// Function cvUpdateFGDStatModel updates statistical model and returns number of foreground regions
// parameters:
// curr_frame - current frame from video sequence
// p_model - pointer to CvFGDStatModel structure
static int CV_CDECL
icvUpdateFGDStatModel( IplImage* curr_frame, CvFGDStatModel* model, double )
{
int mask_step = model->Ftd->widthStep;
CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
IplImage* prev_frame = model->prev_frame;
int region_count = 0;
int FG_pixels_count = 0;
int deltaC = cvRound(model->params.delta * 256 / model->params.Lc);
int deltaCC = cvRound(model->params.delta * 256 / model->params.Lcc);
int i, j, k, l;
//clear storages
cvClearMemStorage(model->storage);
cvZero(model->foreground);
// From foreground pixel candidates using image differencing
// with adaptive thresholding. The algorithm is from:
//
// Thresholding for Change Detection
// Paul L. Rosin 1998 6p
// http://www.cis.temple.edu/~latecki/Courses/CIS750-03/Papers/thresh-iccv.pdf
//
cvChangeDetection( prev_frame, curr_frame, model->Ftd );
cvChangeDetection( model->background, curr_frame, model->Fbd );
for( i = 0; i < model->Ftd->height; i++ )
{
for( j = 0; j < model->Ftd->width; j++ )
{
if( ((uchar*)model->Fbd->imageData)[i*mask_step+j] || ((uchar*)model->Ftd->imageData)[i*mask_step+j] )
{
float Pb = 0;
float Pv = 0;
float Pvb = 0;
CvBGPixelStat* stat = model->pixel_stat + i * model->Ftd->width + j;
CvBGPixelCStatTable* ctable = stat->ctable;
CvBGPixelCCStatTable* cctable = stat->cctable;
uchar* curr_data = (uchar*)(curr_frame->imageData) + i*curr_frame->widthStep + j*3;
uchar* prev_data = (uchar*)(prev_frame->imageData) + i*prev_frame->widthStep + j*3;
int val = 0;
// Is it a motion pixel?
if( ((uchar*)model->Ftd->imageData)[i*mask_step+j] )
{
if( !stat->is_trained_dyn_model ) {
val = 1;
} else {
// Compare with stored CCt vectors:
for( k = 0; PV_CC(k) > model->params.alpha2 && k < model->params.N1cc; k++ )
{
if ( abs( V_CC(k,0) - prev_data[0]) <= deltaCC &&
abs( V_CC(k,1) - prev_data[1]) <= deltaCC &&
abs( V_CC(k,2) - prev_data[2]) <= deltaCC &&
abs( V_CC(k,3) - curr_data[0]) <= deltaCC &&
abs( V_CC(k,4) - curr_data[1]) <= deltaCC &&
abs( V_CC(k,5) - curr_data[2]) <= deltaCC)
{
Pv += PV_CC(k);
Pvb += PVB_CC(k);
}
}
Pb = stat->Pbcc;
if( 2 * Pvb * Pb <= Pv ) val = 1;
}
}
else if( stat->is_trained_st_model )
{
// Compare with stored Ct vectors:
for( k = 0; PV_C(k) > model->params.alpha2 && k < model->params.N1c; k++ )
{
if ( abs( V_C(k,0) - curr_data[0]) <= deltaC &&
abs( V_C(k,1) - curr_data[1]) <= deltaC &&
abs( V_C(k,2) - curr_data[2]) <= deltaC )
{
Pv += PV_C(k);
Pvb += PVB_C(k);
}
}
Pb = stat->Pbc;
if( 2 * Pvb * Pb <= Pv ) val = 1;
}
// Update foreground:
((uchar*)model->foreground->imageData)[i*mask_step+j] = (uchar)(val*255);
FG_pixels_count += val;
} // end if( change detection...
} // for j...
} // for i...
//end BG/FG classification
// Foreground segmentation.
