2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
1069 lines
33 KiB
C++
1069 lines
33 KiB
C++
/*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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// 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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// Copyright (C) 2013, OpenCV Foundation, 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 the copyright holders 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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namespace cv
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{
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// Classical Hough Transform
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struct LinePolar
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{
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float rho;
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float angle;
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};
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struct hough_cmp_gt
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{
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hough_cmp_gt(const int* _aux) : aux(_aux) {}
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bool operator()(int l1, int l2) const { return aux[l1] > aux[l2]; }
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const int* aux;
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};
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/*
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Here image is an input raster;
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step is it's step; size characterizes it's ROI;
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rho and theta are discretization steps (in pixels and radians correspondingly).
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threshold is the minimum number of pixels in the feature for it
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to be a candidate for line. lines is the output
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array of (rho, theta) pairs. linesMax is the buffer size (number of pairs).
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Functions return the actual number of found lines.
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*/
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static void
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HoughLinesStandard( const Mat& img, float rho, float theta,
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int threshold, std::vector<Vec2f>& lines, int linesMax )
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{
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int i, j;
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float irho = 1 / rho;
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CV_Assert( img.type() == CV_8UC1 );
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const uchar* image = img.data;
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int step = (int)img.step;
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int width = img.cols;
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int height = img.rows;
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int numangle = cvRound(CV_PI / theta);
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int numrho = cvRound(((width + height) * 2 + 1) / rho);
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AutoBuffer<int> _accum((numangle+2) * (numrho+2));
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std::vector<int> _sort_buf;
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AutoBuffer<float> _tabSin(numangle);
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AutoBuffer<float> _tabCos(numangle);
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int *accum = _accum;
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float *tabSin = _tabSin, *tabCos = _tabCos;
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memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) );
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float ang = 0;
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for(int n = 0; n < numangle; ang += theta, n++ )
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{
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tabSin[n] = (float)(sin((double)ang) * irho);
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tabCos[n] = (float)(cos((double)ang) * irho);
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}
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// stage 1. fill accumulator
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for( i = 0; i < height; i++ )
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for( j = 0; j < width; j++ )
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{
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if( image[i * step + j] != 0 )
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for(int n = 0; n < numangle; n++ )
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{
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int r = cvRound( j * tabCos[n] + i * tabSin[n] );
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r += (numrho - 1) / 2;
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accum[(n+1) * (numrho+2) + r+1]++;
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}
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}
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// stage 2. find local maximums
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for(int r = 0; r < numrho; r++ )
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for(int n = 0; n < numangle; n++ )
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{
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int base = (n+1) * (numrho+2) + r+1;
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if( accum[base] > threshold &&
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accum[base] > accum[base - 1] && accum[base] >= accum[base + 1] &&
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accum[base] > accum[base - numrho - 2] && accum[base] >= accum[base + numrho + 2] )
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_sort_buf.push_back(base);
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}
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// stage 3. sort the detected lines by accumulator value
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cv::sort(_sort_buf, hough_cmp_gt(accum));
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// stage 4. store the first min(total,linesMax) lines to the output buffer
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linesMax = std::min(linesMax, (int)_sort_buf.size());
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double scale = 1./(numrho+2);
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for( i = 0; i < linesMax; i++ )
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{
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LinePolar line;
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int idx = _sort_buf[i];
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int n = cvFloor(idx*scale) - 1;
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int r = idx - (n+1)*(numrho+2) - 1;
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line.rho = (r - (numrho - 1)*0.5f) * rho;
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line.angle = n * theta;
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lines.push_back(Vec2f(line.rho, line.angle));
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}
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}
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// Multi-Scale variant of Classical Hough Transform
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struct hough_index
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{
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hough_index() : value(0), rho(0.f), theta(0.f) {}
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hough_index(int _val, float _rho, float _theta)
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: value(_val), rho(_rho), theta(_theta) {}
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int value;
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float rho, theta;
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};
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static void
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HoughLinesSDiv( const Mat& img,
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float rho, float theta, int threshold,
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int srn, int stn,
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std::vector<Vec2f>& lines, int linesMax )
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{
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#define _POINT(row, column)\
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(image_src[(row)*step+(column)])
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int index, i;
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int ri, ti, ti1, ti0;
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int row, col;
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float r, t; /* Current rho and theta */
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float rv; /* Some temporary rho value */
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int fn = 0;
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float xc, yc;
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const float d2r = (float)(CV_PI / 180);
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int sfn = srn * stn;
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int fi;
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int count;
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int cmax = 0;
