1105 lines
37 KiB
C++
1105 lines
37 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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// 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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/************************************************************************************\
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This is improved variant of chessboard corner detection algorithm that
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uses a graph of connected quads. It is based on the code contributed
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by Vladimir Vezhnevets and Philip Gruebele.
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Here is the copyright notice from the original Vladimir's code:
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===============================================================
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The algorithms developed and implemented by Vezhnevets Vldimir
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aka Dead Moroz (vvp@graphics.cs.msu.ru)
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See http://graphics.cs.msu.su/en/research/calibration/opencv.html
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for detailed information.
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Reliability additions and modifications made by Philip Gruebele.
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<a href="mailto:pgruebele@cox.net">pgruebele@cox.net</a>
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\************************************************************************************/
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#include "_cv.h"
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//=====================================================================================
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// Implementation for the enhanced calibration object detection
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//=====================================================================================
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#define MAX_CONTOUR_APPROX 7
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typedef struct CvContourEx
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{
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CV_CONTOUR_FIELDS()
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int counter;
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}
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CvContourEx;
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//=====================================================================================
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/// Corner info structure
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/** This structure stores information about the chessboard corner.*/
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typedef struct CvCBCorner
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{
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CvPoint2D32f pt; // Coordinates of the corner
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int row; // Board row index
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int count; // Number of neighbor corners
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struct CvCBCorner* neighbors[4]; // Neighbor corners
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}
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CvCBCorner;
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//=====================================================================================
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/// Quadrangle contour info structure
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/** This structure stores information about the chessboard quadrange.*/
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typedef struct CvCBQuad
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{
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int count; // Number of quad neibors
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int group_idx; // quad group ID
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float edge_len; // quad size characteristic
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CvCBCorner *corners[4]; // Coordinates of quad corners
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struct CvCBQuad *neighbors[4]; // Pointers of quad neighbors
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}
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CvCBQuad;
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//=====================================================================================
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//static CvMat* debug_img = 0;
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static int icvGenerateQuads( CvCBQuad **quads, CvCBCorner **corners,
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CvMemStorage *storage, CvMat *image, int flags );
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static void icvFindQuadNeighbors( CvCBQuad *quads, int quad_count );
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static int icvFindConnectedQuads( CvCBQuad *quads, int quad_count,
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CvCBQuad **quad_group, int group_idx,
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CvMemStorage* storage );
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static int icvCheckQuadGroup( CvCBQuad **quad_group, int count,
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CvCBCorner **out_corners, CvSize pattern_size );
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static int icvCleanFoundConnectedQuads( int quad_count,
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CvCBQuad **quads, CvSize pattern_size );
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CV_IMPL
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int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
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CvPoint2D32f* out_corners, int* out_corner_count,
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int flags )
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{
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const int min_dilations = 1;
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const int max_dilations = 3;
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int found = 0;
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CvMat* norm_img = 0;
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CvMat* thresh_img = 0;
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CvMemStorage* storage = 0;
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CvCBQuad *quads = 0, **quad_group = 0;
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CvCBCorner *corners = 0, **corner_group = 0;
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if( out_corner_count )
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*out_corner_count = 0;
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CV_FUNCNAME( "cvFindChessBoardCornerGuesses2" );
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__BEGIN__;
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int quad_count, group_idx, i, dilations;
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CvMat stub, *img = (CvMat*)arr;
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CV_CALL( img = cvGetMat( img, &stub ));
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//debug_img = img;
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if( CV_MAT_DEPTH( img->type ) != CV_8U || CV_MAT_CN( img->type ) == 2 )
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CV_ERROR( CV_StsUnsupportedFormat, "Only 8-bit grayscale or color images are supported" );
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if( pattern_size.width <= 2 || pattern_size.height <= 2 )
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CV_ERROR( CV_StsOutOfRange, "pattern should have at least 2x2 size" );
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if( !out_corners )
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CV_ERROR( CV_StsNullPtr, "Null pointer to corners" );
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CV_CALL( storage = cvCreateMemStorage(0) );
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CV_CALL( thresh_img = cvCreateMat( img->rows, img->cols, CV_8UC1 ));
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if( CV_MAT_CN(img->type) != 1 || (flags & CV_CALIB_CB_NORMALIZE_IMAGE) )
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{
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// equalize the input image histogram -
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// that should make the contrast between "black" and "white" areas big enough
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CV_CALL( norm_img = cvCreateMat( img->rows, img->cols, CV_8UC1 ));
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if( CV_MAT_CN(img->type) != 1 )
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{
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CV_CALL( cvCvtColor( img, norm_img, CV_BGR2GRAY ));
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img = norm_img;
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}
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if( flags & CV_CALIB_CB_NORMALIZE_IMAGE )
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{
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cvEqualizeHist( img, norm_img );
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img = norm_img;
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}
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}
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// Try our standard "1" dilation, but if the pattern is not found, iterate the whole procedure with higher dilations.
