so +*.so.* +*_pch.h.cpp +*_resource.rc +*.qm +.#* +*.*# +core +!core/ +tags +.DS_Store +.directory +*.debug +Makefile* +*.prl +*.app +moc_*.cpp +ui_*.h +qrc_*.cpp +Thumbs.db +*.res +*.rc +/.qmake.cache +/.qmake.stash + +# qtcreator generated files +*.pro.user* + +# xemacs temporary files +*.flc + +# Vim temporary files +.*.swp + +# Visual Studio generated files +*.ib_pdb_index +*.idb +*.ilk +*.pdb +*.sln +*.suo +*.vcproj +*vcproj.*.*.user +*.ncb +*.sdf +*.opensdf +*.vcxproj +*vcxproj.* + +# MinGW generated files +*.Debug +*.Release + +# Python byte code +*.pyc + +# Binaries +# -------- +*.dll +*.exe + diff --git a/02.png b/02.png new file mode 100644 index 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+ChessboradStruct::~ChessboradStruct() +{ + +} + +inline float distv(cv::Vec2f a, cv::Vec2f b) +{ + return std::sqrt((a[0] - b[0])*(a[0] - b[0]) + (a[1] - b[1])*(a[1] - b[1])); +} + +inline float mean_l(std::vector &resultSet) +{ + double sum = std::accumulate(std::begin(resultSet), std::end(resultSet), 0.0); + double mean = sum / resultSet.size(); //ŸùÖµ + return mean; +} + +inline float stdev_l(std::vector &resultSet, float &mean) +{ + double accum = 0.0; + mean = mean_l(resultSet); + std::for_each(std::begin(resultSet), std::end(resultSet), [&](const double d) { + accum += (d - mean)*(d - mean); + }); + double stdev = sqrt(accum / (resultSet.size() - 1)); //·œ²î + return stdev; +} + +inline float stdevmean(std::vector &resultSet) +{ + float stdvalue, meanvalue; + + stdvalue = stdev_l(resultSet, meanvalue); + + return stdvalue / meanvalue; +} + +int ChessboradStruct::directionalNeighbor(int idx, cv::Vec2f v, cv::Mat chessboard, Corners& corners, int& neighbor_idx, float& min_dist) +{ + +#if 1 + // list of neighboring elements, which are currently not in use + std::vector unused(corners.p.size()); + for (int i = 0; i < unused.size(); i++) + { + unused[i] = i; + } + for (int i = 0; i < chessboard.rows; i++) + for (int j = 0; j < chessboard.cols; j++) + { + int xy = chessboard.at(i, j); + if (xy >= 0) + { + unused[xy] = -1; + } + } + + int nsize = unused.size(); + + for (int i = 0; i < nsize;) + { + if (unused[i] < 0) + { + std::vector::iterator iter = unused.begin() + i; + unused.erase(iter); + i = 0; + nsize = unused.size(); + continue; + } + i++; + } + + std::vector dist_edge; + std::vector dist_point; + + cv::Vec2f idxp = cv::Vec2f(corners.p[idx].x, corners.p[idx].y); + // direction and distance to unused corners + for (int i = 0; i < unused.size(); i++) + { + int ind = unused[i]; + cv::Vec2f diri = cv::Vec2f(corners.p[ind].x, corners.p[ind].y) - idxp; + float disti = diri[0] * v[0] + diri[1] * v[1]; + + cv::Vec2f de = diri - disti*v; + dist_edge.push_back(distv(de, cv::Vec2f(0, 0))); + // distances + dist_point.push_back(disti); + } +#else + // list of neighboring elements, which are currently not in use + std::vector unused(corners.p.size()); + for (int i = 0; i < unused.size(); i++) + { + unused[i] = i; + } + for (int i = 0; i < chessboard.rows; i++) + for (int j = 0; j < chessboard.cols; j++) + { + int xy = chessboard.at(i, j); + if (xy >= 0) + { + unused[xy] = -1;//flag the used idx + } + } + + std::vector dist_edge; + std::vector dist_point; + + cv::Vec2f idxp = cv::Vec2f(corners.p[idx].x, corners.p[idx].y); + // direction and distance to unused corners + for (int i = 0; i < corners.p.size(); i++) + { + if (unused[i] == -1) + { + dist_point.push_back(std::numeric_limits::max()); + dist_edge.push_back(0); + continue; + } + cv::Vec2f diri = cv::Vec2f(corners.p[i].x, corners.p[i].y) - idxp; + float disti = diri[0] * v[0] + diri[1] * v[1]; + + cv::Vec2f de = diri - disti*v; + dist_edge.push_back(distv(de, cv::Vec2f(0, 0))); + // distances + dist_point.push_back(disti); + } + +#endif + + // find best neighbor + int min_idx = 0; + min_dist = std::numeric_limits::max(); + + //min_dist = dist_point[0] + 5 * dist_edge[0]; + for (int i = 0; i < dist_point.size(); i++) + { + if (dist_point[i] > 0) + { + float m = dist_point[i] + 5 * dist_edge[i]; + if (m < min_dist) + { + min_dist = m; + min_idx = i; + } + } + } + neighbor_idx = unused[min_idx]; + + return 1; +} + + +cv::Mat ChessboradStruct::initChessboard(Corners& corners, int idx) +{ + // return if not enough corners + if (corners.p.size() < 9) + { + logd("not enough corners!\n"); + chessboard.release();//return empty! + return chessboard; + } + // init chessboard hypothesis + chessboard = -1 * cv::Mat::ones(3, 3, CV_32S); + + // extract feature index and orientation(central element) + cv::Vec2f v1 = corners.v1[idx]; + cv::Vec2f v2 = corners.v2[idx]; + chessboard.at(1, 1) = idx; + std::vector dist1(2), dist2(6); + + // find left / right / top / bottom neighbors + directionalNeighbor(idx, +1 * v1, chessboard, corners, chessboard.at(1, 2), dist1[0]); + directionalNeighbor(idx, -1 * v1, chessboard, corners, chessboard.at(1, 0), dist1[1]); + directionalNeighbor(idx, +1 * v2, chessboard, corners, chessboard.at(2, 1), dist2[0]); + directionalNeighbor(idx, -1 * v2, chessboard, corners, chessboard.at(0, 1), dist2[1]); + + // find top - left / top - right / bottom - left / bottom - right neighbors + + directionalNeighbor(chessboard.at(1, 0), -1 * v2, chessboard, corners, chessboard.at(0, 0), dist2[2]); + directionalNeighbor(chessboard.at(1, 0), +1 * v2, chessboard, corners, chessboard.at(2, 0), dist2[3]); + directionalNeighbor(chessboard.at(1, 2), -1 * v2, chessboard, corners, chessboard.at(0, 2), dist2[4]); + directionalNeighbor(chessboard.at(1, 2), +1 * v2, chessboard, corners, chessboard.at(2, 2), dist2[5]); + + // initialization must be homogenously distributed + + + bool sigood = false; + sigood = sigood||(dist1[0]<0) || (dist1[1]<0); + sigood = sigood || (dist2[0]<0) || (dist2[1]<0) || (dist2[2]<0) || (dist2[3]<0) || (dist2[4]<0) || (dist2[5]<0); + + + sigood = sigood || (stdevmean(dist1) > 0.3) || (stdevmean(dist2) > 0.3); + + if (sigood == true) + { + chessboard.release(); + return chessboard; + } + return chessboard; +} + +float ChessboradStruct::chessboardEnergy(cv::Mat chessboard, Corners& corners) +{ + float lamda = m_lamda; + //energy: number of corners + float E_corners = -1 * chessboard.size().area(); + //energy: structur + float E_structure = 0; + //walk through rows + for (int i = 0; i < chessboard.rows; i++) + for (int j = 0; j < chessboard.cols-2; j++) + { + std::vector x; + float E_structure0 = 0; + for (int k = j; k <= j + 2; k++) + { + int n = chessboard.at(i, k); + x.push_back(corners.p[n]); + } + E_structure0 = distv(x[0] + x[2] - 2 * x[1], cv::Vec2f(0,0)); + float tv = distv(x[0] - x[2], cv::Vec2f(0, 0)); + E_structure0 = E_structure0 / tv; + if (E_structure < E_structure0) + E_structure = E_structure0; + } + + //walk through columns + for (int i = 0; i < chessboard.cols; i++) + for (int j = 0; j < chessboard.rows-2; j++) + { + std::vector x; + float E_structure0 = 0; + for (int k = j; k <= j + 2; k++) + { + int n = chessboard.at(k, i); + x.push_back(corners.p[n]); + } + E_structure0 = distv(x[0] + x[2] - 2 * x[1], cv::Vec2f(0, 0)); + float tv = distv(x[0] - x[2], cv::Vec2f(0, 0)); + E_structure0 = E_structure0 / tv; + if (E_structure < E_structure0) + E_structure = E_structure0; + } + + // final energy + float E = E_corners + lamda*chessboard.size().area()*E_structure; + + return E; +} + +// replica prediction(new) +void ChessboradStruct::predictCorners(std::vector& p1, std::vector& p2, + std::vector& p3, std::vector& pred) +{ + cv::Vec2f v1, v2; + float a1, a2, a3; + float s1, s2, s3; + pred.resize(p1.size()); + for (int i = 0; i < p1.size(); i++) + { + // compute vectors + v1 = p2[i] - p1[i]; + v2 = p3[i] - p2[i]; + // predict angles + a1 = atan2(v1[1], v1[0]); + a2 = atan2(v1[1], v1[0]); + a3 = 2.0 * a2 - a1; + + //predict scales + s1 = distv(v1, cv::Vec2f(0, 0)); + s2 = distv(v2, cv::Vec2f(0, 0)); + s3 = 2 * s2 - s1; + pred[i] = p3[i] + 0.75*s3*cv::Vec2f(cos(a3), sin(a3)); + } +} + +void ChessboradStruct::assignClosestCorners(std::vector&cand, std::vector&pred, std::vector &idx) +{ + //return error if not enough candidates are available + if (cand.size() < pred.size()) + { + idx.resize(1); + idx[0] = -1; + return; + } + idx.resize(pred.size()); + + //build distance matrix + cv::Mat D = cv::Mat::zeros(cand.size(), pred.size(), CV_32FC1); + float mind = FLT_MAX; + for (int i = 0; i < D.cols; i++)//ÁÐÓÅÏÈ + { + cv::Vec2f delta; + for (int j = 0; j < D.rows; j++) + { + delta = cand[j] - pred[i]; + float s = distv(delta, cv::Vec2f(0, 0)); + D.at(j, i) = s; + if (s < mind) + { + mind = s; + } + } + } + + // search greedily for closest corners + for (int k = 0; k < pred.size(); k++) + { + bool isbreak = false; + for (int i = 0; i < D.rows; i++) + { + for (int j = 0; j < D.cols; j++) + { + if (fabs(D.at(i, j) - mind) < 10e-10) + { + idx[j] = i; + for (int m = 0; m < D.cols; m++) + { + D.at(i, m) = FLT_MAX; + } + for (int m = 0; m < D.rows; m++) + { + D.at(m,j) = FLT_MAX; + } + isbreak = true; + break; + } + } + if (isbreak == true) + break; + } + mind = FLT_MAX; + for (int i = 0; i < D.rows; i++) + { + for (int j = 0; j < D.cols; j++) + { + if (D.at(i, j) < mind) + { + mind = D.at(i, j); + } + } + } + } +} + + + +cv::Mat ChessboradStruct::growChessboard(cv::Mat chessboard, Corners& corners, int border_type) +{ + if (chessboard.empty() == true) + { + return chessboard; + } + std::vector p = corners.p; + // list of unused feature elements + std::vector unused(p.size()); + for (int i = 0; i < unused.size(); i++) + { + unused[i] = i; + } + for (int i = 0; i < chessboard.rows; i++) + for (int j = 0; j < chessboard.cols; j++) + { + int xy = chessboard.at(i, j); + if (xy >= 0) + { + unused[xy] = -1; + } + } + + int nsize = unused.size(); + + for (int i = 0; i < nsize; ) + { + if (unused[i] < 0) + { + std::vector::iterator iter = unused.begin() + i; + unused.erase(iter); + i = 0; + nsize = unused.size(); + continue; + } + i++; + } + + // candidates from unused corners + std::vector cand; + for (int i = 0; i < unused.size(); i++) + { + cand.push_back(corners.p[unused[i]]); + } + // switch border type 1..4 + cv::Mat chesstemp; + + switch (border_type) + { + case 0: + { + std::vector p1, p2, p3,pred; + for (int row = 0; row < chessboard.rows; row++) + for (int col = 0; col < chessboard.cols; col++) + { + if (col == chessboard.cols - 3) + { + int ij = chessboard.at(row, col); + p1.push_back(cv::Vec2f(p[ij])); + } + if (col == chessboard.cols - 2) + { + int ij = chessboard.at(row, col); + p2.push_back(cv::Vec2f(p[ij])); + } + if (col == chessboard.cols - 1) + { + int ij = chessboard.at(row, col); + p3.push_back(cv::Vec2f(p[ij])); + } + } + std::vector idx; + predictCorners(p1, p2, p3, pred); + assignClosestCorners(cand, pred, idx); + if (idx[0] < 0) + { + return chessboard; + } + + cv::copyMakeBorder(chessboard, chesstemp, 0, 0, 0, 1, 0,0); + + for (int i = 0; i < chesstemp.rows; i++) + { + chesstemp.at(i, chesstemp.cols - 1) = unused[idx[i]];//ÓÒ + } + chessboard = chesstemp.clone(); + + break; + } + case 1: + { + std::vector p1, p2, p3, pred; + for (int row = 0; row < chessboard.rows; row++) + for (int col = 0; col < chessboard.cols; col++) + { + if (row == chessboard.rows - 3) + { + int ij = chessboard.at(row, col); + p1.push_back(cv::Vec2f(p[ij])); + } + if (row == chessboard.rows - 2) + { + int ij = chessboard.at(row, col); + p2.push_back(cv::Vec2f(p[ij])); + } + if (row == chessboard.rows - 1) + { + int ij = chessboard.at(row, col); + p3.push_back(cv::Vec2f(p[ij])); + } + } + std::vector idx; + predictCorners(p1, p2, p3, pred); + assignClosestCorners(cand, pred, idx); + if (idx[0] < 0) + { + return chessboard; + } + + cv::copyMakeBorder(chessboard, chesstemp, 0, 1, 0, 0, 0, 0); + for (int i = 0; i < chesstemp.cols; i++) + { + chesstemp.at(chesstemp.rows - 1, i) = unused[idx[i]];//Ï + } + chessboard = chesstemp.clone(); + + break; + } + case 2: + { + std::vector p1, p2, p3, pred; + for (int row = 0; row < chessboard.rows; row++) + for (int col = 0; col < chessboard.cols; col++) + { + if (col == 2) + { + int ij = chessboard.at(row, col); + p1.push_back(cv::Vec2f(p[ij])); + } + if (col == 1) + { + int ij = chessboard.at(row, col); + p2.push_back(cv::Vec2f(p[ij])); + } + if (col == 0) + { + int ij = chessboard.at(row, col); + p3.push_back(cv::Vec2f(p[ij])); + } + } + std::vector idx; + predictCorners(p1, p2, p3, pred); + assignClosestCorners(cand, pred, idx); + if (idx[0] < 0) + { + return chessboard; + } + + cv::copyMakeBorder(chessboard, chesstemp, 0, 0, 1, 0, 0, 0);//×ó + for (int i = 0; i < chesstemp.rows; i++) + { + chesstemp.at(i, 0) = unused[idx[i]]; + } + chessboard = chesstemp.clone(); + + break; + } + case 3: + { + std::vector p1, p2, p3, pred; + for (int row = 0; row < chessboard.rows; row++) + for (int col = 0; col < chessboard.cols; col++) + { + if (row == 2) + { + int ij = chessboard.at(row, col); + p1.push_back(cv::Vec2f(p[ij])); + } + if (row == 1) + { + int ij = chessboard.at(row, col); + p2.push_back(cv::Vec2f(p[ij])); + } + if (row == 0) + { + int