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std::vector<hough_index> lst;
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CV_Assert( img.type() == CV_8UC1 );
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CV_Assert( linesMax > 0 && rho > 0 && theta > 0 );
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threshold = MIN( threshold, 255 );
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const uchar* image_src = img.data;
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int step = (int)img.step;
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int w = img.cols;
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int h = img.rows;
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float irho = 1 / rho;
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float itheta = 1 / theta;
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float srho = rho / srn;
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float stheta = theta / stn;
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float isrho = 1 / srho;
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float istheta = 1 / stheta;
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int rn = cvFloor( std::sqrt( (double)w * w + (double)h * h ) * irho );
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int tn = cvFloor( 2 * CV_PI * itheta );
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lst.push_back(hough_index(threshold, -1.f, 0.f));
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// Precalculate sin table
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std::vector<float> _sinTable( 5 * tn * stn );
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float* sinTable = &_sinTable[0];
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for( index = 0; index < 5 * tn * stn; index++ )
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sinTable[index] = (float)cos( stheta * index * 0.2f );
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std::vector<uchar> _caccum(rn * tn, (uchar)0);
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uchar* caccum = &_caccum[0];
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// Counting all feature pixels
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for( row = 0; row < h; row++ )
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for( col = 0; col < w; col++ )
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fn += _POINT( row, col ) != 0;
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std::vector<int> _x(fn), _y(fn);
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int* x = &_x[0], *y = &_y[0];
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// Full Hough Transform (it's accumulator update part)
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fi = 0;
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for( row = 0; row < h; row++ )
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{
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for( col = 0; col < w; col++ )
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{
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if( _POINT( row, col ))
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{
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int halftn;
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float r0;
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float scale_factor;
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int iprev = -1;
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float phi, phi1;
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float theta_it; // Value of theta for iterating
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// Remember the feature point
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x[fi] = col;
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y[fi] = row;
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fi++;
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yc = (float) row + 0.5f;
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xc = (float) col + 0.5f;
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/* Update the accumulator */
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t = (float) fabs( cvFastArctan( yc, xc ) * d2r );
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r = (float) std::sqrt( (double)xc * xc + (double)yc * yc );
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r0 = r * irho;
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ti0 = cvFloor( (t + CV_PI*0.5) * itheta );
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caccum[ti0]++;
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theta_it = rho / r;
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theta_it = theta_it < theta ? theta_it : theta;
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scale_factor = theta_it * itheta;
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halftn = cvFloor( CV_PI / theta_it );
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for( ti1 = 1, phi = theta_it - (float)(CV_PI*0.5), phi1 = (theta_it + t) * itheta;
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ti1 < halftn; ti1++, phi += theta_it, phi1 += scale_factor )
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{
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rv = r0 * std::cos( phi );
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i = cvFloor( rv ) * tn;
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i += cvFloor( phi1 );
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assert( i >= 0 );
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assert( i < rn * tn );
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caccum[i] = (uchar) (caccum[i] + ((i ^ iprev) != 0));
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iprev = i;
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if( cmax < caccum[i] )
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cmax = caccum[i];
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}
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}
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}
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}
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// Starting additional analysis
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count = 0;
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for( ri = 0; ri < rn; ri++ )
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{
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for( ti = 0; ti < tn; ti++ )
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{
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if( caccum[ri * tn + ti] > threshold )
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count++;
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}
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}
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if( count * 100 > rn * tn )
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{
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HoughLinesStandard( img, rho, theta, threshold, lines, linesMax );
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return;
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}
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std::vector<uchar> _buffer(srn * stn + 2);
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uchar* buffer = &_buffer[0];
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uchar* mcaccum = buffer + 1;
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count = 0;
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for( ri = 0; ri < rn; ri++ )
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{
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for( ti = 0; ti < tn; ti++ )
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{
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if( caccum[ri * tn + ti] > threshold )
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{
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count++;
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memset( mcaccum, 0, sfn * sizeof( uchar ));
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for( index = 0; index < fn; index++ )
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{
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int ti2;
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float r0;
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yc = (float) y[index] + 0.5f;
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xc = (float) x[index] + 0.5f;
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// Update the accumulator
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t = (float) fabs( cvFastArctan( yc, xc ) * d2r );
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r = (float) std::sqrt( (double)xc * xc + (double)yc * yc ) * isrho;
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ti0 = cvFloor( (t + CV_PI * 0.5) * istheta );
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ti2 = (ti * stn - ti0) * 5;
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r0 = (float) ri *srn;
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for( ti1 = 0; ti1 < stn; ti1++, ti2 += 5 )
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{
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rv = r * sinTable[(int) (std::abs( ti2 ))] - r0;
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i = cvFloor( rv ) * stn + ti1;
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i = CV_IMAX( i, -1 );
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i = CV_IMIN( i, sfn );
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mcaccum[i]++;
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assert( i >= -1 );
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assert( i <= sfn );
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}
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}
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// Find peaks in maccum...
