2012-09-10 20:24:55 +08:00
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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2012-09-14 13:34:56 +08:00
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#include <functional>
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2012-09-10 20:24:55 +08:00
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using namespace cv;
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namespace
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{
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/////////////////////////////////////
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// Common
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2013-02-25 00:14:01 +08:00
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template <typename T, class A> void releaseVector(std::vector<T, A>& v)
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2012-09-10 20:24:55 +08:00
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{
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2013-02-25 00:14:01 +08:00
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std::vector<T, A> empty;
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2012-09-10 20:24:55 +08:00
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empty.swap(v);
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}
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double toRad(double a)
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{
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return a * CV_PI / 180.0;
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}
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bool notNull(float v)
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{
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2013-02-25 00:14:01 +08:00
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return fabs(v) > std::numeric_limits<float>::epsilon();
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2012-09-10 20:24:55 +08:00
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}
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class GHT_Pos : public GeneralizedHough
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{
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public:
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GHT_Pos();
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protected:
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void setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter);
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void detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes);
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void releaseImpl();
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virtual void processTempl() = 0;
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virtual void processImage() = 0;
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void filterMinDist();
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void convertTo(OutputArray positions, OutputArray votes);
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double minDist;
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Size templSize;
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Point templCenter;
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Mat templEdges;
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Mat templDx;
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Mat templDy;
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Size imageSize;
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Mat imageEdges;
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Mat imageDx;
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Mat imageDy;
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2013-02-25 00:14:01 +08:00
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std::vector<Vec4f> posOutBuf;
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std::vector<Vec3i> voteOutBuf;
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2012-09-10 20:24:55 +08:00
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};
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GHT_Pos::GHT_Pos()
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{
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minDist = 1.0;
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}
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void GHT_Pos::setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter_)
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{
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templSize = edges.size();
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templCenter = templCenter_;
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edges.copyTo(templEdges);
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dx.copyTo(templDx);
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dy.copyTo(templDy);
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processTempl();
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}
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void GHT_Pos::detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes)
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{
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imageSize = edges.size();
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edges.copyTo(imageEdges);
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dx.copyTo(imageDx);
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dy.copyTo(imageDy);
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posOutBuf.clear();
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voteOutBuf.clear();
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processImage();
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if (!posOutBuf.empty())
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{
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if (minDist > 1)
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filterMinDist();
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convertTo(positions, votes);
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}
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else
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{
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positions.release();
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if (votes.needed())
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votes.release();
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}
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}
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void GHT_Pos::releaseImpl()
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{
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templSize = Size();
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templCenter = Point(-1, -1);
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templEdges.release();
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templDx.release();
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templDy.release();
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imageSize = Size();
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imageEdges.release();
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imageDx.release();
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imageDy.release();
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releaseVector(posOutBuf);
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releaseVector(voteOutBuf);
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}
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2013-03-28 20:12:13 +08:00
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class Vec3iGreaterThanIdx
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{
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public:
