opencv/modules/imgproc/src/generalized_hough.cpp

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2012-09-10 20:24:55 +08:00
/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// If you do not agree to this license, do not download, install,
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//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
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// this list of conditions and the following disclaimer.
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#include "precomp.hpp"
#include <functional>
2012-09-10 20:24:55 +08:00
using namespace std;
using namespace cv;
namespace
{
/////////////////////////////////////
// Common
template <typename T, class A> void releaseVector(vector<T, A>& v)
{
vector<T, A> empty;
empty.swap(v);
}
double toRad(double a)
{
return a * CV_PI / 180.0;
}
bool notNull(float v)
{
return fabs(v) > numeric_limits<float>::epsilon();
}
class GHT_Pos : public GeneralizedHough
{
public:
GHT_Pos();
protected:
void setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter);
void detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes);
void releaseImpl();
virtual void processTempl() = 0;
virtual void processImage() = 0;
void filterMinDist();
void convertTo(OutputArray positions, OutputArray votes);
double minDist;
Size templSize;
Point templCenter;
Mat templEdges;
Mat templDx;
Mat templDy;
Size imageSize;
Mat imageEdges;
Mat imageDx;
Mat imageDy;
vector<Vec4f> posOutBuf;
vector<Vec3i> voteOutBuf;
};
GHT_Pos::GHT_Pos()
{
minDist = 1.0;
}
void GHT_Pos::setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter_)
{
templSize = edges.size();
templCenter = templCenter_;
edges.copyTo(templEdges);
dx.copyTo(templDx);
dy.copyTo(templDy);
processTempl();
}
void GHT_Pos::detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes)
{
imageSize = edges.size();
edges.copyTo(imageEdges);
dx.copyTo(imageDx);
dy.copyTo(imageDy);
posOutBuf.clear();
voteOutBuf.clear();
processImage();
if (!posOutBuf.empty())
{
if (minDist > 1)
filterMinDist();
convertTo(positions, votes);
}
else
{
positions.release();
if (votes.needed())
votes.release();
}
}
void GHT_Pos::releaseImpl()
{
templSize = Size();
templCenter = Point(-1, -1);
templEdges.release();
templDx.release();
templDy.release();
imageSize = Size();
imageEdges.release();
imageDx.release();
imageDy.release();
releaseVector(posOutBuf);
releaseVector(voteOutBuf);
}
#define votes_cmp_gt(l1, l2) (aux[l1][0] > aux[l2][0])
static CV_IMPLEMENT_QSORT_EX( sortIndexies, size_t, votes_cmp_gt, const Vec3i* )
void GHT_Pos::filterMinDist()
{
size_t oldSize = posOutBuf.size();
const bool hasVotes = !voteOutBuf.empty();
CV_Assert(!hasVotes || voteOutBuf.size() == oldSize);
vector<Vec4f> oldPosBuf(posOutBuf);
vector<Vec3i> oldVoteBuf(voteOutBuf);
vector<size_t> indexies(oldSize);
for (size_t i = 0; i < oldSize; ++i)
indexies[i] = i;
sortIndexies(&indexies[0], oldSize, &oldVoteBuf[0]);
posOutBuf.clear();
voteOutBuf.clear();
const int cellSize = cvRound(minDist);
const int gridWidth = (imageSize.width + cellSize - 1) / cellSize;
const int gridHeight = (imageSize.height + cellSize - 1) / cellSize;
vector< vector<Point2f> > grid(gridWidth * gridHeight);
const double minDist2 = minDist * minDist;
for (size_t i = 0; i < oldSize; ++i)
{
const size_t ind = indexies[i];
Point2f p(oldPosBuf[ind][0], oldPosBuf[ind][1]);
bool good = true;
const int xCell = static_cast<int>(p.x / cellSize);
const int yCell = static_cast<int>(p.y / cellSize);
int x1 = xCell - 1;