// Smooth foreground map:
if( model->params.perform_morphing ){
cvMorphologyEx( model->foreground, model->foreground, 0, 0, CV_MOP_OPEN, model->params.perform_morphing );
cvMorphologyEx( model->foreground, model->foreground, 0, 0, CV_MOP_CLOSE, model->params.perform_morphing );
}
if( model->params.minArea > 0 || model->params.is_obj_without_holes ){
// Discard under-size foreground regions:
//
cvFindContours( model->foreground, model->storage, &first_seq, sizeof(CvContour), CV_RETR_LIST );
for( seq = first_seq; seq; seq = seq->h_next )
{
CvContour* cnt = (CvContour*)seq;
if( cnt->rect.width * cnt->rect.height < model->params.minArea ||
(model->params.is_obj_without_holes && CV_IS_SEQ_HOLE(seq)) )
{
// Delete under-size contour:
prev_seq = seq->h_prev;
if( prev_seq )
{
prev_seq->h_next = seq->h_next;
if( seq->h_next ) seq->h_next->h_prev = prev_seq;
}
else
{
first_seq = seq->h_next;
if( seq->h_next ) seq->h_next->h_prev = NULL;
}
}
else
{
region_count++;
}
}
model->foreground_regions = first_seq;
cvZero(model->foreground);
cvDrawContours(model->foreground, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1);
} else {
model->foreground_regions = NULL;
}
// Check ALL BG update condition:
if( ((float)FG_pixels_count/(model->Ftd->width*model->Ftd->height)) > CV_BGFG_FGD_BG_UPDATE_TRESH )
{
for( i = 0; i < model->Ftd->height; i++ )
for( j = 0; j < model->Ftd->width; j++ )
{
CvBGPixelStat* stat = model->pixel_stat + i * model->Ftd->width + j;
stat->is_trained_st_model = stat->is_trained_dyn_model = 1;
}
}
// Update background model:
for( i = 0; i < model->Ftd->height; i++ )
{
for( j = 0; j < model->Ftd->width; j++ )
{
CvBGPixelStat* stat = model->pixel_stat + i * model->Ftd->width + j;
CvBGPixelCStatTable* ctable = stat->ctable;
CvBGPixelCCStatTable* cctable = stat->cctable;
uchar *curr_data = (uchar*)(curr_frame->imageData)+i*curr_frame->widthStep+j*3;
uchar *prev_data = (uchar*)(prev_frame->imageData)+i*prev_frame->widthStep+j*3;
if( ((uchar*)model->Ftd->imageData)[i*mask_step+j] || !stat->is_trained_dyn_model )
{
float alpha = stat->is_trained_dyn_model ? model->params.alpha2 : model->params.alpha3;
float diff = 0;
int dist, min_dist = 2147483647, indx = -1;
//update Pb
stat->Pbcc *= (1.f-alpha);
if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
{
stat->Pbcc += alpha;
}
// Find best Vi match:
for(k = 0; PV_CC(k) && k < model->params.N2cc; k++ )
{
// Exponential decay of memory
PV_CC(k) *= (1-alpha);
PVB_CC(k) *= (1-alpha);
if( PV_CC(k) < MIN_PV )
{
PV_CC(k) = 0;
PVB_CC(k) = 0;
continue;
}
dist = 0;
for( l = 0; l < 3; l++ )
{
int val = abs( V_CC(k,l) - prev_data[l] );
if( val > deltaCC ) break;
dist += val;
val = abs( V_CC(k,l+3) - curr_data[l] );
if( val > deltaCC) break;
dist += val;
}
if( l == 3 && dist < min_dist )
{
min_dist = dist;
indx = k;
}
}
if( indx < 0 )
{ // Replace N2th elem in the table by new feature:
indx = model->params.N2cc - 1;
PV_CC(indx) = alpha;
PVB_CC(indx) = alpha;
//udate Vt
for( l = 0; l < 3; l++ )
{
V_CC(indx,l) = prev_data[l];
V_CC(indx,l+3) = curr_data[l];
}
}
else
{ // Update:
PV_CC(indx) += alpha;
if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
{
PVB_CC(indx) += alpha;
}
}
//re-sort CCt table by Pv
for( k = 0; k < indx; k++ )
{
if( PV_CC(k) <= PV_CC(indx) )