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Vec3iGreaterThanIdx( const Vec3i* _arr ) : arr(_arr) {}
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bool operator()(size_t a, size_t b) const { return arr[a][0] > arr[b][0]; }
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const Vec3i* arr;
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};
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2012-09-10 20:24:55 +08:00
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void GHT_Pos::filterMinDist()
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{
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size_t oldSize = posOutBuf.size();
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const bool hasVotes = !voteOutBuf.empty();
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CV_Assert(!hasVotes || voteOutBuf.size() == oldSize);
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2013-02-25 00:14:01 +08:00
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std::vector<Vec4f> oldPosBuf(posOutBuf);
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std::vector<Vec3i> oldVoteBuf(voteOutBuf);
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2012-09-10 20:24:55 +08:00
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2013-02-25 00:14:01 +08:00
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std::vector<size_t> indexies(oldSize);
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2012-09-10 20:24:55 +08:00
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for (size_t i = 0; i < oldSize; ++i)
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indexies[i] = i;
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2013-03-28 20:12:13 +08:00
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std::sort(indexies.begin(), indexies.end(), Vec3iGreaterThanIdx(&oldVoteBuf[0]));
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2012-09-10 20:24:55 +08:00
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posOutBuf.clear();
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voteOutBuf.clear();
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const int cellSize = cvRound(minDist);
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const int gridWidth = (imageSize.width + cellSize - 1) / cellSize;
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const int gridHeight = (imageSize.height + cellSize - 1) / cellSize;
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2013-02-25 00:14:01 +08:00
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std::vector< std::vector<Point2f> > grid(gridWidth * gridHeight);
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2012-09-10 20:24:55 +08:00
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const double minDist2 = minDist * minDist;
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for (size_t i = 0; i < oldSize; ++i)
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{
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const size_t ind = indexies[i];
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Point2f p(oldPosBuf[ind][0], oldPosBuf[ind][1]);
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bool good = true;
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const int xCell = static_cast<int>(p.x / cellSize);
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const int yCell = static_cast<int>(p.y / cellSize);
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int x1 = xCell - 1;
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int y1 = yCell - 1;
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int x2 = xCell + 1;
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int y2 = yCell + 1;
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// boundary check
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x1 = std::max(0, x1);
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y1 = std::max(0, y1);
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x2 = std::min(gridWidth - 1, x2);
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y2 = std::min(gridHeight - 1, y2);
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for (int yy = y1; yy <= y2; ++yy)
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{
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for (int xx = x1; xx <= x2; ++xx)
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{
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2013-02-25 00:14:01 +08:00
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const std::vector<Point2f>& m = grid[yy * gridWidth + xx];
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2012-09-10 20:24:55 +08:00
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for(size_t j = 0; j < m.size(); ++j)
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{
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const Point2f d = p - m[j];
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if (d.ddot(d) < minDist2)
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{
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good = false;
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goto break_out;
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}
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}
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}
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}
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break_out:
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if(good)
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{
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grid[yCell * gridWidth + xCell].push_back(p);
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posOutBuf.push_back(oldPosBuf[ind]);
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if (hasVotes)
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voteOutBuf.push_back(oldVoteBuf[ind]);
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}
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}
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}
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void GHT_Pos::convertTo(OutputArray _positions, OutputArray _votes)
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{
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const int total = static_cast<int>(posOutBuf.size());
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const bool hasVotes = !voteOutBuf.empty();
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CV_Assert(!hasVotes || voteOutBuf.size() == posOutBuf.size());
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_positions.create(1, total, CV_32FC4);
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Mat positions = _positions.getMat();
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Mat(1, total, CV_32FC4, &posOutBuf[0]).copyTo(positions);
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if (_votes.needed())
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{
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if (!hasVotes)
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_votes.release();
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else
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{
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_votes.create(1, total, CV_32SC3);
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Mat votes = _votes.getMat();
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Mat(1, total, CV_32SC3, &voteOutBuf[0]).copyTo(votes);
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}
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}
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}
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/////////////////////////////////////
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// POSITION Ballard
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class GHT_Ballard_Pos : public GHT_Pos