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for( index = 0; index < sfn; index++ )
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{
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i = 0;
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int pos = (int)(lst.size() - 1);
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if( pos < 0 || lst[pos].value < mcaccum[index] )
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{
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hough_index vi(mcaccum[index],
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index / stn * srho + ri * rho,
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index % stn * stheta + ti * theta - (float)(CV_PI*0.5));
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lst.push_back(vi);
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for( ; pos >= 0; pos-- )
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{
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if( lst[pos].value > vi.value )
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break;
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lst[pos+1] = lst[pos];
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}
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lst[pos+1] = vi;
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if( (int)lst.size() > linesMax )
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lst.pop_back();
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}
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}
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}
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}
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}
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for( size_t idx = 0; idx < lst.size(); idx++ )
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{
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if( lst[idx].rho < 0 )
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continue;
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lines.push_back(Vec2f(lst[idx].rho, lst[idx].theta));
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}
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}
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/****************************************************************************************\
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* Probabilistic Hough Transform *
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\****************************************************************************************/
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static void
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HoughLinesProbabilistic( Mat& image,
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float rho, float theta, int threshold,
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int lineLength, int lineGap,
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std::vector<Vec4i>& lines, int linesMax )
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{
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Point pt;
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float irho = 1 / rho;
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RNG rng((uint64)-1);
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CV_Assert( image.type() == CV_8UC1 );
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int width = image.cols;
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int height = image.rows;
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int numangle = cvRound(CV_PI / theta);
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int numrho = cvRound(((width + height) * 2 + 1) / rho);
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Mat accum = Mat::zeros( numangle, numrho, CV_32SC1 );
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Mat mask( height, width, CV_8UC1 );
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std::vector<float> trigtab(numangle*2);
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for( int n = 0; n < numangle; n++ )
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{
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trigtab[n*2] = (float)(cos((double)n*theta) * irho);
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trigtab[n*2+1] = (float)(sin((double)n*theta) * irho);
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}
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const float* ttab = &trigtab[0];
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uchar* mdata0 = mask.data;
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std::vector<Point> nzloc;
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// stage 1. collect non-zero image points
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for( pt.y = 0; pt.y < height; pt.y++ )
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{
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const uchar* data = image.ptr(pt.y);
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uchar* mdata = mask.ptr(pt.y);
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for( pt.x = 0; pt.x < width; pt.x++ )
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{
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if( data[pt.x] )
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{
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mdata[pt.x] = (uchar)1;
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nzloc.push_back(pt);
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}
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else