{
//shift elements
CvBGPixelCCStatTable tmp1, tmp2 = cctable[indx];
for( l = k; l <= indx; l++ )
{
tmp1 = cctable[l];
cctable[l] = tmp2;
tmp2 = tmp1;
}
break;
}
}
float sum1=0, sum2=0;
//check "once-off" changes
for(k = 0; PV_CC(k) && k < model->params.N1cc; k++ )
{
sum1 += PV_CC(k);
sum2 += PVB_CC(k);
}
if( sum1 > model->params.T ) stat->is_trained_dyn_model = 1;
diff = sum1 - stat->Pbcc * sum2;
// Update stat table:
if( diff > model->params.T )
{
//printf("once off change at motion mode\n");
//new BG features are discovered
for( k = 0; PV_CC(k) && k < model->params.N1cc; k++ )
{
PVB_CC(k) =
(PV_CC(k)-stat->Pbcc*PVB_CC(k))/(1-stat->Pbcc);
}
assert(stat->Pbcc<=1 && stat->Pbcc>=0);
}
}
// Handle "stationary" pixel:
if( !((uchar*)model->Ftd->imageData)[i*mask_step+j] )
{
float alpha = stat->is_trained_st_model ? model->params.alpha2 : model->params.alpha3;
float diff = 0;
int dist, min_dist = 2147483647, indx = -1;
//update Pb
stat->Pbc *= (1.f-alpha);
if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
{
stat->Pbc += alpha;
}
//find best Vi match
for( k = 0; k < model->params.N2c; k++ )
{
// Exponential decay of memory
PV_C(k) *= (1-alpha);
PVB_C(k) *= (1-alpha);
if( PV_C(k) < MIN_PV )
{
PV_C(k) = 0;
PVB_C(k) = 0;
continue;
}
dist = 0;
for( l = 0; l < 3; l++ )
{
int val = abs( V_C(k,l) - curr_data[l] );
if( val > deltaC ) break;
dist += val;
}
if( l == 3 && dist < min_dist )
{
min_dist = dist;
indx = k;
}
}
if( indx < 0 )
{//N2th elem in the table is replaced by a new features
indx = model->params.N2c - 1;
PV_C(indx) = alpha;
PVB_C(indx) = alpha;
//udate Vt
for( l = 0; l < 3; l++ )
{
V_C(indx,l) = curr_data[l];
}
} else
{//update
PV_C(indx) += alpha;
if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
{
PVB_C(indx) += alpha;
}
}
//re-sort Ct table by Pv
for( k = 0; k < indx; k++ )
{
if( PV_C(k) <= PV_C(indx) )
{
//shift elements
CvBGPixelCStatTable tmp1, tmp2 = ctable[indx];
for( l = k; l <= indx; l++ )
{
tmp1 = ctable[l];
ctable[l] = tmp2;
tmp2 = tmp1;
}
break;
}
}
// Check "once-off" changes:
float sum1=0, sum2=0;
for( k = 0; PV_C(k) && k < model->params.N1c; k++ )
{
sum1 += PV_C(k);
sum2 += PVB_C(k);
}
diff = sum1 - stat->Pbc * sum2;
if( sum1 > model->params.T ) stat->is_trained_st_model = 1;
// Update stat table:
if( diff > model->params.T )
{
//printf("once off change at stat mode\n");
//new BG features are discovered
for( k = 0; PV_C(k) && k < model->params.N1c; k++ )
{
PVB_C(k) = (PV_C(k)-stat->Pbc*PVB_C(k))/(1-stat->Pbc);
}
stat->Pbc = 1 - stat->Pbc;
}
} // if !(change detection) at pixel (i,j)
// Update the reference BG image:
if( !((uchar*)model->foreground->imageData)[i*mask_step+j])
{
uchar* ptr = ((uchar*)model->background->imageData) + i*model->background->widthStep+j*3;
if( !((uchar*)model->Ftd->imageData)[i*mask_step+j] &&
!((uchar*)model->Fbd->imageData)[i*mask_step+j] )
{
// Apply IIR filter:
for( l = 0; l < 3; l++ )
{
int a = cvRound(ptr[l]*(1 - model->params.alpha1) + model->params.alpha1*curr_data[l]);
ptr[l] = (uchar)a;
//((uchar*)model->background->imageData)[i*model->background->widthStep+j*3+l]*=(1 - model->params.alpha1);
//((uchar*)model->background->imageData)[i*model->background->widthStep+j*3+l] += model->params.alpha1*curr_data[l];
}
}