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{
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public:
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AlgorithmInfo* info() const;
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GHT_Ballard_Pos();
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protected:
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void releaseImpl();
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void processTempl();
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void processImage();
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virtual void calcHist();
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virtual void findPosInHist();
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int levels;
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int votesThreshold;
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double dp;
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2013-02-25 00:14:01 +08:00
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std::vector< std::vector<Point> > r_table;
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2012-09-10 20:24:55 +08:00
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Mat hist;
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};
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CV_INIT_ALGORITHM(GHT_Ballard_Pos, "GeneralizedHough.POSITION",
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obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
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"Minimum distance between the centers of the detected objects.");
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obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
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"R-Table levels.");
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obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
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"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
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obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
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"Inverse ratio of the accumulator resolution to the image resolution."));
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GHT_Ballard_Pos::GHT_Ballard_Pos()
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{
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levels = 360;
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votesThreshold = 100;
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dp = 1.0;
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}
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void GHT_Ballard_Pos::releaseImpl()
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{
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GHT_Pos::releaseImpl();
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releaseVector(r_table);
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hist.release();
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}
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void GHT_Ballard_Pos::processTempl()
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{
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CV_Assert(templEdges.type() == CV_8UC1);
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CV_Assert(templDx.type() == CV_32FC1 && templDx.size() == templSize);
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CV_Assert(templDy.type() == templDx.type() && templDy.size() == templSize);
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CV_Assert(levels > 0);
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const double thetaScale = levels / 360.0;
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r_table.resize(levels + 1);
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2013-02-25 00:14:01 +08:00
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for_each(r_table.begin(), r_table.end(), mem_fun_ref(&std::vector<Point>::clear));
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2012-09-10 20:24:55 +08:00
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for (int y = 0; y < templSize.height; ++y)
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{
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const uchar* edgesRow = templEdges.ptr(y);
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const float* dxRow = templDx.ptr<float>(y);
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const float* dyRow = templDy.ptr<float>(y);
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for (int x = 0; x < templSize.width; ++x)
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{
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const Point p(x, y);
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if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
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{
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const float theta = fastAtan2(dyRow[x], dxRow[x]);
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const int n = cvRound(theta * thetaScale);
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r_table[n].push_back(p - templCenter);
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}
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}
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}
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}
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void GHT_Ballard_Pos::processImage()
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{
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calcHist();
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findPosInHist();
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}
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void GHT_Ballard_Pos::calcHist()
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{
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CV_Assert(imageEdges.type() == CV_8UC1);
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CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
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CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
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CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
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CV_Assert(dp > 0.0);
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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);
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
const std::vector<Point>& r_row = r_table[n];
|
2012-09-10 20:24:55 +08:00
|
|
|
|
|
|
|
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);
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
const std::vector<Point>& r_row = base->r_table[n];
|
2012-09-10 20:24:55 +08:00
|
|
|
|
|
|
|
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);
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
const std::vector<Point>& r_row = base->r_table[n];
|
2012-09-10 20:24:55 +08:00
|
|
|
|
|
|
|
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;
|
|
|
|
};
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
void buildFeatureList(const Mat& edges, const Mat& dx, const Mat& dy, std::vector< std::vector<Feature> >& features, Point2d center = Point2d());
|
|
|
|
void getContourPoints(const Mat& edges, const Mat& dx, const Mat& dy, std::vector<ContourPoint>& points);
|
2012-09-10 20:24:55 +08:00
|
|
|
|
|
|
|
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;
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
std::vector< std::vector<Feature> > templFeatures;
|
|
|
|
std::vector< std::vector<Feature> > imageFeatures;
|
2012-09-10 20:24:55 +08:00
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
std::vector< std::pair<double, int> > angles;
|
|
|
|
std::vector< std::pair<double, int> > scales;
|
