cvcalibinit.cpp

opencv库在TI DM6437上的移植,目前包括两个库cv.lib和cxcore.lib的工程

/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                        Intel License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
//
//   * The name of Intel Corporation may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/

/************************************************************************************\
    This is improved variant of chessboard corner detection algorithm that
    uses a graph of connected quads. It is based on the code contributed
    by Vladimir Vezhnevets and Philip Gruebele.
    Here is the copyright notice from the original Vladimir's code:
    ===============================================================

    The algorithms developed and implemented by Vezhnevets Vldimir
    aka Dead Moroz ([e-mail])
    See http://graphics.cs.msu.su/en/research/calibration/opencv.html
    for detailed information.

    Reliability additions and modifications made by Philip Gruebele.
    <a href="mailto:[e-mail]">[e-mail]</a>

\************************************************************************************/

#include "_cv.h"

//=====================================================================================
// Implementation for the enhanced calibration object detection
//=====================================================================================

#define MAX_CONTOUR_APPROX  7

typedef struct CvContourEx
{
    CV_CONTOUR_FIELDS()
    int counter;
}
CvContourEx;

//=====================================================================================

/// Corner info structure
/** This structure stores information about the chessboard corner.*/
typedef struct CvCBCorner
{
    CvPoint2D32f pt; // Coordinates of the corner
    int row;         // Board row index
    int count;       // Number of neighbor corners
    struct CvCBCorner* neighbors[4]; // Neighbor corners
}
CvCBCorner;

//=====================================================================================
/// Quadrangle contour info structure
/** This structure stores information about the chessboard quadrange.*/
typedef struct CvCBQuad
{
    int count;      // Number of quad neibors
    int group_idx;  // quad group ID
    float edge_len; // quad size characteristic
    CvCBCorner *corners[4]; // Coordinates of quad corners
    struct CvCBQuad *neighbors[4]; // Pointers of quad neighbors
}
CvCBQuad;

//=====================================================================================

//static CvMat* debug_img = 0;

static int icvGenerateQuads( CvCBQuad **quads, CvCBCorner **corners,
                             CvMemStorage *storage, CvMat *image, int flags );

static void icvFindQuadNeighbors( CvCBQuad *quads, int quad_count );

static int icvFindConnectedQuads( CvCBQuad *quads, int quad_count,
                                  CvCBQuad **quad_group, int group_idx,
                                  CvMemStorage* storage );

static int icvCheckQuadGroup( CvCBQuad **quad_group, int count,
                              CvCBCorner **out_corners, CvSize pattern_size );

static int icvCleanFoundConnectedQuads( int quad_count,
                CvCBQuad **quads, CvSize pattern_size );

CV_IMPL
int cvFindChessboardCorners( const void* arr, CvSize pattern_size,
                             CvPoint2D32f* out_corners, int* out_corner_count,
                             int flags )
{
    const int min_dilations = 1;
    const int max_dilations = 3;
    int found = 0;
    CvMat* norm_img = 0;
    CvMat* thresh_img = 0;
    CvMemStorage* storage = 0;

    CvCBQuad *quads = 0, **quad_group = 0;
    CvCBCorner *corners = 0, **corner_group = 0;

    if( out_corner_count )
        *out_corner_count = 0;

    CV_FUNCNAME( "cvFindChessBoardCornerGuesses2" );

    __BEGIN__;

    int quad_count, group_idx, i, dilations;
    CvMat stub, *img = (CvMat*)arr;

    CV_CALL( img = cvGetMat( img, &stub ));
    //debug_img = img;

    if( CV_MAT_DEPTH( img->type ) != CV_8U || CV_MAT_CN( img->type ) == 2 )
        CV_ERROR( CV_StsUnsupportedFormat, "Only 8-bit grayscale or color images are supported" );

    if( pattern_size.width <= 2 || pattern_size.height <= 2 )
        CV_ERROR( CV_StsOutOfRange, "pattern should have at least 2x2 size" );

    if( !out_corners )
        CV_ERROR( CV_StsNullPtr, "Null pointer to corners" );