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mdata[pt.x] = 0;
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}
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}
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int count = (int)nzloc.size();
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// stage 2. process all the points in random order
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for( ; count > 0; count-- )
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{
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// choose random point out of the remaining ones
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int idx = rng.uniform(0, count);
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int max_val = threshold-1, max_n = 0;
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Point point = nzloc[idx];
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Point line_end[2];
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float a, b;
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int* adata = (int*)accum.data;
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int i = point.y, j = point.x, k, x0, y0, dx0, dy0, xflag;
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int good_line;
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const int shift = 16;
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// "remove" it by overriding it with the last element
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nzloc[idx] = nzloc[count-1];
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// check if it has been excluded already (i.e. belongs to some other line)
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if( !mdata0[i*width + j] )
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continue;
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// update accumulator, find the most probable line
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for( int n = 0; n < numangle; n++, adata += numrho )
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{
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int r = cvRound( j * ttab[n*2] + i * ttab[n*2+1] );
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r += (numrho - 1) / 2;
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int val = ++adata[r];
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if( max_val < val )
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{
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max_val = val;
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max_n = n;
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}
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}
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// if it is too "weak" candidate, continue with another point
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if( max_val < threshold )
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continue;
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// from the current point walk in each direction
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// along the found line and extract the line segment
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a = -ttab[max_n*2+1];
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b = ttab[max_n*2];
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x0 = j;
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y0 = i;
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if( fabs(a) > fabs(b) )
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{
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xflag = 1;
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dx0 = a > 0 ? 1 : -1;
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dy0 = cvRound( b*(1 << shift)/fabs(a) );
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y0 = (y0 << shift) + (1 << (shift-1));
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}
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else
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{
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xflag = 0;
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dy0 = b > 0 ? 1 : -1;
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dx0 = cvRound( a*(1 << shift)/fabs(b) );
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x0 = (x0 << shift) + (1 << (shift-1));
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}
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for( k = 0; k < 2; k++ )
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{
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int gap = 0, x = x0, y = y0, dx = dx0, dy = dy0;
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if( k > 0 )
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dx = -dx, dy = -dy;
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// walk along the line using fixed-point arithmetics,
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// stop at the image border or in case of too big gap
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for( ;; x += dx, y += dy )
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{
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uchar* mdata;
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int i1, j1;
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if( xflag )
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{
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j1 = x;
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i1 = y >> shift;
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}
