/*
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License Agreement
For Open Source Computer Vision Library
(3-clause BSD License)
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*/
#include "precomp.hpp"
#include "opencv2/aruco.hpp"
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "apriltag_quad_thresh.hpp"
#include "zarray.hpp"
//#define APRIL_DEBUG
#ifdef APRIL_DEBUG
#include "opencv2/imgcodecs.hpp"
#endif
namespace cv {
namespace aruco {
using namespace std;
/**
*
*/
DetectorParameters::DetectorParameters()
: adaptiveThreshWinSizeMin(3),
adaptiveThreshWinSizeMax(23),
adaptiveThreshWinSizeStep(10),
adaptiveThreshConstant(7),
minMarkerPerimeterRate(0.03),
maxMarkerPerimeterRate(4.),
polygonalApproxAccuracyRate(0.03),
minCornerDistanceRate(0.05),
minDistanceToBorder(3),
minMarkerDistanceRate(0.05),
cornerRefinementMethod(CORNER_REFINE_NONE),
cornerRefinementWinSize(5),
cornerRefinementMaxIterations(30),
cornerRefinementMinAccuracy(0.1),
markerBorderBits(1),
perspectiveRemovePixelPerCell(4),
perspectiveRemoveIgnoredMarginPerCell(0.13),
maxErroneousBitsInBorderRate(0.35),
minOtsuStdDev(5.0),
errorCorrectionRate(0.6),
aprilTagQuadDecimate(0.0),
aprilTagQuadSigma(0.0),
aprilTagMinClusterPixels(5),
aprilTagMaxNmaxima(10),
aprilTagCriticalRad( (float)(10* CV_PI /180) ),
aprilTagMaxLineFitMse(10.0),
aprilTagMinWhiteBlackDiff(5),
aprilTagDeglitch(0){}
/**
* @brief Create a new set of DetectorParameters with default values.
*/
Ptr<DetectorParameters> DetectorParameters::create() {
Ptr<DetectorParameters> params = makePtr<DetectorParameters>();
return params;
}
/**
* @brief Convert input image to gray if it is a 3-channels image
*/
static void _convertToGrey(InputArray _in, OutputArray _out) {
CV_Assert(_in.getMat().channels() == 1 || _in.getMat().channels() == 3);
_out.create(_in.getMat().size(), CV_8UC1);
if(_in.getMat().type() == CV_8UC3)
cvtColor(_in.getMat(), _out.getMat(), COLOR_BGR2GRAY);
else
_in.getMat().copyTo(_out);
}
/**
* @brief Threshold input image using adaptive thresholding
*/
static void _threshold(InputArray _in, OutputArray _out, int winSize, double constant) {
CV_Assert(winSize >= 3);
if(winSize % 2 == 0) winSize++; // win size must be odd
adaptiveThreshold(_in, _out, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY_INV, winSize, constant);
}
/**
* @brief Given a tresholded image, find the contours, calculate their polygonal approximation
* and take those that accomplish some conditions
*/
static void _findMarkerContours(InputArray _in, vector< vector< Point2f > > &candidates,
vector< vector< Point > > &contoursOut, double minPerimeterRate,
double maxPerimeterRate, double accuracyRate,
double minCornerDistanceRate, int minDistanceToBorder) {
CV_Assert(minPerimeterRate > 0 && maxPerimeterRate > 0 && accuracyRate > 0 &&
minCornerDistanceRate >= 0 && minDistanceToBorder >= 0);
// calculate maximum and minimum sizes in pixels
unsigned int minPerimeterPixels =
(unsigned int)(minPerimeterRate * max(_in.getMat().cols, _in.getMat().rows));
unsigned int maxPerimeterPixels =
(unsigned int)(maxPerimeterRate * max(_in.getMat().cols, _in.getMat().rows));
Mat contoursImg;
_in.getMat().copyTo(contoursImg);
vector< vector< Point > > contours;
findContours(contoursImg, contours, RETR_LIST, CHAIN_APPROX_NONE);
// now filter list of contours
for(unsigned int i = 0; i < contours.size(); i++) {
// check perimeter
if(contours[i].size() < minPerimeterPixels || contours[i].size() > maxPerimeterPixels)
continue;
// check is square and is convex
vector< Point > approxCurve;
approxPolyDP(contours[i], approxCurve, double(contours[i].size()) * accuracyRate, true);
if(approxCurve.size() != 4 || !isContourConvex(approxCurve)) continue;
// check min distance between corners
double minDistSq =
max(contoursImg.cols, contoursImg.rows) * max(contoursImg.cols, contoursImg.rows);
for(int j = 0; j < 4; j++) {
double d = (double)(approxCurve[j].x - approxCurve[(j + 1) % 4].x) *
(double)(approxCurve[j].x - approxCurve[(j + 1) % 4].x) +
(double)(approxCurve[j].y - approxCurve[(j + 1) % 4].y) *
(double)(approxCurve[j].y - approxCurve[(j + 1) % 4].y);
minDistSq = min(minDistSq, d);
}
double minCornerDistancePixels = double(contours[i].size()) * minCornerDistanceRate;
if(minDistSq < minCornerDistancePixels * minCornerDistancePixels) continue;
// check if it is too near to the image border
bool tooNearBorder = false;
for(int j = 0; j < 4; j++) {
if(approxCurve[j].x < minDistanceToBorder || approxCurve[j].y < minDistanceToBorder ||
approxCurve[j].x > contoursImg.cols - 1 - minDistanceToBorder ||
approxCurve[j].y > contoursImg.rows - 1 - minDistanceToBorder)
tooNearBorder = true;
}
if(tooNearBorder) continue;
// if it passes all the test, add to candidates vector
vector< Point2f > currentCandidate;
currentCandidate.resize(4);
for(int j = 0; j < 4; j++) {
currentCandidate[j] = Point2f((float)approxCurve[j].x, (float)approxCurve[j].y);
}
candidates.push_back(currentCandidate);
contoursOut.push_back(contours[i]);
}
}
/**
* @brief Assure order of candidate corners is clockwise direction
*/
static void _reorderCandidatesCorners(vector< vector< Point2f > > &candidates) {
for(unsigned int i = 0; i < candidates.size(); i++) {
double dx1 = candidates[i][1].x - candidates[i][0].x;
double dy1 = candidates[i][1].y - candidates[i][0].y;
double dx2 = candidates[i][2].x - candidates[i][0].x;
double dy2 = candidates[i][2].y - candidates[i][0].y;
double crossProduct = (dx1 * dy2) - (dy1 * dx2);
if(crossProduct < 0.0) { // not clockwise direction
swap(candidates[i][1], candidates[i][3]);
}
}
}
/**
* @brief Check candidates that are too close to each other and remove the smaller one
*/
static void _filterTooCloseCandidates(const vector< vector< Point2f > > &candidatesIn,
vector< vector< Point2f > > &candidatesOut,
const vector< vector< Point > > &contoursIn,
vector< vector< Point > > &contoursOut,
double minMarkerDistanceRate) {
CV_Assert(minMarkerDistanceRate >= 0);
vector< pair< int, int > > nearCandidates;
for(unsigned int i = 0; i < candidatesIn.size(); i++) {
for(unsigned int j = i + 1; j < candidatesIn.size(); j++) {
int minimumPerimeter = min((int)contoursIn[i].size(), (int)contoursIn[j].size() );
// fc is the first corner considered on one of the markers, 4 combinations are possible
for(int fc = 0; fc < 4; fc++) {
double distSq = 0;
for(int c = 0; c < 4; c++) {
// modC is the corner considering first corner is fc
int modC = (c + fc) % 4;
distSq += (candidatesIn[i][modC].x - candidatesIn[j][c].x) *
(candidatesIn[i][modC].x - candidatesIn[j][c].x) +
(candidatesIn[i][modC].y - candidatesIn[j][c].y) *
(candidatesIn[i][modC].y - candidatesIn[j][c].y);
}
distSq /= 4.;
// if mean square distance is too low, remove the smaller one of the two markers
double minMarkerDistancePixels = double(minimumPerimeter) * minMarkerDistanceRate;
if(distSq < minMarkerDistancePixels * minMarkerDistancePixels) {
nearCandidates.push_back(pair< int, int >(i, j));
break;
}
}
}
}
// mark smaller one in pairs to remove
vector< bool > toRemove(candidatesIn.size(), false);
for(unsigned int i = 0; i < nearCandidates.size(); i++) {
// if one of the marker has been already markerd to removed, dont need to do anything
if(toRemove[nearCandidates[i].first] || toRemove[nearCandidates[i].second]) continue;