ij = chessboard.at(row, col); + p3.push_back(cv::Vec2f(p[ij])); + } + } + std::vector idx; + predictCorners(p1, p2, p3, pred); + assignClosestCorners(cand, pred, idx); + if (idx[0] < 0) + { + return chessboard; + } + cv::copyMakeBorder(chessboard, chesstemp, 1, 0, 0, 0, 0, 0);//ÉÏ + for (int i = 0; i < chesstemp.cols; i++) + { + chesstemp.at(0, i) = unused[idx[i]]; + } + chessboard = chesstemp.clone(); + break; + } + default: + break; + } + return chessboard; +} + + + + +void ChessboradStruct::chessboardsFromCorners( Corners& corners, std::vector& chessboards, float lamda) +{ + logd("Structure recovery:\n"); + m_lamda = lamda; + for (int i = 0; i < corners.p.size(); i++) + { + if (i % 128 == 0) + printf("%d, %d\n", i, corners.p.size()); + + cv::Mat csbd = initChessboard(corners, i); + if (csbd.empty() == true) + { + continue; + } + float E =chessboardEnergy(csbd, corners); + if (E > 0){ continue; } + + int s = 0; + //try growing chessboard + while (true) + { + s++; + // compute current energy + float energy = chessboardEnergy(chessboard, corners); + + std::vector proposal(4); + std::vector p_energy(4); + //compute proposals and energies + for (int j = 0; j < 4; j++) + { + proposal[j] = growChessboard(chessboard, corners, j); + p_energy[j] = chessboardEnergy(proposal[j], corners); + } + // find best proposal + float min_value = p_energy[0]; + int min_idx = 0; + for (int i0 = 1; i0 < p_energy.size(); i0++) + { + if (min_value > p_energy[i0]) + { + min_value = p_energy[i0]; + min_idx = i0; + } + } + // accept best proposal, if energy is reduced + cv::Mat chessboardt; + if (p_energy[min_idx] < energy) + { + chessboardt = proposal[min_idx]; + chessboard = chessboardt.clone(); + } + else + { + break; + } + }//end while + + if (chessboardEnergy(chessboard, corners) < -10) + { + //check if new chessboard proposal overlaps with existing chessboards + cv::Mat overlap = cv::Mat::zeros(cv::Size(2,chessboards.size()), CV_32FC1); + for (int j = 0; j < chessboards.size(); j++) + { + bool isbreak = false; + for (int k = 0; k < chessboards[j].size().area(); k++) + { + int refv = chessboards[j].at(k / chessboards[j].cols, k%chessboards[j].cols); + for (int l = 0; l < chessboard.size().area(); l++) + { + int isv = chessboard.at(l/ chessboard.cols, l%chessboard.cols); + if (refv == isv) + { + overlap.at(j, 0) = 1.0; + float s = chessboardEnergy(chessboards[j], corners); + overlap.at(j, 1) = s; + isbreak = true; + break; + } + } + // if (isbreak == true) + // { + // break; + // } + } + //if (isbreak == true) + //{ + // break; + //} + }//endfor + + // add chessboard(and replace overlapping if neccessary) + bool isoverlap = false; + for (int i0 = 0; i0 < overlap.rows; i0++) + { + if (overlap.empty() == false) + { + if (fabs(overlap.at(i0, 0)) > 0.000001)// ==1 + { + isoverlap = true; + break; + } + } + } + if (isoverlap == false) + { + chessboards.push_back(chessboard); + } + else + { + bool flagpush = true; + std::vector flagerase(overlap.rows); + for (int m = 0; m < flagerase.size(); m++) + { + flagerase[m] = false; + } + float ce = chessboardEnergy(chessboard, corners); + for (int i1 = 0; i1 < overlap.rows; i1++) + { + if (fabs(overlap.at(i1, 0)) > 0.0001)// ==1//ÓÐÖصþ + { + bool isb1 = overlap.at(i1, 1) > ce; + + int a = int(overlap.at(i1, 1) * 1000); + int b = int(ce * 1000); + + bool isb2 = a > b; + if (isb1 != isb2) + printf("find bug!\n"); + + if (isb2) + { + flagerase[i1] = true; + } + else + { + flagpush = false; + // break; + + }//endif + }//endif + + }//end for + + if (flagpush == true) + { + for (int i1 = 0; i1 < chessboards.size();) + { + std::vector::iterator it = chessboards.begin() + i1; + std::vector::iterator it1 = flagerase.begin() + i1; + if (*it1 == true) + { + chessboards.erase(it); + flagerase.erase(it1); + i1 = 0; + } + i1++; + } + chessboards.push_back(chessboard); + } + + }//endif + + }//endif + }//end for + +} +#define BOLDGREEN "\033[1m\033[32m" /* Bold Green */ +void ChessboradStruct::drawchessboard(cv::Mat img, Corners& corners, std::vector& chessboards, char * title, int t_, cv::Rect rect) +{ + printf("end!\n"); + + cv::RNG rng(0xFFFFFFFF); + std::string s("If it's useful, please give a star ^-^."); + std::string s1("https://github.com/onlyliucat\n"); + std::cout<(i, j); + cv::circle(disp, cv::Point2f(corners.p[d].x + rect.x, corners.p[d].y + rect.y), n, s, n); + } + } + cv::Mat SmallMat; + cv::resize(disp, SmallMat, cv::Size(), scale, scale); + cv::namedWindow(title); + cv::imshow(title, SmallMat); + cv::waitKey(t_); +} + + + diff --git a/ChessboradStruct.h b/ChessboradStruct.h new file mode 100644 index 0000000..50fb094 --- /dev/null +++ b/ChessboradStruct.h @@ -0,0 +1,32 @@ +/* Copyright 2017 onlyliu(997737609@qq.com). */ +/* */ +/* Automatic Camera and Range Sensor Calibration using a single Shot */ +/* this project realize the papar: Automatic Camera and Range Sensor */ +/* Calibration using a single Shot */ + +#pragma once + +#include "opencv2/opencv.hpp" +#include +#include "HeaderCB.h" + +class ChessboradStruct +{ +public: + ChessboradStruct(); + ~ChessboradStruct(); + + cv::Mat initChessboard(Corners& corners, int idx); + void chessboardsFromCorners(Corners& corners, std::vector& chessboards, float lamda = 0.5); + int directionalNeighbor(int idx, cv::Vec2f v, cv::Mat chessboard, Corners& corners, int& neighbor_idx, float& min_dist); + float chessboardEnergy(cv::Mat chessboard, Corners& corners); + void predictCorners(std::vector& p1, std::vector& p2, std::vector& p3, std::vector& pred); + cv::Mat growChessboard(cv::Mat chessboard, Corners& corners, int border_type); + void assignClosestCorners(std::vector&cand, std::vector&pred, std::vector &idx); + void drawchessboard(cv::Mat img, Corners& corners, std::vector& chessboards, char * title = "chessboard", int t = 0, cv::Rect rect = cv::Rect(0,0,0,0)); + + cv::Mat chessboard; + float m_lamda; + +}; + diff --git a/CornerDetAC.cpp b/CornerDetAC.cpp new file mode 100644 index 0000000..2664713 --- /dev/null +++ b/CornerDetAC.cpp @@ -0,0 +1,826 @@ +/* Copyright 2017 onlyliu(997737609@qq.com). */ +/* */ +/* part of source code come from https://github.com/qibao77/cornerDetect */ +/* Automatic Camera and Range Sensor Calibration using a single Shot */ +/* this project realize the papar: Automatic Camera and Range Sensor */ +/* Calibration using a single Shot */ + + +#include "CornerDetAC.h" +#include "corealgmatlab.h" + + +using namespace cv; +using namespace std; + +//#define show_ + +CornerDetAC::CornerDetAC() +{ +} + + +CornerDetAC::~CornerDetAC() +{ +} +CornerDetAC::CornerDetAC(cv::Mat img) +{ + //3 scales + radius.push_back(4); + radius.push_back(8); + radius.push_back(12); + + templateProps.push_back(Point2f((dtype)0, (dtype)CV_PI / 2)); + templateProps.push_back(Point2f((dtype)CV_PI / 4, (dtype)-CV_PI / 4)); + templateProps.push_back(Point2f((dtype)0, (dtype)CV_PI / 2)); + templateProps.push_back(Point2f((dtype)CV_PI / 4, (dtype)-CV_PI / 4)); + templateProps.push_back(Point2f((dtype)0, (dtype)CV_PI / 2)); + templateProps.push_back(Point2f((dtype)CV_PI / 4, (dtype)-CV_PI / 4)); +} + +//Normal probability density function (pdf). +dtype CornerDetAC::normpdf(dtype dist, dtype mu, dtype sigma) +{ + dtype s = exp(-0.5*(dist - mu)*(dist - mu) / (sigma*sigma)); + s = s / (std::sqrt(2 * CV_PI)*sigma); + return s; +} + + +//**************************ɺ*****************************// +//angleͣ45Ⱥ˺90Ⱥ +//kernelSize˴Сɵĺ˵ĴСΪkernelSize*2+1 +//kernelA...kernelDɵĺ +//*************************************************************************// +void CornerDetAC::createkernel(float angle1, float angle2, int kernelSize, Mat &kernelA, Mat &kernelB, Mat &kernelC, Mat &kernelD) +{ + + int width = (int)kernelSize * 2 + 1; + int height = (int)kernelSize * 2 + 1; + kernelA = cv::Mat::zeros(height, width, mtype); + kernelB = cv::Mat::zeros(height, width, mtype); + kernelC = cv::Mat::zeros(height, width, mtype); + kernelD = cv::Mat::zeros(height, width, mtype); + + for (int u = 0; u < width; ++u){ + for (int v = 0; v < height; ++v){ + dtype vec[] = { u - kernelSize, v - kernelSize };//൱ڽԭƶ + dtype dis = std::sqrt(vec[0] * vec[0] + vec[1] * vec[1]);//൱ڼ㵽ĵľ + dtype side1 = vec[0] * (-sin(angle1)) + vec[1] * cos(angle1);//൱ڽԭƶĺ˽תԴ˲ֺ + dtype side2 = vec[0] * (-sin(angle2)) + vec[1] * cos(angle2);//X=X0*cos+Y0*sin;Y=Y0*cos-X0*sin + if (side1 <= -0.1&&side2 <= -0.1){ + kernelA.ptr(v)[u] = normpdf(dis, 0, kernelSize / 2); + } + if (side1 >= 0.1&&side2 >= 0.1){ + kernelB.ptr(v)[u] = normpdf(dis, 0, kernelSize / 2); + } + if (side1 <= -0.1&&side2 >= 0.1){ + kernelC.ptr(v)[u] = normpdf(dis, 0, kernelSize / 2); + } + if (side1 >= 0.1&&side2 <= -0.1){ + kernelD.ptr(v)[u] = normpdf(dis, 0, kernelSize / 2); + } + } + } + //std::cout << "kernelA:" << kernelA << endl << "kernelB:" << kernelB << endl + // << "kernelC:" << kernelC<< endl << "kernelD:" << kernelD << endl; + //һ + kernelA = kernelA / cv::sum(kernelA)[0]; + kernelB = kernelB / cv::sum(kernelB)[0]; + kernelC = kernelC / cv::sum(kernelC)[0]; + kernelD = kernelD / cv::sum(kernelD)[0]; + +} + +//**************************//ȡСֵ*****************************// +//*************************************************************************// +void CornerDetAC::getMin(Mat src1, Mat src2, Mat &dst){ + int rowsLeft = src1.rows; + int colsLeft = src1.cols; + int rowsRight = src2.rows; + int colsRight = src2.cols; + if (rowsLeft != rowsRight || colsLeft != colsRight)return; + + int channels = src1.channels(); + + int nr = rowsLeft; + int nc = colsLeft; + if (src1.isContinuous()){ + nc = nc*nr; + nr = 1; + //std::cout<<"continue"<(i); + const dtype* dataRight = src2.ptr(i); + dtype* dataResult = dst.ptr(i); + for (int j = 0; j < nc*channels; ++j){ + dataResult[j] = (dataLeft[j] < dataRight[j]) ? dataLeft[j] : dataRight[j]; + } + } +} +//**************************//ȡֵ*****************************// +//*************************************************************************// +void CornerDetAC::getMax(Mat src1, Mat src2, Mat &dst) +{ + int rowsLeft = src1.rows; + int colsLeft = src1.cols; + int rowsRight = src2.rows; + int colsRight = src2.cols; + if (rowsLeft != rowsRight || colsLeft != colsRight)return; + + int channels = src1.channels(); + + int nr = rowsLeft; + int nc = colsLeft; + if (src1.isContinuous()){ + nc = nc*nr; + nr = 1; + //std::cout<<"continue"<(i); + const dtype* dataRight = src2.ptr(i); + dtype* dataResult = dst.ptr(i); + for (int j = 0; j < nc*channels; ++j){ + dataResult[j] = (dataLeft[j] >= dataRight[j]) ? dataLeft[j] : dataRight[j]; + } + } +} +//ȡݶȽǶȺȨ +void CornerDetAC::getImageAngleAndWeight(Mat img, Mat &imgDu, Mat &imgDv, Mat &imgAngle, Mat &imgWeight) +{ + + Mat sobelKernel(3, 3, mtype); + Mat sobelKernelTrs(3, 3, mtype); + //soble˲Ӻ + sobelKernel.col(0).setTo(cv::Scalar(-1.0)); + sobelKernel.col(1).setTo(cv::Scalar(0.0)); + sobelKernel.col(2).setTo(cv::Scalar(1.0)); + + sobelKernelTrs = sobelKernel.t(); + + imgDu = corealgmatlab::conv2(img, sobelKernel, CONVOLUTION_SAME); + imgDv = corealgmatlab::conv2(img, sobelKernelTrs, CONVOLUTION_SAME); + + if (imgDu.size() != imgDv.size())return; + + cartToPolar(imgDu, imgDv, imgWeight, imgAngle, false); + for (int i = 0; i < imgDu.rows; i++) + { + for (int j = 0; j < imgDu.cols; j++) + { + dtype* dataAngle = imgAngle.ptr(i); + if (dataAngle[j] < 0) + dataAngle[j] = dataAngle[j] + CV_PI; + else if (dataAngle[j] > CV_PI) + dataAngle[j] = dataAngle[j] - CV_PI; + } + } + /* + for (int i = 0; i < imgDu.rows; i++) + { + dtype* dataDv = imgDv.ptr(i); + dtype* dataDu = imgDu.ptr(i); + dtype* dataAngle = imgAngle.ptr(i); + dtype* dataWeight = imgWeight.ptr(i); + for (int j = 0; j < imgDu.cols; j++) + { + if (dataDu[j] > 0.000001) + { + dataAngle[j] = atan2((dtype)dataDv[j], (dtype)dataDu[j]); + if (dataAngle[j] < 0)dataAngle[j] = dataAngle[j] + CV_PI; + else if (dataAngle[j] > CV_PI)dataAngle[j] = dataAngle[j] - CV_PI; + } + dataWeight[j] = std::sqrt((dtype)dataDv[j] * (dtype)dataDv[j] + (dtype)dataDu[j] * (dtype)dataDu[j]); + } + } + */ +} +//**************************Ǽֵ*****************************// +//inputCornersǵ㣬outputCornersǷǼֵƺĽǵ +//threshold趨ֵ +//marginǽзǼֵʱ鷽߽ľ룬patchSizeǸ÷ĴС +//*************************************************************************// +void CornerDetAC::nonMaximumSuppression(Mat& inputCorners, vector& outputCorners, int patchSize, dtype threshold, int margin) +{ + if (inputCorners.size <= 0) + { + cout << "The imput mat is empty!" << endl; return; + } + for (int i = margin + patchSize; i <= inputCorners.cols - (margin + patchSize+1); i = i + patchSize + 1)//ƶ鷽飬ÿƶһĴС + { + for (int j = margin + patchSize; j <= inputCorners.rows - (margin + patchSize+1); j = j + patchSize + 1) + { + dtype maxVal = inputCorners.ptr(j)[i]; + int maxX = i; int maxY = j; + for (int m = i; m <= i + patchSize ; m++)//ҳü鷽еľֲֵ + { + for (int n = j; n <= j + patchSize ; n++) + { + dtype temp = inputCorners.ptr(n)[m]; + if (temp > maxVal) + { + maxVal = temp; maxX = m; maxY = n; + } + } + } + if (maxVal < threshold)continue;//þֲֵСֵҪ + int flag = 0; + for (int m = maxX - patchSize; m <= min(maxX + patchSize, inputCorners.cols - margin-1); m++)//μ + { + for (int n = maxY - patchSize; n <= min(maxY + patchSize, inputCorners.rows - margin-1); n++) + { + if (inputCorners.ptr(n)[m]>maxVal && (mi + patchSize || nj + patchSize)) + { + flag = 1; break; + } + } + if (flag)break; + } + if (flag)continue; + outputCorners.push_back(Point(maxX, maxY)); + std::vector e1(2, 0.0); + std::vector e2(2, 0.0); + cornersEdge1.push_back(e1); + cornersEdge2.push_back(e2); + } + } +} + +int cmp(const pair &a, const pair &b) +{ + return a.first > b.first; +} + +//find modes of smoothed histogram +void CornerDetAC::findModesMeanShift(vector hist, vector &hist_smoothed, vector> &modes, dtype sigma){ + //efficient mean - shift approximation by histogram smoothing + //compute smoothed histogram + bool allZeros = true; + for (int i = 0; i < hist.size(); i++) + { + dtype sum = 0; + for (int j = -(int)round(2 * sigma); j <= (int)round(2 * sigma); j++) + { + int idx = 0; + idx = (i + j) % hist.size(); + sum = sum + hist[idx] * normpdf(j, 0, sigma); + } + hist_smoothed[i] = sum; + if (abs(hist_smoothed[i] - hist_smoothed[0]) > 0.0001)allZeros = false;// check if at least one entry is non - zero + //(otherwise mode finding may run infinitly) + } + if (allZeros)return; + + //mode finding + for (int i = 0; i < hist.size(); i++) + { + int j = i; + while (true) + { + float h0 = hist_smoothed[j]; + int j1 = (j + 1) % hist.size(); + int j2 = (j - 1) % hist.size(); + float h1 = hist_smoothed[j1]; + float h2 = hist_smoothed[j2]; + if (h1 >= h0 && h1 >= h2) + j = j1; + else if (h2 > h0 && h2 > h1) + j = j2; + else + break; + } + bool ys = true; + if (modes.size() == 0) + { + ys = true; + } + else + { + for (int k = 0; k < modes.size(); k++) + { + if (modes[k].second == j) + { + ys = false; + break; + } + } + } + if (ys == true) + { + modes.push_back(std::make_pair(hist_smoothed[j], j)); + } + } + std::sort(modes.begin(), modes.end(), cmp); +} + +//estimate edge orientations +void CornerDetAC::edgeOrientations(Mat imgAngle, Mat imgWeight, int index){ + //number of bins (histogram parameter) + int binNum = 32; + + //convert images to vectors + if (imgAngle.size() != imgWeight.size())return; + vector vec_angle, vec_weight; + for (int i = 0; i < imgAngle.cols; i++) + { + for (int j = 0; j < imgAngle.rows; j++) + { + // convert angles from .normals to directions + float angle = imgAngle.ptr(j)[i] + CV_PI / 2; + angle = angle > CV_PI ? (angle - CV_PI) : angle; + vec_angle.push_back(angle); + + vec_weight.push_back(imgWeight.ptr(j)[i]); + } + } + + //create histogram + dtype pin = (CV_PI / binNum); + vector angleHist(binNum, 0); + for (int i = 0; i < vec_angle.size(); i++) + { + int bin = max(min((int)floor(vec_angle[i] / pin), binNum - 1), 0); + angleHist[bin] = angleHist[bin] + vec_weight[i]; + } + + // find modes of smoothed histogram + vector hist_smoothed(angleHist); + vector > modes; + findModesMeanShift(angleHist, hist_smoothed, modes, 1); + + // if only one or no mode = > return invalid corner + if (modes.size() <= 1)return; + + //compute orientation at modes and sort by angle + float fo[2]; + fo[0] = modes[0].second*pin; + fo[1] = modes[1].second*pin; + dtype deltaAngle = 0; + if (fo[0] > fo[1]) + { + dtype t = fo[0]; + fo[0] = fo[1]; + fo[1] = t; + } + + deltaAngle = MIN(fo[1] - fo[0], fo[0] - fo[1] + (dtype)CV_PI); + // if angle too small => return invalid corner + if (deltaAngle <= 0.3)return; + + //set statistics: orientations + cornersEdge1[index][0] = cos(fo[0]); + cornersEdge1[index][1] = sin(fo[0]); + cornersEdge2[index][0] = cos(fo[1]); + cornersEdge2[index][1] = sin(fo[1]); +} + +float CornerDetAC::norm2d(cv::Point2f o) +{ + return sqrt(o.x*o.x + o.y*o.y); +} +//ؾҽǵ +void CornerDetAC::refineCorners(vector &cornors, Mat imgDu, Mat imgDv, Mat imgAngle, Mat imgWeight, float radius){ + // image dimensions + int width = imgDu.cols; + int height = imgDu.rows; + // for all corners do + for (int i = 0; i < cornors.size(); i++) + { + //extract current corner location + int cu = cornors[i].x; + int cv = cornors[i].y; + // estimate edge orientations + int startX, startY, ROIwidth, ROIheight; + startX = MAX(cu - radius, (dtype)0); + startY = MAX(cv - radius, (dtype)0); + ROIwidth = MIN(cu + radius + 1, (dtype)width - 1) - startX; + ROIheight = MIN(cv + radius + 1, (dtype)height - 1) - startY; + + Mat roiAngle, roiWeight; + roiAngle = imgAngle(Rect(startX, startY, ROIwidth, ROIheight)); + roiWeight = imgWeight(Rect(startX, startY, ROIwidth, ROIheight)); + edgeOrientations(roiAngle, roiWeight, i); + + // continue, if invalid edge orientations + if (cornersEdge1[i][0] == 0 && cornersEdge1[i][1] == 0 || cornersEdge2[i][0] == 0 && cornersEdge2[i][1] == 0) + continue; + // continue; + + //%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + //% corner orientation refinement % + //%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + cv::Mat A1 = cv::Mat::zeros(cv::Size(2, 2), mtype); + cv::Mat A2 = cv::Mat::zeros(cv::Size(2, 2), mtype); + + for (int u = startX; u < startX + ROIwidth; u++) + for (int v = startY; v < startY + ROIheight; v++) + { + // pixel orientation vector + cv::Point2f o(imgDu.at(v, u), imgDv.at(v, u)); + float no = norm2d(o); + if (no < 0.1) + continue; + o = o / no; + // robust refinement of orientation 1 + dtype t0 = abs(o.x*cornersEdge1[i][0] + o.y*cornersEdge1[i][1]); + if (t0 < 0.25) // inlier ? + { + Mat addtion(1, 2, mtype); + addtion.col(0).setTo(imgDu.at(v, u)); + addtion.col(1).setTo(imgDv.at(v, u)); + Mat addtionu = imgDu.at(v, u)*addtion; + Mat addtionv = imgDv.at(v, u)*addtion; + for (int j = 0; j < A1.cols; j++) + { + A1.at(0, j) = A1.at(0, j) + addtionu.at(0, j); + A1.at(1, j) = A1.at(1, j) + addtionv.at(0, j); + } + } + // robust refinement of orientation 2 + dtype t1 = abs(o.x*cornersEdge2[i][0] + o.y*cornersEdge2[i][1]); + if (t1 < 0.25) // inlier ? + { + Mat addtion(1, 2, mtype); + addtion.col(0).setTo(imgDu.at(v, u)); + addtion.col(1).setTo(imgDv.at(v, u)); + Mat addtionu = imgDu.at(v, u)*addtion; + Mat addtionv = imgDv.at(v, u)*addtion; + for (int j = 0; j < A2.cols; j++) + { + A2.at(0, j) = A2.at(0, j) + addtionu.at(0, j); + A2.at(1, j) = A2.at(1, j) + addtionv.at(0, j); + } + } + }//end for + // set new corner orientation + cv::Mat v1, foo1; + cv::Mat v2, foo2; + cv::eigen(A1, v1, foo1); + cv::eigen(A2, v2, foo2); + cornersEdge1[i][0] = -foo1.at(1, 