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// This is necessary because some squares simply do not separate properly with a single dilation. However,
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// we want to use the minimum number of dilations possible since dilations cause the squares to become smaller,
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// making it difficult to detect smaller squares.
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for( dilations = min_dilations; dilations <= max_dilations; dilations++ )
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{
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// convert the input grayscale image to binary (black-n-white)
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if( flags & CV_CALIB_CB_ADAPTIVE_THRESH )
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{
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int block_size = cvRound(MIN(img->cols,img->rows)*0.2)|1;
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cvDilate( img, thresh_img, 0, dilations );
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// convert to binary
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cvAdaptiveThreshold( img, thresh_img, 255,
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CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, block_size, 0 );
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cvDilate( thresh_img, thresh_img, 0, dilations-1 );
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}
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else
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{
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// Make dilation before the thresholding.
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// It splits chessboard corners
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//cvDilate( img, thresh_img, 0, 1 );
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// empiric threshold level
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double mean = cvMean( img );
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int thresh_level = cvRound( mean - 10 );
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thresh_level = MAX( thresh_level, 10 );
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cvThreshold( img, thresh_img, thresh_level, 255, CV_THRESH_BINARY );
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cvDilate( thresh_img, thresh_img, 0, dilations );
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}
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// So we can find rectangles that go to the edge, we draw a white line around the image edge.
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// Otherwise FindContours will miss those clipped rectangle contours.
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// The border color will be the image mean, because otherwise we risk screwing up filters like cvSmooth()...
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cvRectangle( thresh_img, cvPoint(0,0), cvPoint(thresh_img->cols-1,
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thresh_img->rows-1), CV_RGB(255,255,255), 3, 8);
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CV_CALL( quad_count = icvGenerateQuads( &quads, &corners, storage, thresh_img, flags ));
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if( quad_count <= 0 )
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continue;
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// Find quad's neighbors
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CV_CALL( icvFindQuadNeighbors( quads, quad_count ));
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CV_CALL( quad_group = (CvCBQuad**)cvAlloc( sizeof(quad_group[0]) * quad_count));
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CV_CALL( corner_group = (CvCBCorner**)cvAlloc( sizeof(corner_group[0]) * quad_count*4 ));
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for( group_idx = 0; ; group_idx++ )
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{
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int count;
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CV_CALL( count = icvFindConnectedQuads( quads, quad_count, quad_group, group_idx, storage ));
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if( count == 0 )
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break;
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// If count is more than it should be, this will remove those quads
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// which cause maximum deviation from a nice square pattern.
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CV_CALL( count = icvCleanFoundConnectedQuads( count, quad_group, pattern_size ));
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CV_CALL( count = icvCheckQuadGroup( quad_group, count, corner_group, pattern_size ));
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if( count > 0 || (out_corner_count && -count > *out_corner_count) )
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{
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int n = count > 0 ? pattern_size.width * pattern_size.height : -count;
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n = MIN( n, pattern_size.width * pattern_size.height );
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// copy corners to output array
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for( i = 0; i < n; i++ )
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out_corners[i] = corner_group[i]->pt;
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if( out_corner_count )
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*out_corner_count = n;
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if( count > 0 )
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{
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found = 1;
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EXIT;
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}
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}
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}
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cvFree( &quads );
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cvFree( &corners );
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}
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__END__;
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cvReleaseMemStorage( &storage );
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cvReleaseMat( &norm_img );
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cvReleaseMat( &thresh_img );
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cvFree( &quads );
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cvFree( &corners );
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cvFree( &quad_group );
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cvFree( &corner_group );
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return found;
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}
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// if we found too many connect quads, remove those which probably do not belong.
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static int
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icvCleanFoundConnectedQuads( int quad_count, CvCBQuad **quad_group, CvSize pattern_size )
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{
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CvMemStorage *temp_storage = 0;
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CvPoint2D32f *centers = 0;
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CV_FUNCNAME( "icvCleanFoundConnectedQuads" );
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__BEGIN__;
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CvPoint2D32f center = {0,0};
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int i, j, k;
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// number of quads this pattern should contain
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int count = ((pattern_size.width + 1)*(pattern_size.height + 1) + 1)/2;
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// if we have more quadrangles than we should,
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// try to eliminate duplicates or ones which don't belong to the pattern rectangle...
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if( quad_count <= count )
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EXIT;
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// create an array of quadrangle centers
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CV_CALL( centers = (CvPoint2D32f *)cvAlloc( sizeof(centers[0])*quad_count ));
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CV_CALL( temp_storage = cvCreateMemStorage(0));
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for( i = 0; i < quad_count; i++ )
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{
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CvPoint2D32f ci = {0,0};
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CvCBQuad* q = quad_group[i];
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for( j = 0; j < 4; j++ )
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{
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CvPoint2D32f pt = q->corners[j]->pt;
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ci.x += pt.x;
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ci.y += pt.y;
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}
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ci.x *= 0.25f;
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ci.y *= 0.25f;
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centers[i] = ci;
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center.x += ci.x;
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center.y += ci.y;
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}
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center.x /= quad_count;
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center.y /= quad_count;
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// If we still have more quadrangles than we should,
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// we try to eliminate bad ones based on minimizing the bounding box.
