void setSize( Mat& m, int _dims, const int* _sz, const size_t* _steps, bool autoSteps)
{
CV_Assert( 0 <= _dims && _dims <= CV_MAX_DIM );
if( m.dims != _dims )
{
if( m.step.p != m.step.buf )
{
fastFree(m.step.p);
m.step.p = m.step.buf;
m.size.p = &m.rows;
}
if( _dims > 2 )
{
m.step.p = (size_t*)fastMalloc(_dims*sizeof(m.step.p[0]) + (_dims+1)*sizeof(m.size.p[0]));
m.size.p = (int*)(m.step.p + _dims) + 1;
m.size.p[-1] = _dims;
m.rows = m.cols = -1;
}
}
m.dims = _dims;
if( !_sz )
return;
size_t esz = CV_ELEM_SIZE(m.flags), esz1 = CV_ELEM_SIZE1(m.flags), total = esz;
for( int i = _dims-1; i >= 0; i-- )
{
int s = _sz[i];
CV_Assert( s >= 0 );
m.size.p[i] = s;
if( _steps )
{
if (_steps[i] % esz1 != 0)
{
CV_Error(Error::BadStep, "Step must be a multiple of esz1");
}
m.step.p[i] = i < _dims-1 ? _steps[i] : esz;
}
else if( autoSteps )
{
m.step.p[i] = total;
int64 total1 = (int64)total*s;
if( (uint64)total1 != (size_t)total1 )
CV_Error( CV_StsOutOfRange, "The total matrix size does not fit to \"size_t\" type" );
total = (size_t)total1;
}
}
if( _dims == 1 )
{
m.dims = 2;
m.cols = 1;
m.step[1] = esz;
}
}
UMat& UMat::setTo(InputArray _value, InputArray _mask)
{
bool haveMask = !_mask.empty();
#ifdef HAVE_OPENCL
int tp = type(), cn = CV_MAT_CN(tp);
if( dims <= 2 && cn <= 4 && CV_MAT_DEPTH(tp) < CV_64F && ocl::useOpenCL() )
{
Mat value = _value.getMat();
CV_Assert( checkScalar(value, type(), _value.kind(), _InputArray::UMAT) );
double buf[4] = { 0, 0, 0, 0 };
convertAndUnrollScalar(value, tp, (uchar *)buf, 1);
int scalarcn = cn == 3 ? 4 : cn, rowsPerWI = ocl::Device::getDefault().isIntel() ? 4 : 1;
String opts = format("-D dstT=%s -D rowsPerWI=%d -D dstST=%s -D dstT1=%s -D cn=%d",
ocl::memopTypeToStr(tp), rowsPerWI,
ocl::memopTypeToStr(CV_MAKETYPE(tp, scalarcn)),
ocl::memopTypeToStr(CV_MAT_DEPTH(tp)), cn);
ocl::Kernel setK(haveMask ? "setMask" : "set", ocl::core::copyset_oclsrc, opts);
if( !setK.empty() )
{
ocl::KernelArg scalararg(0, 0, 0, 0, buf, CV_ELEM_SIZE1(tp) * scalarcn);
UMat mask;
if( haveMask )
{
mask = _mask.getUMat();
CV_Assert( mask.size() == size() && mask.type() == CV_8UC1 );
ocl::KernelArg maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
dstarg = ocl::KernelArg::ReadWrite(*this);
setK.args(maskarg, dstarg, scalararg);
}
else
{
ocl::KernelArg dstarg = ocl::KernelArg::WriteOnly(*this);
setK.args(dstarg, scalararg);
}
size_t globalsize[] = { cols, (rows + rowsPerWI - 1) / rowsPerWI };
if( setK.run(2, globalsize, NULL, false) )
return *this;
}
}
#endif
Mat m = getMat(haveMask ? ACCESS_RW : ACCESS_WRITE);
m.setTo(_value, _mask);
return *this;
}
pstable_l2_func(int _d, int _k, double _r, CvRNG& rng)
: d(_d), k(_k), r(_r) {
assert(sizeof(T) == CV_ELEM_SIZE1(cvtype));
a = cvCreateMat(k, d, cvtype);
b = cvCreateMat(k, 1, cvtype);
r1 = cvCreateMat(k, 1, CV_32SC1);
r2 = cvCreateMat(k, 1, CV_32SC1);
cvRandArr(&rng, a, CV_RAND_NORMAL, cvScalar(0), cvScalar(1));
cvRandArr(&rng, b, CV_RAND_UNI, cvScalar(0), cvScalar(r));
cvRandArr(&rng, r1, CV_RAND_UNI,
cvScalar(std::numeric_limits<int>::min()),
cvScalar(std::numeric_limits<int>::max()));
cvRandArr(&rng, r2, CV_RAND_UNI,
cvScalar(std::numeric_limits<int>::min()),
cvScalar(std::numeric_limits<int>::max()));
}
inline
size_t HostMem::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
inline
size_t GpuMat::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
bool PxMDecoder::readData( Mat& img )
{
int color = img.channels() > 1;
uchar* data = img.data;
int step = (int)img.step;
PaletteEntry palette[256];
bool result = false;
int bit_depth = CV_ELEM_SIZE1(m_type)*8;
int src_pitch = (m_width*m_bpp*bit_depth/8 + 7)/8;