0); + cornersEdge1[i][1] = -foo1.at(1, 1); + cornersEdge2[i][0] = -foo2.at(1, 0); + cornersEdge2[i][1] = -foo2.at(1, 1); + + //%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + //% corner location refinement % + //%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + cv::Mat G = cv::Mat::zeros(cv::Size(2, 2), mtype); + cv::Mat b = cv::Mat::zeros(cv::Size(1, 2), mtype); + for (int u = startX; u < startX + ROIwidth; u++) + for (int v = startY; v < startY + ROIheight; v++) + { + // pixel orientation vector + cv::Point2f o(imgDu.at(v, u), imgDv.at(v, u)); + float no = norm2d(o); + if (no < 0.1) + continue; + o = o / no; + //robust subpixel corner estimation + if (u != cu || v != cv)// % do not consider center pixel + { + //compute rel. position of pixel and distance to vectors + cv::Point2f w(u - cu, v - cv); + float wvv1 = w.x*cornersEdge1[i][0] + w.y*cornersEdge1[i][1]; + float wvv2 = w.x*cornersEdge2[i][0] + w.y*cornersEdge2[i][1]; + + cv::Point2f wv1(wvv1 * cornersEdge1[i][0], wvv1 * cornersEdge1[i][1]); + cv::Point2f wv2(wvv2 * cornersEdge2[i][0], wvv2 * cornersEdge2[i][1]); + cv::Point2f vd1(w.x - wv1.x, w.y - wv1.y); + cv::Point2f vd2(w.x - wv2.x, w.y - wv2.y); + dtype d1 = norm2d(vd1), d2 = norm2d(vd2); + //if pixel corresponds with either of the vectors / directions + if ((d1 < 3) && abs(o.x*cornersEdge1[i][0] + o.y*cornersEdge1[i][1]) < 0.25 \ + || (d2 < 3) && abs(o.x*cornersEdge2[i][0] + o.y*cornersEdge2[i][1]) < 0.25) + { + dtype du = imgDu.at(v, u), dv = imgDv.at(v, u); + cv::Mat uvt = (Mat_(2, 1) << u, v); + cv::Mat H = (Mat_(2, 2) << du*du, du*dv, dv*du, dv*dv); + G = G + H; + cv::Mat t = H*(uvt); + b = b + t; + } + } + }//endfor + //set new corner location if G has full rank + Mat s, u, v; + SVD::compute(G, s, u, v); + int rank = 0; + for (int k = 0; k < s.rows; k++) + { + if (s.at(k, 0) > 0.0001 || s.at(k, 0) < -0.0001)// not equal zero + { + rank++; + } + } + if (rank == 2) + { + cv::Mat mp = G.inv()*b; + cv::Point2f corner_pos_new(mp.at(0, 0), mp.at(1, 0)); + // % set corner to invalid, if position update is very large + if (norm2d(cv::Point2f(corner_pos_new.x - cu, corner_pos_new.y - cv)) >= 4) + { + cornersEdge1[i][0] = 0; + cornersEdge1[i][1] = 0; + cornersEdge2[i][0] = 0; + cornersEdge2[i][1] = 0; + } + else + { + cornors[i].x = mp.at(0, 0); + cornors[i].y = mp.at(1, 0); + } + } + else//otherwise: set corner to invalid + { + cornersEdge1[i][0] = 0; + cornersEdge1[i][1] = 0; + cornersEdge2[i][0] = 0; + cornersEdge2[i][1] = 0; + } + } +} + +//compute corner statistics +void CornerDetAC::cornerCorrelationScore(Mat img, Mat imgWeight, vector cornersEdge, float &score){ + //center + int c[] = { imgWeight.cols / 2, imgWeight.cols / 2 }; + + //compute gradient filter kernel(bandwith = 3 px) + Mat img_filter = Mat::ones(imgWeight.size(), imgWeight.type()); + img_filter = img_filter*-1; + for (int i = 0; i < imgWeight.cols; i++) + { + for (int j = 0; j < imgWeight.rows; j++) + { + Point2f p1 = Point2f(i - c[0], j - c[1]); + Point2f p2 = Point2f(p1.x*cornersEdge[0].x*cornersEdge[0].x + p1.y*cornersEdge[0].x*cornersEdge[0].y, + p1.x*cornersEdge[0].x*cornersEdge[0].y + p1.y*cornersEdge[0].y*cornersEdge[0].y); + Point2f p3 = Point2f(p1.x*cornersEdge[1].x*cornersEdge[1].x + p1.y*cornersEdge[1].x*cornersEdge[1].y, + p1.x*cornersEdge[1].x*cornersEdge[1].y + p1.y*cornersEdge[1].y*cornersEdge[1].y); + float norm1 = sqrt((p1.x - p2.x)*(p1.x - p2.x) + (p1.y - p2.y)*(p1.y - p2.y)); + float norm2 = sqrt((p1.x - p3.x)*(p1.x - p3.x) + (p1.y - p3.y)*(p1.y - p3.y)); + if (norm1 <= 1.5 || norm2 <= 1.5) + { + img_filter.ptr(j)[i] = 1; + } + } + } + + //normalize + Mat mean, std, mean1, std1; + meanStdDev(imgWeight, mean, std); + meanStdDev(img_filter, mean1, std1); + for (int i = 0; i < imgWeight.cols; i++) + { + for (int j = 0; j < imgWeight.rows; j++) + { + imgWeight.ptr(j)[i] = (dtype)(imgWeight.ptr(j)[i] - mean.ptr(0)[0]) / (dtype)std.ptr(0)[0]; + img_filter.ptr(j)[i] = (dtype)(img_filter.ptr(j)[i] - mean1.ptr(0)[0]) / (dtype)std1.ptr(0)[0]; + } + } + + //convert into vectors + vector vec_filter, vec_weight; + for (int i = 0; i < imgWeight.cols; i++) + { + for (int j = 0; j < imgWeight.rows; j++) + { + vec_filter.push_back(img_filter.ptr(j)[i]); + vec_weight.push_back(imgWeight.ptr(j)[i]); + } + } + + //compute gradient score + float sum = 0; + for (int i = 0; i < vec_weight.size(); i++) + { + sum += vec_weight[i] * vec_filter[i]; + } + sum = (dtype)sum / (dtype)(vec_weight.size() - 1); + dtype score_gradient = sum >= 0 ? sum : 0; + + //create intensity filter kernel + Mat kernelA, kernelB, kernelC, kernelD; + createkernel(atan2(cornersEdge[0].y, cornersEdge[0].x), atan2(cornersEdge[1].y, cornersEdge[1].x), c[0], kernelA, kernelB, kernelC, kernelD);//1.1 ֺ + + //checkerboard responses + float a1, a2, b1, b2; + a1 = kernelA.dot(img); + a2 = kernelB.dot(img); + b1 = kernelC.dot(img); + b2 = kernelD.dot(img); + + float mu = (a1 + a2 + b1 + b2) / 4; + + float score_a = (a1 - mu) >= (a2 - mu) ? (a2 - mu) : (a1 - mu); + float score_b = (mu - b1) >= (mu - b2) ? (mu - b2) : (mu - b1); + float score_1 = score_a >= score_b ? score_b : score_a; + + score_b = (b1 - mu) >= (b2 - mu) ? (b2 - mu) : (b1 - mu); + score_a = (mu - a1) >= (mu - a2) ? (mu - a2) : (mu - a1); + float score_2 = score_a >= score_b ? score_b : score_a; + + float score_intensity = score_1 >= score_2 ? score_1 : score_2; + score_intensity = score_intensity > 0.0 ? score_intensity : 0.0; + + score = score_gradient*score_intensity; +} +//score corners +void CornerDetAC::scoreCorners(Mat img, Mat imgAngle, Mat imgWeight, vector &cornors, vector radius, vector &score){ + //for all corners do + for (int i = 0; i < cornors.size(); i++) + { + //corner location + int u = cornors[i].x+0.5; + int v = cornors[i].y+0.5; + if (i == 278) + { + int aaa = 0; + } + //compute corner statistics @ radius 1 + vector scores; + for (int j = 0; j < radius.size(); j++) + { + scores.push_back(0); + int r = radius[j]; + if (u > r&&u <= (img.cols - r - 1) && v>r && v <= (img.rows - r - 1)) + { + int startX, startY, ROIwidth, ROIheight; + startX = u - r; + startY = v - r; + ROIwidth = 2 * r + 1; + ROIheight = 2 * r + 1; + + Mat sub_img = img(Rect(startX, startY, ROIwidth, ROIheight)).clone(); + Mat sub_imgWeight = imgWeight(Rect(startX, startY, ROIwidth, ROIheight)).clone(); + vector cornersEdge; + cornersEdge.push_back(Point2f((float)cornersEdge1[i][0], (float)cornersEdge1[i][1])); + cornersEdge.push_back(Point2f((float)cornersEdge2[i][0], (float)cornersEdge2[i][1])); + cornerCorrelationScore(sub_img, sub_imgWeight, cornersEdge, scores[j]); + } + } + //take