size_t perimeter1 = contoursIn[nearCandidates[i].first].size();
size_t perimeter2 = contoursIn[nearCandidates[i].second].size();
if(perimeter1 > perimeter2)
toRemove[nearCandidates[i].second] = true;
else
toRemove[nearCandidates[i].first] = true;
}
// remove extra candidates
candidatesOut.clear();
unsigned long totalRemaining = 0;
for(unsigned int i = 0; i < toRemove.size(); i++)
if(!toRemove[i]) totalRemaining++;
candidatesOut.resize(totalRemaining);
contoursOut.resize(totalRemaining);
for(unsigned int i = 0, currIdx = 0; i < candidatesIn.size(); i++) {
if(toRemove[i]) continue;
candidatesOut[currIdx] = candidatesIn[i];
contoursOut[currIdx] = contoursIn[i];
currIdx++;
}
}
/**
* ParallelLoopBody class for the parallelization of the basic candidate detections using
* different threhold window sizes. Called from function _detectInitialCandidates()
*/
class DetectInitialCandidatesParallel : public ParallelLoopBody {
public:
DetectInitialCandidatesParallel(const Mat *_grey,
vector< vector< vector< Point2f > > > *_candidatesArrays,
vector< vector< vector< Point > > > *_contoursArrays,
const Ptr<DetectorParameters> &_params)
: grey(_grey), candidatesArrays(_candidatesArrays), contoursArrays(_contoursArrays),
params(_params) {}
void operator()(const Range &range) const CV_OVERRIDE {
const int begin = range.start;
const int end = range.end;
for(int i = begin; i < end; i++) {
int currScale =
params->adaptiveThreshWinSizeMin + i * params->adaptiveThreshWinSizeStep;
// threshold
Mat thresh;
_threshold(*grey, thresh, currScale, params->adaptiveThreshConstant);
// detect rectangles
_findMarkerContours(thresh, (*candidatesArrays)[i], (*contoursArrays)[i],
params->minMarkerPerimeterRate, params->maxMarkerPerimeterRate,
params->polygonalApproxAccuracyRate, params->minCornerDistanceRate,
params->minDistanceToBorder);
}
}
private:
DetectInitialCandidatesParallel &operator=(const DetectInitialCandidatesParallel &);
const Mat *grey;
vector< vector< vector< Point2f > > > *candidatesArrays;
vector< vector< vector< Point > > > *contoursArrays;
const Ptr<DetectorParameters> ¶ms;
};
/**
* @brief Initial steps on finding square candidates
*/
static void _detectInitialCandidates(const Mat &grey, vector< vector< Point2f > > &candidates,
vector< vector< Point > > &contours,
const Ptr<DetectorParameters> ¶ms) {
CV_Assert(params->adaptiveThreshWinSizeMin >= 3 && params->adaptiveThreshWinSizeMax >= 3);
CV_Assert(params->adaptiveThreshWinSizeMax >= params->adaptiveThreshWinSizeMin);
CV_Assert(params->adaptiveThreshWinSizeStep > 0);
// number of window sizes (scales) to apply adaptive thresholding
int nScales = (params->adaptiveThreshWinSizeMax - params->adaptiveThreshWinSizeMin) /
params->adaptiveThreshWinSizeStep + 1;
vector< vector< vector< Point2f > > > candidatesArrays((size_t) nScales);
vector< vector< vector< Point > > > contoursArrays((size_t) nScales);
////for each value in the interval of thresholding window sizes
// for(int i = 0; i < nScales; i++) {
// int currScale = params.adaptiveThreshWinSizeMin + i*params.adaptiveThreshWinSizeStep;
// // treshold
// Mat thresh;
// _threshold(grey, thresh, currScale, params.adaptiveThreshConstant);
// // detect rectangles
// _findMarkerContours(thresh, candidatesArrays[i], contoursArrays[i],
// params.minMarkerPerimeterRate,
// params.maxMarkerPerimeterRate, params.polygonalApproxAccuracyRate,
// params.minCornerDistance, params.minDistanceToBorder);
//}
// this is the parallel call for the previous commented loop (result is equivalent)
parallel_for_(Range(0, nScales), DetectInitialCandidatesParallel(&grey, &candidatesArrays,
&contoursArrays, params));
// join candidates
for(int i = 0; i < nScales; i++) {
for(unsigned int j = 0; j < candidatesArrays[i].size(); j++) {
candidates.push_back(candidatesArrays[i][j]);
contours.push_back(contoursArrays[i][j]);
}
}
}
/**
* @brief Detect square candidates in the input image
*/
static void _detectCandidates(InputArray _image, vector< vector< Point2f > >& candidatesOut,
vector< vector< Point > >& contoursOut, const Ptr<DetectorParameters> &_params) {
Mat image = _image.getMat();
CV_Assert(image.total() != 0);
/// 1. CONVERT TO GRAY
Mat grey;
_convertToGrey(image, grey);
vector< vector< Point2f > > candidates;
vector< vector< Point > > contours;
/// 2. DETECT FIRST SET OF CANDIDATES
_detectInitialCandidates(grey, candidates, contours, _params);
/// 3. SORT CORNERS
_reorderCandidatesCorners(candidates);
/// 4. FILTER OUT NEAR CANDIDATE PAIRS
_filterTooCloseCandidates(candidates, candidatesOut, contours, contoursOut,
_params->minMarkerDistanceRate);
}
/**
* @brief Given an input image and candidate corners, extract the bits of the candidate, including
* the border bits
*/
static Mat _extractBits(InputArray _image, InputArray _corners, int markerSize,
int markerBorderBits, int cellSize, double cellMarginRate,
double minStdDevOtsu) {
CV_Assert(_image.getMat().channels() == 1);
CV_Assert(_corners.total() == 4);
CV_Assert(markerBorderBits > 0 && cellSize > 0 && cellMarginRate >= 0 && cellMarginRate <= 1);
CV_Assert(minStdDevOtsu >= 0);
// number of bits in the marker
int markerSizeWithBorders = markerSize + 2 * markerBorderBits;
int cellMarginPixels = int(cellMarginRate * cellSize);
Mat resultImg; // marker image after removing perspective
int resultImgSize = markerSizeWithBorders * cellSize;
Mat resultImgCorners(4, 1, CV_32FC2);
resultImgCorners.ptr< Point2f >(0)[0] = Point2f(0, 0);
resultImgCorners.ptr< Point2f >(0)[1] = Point2f((float)resultImgSize - 1, 0);
resultImgCorners.ptr< Point2f >(0)[2] =
Point2f((float)resultImgSize - 1, (float)resultImgSize - 1);
resultImgCorners.ptr< Point2f >(0)[3] = Point2f(0, (float)resultImgSize - 1);
// remove perspective
Mat transformation = getPerspectiveTransform(_corners, resultImgCorners);
warpPerspective(_image, resultImg, transformation, Size(resultImgSize, resultImgSize),
INTER_NEAREST);
// output image containing the bits
Mat bits(markerSizeWithBorders, markerSizeWithBorders, CV_8UC1, Scalar::all(0));
// check if standard deviation is enough to apply Otsu
// if not enough, it probably means all bits are the same color (black or white)
Mat mean, stddev;
// Remove some border just to avoid border noise from perspective transformation
Mat innerRegion = resultImg.colRange(cellSize / 2, resultImg.cols - cellSize / 2)
.rowRange(cellSize / 2, resultImg.rows - cellSize / 2);
meanStdDev(innerRegion, mean, stddev);
if(stddev.ptr< double >(0)[0] < minStdDevOtsu) {
// all black or all white, depending on mean value
if(mean.ptr< double >(0)[0] > 127)
bits.setTo(1);
else
bits.setTo(0);
return bits;
}
// now extract code, first threshold using Otsu
threshold(resultImg, resultImg, 125, 255, THRESH_BINARY | THRESH_OTSU);
// for each cell
for(int y = 0; y < markerSizeWithBorders; y++) {
for(int x = 0; x < markerSizeWithBorders; x++) {
int Xstart = x * (cellSize) + cellMarginPixels;
int Ystart = y * (cellSize) + cellMarginPixels;
Mat square = resultImg(Rect(Xstart, Ystart, cellSize - 2 * cellMarginPixels,
cellSize - 2 * cellMarginPixels));
// count white pixels on each cell to assign its value
size_t nZ = (size_t) countNonZero(square);
if(nZ > square.total() / 2) bits.at< unsigned char >(y, x) = 1;
}
}
return bits;
}
/**
* @brief Return number of erroneous bits in border, i.e. number of white bits in border.