2012-09-10 20:24:55 +08:00
|
|
|
};
|
|
|
|
|
|
|
|
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);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
void GHT_Guil_Full::buildFeatureList(const Mat& edges, const Mat& dx, const Mat& dy, std::vector< std::vector<Feature> >& features, Point2d center)
|
2012-09-10 20:24:55 +08:00
|
|
|
{
|
|
|
|
CV_Assert(levels > 0);
|
|
|
|
|
|
|
|
const double maxDist = sqrt((double) templSize.width * templSize.width + templSize.height * templSize.height) * maxScale;
|
|
|
|
|
|
|
|
const double alphaScale = levels / 360.0;
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
std::vector<ContourPoint> points;
|
2012-09-10 20:24:55 +08:00
|
|
|
getContourPoints(edges, dx, dy, points);
|
|
|
|
|
|
|
|
features.resize(levels + 1);
|
2013-02-25 00:14:01 +08:00
|
|
|
for_each(features.begin(), features.end(), mem_fun_ref(&std::vector<Feature>::clear));
|
|
|
|
for_each(features.begin(), features.end(), bind2nd(mem_fun_ref(&std::vector<Feature>::reserve), maxSize));
|
2012-09-10 20:24:55 +08:00
|
|
|
|
|
|
|
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);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
void GHT_Guil_Full::getContourPoints(const Mat& edges, const Mat& dx, const Mat& dy, std::vector<ContourPoint>& points)
|
2012-09-10 20:24:55 +08:00
|
|
|
{
|
|
|
|
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);
|
|
|
|
|
2013-02-25 00:14:01 +08:00
|
|
|
std::vector<int> OHist(angleRange + 1, 0);
|
2012-09-10 20:24:55 +08:00
|
|
|
for (int i = 0; i <= levels; ++i)
|
|
|
|
{
|
2013-02-25 00:14:01 +08:00
|
|
|
const std::vector<Feature>& templRow = templFeatures[i];
|
|
|
|
const std::vector<Feature>& imageRow = imageFeatures[i];
|
2012-09-10 20:24:55 +08:00
|
|
|
|
|
|
|
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;
|
2013-02-25 00:14:01 +08:00
|
|
|
angles.push_back(std::make_pair(angle, OHist[n]));
|
2012-09-10 20:24:55 +08:00
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
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void GHT_Guil_Full::calcScale(double angle)
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{
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CV_Assert(levels > 0);
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CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
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CV_Assert(imageFeatures.size() == templFeatures.size());
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CV_Assert(minScale > 0.0 && minScale < maxScale);
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CV_Assert(scaleStep > 0.0);
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CV_Assert(scaleThresh > 0);
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const double iScaleStep = 1.0 / scaleStep;
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const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
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2013-02-25 00:14:01 +08:00
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std::vector<int> SHist(scaleRange + 1, 0);
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2012-09-10 20:24:55 +08:00
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for (int i = 0; i <= levels; ++i)
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{
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2013-02-25 00:14:01 +08:00
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const std::vector<Feature>& templRow = templFeatures[i];
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const std::vector<Feature>& imageRow = imageFeatures[i];
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2012-09-10 20:24:55 +08:00
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for (size_t j = 0; j < templRow.size(); ++j)
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{
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Feature templF = templRow[j];
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templF.p1.theta += angle;
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for (size_t k = 0; k < imageRow.size(); ++k)
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{
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Feature imF = imageRow[k];
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if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon))
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{
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const double scale = imF.d12 / templF.d12;
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if (scale >= minScale && scale <= maxScale)
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{
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const int s = cvRound((scale - minScale) * iScaleStep);
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++SHist[s];
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}
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}
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}
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}
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}
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scales.clear();
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for (int s = 0; s < scaleRange; ++s)
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{
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if (SHist[s] >= scaleThresh)
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{
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const double scale = minScale + s * scaleStep;
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2013-02-25 00:14:01 +08:00
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scales.push_back(std::make_pair(scale, SHist[s]));
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2012-09-10 20:24:55 +08:00
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}
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}
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}
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void GHT_Guil_Full::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
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{
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CV_Assert(levels > 0);
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CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
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CV_Assert(imageFeatures.size() == templFeatures.size());
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CV_Assert(dp > 0.0);
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CV_Assert(posThresh > 0);
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const double sinVal = sin(toRad(angle));
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const double cosVal = cos(toRad(angle));
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const double idp = 1.0 / dp;
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const int histRows = cvCeil(imageSize.height * idp);
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const int histCols = cvCeil(imageSize.width * idp);
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Mat DHist(histRows + 2, histCols + 2, CV_32SC1, Scalar::all(0));
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for (int i = 0; i <= levels; ++i)
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{
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2013-02-25 00:14:01 +08:00
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const std::vector<Feature>& templRow = templFeatures[i];
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const std::vector<Feature>& imageRow = imageFeatures[i];
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2012-09-10 20:24:55 +08:00
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for (size_t j = 0; j < templRow.size(); ++j)
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{
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Feature templF = templRow[j];
