www.pudn.com > OpenCV-Intel.zip > cvcalibinit.cpp
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/************************************************************************************\
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 (vvp@graphics.cs.msu.ru)
See http://graphics.cs.msu.su/en/research/calibration/opencv.html
for detailed information.
Reliability additions and modifications made by Philip Gruebele.
pgruebele@cox.net
\************************************************************************************/
#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( (void**)&quads );
cvFree( (void**)&corners );
}
__END__;
cvReleaseMemStorage( &storage );
cvReleaseMat( &norm_img );
cvReleaseMat( &thresh_img );
cvFree( (void**)&quads );
cvFree( (void**)&corners );
cvFree( (void**)&quad_group );
cvFree( (void**)&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++ )
if( q0->neighbors[k] == q )
{
q0->neighbors[k] = 0;
q0->count--;
break;
}
break;
}
}
}
// remove the quad
quad_count--;
quad_group[min_box_area_index] = quad_group[quad_count];
centers[min_box_area_index] = centers[quad_count];
}
__END__;
cvReleaseMemStorage( &temp_storage );
cvFree( (void**)¢ers );
return quad_count;
}
//=====================================================================================
static int
icvFindConnectedQuads( CvCBQuad *quad, int quad_count, CvCBQuad **out_group,
int group_idx, CvMemStorage* storage )
{
CvMemStorage* temp_storage = cvCreateChildMemStorage( storage );
CvSeq* stack = cvCreateSeq( 0, sizeof(*stack), sizeof(void*), temp_storage );
int i, count = 0;
// Scan the array for a first unlabeled quad
for( i = 0; i < quad_count; i++ )
{
if( quad[i].count > 0 && quad[i].group_idx < 0)
break;
}
// Recursively find a group of connected quads starting from the seed quad[i]
if( i < quad_count )
{
CvCBQuad* q = &quad[i];
cvSeqPush( stack, &q );
out_group[count++] = q;
q->group_idx = group_idx;
while( stack->total )
{
cvSeqPop( stack, &q );
for( i = 0; i < 4; i++ )
{
CvCBQuad *neighbor = q->neighbors[i];
if( neighbor && neighbor->count > 0 && neighbor->group_idx < 0 )
{
cvSeqPush( stack, &neighbor );
out_group[count++] = neighbor;
neighbor->group_idx = group_idx;
}
}
}
}
cvReleaseMemStorage( &temp_storage );
return count;
}
//=====================================================================================
static int
icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count,
CvCBCorner **out_corners, CvSize pattern_size )
{
const int ROW1 = 1000000;
const int ROW2 = 2000000;
const int ROW_ = 3000000;
int result = 0;
int i, out_corner_count = 0, corner_count = 0;
CvCBCorner** corners = 0;
CV_FUNCNAME( "icvCheckQuadGroup" );
__BEGIN__;
int j, k, kk;
int width = 0, height = 0;
int hist[5] = {0,0,0,0,0};
CvCBCorner* first = 0, *first2 = 0, *right, *cur, *below, *c;
CV_CALL( corners = (CvCBCorner**)cvAlloc( quad_count*4*sizeof(corners[0]) ));
// build dual graph, which vertices are internal quad corners
// and two vertices are connected iff they lie on the same quad edge
for( i = 0; i < quad_count; i++ )
{
CvCBQuad* q = quad_group[i];
/*CvScalar color = q->count == 0 ? cvScalar(0,255,255) :
q->count == 1 ? cvScalar(0,0,255) :
q->count == 2 ? cvScalar(0,255,0) :
q->count == 3 ? cvScalar(255,255,0) :
cvScalar(255,0,0);*/
for( j = 0; j < 4; j++ )
{
//cvLine( debug_img, cvPointFrom32f(q->corners[j]->pt), cvPointFrom32f(q->corners[(j+1)&3]->pt), color, 1, CV_AA, 0 );
if( q->neighbors[j] )
{
CvCBCorner *a = q->corners[j], *b = q->corners[(j+1)&3];
// mark internal corners that belong to:
// - a quad with a single neighbor - with ROW1,
// - a quad with two neighbors - with ROW2
// make the rest of internal corners with ROW_
int row_flag = q->count == 1 ? ROW1 : q->count == 2 ? ROW2 : ROW_;
if( a->row == 0 )
{
corners[corner_count++] = a;
a->row = row_flag;
}
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( (void**)&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( (void**)out_quads );
if( out_corners )
cvFree( (void**)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<