CopterExpress/clover
1
0
Fork 0
mirror of https://github.com/CopterExpress/clover.git synced 2026-05-26 11:43:25 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
98d5d506077dd64348a9d8cfebfe3569758aad3c
clover/aruco_pose/vendor/aruco/src/aruco.cpp
Alexey Rogachevskiy 98d5d50607 aruco_pose: Prevent OpenCV from crashing (#238) 
* aruco_pose: Add tests that crash OpenCV

* aruco_pose: Don't try to interpolate single points
2020-05-30 01:57:14 +03:00

1919 lines
71 KiB
C++
Raw Blame History

/*
By downloading, copying, installing or using the software you agree to this
license. If you do not agree to this license, do not download, install,
copy or use the software.
License Agreement
For Open Source Computer Vision Library
(3-clause BSD License)
Copyright (C) 2013, OpenCV Foundation, all rights reserved.
Third party copyrights are property of their respective owners.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the names of the copyright holders nor the names of the contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and
any express or implied warranties, including, but not limited to, the implied
warranties of merchantability and fitness for a particular purpose are
disclaimed. In no event shall copyright holders or contributors be liable for
any direct, indirect, incidental, special, exemplary, or consequential damages
(including, but not limited to, procurement of substitute goods or services;
loss of use, data, or profits; or business interruption) however caused
and on any theory of liability, whether in contract, strict liability,
or tort (including negligence or otherwise) arising in any way out of
the use of this software, even if advised of the possibility of such damage.
*/
#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),
detectInvertedMarker(false){}
/**
* @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.type() == CV_8UC1 || _in.type() == CV_8UC3);
if(_in.type() == CV_8UC3)
cvtColor(_in, _out, COLOR_BGR2GRAY);
else
_in.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> &params;
};
/**
* @brief Initial steps on finding square candidates
*/
static void _detectInitialCandidates(const Mat &grey, vector< vector< Point2f > > &candidates,
vector< vector< Point > > &contours,
const Ptr<DetectorParameters> &params) {
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);
// check if it is a white marker
if(params->detectInvertedMarker){
// to get from 255 to 1
Mat invertedImg = ~candidateBits-254;
int invBError = _getBorderErrors(invertedImg, dictionary->markerSize, params->markerBorderBits);
// white marker
if(invBError<borderErrors){
borderErrors = invBError;
invertedImg.copyTo(candidateBits);
}
}
if(borderErrors > maximumErrorsInBorder) return false;
// 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> &params;
};
/**
* @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> &params,
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> &params;
};
/**
* 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++){
// Don't try to "interpolate" single points
if (cntPts[i].size() < 2) return;
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);
}
_zarray_destroy(quads);
}
/**
*/
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 &params = *_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)
{
/* no-op since we're using our own _drawAxis routine */
/*drawFrameAxes(_image, _cameraMatrix, _distCoeffs, _rvec, _tvec, length, 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) {
/* NOTE: OpenCV v<3.3 does not have .empty() as a Size member */
CV_Assert(outSize.height > 0 && outSize.width > 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);
}
}
}
Reference in New Issue View Git Blame Copy Permalink