highest score + score.push_back(*max_element(begin(scores), end(scores))); + } + +} + + +void CornerDetAC::detectCorners(Mat &Src, vector &resultCornors, Corners& mcorners, dtype scoreThreshold, bool isrefine) +{ + Mat gray, imageNorm; + gray = Mat(Src.size(), CV_8U); + + // convert to double grayscale image + if (Src.channels() == 3) + { + cvtColor(Src, gray, COLOR_BGR2GRAY); + } + else + { + gray = Src.clone(); + } + + cv::GaussianBlur(gray, gray, cv::Size(9,9), 1.5); + + + // scale input image + normalize(gray, imageNorm, 0, 1, cv::NORM_MINMAX, mtype); + //gray.convertTo(imageNorm, CV_32F, 1 / 255.0); + + // filter image + Mat imgCorners = Mat::zeros(imageNorm.size(), mtype); + + Mat imgCornerA1(imageNorm.size(), mtype); + Mat imgCornerB1(imageNorm.size(), mtype); + Mat imgCornerC1(imageNorm.size(), mtype); + Mat imgCornerD1(imageNorm.size(), mtype); + + Mat imgCornerA(imageNorm.size(), mtype); + Mat imgCornerB(imageNorm.size(), mtype); + Mat imgCorner1(imageNorm.size(), mtype); + Mat imgCorner2(imageNorm.size(), mtype); + Mat imgCornerMean(imageNorm.size(), mtype); + + std::cout << "begin filtering !" << std::endl; + double t = (double)getTickCount(); + + //#pragma omp parallel for num_threads(4) + for (int i = 0; i < 6; i++) + { + Mat kernelA1, kernelB1, kernelC1, kernelD1; + createkernel(templateProps[i].x, templateProps[i].y, radius[i / 2], kernelA1, kernelB1, kernelC1, kernelD1);//1.1 ֺ + + //std::cout << "kernelA:" << kernelA1 << endl << "kernelB:" << kernelB1 << endl + // << "kernelC:" << kernelC1 << endl << "kernelD:" << kernelD1 << endl; + // filter image according with current template +#if 1 + imgCornerA1 = corealgmatlab::conv2(imageNorm, kernelA1, CONVOLUTION_SAME); + imgCornerB1 = corealgmatlab::conv2(imageNorm, kernelB1, CONVOLUTION_SAME); + imgCornerC1 = corealgmatlab::conv2(imageNorm, kernelC1, CONVOLUTION_SAME); + imgCornerD1 = corealgmatlab::conv2(imageNorm, kernelD1, CONVOLUTION_SAME); +#else + filter2D(imageNorm, imgCornerA1, mtype, kernelA1);//a1 + filter2D(imageNorm, imgCornerB1, mtype, kernelB1);//a2 + filter2D(imageNorm, imgCornerC1, mtype, kernelC1);//b1 + filter2D(imageNorm, imgCornerD1, mtype, kernelD1);//b2 +#endif + //compute mean + imgCornerMean = (imgCornerA1 + imgCornerB1 + imgCornerC1 + imgCornerD1) / 4.0;//1.3 չʽм + // case 1: a = white, b = black + getMin(imgCornerA1 - imgCornerMean, imgCornerB1 - imgCornerMean, imgCornerA); + getMin(imgCornerMean - imgCornerC1, imgCornerMean - imgCornerD1, imgCornerB); + getMin(imgCornerA, imgCornerB, imgCorner1); + // case 2: b = white, a = black + getMin(imgCornerMean - imgCornerA1, imgCornerMean - imgCornerB1, imgCornerA); + getMin(imgCornerC1 - imgCornerMean, imgCornerD1 - imgCornerMean, imgCornerB); + getMin(imgCornerA, imgCornerB, imgCorner2); + + // update corner map + getMax(imgCorners, imgCorner1, imgCorners); + getMax(imgCorners, imgCorner2, imgCorners); + } +#ifdef show_ + namedWindow("ROI");//ڣʾԭʼͼ + imshow("ROI", imgCorners); waitKey(10); +#endif + + t = ((double)getTickCount() - t) / getTickFrequency(); + std::cout << "filtering time cost :" << t << std::endl; + + // extract corner candidates via non maximum suppression + nonMaximumSuppression(imgCorners, cornerPoints, 3, 0.025, 5);//1.5 Ǽֵ㷨йˣȡ̸ǵ + + //post processing + + Mat imageDu(gray.size(), mtype); + Mat imageDv(gray.size(), mtype); + + Mat img_angle = cv::Mat::zeros(gray.size(), mtype); + Mat img_weight = cv::Mat::zeros(gray.size(), mtype); + + getImageAngleAndWeight(imageNorm, imageDu, imageDv, img_angle, img_weight); + if (isrefine == true) + { + //subpixel refinement + refineCorners(cornerPoints, imageDu, imageDv, img_angle, img_weight, 10); + if (cornerPoints.size() > 0) + { + for (int i = 0; i < cornerPoints.size(); i++) + { + if (cornersEdge1[i][0] == 0 && cornersEdge1[i][0] == 0) + { + cornerPoints[i].x = 0; cornerPoints[i].y = 0; + } + } + } + } + + //remove corners without edges + + //score corners + vector score; + scoreCorners(imageNorm, img_angle, img_weight, cornerPoints, radius, score); + +#ifdef show_ + namedWindow("src");//ڣʾԭʼͼ + imshow("src", Src); + waitKey(0); +#endif + + // remove low scoring corners + int nlen = cornerPoints.size(); + if (nlen > 0) + { + for (int i = 0; i < nlen;i++) + { + if (score[i] > scoreThreshold) + { + mcorners.p.push_back(cornerPoints[i]); + mcorners.v1.push_back(cv::Vec2f(cornersEdge1[i][0], cornersEdge1[i][1])); + mcorners.v2.push_back(cv::Vec2f(cornersEdge2[i][0], cornersEdge2[i][1])); + mcorners.score.push_back(score[i]); + } + } + } + + std::vector corners_n1(mcorners.p.size()); + for (int i = 0; i < corners_n1.size(); i++) + { + if (mcorners.v1[i][0] + mcorners.v1[i][1] < 0.0) + { + mcorners.v1[i] = -mcorners.v1[i]; + } + corners_n1[i] = mcorners.v1[i]; + float flipflag = corners_n1[i][0] * mcorners.v2[i][0] + corners_n1[0][1] * mcorners.v2[i][1]; + if (flipflag > 0) + flipflag = -1; + else + flipflag = 1; + mcorners.v2[i] = flipflag * mcorners.v2[i]; + } +} + diff --git a/CornerDetAC.h b/CornerDetAC.h new file mode 100644 index 0000000..855e9f0 --- /dev/null +++ b/CornerDetAC.h @@ -0,0 +1,65 @@ +/* Copyright 2017 onlyliu(997737609@qq.com). */ +/* */ +/* part of source code come from https://github.com/qibao77/cornerDetect */ +/* Automatic Camera and Range Sensor Calibration using a single Shot */ +/* this project realize the papar: Automatic Camera and Range Sensor */ +/* Calibration using a single Shot */ + + +#pragma once + +#include "opencv2/opencv.hpp" +#include +#include + +#include "HeaderCB.h" + +#if 1 +#define mtype CV_32F +#define dtype float +#else +#define mtype CV_64F +#define dtype double +#endif + +class CornerDetAC +{ +public: + CornerDetAC(); + ~CornerDetAC(); + CornerDetAC(cv::Mat img); + void detectCorners(cv::Mat &Src, std::vector &resultCornors, Corners& mcorners, dtype scoreThreshold, bool isrefine = true); + void chessboardsFromCorners(std::vector>chessboards); +private: + + dtype normpdf(dtype dist, dtype mu, dtype sigma); + + void getMin(cv::Mat src1, cv::Mat src2, cv::Mat &dst); + + void getMax(cv::Mat src1, cv::Mat src2, cv::Mat &dst); + + void getImageAngleAndWeight(cv::Mat img, cv::Mat &imgDu, cv::Mat &imgDv, cv::Mat &imgAngle, cv::Mat &imgWeight); + + void edgeOrientations(cv::Mat imgAngle, cv::Mat imgWeight, int index); + + void findModesMeanShift(std::vector hist, std::vector &hist_smoothed, std::vector> &modes, dtype