*/
static int _getBorderErrors(const Mat &bits, int markerSize, int borderSize) {
int sizeWithBorders = markerSize + 2 * borderSize;
CV_Assert(markerSize > 0 && bits.cols == sizeWithBorders && bits.rows == sizeWithBorders);
int totalErrors = 0;
for(int y = 0; y < sizeWithBorders; y++) {
for(int k = 0; k < borderSize; k++) {
if(bits.ptr< unsigned char >(y)[k] != 0) totalErrors++;
if(bits.ptr< unsigned char >(y)[sizeWithBorders - 1 - k] != 0) totalErrors++;
}
}
for(int x = borderSize; x < sizeWithBorders - borderSize; x++) {
for(int k = 0; k < borderSize; k++) {
if(bits.ptr< unsigned char >(k)[x] != 0) totalErrors++;
if(bits.ptr< unsigned char >(sizeWithBorders - 1 - k)[x] != 0) totalErrors++;
}
}
return totalErrors;
}
/**
* @brief Tries to identify one candidate given the dictionary
*/
static bool _identifyOneCandidate(const Ptr<Dictionary>& dictionary, InputArray _image,
vector<Point2f>& _corners, int& idx,
const Ptr<DetectorParameters>& params)
{
CV_Assert(_corners.size() == 4);
CV_Assert(_image.getMat().total() != 0);
CV_Assert(params->markerBorderBits > 0);
// get bits
Mat candidateBits =
_extractBits(_image, _corners, dictionary->markerSize, params->markerBorderBits,
params->perspectiveRemovePixelPerCell,
params->perspectiveRemoveIgnoredMarginPerCell, params->minOtsuStdDev);
// analyze border bits
int maximumErrorsInBorder =
int(dictionary->markerSize * dictionary->markerSize * params->maxErroneousBitsInBorderRate);
int borderErrors =
_getBorderErrors(candidateBits, dictionary->markerSize, params->markerBorderBits);
if(borderErrors > maximumErrorsInBorder) return false; // border is wrong
// take only inner bits
Mat onlyBits =
candidateBits.rowRange(params->markerBorderBits,
candidateBits.rows - params->markerBorderBits)
.colRange(params->markerBorderBits, candidateBits.rows - params->markerBorderBits);
// try to indentify the marker
int rotation;
if(!dictionary->identify(onlyBits, idx, rotation, params->errorCorrectionRate))
return false;
// shift corner positions to the correct rotation
if(rotation != 0) {
std::rotate(_corners.begin(), _corners.begin() + 4 - rotation, _corners.end());
}
return true;
}
/**
* ParallelLoopBody class for the parallelization of the marker identification step
* Called from function _identifyCandidates()
*/
class IdentifyCandidatesParallel : public ParallelLoopBody {
public:
IdentifyCandidatesParallel(const Mat& _grey, vector< vector< Point2f > >& _candidates,
const Ptr<Dictionary> &_dictionary,
vector< int >& _idsTmp, vector< char >& _validCandidates,
const Ptr<DetectorParameters> &_params)
: grey(_grey), candidates(_candidates), dictionary(_dictionary),
idsTmp(_idsTmp), validCandidates(_validCandidates), params(_params) {}
void operator()(const Range &range) const CV_OVERRIDE {
const int begin = range.start;
const int end = range.end;
for(int i = begin; i < end; i++) {
int currId;
if(_identifyOneCandidate(dictionary, grey, candidates[i], currId, params)) {
validCandidates[i] = 1;
idsTmp[i] = currId;
}
}
}
private:
IdentifyCandidatesParallel &operator=(const IdentifyCandidatesParallel &); // to quiet MSVC
const Mat &grey;
vector< vector< Point2f > >& candidates;
const Ptr<Dictionary> &dictionary;
vector< int > &idsTmp;
vector< char > &validCandidates;
const Ptr<DetectorParameters> ¶ms;
};
/**
* @brief Copy the contents of a corners vector to an OutputArray, settings its size.
*/
static void _copyVector2Output(vector< vector< Point2f > > &vec, OutputArrayOfArrays out) {
out.create((int)vec.size(), 1, CV_32FC2);
if(out.isMatVector()) {
for (unsigned int i = 0; i < vec.size(); i++) {
out.create(4, 1, CV_32FC2, i);
Mat &m = out.getMatRef(i);
Mat(Mat(vec[i]).t()).copyTo(m);
}
}
else if(out.isUMatVector()) {
for (unsigned int i = 0; i < vec.size(); i++) {
out.create(4, 1, CV_32FC2, i);
UMat &m = out.getUMatRef(i);
Mat(Mat(vec[i]).t()).copyTo(m);
}
}
else if(out.kind() == _OutputArray::STD_VECTOR_VECTOR){
for (unsigned int i = 0; i < vec.size(); i++) {
out.create(4, 1, CV_32FC2, i);
Mat m = out.getMat(i);
Mat(Mat(vec[i]).t()).copyTo(m);
}
}
else {
CV_Error(cv::Error::StsNotImplemented,
"Only Mat vector, UMat vector, and vector<vector> OutputArrays are currently supported.");
}
}
/**
* @brief Identify square candidates according to a marker dictionary
*/
static void _identifyCandidates(InputArray _image, vector< vector< Point2f > >& _candidates,
vector< vector<Point> >& _contours, const Ptr<Dictionary> &_dictionary,
vector< vector< Point2f > >& _accepted, vector< int >& ids,
const Ptr<DetectorParameters> ¶ms,
OutputArrayOfArrays _rejected = noArray()) {
int ncandidates = (int)_candidates.size();
vector< vector< Point2f > > accepted;
vector< vector< Point2f > > rejected;
vector< vector< Point > > contours;
CV_Assert(_image.getMat().total() != 0);
Mat grey;
_convertToGrey(_image.getMat(), grey);
vector< int > idsTmp(ncandidates, -1);
vector< char > validCandidates(ncandidates, 0);
//// Analyze each of the candidates
// for (int i = 0; i < ncandidates; i++) {
// int currId = i;
// Mat currentCandidate = _candidates.getMat(i);
// if (_identifyOneCandidate(dictionary, grey, currentCandidate, currId, params)) {
// validCandidates[i] = 1;
// idsTmp[i] = currId;
// }
//}
// this is the parallel call for the previous commented loop (result is equivalent)
parallel_for_(Range(0, ncandidates),
IdentifyCandidatesParallel(grey, _candidates, _dictionary, idsTmp,
validCandidates, params));
for(int i = 0; i < ncandidates; i++) {
if(validCandidates[i] == 1) {
accepted.push_back(_candidates[i]);
ids.push_back(idsTmp[i]);
contours.push_back(_contours[i]);
} else {
rejected.push_back(_candidates[i]);
}
}
// parse output
_accepted = accepted;
_contours= contours;
if(_rejected.needed()) {
_copyVector2Output(rejected, _rejected);
}
}
/**
* @brief Final filter of markers after its identification
*/
static void _filterDetectedMarkers(vector< vector< Point2f > >& _corners, vector< int >& _ids, vector< vector< Point> >& _contours) {
CV_Assert(_corners.size() == _ids.size());
if(_corners.empty()) return;
// mark markers that will be removed
vector< bool > toRemove(_corners.size(), false);
bool atLeastOneRemove = false;
// remove repeated markers with same id, if one contains the other (doble border bug)
for(unsigned int i = 0; i < _corners.size() - 1; i++) {
for(unsigned int j = i + 1; j < _corners.size(); j++) {
if(_ids[i] != _ids[j]) continue;
// check if first marker is inside second
bool inside = true;
for(unsigned int p = 0; p < 4; p++) {
Point2f point = _corners[j][p];
if(pointPolygonTest(_corners[i], point, false) < 0) {
inside = false;
break;
}
}
if(inside) {
toRemove[j] = true;
atLeastOneRemove = true;
continue;
}
// check the second marker
inside = true;
for(unsigned int p = 0; p < 4; p++) {
Point2f point = _corners[i][p];
if(pointPolygonTest(_corners[j], point, false) < 0) {
inside = false;
break;
}
}
if(inside) {
toRemove[i] = true;
atLeastOneRemove = true;
continue;
}
}
}
// parse output
if(atLeastOneRemove) {
vector< vector< Point2f > >::iterator filteredCorners = _corners.begin();
vector< int >::iterator filteredIds = _ids.begin();
vector< vector< Point > >::iterator filteredContours = _contours.begin();
for(unsigned int i = 0; i < toRemove.size(); i++) {
if(!toRemove[i]) {
*filteredCorners++ = _corners[i];
*filteredIds++ = _ids[i];
*filteredContours++ = _contours[i];
}
}
_ids.erase(filteredIds, _ids.end());
