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// For Open Source Computer Vision Library
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#include "_cv.h"
#define cmp_features( f1, f2 ) (*(f1) > *(f2))
static CV_IMPLEMENT_QSORT( icvSortFeatures, int *, cmp_features )
CV_IMPL void
cvGoodFeaturesToTrack( const void* image, void* eigImage, void* tempImage,
CvPoint2D32f* corners, int *corner_count,
double quality_level, double min_distance,
const void* maskImage, int block_size,
int use_harris, double harris_k )
{
CvMat* _eigImg = 0;
CvMat* _tmpImg = 0;
CV_FUNCNAME( "cvGoodFeaturesToTrack" );
__BEGIN__;
double max_val = 0;
int max_count = 0;
int count = 0;
int x, y, i, k = 0;
int min_dist;
int eig_step, tmp_step;
/* when selecting points, use integer coordinates */
CvPoint *ptr = (CvPoint *) corners;
/* process floating-point images using integer arithmetics */
int *eig_data = 0;
int *tmp_data = 0;
int **ptr_data = 0;
uchar *mask_data = 0;
int mask_step = 0;
CvSize size;
int coi1 = 0, coi2 = 0, coi3 = 0;
CvMat stub, *img = (CvMat*)image;
CvMat eig_stub, *eig = (CvMat*)eigImage;
CvMat tmp_stub, *tmp = (CvMat*)tempImage;
CvMat mask_stub, *mask = (CvMat*)maskImage;
if( corner_count )
{
max_count = *corner_count;
*corner_count = 0;
}
CV_CALL( img = cvGetMat( img, &stub, &coi1 ));
if( eig )
{
CV_CALL( eig = cvGetMat( eig, &eig_stub, &coi2 ));
}
else
{
CV_CALL( _eigImg = cvCreateMat( img->rows, img->cols, CV_32FC1 ));
eig = _eigImg;
}
if( tmp )
{
CV_CALL( tmp = cvGetMat( tmp, &tmp_stub, &coi3 ));
}
else
{
CV_CALL( _tmpImg = cvCreateMat( img->rows, img->cols, CV_32FC1 ));
tmp = _tmpImg;
}
if( mask )
{
CV_CALL( mask = cvGetMat( mask, &mask_stub ));
if( !CV_IS_MASK_ARR( mask ))
{
CV_ERROR( CV_StsBadMask, "" );
}
}
if( coi1 != 0 || coi2 != 0 || coi3 != 0 )
CV_ERROR( CV_BadCOI, "" );
if( CV_MAT_CN(img->type) != 1 ||
CV_MAT_CN(eig->type) != 1 ||
CV_MAT_CN(tmp->type) != 1 )
CV_ERROR( CV_BadNumChannels, cvUnsupportedFormat );
if( CV_MAT_DEPTH(tmp->type) != CV_32F ||
CV_MAT_DEPTH(eig->type) != CV_32F )
CV_ERROR( CV_BadDepth, cvUnsupportedFormat );
if( !corners || !corner_count )
CV_ERROR( CV_StsNullPtr, "" );
if( max_count <= 0 )
CV_ERROR( CV_StsBadArg, "maximal corners number is non positive" );
if( quality_level <= 0 || min_distance < 0 )
CV_ERROR( CV_StsBadArg, "quality level or min distance are non positive" );
if( use_harris )
{
CV_CALL( cvCornerHarris( img, eig, block_size, 3, harris_k ));
}
else
{
CV_CALL( cvCornerMinEigenVal( img, eig, block_size, 3 ));
}
CV_CALL( cvMinMaxLoc( eig, 0, &max_val, 0, 0, mask ));
CV_CALL( cvThreshold( eig, eig, max_val * quality_level,
0, CV_THRESH_TOZERO ));
CV_CALL( cvDilate( eig, tmp ));
min_dist = cvRound( min_distance * min_distance );
size = cvGetMatSize( img );
ptr_data = (int**)(tmp->data.ptr);
eig_data = (int*)(eig->data.ptr);
tmp_data = (int*)(tmp->data.ptr);
if( mask )
{
mask_data = (uchar*)(mask->data.ptr);
mask_step = mask->step;
}
eig_step = eig->step / sizeof(eig_data[0]);
tmp_step = tmp->step / sizeof(tmp_data[0]);
/* collect list of pointers to features - put them into temporary image */
for( y = 1, k = 0; y < size.height - 1; y++ )
{
eig_data += eig_step;
tmp_data += tmp_step;
mask_data += mask_step;
for( x = 1; x < size.width - 1; x++ )
{
int val = eig_data[x];
if( val != 0 && val == tmp_data[x] && (!mask || mask_data[x]) )
ptr_data[k++] = eig_data + x;
}
}
icvSortFeatures( ptr_data, k, 0 );
/* select the strongest features */
for( i = 0; i < k; i++ )
{
int j = count, ofs = (int)((uchar*)(ptr_data[i]) - eig->data.ptr);
y = ofs / eig->step;
x = (ofs - y * eig->step)/sizeof(float);
if( min_dist != 0 )
{
for( j = 0; j < count; j++ )
{
int dx = x - ptr[j].x;
int dy = y - ptr[j].y;
int dist = dx * dx + dy * dy;
if( dist < min_dist )
break;
}
}
if( j == count )
{
ptr[count].x = x;
ptr[count].y = y;
if( ++count >= max_count )
break;
}
}
/* convert points to floating-point format */
for( i = 0; i < count; i++ )
{
assert( (unsigned)ptr[i].x < (unsigned)size.width &&
(unsigned)ptr[i].y < (unsigned)size.height );
corners[i].x = (float)ptr[i].x;
corners[i].y = (float)ptr[i].y;
}
*corner_count = count;
__END__;
cvReleaseMat( &_eigImg );
cvReleaseMat( &_tmpImg );
}
/* End of file. */