Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
29005 Commits
6 Branches
128 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: c9e10e1d0b
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'c9e10e1d0b'
${ noResults }
opencv/modules/calib3d/src/circlesgrid.cpp

1642 lines
49 KiB
Raw Blame History

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of the copyright holders may not 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 the Intel Corporation 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.
//
//M*/
#include "precomp.hpp"
#include "circlesgrid.hpp"
#include <limits>
// Requires CMake flag: DEBUG_opencv_calib3d=ON
//#define DEBUG_CIRCLES
#ifdef DEBUG_CIRCLES
# include <iostream>
# include "opencv2/opencv_modules.hpp"
# ifdef HAVE_OPENCV_HIGHGUI
# include "opencv2/highgui.hpp"
# else
# undef DEBUG_CIRCLES
# endif
#endif
using namespace cv;
#ifdef DEBUG_CIRCLES
void drawPoints(const std::vector<Point2f> &points, Mat &outImage, int radius = 2, Scalar color = Scalar::all(255), int thickness = -1)
{
for(size_t i=0; i<points.size(); i++)
{
circle(outImage, points[i], radius, color, thickness);
}
}
#endif
void CirclesGridClusterFinder::hierarchicalClustering(const std::vector<Point2f> &points, const Size &patternSz, std::vector<Point2f> &patternPoints)
{
#ifdef HAVE_TEGRA_OPTIMIZATION
if(tegra::useTegra() && tegra::hierarchicalClustering(points, patternSz, patternPoints))
return;
#endif
int j, n = (int)points.size();
size_t pn = static_cast<size_t>(patternSz.area());
patternPoints.clear();
if (pn >= points.size())
{
if (pn == points.size())
patternPoints = points;
return;
}
Mat dists(n, n, CV_32FC1, Scalar(0));
Mat distsMask(dists.size(), CV_8UC1, Scalar(0));
for(int i = 0; i < n; i++)
{
for(j = i+1; j < n; j++)
{
dists.at<float>(i, j) = (float)norm(points[i] - points[j]);
distsMask.at<uchar>(i, j) = 255;
//TODO: use symmetry
distsMask.at<uchar>(j, i) = 255;//distsMask.at<uchar>(i, j);
dists.at<float>(j, i) = dists.at<float>(i, j);
}
}
std::vector<std::list<size_t> > clusters(points.size());
for(size_t i=0; i<points.size(); i++)
{
clusters[i].push_back(i);
}
int patternClusterIdx = 0;
while(clusters[patternClusterIdx].size() < pn)
{
Point minLoc;
minMaxLoc(dists, 0, 0, &minLoc, 0, distsMask);
int minIdx = std::min(minLoc.x, minLoc.y);
int maxIdx = std::max(minLoc.x, minLoc.y);
distsMask.row(maxIdx).setTo(0);
distsMask.col(maxIdx).setTo(0);
Mat tmpRow = dists.row(minIdx);
Mat tmpCol = dists.col(minIdx);
cv::min(dists.row(minLoc.x), dists.row(minLoc.y), tmpRow);
tmpRow = tmpRow.t();
tmpRow.copyTo(tmpCol);
clusters[minIdx].splice(clusters[minIdx].end(), clusters[maxIdx]);
patternClusterIdx = minIdx;
}
//the largest cluster can have more than pn points -- we need to filter out such situations
if(clusters[patternClusterIdx].size() != static_cast<size_t>(patternSz.area()))
{
return;
}
patternPoints.reserve(clusters[patternClusterIdx].size());
for(std::list<size_t>::iterator it = clusters[patternClusterIdx].begin(); it != clusters[patternClusterIdx].end();++it)
{
patternPoints.push_back(points[*it]);
}
}
void CirclesGridClusterFinder::findGrid(const std::vector<cv::Point2f> &points, cv::Size _patternSize, std::vector<Point2f>& centers)
{
patternSize = _patternSize;
centers.clear();
if(points.empty())
{
return;
}
std::vector<Point2f> patternPoints;
hierarchicalClustering(points, patternSize, patternPoints);
if(patternPoints.empty())
{
return;
}
#ifdef DEBUG_CIRCLES
Mat patternPointsImage(1024, 1248, CV_8UC1, Scalar(0));
drawPoints(patternPoints, patternPointsImage);
imshow("pattern points", patternPointsImage);
#endif
std::vector<Point2f> hull2f;
convexHull(patternPoints, hull2f, false);
const size_t cornersCount = isAsymmetricGrid ? 6 : 4;
if(hull2f.size() < cornersCount)
return;
std::vector<Point2f> corners;
findCorners(hull2f, corners);
if(corners.size() != cornersCount)
return;
std::vector<Point2f> outsideCorners, sortedCorners;
if(isAsymmetricGrid)
{
findOutsideCorners(corners, outsideCorners);
const size_t outsideCornersCount = 2;
if(outsideCorners.size() != outsideCornersCount)
return;
}
getSortedCorners(hull2f, patternPoints, corners, outsideCorners, sortedCorners);
if(sortedCorners.size() != cornersCount)
return;
std::vector<Point2f> rectifiedPatternPoints;
rectifyPatternPoints(patternPoints, sortedCorners, rectifiedPatternPoints);
if(patternPoints.size() != rectifiedPatternPoints.size())
return;
parsePatternPoints(patternPoints, rectifiedPatternPoints, centers);
}
void CirclesGridClusterFinder::findCorners(const std::vector<cv::Point2f> &hull2f, std::vector<cv::Point2f> &corners)
{
//find angles (cosines) of vertices in convex hull
std::vector<float> angles;
for(size_t i=0; i<hull2f.size(); i++)
{
Point2f vec1 = hull2f[(i+1) % hull2f.size()] - hull2f[i % hull2f.size()];
Point2f vec2 = hull2f[(i-1 + static_cast<int>(hull2f.size())) % hull2f.size()] - hull2f[i % hull2f.size()];
float angle = (float)(vec1.ddot(vec2) / (norm(vec1) * norm(vec2)));
angles.push_back(angle);
}
//sort angles by cosine
//corners are the most sharp angles (6)
Mat anglesMat = Mat(angles);
Mat sortedIndices;
sortIdx(anglesMat, sortedIndices, SORT_EVERY_COLUMN + SORT_DESCENDING);
CV_Assert(sortedIndices.type() == CV_32SC1);