_corners.erase(filteredCorners, _corners.end());
_contours.erase(filteredContours, _contours.end());
}
}
/**
* @brief Return object points for the system centered in a single marker, given the marker length
*/
static void _getSingleMarkerObjectPoints(float markerLength, OutputArray _objPoints) {
CV_Assert(markerLength > 0);
_objPoints.create(4, 1, CV_32FC3);
Mat objPoints = _objPoints.getMat();
// set coordinate system in the middle of the marker, with Z pointing out
objPoints.ptr< Vec3f >(0)[0] = Vec3f(-markerLength / 2.f, markerLength / 2.f, 0);
objPoints.ptr< Vec3f >(0)[1] = Vec3f(markerLength / 2.f, markerLength / 2.f, 0);
objPoints.ptr< Vec3f >(0)[2] = Vec3f(markerLength / 2.f, -markerLength / 2.f, 0);
objPoints.ptr< Vec3f >(0)[3] = Vec3f(-markerLength / 2.f, -markerLength / 2.f, 0);
}
/**
* ParallelLoopBody class for the parallelization of the marker corner subpixel refinement
* Called from function detectMarkers()
*/
class MarkerSubpixelParallel : public ParallelLoopBody {
public:
MarkerSubpixelParallel(const Mat *_grey, OutputArrayOfArrays _corners,
const Ptr<DetectorParameters> &_params)
: grey(_grey), corners(_corners), params(_params) {}
void operator()(const Range &range) const CV_OVERRIDE {
const int begin = range.start;
const int end = range.end;
for(int i = begin; i < end; i++) {
cornerSubPix(*grey, corners.getMat(i),
Size(params->cornerRefinementWinSize, params->cornerRefinementWinSize),
Size(-1, -1), TermCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS,
params->cornerRefinementMaxIterations,
params->cornerRefinementMinAccuracy));
}
}
private:
MarkerSubpixelParallel &operator=(const MarkerSubpixelParallel &); // to quiet MSVC
const Mat *grey;
OutputArrayOfArrays corners;
const Ptr<DetectorParameters> ¶ms;
};
/**
* Line fitting A * B = C :: Called from function refineCandidateLines
* @param nContours, contour-container
*/
static Point3f _interpolate2Dline(const std::vector<cv::Point2f>& nContours){
float minX, minY, maxX, maxY;
minX = maxX = nContours[0].x;
minY = maxY = nContours[0].y;
for(unsigned int i = 0; i< nContours.size(); i++){
minX = nContours[i].x < minX ? nContours[i].x : minX;
minY = nContours[i].y < minY ? nContours[i].y : minY;
maxX = nContours[i].x > maxX ? nContours[i].x : maxX;
maxY = nContours[i].y > maxY ? nContours[i].y : maxY;
}
Mat A = Mat::ones((int)nContours.size(), 2, CV_32F); // Coefficient Matrix (N x 2)
Mat B((int)nContours.size(), 1, CV_32F); // Variables Matrix (N x 1)
Mat C; // Constant
if(maxX - minX > maxY - minY){
for(unsigned int i =0; i < nContours.size(); i++){
A.at<float>(i,0)= nContours[i].x;
B.at<float>(i,0)= nContours[i].y;
}
solve(A, B, C, DECOMP_NORMAL);
return Point3f(C.at<float>(0, 0), -1., C.at<float>(1, 0));
}
else{
for(unsigned int i =0; i < nContours.size(); i++){
A.at<float>(i,0)= nContours[i].y;
B.at<float>(i,0)= nContours[i].x;
}
solve(A, B, C, DECOMP_NORMAL);
return Point3f(-1., C.at<float>(0, 0), C.at<float>(1, 0));
}
}
/**
* Find the Point where the lines crosses :: Called from function refineCandidateLines
* @param nLine1
* @param nLine2
* @return Crossed Point
*/
static Point2f _getCrossPoint(Point3f nLine1, Point3f nLine2){
Matx22f A(nLine1.x, nLine1.y, nLine2.x, nLine2.y);
Vec2f B(-nLine1.z, -nLine2.z);
return Vec2f(A.solve(B).val);
}
static void _distortPoints(vector<cv::Point2f>& in, const Mat& camMatrix, const Mat& distCoeff) {
// trivial extrinsics
Matx31f Rvec(0,0,0);
Matx31f Tvec(0,0,0);
// calculate 3d points and then reproject, so opencv makes the distortion internally
vector<cv::Point3f> cornersPoints3d;
for (unsigned int i = 0; i < in.size(); i++){
float x= (in[i].x - float(camMatrix.at<double>(0, 2))) / float(camMatrix.at<double>(0, 0));
float y= (in[i].y - float(camMatrix.at<double>(1, 2))) / float(camMatrix.at<double>(1, 1));
cornersPoints3d.push_back(Point3f(x,y,1));
}
cv::projectPoints(cornersPoints3d, Rvec, Tvec, camMatrix, distCoeff, in);
}
/**
* Refine Corners using the contour vector :: Called from function detectMarkers
* @param nContours, contour-container
* @param nCorners, candidate Corners
* @param camMatrix, cameraMatrix input 3x3 floating-point camera matrix
* @param distCoeff, distCoeffs vector of distortion coefficient
*/
static void _refineCandidateLines(std::vector<Point>& nContours, std::vector<Point2f>& nCorners, const Mat& camMatrix, const Mat& distCoeff){
vector<Point2f> contour2f(nContours.begin(), nContours.end());
if(!camMatrix.empty() && !distCoeff.empty()){
undistortPoints(contour2f, contour2f, camMatrix, distCoeff);
}
/* 5 groups :: to group the edges
* 4 - classified by its corner
* extra group - (temporary) if contours do not begin with a corner
*/
vector<Point2f> cntPts[5];
int cornerIndex[4]={-1};
int group=4;
for ( unsigned int i =0; i < nContours.size(); i++ ) {
for(unsigned int j=0; j<4; j++){
if ( nCorners[j] == contour2f[i] ){
cornerIndex[j] = i;
group=j;
}
}
cntPts[group].push_back(contour2f[i]);
}
// saves extra group into corresponding
if( !cntPts[4].empty() ){
for( unsigned int i=0; i < cntPts[4].size() ; i++ )
cntPts[group].push_back(cntPts[4].at(i));
cntPts[4].clear();
}
//Evaluate contour direction :: using the position of the detected corners
int inc=1;
inc = ( (cornerIndex[0] > cornerIndex[1]) && (cornerIndex[3] > cornerIndex[0]) ) ? -1:inc;
inc = ( (cornerIndex[2] > cornerIndex[3]) && (cornerIndex[1] > cornerIndex[2]) ) ? -1:inc;
// calculate the line :: who passes through the grouped points
Point3f lines[4];
for(int i=0; i<4; i++){
lines[i]=_interpolate2Dline(cntPts[i]);
}
/*
* calculate the corner :: where the lines crosses to each other
* clockwise direction no clockwise direction
* 0 1
* .---. 1 .---. 2
* | | | |
* 3 .___. 0 .___.
* 2 3
*/
for(int i=0; i < 4; i++){
if(inc<0)
nCorners[i] = _getCrossPoint(lines[ i ], lines[ (i+1)%4 ]); // 01 12 23 30
else
nCorners[i] = _getCrossPoint(lines[ i ], lines[ (i+3)%4 ]); // 30 01 12 23
}
if(!camMatrix.empty() && !distCoeff.empty()){
_distortPoints(nCorners, camMatrix, distCoeff);
}
}
/**
* ParallelLoopBody class for the parallelization of the marker corner contour refinement
* Called from function detectMarkers()
*/
class MarkerContourParallel : public ParallelLoopBody {
public:
MarkerContourParallel( vector< vector< Point > >& _contours, vector< vector< Point2f > >& _candidates, const Mat& _camMatrix, const Mat& _distCoeff)
: contours(_contours), candidates(_candidates), camMatrix(_camMatrix), distCoeff(_distCoeff){}
void operator()(const Range &range) const CV_OVERRIDE {
for(int i = range.start; i < range.end; i++) {
_refineCandidateLines(contours[i], candidates[i], camMatrix, distCoeff);
}
}
private:
MarkerContourParallel &operator=(const MarkerContourParallel &){
return *this;
}
vector< vector< Point > >& contours;
vector< vector< Point2f > >& candidates;
const Mat& camMatrix;
const Mat& distCoeff;
};
#ifdef APRIL_DEBUG
static void _darken(const Mat &im){
for (int y = 0; y < im.rows; y++) {
for (int x = 0; x < im.cols; x++) {
im.data[im.cols*y+x] /= 2;
}
}
}
#endif
/**
*
* @param im_orig
* @param _params
* @param candidates
* @param contours
*/
static void _apriltag(Mat im_orig, const Ptr<DetectorParameters> & _params, std::vector< std::vector< Point2f > > &candidates,
std::vector< std::vector< Point > > &contours){
///////////////////////////////////////////////////////////
/// Step 1. Detect quads according to requested image decimation
/// and blurring parameters.