int y1 = yCell - 1;
int x2 = xCell + 1;
int y2 = yCell + 1;
// boundary check
x1 = std::max(0, x1);
y1 = std::max(0, y1);
x2 = std::min(gridWidth - 1, x2);
y2 = std::min(gridHeight - 1, y2);
for (int yy = y1; yy <= y2; ++yy)
{
for (int xx = x1; xx <= x2; ++xx)
{
const vector<Point2f>& m = grid[yy * gridWidth + xx];
for(size_t j = 0; j < m.size(); ++j)
{
const Point2f d = p - m[j];
if (d.ddot(d) < minDist2)
{
good = false;
goto break_out;
}
}
}
}
break_out:
if(good)
{
grid[yCell * gridWidth + xCell].push_back(p);
posOutBuf.push_back(oldPosBuf[ind]);
if (hasVotes)
voteOutBuf.push_back(oldVoteBuf[ind]);
}
}
}
void GHT_Pos::convertTo(OutputArray _positions, OutputArray _votes)
{
const int total = static_cast<int>(posOutBuf.size());
const bool hasVotes = !voteOutBuf.empty();
CV_Assert(!hasVotes || voteOutBuf.size() == posOutBuf.size());
_positions.create(1, total, CV_32FC4);
Mat positions = _positions.getMat();
Mat(1, total, CV_32FC4, &posOutBuf[0]).copyTo(positions);
if (_votes.needed())
{
if (!hasVotes)
_votes.release();
else
{
_votes.create(1, total, CV_32SC3);
Mat votes = _votes.getMat();
Mat(1, total, CV_32SC3, &voteOutBuf[0]).copyTo(votes);
}
}
}
/////////////////////////////////////
// POSITION Ballard
class GHT_Ballard_Pos : public GHT_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Ballard_Pos();
protected:
void releaseImpl();
void processTempl();
void processImage();
virtual void calcHist();
virtual void findPosInHist();
int levels;
int votesThreshold;
double dp;
vector< vector<Point> > r_table;
Mat hist;
};
CV_INIT_ALGORITHM(GHT_Ballard_Pos, "GeneralizedHough.POSITION",
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"R-Table levels.");
obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution."));
GHT_Ballard_Pos::GHT_Ballard_Pos()
{
levels = 360;
votesThreshold = 100;
dp = 1.0;
}
void GHT_Ballard_Pos::releaseImpl()
{
GHT_Pos::releaseImpl();
releaseVector(r_table);
hist.release();
}
void GHT_Ballard_Pos::processTempl()
{
CV_Assert(templEdges.type() == CV_8UC1);
CV_Assert(templDx.type() == CV_32FC1 && templDx.size() == templSize);
CV_Assert(templDy.type() == templDx.type() && templDy.size() == templSize);
CV_Assert(levels > 0);
const double thetaScale = levels / 360.0;
r_table.resize(levels + 1);
for_each(r_table.begin(), r_table.end(), mem_fun_ref(&vector<Point>::clear));
for (int y = 0; y < templSize.height; ++y)
{
const uchar* edgesRow = templEdges.ptr(y);
const float* dxRow = templDx.ptr<float>(y);
const float* dyRow = templDy.ptr<float>(y);
for (int x = 0; x < templSize.width; ++x)
{
const Point p(x, y);
if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
{
const float theta = fastAtan2(dyRow[x], dxRow[x]);
const int n = cvRound(theta * thetaScale);
r_table[n].push_back(p - templCenter);
}
}
}
}
void GHT_Ballard_Pos::processImage()
{
calcHist();
findPosInHist();
}
void GHT_Ballard_Pos::calcHist()
{
CV_Assert(imageEdges.type() == CV_8UC1);
CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
CV_Assert(dp > 0.0);
const double thetaScale = levels / 360.0;
const double idp = 1.0 / dp;
hist.create(cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2, CV_32SC1);
hist.setTo(0);
const int rows = hist.rows - 2;
const int cols = hist.cols - 2;
for (int y = 0; y < imageSize.height; ++y)
{
const uchar* edgesRow = imageEdges.ptr(y);
const float* dxRow = imageDx.ptr<float>(y);
const float* dyRow = imageDy.ptr<float>(y);
for (int x = 0; x < imageSize.width; ++x)
{
const Point p(x, y);
if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