int nch = CV_MAT_CN(m_type);
int width3 = m_width*nch;
int i, x, y;
if( m_offset < 0 || !m_strm.isOpened())
return false;
AutoBuffer<uchar> _src(src_pitch + 32);
uchar* src = _src;
AutoBuffer<uchar> _gray_palette;
uchar* gray_palette = _gray_palette;
// create LUT for converting colors
if( bit_depth == 8 )
{
_gray_palette.allocate(m_maxval + 1);
gray_palette = _gray_palette;
for( i = 0; i <= m_maxval; i++ )
gray_palette[i] = (uchar)((i*255/m_maxval)^(m_bpp == 1 ? 255 : 0));
FillGrayPalette( palette, m_bpp==1 ? 1 : 8 , m_bpp == 1 );
}
try
{
m_strm.setPos( m_offset );
switch( m_bpp )
{
////////////////////////// 1 BPP /////////////////////////
case 1:
if( !m_binary )
{
for( y = 0; y < m_height; y++, data += step )
{
for( x = 0; x < m_width; x++ )
src[x] = ReadNumber( m_strm, 1 ) != 0;
if( color )
FillColorRow8( data, src, m_width, palette );
else
FillGrayRow8( data, src, m_width, gray_palette );
}
}
else
{
for( y = 0; y < m_height; y++, data += step )
{
m_strm.getBytes( src, src_pitch );
if( color )
FillColorRow1( data, src, m_width, palette );
else
FillGrayRow1( data, src, m_width, gray_palette );
}
}
result = true;
break;
////////////////////////// 8 BPP /////////////////////////
case 8:
case 24:
for( y = 0; y < m_height; y++, data += step )
{
if( !m_binary )
{
for( x = 0; x < width3; x++ )
{
int code = ReadNumber( m_strm, INT_MAX );
if( (unsigned)code > (unsigned)m_maxval ) code = m_maxval;
if( bit_depth == 8 )
src[x] = gray_palette[code];
else
((ushort *)src)[x] = (ushort)code;
}
}
else
{
m_strm.getBytes( src, src_pitch );
if( bit_depth == 16 && !isBigEndian() )
{
for( x = 0; x < width3; x++ )
{
uchar v = src[x * 2];
src[x * 2] = src[x * 2 + 1];
src[x * 2 + 1] = v;
}
}
}
if( img.depth() == CV_8U && bit_depth == 16 )
{
for( x = 0; x < width3; x++ )
{
int v = ((ushort *)src)[x];
src[x] = (uchar)(v >> 8);
}
}
if( m_bpp == 8 ) // image has one channel
{
if( color )
{
if( img.depth() == CV_8U ) {
uchar *d = data, *s = src, *end = src + m_width;
for( ; s < end; d += 3, s++)
d[0] = d[1] = d[2] = *s;
} else {
ushort *d = (ushort *)data, *s = (ushort *)src, *end = ((ushort *)src) + m_width;
for( ; s < end; s++, d += 3)
d[0] = d[1] = d[2] = *s;
}
}
else
memcpy( data, src, m_width*(bit_depth/8) );
}
else
{
if( color )
{
if( img.depth() == CV_8U )
icvCvt_RGB2BGR_8u_C3R( src, 0, data, 0, cvSize(m_width,1) );
else
icvCvt_RGB2BGR_16u_C3R( (ushort *)src, 0, (ushort *)data, 0, cvSize(m_width,1) );
}
else if( img.depth() == CV_8U )
icvCvt_BGR2Gray_8u_C3C1R( src, 0, data, 0, cvSize(m_width,1), 2 );
else
icvCvt_BGRA2Gray_16u_CnC1R( (ushort *)src, 0, (ushort *)data, 0, cvSize(m_width,1), 3, 2 );
}
}
result = true;
break;
default:
assert(0);
}
}
catch(...)
{
}
return result;
}
int elemSize1() const { return CV_ELEM_SIZE1(type_); }
/// @brief Construct a Mat from an NDArray object.
static void construct(PyObject* object,
boost::python::converter::rvalue_from_python_stage1_data* data) {
namespace python = boost::python;
// Object is a borrowed reference, so create a handle indicting it is
// borrowed for proper reference counting.
python::handle<> handle(python::borrowed(object));
// Obtain a handle to the memory block that the converter has allocated
// for the C++ type.
typedef python::converter::rvalue_from_python_storage<Mat> storage_type;
void* storage = reinterpret_cast<storage_type*>(data)->storage.bytes;
// Allocate the C++ type into the converter's memory block, and assign
// its handle to the converter's convertible variable. The C++
// container is populated by passing the begin and end iterators of
// the python object to the container's constructor.