Mat quad_im;
im_orig.copyTo(quad_im);
if (_params->aprilTagQuadDecimate > 1){
resize(im_orig, quad_im, Size(), 1/_params->aprilTagQuadDecimate, 1/_params->aprilTagQuadDecimate, INTER_AREA );
}
// Apply a Blur
if (_params->aprilTagQuadSigma != 0) {
// compute a reasonable kernel width by figuring that the
// kernel should go out 2 std devs.
//
// max sigma ksz
// 0.499 1 (disabled)
// 0.999 3
// 1.499 5
// 1.999 7
float sigma = fabsf((float) _params->aprilTagQuadSigma);
int ksz = cvFloor(4 * sigma); // 2 std devs in each direction
ksz |= 1; // make odd number
if (ksz > 1) {
if (_params->aprilTagQuadSigma > 0)
GaussianBlur(quad_im, quad_im, Size(ksz, ksz), sigma, sigma, BORDER_REPLICATE);
else {
Mat orig;
quad_im.copyTo(orig);
GaussianBlur(quad_im, quad_im, Size(ksz, ksz), sigma, sigma, BORDER_REPLICATE);
// SHARPEN the image by subtracting the low frequency components.
for (int y = 0; y < orig.rows; y++) {
for (int x = 0; x < orig.cols; x++) {
int vorig = orig.data[y*orig.step + x];
int vblur = quad_im.data[y*quad_im.step + x];
int v = 2*vorig - vblur;
if (v < 0)
v = 0;
if (v > 255)
v = 255;
quad_im.data[y*quad_im.step + x] = (uint8_t) v;
}
}
}
}
}
#ifdef APRIL_DEBUG
imwrite("1.1 debug_preprocess.pnm", quad_im);
#endif
///////////////////////////////////////////////////////////
/// Step 2. do the Threshold :: get the set of candidate quads
zarray_t *quads = apriltag_quad_thresh(_params, quad_im, contours);
CV_Assert(quads != NULL);
// adjust centers of pixels so that they correspond to the
// original full-resolution image.
if (_params->aprilTagQuadDecimate > 1) {
for (int i = 0; i < _zarray_size(quads); i++) {
struct sQuad *q;
_zarray_get_volatile(quads, i, &q);
for (int j = 0; j < 4; j++) {
q->p[j][0] *= _params->aprilTagQuadDecimate;
q->p[j][1] *= _params->aprilTagQuadDecimate;
}
}
}
#ifdef APRIL_DEBUG
Mat im_quads = im_orig.clone();
im_quads = im_quads*0.5;
srandom(0);
for (int i = 0; i < _zarray_size(quads); i++) {
struct sQuad *quad;
_zarray_get_volatile(quads, i, &quad);
const int bias = 100;
int color = bias + (random() % (255-bias));
line(im_quads, Point(quad->p[0][0], quad->p[0][1]), Point(quad->p[1][0], quad->p[1][1]), color, 1);
line(im_quads, Point(quad->p[1][0], quad->p[1][1]), Point(quad->p[2][0], quad->p[2][1]), color, 1);
line(im_quads, Point(quad->p[2][0], quad->p[2][1]), Point(quad->p[3][0], quad->p[3][1]), color, 1);
line(im_quads, Point(quad->p[3][0], quad->p[3][1]), Point(quad->p[0][0], quad->p[0][1]), color, 1);
}
imwrite("1.2 debug_quads_raw.pnm", im_quads);
#endif
////////////////////////////////////////////////////////////////
/// Step 3. Save the output :: candidate corners
for (int i = 0; i < _zarray_size(quads); i++) {
struct sQuad *quad;
_zarray_get_volatile(quads, i, &quad);
std::vector< Point2f > corners;
corners.push_back(Point2f(quad->p[3][0], quad->p[3][1])); //pA
corners.push_back(Point2f(quad->p[0][0], quad->p[0][1])); //pB
corners.push_back(Point2f(quad->p[1][0], quad->p[1][1])); //pC
corners.push_back(Point2f(quad->p[2][0], quad->p[2][1])); //pD
candidates.push_back(corners);
}
}
/**
*/
void detectMarkers(InputArray _image, const Ptr<Dictionary> &_dictionary, OutputArrayOfArrays _corners,
OutputArray _ids, const Ptr<DetectorParameters> &_params,
OutputArrayOfArrays _rejectedImgPoints, InputArrayOfArrays camMatrix, InputArrayOfArrays distCoeff) {
CV_Assert(!_image.empty());
Mat grey;
_convertToGrey(_image.getMat(), grey);
/// STEP 1: Detect marker candidates
vector< vector< Point2f > > candidates;
vector< vector< Point > > contours;
vector< int > ids;
/// STEP 1.a Detect marker candidates :: using AprilTag
if(_params->cornerRefinementMethod == CORNER_REFINE_APRILTAG)
_apriltag(grey, _params, candidates, contours);
/// STEP 1.b Detect marker candidates :: traditional way
else
_detectCandidates(grey, candidates, contours, _params);
/// STEP 2: Check candidate codification (identify markers)
_identifyCandidates(grey, candidates, contours, _dictionary, candidates, ids, _params,
_rejectedImgPoints);
/// STEP 3: Filter detected markers;
_filterDetectedMarkers(candidates, ids, contours);
// copy to output arrays
_copyVector2Output(candidates, _corners);
Mat(ids).copyTo(_ids);
/// STEP 4: Corner refinement :: use corner subpix
if( _params->cornerRefinementMethod == CORNER_REFINE_SUBPIX ) {
CV_Assert(_params->cornerRefinementWinSize > 0 && _params->cornerRefinementMaxIterations > 0 &&
_params->cornerRefinementMinAccuracy > 0);
//// do corner refinement for each of the detected markers
// for (unsigned int i = 0; i < _corners.cols(); i++) {
// cornerSubPix(grey, _corners.getMat(i),
// Size(params.cornerRefinementWinSize, params.cornerRefinementWinSize),
// Size(-1, -1), TermCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS,
// params.cornerRefinementMaxIterations,
// params.cornerRefinementMinAccuracy));
//}
// this is the parallel call for the previous commented loop (result is equivalent)
parallel_for_(Range(0, _corners.cols()),
MarkerSubpixelParallel(&grey, _corners, _params));
}
/// STEP 4, Optional : Corner refinement :: use contour container
if( _params->cornerRefinementMethod == CORNER_REFINE_CONTOUR){
if(! _ids.empty()){
// do corner refinement using the contours for each detected markers
parallel_for_(Range(0, _corners.cols()), MarkerContourParallel(contours, candidates, camMatrix.getMat(), distCoeff.getMat()));
// copy the corners to the output array
_copyVector2Output(candidates, _corners);
}
}
}
/**
* ParallelLoopBody class for the parallelization of the single markers pose estimation
* Called from function estimatePoseSingleMarkers()
*/
class SinglePoseEstimationParallel : public ParallelLoopBody {
public:
SinglePoseEstimationParallel(Mat& _markerObjPoints, InputArrayOfArrays _corners,
InputArray _cameraMatrix, InputArray _distCoeffs,
Mat& _rvecs, Mat& _tvecs)
: markerObjPoints(_markerObjPoints), corners(_corners), cameraMatrix(_cameraMatrix),
distCoeffs(_distCoeffs), rvecs(_rvecs), tvecs(_tvecs) {}
void operator()(const Range &range) const CV_OVERRIDE {
const int begin = range.start;
const int end = range.end;
for(int i = begin; i < end; i++) {
solvePnP(markerObjPoints, corners.getMat(i), cameraMatrix, distCoeffs,
rvecs.at<Vec3d>(i), tvecs.at<Vec3d>(i));
}
}
private:
SinglePoseEstimationParallel &operator=(const SinglePoseEstimationParallel &); // to quiet MSVC
Mat& markerObjPoints;
InputArrayOfArrays corners;
InputArray cameraMatrix, distCoeffs;
Mat& rvecs, tvecs;
};
/**
*/
void estimatePoseSingleMarkers(InputArrayOfArrays _corners, float markerLength,
InputArray _cameraMatrix, InputArray _distCoeffs,
OutputArray _rvecs, OutputArray _tvecs, OutputArray _objPoints) {
CV_Assert(markerLength > 0);
Mat markerObjPoints;
_getSingleMarkerObjectPoints(markerLength, markerObjPoints);
int nMarkers = (int)_corners.total();
_rvecs.create(nMarkers, 1, CV_64FC3);
_tvecs.create(nMarkers, 1, CV_64FC3);
Mat rvecs = _rvecs.getMat(), tvecs = _tvecs.getMat();
//// for each marker, calculate its pose
// for (int i = 0; i < nMarkers; i++) {
// solvePnP(markerObjPoints, _corners.getMat(i), _cameraMatrix, _distCoeffs,
// _rvecs.getMat(i), _tvecs.getMat(i));
//}
// this is the parallel call for the previous commented loop (result is equivalent)