    CV_CALL( storage = cvCreateMemStorage(0) );
    CV_CALL( thresh_img = cvCreateMat( img->rows, img->cols, CV_8UC1 ));

    if( CV_MAT_CN(img->type) != 1 || (flags & CV_CALIB_CB_NORMALIZE_IMAGE) )
    {
        // equalize the input image histogram -
        // that should make the contrast between "black" and "white" areas big enough
        CV_CALL( norm_img = cvCreateMat( img->rows, img->cols, CV_8UC1 ));

        if( CV_MAT_CN(img->type) != 1 )
        {
            CV_CALL( cvCvtColor( img, norm_img, CV_BGR2GRAY ));
            img = norm_img;
        }

        if( flags & CV_CALIB_CB_NORMALIZE_IMAGE )
        {
            cvEqualizeHist( img, norm_img );
            img = norm_img;
        }
    }

    // Try our standard "1" dilation, but if the pattern is not found, iterate the whole procedure with higher dilations.
    // This is necessary because some squares simply do not separate properly with a single dilation.  However,
    // we want to use the minimum number of dilations possible since dilations cause the squares to become smaller,
    // making it difficult to detect smaller squares.
    for( dilations = min_dilations; dilations <= max_dilations; dilations++ )
    {
        // convert the input grayscale image to binary (black-n-white)
        if( flags & CV_CALIB_CB_ADAPTIVE_THRESH )
        {
            int block_size = cvRound(MIN(img->cols,img->rows)*0.2)|1;
            cvDilate( img, thresh_img, 0, dilations );

            // convert to binary
            cvAdaptiveThreshold( img, thresh_img, 255,
                CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, block_size, 0 );
            cvDilate( thresh_img, thresh_img, 0, dilations-1 );
        }
        else
        {
            // Make dilation before the thresholding.
            // It splits chessboard corners
            //cvDilate( img, thresh_img, 0, 1 );

            // empiric threshold level
            double mean = cvMean( img );
            int thresh_level = cvRound( mean - 10 );
            thresh_level = MAX( thresh_level, 10 );

            cvThreshold( img, thresh_img, thresh_level, 255, CV_THRESH_BINARY );
            cvDilate( thresh_img, thresh_img, 0, dilations );
        }

        // So we can find rectangles that go to the edge, we draw a white line around the image edge.
        // Otherwise FindContours will miss those clipped rectangle contours.
        // The border color will be the image mean, because otherwise we risk screwing up filters like cvSmooth()...
        cvRectangle( thresh_img, cvPoint(0,0), cvPoint(thresh_img->cols-1,
                     thresh_img->rows-1), CV_RGB(255,255,255), 3, 8);

        CV_CALL( quad_count = icvGenerateQuads( &quads, &corners, storage, thresh_img, flags ));
        if( quad_count <= 0 )
            continue;

        // Find quad's neighbors
        CV_CALL( icvFindQuadNeighbors( quads, quad_count ));

        CV_CALL( quad_group = (CvCBQuad**)cvAlloc( sizeof(quad_group[0]) * quad_count));
        CV_CALL( corner_group = (CvCBCorner**)cvAlloc( sizeof(corner_group[0]) * quad_count*4 ));

        for( group_idx = 0; ; group_idx++ )
        {
            int count;
            CV_CALL( count = icvFindConnectedQuads( quads, quad_count, quad_group, group_idx, storage ));

            if( count == 0 )
                break;

            // If count is more than it should be, this will remove those quads
            // which cause maximum deviation from a nice square pattern.
            CV_CALL( count = icvCleanFoundConnectedQuads( count, quad_group, pattern_size ));
            CV_CALL( count = icvCheckQuadGroup( quad_group, count, corner_group, pattern_size ));

            if( count > 0 || (out_corner_count && -count > *out_corner_count) )
            {
                int n = count > 0 ? pattern_size.width * pattern_size.height : -count;
                n = MIN( n, pattern_size.width * pattern_size.height );