CV_Assert(sortedIndices.cols == 1);
const int cornersCount = isAsymmetricGrid ? 6 : 4;
Mat cornersIndices;
cv::sort(sortedIndices.rowRange(0, cornersCount), cornersIndices, SORT_EVERY_COLUMN + SORT_ASCENDING);
corners.clear();
for(int i=0; i<cornersCount; i++)
{
corners.push_back(hull2f[cornersIndices.at<int>(i, 0)]);
}
}
void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f> &corners, std::vector<cv::Point2f> &outsideCorners)
{
CV_Assert(!corners.empty());
outsideCorners.clear();
//find two pairs of the most nearest corners
const size_t n = corners.size();
#ifdef DEBUG_CIRCLES
Mat cornersImage(1024, 1248, CV_8UC1, Scalar(0));
drawPoints(corners, cornersImage);
imshow("corners", cornersImage);
#endif
std::vector<Point2f> tangentVectors(n);
for(size_t k=0; k < n; k++)
{
Point2f diff = corners[(k + 1) % n] - corners[k];
tangentVectors[k] = diff * (1.0f / norm(diff));
}
//compute angles between all sides
Mat cosAngles((int)n, (int)n, CV_32FC1, 0.0f);
for(size_t i = 0; i < n; i++)
{
for(size_t j = i + 1; j < n; j++)
{
float val = fabs(tangentVectors[i].dot(tangentVectors[j]));
cosAngles.at<float>((int)i, (int)j) = val;
cosAngles.at<float>((int)j, (int)i) = val;
}
}
//find two parallel sides to which outside corners belong
Point maxLoc;
minMaxLoc(cosAngles, 0, 0, 0, &maxLoc);
const int diffBetweenFalseLines = 3;
if(abs(maxLoc.x - maxLoc.y) == diffBetweenFalseLines)
{
cosAngles.row(maxLoc.x).setTo(0.0f);
cosAngles.col(maxLoc.x).setTo(0.0f);
cosAngles.row(maxLoc.y).setTo(0.0f);
cosAngles.col(maxLoc.y).setTo(0.0f);
minMaxLoc(cosAngles, 0, 0, 0, &maxLoc);
}
#ifdef DEBUG_CIRCLES
Mat linesImage(1024, 1248, CV_8UC1, Scalar(0));
line(linesImage, corners[maxLoc.y], corners[(maxLoc.y + 1) % n], Scalar(255));
line(linesImage, corners[maxLoc.x], corners[(maxLoc.x + 1) % n], Scalar(255));
imshow("lines", linesImage);
#endif
int maxIdx = std::max(maxLoc.x, maxLoc.y);
int minIdx = std::min(maxLoc.x, maxLoc.y);
const int bigDiff = 4;
if(maxIdx - minIdx == bigDiff)
{
minIdx += (int)n;
std::swap(maxIdx, minIdx);
}
if(maxIdx - minIdx != (int)n - bigDiff)
{
return;
}
int outsidersSegmentIdx = (minIdx + maxIdx) / 2;
outsideCorners.push_back(corners[outsidersSegmentIdx % n]);
outsideCorners.push_back(corners[(outsidersSegmentIdx + 1) % n]);
#ifdef DEBUG_CIRCLES
drawPoints(outsideCorners, cornersImage, 2, Scalar(128));
imshow("corners", cornersImage);
#endif
}
namespace {
double pointLineDistance(const cv::Point2f &p, const cv::Vec4f &line)
{
Vec3f pa( line[0], line[1], 1 );
Vec3f pb( line[2], line[3], 1 );
Vec3f l = pa.cross(pb);
return std::abs((p.x * l[0] + p.y * l[1] + l[2])) * 1.0 /
std::sqrt(double(l[0] * l[0] + l[1] * l[1]));
}
}
void CirclesGridClusterFinder::getSortedCorners(const std::vector<cv::Point2f> &hull2f, const std::vector<cv::Point2f> &patternPoints, const std::vector<cv::Point2f> &corners, const std::vector<cv::Point2f> &outsideCorners, std::vector<cv::Point2f> &sortedCorners)
{
Point2f firstCorner;
if(isAsymmetricGrid)
{
Point2f center = std::accumulate(corners.begin(), corners.end(), Point2f(0.0f, 0.0f));
center *= 1.0 / corners.size();
std::vector<Point2f> centerToCorners;
for(size_t i=0; i<outsideCorners.size(); i++)
{
centerToCorners.push_back(outsideCorners[i] - center);
}
//TODO: use CirclesGridFinder::getDirection
float crossProduct = centerToCorners[0].x * centerToCorners[1].y - centerToCorners[0].y * centerToCorners[1].x;
//y axis is inverted in computer vision so we check > 0
bool isClockwise = crossProduct > 0;
firstCorner = isClockwise ? outsideCorners[1] : outsideCorners[0];
}
else
{
firstCorner = corners[0];
}
std::vector<Point2f>::const_iterator firstCornerIterator = std::find(hull2f.begin(), hull2f.end(), firstCorner);
sortedCorners.clear();
for(std::vector<Point2f>::const_iterator it = firstCornerIterator; it != hull2f.end();++it)
{
std::vector<Point2f>::const_iterator itCorners = std::find(corners.begin(), corners.end(), *it);
if(itCorners != corners.end())
{
sortedCorners.push_back(*it);
}
}
for(std::vector<Point2f>::const_iterator it = hull2f.begin(); it != firstCornerIterator;++it)
{
std::vector<Point2f>::const_iterator itCorners = std::find(corners.begin(), corners.end(), *it);
if(itCorners != corners.end())
{
sortedCorners.push_back(*it);
}
}
if(!isAsymmetricGrid)
{
double dist01 = norm(sortedCorners[0] - sortedCorners[1]);
double dist12 = norm(sortedCorners[1] - sortedCorners[2]);
// Use half the average distance between circles on the shorter side as threshold for determining whether a point lies on an edge.
double thresh = min(dist01, dist12) / min(patternSize.width, patternSize.height) / 2;
size_t circleCount01 = 0;
size_t circleCount12 = 0;
Vec4f line01( sortedCorners[0].x, sortedCorners[0].y, sortedCorners[1].x, sortedCorners[1].y );
Vec4f line12( sortedCorners[1].x, sortedCorners[1].y, sortedCorners[2].x, sortedCorners[2].y );
// Count the circles along both edges.
for (size_t i = 0; i < patternPoints.size(); i++)
{
if (pointLineDistance(patternPoints[i], line01) < thresh)
circleCount01++;
if (pointLineDistance(patternPoints[i], line12) < thresh)
circleCount12++;
}
// Ensure that the edge from sortedCorners[0] to sortedCorners[1] is the one with more circles (i.e. it is interpreted as the pattern's width).