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// We iteratively remove the point which reduces the size of
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// the bounding box of the blobs the most
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// (since we want the rectangle to be as small as possible)
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// remove the quadrange that causes the biggest reduction
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// in pattern size until we have the correct number
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for( ; quad_count > count; quad_count-- )
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{
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double min_box_area = DBL_MAX;
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int skip, min_box_area_index = -1;
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CvCBQuad *q0, *q;
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// For each point, calculate box area without that point
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for( skip = 0; skip < quad_count; skip++ )
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{
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// get bounding rectangle
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CvPoint2D32f temp = centers[skip]; // temporarily make index 'skip' the same as
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centers[skip] = center; // pattern center (so it is not counted for convex hull)
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CvMat pointMat = cvMat(1, quad_count, CV_32FC2, centers);
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CvSeq *hull = cvConvexHull2( &pointMat, temp_storage, CV_CLOCKWISE, 1 );
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centers[skip] = temp;
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double hull_area = fabs(cvContourArea(hull, CV_WHOLE_SEQ));
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// remember smallest box area
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if( hull_area < min_box_area )
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{
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min_box_area = hull_area;
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min_box_area_index = skip;
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}
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cvClearMemStorage( temp_storage );
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}
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q0 = quad_group[min_box_area_index];
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// remove any references to this quad as a neighbor
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for( i = 0; i < quad_count; i++ )
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{
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q = quad_group[i];
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for( j = 0; j < 4; j++ )
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{
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if( q->neighbors[j] == q0 )
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{
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q->neighbors[j] = 0;
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q->count--;
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for( k = 0; k < 4; k++ )
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if( q0->neighbors[k] == q )
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{
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q0->neighbors[k] = 0;
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q0->count--;
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break;
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}
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break;
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}
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}
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}
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// remove the quad
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quad_count--;
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quad_group[min_box_area_index] = quad_group[quad_count];
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centers[min_box_area_index] = centers[quad_count];
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}
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__END__;
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cvReleaseMemStorage( &temp_storage );
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cvFree( ¢ers );
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return quad_count;
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}
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//=====================================================================================
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static int
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icvFindConnectedQuads( CvCBQuad *quad, int quad_count, CvCBQuad **out_group,
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int group_idx, CvMemStorage* storage )
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{
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CvMemStorage* temp_storage = cvCreateChildMemStorage( storage );
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CvSeq* stack = cvCreateSeq( 0, sizeof(*stack), sizeof(void*), temp_storage );
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int i, count = 0;
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// Scan the array for a first unlabeled quad
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for( i = 0; i < quad_count; i++ )
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{
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if( quad[i].count > 0 && quad[i].group_idx < 0)
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break;
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}