{
const float theta = fastAtan2(dyRow[x], dxRow[x]);
const int n = cvRound(theta * thetaScale);
const vector<Point>& r_row = r_table[n];
for (size_t j = 0; j < r_row.size(); ++j)
{
Point c = p - r_row[j];
c.x = cvRound(c.x * idp);
c.y = cvRound(c.y * idp);
if (c.x >= 0 && c.x < cols && c.y >= 0 && c.y < rows)
++hist.at<int>(c.y + 1, c.x + 1);
}
}
}
}
}
void GHT_Ballard_Pos::findPosInHist()
{
CV_Assert(votesThreshold > 0);
const int histRows = hist.rows - 2;
const int histCols = hist.cols - 2;
for(int y = 0; y < histRows; ++y)
{
const int* prevRow = hist.ptr<int>(y);
const int* curRow = hist.ptr<int>(y + 1);
const int* nextRow = hist.ptr<int>(y + 2);
for(int x = 0; x < histCols; ++x)
{
const int votes = curRow[x + 1];
if (votes > votesThreshold && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
{
posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), 1.0f, 0.0f));
voteOutBuf.push_back(Vec3i(votes, 0, 0));
}
}
}
}
/////////////////////////////////////
// POSITION & SCALE
class GHT_Ballard_PosScale : public GHT_Ballard_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Ballard_PosScale();
protected:
void calcHist();
void findPosInHist();
double minScale;
double maxScale;
double scaleStep;
class Worker;
friend class Worker;
};
CV_INIT_ALGORITHM(GHT_Ballard_PosScale, "GeneralizedHough.POSITION_SCALE",
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"R-Table levels.");
obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution.");
obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
"Minimal scale to detect.");
obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
"Maximal scale to detect.");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Scale step."));
GHT_Ballard_PosScale::GHT_Ballard_PosScale()
{
minScale = 0.5;
maxScale = 2.0;
scaleStep = 0.05;
}
class GHT_Ballard_PosScale::Worker : public ParallelLoopBody
{
public:
explicit Worker(GHT_Ballard_PosScale* base_) : base(base_) {}
void operator ()(const Range& range) const;
private:
GHT_Ballard_PosScale* base;
};
void GHT_Ballard_PosScale::Worker::operator ()(const Range& range) const
{
const double thetaScale = base->levels / 360.0;
const double idp = 1.0 / base->dp;
for (int s = range.start; s < range.end; ++s)
{
const double scale = base->minScale + s * base->scaleStep;
Mat curHist(base->hist.size[1], base->hist.size[2], CV_32SC1, base->hist.ptr(s + 1), base->hist.step[1]);
for (int y = 0; y < base->imageSize.height; ++y)
{
const uchar* edgesRow = base->imageEdges.ptr(y);
const float* dxRow = base->imageDx.ptr<float>(y);
const float* dyRow = base->imageDy.ptr<float>(y);
for (int x = 0; x < base->imageSize.width; ++x)
{
const Point2d p(x, y);
if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
{
const float theta = fastAtan2(dyRow[x], dxRow[x]);
const int n = cvRound(theta * thetaScale);
const vector<Point>& r_row = base->r_table[n];
for (size_t j = 0; j < r_row.size(); ++j)
{
Point2d d = r_row[j];
Point2d c = p - d * scale;
c.x *= idp;
c.y *= idp;
if (c.x >= 0 && c.x < base->hist.size[2] - 2 && c.y >= 0 && c.y < base->hist.size[1] - 2)
++curHist.at<int>(cvRound(c.y + 1), cvRound(c.x + 1));
}
}
}
}
}
}
void GHT_Ballard_PosScale::calcHist()
{
CV_Assert(imageEdges.type() == CV_8UC1);
CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
CV_Assert(dp > 0.0);
CV_Assert(minScale > 0.0 && minScale < maxScale);
CV_Assert(scaleStep > 0.0);
const double idp = 1.0 / dp;
const int scaleRange = cvCeil((maxScale - minScale) / scaleStep);
const int sizes[] = {scaleRange + 2, cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2};