if ((circleCount01 > circleCount12 && patternSize.height > patternSize.width) || (circleCount01 < circleCount12 && patternSize.height < patternSize.width))
{
for(size_t i=0; i<sortedCorners.size()-1; i++)
{
sortedCorners[i] = sortedCorners[i+1];
}
sortedCorners[sortedCorners.size() - 1] = firstCorner;
}
}
}
void CirclesGridClusterFinder::rectifyPatternPoints(const std::vector<cv::Point2f> &patternPoints, const std::vector<cv::Point2f> &sortedCorners, std::vector<cv::Point2f> &rectifiedPatternPoints)
{
//indices of corner points in pattern
std::vector<Point> trueIndices;
trueIndices.push_back(Point(0, 0));
trueIndices.push_back(Point(patternSize.width - 1, 0));
if(isAsymmetricGrid)
{
trueIndices.push_back(Point(patternSize.width - 1, 1));
trueIndices.push_back(Point(patternSize.width - 1, patternSize.height - 2));
}
trueIndices.push_back(Point(patternSize.width - 1, patternSize.height - 1));
trueIndices.push_back(Point(0, patternSize.height - 1));
std::vector<Point2f> idealPoints;
for(size_t idx=0; idx<trueIndices.size(); idx++)
{
int i = trueIndices[idx].y;
int j = trueIndices[idx].x;
if(isAsymmetricGrid)
{
idealPoints.push_back(Point2f((2*j + i % 2)*squareSize, i*squareSize));
}
else
{
idealPoints.push_back(Point2f(j*squareSize, i*squareSize));
}
}
Mat homography = findHomography(sortedCorners, idealPoints, 0);
Mat rectifiedPointsMat;
transform(patternPoints, rectifiedPointsMat, homography);
rectifiedPatternPoints.clear();
convertPointsFromHomogeneous(rectifiedPointsMat, rectifiedPatternPoints);
}
void CirclesGridClusterFinder::parsePatternPoints(const std::vector<cv::Point2f> &patternPoints, const std::vector<cv::Point2f> &rectifiedPatternPoints, std::vector<cv::Point2f> &centers)
{
#ifndef HAVE_OPENCV_FLANN
CV_UNUSED(patternPoints);
CV_UNUSED(rectifiedPatternPoints);
CV_UNUSED(centers);
CV_Error(Error::StsNotImplemented, "The desired functionality requires flann module, which was disabled.");
#else
flann::LinearIndexParams flannIndexParams;
flann::Index flannIndex(Mat(rectifiedPatternPoints).reshape(1), flannIndexParams);
centers.clear();
for( int i = 0; i < patternSize.height; i++ )
{
for( int j = 0; j < patternSize.width; j++ )
{
Point2f idealPt;
if(isAsymmetricGrid)
idealPt = Point2f((2*j + i % 2)*squareSize, i*squareSize);
else
idealPt = Point2f(j*squareSize, i*squareSize);
Mat query(1, 2, CV_32F, &idealPt);
const int knn = 1;
int indicesbuf[knn] = {0};
float distsbuf[knn] = {0.f};
Mat indices(1, knn, CV_32S, &indicesbuf);
Mat dists(1, knn, CV_32F, &distsbuf);
flannIndex.knnSearch(query, indices, dists, knn, flann::SearchParams());
centers.push_back(patternPoints.at(indicesbuf[0]));
if(distsbuf[0] > maxRectifiedDistance)
{
#ifdef DEBUG_CIRCLES
std::cout << "Pattern not detected: too large rectified distance" << std::endl;
#endif
centers.clear();
return;
}
}
}
#endif
}
Graph::Graph(size_t n)
{
for (size_t i = 0; i < n; i++)
{
addVertex(i);
}
}
bool Graph::doesVertexExist(size_t id) const
{
return (vertices.find(id) != vertices.end());
}
void Graph::addVertex(size_t id)
{
CV_Assert( !doesVertexExist( id ) );
vertices.insert(std::pair<size_t, Vertex> (id, Vertex()));
}
void Graph::addEdge(size_t id1, size_t id2)
{
CV_Assert( doesVertexExist( id1 ) );
CV_Assert( doesVertexExist( id2 ) );
vertices[id1].neighbors.insert(id2);
vertices[id2].neighbors.insert(id1);
}
void Graph::removeEdge(size_t id1, size_t id2)
{
CV_Assert( doesVertexExist( id1 ) );
CV_Assert( doesVertexExist( id2 ) );
vertices[id1].neighbors.erase(id2);
vertices[id2].neighbors.erase(id1);
}
bool Graph::areVerticesAdjacent(size_t id1, size_t id2) const
{
Vertices::const_iterator it = vertices.find(id1);
CV_Assert(it != vertices.end());
const Neighbors & neighbors = it->second.neighbors;
return neighbors.find(id2) != neighbors.end();
}
size_t Graph::getVerticesCount() const
{
return vertices.size();
}
size_t Graph::getDegree(size_t id) const
{
Vertices::const_iterator it = vertices.find(id);
CV_Assert( it != vertices.end() );
return it->second.neighbors.size();
}
void Graph::floydWarshall(cv::Mat &distanceMatrix, int infinity) const
{
const int edgeWeight = 1;
const int n = (int)getVerticesCount();
distanceMatrix.create(n, n, CV_32SC1);
distanceMatrix.setTo(infinity);
for (Vertices::const_iterator it1 = vertices.begin(); it1 != vertices.end();++it1)
{
distanceMatrix.at<int> ((int)it1->first, (int)it1->first) = 0;
for (Neighbors::const_iterator it2 = it1->second.neighbors.begin(); it2 != it1->second.neighbors.end();++it2)
{
CV_Assert( it1->first != *it2 );
distanceMatrix.at<int> ((int)it1->first, (int)*it2) = edgeWeight;
}
}
for (Vertices::const_iterator it1 = vertices.begin(); it1 != vertices.end();++it1)
{
for (Vertices::const_iterator it2 = vertices.begin(); it2 != vertices.end();++it2)
{
for (Vertices::const_iterator it3 = vertices.begin(); it3 != vertices.end();++it3)
{
int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first;
int val1 = distanceMatrix.at<int> (i2, i3);
int val2;
if (distanceMatrix.at<int> (i2, i1) == infinity ||
distanceMatrix.at<int> (i1, i3) == infinity)
val2 = val1;
else
{
val2 = distanceMatrix.at<int> (i2, i1) + distanceMatrix.at<int> (i1, i3);
}
distanceMatrix.at<int> (i2, i3) = (val1 == infinity) ? val2 : std::min(val1, val2);
}
}
}
}
const Graph::Neighbors& Graph::getNeighbors(size_t id) const
{
Vertices::const_iterator it = vertices.find(id);
CV_Assert( it != vertices.end() );
return it->second.neighbors;
}
CirclesGridFinder::Segment::Segment(cv::Point2f _s, cv::Point2f _e) :
s(_s), e(_e)