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// Recursively find a group of connected quads starting from the seed quad[i]
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if( i < quad_count )
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{
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CvCBQuad* q = &quad[i];
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cvSeqPush( stack, &q );
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out_group[count++] = q;
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q->group_idx = group_idx;
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while( stack->total )
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{
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cvSeqPop( stack, &q );
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for( i = 0; i < 4; i++ )
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{
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CvCBQuad *neighbor = q->neighbors[i];
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if( neighbor && neighbor->count > 0 && neighbor->group_idx < 0 )
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{
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cvSeqPush( stack, &neighbor );
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out_group[count++] = neighbor;
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neighbor->group_idx = group_idx;
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}
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}
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}
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}
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cvReleaseMemStorage( &temp_storage );
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return count;
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}
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//=====================================================================================
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static int
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icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count,
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CvCBCorner **out_corners, CvSize pattern_size )
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{
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const int ROW1 = 1000000;
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const int ROW2 = 2000000;
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const int ROW_ = 3000000;
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int result = 0;
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int i, out_corner_count = 0, corner_count = 0;
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CvCBCorner** corners = 0;
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CV_FUNCNAME( "icvCheckQuadGroup" );
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__BEGIN__;
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int j, k, kk;
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int width = 0, height = 0;
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int hist[5] = {0,0,0,0,0};
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CvCBCorner* first = 0, *first2 = 0, *right, *cur, *below, *c;
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CV_CALL( corners = (CvCBCorner**)cvAlloc( quad_count*4*sizeof(corners[0]) ));
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// build dual graph, which vertices are internal quad corners
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// and two vertices are connected iff they lie on the same quad edge
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for( i = 0; i < quad_count; i++ )
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{
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CvCBQuad* q = quad_group[i];
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/*CvScalar color = q->count == 0 ? cvScalar(0,255,255) :
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q->count == 1 ? cvScalar(0,0,255) :
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q->count == 2 ? cvScalar(0,255,0) :
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q->count == 3 ? cvScalar(255,255,0) :
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cvScalar(255,0,0);*/
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for( j = 0; j < 4; j++ )
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{
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//cvLine( debug_img, cvPointFrom32f(q->corners[j]->pt), cvPointFrom32f(q->corners[(j+1)&3]->pt), color, 1, CV_AA, 0 );
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if( q->neighbors[j] )
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{
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CvCBCorner *a = q->corners[j], *b = q->corners[(j+1)&3];
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// mark internal corners that belong to:
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// - a quad with a single neighbor - with ROW1,
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// - a quad with two neighbors - with ROW2
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// make the rest of internal corners with ROW_
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int row_flag = q->count == 1 ? ROW1 : q->count == 2 ? ROW2 : ROW_;
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if( a->row == 0 )
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{
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corners[corner_count++] = a;
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a->row = row_flag;
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}
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else if( a->row > row_flag )
|
|
a->row = row_flag;
|
|
|
|
if( q->neighbors[(j+1)&3] )
|
|
{
|
|
if( a->count >= 4 || b->count >= 4 )
|
|
EXIT;
|
|
for( k = 0; k < 4; k++ )
|
|
{
|
|
if( a->neighbors[k] == b )
|
|
EXIT;
|
|
if( b->neighbors[k] == a )
|
|
EXIT;
|
|
}
|
|
a->neighbors[a->count++] = b;