else
{
// Background change detected:
for( l = 0; l < 3; l++ )
{
//((uchar*)model->background->imageData)[i*model->background->widthStep+j*3+l] = curr_data[l];
ptr[l] = curr_data[l];
}
}
}
} // j
} // i
// Keep previous frame:
cvCopy( curr_frame, model->prev_frame );
return region_count;
}
static CvStatus
icvFetchContourEx( char* ptr,
int step,
CvPoint pt,
CvSeq* contour,
int _method,
int nbd,
CvRect* _rect )
{
int deltas[16];
CvSeqWriter writer;
char *i0 = ptr, *i1, *i3, *i4;
CvRect rect;
int prev_s = -1, s, s_end;
int method = _method - 1;
assert( (unsigned) _method <= CV_CHAIN_APPROX_SIMPLE );
assert( 1 < nbd && nbd < 128 );
/* initialize local state */
CV_INIT_3X3_DELTAS( deltas, step, 1 );
memcpy( deltas + 8, deltas, 8 * sizeof( deltas[0] ));
/* initialize writer */
cvStartAppendToSeq( contour, &writer );
if( method < 0 )
((CvChain *)contour)->origin = pt;
rect.x = rect.width = pt.x;
rect.y = rect.height = pt.y;
s_end = s = CV_IS_SEQ_HOLE( contour ) ? 0 : 4;
do
{
s = (s - 1) & 7;
i1 = i0 + deltas[s];
if( *i1 != 0 )
break;
}
while( s != s_end );
if( s == s_end ) /* single pixel domain */
{
*i0 = (char) (nbd | 0x80);
if( method >= 0 )
{
CV_WRITE_SEQ_ELEM( pt, writer );
}
}
else
{
i3 = i0;
prev_s = s ^ 4;
/* follow border */
for( ;; )
{
s_end = s;
for( ;; )
{
i4 = i3 + deltas[++s];
if( *i4 != 0 )
break;
}
s &= 7;
/* check "right" bound */
if( (unsigned) (s - 1) < (unsigned) s_end )
{
*i3 = (char) (nbd | 0x80);
}
else if( *i3 == 1 )
{
*i3 = (char) nbd;
}
if( method < 0 )
{
char _s = (char) s;
CV_WRITE_SEQ_ELEM( _s, writer );
}
else if( s != prev_s || method == 0 )
{
CV_WRITE_SEQ_ELEM( pt, writer );
}
if( s != prev_s )
{
/* update bounds */
if( pt.x < rect.x )
rect.x = pt.x;
else if( pt.x > rect.width )
rect.width = pt.x;
if( pt.y < rect.y )
rect.y = pt.y;
else if( pt.y > rect.height )
rect.height = pt.y;
}
prev_s = s;
pt.x += icvCodeDeltas[s].x;
pt.y += icvCodeDeltas[s].y;
if( i4 == i0 && i3 == i1 ) break;
i3 = i4;
s = (s + 4) & 7;
} /* end of border following loop */
}
rect.width -= rect.x - 1;
rect.height -= rect.y - 1;
cvEndWriteSeq( &writer );
if( _method != CV_CHAIN_CODE )
((CvContour*)contour)->rect = rect;
assert( writer.seq->total == 0 && writer.seq->first == 0 ||
writer.seq->total > writer.seq->first->count ||
(writer.seq->first->prev == writer.seq->first &&
writer.seq->first->next == writer.seq->first) );
if( _rect ) *_rect = rect;
return CV_OK;
}
IplImage * find_macbeth( const char *img )
{
IplImage * macbeth_img = cvLoadImage( img,
CV_LOAD_IMAGE_ANYCOLOR|CV_LOAD_IMAGE_ANYDEPTH );
IplImage * macbeth_original = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), macbeth_img->depth, macbeth_img->nChannels );
cvCopy(macbeth_img, macbeth_original);
IplImage * macbeth_split[3];
IplImage * macbeth_split_thresh[3];
for(int i = 0; i < 3; i++) {
macbeth_split[i] = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), macbeth_img->depth, 1 );
macbeth_split_thresh[i] = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), macbeth_img->depth, 1 );
}
cvSplit(macbeth_img, macbeth_split[0], macbeth_split[1], macbeth_split[2], NULL);
if( macbeth_img )
{
int adaptive_method = CV_ADAPTIVE_THRESH_MEAN_C;
int threshold_type = CV_THRESH_BINARY_INV;
int block_size = cvRound(