hist.create(3, sizes, CV_32SC1);
hist.setTo(0);
parallel_for_(Range(0, scaleRange), Worker(this));
}
void GHT_Ballard_PosScale::findPosInHist()
{
CV_Assert(votesThreshold > 0);
const int scaleRange = hist.size[0] - 2;
const int histRows = hist.size[1] - 2;
const int histCols = hist.size[2] - 2;
for (int s = 0; s < scaleRange; ++s)
{
const float scale = static_cast<float>(minScale + s * scaleStep);
const Mat prevHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(s), hist.step[1]);
const Mat curHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(s + 1), hist.step[1]);
const Mat nextHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(s + 2), hist.step[1]);
for(int y = 0; y < histRows; ++y)
{
const int* prevHistRow = prevHist.ptr<int>(y + 1);
const int* prevRow = curHist.ptr<int>(y);
const int* curRow = curHist.ptr<int>(y + 1);
const int* nextRow = curHist.ptr<int>(y + 2);
const int* nextHistRow = nextHist.ptr<int>(y + 1);
for(int x = 0; x < histCols; ++x)
{
const int votes = curRow[x + 1];
if (votes > votesThreshold &&
votes > curRow[x] &&
votes >= curRow[x + 2] &&
votes > prevRow[x + 1] &&
votes >= nextRow[x + 1] &&
votes > prevHistRow[x + 1] &&
votes >= nextHistRow[x + 1])
{
posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), scale, 0.0f));
voteOutBuf.push_back(Vec3i(votes, votes, 0));
}
}
}
}
}
/////////////////////////////////////
// POSITION & ROTATION
class GHT_Ballard_PosRotation : public GHT_Ballard_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Ballard_PosRotation();
protected:
void calcHist();
void findPosInHist();
double minAngle;
double maxAngle;
double angleStep;
class Worker;
friend class Worker;
};
CV_INIT_ALGORITHM(GHT_Ballard_PosRotation, "GeneralizedHough.POSITION_ROTATION",
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"R-Table levels.");
obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution.");
obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
"Minimal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
"Maximal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
"Angle step in degrees."));
GHT_Ballard_PosRotation::GHT_Ballard_PosRotation()
{
minAngle = 0.0;
maxAngle = 360.0;
angleStep = 1.0;
}
class GHT_Ballard_PosRotation::Worker : public ParallelLoopBody
{
public:
explicit Worker(GHT_Ballard_PosRotation* base_) : base(base_) {}
void operator ()(const Range& range) const;
private:
GHT_Ballard_PosRotation* base;
};
void GHT_Ballard_PosRotation::Worker::operator ()(const Range& range) const
{
const double thetaScale = base->levels / 360.0;
const double idp = 1.0 / base->dp;
for (int a = range.start; a < range.end; ++a)
{
const double angle = base->minAngle + a * base->angleStep;
const double sinA = ::sin(toRad(angle));
const double cosA = ::cos(toRad(angle));
Mat curHist(base->hist.size[1], base->hist.size[2], CV_32SC1, base->hist.ptr(a + 1), base->hist.step[1]);
for (int y = 0; y < base->imageSize.height; ++y)
{
const uchar* edgesRow = base->imageEdges.ptr(y);
const float* dxRow = base->imageDx.ptr<float>(y);
const float* dyRow = base->imageDy.ptr<float>(y);
for (int x = 0; x < base->imageSize.width; ++x)
{
const Point2d p(x, y);
if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
{
double theta = fastAtan2(dyRow[x], dxRow[x]) - angle;
if (theta < 0)
theta += 360.0;
const int n = cvRound(theta * thetaScale);
const vector<Point>& r_row = base->r_table[n];
for (size_t j = 0; j < r_row.size(); ++j)
{
Point2d d = r_row[j];