|
|
b->neighbors[b->count++] = a;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if( corner_count != pattern_size.width*pattern_size.height )
|
|
EXIT;
|
|
|
|
for( i = 0; i < corner_count; i++ )
|
|
{
|
|
int n = corners[i]->count;
|
|
assert( 0 <= n && n <= 4 );
|
|
hist[n]++;
|
|
if( !first && n == 2 )
|
|
{
|
|
if( corners[i]->row == ROW1 )
|
|
first = corners[i];
|
|
else if( !first2 && corners[i]->row == ROW2 )
|
|
first2 = corners[i];
|
|
}
|
|
}
|
|
|
|
// start with a corner that belongs to a quad with a signle neighbor.
|
|
// if we do not have such, start with a corner of a quad with two neighbors.
|
|
if( !first )
|
|
first = first2;
|
|
|
|
if( !first || hist[0] != 0 || hist[1] != 0 || hist[2] != 4 ||
|
|
hist[3] != (pattern_size.width + pattern_size.height)*2 - 8 )
|
|
EXIT;
|
|
|
|
cur = first;
|
|
right = below = 0;
|
|
out_corners[out_corner_count++] = cur;
|
|
|
|
for( k = 0; k < 4; k++ )
|
|
{
|
|
c = cur->neighbors[k];
|
|
if( c )
|
|
{
|
|
if( !right )
|
|
right = c;
|
|
else if( !below )
|
|
below = c;
|
|
}
|
|
}
|
|
|
|
if( !right || right->count != 2 && right->count != 3 ||
|
|
!below || below->count != 2 && below->count != 3 )
|
|
EXIT;
|
|
|
|
cur->row = 0;
|
|
//cvCircle( debug_img, cvPointFrom32f(cur->pt), 3, cvScalar(0,255,0), -1, 8, 0 );
|
|
|
|
first = below; // remember the first corner in the next row
|
|
// find and store the first row (or column)
|
|
for(j=1;;j++)
|
|
{
|
|
right->row = 0;
|
|
out_corners[out_corner_count++] = right;
|
|
//cvCircle( debug_img, cvPointFrom32f(right->pt), 3, cvScalar(0,255-j*10,0), -1, 8, 0 );
|
|
if( right->count == 2 )
|
|
break;
|
|
if( right->count != 3 || out_corner_count >= MAX(pattern_size.width,pattern_size.height) )
|
|
EXIT;
|
|
cur = right;
|
|
for( k = 0; k < 4; k++ )
|
|
{
|
|
c = cur->neighbors[k];
|
|
if( c && c->row > 0 )
|
|
{
|
|
for( kk = 0; kk < 4; kk++ )
|
|
{
|
|
if( c->neighbors[kk] == below )
|
|
break;
|
|
}
|
|
if( kk < 4 )
|
|
below = c;
|
|
else
|
|
right = c;
|
|
}
|
|
}
|
|
}
|
|
|
|
width = out_corner_count;
|
|
if( width == pattern_size.width )
|
|
height = pattern_size.height;
|
|
else if( width == pattern_size.height )
|
|
height = pattern_size.width;
|
|
else
|
|
EXIT;
|
|
|
|
// find and store all the other rows
|
|
for( i = 1; ; i++ )
|
|
{
|
|
if( !first )
|
|
break;
|
|
cur = first;
|
|
first = 0;
|
|
for( j = 0;; j++ )
|
|
{
|
|
cur->row = i;
|
|
out_corners[out_corner_count++] = cur;
|
|
//cvCircle( debug_img, cvPointFrom32f(cur->pt), 3, cvScalar(0,0,255-j*10), -1, 8, 0 );
|
|
if( cur->count == 2 + (i < height-1) && j > 0 )
|
|
break;
|
|
|
|
right = 0;
|
|
|
|
// find a neighbor that has not been processed yet
|
|
// and that has a neighbor from the previous row
|
|
for( k = 0; k < 4; k++ )
|
|
{
|
|
c = cur->neighbors[k];
|
|
if( c && c->row > i )
|
|
{
|
|
for( kk = 0; kk < 4; kk++ )
|
|
{
|
|
if( c->neighbors[kk] && c->neighbors[kk]->row == i-1 )
|
|
break;
|
|
}
|
|
if( kk < 4 )
|
|
{
|
|
right = c;
|
|
if( j > 0 )
|
|
break;
|
|
}
|
|
else if( j == 0 )
|
|
first = c;
|
|
}
|
|
}
|
|
if( !right )
|
|
EXIT;
|
|
cur = right;
|
|
}
|
|
|
|
if( j != width - 1 )
|
|
EXIT;
|
|
}
|
|
|
|
if( out_corner_count != corner_count )
|
|
EXIT;
|
|
|
|
// check if we need to transpose the board
|
|
if( width != pattern_size.width )
|
|
{
|
|
CV_SWAP( width, height, k );
|
|
|
|
memcpy( corners, out_corners, corner_count*sizeof(corners[0]) );
|
|
for( i = 0; i < height; i++ )
|
|
for( j = 0; j < width; j++ )
|
|
out_corners[i*width + j] = corners[j*height + i];
|
|
}
|
|
|
|
// check if we need to revert the order in each row
|
|
{
|
|
CvPoint2D32f p0 = out_corners[0]->pt, p1 = out_corners[pattern_size.width-1]->pt,
|
|
p2 = out_corners[pattern_size.width]->pt;
|
|
if( (p1.x - p0.x)*(p2.y - p1.y) - (p1.y - p0.y)*(p2.x - p1.x) < 0 )
|
|
{
|
|
if( width % 2 == 0 )
|
|
{
|
|
for( i = 0; i < height; i++ )
|
|
for( j = 0; j < width/2; j++ )
|
|
CV_SWAP( out_corners[i*width+j], out_corners[i*width+width-j-1], c );
|
|
}
|
|
else
|
|
{
|
|
for( j = 0; j < width; j++ )
|
|
for( i = 0; i < height/2; i++ )
|
|
CV_SWAP( out_corners[i*width+j], out_corners[(height - i - 1)*width+j], c );
|
|
}
|
|
}
|
|
}
|
|
|
|
result = corner_count;
|
|
|
|
__END__;
|
|
|
|
if( result <= 0 && corners )
|
|
{
|
|
corner_count = MIN( corner_count, pattern_size.width*pattern_size.height );
|
|
for( i = 0; i < corner_count; i++ )
|
|
out_corners[i] = corners[i];
|
|
result = -corner_count;
|
|
}
|
|
|
|
cvFree( &corners );
|
|
|
|
return result;
|
|
}
|
|
|
|
//=====================================================================================
|
|
|
|
static void icvFindQuadNeighbors( CvCBQuad *quads, int quad_count )
|
|
{
|
|
const float thresh_scale = 1.f;
|
|
int idx, i, k, j;
|
|
float dx, dy, dist;
|
|
|
|
// find quad neighbors
|
|
for( idx = 0; idx < quad_count; idx++ )
|
|
{
|
|
CvCBQuad* cur_quad = &quads[idx];
|
|
|
|
// choose the points of the current quadrangle that are close to
|
|
// some points of the other quadrangles
|
|
// (it can happen for split corners (due to dilation) of the
|
|
// checker board). Search only in other quadrangles!
|
|
|
|
// for each corner of this quadrangle
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
CvPoint2D32f pt;
|
|
float min_dist = FLT_MAX;
|
|
int closest_corner_idx = -1;
|
|
CvCBQuad *closest_quad = 0;
|
|
CvCBCorner *closest_corner = 0;
|
|
|
|
if( cur_quad->neighbors[i] )
|
|
continue;
|
|
|
|
pt = cur_quad->corners[i]->pt;
|
|
|
|
// find the closest corner in all other quadrangles
|
|
for( k = 0; k < quad_count; k++ )
|
|
{
|
|
if( k == idx )
|
|
continue;
|
|
|
|
for( j = 0; j < 4; j++ )
|
|
{
|
|
if( quads[k].neighbors[j] )
|
|
continue;
|
|
|
|
dx = pt.x - quads[k].corners[j]->pt.x;
|
|
dy = pt.y - quads[k].corners[j]->pt.y;
|
|
dist = dx * dx + dy * dy;
|
|
|
|
if( dist < min_dist &&
|
|
dist <= cur_quad->edge_len*thresh_scale &&
|
|
dist <= quads[k].edge_len*thresh_scale )
|
|
{
|
|
closest_corner_idx = j;
|
|
closest_quad = &quads[k];
|
|
min_dist = dist;
|
|
}
|
|
}
|
|
}
|
|
|
|
// we found a matching corner point?