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templF.p1.theta += angle;
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templF.r1 *= scale;
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templF.r2 *= scale;
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templF.r1 = Point2d(cosVal * templF.r1.x - sinVal * templF.r1.y, sinVal * templF.r1.x + cosVal * templF.r1.y);
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templF.r2 = Point2d(cosVal * templF.r2.x - sinVal * templF.r2.y, sinVal * templF.r2.x + cosVal * templF.r2.y);
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for (size_t k = 0; k < imageRow.size(); ++k)
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{
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Feature imF = imageRow[k];
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if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon))
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{
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Point2d c1, c2;
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c1 = imF.p1.pos - templF.r1;
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c1 *= idp;
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c2 = imF.p2.pos - templF.r2;
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c2 *= idp;
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if (fabs(c1.x - c2.x) > 1 || fabs(c1.y - c2.y) > 1)
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continue;
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if (c1.y >= 0 && c1.y < histRows && c1.x >= 0 && c1.x < histCols)
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++DHist.at<int>(cvRound(c1.y) + 1, cvRound(c1.x) + 1);
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}
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}
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}
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}
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for(int y = 0; y < histRows; ++y)
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{
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const int* prevRow = DHist.ptr<int>(y);
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const int* curRow = DHist.ptr<int>(y + 1);
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const int* nextRow = DHist.ptr<int>(y + 2);
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for(int x = 0; x < histCols; ++x)
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{
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const int votes = curRow[x + 1];
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if (votes > posThresh && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
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{
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posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), static_cast<float>(scale), static_cast<float>(angle)));
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voteOutBuf.push_back(Vec3i(votes, scaleVotes, angleVotes));
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}
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}
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}
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}
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}
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Ptr<GeneralizedHough> cv::GeneralizedHough::create(int method)
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|
|
{
|
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|
switch (method)
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|
{
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|
case GHT_POSITION:
|
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CV_Assert( !GHT_Ballard_Pos_info_auto.name().empty() );
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return new GHT_Ballard_Pos();
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case (GHT_POSITION | GHT_SCALE):
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CV_Assert( !GHT_Ballard_PosScale_info_auto.name().empty() );
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return new GHT_Ballard_PosScale();
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case (GHT_POSITION | GHT_ROTATION):
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CV_Assert( !GHT_Ballard_PosRotation_info_auto.name().empty() );
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|
return new GHT_Ballard_PosRotation();
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case (GHT_POSITION | GHT_SCALE | GHT_ROTATION):
|
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|
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CV_Assert( !GHT_Guil_Full_info_auto.name().empty() );
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|
|
return new GHT_Guil_Full();
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|
}
|
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|
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|
|
CV_Error(CV_StsBadArg, "Unsupported method");
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|
|
|
return Ptr<GeneralizedHough>();
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|
|
|
}
|
|
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|
|
cv::GeneralizedHough::~GeneralizedHough()
|
|
|
|
{
|
|
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|
}
|
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void cv::GeneralizedHough::setTemplate(InputArray _templ, int cannyThreshold, Point templCenter)
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|
|
|
{
|
|
|
|
Mat templ = _templ.getMat();
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|
|
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|
|
CV_Assert(templ.type() == CV_8UC1);
|
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|
|
CV_Assert(cannyThreshold > 0);
|
|
|
|
|
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|
|
Canny(templ, edges_, cannyThreshold / 2, cannyThreshold);
|
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|
|
Sobel(templ, dx_, CV_32F, 1, 0);
|
|
|
|
Sobel(templ, dy_, CV_32F, 0, 1);
|
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|
|
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|
if (templCenter == Point(-1, -1))
|
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|
|
templCenter = Point(templ.cols / 2, templ.rows / 2);
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setTemplateImpl(edges_, dx_, dy_, templCenter);
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}
|
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void cv::GeneralizedHough::setTemplate(InputArray _edges, InputArray _dx, InputArray _dy, Point templCenter)
|
|
|
|
{
|
|
|
|
Mat edges = _edges.getMat();
|
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|
Mat dx = _dx.getMat();
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|
Mat dy = _dy.getMat();
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if (templCenter == Point(-1, -1))
|
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|
|
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);
|
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|
|
CV_Assert(cannyThreshold > 0);
|
|
|
|
|
|
|
|
Canny(image, edges_, cannyThreshold / 2, cannyThreshold);
|
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|
|
Sobel(image, dx_, CV_32F, 1, 0);
|
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|
|
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();
|
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|
|
cv::Mat dy = _dy.getMat();
|
|
|
|
|
|
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|
detectImpl(edges, dx, dy, positions, votes);
|
|
|
|
}
|
|
|
|
|
|
|
|
void cv::GeneralizedHough::release()
|
|
|
|
{
|
|
|
|
edges_.release();
|
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|
|
dx_.release();
|
|
|
|
dy_.release();
|
|
|
|
releaseImpl();
|
|
|
|
}
|