MIN(macbeth_img->width,macbeth_img->height)*0.02)|1;
fprintf(stderr,"Using %d as block size\n", block_size);
double offset = 6;
// do an adaptive threshold on each channel
for(int i = 0; i < 3; i++) {
cvAdaptiveThreshold(macbeth_split[i], macbeth_split_thresh[i], 255, adaptive_method, threshold_type, block_size, offset);
}
IplImage * adaptive = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), IPL_DEPTH_8U, 1 );
// OR the binary threshold results together
cvOr(macbeth_split_thresh[0],macbeth_split_thresh[1],adaptive);
cvOr(macbeth_split_thresh[2],adaptive,adaptive);
for(int i = 0; i < 3; i++) {
cvReleaseImage( &(macbeth_split[i]) );
cvReleaseImage( &(macbeth_split_thresh[i]) );
}
int element_size = (block_size/10)+2;
fprintf(stderr,"Using %d as element size\n", element_size);
// do an opening on the threshold image
IplConvKernel * element = cvCreateStructuringElementEx(element_size,element_size,element_size/2,element_size/2,CV_SHAPE_RECT);
cvMorphologyEx(adaptive,adaptive,NULL,element,CV_MOP_OPEN);
cvReleaseStructuringElement(&element);
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* initial_quads = cvCreateSeq( 0, sizeof(*initial_quads), sizeof(void*), storage );
CvSeq* initial_boxes = cvCreateSeq( 0, sizeof(*initial_boxes), sizeof(CvBox2D), storage );
// find contours in the threshold image
CvSeq * contours = NULL;
cvFindContours(adaptive,storage,&contours);
int min_size = (macbeth_img->width*macbeth_img->height)/
(MACBETH_SQUARES*100);
if(contours) {
int count = 0;
for( CvSeq* c = contours; c != NULL; c = c->h_next) {
CvRect rect = ((CvContour*)c)->rect;
// only interested in contours with these restrictions
if(CV_IS_SEQ_HOLE(c) && rect.width*rect.height >= min_size) {
// only interested in quad-like contours
CvSeq * quad_contour = find_quad(c, storage, min_size);
if(quad_contour) {
cvSeqPush( initial_quads, &quad_contour );
count++;
rect = ((CvContour*)quad_contour)->rect;
CvScalar average = contour_average((CvContour*)quad_contour, macbeth_img);
CvBox2D box = cvMinAreaRect2(quad_contour,storage);
cvSeqPush( initial_boxes, &box );
// fprintf(stderr,"Center: %f %f\n", box.center.x, box.center.y);
double min_distance = MAX_RGB_DISTANCE;
CvPoint closest_color_idx = cvPoint(-1,-1);
for(int y = 0; y < MACBETH_HEIGHT; y++) {
for(int x = 0; x < MACBETH_WIDTH; x++) {
double distance = euclidean_distance_lab(average,colorchecker_srgb[y][x]);
if(distance < min_distance) {
closest_color_idx.x = x;
closest_color_idx.y = y;
min_distance = distance;
}
}
}
CvScalar closest_color = colorchecker_srgb[closest_color_idx.y][closest_color_idx.x];
// fprintf(stderr,"Closest color: %f %f %f (%d %d)\n",
// closest_color.val[2],
// closest_color.val[1],
// closest_color.val[0],
// closest_color_idx.x,
// closest_color_idx.y
// );
// cvDrawContours(
// macbeth_img,
// quad_contour,
// cvScalar(255,0,0),
// cvScalar(0,0,255),
// 0,
// element_size
// );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(box.center),
// element_size*6,
// cvScalarAll(255),
// -1
// );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(box.center),
// element_size*6,
// closest_color,
// -1
// );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(box.center),
// element_size*4,
// average,
// -1
// );
// CvRect rect = contained_rectangle(box);