|
|
if( closest_corner_idx >= 0 && min_dist < FLT_MAX )
|
|
{
|
|
// If another point from our current quad is closer to the found corner
|
|
// than the current one, then we don't count this one after all.
|
|
// This is necessary to support small squares where otherwise the wrong
|
|
// corner will get matched to closest_quad;
|
|
closest_corner = closest_quad->corners[closest_corner_idx];
|
|
|
|
for( j = 0; j < 4; j++ )
|
|
{
|
|
if( cur_quad->neighbors[j] == closest_quad )
|
|
break;
|
|
|
|
dx = closest_corner->pt.x - cur_quad->corners[j]->pt.x;
|
|
dy = closest_corner->pt.y - cur_quad->corners[j]->pt.y;
|
|
|
|
if( dx * dx + dy * dy < min_dist )
|
|
break;
|
|
}
|
|
|
|
if( j < 4 || cur_quad->count >= 4 || closest_quad->count >= 4 )
|
|
continue;
|
|
|
|
// Check that each corner is a neighbor of different quads
|
|
for( j = 0; j < closest_quad->count; j++ )
|
|
{
|
|
if( closest_quad->neighbors[j] == cur_quad )
|
|
break;
|
|
}
|
|
if( j < closest_quad->count )
|
|
continue;
|
|
|
|
// check whether the closest corner to closest_corner
|
|
// is different from cur_quad->corners[i]->pt
|
|
for( k = 0; k < quad_count; k++ )
|
|
{
|
|
CvCBQuad* q = &quads[k];
|
|
if( k == idx || q == closest_quad )
|
|
continue;
|
|
|
|
for( j = 0; j < 4; j++ )
|
|
if( !q->neighbors[j] )
|
|
{
|
|
dx = closest_corner->pt.x - q->corners[j]->pt.x;
|
|
dy = closest_corner->pt.y - q->corners[j]->pt.y;
|
|
dist = dx*dx + dy*dy;
|
|
if( dist < min_dist )
|
|
break;
|
|
}
|
|
if( j < 4 )
|
|
break;
|
|
}
|
|
|
|
if( k < quad_count )
|
|
continue;
|
|
|
|
closest_corner->pt.x = (pt.x + closest_corner->pt.x) * 0.5f;
|
|
closest_corner->pt.y = (pt.y + closest_corner->pt.y) * 0.5f;
|
|
|
|
// We've found one more corner - remember it
|
|
cur_quad->count++;
|
|
cur_quad->neighbors[i] = closest_quad;
|
|
cur_quad->corners[i] = closest_corner;
|
|
|
|
closest_quad->count++;
|
|
closest_quad->neighbors[closest_corner_idx] = cur_quad;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
//=====================================================================================
|
|
|
|
static int
|
|
icvGenerateQuads( CvCBQuad **out_quads, CvCBCorner **out_corners,
|
|
CvMemStorage *storage, CvMat *image, int flags )
|
|
{
|
|
int quad_count = 0;
|
|
CvMemStorage *temp_storage = 0;
|
|
|
|
if( out_quads )
|
|
*out_quads = 0;
|
|
|
|
if( out_corners )
|
|
*out_corners = 0;
|
|
|
|
CV_FUNCNAME( "icvGenerateQuads" );
|
|
|
|
__BEGIN__;
|
|
|
|
CvSeq *src_contour = 0;
|
|
CvSeq *root;
|
|
CvContourEx* board = 0;
|
|
CvContourScanner scanner;
|
|
int i, idx, min_size;
|
|
|
|
CV_ASSERT( out_corners && out_quads );
|
|
|
|
// empiric bound for minimal allowed perimeter for squares
|
|
min_size = cvRound( image->cols * image->rows * .03 * 0.01 * 0.92 );
|
|
|
|
// create temporary storage for contours and the sequence of pointers to found quadrangles
|
|
CV_CALL( temp_storage = cvCreateChildMemStorage( storage ));
|
|
CV_CALL( root = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvSeq*), temp_storage ));
|
|
|
|
// initialize contour retrieving routine
|
|
CV_CALL( scanner = cvStartFindContours( image, temp_storage, sizeof(CvContourEx),
|
|
CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ));
|
|
|
|
// get all the contours one by one
|
|
while( (src_contour = cvFindNextContour( scanner )) != 0 )
|
|
{
|
|
CvSeq *dst_contour = 0;
|
|
CvRect rect = ((CvContour*)src_contour)->rect;
|
|
|
|
// reject contours with too small perimeter
|
|
if( CV_IS_SEQ_HOLE(src_contour) && rect.width*rect.height >= min_size )
|
|
{
|
|
const int min_approx_level = 2, max_approx_level = MAX_CONTOUR_APPROX;
|
|
int approx_level;
|
|
for( approx_level = min_approx_level; approx_level <= max_approx_level; approx_level++ )
|
|
{
|
|
dst_contour = cvApproxPoly( src_contour, sizeof(CvContour), temp_storage,
|
|
CV_POLY_APPROX_DP, (float)approx_level );
|
|
// we call this again on its own output, because sometimes
|
|
// cvApproxPoly() does not simplify as much as it should.
