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// 2008-04-07, Xavier Delacour <xavier.delacour@gmail.com>
// The trouble here is that Octave arrays are in Fortran order, while OpenCV
// arrays are in C order. Neither Octave nor OpenCV seem to provide n-dim
// transpose, so we do that here.
// For images, we also scale the result to lie within [0-1].
// * add support for sparse matrices
// * add support for complex matrices
// * add support for roi and coi, or complain if either is set
// * test case for channel==1
// * test case for channel=={2,3,4}
// * test case for 2 dim, 1 dim, n dim cases
%{
class ndim_iterator {
int nd;
int dims[CV_MAX_DIM];
int step[CV_MAX_DIM];
int curr[CV_MAX_DIM];
uchar* _data;
int _type;
bool done;
public:
ndim_iterator() {}
ndim_iterator(CvMat* m) {
int c = CV_MAT_CN(m->type);
int elem_size = CV_ELEM_SIZE1(m->type);
nd = c == 1 ? 2 : 3;
dims[0] = m->rows;
dims[1] = m->cols;
dims[2] = c;
step[0] = m->step;
step[1] = c * elem_size;
step[2] = elem_size;
curr[0] = curr[1] = curr[2] = 0;
_data = m->data.ptr;
_type = m->type;
done = false;
}
ndim_iterator(CvMatND* m) {
int c = CV_MAT_CN(m->type);
int elem_size = CV_ELEM_SIZE1(m->type);
nd = m->dims + (c == 1 ? 0 : 1);
for (int j = 0; j < m->dims; ++j) {
dims[j] = m->dim[j].size;
step[j] = m->dim[j].step;
curr[j] = 0;
}
if (c > 1) {
dims[m->dims] = c;
step[m->dims] = elem_size;
curr[m->dims] = 0;
}
_data = m->data.ptr;
_type = m->type;
done = false;
}
ndim_iterator(IplImage* img) {
nd = img->nChannels == 1 ? 2 : 3;
dims[0] = img->height;
dims[1] = img->width;
dims[2] = img->nChannels;
switch (img->depth) {
case IPL_DEPTH_8U: _type = CV_8U; break;
case IPL_DEPTH_8S: _type = CV_8S; break;
case IPL_DEPTH_16U: _type = CV_16U; break;
case IPL_DEPTH_16S: _type = CV_16S; break;
case IPL_DEPTH_32S: _type = CV_32S; break;
case IPL_DEPTH_32F: _type = CV_32F; break;
case IPL_DEPTH_1U: _type = CV_64F; break;
default:
error("unsupported image depth");
return;
}
int elem_size = CV_ELEM_SIZE1(_type);
step[0] = img->widthStep;
step[1] = img->nChannels * elem_size;
step[2] = elem_size;
curr[0] = curr[1] = curr[2] = 0;
_data = (uchar*)img->imageData;
done = false;
}
ndim_iterator(NDArray& nda) {
dim_vector d(nda.dims());
nd = d.length();
int last_step = sizeof(double);
for (int j = 0; j < d.length(); ++j) {
dims[j] = d(j);
step[j] = last_step;
last_step *= dims[j];
curr[j] = 0;
}
_data = (uchar*)const_cast<double*>(nda.data());
_type = CV_64F;
done = false;
}
operator bool () const {
return !done;
}
uchar* data() {
return _data;
}
int type() const {
return _type;
}
ndim_iterator& operator++ () {
int curr_dim = 0;
for (;;) {
_data += step[curr_dim];
if (++curr[curr_dim] < dims[curr_dim])
break;
curr[curr_dim] = 0;
_data -= step[curr_dim] * dims[curr_dim];
++curr_dim;
if (curr_dim == nd) {
done = true;
break;
}
}
return *this;
}
};
template <class T1, class T2>
void transpose_copy_typed(ndim_iterator src_it, ndim_iterator dst_it,
double scale) {
assert(sizeof(T1) == CV_ELEM_SIZE1(src_it.type()));
assert(sizeof(T2) == CV_ELEM_SIZE1(dst_it.type()));
if (scale == 1) {
while (src_it) {
*(T2*)dst_it.data() = (T2)*(T1*)src_it.data();
++src_it;
++dst_it;
}
} else {
while (src_it) {
*(T2*)dst_it.data() = (T2)(scale * (*(T1*)src_it.data()));