{
}
void computeShortestPath(Mat &predecessorMatrix, int v1, int v2, std::vector<int> &path);
void computePredecessorMatrix(const Mat &dm, int verticesCount, Mat &predecessorMatrix);
CirclesGridFinderParameters::CirclesGridFinderParameters()
{
minDensity = 10;
densityNeighborhoodSize = Size2f(16, 16);
minDistanceToAddKeypoint = 20;
kmeansAttempts = 100;
convexHullFactor = 1.1f;
keypointScale = 1;
minGraphConfidence = 9;
vertexGain = 1;
vertexPenalty = -0.6f;
edgeGain = 1;
edgePenalty = -0.6f;
existingVertexGain = 10000;
minRNGEdgeSwitchDist = 5.f;
gridType = SYMMETRIC_GRID;
}
CirclesGridFinderParameters2::CirclesGridFinderParameters2()
: CirclesGridFinderParameters()
{
squareSize = 1.0f;
maxRectifiedDistance = squareSize/2.0f;
}
CirclesGridFinder::CirclesGridFinder(Size _patternSize, const std::vector<Point2f> &testKeypoints,
const CirclesGridFinderParameters &_parameters) :
patternSize(static_cast<size_t> (_patternSize.width), static_cast<size_t> (_patternSize.height))
{
CV_Assert(_patternSize.height >= 0 && _patternSize.width >= 0);
keypoints = testKeypoints;
parameters = _parameters;
largeHoles = 0;
smallHoles = 0;
}
bool CirclesGridFinder::findHoles()
{
switch (parameters.gridType)
{
case CirclesGridFinderParameters::SYMMETRIC_GRID:
{
std::vector<Point2f> vectors, filteredVectors, basis;
Graph rng(0);
computeRNG(rng, vectors);
filterOutliersByDensity(vectors, filteredVectors);
std::vector<Graph> basisGraphs;
findBasis(filteredVectors, basis, basisGraphs);
findMCS(basis, basisGraphs);
break;
}
case CirclesGridFinderParameters::ASYMMETRIC_GRID:
{
std::vector<Point2f> vectors, tmpVectors, filteredVectors, basis;
Graph rng(0);
computeRNG(rng, tmpVectors);
rng2gridGraph(rng, vectors);
filterOutliersByDensity(vectors, filteredVectors);
std::vector<Graph> basisGraphs;
findBasis(filteredVectors, basis, basisGraphs);
findMCS(basis, basisGraphs);
eraseUsedGraph(basisGraphs);
holes2 = holes;
holes.clear();
findMCS(basis, basisGraphs);
break;
}
default:
CV_Error(Error::StsBadArg, "Unknown pattern type");
}
return (isDetectionCorrect());
//CV_Error( 0, "Detection is not correct" );
}
void CirclesGridFinder::rng2gridGraph(Graph &rng, std::vector<cv::Point2f> &vectors) const
{
for (size_t i = 0; i < rng.getVerticesCount(); i++)
{
Graph::Neighbors neighbors1 = rng.getNeighbors(i);
for (Graph::Neighbors::iterator it1 = neighbors1.begin(); it1 != neighbors1.end(); ++it1)
{
Graph::Neighbors neighbors2 = rng.getNeighbors(*it1);
for (Graph::Neighbors::iterator it2 = neighbors2.begin(); it2 != neighbors2.end(); ++it2)
{
if (i < *it2)
{
Point2f vec1 = keypoints[i] - keypoints[*it1];
Point2f vec2 = keypoints[*it1] - keypoints[*it2];
if (norm(vec1 - vec2) < parameters.minRNGEdgeSwitchDist || norm(vec1 + vec2)
< parameters.minRNGEdgeSwitchDist)
continue;
vectors.push_back(keypoints[i] - keypoints[*it2]);
vectors.push_back(keypoints[*it2] - keypoints[i]);
}
}
}
}
}
void CirclesGridFinder::eraseUsedGraph(std::vector<Graph> &basisGraphs) const
{
for (size_t i = 0; i < holes.size(); i++)
{
for (size_t j = 0; j < holes[i].size(); j++)
{
for (size_t k = 0; k < basisGraphs.size(); k++)
{
if (i != holes.size() - 1 && basisGraphs[k].areVerticesAdjacent(holes[i][j], holes[i + 1][j]))
{
basisGraphs[k].removeEdge(holes[i][j], holes[i + 1][j]);
}
if (j != holes[i].size() - 1 && basisGraphs[k].areVerticesAdjacent(holes[i][j], holes[i][j + 1]))
{
basisGraphs[k].removeEdge(holes[i][j], holes[i][j + 1]);
}
}
}
}
}
bool CirclesGridFinder::isDetectionCorrect()
{
switch (parameters.gridType)
{
case CirclesGridFinderParameters::SYMMETRIC_GRID:
{
if (holes.size() != patternSize.height)
return false;
std::set<size_t> vertices;
for (size_t i = 0; i < holes.size(); i++)
{
if (holes[i].size() != patternSize.width)
return false;
for (size_t j = 0; j < holes[i].size(); j++)
{
vertices.insert(holes[i][j]);
}
}
return vertices.size() == patternSize.area();
}
case CirclesGridFinderParameters::ASYMMETRIC_GRID:
{
if (holes.size() < holes2.size() || holes[0].size() < holes2[0].size())
{
largeHoles = &holes2;
smallHoles = &holes;
}
else
{
largeHoles = &holes;
smallHoles = &holes2;
}
size_t largeWidth = patternSize.width;
size_t largeHeight = (size_t)ceil(patternSize.height / 2.);
size_t smallWidth = patternSize.width;
size_t smallHeight = (size_t)floor(patternSize.height / 2.);
size_t sw = smallWidth, sh = smallHeight, lw = largeWidth, lh = largeHeight;
if (largeHoles->size() != largeHeight)
{
std::swap(lh, lw);
}
if (smallHoles->size() != smallHeight)
{
std::swap(sh, sw);
}
if (largeHoles->size() != lh || smallHoles->size() != sh)
{
return false;
}
std::set<size_t> vertices;
for (size_t i = 0; i < largeHoles->size(); i++)
{
if (largeHoles->at(i).size() != lw)
{
return false;
}
for (size_t j = 0; j < largeHoles->at(i).size(); j++)
{
vertices.insert(largeHoles->at(i)[j]);
}
if (i < smallHoles->size())
{
if (smallHoles->at(i).size() != sw)
{
return false;
}
for (size_t j = 0; j < smallHoles->at(i).size(); j++)
{
vertices.insert(smallHoles->at(i)[j]);
}
}
}
return (vertices.size() == largeHeight * largeWidth + smallHeight * smallWidth);
}
}
CV_Error(Error::StsBadArg, "Unknown pattern type");
}
void CirclesGridFinder::findMCS(const std::vector<Point2f> &basis, std::vector<Graph> &basisGraphs)
{
holes.clear();
Path longestPath;
size_t bestGraphIdx = findLongestPath(basisGraphs, longestPath);
std::vector<size_t> holesRow = longestPath.vertices;
while (holesRow.size() > std::max(patternSize.width, patternSize.height))
{
holesRow.pop_back();
