84 lines
3.0 KiB
C++
84 lines
3.0 KiB
C++
#include <cxcore.h>
|
|
#include <cv.h>
|
|
#include <stdio.h>
|
|
#include "cvshadow.h"
|
|
|
|
CvArr * cvCvtSeqToArray_Shadow( const CvSeq* seq, CvArr * elements, CvSlice slice){
|
|
CvMat stub, *mat=(CvMat *)elements;
|
|
if(!CV_IS_MAT(mat)){
|
|
mat = cvGetMat(elements, &stub);
|
|
}
|
|
cvCvtSeqToArray( seq, mat->data.ptr, slice );
|
|
return elements;
|
|
}
|
|
|
|
double cvArcLength_Shadow( const CvSeq * seq, CvSlice slice, int is_closed){
|
|
return cvArcLength( seq, slice, is_closed );
|
|
}
|
|
double cvArcLength_Shadow( const CvArr * arr, CvSlice slice, int is_closed){
|
|
return cvArcLength( arr, slice, is_closed );
|
|
}
|
|
|
|
void cvMoments_Shadow( const CvSeq * seq, CvMoments * moments, int binary ){
|
|
cvMoments( seq, moments, binary );
|
|
}
|
|
|
|
void cvMoments_Shadow( const CvArr * seq, CvMoments * moments, int binary ){
|
|
cvMoments( seq, moments, binary );
|
|
}
|
|
|
|
|
|
CvTypedSeq<CvRect> * cvHaarDetectObjects_Shadow( const CvArr* image, CvHaarClassifierCascade* cascade,
|
|
CvMemStorage* storage, double scale_factor, int min_neighbors, int flags,
|
|
CvSize min_size )
|
|
{
|
|
return (CvTypedSeq<CvRect> *) cvHaarDetectObjects( image, cascade, storage, scale_factor,
|
|
min_neighbors, flags, min_size);
|
|
}
|
|
|
|
CvTypedSeq<CvConnectedComp> * cvSegmentMotion_Shadow( const CvArr* mhi, CvArr* seg_mask, CvMemStorage* storage,
|
|
double timestamp, double seg_thresh ){
|
|
return (CvTypedSeq<CvConnectedComp> *) cvSegmentMotion( mhi, seg_mask, storage, timestamp, seg_thresh );
|
|
}
|
|
|
|
CvTypedSeq<CvPoint> * cvApproxPoly_Shadow( const void* src_seq, int header_size, CvMemStorage* storage,
|
|
int method, double parameter, int parameter2)
|
|
{
|
|
return (CvTypedSeq<CvPoint> *) cvApproxPoly( src_seq, header_size, storage, method, parameter, parameter2 );
|
|
}
|
|
|
|
// Always return a new Mat of indices
|
|
CvMat * cvConvexHull2_Shadow( const CvArr * points, int orientation, int return_points){
|
|
CvMat * hull=0;
|
|
CvMat * points_mat=(CvMat *) points;
|
|
CvSeq * points_seq=(CvSeq *) points;
|
|
int npoints, type;
|
|
|
|
if(CV_IS_MAT(points_mat)){
|
|
npoints = MAX(points_mat->rows, points_mat->cols);
|
|
type = return_points ? points_mat->type : CV_32S;
|
|
}
|
|
else if(CV_IS_SEQ(points_seq)){
|
|
npoints = points_seq->total;
|
|
type = return_points ? CV_SEQ_ELTYPE(points_seq) : 1;
|
|
}
|
|
else{
|
|
CV_Error(CV_StsBadArg, "points must be a CvSeq or CvMat");
|
|
}
|
|
hull=cvCreateMat(1,npoints,type);
|
|
cvConvexHull2(points, hull, orientation, return_points);
|
|
|
|
return hull;
|
|
}
|
|
std::vector<CvPoint> cvSnakeImage_Shadow( const CvMat * image, std::vector<CvPoint> points,
|
|
std::vector<float> alpha, std::vector<float> beta,
|
|
std::vector<float> gamma,
|
|
CvSize win, CvTermCriteria criteria, int calc_gradient ){
|
|
IplImage ipl_stub;
|
|
cvSnakeImage( cvGetImage(image, &ipl_stub), &(points[0]), points.size(),
|
|
&((alpha)[0]), &((beta)[0]), &((gamma)[0]),
|
|
(alpha.size()>1 && beta.size()>1 && gamma.size()>1 ? CV_ARRAY : CV_VALUE),
|
|
win, criteria, calc_gradient );
|
|
return points;
|
|
}
|