parallel_for_(Range(0, nMarkers),
SinglePoseEstimationParallel(markerObjPoints, _corners, _cameraMatrix,
_distCoeffs, rvecs, tvecs));
if(_objPoints.needed()){
markerObjPoints.convertTo(_objPoints, -1);
}
}
void getBoardObjectAndImagePoints(const Ptr<Board> &board, InputArrayOfArrays detectedCorners,
InputArray detectedIds, OutputArray objPoints, OutputArray imgPoints) {
CV_Assert(board->ids.size() == board->objPoints.size());
CV_Assert(detectedIds.total() == detectedCorners.total());
size_t nDetectedMarkers = detectedIds.total();
vector< Point3f > objPnts;
objPnts.reserve(nDetectedMarkers);
vector< Point2f > imgPnts;
imgPnts.reserve(nDetectedMarkers);
// look for detected markers that belong to the board and get their information
for(unsigned int i = 0; i < nDetectedMarkers; i++) {
int currentId = detectedIds.getMat().ptr< int >(0)[i];
for(unsigned int j = 0; j < board->ids.size(); j++) {
if(currentId == board->ids[j]) {
for(int p = 0; p < 4; p++) {
objPnts.push_back(board->objPoints[j][p]);
imgPnts.push_back(detectedCorners.getMat(i).ptr< Point2f >(0)[p]);
}
}
}
}
// create output
Mat(objPnts).copyTo(objPoints);
Mat(imgPnts).copyTo(imgPoints);
}
/**
* Project board markers that are not included in the list of detected markers
*/
static void _projectUndetectedMarkers(const Ptr<Board> &_board, InputOutputArrayOfArrays _detectedCorners,
InputOutputArray _detectedIds, InputArray _cameraMatrix,
InputArray _distCoeffs,
vector< vector< Point2f > >& _undetectedMarkersProjectedCorners,
OutputArray _undetectedMarkersIds) {
// first estimate board pose with the current avaible markers
Mat rvec, tvec;
int boardDetectedMarkers;
boardDetectedMarkers = aruco::estimatePoseBoard(_detectedCorners, _detectedIds, _board,
_cameraMatrix, _distCoeffs, rvec, tvec);
// at least one marker from board so rvec and tvec are valid
if(boardDetectedMarkers == 0) return;
// search undetected markers and project them using the previous pose
vector< vector< Point2f > > undetectedCorners;
vector< int > undetectedIds;
for(unsigned int i = 0; i < _board->ids.size(); i++) {
int foundIdx = -1;
for(unsigned int j = 0; j < _detectedIds.total(); j++) {
if(_board->ids[i] == _detectedIds.getMat().ptr< int >()[j]) {
foundIdx = j;
break;
}
}
// not detected
if(foundIdx == -1) {
undetectedCorners.push_back(vector< Point2f >());
undetectedIds.push_back(_board->ids[i]);
projectPoints(_board->objPoints[i], rvec, tvec, _cameraMatrix, _distCoeffs,
undetectedCorners.back());
}
}
// parse output
Mat(undetectedIds).copyTo(_undetectedMarkersIds);
_undetectedMarkersProjectedCorners = undetectedCorners;
}
/**
* Interpolate board markers that are not included in the list of detected markers using
* global homography
*/
static void _projectUndetectedMarkers(const Ptr<Board> &_board, InputOutputArrayOfArrays _detectedCorners,
InputOutputArray _detectedIds,
vector< vector< Point2f > >& _undetectedMarkersProjectedCorners,
OutputArray _undetectedMarkersIds) {
// check board points are in the same plane, if not, global homography cannot be applied
CV_Assert(_board->objPoints.size() > 0);
CV_Assert(_board->objPoints[0].size() > 0);
float boardZ = _board->objPoints[0][0].z;
for(unsigned int i = 0; i < _board->objPoints.size(); i++) {
for(unsigned int j = 0; j < _board->objPoints[i].size(); j++) {
CV_Assert(boardZ == _board->objPoints[i][j].z);
}
}
vector< Point2f > detectedMarkersObj2DAll; // Object coordinates (without Z) of all the detected
// marker corners in a single vector
vector< Point2f > imageCornersAll; // Image corners of all detected markers in a single vector
vector< vector< Point2f > > undetectedMarkersObj2D; // Object coordinates (without Z) of all
// missing markers in different vectors
vector< int > undetectedMarkersIds; // ids of missing markers
// find markers included in board, and missing markers from board. Fill the previous vectors
for(unsigned int j = 0; j < _board->ids.size(); j++) {
bool found = false;
for(unsigned int i = 0; i < _detectedIds.total(); i++) {
if(_detectedIds.getMat().ptr< int >()[i] == _board->ids[j]) {
for(int c = 0; c < 4; c++) {
imageCornersAll.push_back(_detectedCorners.getMat(i).ptr< Point2f >()[c]);
detectedMarkersObj2DAll.push_back(
Point2f(_board->objPoints[j][c].x, _board->objPoints[j][c].y));
}
found = true;
break;
}
}
if(!found) {
undetectedMarkersObj2D.push_back(vector< Point2f >());
for(int c = 0; c < 4; c++) {
undetectedMarkersObj2D.back().push_back(
Point2f(_board->objPoints[j][c].x, _board->objPoints[j][c].y));
}
undetectedMarkersIds.push_back(_board->ids[j]);
}
}
if(imageCornersAll.size() == 0) return;
// get homography from detected markers
Mat transformation = findHomography(detectedMarkersObj2DAll, imageCornersAll);
_undetectedMarkersProjectedCorners.resize(undetectedMarkersIds.size());
// for each undetected marker, apply transformation
for(unsigned int i = 0; i < undetectedMarkersObj2D.size(); i++) {
perspectiveTransform(undetectedMarkersObj2D[i], _undetectedMarkersProjectedCorners[i], transformation);
}
Mat(undetectedMarkersIds).copyTo(_undetectedMarkersIds);
}
/**
*/
void refineDetectedMarkers(InputArray _image, const Ptr<Board> &_board,
InputOutputArrayOfArrays _detectedCorners, InputOutputArray _detectedIds,
InputOutputArrayOfArrays _rejectedCorners, InputArray _cameraMatrix,
InputArray _distCoeffs, float minRepDistance, float errorCorrectionRate,
bool checkAllOrders, OutputArray _recoveredIdxs,
const Ptr<DetectorParameters> &_params) {
CV_Assert(minRepDistance > 0);
if(_detectedIds.total() == 0 || _rejectedCorners.total() == 0) return;
DetectorParameters ¶ms = *_params;
// get projections of missing markers in the board
vector< vector< Point2f > > undetectedMarkersCorners;
vector< int > undetectedMarkersIds;
if(_cameraMatrix.total() != 0) {
// reproject based on camera projection model
_projectUndetectedMarkers(_board, _detectedCorners, _detectedIds, _cameraMatrix, _distCoeffs,
undetectedMarkersCorners, undetectedMarkersIds);
} else {
// reproject based on global homography
_projectUndetectedMarkers(_board, _detectedCorners, _detectedIds, undetectedMarkersCorners,
undetectedMarkersIds);
}
// list of missing markers indicating if they have been assigned to a candidate
vector< bool > alreadyIdentified(_rejectedCorners.total(), false);
// maximum bits that can be corrected
Dictionary &dictionary = *(_board->dictionary);
int maxCorrectionRecalculated =
int(double(dictionary.maxCorrectionBits) * errorCorrectionRate);
Mat grey;
_convertToGrey(_image, grey);
// vector of final detected marker corners and ids
vector< Mat > finalAcceptedCorners;
vector< int > finalAcceptedIds;
// fill with the current markers
finalAcceptedCorners.resize(_detectedCorners.total());
finalAcceptedIds.resize(_detectedIds.total());
for(unsigned int i = 0; i < _detectedIds.total(); i++) {
finalAcceptedCorners[i] = _detectedCorners.getMat(i).clone();
finalAcceptedIds[i] = _detectedIds.getMat().ptr< int >()[i];
}
vector< int > recoveredIdxs; // original indexes of accepted markers in _rejectedCorners
// for each missing marker, try to find a correspondence
for(unsigned int i = 0; i < undetectedMarkersIds.size(); i++) {
// best match at the moment
int closestCandidateIdx = -1;
double closestCandidateDistance = minRepDistance * minRepDistance + 1;