// cvRectangle(
// macbeth_img,
// cvPoint(rect.x,rect.y),
// cvPoint(rect.x+rect.width, rect.y+rect.height),
// cvScalarAll(0),
// element_size
// );
}
}
}
ColorChecker found_colorchecker;
fprintf(stderr,"%d initial quads found", initial_quads->total);
if(count > MACBETH_SQUARES) {
fprintf(stderr," (probably a Passport)\n");
CvMat* points = cvCreateMat( initial_quads->total , 1, CV_32FC2 );
CvMat* clusters = cvCreateMat( initial_quads->total , 1, CV_32SC1 );
CvSeq* partitioned_quads[2];
CvSeq* partitioned_boxes[2];
for(int i = 0; i < 2; i++) {
partitioned_quads[i] = cvCreateSeq( 0, sizeof(**partitioned_quads), sizeof(void*), storage );
partitioned_boxes[i] = cvCreateSeq( 0, sizeof(**partitioned_boxes), sizeof(CvBox2D), storage );
}
// set up the points sequence for cvKMeans2, using the box centers
for(int i = 0; i < initial_quads->total; i++) {
CvBox2D box = (*(CvBox2D*)cvGetSeqElem(initial_boxes, i));
cvSet1D(points, i, cvScalar(box.center.x,box.center.y));
}
// partition into two clusters: passport and colorchecker
cvKMeans2( points, 2, clusters,
cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,
10, 1.0 ) );
for(int i = 0; i < initial_quads->total; i++) {
CvPoint2D32f pt = ((CvPoint2D32f*)points->data.fl)[i];
int cluster_idx = clusters->data.i[i];
cvSeqPush( partitioned_quads[cluster_idx],
cvGetSeqElem(initial_quads, i) );
cvSeqPush( partitioned_boxes[cluster_idx],
cvGetSeqElem(initial_boxes, i) );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(pt),
// element_size*2,
// cvScalar(255*cluster_idx,0,255-(255*cluster_idx)),
// -1
// );
}
ColorChecker partitioned_checkers[2];
// check each of the two partitioned sets for the best colorchecker
for(int i = 0; i < 2; i++) {
partitioned_checkers[i] =
find_colorchecker(partitioned_quads[i], partitioned_boxes[i],
storage, macbeth_img, macbeth_original);
}
// use the colorchecker with the lowest error
found_colorchecker = partitioned_checkers[0].error < partitioned_checkers[1].error ?
partitioned_checkers[0] : partitioned_checkers[1];
cvReleaseMat( &points );
cvReleaseMat( &clusters );
}
else { // just one colorchecker to test
fprintf(stderr,"\n");
found_colorchecker = find_colorchecker(initial_quads, initial_boxes,
storage, macbeth_img, macbeth_original);
}
// render the found colorchecker
draw_colorchecker(found_colorchecker.values,found_colorchecker.points,macbeth_img,found_colorchecker.size);
// print out the colorchecker info
for(int y = 0; y < MACBETH_HEIGHT; y++) {
for(int x = 0; x < MACBETH_WIDTH; x++) {
CvScalar this_value = cvGet2D(found_colorchecker.values,y,x);
CvScalar this_point = cvGet2D(found_colorchecker.points,y,x);
printf("%.0f,%.0f,%.0f,%.0f,%.0f\n",
this_point.val[0],this_point.val[1],
this_value.val[2],this_value.val[1],this_value.val[0]);
}
}
printf("%0.f\n%f\n",found_colorchecker.size,found_colorchecker.error);
}
cvReleaseMemStorage( &storage );
if( macbeth_original ) cvReleaseImage( &macbeth_original );
if( adaptive ) cvReleaseImage( &adaptive );
return macbeth_img;
}
if( macbeth_img ) cvReleaseImage( &macbeth_img );
return NULL;
}
/*
* If curve is hole(inner contour), return true. Otherwise return false.
*/
VALUE
rb_hole_q(VALUE self)
{
return CV_IS_SEQ_HOLE(CVSEQ(self)) ? Qtrue : Qfalse;
}