|
|
dst_contour = cvApproxPoly( dst_contour, sizeof(CvContour), temp_storage,
|
|
CV_POLY_APPROX_DP, (float)approx_level );
|
|
|
|
if( dst_contour->total == 4 )
|
|
break;
|
|
}
|
|
|
|
// reject non-quadrangles
|
|
if( dst_contour->total == 4 && cvCheckContourConvexity(dst_contour) )
|
|
{
|
|
CvPoint pt[4];
|
|
double d1, d2, p = cvContourPerimeter(dst_contour);
|
|
double area = fabs(cvContourArea(dst_contour, CV_WHOLE_SEQ));
|
|
double dx, dy;
|
|
|
|
for( i = 0; i < 4; i++ )
|
|
pt[i] = *(CvPoint*)cvGetSeqElem(dst_contour, i);
|
|
|
|
dx = pt[0].x - pt[2].x;
|
|
dy = pt[0].y - pt[2].y;
|
|
d1 = sqrt(dx*dx + dy*dy);
|
|
|
|
dx = pt[1].x - pt[3].x;
|
|
dy = pt[1].y - pt[3].y;
|
|
d2 = sqrt(dx*dx + dy*dy);
|
|
|
|
// philipg. Only accept those quadrangles which are more square
|
|
// than rectangular and which are big enough
|
|
double d3, d4;
|
|
dx = pt[0].x - pt[1].x;
|
|
dy = pt[0].y - pt[1].y;
|
|
d3 = sqrt(dx*dx + dy*dy);
|
|
dx = pt[1].x - pt[2].x;
|
|
dy = pt[1].y - pt[2].y;
|
|
d4 = sqrt(dx*dx + dy*dy);
|
|
if( !(flags & CV_CALIB_CB_FILTER_QUADS) ||
|
|
d3*4 > d4 && d4*4 > d3 && d3*d4 < area*1.5 && area > min_size &&
|
|
d1 >= 0.15 * p && d2 >= 0.15 * p )
|
|
{
|
|
CvContourEx* parent = (CvContourEx*)(src_contour->v_prev);
|
|
parent->counter++;
|
|
if( !board || board->counter < parent->counter )
|
|
board = parent;
|
|
dst_contour->v_prev = (CvSeq*)parent;
|
|
//for( i = 0; i < 4; i++ ) cvLine( debug_img, pt[i], pt[(i+1)&3], cvScalar(200,255,255), 1, CV_AA, 0 );
|
|
cvSeqPush( root, &dst_contour );
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// finish contour retrieving
|
|
cvEndFindContours( &scanner );
|
|
|
|
// allocate quad & corner buffers
|
|
CV_CALL( *out_quads = (CvCBQuad*)cvAlloc(root->total * sizeof((*out_quads)[0])));
|
|
CV_CALL( *out_corners = (CvCBCorner*)cvAlloc(root->total * 4 * sizeof((*out_corners)[0])));
|
|
|
|
// Create array of quads structures
|
|
for( idx = 0; idx < root->total; idx++ )
|
|
{
|
|
CvCBQuad* q = &(*out_quads)[quad_count];
|
|
src_contour = *(CvSeq**)cvGetSeqElem( root, idx );
|
|
if( (flags & CV_CALIB_CB_FILTER_QUADS) && src_contour->v_prev != (CvSeq*)board )
|
|
continue;
|
|
|
|
// reset group ID
|
|
memset( q, 0, sizeof(*q) );
|
|
q->group_idx = -1;
|
|
assert( src_contour->total == 4 );
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
CvPoint2D32f pt = cvPointTo32f(*(CvPoint*)cvGetSeqElem(src_contour, i));
|
|
CvCBCorner* corner = &(*out_corners)[quad_count*4 + i];
|
|
|
|
memset( corner, 0, sizeof(*corner) );
|
|
corner->pt = pt;
|
|
q->corners[i] = corner;
|
|
}
|
|
q->edge_len = FLT_MAX;
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
float dx = q->corners[i]->pt.x - q->corners[(i+1)&3]->pt.x;
|
|
float dy = q->corners[i]->pt.y - q->corners[(i+1)&3]->pt.y;
|
|
float d = dx*dx + dy*dy;
|
|
if( q->edge_len > d )
|
|
q->edge_len = d;
|
|
}
|
|
quad_count++;
|
|
}
|
|
|
|
__END__;
|
|
|
|
if( cvGetErrStatus() < 0 )
|
|
{
|
|
if( out_quads )
|
|
cvFree( out_quads );
|
|
if( out_corners )