Point2d c = p - Point2d(d.x * cosA - d.y * sinA, d.x * sinA + d.y * cosA);
c.x *= idp;
c.y *= idp;
if (c.x >= 0 && c.x < base->hist.size[2] - 2 && c.y >= 0 && c.y < base->hist.size[1] - 2)
++curHist.at<int>(cvRound(c.y + 1), cvRound(c.x + 1));
}
}
}
}
}
}
void GHT_Ballard_PosRotation::calcHist()
{
CV_Assert(imageEdges.type() == CV_8UC1);
CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
CV_Assert(dp > 0.0);
CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
CV_Assert(angleStep > 0.0 && angleStep < 360.0);
const double idp = 1.0 / dp;
const int angleRange = cvCeil((maxAngle - minAngle) / angleStep);
const int sizes[] = {angleRange + 2, cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2};
hist.create(3, sizes, CV_32SC1);
hist.setTo(0);
parallel_for_(Range(0, angleRange), Worker(this));
}
void GHT_Ballard_PosRotation::findPosInHist()
{
CV_Assert(votesThreshold > 0);
const int angleRange = hist.size[0] - 2;
const int histRows = hist.size[1] - 2;
const int histCols = hist.size[2] - 2;
for (int a = 0; a < angleRange; ++a)
{
const float angle = static_cast<float>(minAngle + a * angleStep);
const Mat prevHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(a), hist.step[1]);
const Mat curHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(a + 1), hist.step[1]);
const Mat nextHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(a + 2), hist.step[1]);
for(int y = 0; y < histRows; ++y)
{
const int* prevHistRow = prevHist.ptr<int>(y + 1);
const int* prevRow = curHist.ptr<int>(y);
const int* curRow = curHist.ptr<int>(y + 1);
const int* nextRow = curHist.ptr<int>(y + 2);
const int* nextHistRow = nextHist.ptr<int>(y + 1);
for(int x = 0; x < histCols; ++x)
{
const int votes = curRow[x + 1];
if (votes > votesThreshold &&
votes > curRow[x] &&
votes >= curRow[x + 2] &&
votes > prevRow[x + 1] &&
votes >= nextRow[x + 1] &&
votes > prevHistRow[x + 1] &&
votes >= nextHistRow[x + 1])
{
posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), 1.0f, angle));
voteOutBuf.push_back(Vec3i(votes, 0, votes));
}
}
}
}
}
/////////////////////////////////////////
// POSITION & SCALE & ROTATION
double clampAngle(double a)
{
double res = a;
while (res > 360.0)
res -= 360.0;
while (res < 0)
res += 360.0;
return res;
}
bool angleEq(double a, double b, double eps = 1.0)
{
return (fabs(clampAngle(a - b)) <= eps);
}
class GHT_Guil_Full : public GHT_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Guil_Full();
protected:
void releaseImpl();
void processTempl();
void processImage();
struct ContourPoint
{
Point2d pos;
double theta;
};
struct Feature
{
ContourPoint p1;
ContourPoint p2;
double alpha12;
double d12;
Point2d r1;
Point2d r2;
};
void buildFeatureList(const Mat& edges, const Mat& dx, const Mat& dy, vector< vector<Feature> >& features, Point2d center = Point2d());
void getContourPoints(const Mat& edges, const Mat& dx, const Mat& dy, vector<ContourPoint>& points);
void calcOrientation();
void calcScale(double angle);
void calcPosition(double angle, int angleVotes, double scale, int scaleVotes);
int maxSize;
double xi;
int levels;
double angleEpsilon;
double minAngle;
double maxAngle;
double angleStep;
int angleThresh;
double minScale;
double maxScale;
double scaleStep;
int scaleThresh;
double dp;
int posThresh;
vector< vector<Feature> > templFeatures;
vector< vector<Feature> > imageFeatures;
vector< pair<double, int> > angles;
vector< pair<double, int> > scales;
};
CV_INIT_ALGORITHM(GHT_Guil_Full, "GeneralizedHough.POSITION_SCALE_ROTATION",
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