++src_it;
++dst_it;
}
}
}
template <class T1>
void transpose_copy2(ndim_iterator src_it, ndim_iterator dst_it,
double scale) {
switch (CV_MAT_DEPTH(dst_it.type())) {
case CV_8U: transpose_copy_typed<T1,unsigned char>(src_it,dst_it,scale); break;
case CV_8S: transpose_copy_typed<T1,signed char>(src_it,dst_it,scale); break;
case CV_16U: transpose_copy_typed<T1,unsigned short>(src_it,dst_it,scale); break;
case CV_16S: transpose_copy_typed<T1,signed short>(src_it,dst_it,scale); break;
case CV_32S: transpose_copy_typed<T1,signed int>(src_it,dst_it,scale); break;
case CV_32F: transpose_copy_typed<T1,float>(src_it,dst_it,scale); break;
case CV_64F: transpose_copy_typed<T1,double>(src_it,dst_it,scale); break;
default:
error("unsupported dest array type (supported types are CV_8U, CV_8S, "
"CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)");
}
}
void transpose_copy(ndim_iterator src_it, ndim_iterator dst_it,
double scale = 1) {
switch (CV_MAT_DEPTH(src_it.type())) {
case CV_8U: transpose_copy2<unsigned char>(src_it,dst_it,scale); break;
case CV_8S: transpose_copy2<signed char>(src_it,dst_it,scale); break;
case CV_16U: transpose_copy2<unsigned short>(src_it,dst_it,scale); break;
case CV_16S: transpose_copy2<signed short>(src_it,dst_it,scale); break;
case CV_32S: transpose_copy2<signed int>(src_it,dst_it,scale); break;
case CV_32F: transpose_copy2<float>(src_it,dst_it,scale); break;
case CV_64F: transpose_copy2<double>(src_it,dst_it,scale); break;
default:
error("unsupported source array type (supported types are CV_8U, CV_8S, "
"CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)");
}
}
octave_value cv2mat(CvArr* arr) {
dim_vector d;
NDArray nda;
if (CV_IS_MAT(arr)) {
// m x n x c
CvMat* m = (CvMat*)arr;
int c = CV_MAT_CN(m->type);
if (c == 1) {
d.resize(2);
d(0) = m->rows;
d(1) = m->cols;
} else {
d.resize(3);
d(0) = m->rows;
d(1) = m->cols;
d(2) = c;
}
nda = NDArray(d);
transpose_copy(m, nda);
}
else if (CV_IS_MATND(arr)) {
// m1 x m2 x ... x mn x c
CvMatND* m = (CvMatND*)arr;
int c = CV_MAT_CN(m->type);
if (c == 1) {
d.resize(m->dims);
for (int j = 0; j < m->dims; ++j)
d(j) = m->dim[j].size;
} else {
d.resize(m->dims + 1);
for (int j = 0; j < m->dims; ++j)
d(j) = m->dim[j].size;
d(m->dims) = c;
}
nda = NDArray(d);
transpose_copy(m, nda);
}
else if (CV_IS_IMAGE(arr)) {
// m x n x c
IplImage* img = (IplImage*)arr;
if (img->nChannels == 1) {
d.resize(2);
d(0) = img->height;
d(1) = img->width;
} else {
d.resize(3);
d(0) = img->height;
d(1) = img->width;
d(2) = img->nChannels;
}
nda = NDArray(d);
transpose_copy(img, nda);
}
else {
error("unsupported array type (supported types are CvMat, CvMatND, IplImage)");
return octave_value();
}
return nda;
}
octave_value mat2cv(const octave_value& ov, int type) {
NDArray nda(ov.array_value());
if (error_state)
return 0;
dim_vector d = ov.dims();
assert(d.length() > 0);
int nd = d.length();
int last_dim = d(d.length() - 1);
int c = CV_MAT_CN(type);
if (c != 1 && c != last_dim) {
error("last dimension and channel must agree, or channel must equal one");
return 0;
}
if (c > 1)
--nd;
if (nd == 2) {
CvMat *m = cvCreateMat(d(0), d(1), type);
transpose_copy(nda, m);
return SWIG_NewPointerObj(m, SWIGTYPE_p_CvMat, SWIG_POINTER_OWN);