Mat closestRotatedMarker;
for(unsigned int j = 0; j < _rejectedCorners.total(); j++) {
if(alreadyIdentified[j]) continue;
// check distance
double minDistance = closestCandidateDistance + 1;
bool valid = false;
int validRot = 0;
for(int c = 0; c < 4; c++) { // first corner in rejected candidate
double currentMaxDistance = 0;
for(int k = 0; k < 4; k++) {
Point2f rejCorner = _rejectedCorners.getMat(j).ptr< Point2f >()[(c + k) % 4];
Point2f distVector = undetectedMarkersCorners[i][k] - rejCorner;
double cornerDist = distVector.x * distVector.x + distVector.y * distVector.y;
currentMaxDistance = max(currentMaxDistance, cornerDist);
}
// if distance is better than current best distance
if(currentMaxDistance < closestCandidateDistance) {
valid = true;
validRot = c;
minDistance = currentMaxDistance;
}
if(!checkAllOrders) break;
}
if(!valid) continue;
// apply rotation
Mat rotatedMarker;
if(checkAllOrders) {
rotatedMarker = Mat(4, 1, CV_32FC2);
for(int c = 0; c < 4; c++)
rotatedMarker.ptr< Point2f >()[c] =
_rejectedCorners.getMat(j).ptr< Point2f >()[(c + 4 + validRot) % 4];
}
else rotatedMarker = _rejectedCorners.getMat(j);
// last filter, check if inner code is close enough to the assigned marker code
int codeDistance = 0;
// if errorCorrectionRate, dont check code
if(errorCorrectionRate >= 0) {
// extract bits
Mat bits = _extractBits(
grey, rotatedMarker, dictionary.markerSize, params.markerBorderBits,
params.perspectiveRemovePixelPerCell,
params.perspectiveRemoveIgnoredMarginPerCell, params.minOtsuStdDev);
Mat onlyBits =
bits.rowRange(params.markerBorderBits, bits.rows - params.markerBorderBits)
.colRange(params.markerBorderBits, bits.rows - params.markerBorderBits);
codeDistance =
dictionary.getDistanceToId(onlyBits, undetectedMarkersIds[i], false);
}
// if everythin is ok, assign values to current best match
if(errorCorrectionRate < 0 || codeDistance < maxCorrectionRecalculated) {
closestCandidateIdx = j;
closestCandidateDistance = minDistance;
closestRotatedMarker = rotatedMarker;
}
}
// if at least one good match, we have rescue the missing marker
if(closestCandidateIdx >= 0) {
// subpixel refinement
if(_params->cornerRefinementMethod == CORNER_REFINE_SUBPIX) {
CV_Assert(params.cornerRefinementWinSize > 0 &&
params.cornerRefinementMaxIterations > 0 &&
params.cornerRefinementMinAccuracy > 0);
cornerSubPix(grey, closestRotatedMarker,
Size(params.cornerRefinementWinSize, params.cornerRefinementWinSize),
Size(-1, -1), TermCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS,
params.cornerRefinementMaxIterations,
params.cornerRefinementMinAccuracy));
}
// remove from rejected
alreadyIdentified[closestCandidateIdx] = true;
// add to detected
finalAcceptedCorners.push_back(closestRotatedMarker);
finalAcceptedIds.push_back(undetectedMarkersIds[i]);
// add the original index of the candidate
recoveredIdxs.push_back(closestCandidateIdx);
}
}
// parse output
if(finalAcceptedIds.size() != _detectedIds.total()) {
_detectedCorners.clear();
_detectedIds.clear();
// parse output
Mat(finalAcceptedIds).copyTo(_detectedIds);
_detectedCorners.create((int)finalAcceptedCorners.size(), 1, CV_32FC2);
for(unsigned int i = 0; i < finalAcceptedCorners.size(); i++) {
_detectedCorners.create(4, 1, CV_32FC2, i, true);
for(int j = 0; j < 4; j++) {
_detectedCorners.getMat(i).ptr< Point2f >()[j] =
finalAcceptedCorners[i].ptr< Point2f >()[j];
}
}
// recalculate _rejectedCorners based on alreadyIdentified
vector< Mat > finalRejected;
for(unsigned int i = 0; i < alreadyIdentified.size(); i++) {
if(!alreadyIdentified[i]) {
finalRejected.push_back(_rejectedCorners.getMat(i).clone());
}
}
_rejectedCorners.clear();
_rejectedCorners.create((int)finalRejected.size(), 1, CV_32FC2);
for(unsigned int i = 0; i < finalRejected.size(); i++) {
_rejectedCorners.create(4, 1, CV_32FC2, i, true);
for(int j = 0; j < 4; j++) {
_rejectedCorners.getMat(i).ptr< Point2f >()[j] =
finalRejected[i].ptr< Point2f >()[j];
}
}
if(_recoveredIdxs.needed()) {
Mat(recoveredIdxs).copyTo(_recoveredIdxs);
}
}
}
/**
*/
int estimatePoseBoard(InputArrayOfArrays _corners, InputArray _ids, const Ptr<Board> &board,
InputArray _cameraMatrix, InputArray _distCoeffs, OutputArray _rvec,
OutputArray _tvec, bool useExtrinsicGuess) {
CV_Assert(_corners.total() == _ids.total());
// get object and image points for the solvePnP function
Mat objPoints, imgPoints;
getBoardObjectAndImagePoints(board, _corners, _ids, objPoints, imgPoints);
CV_Assert(imgPoints.total() == objPoints.total());
if(objPoints.total() == 0) // 0 of the detected markers in board
return 0;
solvePnP(objPoints, imgPoints, _cameraMatrix, _distCoeffs, _rvec, _tvec, useExtrinsicGuess);
// divide by four since all the four corners are concatenated in the array for each marker
return (int)objPoints.total() / 4;
}
/**
*/
void GridBoard::draw(Size outSize, OutputArray _img, int marginSize, int borderBits) {
_drawPlanarBoardImpl(this, outSize, _img, marginSize, borderBits);
}
/**
*/
Ptr<Board> Board::create(InputArrayOfArrays objPoints, const Ptr<Dictionary> &dictionary, InputArray ids) {
CV_Assert(objPoints.total() == ids.total());
CV_Assert(objPoints.type() == CV_32FC3 || objPoints.type() == CV_32FC1);
std::vector< std::vector< Point3f > > obj_points_vector;
for (unsigned int i = 0; i < objPoints.total(); i++) {
std::vector<Point3f> corners;
Mat corners_mat = objPoints.getMat(i);
if(corners_mat.type() == CV_32FC1)
corners_mat = corners_mat.reshape(3);
CV_Assert(corners_mat.total() == 4);
for (int j = 0; j < 4; j++) {
corners.push_back(corners_mat.at<Point3f>(j));
}
obj_points_vector.push_back(corners);
}
Ptr<Board> res = makePtr<Board>();
ids.copyTo(res->ids);
res->objPoints = obj_points_vector;
res->dictionary = cv::makePtr<Dictionary>(dictionary);
return res;
}
/**
*/
Ptr<GridBoard> GridBoard::create(int markersX, int markersY, float markerLength, float markerSeparation,
const Ptr<Dictionary> &dictionary, int firstMarker) {
CV_Assert(markersX > 0 && markersY > 0 && markerLength > 0 && markerSeparation > 0);
Ptr<GridBoard> res = makePtr<GridBoard>();
res->_markersX = markersX;
res->_markersY = markersY;
res->_markerLength = markerLength;
res->_markerSeparation = markerSeparation;
res->dictionary = dictionary;
size_t totalMarkers = (size_t) markersX * markersY;
res->ids.resize(totalMarkers);
res->objPoints.reserve(totalMarkers);
// fill ids with first identifiers
for(unsigned int i = 0; i < totalMarkers; i++) {
res->ids[i] = i + firstMarker;
}
// calculate Board objPoints
float maxY = (float)markersY * markerLength + (markersY - 1) * markerSeparation;
for(int y = 0; y < markersY; y++) {
for(int x = 0; x < markersX; x++) {
vector< Point3f > corners;
corners.resize(4);
corners[0] = Point3f(x * (markerLength + markerSeparation),
maxY - y * (markerLength + markerSeparation), 0);
corners[1] = corners[0] + Point3f(markerLength, 0, 0);
corners[2] = corners[0] + Point3f(markerLength, -markerLength, 0);
corners[3] = corners[0] + Point3f(0, -markerLength, 0);
res->objPoints.push_back(corners);
}
}
return res;
}
/**
*/
void drawDetectedMarkers(InputOutputArray _image, InputArrayOfArrays _corners,
InputArray _ids, Scalar borderColor) {
CV_Assert(_image.getMat().total() != 0 &&