"Maximal size of inner buffers.");
obj.info()->addParam(obj, "xi", obj.xi, false, 0, 0,
"Angle difference in degrees between two points in feature.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"Feature table levels.");
obj.info()->addParam(obj, "angleEpsilon", obj.angleEpsilon, false, 0, 0,
"Maximal difference between angles that treated as equal.");
obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
"Minimal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
"Maximal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
"Angle step in degrees.");
obj.info()->addParam(obj, "angleThresh", obj.angleThresh, false, 0, 0,
"Angle threshold.");
obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
"Minimal scale to detect.");
obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
"Maximal scale to detect.");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Scale step.");
obj.info()->addParam(obj, "scaleThresh", obj.scaleThresh, false, 0, 0,
"Scale threshold.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution.");
obj.info()->addParam(obj, "posThresh", obj.posThresh, false, 0, 0,
"Position threshold."));
GHT_Guil_Full::GHT_Guil_Full()
{
maxSize = 1000;
xi = 90.0;
levels = 360;
angleEpsilon = 1.0;
minAngle = 0.0;
maxAngle = 360.0;
angleStep = 1.0;
angleThresh = 15000;
minScale = 0.5;
maxScale = 2.0;
scaleStep = 0.05;
scaleThresh = 1000;
dp = 1.0;
posThresh = 100;
}
void GHT_Guil_Full::releaseImpl()
{
GHT_Pos::releaseImpl();
releaseVector(templFeatures);
releaseVector(imageFeatures);
releaseVector(angles);
releaseVector(scales);
}
void GHT_Guil_Full::processTempl()
{
buildFeatureList(templEdges, templDx, templDy, templFeatures, templCenter);
}
void GHT_Guil_Full::processImage()
{
buildFeatureList(imageEdges, imageDx, imageDy, imageFeatures);
calcOrientation();
for (size_t i = 0; i < angles.size(); ++i)
{
const double angle = angles[i].first;
const int angleVotes = angles[i].second;
calcScale(angle);
for (size_t j = 0; j < scales.size(); ++j)
{
const double scale = scales[j].first;
const int scaleVotes = scales[j].second;
calcPosition(angle, angleVotes, scale, scaleVotes);
}
}
}
void GHT_Guil_Full::buildFeatureList(const Mat& edges, const Mat& dx, const Mat& dy, vector< vector<Feature> >& features, Point2d center)
{
CV_Assert(levels > 0);
const double maxDist = sqrt((double) templSize.width * templSize.width + templSize.height * templSize.height) * maxScale;
const double alphaScale = levels / 360.0;
vector<ContourPoint> points;
getContourPoints(edges, dx, dy, points);
features.resize(levels + 1);
for_each(features.begin(), features.end(), mem_fun_ref(&vector<Feature>::clear));
for_each(features.begin(), features.end(), bind2nd(mem_fun_ref(&vector<Feature>::reserve), maxSize));
for (size_t i = 0; i < points.size(); ++i)
{
ContourPoint p1 = points[i];
for (size_t j = 0; j < points.size(); ++j)
{
ContourPoint p2 = points[j];
if (angleEq(p1.theta - p2.theta, xi, angleEpsilon))
{
const Point2d d = p1.pos - p2.pos;
Feature f;
f.p1 = p1;
f.p2 = p2;
2012-09-21 17:41:36 +08:00
f.alpha12 = clampAngle(fastAtan2((float)d.y, (float)d.x) - p1.theta);
2012-09-10 20:24:55 +08:00
f.d12 = norm(d);
if (f.d12 > maxDist)
continue;
f.r1 = p1.pos - center;
f.r2 = p2.pos - center;
const int n = cvRound(f.alpha12 * alphaScale);
if (features[n].size() < static_cast<size_t>(maxSize))
features[n].push_back(f);
}
}
}
}
void GHT_Guil_Full::getContourPoints(const Mat& edges, const Mat& dx, const Mat& dy, vector<ContourPoint>& points)
{
CV_Assert(edges.type() == CV_8UC1);
