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aruco.cpp
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aruco.cpp
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/*
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, save the potential candidates
* (i.e. biggest/smallest contour) and remove the rest
*/
static void _filterTooCloseCandidates(const vector< vector< Point2f > > &candidatesIn,
vector< vector< vector< Point2f > > > &candidatesSetOut,
const vector< vector< Point > > &contoursIn,
vector< vector< vector< Point > > > &contoursSetOut,
double minMarkerDistanceRate, bool detectInvertedMarker) {
CV_Assert(minMarkerDistanceRate >= 0);
vector<int> candGroup;
candGroup.resize(candidatesIn.size(), -1);
vector< vector<unsigned int> > groupedCandidates;
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) {
// i and j are not related to a group
if(candGroup[i]<0 && candGroup[j]<0){
// mark candidates with their corresponding group number
candGroup[i] = candGroup[j] = (int)groupedCandidates.size();
// create group
vector<unsigned int> grouped;
grouped.push_back(i);
grouped.push_back(j);
groupedCandidates.push_back( grouped );
}
// i is related to a group
else if(candGroup[i] > -1 && candGroup[j] == -1){
int group = candGroup[i];
candGroup[j] = group;
// add to group
groupedCandidates[group].push_back( j );
}
// j is related to a group
else if(candGroup[j] > -1 && candGroup[i] == -1){
int group = candGroup[j];
candGroup[i] = group;
// add to group
groupedCandidates[group].push_back( i );
}
}
}
}
}
// save possible candidates
candidatesSetOut.clear();
contoursSetOut.clear();
vector< vector< Point2f > > biggerCandidates;
vector< vector< Point > > biggerContours;
vector< vector< Point2f > > smallerCandidates;
vector< vector< Point > > smallerContours;
// save possible candidates
for( unsigned int i = 0; i < groupedCandidates.size(); i++ ) {
int smallerIdx = groupedCandidates[i][0];
int biggerIdx = -1;
// evaluate group elements
for( unsigned int j = 1; j < groupedCandidates[i].size(); j++ ) {
size_t currPerim = contoursIn[ groupedCandidates[i][j] ].size();
// check if current contour is bigger
if ( biggerIdx < 0 )
biggerIdx = groupedCandidates[i][j];
else if(currPerim >= contoursIn[ biggerIdx ].size())
biggerIdx = groupedCandidates[i][j];
// check if current contour is smaller
if(currPerim < contoursIn[ smallerIdx ].size() && detectInvertedMarker)
smallerIdx = groupedCandidates[i][j];
}
// add contours und candidates
if(biggerIdx > -1){
biggerCandidates.push_back(candidatesIn[biggerIdx]);
biggerContours.push_back(contoursIn[biggerIdx]);
if( detectInvertedMarker ){
smallerCandidates.push_back(candidatesIn[smallerIdx]);
smallerContours.push_back(contoursIn[smallerIdx]);
}
}
}
// to preserve the structure :: candidateSet< defaultCandidates, whiteCandidates >
// default candidates
candidatesSetOut.push_back(biggerCandidates);
contoursSetOut.push_back(biggerContours);
// white candidates
candidatesSetOut.push_back(smallerCandidates);
contoursSetOut.push_back(smallerContours);
}
/**
* 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< vector< Point2f > > >& candidatesSetOut,
vector< vector< vector< Point > > >& contoursSetOut, 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
// save the outter/inner border (i.e. potential candidates)
_filterTooCloseCandidates(candidates, candidatesSetOut, contours, contoursSetOut,
_params->minMarkerDistanceRate, _params->detectInvertedMarker);
}
/**
* @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
* @return candidate typ. zero if the candidate is not valid,
* 1 if the candidate is a black candidate (default candidate)
* 2 if the candidate is a white candidate
*/
static uint8_t _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);
uint8_t typ=1;
// 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);
typ=2;
}
}
if(borderErrors > maximumErrorsInBorder) return 0; // 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 0;
// shift corner positions to the correct rotation
if(rotation != 0) {
std::rotate(_corners.begin(), _corners.begin() + 4 - rotation, _corners.end());
}
return typ;
}
/**
* 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< uint8_t >& _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;
validCandidates[i] = _identifyOneCandidate(dictionary, grey, candidates[i], currId, params);
if(validCandidates[i] > 0)
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< uint8_t > &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< vector< Point2f > > >& _candidatesSet,
vector< vector< vector<Point> > >& _contoursSet, const Ptr<Dictionary> &_dictionary,
vector< vector< Point2f > >& _accepted, vector< vector<Point> >& _contours, vector< int >& ids,
const Ptr<DetectorParameters> ¶ms,
OutputArrayOfArrays _rejected = noArray()) {
int ncandidates = (int)_candidatesSet[0].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< uint8_t > validCandidates(ncandidates, 0);
//// Analyze each of the candidates
parallel_for_(Range(0, ncandidates),
IdentifyCandidatesParallel(grey,
params->detectInvertedMarker ? _candidatesSet[1] : _candidatesSet[0],
_dictionary, idsTmp,
validCandidates, params));
for(int i = 0; i < ncandidates; i++) {
if(validCandidates[i] > 0) {
// add the white valid candidate
if( params->detectInvertedMarker && validCandidates[i] == 2 ){
accepted.push_back(_candidatesSet[1][i]);
ids.push_back(idsTmp[i]);
contours.push_back(_contoursSet[1][i]);
continue;
}
// add the default (black) valid candidate
accepted.push_back(_candidatesSet[0][i]);
ids.push_back(idsTmp[i]);
contours.push_back(_contoursSet[0][i]);
} else {
rejected.push_back(_candidatesSet[0][i]);
}
}
// parse output
_accepted = accepted;
_contours= contours;
if(_rejected.needed()) {
_copyVector2Output(rejected, _rejected);
}
}
/**
* @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