(_image.getMat().channels() == 1 || _image.getMat().channels() == 3));
CV_Assert((_corners.total() == _ids.total()) || _ids.total() == 0);
// calculate colors
Scalar textColor, cornerColor;
textColor = cornerColor = borderColor;
swap(textColor.val[0], textColor.val[1]); // text color just sawp G and R
swap(cornerColor.val[1], cornerColor.val[2]); // corner color just sawp G and B
int nMarkers = (int)_corners.total();
for(int i = 0; i < nMarkers; i++) {
Mat currentMarker = _corners.getMat(i);
CV_Assert(currentMarker.total() == 4 && currentMarker.type() == CV_32FC2);
// draw marker sides
for(int j = 0; j < 4; j++) {
Point2f p0, p1;
p0 = currentMarker.ptr< Point2f >(0)[j];
p1 = currentMarker.ptr< Point2f >(0)[(j + 1) % 4];
line(_image, p0, p1, borderColor, 1);
}
// draw first corner mark
rectangle(_image, currentMarker.ptr< Point2f >(0)[0] - Point2f(3, 3),
currentMarker.ptr< Point2f >(0)[0] + Point2f(3, 3), cornerColor, 1, LINE_AA);
// draw ID
if(_ids.total() != 0) {
Point2f cent(0, 0);
for(int p = 0; p < 4; p++)
cent += currentMarker.ptr< Point2f >(0)[p];
cent = cent / 4.;
stringstream s;
s << "id=" << _ids.getMat().ptr< int >(0)[i];
putText(_image, s.str(), cent, FONT_HERSHEY_SIMPLEX, 0.5, textColor, 2);
}
}
}
/**
*/
void drawAxis(InputOutputArray _image, InputArray _cameraMatrix, InputArray _distCoeffs,
InputArray _rvec, InputArray _tvec, float length) {
CV_Assert(_image.getMat().total() != 0 &&
(_image.getMat().channels() == 1 || _image.getMat().channels() == 3));
CV_Assert(length > 0);
// project axis points
vector< Point3f > axisPoints;
axisPoints.push_back(Point3f(0, 0, 0));
axisPoints.push_back(Point3f(length, 0, 0));
axisPoints.push_back(Point3f(0, length, 0));
axisPoints.push_back(Point3f(0, 0, length));
vector< Point2f > imagePoints;
projectPoints(axisPoints, _rvec, _tvec, _cameraMatrix, _distCoeffs, imagePoints);
// draw axis lines
line(_image, imagePoints[0], imagePoints[1], Scalar(0, 0, 255), 3);
line(_image, imagePoints[0], imagePoints[2], Scalar(0, 255, 0), 3);
line(_image, imagePoints[0], imagePoints[3], Scalar(255, 0, 0), 3);
}
/**
*/
void drawMarker(const Ptr<Dictionary> &dictionary, int id, int sidePixels, OutputArray _img, int borderBits) {
dictionary->drawMarker(id, sidePixels, _img, borderBits);
}
void _drawPlanarBoardImpl(Board *_board, Size outSize, OutputArray _img, int marginSize,
int borderBits) {
CV_Assert(outSize.area() > 0);
CV_Assert(marginSize >= 0);
_img.create(outSize, CV_8UC1);
Mat out = _img.getMat();
out.setTo(Scalar::all(255));
out.adjustROI(-marginSize, -marginSize, -marginSize, -marginSize);
// calculate max and min values in XY plane
CV_Assert(_board->objPoints.size() > 0);
float minX, maxX, minY, maxY;
minX = maxX = _board->objPoints[0][0].x;
minY = maxY = _board->objPoints[0][0].y;
for(unsigned int i = 0; i < _board->objPoints.size(); i++) {
for(int j = 0; j < 4; j++) {
minX = min(minX, _board->objPoints[i][j].x);
maxX = max(maxX, _board->objPoints[i][j].x);
minY = min(minY, _board->objPoints[i][j].y);
maxY = max(maxY, _board->objPoints[i][j].y);
}
}
float sizeX = maxX - minX;
float sizeY = maxY - minY;
// proportion transformations
float xReduction = sizeX / float(out.cols);
float yReduction = sizeY / float(out.rows);
// determine the zone where the markers are placed
if(xReduction > yReduction) {
int nRows = int(sizeY / xReduction);
int rowsMargins = (out.rows - nRows) / 2;
out.adjustROI(-rowsMargins, -rowsMargins, 0, 0);
} else {
int nCols = int(sizeX / yReduction);
int colsMargins = (out.cols - nCols) / 2;
out.adjustROI(0, 0, -colsMargins, -colsMargins);
}
// now paint each marker
Dictionary &dictionary = *(_board->dictionary);
Mat marker;
Point2f outCorners[3];
Point2f inCorners[3];
for(unsigned int m = 0; m < _board->objPoints.size(); m++) {
// transform corners to markerZone coordinates
for(int j = 0; j < 3; j++) {
Point2f pf = Point2f(_board->objPoints[m][j].x, _board->objPoints[m][j].y);
// move top left to 0, 0
pf -= Point2f(minX, minY);
pf.x = pf.x / sizeX * float(out.cols);
pf.y = (1.0f - pf.y / sizeY) * float(out.rows);
outCorners[j] = pf;
}
// get marker
Size dst_sz(outCorners[2] - outCorners[0]); // assuming CCW order
dst_sz.width = dst_sz.height = std::min(dst_sz.width, dst_sz.height); //marker should be square
dictionary.drawMarker(_board->ids[m], dst_sz.width, marker, borderBits);
if((outCorners[0].y == outCorners[1].y) && (outCorners[1].x == outCorners[2].x)) {
// marker is aligned to image axes
marker.copyTo(out(Rect(outCorners[0], dst_sz)));
continue;
}
// interpolate tiny marker to marker position in markerZone
inCorners[0] = Point2f(-0.5f, -0.5f);
inCorners[1] = Point2f(marker.cols - 0.5f, -0.5f);
inCorners[2] = Point2f(marker.cols - 0.5f, marker.rows - 0.5f);
// remove perspective
Mat transformation = getAffineTransform(inCorners, outCorners);
warpAffine(marker, out, transformation, out.size(), INTER_LINEAR,
BORDER_TRANSPARENT);
}
}
/**
*/
void drawPlanarBoard(const Ptr<Board> &_board, Size outSize, OutputArray _img, int marginSize,
int borderBits) {
_drawPlanarBoardImpl(_board, outSize, _img, marginSize, borderBits);
}
/**
*/
double calibrateCameraAruco(InputArrayOfArrays _corners, InputArray _ids, InputArray _counter,
const Ptr<Board> &board, Size imageSize, InputOutputArray _cameraMatrix,
InputOutputArray _distCoeffs, OutputArrayOfArrays _rvecs,
OutputArrayOfArrays _tvecs,
OutputArray _stdDeviationsIntrinsics,
OutputArray _stdDeviationsExtrinsics,
OutputArray _perViewErrors,
int flags, TermCriteria criteria) {
// for each frame, get properly processed imagePoints and objectPoints for the calibrateCamera
// function
vector< Mat > processedObjectPoints, processedImagePoints;
size_t nFrames = _counter.total();
int markerCounter = 0;
for(size_t frame = 0; frame < nFrames; frame++) {
int nMarkersInThisFrame = _counter.getMat().ptr< int >()[frame];
vector< Mat > thisFrameCorners;
vector< int > thisFrameIds;
CV_Assert(nMarkersInThisFrame > 0);
thisFrameCorners.reserve((size_t) nMarkersInThisFrame);
thisFrameIds.reserve((size_t) nMarkersInThisFrame);
for(int j = markerCounter; j < markerCounter + nMarkersInThisFrame; j++) {
thisFrameCorners.push_back(_corners.getMat(j));
thisFrameIds.push_back(_ids.getMat().ptr< int >()[j]);
}
markerCounter += nMarkersInThisFrame;
Mat currentImgPoints, currentObjPoints;
getBoardObjectAndImagePoints(board, thisFrameCorners, thisFrameIds, currentObjPoints,
currentImgPoints);
if(currentImgPoints.total() > 0 && currentObjPoints.total() > 0) {
processedImagePoints.push_back(currentImgPoints);
processedObjectPoints.push_back(currentObjPoints);
}
}
return calibrateCamera(processedObjectPoints, processedImagePoints, imageSize, _cameraMatrix,
_distCoeffs, _rvecs, _tvecs, _stdDeviationsIntrinsics, _stdDeviationsExtrinsics,
_perViewErrors, flags, criteria);
}
/**
*/
double calibrateCameraAruco(InputArrayOfArrays _corners, InputArray _ids, InputArray _counter,
const Ptr<Board> &board, Size imageSize, InputOutputArray _cameraMatrix,
InputOutputArray _distCoeffs, OutputArrayOfArrays _rvecs,
OutputArrayOfArrays _tvecs, int flags, TermCriteria criteria) {
return calibrateCameraAruco(_corners, _ids, _counter, board, imageSize, _cameraMatrix, _distCoeffs, _rvecs, _tvecs,
noArray(), noArray(), noArray(), flags, criteria);
}
}
}