holesRow.erase(holesRow.begin());
}
if (bestGraphIdx == 0)
{
holes.push_back(holesRow);
size_t w = holes[0].size();
size_t h = holes.size();
//parameters.minGraphConfidence = holes[0].size() * parameters.vertexGain + (holes[0].size() - 1) * parameters.edgeGain;
//parameters.minGraphConfidence = holes[0].size() * parameters.vertexGain + (holes[0].size() / 2) * parameters.edgeGain;
//parameters.minGraphConfidence = holes[0].size() * parameters.existingVertexGain + (holes[0].size() / 2) * parameters.edgeGain;
parameters.minGraphConfidence = holes[0].size() * parameters.existingVertexGain;
for (size_t i = h; i < patternSize.height; i++)
{
addHolesByGraph(basisGraphs, true, basis[1]);
}
//parameters.minGraphConfidence = holes.size() * parameters.existingVertexGain + (holes.size() / 2) * parameters.edgeGain;
parameters.minGraphConfidence = holes.size() * parameters.existingVertexGain;
for (size_t i = w; i < patternSize.width; i++)
{
addHolesByGraph(basisGraphs, false, basis[0]);
}
}
else
{
holes.resize(holesRow.size());
for (size_t i = 0; i < holesRow.size(); i++)
holes[i].push_back(holesRow[i]);
size_t w = holes[0].size();
size_t h = holes.size();
parameters.minGraphConfidence = holes.size() * parameters.existingVertexGain;
for (size_t i = w; i < patternSize.width; i++)
{
addHolesByGraph(basisGraphs, false, basis[0]);
}
parameters.minGraphConfidence = holes[0].size() * parameters.existingVertexGain;
for (size_t i = h; i < patternSize.height; i++)
{
addHolesByGraph(basisGraphs, true, basis[1]);
}
}
}
Mat CirclesGridFinder::rectifyGrid(Size detectedGridSize, const std::vector<Point2f>& centers,
const std::vector<Point2f> &keypoints, std::vector<Point2f> &warpedKeypoints)
{
CV_Assert( !centers.empty() );
const float edgeLength = 30;
const Point2f offset(150, 150);
std::vector<Point2f> dstPoints;
bool isClockwiseBefore =
getDirection(centers[0], centers[detectedGridSize.width - 1], centers[centers.size() - 1]) < 0;
int iStart = isClockwiseBefore ? 0 : detectedGridSize.height - 1;
int iEnd = isClockwiseBefore ? detectedGridSize.height : -1;
int iStep = isClockwiseBefore ? 1 : -1;
for (int i = iStart; i != iEnd; i += iStep)
{
for (int j = 0; j < detectedGridSize.width; j++)
{
dstPoints.push_back(offset + Point2f(edgeLength * j, edgeLength * i));
}
}
Mat H = findHomography(centers, dstPoints, RANSAC);
//Mat H = findHomography(corners, dstPoints);
if (H.empty())
{
H = Mat::zeros(3, 3, CV_64FC1);
warpedKeypoints.clear();
return H;
}
std::vector<Point2f> srcKeypoints;
for (size_t i = 0; i < keypoints.size(); i++)
{
srcKeypoints.push_back(keypoints[i]);
}
Mat dstKeypointsMat;
transform(srcKeypoints, dstKeypointsMat, H);
std::vector<Point2f> dstKeypoints;
convertPointsFromHomogeneous(dstKeypointsMat, dstKeypoints);
warpedKeypoints.clear();
for (size_t i = 0; i < dstKeypoints.size(); i++)
{
Point2f pt = dstKeypoints[i];
warpedKeypoints.push_back(pt);
}
return H;
}
size_t CirclesGridFinder::findNearestKeypoint(Point2f pt) const
{
size_t bestIdx = 0;
double minDist = std::numeric_limits<double>::max();
for (size_t i = 0; i < keypoints.size(); i++)
{
double dist = norm(pt - keypoints[i]);
if (dist < minDist)
{
minDist = dist;
bestIdx = i;
}
}
return bestIdx;
}
void CirclesGridFinder::addPoint(Point2f pt, std::vector<size_t> &points)
{
size_t ptIdx = findNearestKeypoint(pt);
if (norm(keypoints[ptIdx] - pt) > parameters.minDistanceToAddKeypoint)
{
Point2f kpt = Point2f(pt);
keypoints.push_back(kpt);
points.push_back(keypoints.size() - 1);
}
else
{
points.push_back(ptIdx);
}
}
void CirclesGridFinder::findCandidateLine(std::vector<size_t> &line, size_t seedLineIdx, bool addRow, Point2f basisVec,
std::vector<size_t> &seeds)
{
line.clear();
seeds.clear();
if (addRow)
{
for (size_t i = 0; i < holes[seedLineIdx].size(); i++)
{
Point2f pt = keypoints[holes[seedLineIdx][i]] + basisVec;
addPoint(pt, line);
seeds.push_back(holes[seedLineIdx][i]);
}
}
else
{
for (size_t i = 0; i < holes.size(); i++)
{
Point2f pt = keypoints[holes[i][seedLineIdx]] + basisVec;
addPoint(pt, line);
seeds.push_back(holes[i][seedLineIdx]);
}
}
CV_Assert( line.size() == seeds.size() );
}
void CirclesGridFinder::findCandidateHoles(std::vector<size_t> &above, std::vector<size_t> &below, bool addRow, Point2f basisVec,
std::vector<size_t> &aboveSeeds, std::vector<size_t> &belowSeeds)
{
above.clear();
below.clear();
aboveSeeds.clear();
belowSeeds.clear();
findCandidateLine(above, 0, addRow, -basisVec, aboveSeeds);
size_t lastIdx = addRow ? holes.size() - 1 : holes[0].size() - 1;
findCandidateLine(below, lastIdx, addRow, basisVec, belowSeeds);
CV_Assert( below.size() == above.size() );
CV_Assert( belowSeeds.size() == aboveSeeds.size() );
CV_Assert( below.size() == belowSeeds.size() );
}
bool CirclesGridFinder::areCentersNew(const std::vector<size_t> &newCenters, const std::vector<std::vector<size_t> > &holes)
{
for (size_t i = 0; i < newCenters.size(); i++)
{
for (size_t j = 0; j < holes.size(); j++)
{
if (holes[j].end() != std::find(holes[j].begin(), holes[j].end(), newCenters[i]))
{
return false;
}
}
}
return true;
}
void CirclesGridFinder::insertWinner(float aboveConfidence, float belowConfidence, float minConfidence, bool addRow,
const std::vector<size_t> &above, const std::vector<size_t> &below,
std::vector<std::vector<size_t> > &holes)
{
if (aboveConfidence < minConfidence && belowConfidence < minConfidence)
return;
if (addRow)
{
if (aboveConfidence >= belowConfidence)
{
if (!areCentersNew(above, holes))
CV_Error( 0, "Centers are not new" );