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else
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{
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j1 = x >> shift;
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i1 = y;
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}
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|
|
|
if( j1 < 0 || j1 >= width || i1 < 0 || i1 >= height )
|
|
break;
|
|
|
|
mdata = mdata0 + i1*width + j1;
|
|
|
|
// for each non-zero point:
|
|
// update line end,
|
|
// clear the mask element
|
|
// reset the gap
|
|
if( *mdata )
|
|
{
|
|
gap = 0;
|
|
line_end[k].y = i1;
|
|
line_end[k].x = j1;
|
|
}
|
|
else if( ++gap > lineGap )
|
|
break;
|
|
}
|
|
}
|
|
|
|
good_line = std::abs(line_end[1].x - line_end[0].x) >= lineLength ||
|
|
std::abs(line_end[1].y - line_end[0].y) >= lineLength;
|
|
|
|
for( k = 0; k < 2; k++ )
|
|
{
|
|
int x = x0, y = y0, dx = dx0, dy = dy0;
|
|
|
|
if( k > 0 )
|
|
dx = -dx, dy = -dy;
|
|
|
|
// walk along the line using fixed-point arithmetics,
|
|
// stop at the image border or in case of too big gap
|
|
for( ;; x += dx, y += dy )
|
|
{
|
|
uchar* mdata;
|
|
int i1, j1;
|
|
|
|
if( xflag )
|
|
{
|
|
j1 = x;
|
|
i1 = y >> shift;
|
|
}
|
|
else
|
|
{
|
|
j1 = x >> shift;
|
|
i1 = y;
|
|
}
|
|
|
|
mdata = mdata0 + i1*width + j1;
|
|
|
|
// for each non-zero point:
|
|
// update line end,
|
|
// clear the mask element
|
|
// reset the gap
|
|
if( *mdata )
|
|
{
|
|
if( good_line )
|
|
{
|
|
adata = (int*)accum.data;
|
|
for( int n = 0; n < numangle; n++, adata += numrho )
|
|
{
|
|
int r = cvRound( j1 * ttab[n*2] + i1 * ttab[n*2+1] );
|
|
r += (numrho - 1) / 2;
|
|
adata[r]--;
|
|
}
|
|
}
|
|
*mdata = 0;
|
|
}
|
|
|
|
if( i1 == line_end[k].y && j1 == line_end[k].x )
|
|
break;
|
|
}
|
|
}
|
|
|
|
if( good_line )
|
|
{
|
|
Vec4i lr(line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y);
|
|
lines.push_back(lr);
|
|
if( (int)lines.size() >= linesMax )
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
|
|
void cv::HoughLines( InputArray _image, OutputArray _lines,
|
|
double rho, double theta, int threshold,
|
|
double srn, double stn )
|
|
{
|
|
Mat image = _image.getMat();
|
|
std::vector<Vec2f> lines;
|
|
|
|
if( srn == 0 && stn == 0 )
|
|
HoughLinesStandard(image, (float)rho, (float)theta, threshold, lines, INT_MAX);
|
|
else
|
|
HoughLinesSDiv(image, (float)rho, (float)theta, threshold, cvRound(srn), cvRound(stn), lines, INT_MAX);
|
|
|
|
Mat(lines).copyTo(_lines);
|
|
}
|
|
|
|
|
|
void cv::HoughLinesP(InputArray _image, OutputArray _lines,
|
|
double rho, double theta, int threshold,
|
|
double minLineLength, double maxGap )
|
|
{
|
|
Mat image = _image.getMat();
|
|
std::vector<Vec4i> lines;
|
|
HoughLinesProbabilistic(image, (float)rho, (float)theta, threshold, cvRound(minLineLength), cvRound(maxGap), lines, INT_MAX);
|
|
Mat(lines).copyTo(_lines);
|
|
}
|
|
|
|
|
|
|
|
/* Wrapper function for standard hough transform */
|
|
CV_IMPL CvSeq*
|
|
cvHoughLines2( CvArr* src_image, void* lineStorage, int method,
|
|
double rho, double theta, int threshold,
|
|
double param1, double param2 )
|
|
{
|
|
cv::Mat image = cv::cvarrToMat(src_image);
|
|
std::vector<cv::Vec2f> l2;
|
|
std::vector<cv::Vec4i> l4;
|
|
CvSeq* result = 0;
|
|
|
|
CvMat* mat = 0;
|
|
CvSeq* lines = 0;
|
|
CvSeq lines_header;
|
|
CvSeqBlock lines_block;
|
|
int lineType, elemSize;
|
|
int linesMax = INT_MAX;
|
|
int iparam1, iparam2;
|
|
|
|
if( !lineStorage )
|
|
CV_Error( CV_StsNullPtr, "NULL destination" );
|
|
|
|
if( rho <= 0 || theta <= 0 || threshold <= 0 )
|
|
CV_Error( CV_StsOutOfRange, "rho, theta and threshold must be positive" );
|
|
|
|
if( method != CV_HOUGH_PROBABILISTIC )
|
|
{
|
|
lineType = CV_32FC2;
|
|
elemSize = sizeof(float)*2;
|
|
}
|
|
else
|
|
{
|
|
lineType = CV_32SC4;
|
|
elemSize = sizeof(int)*4;
|
|
}
|
|
|
|
if( CV_IS_STORAGE( lineStorage ))
|
|
{
|
|
lines = cvCreateSeq( lineType, sizeof(CvSeq), elemSize, (CvMemStorage*)lineStorage );
|
|
}
|
|
else if( CV_IS_MAT( lineStorage ))
|
|
{
|
|
mat = (CvMat*)lineStorage;
|
|
|
|
if( !CV_IS_MAT_CONT( mat->type ) || (mat->rows != 1 && mat->cols != 1) )
|
|
CV_Error( CV_StsBadArg,
|
|
"The destination matrix should be continuous and have a single row or a single column" );
|
|
|
|
if( CV_MAT_TYPE( mat->type ) != lineType )
|
|
CV_Error( CV_StsBadArg,
|
|
"The destination matrix data type is inappropriate, see the manual" );
|
|
|
|
lines = cvMakeSeqHeaderForArray( lineType, sizeof(CvSeq), elemSize, mat->data.ptr,
|
|
mat->rows + mat->cols - 1, &lines_header, &lines_block );
|
|
linesMax = lines->total;
|
|
cvClearSeq( lines );
|
|
}
|
|
else
|
|
CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
|
|
|
|
iparam1 = cvRound(param1);
|
|
iparam2 = cvRound(param2);
|
|
|
|
switch( method )
|
|
{
|
|
case CV_HOUGH_STANDARD:
|
|
HoughLinesStandard( image, (float)rho,
|
|
(float)theta, threshold, l2, linesMax );
|
|
break;
|
|
case CV_HOUGH_MULTI_SCALE:
|
|
HoughLinesSDiv( image, (float)rho, (float)theta,
|
|
threshold, iparam1, iparam2, l2, linesMax );
|
|
break;
|
|
case CV_HOUGH_PROBABILISTIC:
|
|
HoughLinesProbabilistic( image, (float)rho, (float)theta,
|
|
threshold, iparam1, iparam2, l4, linesMax );
|
|
break;
|
|
default:
|
|
CV_Error( CV_StsBadArg, "Unrecognized method id" );
|
|
}
|
|
|
|
int nlines = (int)(l2.size() + l4.size());
|
|
|
|
if( mat )
|
|
{
|
|
if( mat->cols > mat->rows )
|
|
mat->cols = nlines;
|
|
else
|
|
mat->rows = nlines;
|
|
}
|
|
|
|
if( nlines )
|
|
{
|
|
cv::Mat lx = method == CV_HOUGH_STANDARD || method == CV_HOUGH_MULTI_SCALE ?