|
|
cvFree( out_corners );
|
|
quad_count = 0;
|
|
}
|
|
|
|
cvReleaseMemStorage( &temp_storage );
|
|
return quad_count;
|
|
}
|
|
|
|
|
|
CV_IMPL void
|
|
cvDrawChessboardCorners( CvArr* _image, CvSize pattern_size,
|
|
CvPoint2D32f* corners, int count, int found )
|
|
{
|
|
CV_FUNCNAME( "cvDrawChessboardCorners" );
|
|
|
|
__BEGIN__;
|
|
|
|
const int shift = 0;
|
|
const int radius = 4;
|
|
const int r = radius*(1 << shift);
|
|
int i;
|
|
CvMat stub, *image;
|
|
double scale = 1;
|
|
int type, cn, line_type;
|
|
|
|
CV_CALL( image = cvGetMat( _image, &stub ));
|
|
|
|
type = CV_MAT_TYPE(image->type);
|
|
cn = CV_MAT_CN(type);
|
|
if( cn != 1 && cn != 3 && cn != 4 )
|
|
CV_ERROR( CV_StsUnsupportedFormat, "Number of channels must be 1, 3 or 4" );
|
|
|
|
switch( CV_MAT_DEPTH(image->type) )
|
|
{
|
|
case CV_8U:
|
|
scale = 1;
|
|
break;
|
|
case CV_16U:
|
|
scale = 256;
|
|
break;
|
|
case CV_32F:
|
|
scale = 1./255;
|
|
break;
|
|
default:
|
|
CV_ERROR( CV_StsUnsupportedFormat,
|
|
"Only 8-bit, 16-bit or floating-point 32-bit images are supported" );
|
|
}
|
|
|
|
line_type = type == CV_8UC1 || type == CV_8UC3 ? CV_AA : 8;
|
|
|
|
if( !found )
|
|
{
|
|
CvScalar color = {{0,0,255}};
|
|
if( cn == 1 )
|
|
color = cvScalarAll(200);
|
|
color.val[0] *= scale;
|
|
color.val[1] *= scale;
|
|
color.val[2] *= scale;
|
|
color.val[3] *= scale;
|
|
|
|
for( i = 0; i < count; i++ )
|
|
{
|
|
CvPoint pt;
|
|
pt.x = cvRound(corners[i].x*(1 << shift));
|
|
pt.y = cvRound(corners[i].y*(1 << shift));
|
|
cvLine( image, cvPoint( pt.x - r, pt.y - r ),
|
|
cvPoint( pt.x + r, pt.y + r ), color, 1, line_type, shift );
|
|
cvLine( image, cvPoint( pt.x - r, pt.y + r),
|
|
cvPoint( pt.x + r, pt.y - r), color, 1, line_type, shift );
|
|
cvCircle( image, pt, r+(1<<shift), color, 1, line_type, shift );
|
|
}
|
|
}
|
|
else
|
|
{
|
|
int x, y;
|
|
CvPoint prev_pt = {0, 0};
|
|
const int line_max = 7;
|
|
static const CvScalar line_colors[line_max] =
|
|
{
|
|
{{0,0,255}},
|
|
{{0,128,255}},
|
|
{{0,200,200}},
|
|
{{0,255,0}},
|
|
{{200,200,0}},
|
|
{{255,0,0}},
|
|
{{255,0,255}}
|
|
};
|
|
|
|
for( y = 0, i = 0; y < pattern_size.height; y++ )
|
|
{
|
|
CvScalar color = line_colors[y % line_max];
|
|
if( cn == 1 )
|
|
color = cvScalarAll(200);
|
|
color.val[0] *= scale;
|
|
color.val[1] *= scale;
|
|
color.val[2] *= scale;
|
|
color.val[3] *= scale;
|
|
|
|
for( x = 0; x < pattern_size.width; x++, i++ )
|
|
{
|
|
CvPoint pt;
|
|
pt.x = cvRound(corners[i].x*(1 << shift));
|
|
pt.y = cvRound(corners[i].y*(1 << shift));
|
|
|
|
if( i != 0 )
|
|
cvLine( image, prev_pt, pt, color, 1, line_type, shift );
|
|
|
|
cvLine( image, cvPoint(pt.x - r, pt.y - r),
|
|
cvPoint(pt.x + r, pt.y + r), color, 1, line_type, shift );
|
|
cvLine( image, cvPoint(pt.x - r, pt.y + r),
|
|
cvPoint(pt.x + r, pt.y - r), color, 1, line_type, shift );
|
|
cvCircle( image, pt, r+(1<<shift), color, 1, line_type, shift );
|
|
prev_pt = pt;
|
|
}
|
|
}
|
|
}
|
|
|
|
__END__;
|
|
}
|
|
|
|
|
|
/* End of file. */
|