holes.insert(holes.begin(), above);
}
else
{
if (!areCentersNew(below, holes))
CV_Error( 0, "Centers are not new" );
holes.insert(holes.end(), below);
}
}
else
{
if (aboveConfidence >= belowConfidence)
{
if (!areCentersNew(above, holes))
CV_Error( 0, "Centers are not new" );
for (size_t i = 0; i < holes.size(); i++)
{
holes[i].insert(holes[i].begin(), above[i]);
}
}
else
{
if (!areCentersNew(below, holes))
CV_Error( 0, "Centers are not new" );
for (size_t i = 0; i < holes.size(); i++)
{
holes[i].insert(holes[i].end(), below[i]);
}
}
}
}
float CirclesGridFinder::computeGraphConfidence(const std::vector<Graph> &basisGraphs, bool addRow,
const std::vector<size_t> &points, const std::vector<size_t> &seeds)
{
CV_Assert( points.size() == seeds.size() );
float confidence = 0;
const size_t vCount = basisGraphs[0].getVerticesCount();
CV_Assert( basisGraphs[0].getVerticesCount() == basisGraphs[1].getVerticesCount() );
for (size_t i = 0; i < seeds.size(); i++)
{
if (seeds[i] < vCount && points[i] < vCount)
{
if (!basisGraphs[addRow].areVerticesAdjacent(seeds[i], points[i]))
{
confidence += parameters.vertexPenalty;
}
else
{
confidence += parameters.vertexGain;
}
}
if (points[i] < vCount)
{
confidence += parameters.existingVertexGain;
}
}
for (size_t i = 1; i < points.size(); i++)
{
if (points[i - 1] < vCount && points[i] < vCount)
{
if (!basisGraphs[!addRow].areVerticesAdjacent(points[i - 1], points[i]))
{
confidence += parameters.edgePenalty;
}
else
{
confidence += parameters.edgeGain;
}
}
}
return confidence;
}
void CirclesGridFinder::addHolesByGraph(const std::vector<Graph> &basisGraphs, bool addRow, Point2f basisVec)
{
std::vector<size_t> above, below, aboveSeeds, belowSeeds;
findCandidateHoles(above, below, addRow, basisVec, aboveSeeds, belowSeeds);
float aboveConfidence = computeGraphConfidence(basisGraphs, addRow, above, aboveSeeds);
float belowConfidence = computeGraphConfidence(basisGraphs, addRow, below, belowSeeds);
insertWinner(aboveConfidence, belowConfidence, parameters.minGraphConfidence, addRow, above, below, holes);
}
void CirclesGridFinder::filterOutliersByDensity(const std::vector<Point2f> &samples, std::vector<Point2f> &filteredSamples)
{
if (samples.empty())
CV_Error( 0, "samples is empty" );
filteredSamples.clear();
for (size_t i = 0; i < samples.size(); i++)
{
Rect_<float> rect(samples[i] - Point2f(parameters.densityNeighborhoodSize) * 0.5,
parameters.densityNeighborhoodSize);
int neighborsCount = 0;
for (size_t j = 0; j < samples.size(); j++)
{
if (rect.contains(samples[j]))
neighborsCount++;
}
if (neighborsCount >= parameters.minDensity)
filteredSamples.push_back(samples[i]);
}
if (filteredSamples.empty())
CV_Error( 0, "filteredSamples is empty" );
}
void CirclesGridFinder::findBasis(const std::vector<Point2f> &samples, std::vector<Point2f> &basis, std::vector<Graph> &basisGraphs)
{
basis.clear();
Mat bestLabels;
TermCriteria termCriteria;
Mat centers;
const int clustersCount = 4;
kmeans(Mat(samples).reshape(1, 0), clustersCount, bestLabels, termCriteria, parameters.kmeansAttempts,
KMEANS_RANDOM_CENTERS, centers);
CV_Assert( centers.type() == CV_32FC1 );
std::vector<int> basisIndices;
//TODO: only remove duplicate
for (int i = 0; i < clustersCount; i++)
{
int maxIdx = (fabs(centers.at<float> (i, 0)) < fabs(centers.at<float> (i, 1)));
if (centers.at<float> (i, maxIdx) > 0)
{
Point2f vec(centers.at<float> (i, 0), centers.at<float> (i, 1));
basis.push_back(vec);
basisIndices.push_back(i);
}
}
if (basis.size() != 2)
CV_Error(0, "Basis size is not 2");
if (basis[1].x > basis[0].x)
{
std::swap(basis[0], basis[1]);
std::swap(basisIndices[0], basisIndices[1]);
}
const float minBasisDif = 2;
if (norm(basis[0] - basis[1]) < minBasisDif)
CV_Error(0, "degenerate basis" );
std::vector<std::vector<Point2f> > clusters(2), hulls(2);
for (int k = 0; k < (int)samples.size(); k++)
{
int label = bestLabels.at<int> (k, 0);
int idx = -1;
if (label == basisIndices[0])
idx = 0;
if (label == basisIndices[1])
idx = 1;
if (idx >= 0)
{
clusters[idx].push_back(basis[idx] + parameters.convexHullFactor * (samples[k] - basis[idx]));
}
}
for (size_t i = 0; i < basis.size(); i++)
{
convexHull(clusters[i], hulls[i]);
}
basisGraphs.resize(basis.size(), Graph(keypoints.size()));
for (size_t i = 0; i < keypoints.size(); i++)
{
for (size_t j = 0; j < keypoints.size(); j++)
{
if (i == j)
continue;
Point2f vec = keypoints[i] - keypoints[j];
for (size_t k = 0; k < hulls.size(); k++)
{
if (pointPolygonTest(hulls[k], vec, false) >= 0)
{
basisGraphs[k].addEdge(i, j);
}
}
}
}
if (basisGraphs.size() != 2)
CV_Error(0, "Number of basis graphs is not 2");
}
void CirclesGridFinder::computeRNG(Graph &rng, std::vector<cv::Point2f> &vectors, Mat *drawImage) const
{
rng = Graph(keypoints.size());
vectors.clear();
//TODO: use more fast algorithm instead of naive N^3
for (size_t i = 0; i < keypoints.size(); i++)
{
for (size_t j = 0; j < keypoints.size(); j++)
{
if (i == j)
continue;
Point2f vec = keypoints[i] - keypoints[j];
double dist = norm(vec);
bool isNeighbors = true;
for (size_t k = 0; k < keypoints.size(); k++)
{
if (k == i || k == j)
continue;
double dist1 = norm(keypoints[i] - keypoints[k]);
double dist2 = norm(keypoints[j] - keypoints[k]);
if (dist1 < dist && dist2 < dist)
{
isNeighbors = false;
break;
}
}
if (isNeighbors)
{
rng.addEdge(i, j);
vectors.push_back(keypoints[i] - keypoints[j]);
if (drawImage != 0)
{
line(*drawImage, keypoints[i], keypoints[j], Scalar(255, 0, 0), 2);
circle(*drawImage, keypoints[i], 3, Scalar(0, 0, 255), -1);