sigma); + + void scoreCorners(cv::Mat img, cv::Mat imgAngle, cv::Mat imgWeight, std::vector &cornors, std::vector radius, std::vector &score); + + void cornerCorrelationScore(cv::Mat img, cv::Mat imgWeight, std::vector cornersEdge, float &score); + + void refineCorners(std::vector &cornors, cv::Mat imgDu, cv::Mat imgDv, cv::Mat imgAngle, cv::Mat imgWeight, float radius); + + void createkernel(float angle1, float angle2, int kernelSize, cv::Mat &kernelA, cv::Mat &kernelB, cv::Mat &kernelC, cv::Mat &kernelD); + + void nonMaximumSuppression(cv::Mat& inputCorners, std::vector& outputCorners, int patchSize, dtype threshold, int margin); + float norm2d(cv::Point2f o); + std::vector templateProps; + std::vector radius; + std::vector cornerPoints; + std::vector> cornersEdge1; + std::vector > cornersEdge2; + std::vector cornerPointsRefined; + +}; + diff --git a/HeaderCB.h b/HeaderCB.h new file mode 100644 index 0000000..8c6208d --- /dev/null +++ b/HeaderCB.h @@ -0,0 +1,75 @@ +/* Copyright 2017 onlyliu(997737609@qq.com). */ +/* */ +/* part of source code come from https://github.com/qibao77/cornerDetect */ +/* Automatic Camera and Range Sensor Calibration using a single Shot */ +/* this project realize the papar: Automatic Camera and Range Sensor */ +/* Calibration using a single Shot */ +#pragma once + +#include "opencv2/opencv.hpp" +#include +#include "stdio.h" + +#define logd std::printf; + +struct Corners +{ + std::vector p; + std::vector v1; + std::vector v2; + std::vector score; +} ; + + +struct ConerInfo +{ + cv::Point2f p; + int chessidx; + int row, col; + int idx; + float x, y; + bool vaild; + int neardiskidx; + float nearestdiskdistance; + + ConerInfo operator=(const ConerInfo& value) + { + p = value.p; + chessidx = value.chessidx; + row = value.row; + col = value.col; + idx = value.idx; + x = value.x; + y = value.y; + vaild = value.vaild; + neardiskidx = value.neardiskidx; + nearestdiskdistance = value.nearestdiskdistance; + + return *this; + } + ConerInfo() + { + vaild = true; + neardiskidx = -1; + nearestdiskdistance = -1.0; + } + +}; +struct ImageChessesStruct +{ + std::vector > flagpostion; + std::vector > > idxconersflags; + std::vectorchoosecorneri; + + cv::Rect rt; + int cbnum; + std::vector > chesscorners; + bool flagbeginfromzero; + ImageChessesStruct& operator=( ImageChessesStruct& value) + { + cbnum = value.cbnum; + chesscorners = value.chesscorners; + return *this; + } + +}; diff --git a/corealgmatlab.cpp b/corealgmatlab.cpp new file mode 100644 index 0000000..4d8c1a6 --- /dev/null +++ b/corealgmatlab.cpp @@ -0,0 +1,41 @@ +/* Copyright 2017 onlyliu(997737609@qq.com). */ +/* */ +/* part of source code come from https://github.com/qibao77/cornerDetect */ +/* Automatic Camera and Range Sensor Calibration using a single Shot */ +/* this project realize the papar: Automatic Camera and Range Sensor */ +/* Calibration using a single Shot */ + +#include "corealgmatlab.h" + +using namespace cv; + +corealgmatlab::corealgmatlab() +{ +} + + +corealgmatlab::~corealgmatlab() +{ +} +Mat corealgmatlab::conv2(const Mat &img, const Mat& ikernel, ConvolutionType type) +{ + Mat dest; + Mat kernel; + flip(ikernel, kernel, -1); + Mat source = img; + if (CONVOLUTION_FULL == type) + { + source = Mat(); + const int additionalRows = kernel.rows - 1, additionalCols = kernel.cols - 1; + copyMakeBorder(img, source, (additionalRows + 1) / 2, additionalRows / 2, (additionalCols + 1) / 2, additionalCols / 2, BORDER_CONSTANT, Scalar(0)); + } + Point anchor(kernel.cols - kernel.cols / 2 - 1, kernel.rows - kernel.rows / 2 - 1); + int borderMode = BORDER_CONSTANT; + filter2D(source, dest, img.depth(), kernel, anchor, 0, borderMode); + + if (CONVOLUTION_VALID == type) + { + dest = dest.colRange((kernel.cols - 1) / 2, dest.cols - kernel.cols / 2).rowRange((kernel.rows - 1) / 2, dest.rows - kernel.rows / 2); + } + return dest; +} diff --git a/corealgmatlab.h b/corealgmatlab.h new file mode 100644 index 0000000..e796b26 --- /dev/null +++ b/corealgmatlab.h @@ -0,0 +1,21 @@ +#pragma once + +#include "opencv2/opencv.hpp" +enum ConvolutionType { + /* Return the full convolution, including border */ + CONVOLUTION_FULL, + + /* Return only the part that corresponds to the original image */ + CONVOLUTION_SAME, + /* Return only the submatrix containing elements that were not influenced by the border */ + CONVOLUTION_VALID +}; + +class corealgmatlab +{ +public: + corealgmatlab(); + ~corealgmatlab(); + static cv::Mat conv2(const cv::Mat &img, const cv::Mat& ikernel, ConvolutionType type); +}; + diff --git a/main.cpp b/main.cpp new file mode 100644 index 0000000..e41e614 --- /dev/null +++ b/main.cpp @@ -0,0 +1,88 @@ +/* Copyright 2017 onlyliu(997737609@qq.com). */ +/* */ +/* Automatic Camera and Range Sensor Calibration using a single Shot */ +/* this project realize the papar: Automatic Camera and Range Sensor */ +/* Calibration using a single Shot */ + + + +#include "opencv2/opencv.hpp" + +#include +#include "CornerDetAC.h" +#include "ChessboradStruct.h" + +#include +#include +#include + +using namespace cv; +using namespace std; +using std::vector; +vector points; + +int main(int argc, char* argv[]) +{ + Mat src1; + cv::Mat src; + printf("read file...\n"); + string sr; + if(argc < 2) + sr = string("02.png"); + else + sr = string(argv[1]); + string simage, stxt, ssave; + simage = sr ;//+ ".bmp"; + stxt = sr + ".txt"; + ssave = sr + ".png"; + ssave = "./t/"+ssave; + src1 = imread(simage.c_str(), -1);//载入测试图像 + if (src1.channels() == 1) + { + src = src1.clone(); + } + else + { + if (src1.channels() == 3) + { + cv::cvtColor(src1, src, CV_BGR2GRAY); + } + else + { + if (src1.channels() == 4) + { + cv::cvtColor(src1, src, CV_BGRA2GRAY); + } + } + } + + if (src.empty()) + { + printf("Cannot read image file: %s\n", simage.c_str()); + return -1; + } + else + { + printf("read image file ok\n"); + } + + vector corners_p;//存储找到的角点 + + double t = (double)getTickCount(); + std::vector chessboards; + CornerDetAC corner_detector(src); + ChessboradStruct chessboardstruct; + + Corners corners_s; + corner_detector.detectCorners(src, corners_p, corners_s, 0.01); + + t = ((double)getTickCount() - t) / getTickFrequency(); + std::cout << "time cost :" << t << std::endl; + + ImageChessesStruct ics; + chessboardstruct.chessboardsFromCorners(corners_s, chessboards, 0.6); + chessboardstruct.drawchessboard(src1, corners_s, chessboards, "cb", 0); + + return 0; +} +
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