|
|
cv::Mat(nlines, 1, CV_32FC2, &l2[0]) : cv::Mat(nlines, 1, CV_32SC4, &l4[0]);
|
|
|
|
if( mat )
|
|
{
|
|
cv::Mat dst(nlines, 1, lx.type(), mat->data.ptr);
|
|
lx.copyTo(dst);
|
|
}
|
|
else
|
|
{
|
|
cvSeqPushMulti(lines, lx.data, nlines);
|
|
}
|
|
}
|
|
|
|
if( !mat )
|
|
result = lines;
|
|
return result;
|
|
}
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Circle Detection *
|
|
\****************************************************************************************/
|
|
|
|
static void
|
|
icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
|
|
int min_radius, int max_radius,
|
|
int canny_threshold, int acc_threshold,
|
|
CvSeq* circles, int circles_max )
|
|
{
|
|
const int SHIFT = 10, ONE = 1 << SHIFT;
|
|
cv::Ptr<CvMat> dx, dy;
|
|
cv::Ptr<CvMat> edges, accum, dist_buf;
|
|
std::vector<int> sort_buf;
|
|
cv::Ptr<CvMemStorage> storage;
|
|
|
|
int x, y, i, j, k, center_count, nz_count;
|
|
float min_radius2 = (float)min_radius*min_radius;
|
|
float max_radius2 = (float)max_radius*max_radius;
|
|
int rows, cols, arows, acols;
|
|
int astep, *adata;
|
|
float* ddata;
|
|
CvSeq *nz, *centers;
|
|
float idp, dr;
|
|
CvSeqReader reader;
|
|
|
|
edges = cvCreateMat( img->rows, img->cols, CV_8UC1 );
|
|
cvCanny( img, edges, MAX(canny_threshold/2,1), canny_threshold, 3 );
|
|
|
|
dx = cvCreateMat( img->rows, img->cols, CV_16SC1 );
|
|
dy = cvCreateMat( img->rows, img->cols, CV_16SC1 );
|
|
cvSobel( img, dx, 1, 0, 3 );
|
|
cvSobel( img, dy, 0, 1, 3 );
|
|
|
|
if( dp < 1.f )
|
|
dp = 1.f;
|
|
idp = 1.f/dp;
|
|
accum = cvCreateMat( cvCeil(img->rows*idp)+2, cvCeil(img->cols*idp)+2, CV_32SC1 );
|
|
cvZero(accum);
|
|
|
|
storage = cvCreateMemStorage();
|
|
nz = cvCreateSeq( CV_32SC2, sizeof(CvSeq), sizeof(CvPoint), storage );
|
|
centers = cvCreateSeq( CV_32SC1, sizeof(CvSeq), sizeof(int), storage );
|
|
|
|
rows = img->rows;
|
|
cols = img->cols;
|
|
arows = accum->rows - 2;
|
|
acols = accum->cols - 2;
|
|
adata = accum->data.i;
|
|
astep = accum->step/sizeof(adata[0]);
|
|
// Accumulate circle evidence for each edge pixel
|
|
for( y = 0; y < rows; y++ )
|
|
{
|
|
const uchar* edges_row = edges->data.ptr + y*edges->step;
|
|
const short* dx_row = (const short*)(dx->data.ptr + y*dx->step);
|
|
const short* dy_row = (const short*)(dy->data.ptr + y*dy->step);
|
|
|
|
for( x = 0; x < cols; x++ )
|
|
{
|
|
float vx, vy;
|
|
int sx, sy, x0, y0, x1, y1, r;
|
|
CvPoint pt;
|
|
|
|
vx = dx_row[x];
|
|
vy = dy_row[x];
|
|
|
|
if( !edges_row[x] || (vx == 0 && vy == 0) )
|
|
continue;
|
|
|
|
float mag = std::sqrt(vx*vx+vy*vy);
|
|
assert( mag >= 1 );
|
|
sx = cvRound((vx*idp)*ONE/mag);
|
|
sy = cvRound((vy*idp)*ONE/mag);
|
|
|
|
x0 = cvRound((x*idp)*ONE);
|
|
y0 = cvRound((y*idp)*ONE);
|
|
// Step from min_radius to max_radius in both directions of the gradient
|
|
for(int k1 = 0; k1 < 2; k1++ )
|
|
{
|
|
x1 = x0 + min_radius * sx;
|
|
y1 = y0 + min_radius * sy;
|
|
|
|
for( r = min_radius; r <= max_radius; x1 += sx, y1 += sy, r++ )
|
|
{
|
|
int x2 = x1 >> SHIFT, y2 = y1 >> SHIFT;
|
|
if( (unsigned)x2 >= (unsigned)acols ||
|
|
(unsigned)y2 >= (unsigned)arows )
|
|
break;
|
|
adata[y2*astep + x2]++;
|
|
}
|
|
|
|
sx = -sx; sy = -sy;
|
|
}
|
|
|
|