circle(*drawImage, keypoints[j], 3, Scalar(0, 0, 255), -1);
}
}
}
}
}
void computePredecessorMatrix(const Mat &dm, int verticesCount, Mat &predecessorMatrix)
{
CV_Assert( dm.type() == CV_32SC1 );
predecessorMatrix.create(verticesCount, verticesCount, CV_32SC1);
predecessorMatrix = -1;
for (int i = 0; i < predecessorMatrix.rows; i++)
{
for (int j = 0; j < predecessorMatrix.cols; j++)
{
int dist = dm.at<int> (i, j);
for (int k = 0; k < verticesCount; k++)
{
if (dm.at<int> (i, k) == dist - 1 && dm.at<int> (k, j) == 1)
{
predecessorMatrix.at<int> (i, j) = k;
break;
}
}
}
}
}
static void computeShortestPath(Mat &predecessorMatrix, size_t v1, size_t v2, std::vector<size_t> &path)
{
if (predecessorMatrix.at<int> ((int)v1, (int)v2) < 0)
{
path.push_back(v1);
return;
}
computeShortestPath(predecessorMatrix, v1, predecessorMatrix.at<int> ((int)v1, (int)v2), path);
path.push_back(v2);
}
size_t CirclesGridFinder::findLongestPath(std::vector<Graph> &basisGraphs, Path &bestPath)
{
std::vector<Path> longestPaths(1);
std::vector<int> confidences;
size_t bestGraphIdx = 0;
const int infinity = -1;
for (size_t graphIdx = 0; graphIdx < basisGraphs.size(); graphIdx++)
{
const Graph &g = basisGraphs[graphIdx];
Mat distanceMatrix;
g.floydWarshall(distanceMatrix, infinity);
Mat predecessorMatrix;
computePredecessorMatrix(distanceMatrix, (int)g.getVerticesCount(), predecessorMatrix);
double maxVal;
Point maxLoc;
minMaxLoc(distanceMatrix, 0, &maxVal, 0, &maxLoc);
if (maxVal > longestPaths[0].length)
{
longestPaths.clear();
confidences.clear();
bestGraphIdx = graphIdx;
}
if (longestPaths.empty() || (maxVal == longestPaths[0].length && graphIdx == bestGraphIdx))
{
Path path = Path(maxLoc.x, maxLoc.y, cvRound(maxVal));
CV_Assert(maxLoc.x >= 0 && maxLoc.y >= 0)
;
size_t id1 = static_cast<size_t> (maxLoc.x);
size_t id2 = static_cast<size_t> (maxLoc.y);
computeShortestPath(predecessorMatrix, id1, id2, path.vertices);
longestPaths.push_back(path);
int conf = 0;
for (int v2 = 0; v2 < (int)path.vertices.size(); v2++)
{
conf += (int)basisGraphs[1 - (int)graphIdx].getDegree(v2);
}
confidences.push_back(conf);
}
}
//if( bestGraphIdx != 0 )
//CV_Error( 0, "" );
int maxConf = -1;
int bestPathIdx = -1;
for (int i = 0; i < (int)confidences.size(); i++)
{
if (confidences[i] > maxConf)
{
maxConf = confidences[i];
bestPathIdx = i;
}
}
//int bestPathIdx = rand() % longestPaths.size();
bestPath = longestPaths.at(bestPathIdx);
bool needReverse = (bestGraphIdx == 0 && keypoints[bestPath.lastVertex].x < keypoints[bestPath.firstVertex].x)
|| (bestGraphIdx == 1 && keypoints[bestPath.lastVertex].y < keypoints[bestPath.firstVertex].y);
if (needReverse)
{
std::swap(bestPath.lastVertex, bestPath.firstVertex);
std::reverse(bestPath.vertices.begin(), bestPath.vertices.end());
}
return bestGraphIdx;
}
void CirclesGridFinder::drawBasis(const std::vector<Point2f> &basis, Point2f origin, Mat &drawImg) const
{
for (size_t i = 0; i < basis.size(); i++)
{
Point2f pt(basis[i]);
line(drawImg, origin, origin + pt, Scalar(0, (double)(i * 255), 0), 2);
}
}
void CirclesGridFinder::drawBasisGraphs(const std::vector<Graph> &basisGraphs, Mat &drawImage, bool drawEdges,
bool drawVertices) const
{
//const int vertexRadius = 1;
const int vertexRadius = 3;
const Scalar vertexColor = Scalar(0, 0, 255);
const int vertexThickness = -1;
const Scalar edgeColor = Scalar(255, 0, 0);
//const int edgeThickness = 1;
const int edgeThickness = 2;
if (drawEdges)
{
for (size_t i = 0; i < basisGraphs.size(); i++)
{
for (size_t v1 = 0; v1 < basisGraphs[i].getVerticesCount(); v1++)
{
for (size_t v2 = 0; v2 < basisGraphs[i].getVerticesCount(); v2++)
{
if (basisGraphs[i].areVerticesAdjacent(v1, v2))
{
line(drawImage, keypoints[v1], keypoints[v2], edgeColor, edgeThickness);
}
}
}
}
}
if (drawVertices)
{
for (size_t v = 0; v < basisGraphs[0].getVerticesCount(); v++)
{
circle(drawImage, keypoints[v], vertexRadius, vertexColor, vertexThickness);
}
}
}
void CirclesGridFinder::drawHoles(const Mat &srcImage, Mat &drawImage) const
{
//const int holeRadius = 4;
//const int holeRadius = 2;
//const int holeThickness = 1;
const int holeRadius = 3;
const int holeThickness = -1;
//const Scalar holeColor = Scalar(0, 0, 255);
const Scalar holeColor = Scalar(0, 255, 0);
if (srcImage.channels() == 1)
cvtColor(srcImage, drawImage, COLOR_GRAY2RGB);
else
srcImage.copyTo(drawImage);
for (size_t i = 0; i < holes.size(); i++)
{
for (size_t j = 0; j < holes[i].size(); j++)
{
if (j != holes[i].size() - 1)
line(drawImage, keypoints[holes[i][j]], keypoints[holes[i][j + 1]], Scalar(255, 0, 0), 2);
if (i != holes.size() - 1)
line(drawImage, keypoints[holes[i][j]], keypoints[holes[i + 1][j]], Scalar(255, 0, 0), 2);
circle(drawImage, keypoints[holes[i][j]], holeRadius, holeColor, holeThickness);
}
}
}
Size CirclesGridFinder::getDetectedGridSize() const
{
if (holes.size() == 0)
return Size(0, 0);
return Size((int)holes[0].size(), (int)holes.size());
}
void CirclesGridFinder::getHoles(std::vector<Point2f> &outHoles) const
{
outHoles.clear();
for (size_t i = 0; i < holes.size(); i++)
{
for (size_t j = 0; j < holes[i].size(); j++)
{
outHoles.push_back(keypoints[holes[i][j]]);
}
}
}
static bool areIndicesCorrect(Point pos, std::vector<std::vector<size_t> > *points)
{
if (pos.y < 0 || pos.x < 0)
return false;
return (static_cast<size_t> (pos.y) < points->size() && static_cast<size_t> (pos.x) < points->at(pos.y).size());
}
void CirclesGridFinder::getAsymmetricHoles(std::vector<cv::Point2f> &outHoles) const