CV_Assert(dx.type() == CV_32FC1 && dx.size == edges.size);
CV_Assert(dy.type() == dx.type() && dy.size == edges.size);
points.clear();
points.reserve(edges.size().area());
for (int y = 0; y < edges.rows; ++y)
{
const uchar* edgesRow = edges.ptr(y);
const float* dxRow = dx.ptr<float>(y);
const float* dyRow = dy.ptr<float>(y);
for (int x = 0; x < edges.cols; ++x)
{
if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
{
ContourPoint p;
p.pos = Point2d(x, y);
p.theta = fastAtan2(dyRow[x], dxRow[x]);
points.push_back(p);
}
}
}
}
void GHT_Guil_Full::calcOrientation()
{
CV_Assert(levels > 0);
CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
CV_Assert(imageFeatures.size() == templFeatures.size());
CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
CV_Assert(angleStep > 0.0 && angleStep < 360.0);
CV_Assert(angleThresh > 0);
const double iAngleStep = 1.0 / angleStep;
const int angleRange = cvCeil((maxAngle - minAngle) * iAngleStep);
vector<int> OHist(angleRange + 1, 0);
for (int i = 0; i <= levels; ++i)
{
const vector<Feature>& templRow = templFeatures[i];
const vector<Feature>& imageRow = imageFeatures[i];
for (size_t j = 0; j < templRow.size(); ++j)
{
Feature templF = templRow[j];
for (size_t k = 0; k < imageRow.size(); ++k)
{
Feature imF = imageRow[k];
const double angle = clampAngle(imF.p1.theta - templF.p1.theta);
if (angle >= minAngle && angle <= maxAngle)
{
const int n = cvRound((angle - minAngle) * iAngleStep);
++OHist[n];
}
}
}
}
angles.clear();
for (int n = 0; n < angleRange; ++n)
{
if (OHist[n] >= angleThresh)
{
const double angle = minAngle + n * angleStep;
angles.push_back(make_pair(angle, OHist[n]));
}
}
}
void GHT_Guil_Full::calcScale(double angle)
{
CV_Assert(levels > 0);
CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
CV_Assert(imageFeatures.size() == templFeatures.size());
CV_Assert(minScale > 0.0 && minScale < maxScale);
CV_Assert(scaleStep > 0.0);
CV_Assert(scaleThresh > 0);
const double iScaleStep = 1.0 / scaleStep;
const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
vector<int> SHist(scaleRange + 1, 0);
for (int i = 0; i <= levels; ++i)
{
const vector<Feature>& templRow = templFeatures[i];
const vector<Feature>& imageRow = imageFeatures[i];
for (size_t j = 0; j < templRow.size(); ++j)
{
Feature templF = templRow[j];
templF.p1.theta += angle;
for (size_t k = 0; k < imageRow.size(); ++k)
{
Feature imF = imageRow[k];
if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon))
{
const double scale = imF.d12 / templF.d12;
if (scale >= minScale && scale <= maxScale)
{
const int s = cvRound((scale - minScale) * iScaleStep);
++SHist[s];
}
}
}
}
}
scales.clear();
for (int s = 0; s < scaleRange; ++s)
{
if (SHist[s] >= scaleThresh)
{
const double scale = minScale + s * scaleStep;
scales.push_back(make_pair(scale, SHist[s]));
}
}
}
void GHT_Guil_Full::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
{
CV_Assert(levels > 0);
CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
CV_Assert(imageFeatures.size() == templFeatures.size());
CV_Assert(dp > 0.0);
CV_Assert(posThresh > 0);
const double sinVal = sin(toRad(angle));
const double cosVal = cos(toRad(angle));
const double idp = 1.0 / dp;
const int histRows = cvCeil(imageSize.height * idp);
const int histCols = cvCeil(imageSize.width * idp);
Mat DHist(histRows + 2, histCols + 2, CV_32SC1, Scalar::all(0));
for (int i = 0; i <= levels; ++i)
{
const vector<Feature>& templRow = templFeatures[i];
const vector<Feature>& imageRow = imageFeatures[i];