pt.x = x; pt.y = y;
|
|
cvSeqPush( nz, &pt );
|
|
}
|
|
}
|
|
|
|
nz_count = nz->total;
|
|
if( !nz_count )
|
|
return;
|
|
//Find possible circle centers
|
|
for( y = 1; y < arows - 1; y++ )
|
|
{
|
|
for( x = 1; x < acols - 1; x++ )
|
|
{
|
|
int base = y*(acols+2) + x;
|
|
if( adata[base] > acc_threshold &&
|
|
adata[base] > adata[base-1] && adata[base] > adata[base+1] &&
|
|
adata[base] > adata[base-acols-2] && adata[base] > adata[base+acols+2] )
|
|
cvSeqPush(centers, &base);
|
|
}
|
|
}
|
|
|
|
center_count = centers->total;
|
|
if( !center_count )
|
|
return;
|
|
|
|
sort_buf.resize( MAX(center_count,nz_count) );
|
|
cvCvtSeqToArray( centers, &sort_buf[0] );
|
|
|
|
std::sort(sort_buf.begin(), sort_buf.begin() + center_count, cv::hough_cmp_gt(adata));
|
|
cvClearSeq( centers );
|
|
cvSeqPushMulti( centers, &sort_buf[0], center_count );
|
|
|
|
dist_buf = cvCreateMat( 1, nz_count, CV_32FC1 );
|
|
ddata = dist_buf->data.fl;
|
|
|
|
dr = dp;
|
|
min_dist = MAX( min_dist, dp );
|
|
min_dist *= min_dist;
|
|
// For each found possible center
|
|
// Estimate radius and check support
|
|
for( i = 0; i < centers->total; i++ )
|
|
{
|
|
int ofs = *(int*)cvGetSeqElem( centers, i );
|
|
y = ofs/(acols+2);
|
|
x = ofs - (y)*(acols+2);
|
|
//Calculate circle's center in pixels
|
|
float cx = (float)((x + 0.5f)*dp), cy = (float)(( y + 0.5f )*dp);
|
|
float start_dist, dist_sum;
|
|
float r_best = 0;
|
|
int max_count = 0;
|
|
// Check distance with previously detected circles
|
|
for( j = 0; j < circles->total; j++ )
|
|
{
|
|
float* c = (float*)cvGetSeqElem( circles, j );
|
|
if( (c[0] - cx)*(c[0] - cx) + (c[1] - cy)*(c[1] - cy) < min_dist )
|
|
break;
|
|
}
|
|
|
|
if( j < circles->total )
|
|
continue;
|
|
// Estimate best radius
|
|
cvStartReadSeq( nz, &reader );
|
|
for( j = k = 0; j < nz_count; j++ )
|
|
{
|
|
CvPoint pt;
|
|
float _dx, _dy, _r2;
|
|
CV_READ_SEQ_ELEM( pt, reader );
|
|
_dx = cx - pt.x; _dy = cy - pt.y;
|
|
_r2 = _dx*_dx + _dy*_dy;
|
|
if(min_radius2 <= _r2 && _r2 <= max_radius2 )
|
|
{
|
|
ddata[k] = _r2;
|
|
sort_buf[k] = k;
|
|
k++;
|
|
}
|
|
}
|
|
|
|
int nz_count1 = k, start_idx = nz_count1 - 1;
|
|
if( nz_count1 == 0 )
|
|
continue;
|
|
dist_buf->cols = nz_count1;
|
|
cvPow( dist_buf, dist_buf, 0.5 );
|
|
std::sort(sort_buf.begin(), sort_buf.begin() + nz_count1, cv::hough_cmp_gt((int*)ddata));
|
|
|
|
dist_sum = start_dist = ddata[sort_buf[nz_count1-1]];
|
|
for( j = nz_count1 - 2; j >= 0; j-- )
|
|
{
|
|
float d = ddata[sort_buf[j]];
|
|
|
|
if( d > max_radius )
|
|
break;
|
|
|
|
if( d - start_dist > dr )
|
|
{
|
|
float r_cur = ddata[sort_buf[(j + start_idx)/2]];
|
|
if( (start_idx - j)*r_best >= max_count*r_cur ||
|
|
(r_best < FLT_EPSILON && start_idx - j >= max_count) )
|
|
{
|
|
r_best = r_cur;
|
|
max_count = start_idx - j;
|
|
}
|
|
start_dist = d;
|
|
start_idx = j;
|
|
dist_sum = 0;
|
|
}
|
|
dist_sum += d;
|
|
}
|
|
// Check if the circle has enough support
|
|
if( max_count > acc_threshold )
|
|
{
|
|
float c[3];
|
|
c[0] = cx;
|
|
c[1] = cy;
|
|
c[2] = (float)r_best;
|
|
cvSeqPush( circles, c );
|
|
if( circles->total > circles_max )
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
CV_IMPL CvSeq*
|
|