{
outHoles.clear();
std::vector<Point> largeCornerIndices, smallCornerIndices;
std::vector<Point> firstSteps, secondSteps;
size_t cornerIdx = getFirstCorner(largeCornerIndices, smallCornerIndices, firstSteps, secondSteps);
CV_Assert(largeHoles != 0 && smallHoles != 0)
;
Point srcLargePos = largeCornerIndices[cornerIdx];
Point srcSmallPos = smallCornerIndices[cornerIdx];
while (areIndicesCorrect(srcLargePos, largeHoles) || areIndicesCorrect(srcSmallPos, smallHoles))
{
Point largePos = srcLargePos;
while (areIndicesCorrect(largePos, largeHoles))
{
outHoles.push_back(keypoints[largeHoles->at(largePos.y)[largePos.x]]);
largePos += firstSteps[cornerIdx];
}
srcLargePos += secondSteps[cornerIdx];
Point smallPos = srcSmallPos;
while (areIndicesCorrect(smallPos, smallHoles))
{
outHoles.push_back(keypoints[smallHoles->at(smallPos.y)[smallPos.x]]);
smallPos += firstSteps[cornerIdx];
}
srcSmallPos += secondSteps[cornerIdx];
}
}
double CirclesGridFinder::getDirection(Point2f p1, Point2f p2, Point2f p3)
{
Point2f a = p3 - p1;
Point2f b = p2 - p1;
return a.x * b.y - a.y * b.x;
}
bool CirclesGridFinder::areSegmentsIntersecting(Segment seg1, Segment seg2)
{
bool doesStraddle1 = (getDirection(seg2.s, seg2.e, seg1.s) * getDirection(seg2.s, seg2.e, seg1.e)) < 0;
bool doesStraddle2 = (getDirection(seg1.s, seg1.e, seg2.s) * getDirection(seg1.s, seg1.e, seg2.e)) < 0;
return doesStraddle1 && doesStraddle2;
/*
Point2f t1 = e1-s1;
Point2f n1(t1.y, -t1.x);
double c1 = -n1.ddot(s1);
Point2f t2 = e2-s2;
Point2f n2(t2.y, -t2.x);
double c2 = -n2.ddot(s2);
bool seg1 = ((n1.ddot(s2) + c1) * (n1.ddot(e2) + c1)) <= 0;
bool seg1 = ((n2.ddot(s1) + c2) * (n2.ddot(e1) + c2)) <= 0;
return seg1 && seg2;
*/
}
void CirclesGridFinder::getCornerSegments(const std::vector<std::vector<size_t> > &points, std::vector<std::vector<Segment> > &segments,
std::vector<Point> &cornerIndices, std::vector<Point> &firstSteps,
std::vector<Point> &secondSteps) const
{
segments.clear();
cornerIndices.clear();
firstSteps.clear();
secondSteps.clear();
int h = (int)points.size();
int w = (int)points[0].size();
CV_Assert(h >= 2 && w >= 2)
;
//all 8 segments with one end in a corner
std::vector<Segment> corner;
corner.push_back(Segment(keypoints[points[1][0]], keypoints[points[0][0]]));
corner.push_back(Segment(keypoints[points[0][0]], keypoints[points[0][1]]));
segments.push_back(corner);
cornerIndices.push_back(Point(0, 0));
firstSteps.push_back(Point(1, 0));
secondSteps.push_back(Point(0, 1));
corner.clear();
corner.push_back(Segment(keypoints[points[0][w - 2]], keypoints[points[0][w - 1]]));
corner.push_back(Segment(keypoints[points[0][w - 1]], keypoints[points[1][w - 1]]));
segments.push_back(corner);
cornerIndices.push_back(Point(w - 1, 0));
firstSteps.push_back(Point(0, 1));
secondSteps.push_back(Point(-1, 0));
corner.clear();
corner.push_back(Segment(keypoints[points[h - 2][w - 1]], keypoints[points[h - 1][w - 1]]));
corner.push_back(Segment(keypoints[points[h - 1][w - 1]], keypoints[points[h - 1][w - 2]]));
segments.push_back(corner);
cornerIndices.push_back(Point(w - 1, h - 1));
firstSteps.push_back(Point(-1, 0));
secondSteps.push_back(Point(0, -1));
corner.clear();
corner.push_back(Segment(keypoints[points[h - 1][1]], keypoints[points[h - 1][0]]));
corner.push_back(Segment(keypoints[points[h - 1][0]], keypoints[points[h - 2][0]]));
cornerIndices.push_back(Point(0, h - 1));
firstSteps.push_back(Point(0, -1));
secondSteps.push_back(Point(1, 0));
segments.push_back(corner);
corner.clear();
//y axis is inverted in computer vision so we check < 0
bool isClockwise =
getDirection(keypoints[points[0][0]], keypoints[points[0][w - 1]], keypoints[points[h - 1][w - 1]]) < 0;
if (!isClockwise)
{
#ifdef DEBUG_CIRCLES
std::cout << "Corners are counterclockwise" << std::endl;
#endif
std::reverse(segments.begin(), segments.end());
std::reverse(cornerIndices.begin(), cornerIndices.end());
std::reverse(firstSteps.begin(), firstSteps.end());
std::reverse(secondSteps.begin(), secondSteps.end());
std::swap(firstSteps, secondSteps);
}
}
bool CirclesGridFinder::doesIntersectionExist(const std::vector<Segment> &corner, const std::vector<std::vector<Segment> > &segments)
{
for (size_t i = 0; i < corner.size(); i++)
{
for (size_t j = 0; j < segments.size(); j++)
{
for (size_t k = 0; k < segments[j].size(); k++)
{
if (areSegmentsIntersecting(corner[i], segments[j][k]))
return true;
}
}
}
return false;
}
size_t CirclesGridFinder::getFirstCorner(std::vector<Point> &largeCornerIndices, std::vector<Point> &smallCornerIndices, std::vector<
Point> &firstSteps, std::vector<Point> &secondSteps) const
{
std::vector<std::vector<Segment> > largeSegments;
std::vector<std::vector<Segment> > smallSegments;
getCornerSegments(*largeHoles, largeSegments, largeCornerIndices, firstSteps, secondSteps);
getCornerSegments(*smallHoles, smallSegments, smallCornerIndices, firstSteps, secondSteps);
const size_t cornersCount = 4;
CV_Assert(largeSegments.size() == cornersCount)
;
bool isInsider[cornersCount];
for (size_t i = 0; i < cornersCount; i++)
{
isInsider[i] = doesIntersectionExist(largeSegments[i], smallSegments);
}
int cornerIdx = 0;
bool waitOutsider = true;
for (size_t i = 0; i < cornersCount * 2; ++i)
{
if (waitOutsider)
{
if (!isInsider[(cornerIdx + 1) % cornersCount])
waitOutsider = false;
}
else
{
if (isInsider[(cornerIdx + 1) % cornersCount])
return cornerIdx;
}
cornerIdx = (cornerIdx + 1) % cornersCount;
}
CV_Error(Error::StsNoConv, "isInsider array has the same values");
}