}
else {
int tmp[CV_MAX_DIM];
for (int j = 0; j < nd; ++j)
tmp[j] = d(j);
CvMatND *m = cvCreateMatND(nd, tmp, type);
transpose_copy(nda, m);
return SWIG_NewPointerObj(m, SWIGTYPE_p_CvMatND, SWIG_POINTER_OWN);
}
}
octave_value cv2im(CvArr* arr) {
if (!CV_IS_IMAGE(arr) && !CV_IS_MAT(arr)) {
error("input is not an OpenCV image or 2D matrix");
return octave_value();
}
dim_vector d;
NDArray nda;
if (CV_IS_MAT(arr)) {
// m x n x c
CvMat* m = (CvMat*)arr;
int c = CV_MAT_CN(m->type);
if (c == 1) {
d.resize(2);
d(0) = m->rows;
d(1) = m->cols;
} else {
d.resize(3);
d(0) = m->rows;
d(1) = m->cols;
d(2) = c;
}
nda = NDArray(d);
transpose_copy(m, nda, 1/256.0);
}
else if (CV_IS_IMAGE(arr)) {
// m x n x c
IplImage* img = (IplImage*)arr;
if (img->nChannels == 1) {
d.resize(2);
d(0) = img->height;
d(1) = img->width;
} else {
d.resize(3);
d(0) = img->height;
d(1) = img->width;
d(2) = img->nChannels;
}
nda = NDArray(d);
transpose_copy(img, nda, 1/256.0);
}
return nda;
}
CvMat* im2cv(const octave_value& ov, int depth) {
NDArray nda(ov.array_value());
if (error_state)
return 0;
dim_vector d = ov.dims();
assert(d.length() > 0);
if (d.length() != 2 && d.length() != 3 &&
!(d.length() == 3 && d(2) <= 4)) {
error("input must be m x n or m x n x c matrix, where 1<=c<=4");
return 0;
}
int channels = d.length() == 2 ? 1 : d(2);
int type = CV_MAKETYPE(depth, channels);
CvMat *m = cvCreateMat(d(0), d(1), type);
transpose_copy(nda, m, 256);
return m;
}
%}
%newobject im2cv;
%feature("autodoc", 0) cv2mat;
%feature("autodoc", 0) mat2cv;
%feature("autodoc", 0) cv2im;
%feature("autodoc", 0) im2cv;
%docstring cv2mat {
@deftypefn {Loadable Function} @var{m1} = cv2mat (@var{m2})
Convert the CvMat, CvMatND, or IplImage @var{m2} into an Octave real matrix @var{m1}.
The dimensions @var{m1} are those of @var{m2}, plus an addition dimension
if @var{m2} has more than one channel.
@end deftypefn
}
%docstring mat2cv {
@deftypefn {Loadable Function} @var{m1} = mat2cv (@var{m2}, @var{type})
Convert the Octave array @var{m2} into either a CvMat or a CvMatND of type
@var{type}.
@var{type} is one of CV_8UC(n), CV_8SC(n), CV_16UC(n), CV_16SC(n), CV_32SC(n),
CV_32FC(n), CV_64FC(n), where n indicates channel and is between 1 and 4.
If the dimension of @var{m2} is equal to 2 (not counting channels),
a CvMat is returned. Otherwise, a CvMatND is returned.
@end deftypefn
}
%docstring cv2im {
@deftypefn {Loadable Function} @var{im} = cv2im (@var{I})
Convert the OpenCV image or 2D matrix @var{I} into an Octave image @var{im}.
@var{im} is a real matrix of dimension height x width or
height x width x channels, with values within the interval [0,1]).
@end deftypefn
}
%docstring im2cv {
@deftypefn {Loadable Function} @var{I} = im2cv (@var{im}, @var{depth})
Convert the Octave image @var{im} into the OpenCV image @var{I} of depth
@var{depth}.
@var{im} is a real matrix of dimension height x width or
height x width x channels, with values within the interval [0,1].
@var{depth} must be one of CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F.
@end deftypefn
}
octave_value cv2mat(CvArr* arr);
octave_value mat2cv(const octave_value& ov, int type);
octave_value cv2im(CvArr* arr);
CvMat* im2cv(const octave_value& ov, int depth);