cvHoughCircles( CvArr* src_image, void* circle_storage,
|
|
int method, double dp, double min_dist,
|
|
double param1, double param2,
|
|
int min_radius, int max_radius )
|
|
{
|
|
CvSeq* result = 0;
|
|
|
|
CvMat stub, *img = (CvMat*)src_image;
|
|
CvMat* mat = 0;
|
|
CvSeq* circles = 0;
|
|
CvSeq circles_header;
|
|
CvSeqBlock circles_block;
|
|
int circles_max = INT_MAX;
|
|
int canny_threshold = cvRound(param1);
|
|
int acc_threshold = cvRound(param2);
|
|
|
|
img = cvGetMat( img, &stub );
|
|
|
|
if( !CV_IS_MASK_ARR(img))
|
|
CV_Error( CV_StsBadArg, "The source image must be 8-bit, single-channel" );
|
|
|
|
if( !circle_storage )
|
|
CV_Error( CV_StsNullPtr, "NULL destination" );
|
|
|
|
if( dp <= 0 || min_dist <= 0 || canny_threshold <= 0 || acc_threshold <= 0 )
|
|
CV_Error( CV_StsOutOfRange, "dp, min_dist, canny_threshold and acc_threshold must be all positive numbers" );
|
|
|
|
min_radius = MAX( min_radius, 0 );
|
|
if( max_radius <= 0 )
|
|
max_radius = MAX( img->rows, img->cols );
|
|
else if( max_radius <= min_radius )
|
|
max_radius = min_radius + 2;
|
|
|
|
if( CV_IS_STORAGE( circle_storage ))
|
|
{
|
|
circles = cvCreateSeq( CV_32FC3, sizeof(CvSeq),
|
|
sizeof(float)*3, (CvMemStorage*)circle_storage );
|
|
}
|
|
else if( CV_IS_MAT( circle_storage ))
|
|
{
|
|
mat = (CvMat*)circle_storage;
|
|
|
|
if( !CV_IS_MAT_CONT( mat->type ) || (mat->rows != 1 && mat->cols != 1) ||
|
|
CV_MAT_TYPE(mat->type) != CV_32FC3 )
|
|
CV_Error( CV_StsBadArg,
|
|
"The destination matrix should be continuous and have a single row or a single column" );
|
|
|
|
circles = cvMakeSeqHeaderForArray( CV_32FC3, sizeof(CvSeq), sizeof(float)*3,
|
|
mat->data.ptr, mat->rows + mat->cols - 1, &circles_header, &circles_block );
|
|
circles_max = circles->total;
|
|
cvClearSeq( circles );
|
|
}
|
|
else
|
|
CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
|
|
|
|
switch( method )
|
|
{
|
|
case CV_HOUGH_GRADIENT:
|
|
icvHoughCirclesGradient( img, (float)dp, (float)min_dist,
|
|
min_radius, max_radius, canny_threshold,
|
|
acc_threshold, circles, circles_max );
|
|
break;
|
|
default:
|
|
CV_Error( CV_StsBadArg, "Unrecognized method id" );
|
|
}
|
|
|
|
if( mat )
|
|
{
|
|
if( mat->cols > mat->rows )
|
|
mat->cols = circles->total;
|
|
else
|
|
mat->rows = circles->total;
|
|
}
|
|
else
|
|
result = circles;
|
|
|
|
return result;
|
|
}
|
|
|
|
|
|
namespace cv
|
|
{
|
|
|
|
const int STORAGE_SIZE = 1 << 12;
|
|
|
|
static void seqToMat(const CvSeq* seq, OutputArray _arr)
|
|
{
|
|
if( seq && seq->total > 0 )
|
|
{
|
|
_arr.create(1, seq->total, seq->flags, -1, true);
|
|
Mat arr = _arr.getMat();
|
|
cvCvtSeqToArray(seq, arr.data);
|
|
}
|
|
else
|
|
_arr.release();
|
|
}
|
|
|
|
}
|
|
|
|
void cv::HoughCircles( InputArray _image, OutputArray _circles,
|
|
int method, double dp, double min_dist,
|
|
double param1, double param2,
|
|
int minRadius, int maxRadius )
|
|
{
|
|
Ptr<CvMemStorage> storage = cvCreateMemStorage(STORAGE_SIZE);
|
|
Mat image = _image.getMat();
|
|
CvMat c_image = image;
|
|
CvSeq* seq = cvHoughCircles( &c_image, storage, method,
|
|
dp, min_dist, param1, param2, minRadius, maxRadius );
|
|
seqToMat(seq, _circles);
|
|
}
|
|
|
|
/* End of file. */
|