for (size_t j = 0; j < templRow.size(); ++j)
{
Feature templF = templRow[j];
templF.p1.theta += angle;
templF.r1 *= scale;
templF.r2 *= scale;
templF.r1 = Point2d(cosVal * templF.r1.x - sinVal * templF.r1.y, sinVal * templF.r1.x + cosVal * templF.r1.y);
templF.r2 = Point2d(cosVal * templF.r2.x - sinVal * templF.r2.y, sinVal * templF.r2.x + cosVal * templF.r2.y);
for (size_t k = 0; k < imageRow.size(); ++k)
{
Feature imF = imageRow[k];
if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon))
{
Point2d c1, c2;
c1 = imF.p1.pos - templF.r1;
c1 *= idp;
c2 = imF.p2.pos - templF.r2;
c2 *= idp;
if (fabs(c1.x - c2.x) > 1 || fabs(c1.y - c2.y) > 1)
continue;
if (c1.y >= 0 && c1.y < histRows && c1.x >= 0 && c1.x < histCols)
++DHist.at<int>(cvRound(c1.y) + 1, cvRound(c1.x) + 1);
}
}
}
}
for(int y = 0; y < histRows; ++y)
{
const int* prevRow = DHist.ptr<int>(y);
const int* curRow = DHist.ptr<int>(y + 1);
const int* nextRow = DHist.ptr<int>(y + 2);
for(int x = 0; x < histCols; ++x)
{
const int votes = curRow[x + 1];
if (votes > posThresh && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
{
posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), static_cast<float>(scale), static_cast<float>(angle)));
voteOutBuf.push_back(Vec3i(votes, scaleVotes, angleVotes));
}
}
}
}
}
Ptr<GeneralizedHough> cv::GeneralizedHough::create(int method)
{
switch (method)
{
case GHT_POSITION:
CV_Assert( !GHT_Ballard_Pos_info_auto.name().empty() );
return new GHT_Ballard_Pos();
case (GHT_POSITION | GHT_SCALE):
CV_Assert( !GHT_Ballard_PosScale_info_auto.name().empty() );
return new GHT_Ballard_PosScale();
case (GHT_POSITION | GHT_ROTATION):
CV_Assert( !GHT_Ballard_PosRotation_info_auto.name().empty() );
return new GHT_Ballard_PosRotation();
case (GHT_POSITION | GHT_SCALE | GHT_ROTATION):
CV_Assert( !GHT_Guil_Full_info_auto.name().empty() );
return new GHT_Guil_Full();
}
CV_Error(CV_StsBadArg, "Unsupported method");
return Ptr<GeneralizedHough>();
}
cv::GeneralizedHough::~GeneralizedHough()
{
}
void cv::GeneralizedHough::setTemplate(InputArray _templ, int cannyThreshold, Point templCenter)
{
Mat templ = _templ.getMat();
CV_Assert(templ.type() == CV_8UC1);
CV_Assert(cannyThreshold > 0);
Canny(templ, edges_, cannyThreshold / 2, cannyThreshold);
Sobel(templ, dx_, CV_32F, 1, 0);
Sobel(templ, dy_, CV_32F, 0, 1);
if (templCenter == Point(-1, -1))
templCenter = Point(templ.cols / 2, templ.rows / 2);
setTemplateImpl(edges_, dx_, dy_, templCenter);
}
void cv::GeneralizedHough::setTemplate(InputArray _edges, InputArray _dx, InputArray _dy, Point templCenter)
{
Mat edges = _edges.getMat();
Mat dx = _dx.getMat();
Mat dy = _dy.getMat();
if (templCenter == Point(-1, -1))
templCenter = Point(edges.cols / 2, edges.rows / 2);
setTemplateImpl(edges, dx, dy, templCenter);
}
void cv::GeneralizedHough::detect(InputArray _image, OutputArray positions, OutputArray votes, int cannyThreshold)
{
Mat image = _image.getMat();
CV_Assert(image.type() == CV_8UC1);
CV_Assert(cannyThreshold > 0);
Canny(image, edges_, cannyThreshold / 2, cannyThreshold);
Sobel(image, dx_, CV_32F, 1, 0);
Sobel(image, dy_, CV_32F, 0, 1);
detectImpl(edges_, dx_, dy_, positions, votes);
}
void cv::GeneralizedHough::detect(InputArray _edges, InputArray _dx, InputArray _dy, OutputArray positions, OutputArray votes)
{
cv::Mat edges = _edges.getMat();
cv::Mat dx = _dx.getMat();
cv::Mat dy = _dy.getMat();
detectImpl(edges, dx, dy, positions, votes);
}
void cv::GeneralizedHough::release()
{
edges_.release();
dx_.release();
dy_.release();
releaseImpl();
}