                // copy corners to output array
                for( i = 0; i < n; i++ )
                    out_corners[i] = corner_group[i]->pt;

                if( out_corner_count )
                    *out_corner_count = n;

                if( count > 0 )
                {
                    found = 1;
                    EXIT;
                }
            }
        }

        cvFree( &quads );
        cvFree( &corners );
    }

    __END__;

    cvReleaseMemStorage( &storage );
    cvReleaseMat( &norm_img );
    cvReleaseMat( &thresh_img );
    cvFree( &quads );
    cvFree( &corners );
    cvFree( &quad_group );
    cvFree( &corner_group );

    return found;
}


// if we found too many connect quads, remove those which probably do not belong.
static int
icvCleanFoundConnectedQuads( int quad_count, CvCBQuad **quad_group, CvSize pattern_size )
{
    CvMemStorage *temp_storage = 0;
    CvPoint2D32f *centers = 0;

    CV_FUNCNAME( "icvCleanFoundConnectedQuads" );

    __BEGIN__;

    CvPoint2D32f center = {0,0};
    int i, j, k;
    // number of quads this pattern should contain
    int count = ((pattern_size.width + 1)*(pattern_size.height + 1) + 1)/2;

    // if we have more quadrangles than we should,
    // try to eliminate duplicates or ones which don't belong to the pattern rectangle...
    if( quad_count <= count )
        EXIT;

    // create an array of quadrangle centers
    CV_CALL( centers = (CvPoint2D32f *)cvAlloc( sizeof(centers[0])*quad_count ));
    CV_CALL( temp_storage = cvCreateMemStorage(0));

    for( i = 0; i < quad_count; i++ )
    {
        CvPoint2D32f ci = {0,0};
        CvCBQuad* q = quad_group[i];

        for( j = 0; j < 4; j++ )
        {
            CvPoint2D32f pt = q->corners[j]->pt;
            ci.x += pt.x;
            ci.y += pt.y;
        }

        ci.x *= 0.25f;
        ci.y *= 0.25f;

        centers[i] = ci;
        center.x += ci.x;
        center.y += ci.y;
    }
    center.x /= quad_count;
    center.y /= quad_count;

    // If we still have more quadrangles than we should,
    // we try to eliminate bad ones based on minimizing the bounding box.
    // We iteratively remove the point which reduces the size of
    // the bounding box of the blobs the most
    // (since we want the rectangle to be as small as possible)
    // remove the quadrange that causes the biggest reduction
    // in pattern size until we have the correct number
    for( ; quad_count > count; quad_count-- )
    {
        double min_box_area = DBL_MAX;
        int skip, min_box_area_index = -1;
        CvCBQuad *q0, *q;

        // For each point, calculate box area without that point
        for( skip = 0; skip < quad_count; skip++ )
        {
            // get bounding rectangle
            CvPoint2D32f temp = centers[skip]; // temporarily make index 'skip' the same as
            centers[skip] = center;            // pattern center (so it is not counted for convex hull)
            CvMat pointMat = cvMat(1, quad_count, CV_32FC2, centers);
            CvSeq *hull = cvConvexHull2( &pointMat, temp_storage, CV_CLOCKWISE, 1 );
            centers[skip] = temp;
            double hull_area = fabs(cvContourArea(hull, CV_WHOLE_SEQ));

            // remember smallest box area
            if( hull_area < min_box_area )
            {
                min_box_area = hull_area;
                min_box_area_index = skip;
            }
            cvClearMemStorage( temp_storage );
        }

        q0 = quad_group[min_box_area_index];

        // remove any references to this quad as a neighbor
        for( i = 0; i < quad_count; i++ )
        {
            q = quad_group[i];
            for( j = 0; j < 4; j++ )
            {
                if( q->neighbors[j] == q0 )
                {
                    q->neighbors[j] = 0;
                    q->count--;
                    for( k = 0; k < 4; k++ )