PyArrayObject* oarr = (PyArrayObject*) object;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if (type < 0) {
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
int size[CV_MAX_DIM + 1];
size_t step[CV_MAX_DIM + 1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
|| (i < ndims - 1 && _strides[i] < _strides[i + 1]))
needcopy = true;
}
if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
needcopy = true;
if (needcopy) {
if (needcast) {
object = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) object;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
object = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for (int i = 0; i < ndims; i++) {
size[i] = (int) _sizes[i];
step[i] = (size_t) _strides[i];
}
// handle degenerate case
if (ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if (ismultichannel) {
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if (!needcopy) {
Py_INCREF(object);
}
cv::Mat* m = new (storage) cv::Mat(ndims, size, type, PyArray_DATA(oarr), step);
m->u = g_numpyAllocator.allocate(object, ndims, size, type, step);
m->allocator = &g_numpyAllocator;
m->addref();
data->convertible = storage;
}
Mat fromNDArrayToMat(PyObject* o) {
cv::Mat m;
bool allowND = true;
if (!PyArray_Check(o)) {
failmsg("argument is not a numpy array");
if (!m.data)
m.allocator = &g_numpyAllocator;
} else {
PyArrayObject* oarr = (PyArrayObject*) o;
bool needcopy = false, needcast = false;
int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U :
typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT ? CV_32S :
typenum == NPY_INT32 ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if (type < 0) {
if (typenum == NPY_INT64 || typenum == NPY_UINT64
|| type == NPY_LONG) {
needcopy = needcast = true;
new_typenum = NPY_INT;
type = CV_32S;
} else {
failmsg("Argument data type is not supported");
m.allocator = &g_numpyAllocator;
return m;
}
}
#ifndef CV_MAX_DIM
const int CV_MAX_DIM = 32;
#endif
int ndims = PyArray_NDIM(oarr);
if (ndims >= CV_MAX_DIM) {
failmsg("Dimensionality of argument is too high");
if (!m.data)
m.allocator = &g_numpyAllocator;
return m;
}
int size[CV_MAX_DIM + 1];
size_t step[CV_MAX_DIM + 1];
size_t elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(oarr);
const npy_intp* _strides = PyArray_STRIDES(oarr);
bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;
for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
// these checks handle cases of
// a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
// b) transposed arrays, where _strides[] elements go in non-descending order
// c) flipped arrays, where some of _strides[] elements are negative
if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
|| (i < ndims - 1 && _strides[i] < _strides[i + 1]))
needcopy = true;
}
if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
needcopy = true;
if (needcopy) {
if (needcast) {
o = PyArray_Cast(oarr, new_typenum);
oarr = (PyArrayObject*) o;
} else {
oarr = PyArray_GETCONTIGUOUS(oarr);
o = (PyObject*) oarr;
}
_strides = PyArray_STRIDES(oarr);
}
for (int i = 0; i < ndims; i++) {
size[i] = (int) _sizes[i];
step[i] = (size_t) _strides[i];
}
// handle degenerate case
if (ndims == 0) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if (ismultichannel) {
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if (ndims > 2 && !allowND) {
failmsg("%s has more than 2 dimensions");
} else {
m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
m.addref();
if (!needcopy) {
Py_INCREF(o);
}
}
m.allocator = &g_numpyAllocator;
}
return m;
}
CV_IMPL void
cvLUT( const void* srcarr, void* dstarr, const void* lutarr )
{
static CvFuncTable lut_c1_tab, lut_cn_tab;
static CvLUT_TransformFunc lut_8u_tab[4];
static int inittab = 0;
CV_FUNCNAME( "cvLUT" );
__BEGIN__;
int coi1 = 0, coi2 = 0;
int depth, cn, lut_cn;
CvMat srcstub, *src = (CvMat*)srcarr;
CvMat dststub, *dst = (CvMat*)dstarr;
CvMat lutstub, *lut = (CvMat*)lutarr;
uchar* lut_data;
uchar* shuffled_lut = 0;
CvSize size;
if( !inittab )
{
icvInitLUT_Transform8uC1RTable( &lut_c1_tab );
icvInitLUT_Transform8uCnRTable( &lut_cn_tab );
lut_8u_tab[0] = (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C1R;
lut_8u_tab[1] = (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C2R;
lut_8u_tab[2] = (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C3R;
lut_8u_tab[3] = (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C4R;
inittab = 1;
}
if( !CV_IS_MAT(src) )
CV_CALL( src = cvGetMat( src, &srcstub, &coi1 ));
if( !CV_IS_MAT(dst) )
CV_CALL( dst = cvGetMat( dst, &dststub, &coi2 ));
if( !CV_IS_MAT(lut) )
CV_CALL( lut = cvGetMat( lut, &lutstub ));
if( coi1 != 0 || coi2 != 0 )
CV_ERROR( CV_BadCOI, "" );
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
if( !CV_ARE_CNS_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( CV_MAT_DEPTH( src->type ) > CV_8S )
CV_ERROR( CV_StsUnsupportedFormat, "" );
depth = CV_MAT_DEPTH( dst->type );
cn = CV_MAT_CN( dst->type );
lut_cn = CV_MAT_CN( lut->type );
if( !CV_IS_MAT_CONT(lut->type) || (lut_cn != 1 && lut_cn != cn) ||
!CV_ARE_DEPTHS_EQ( dst, lut ) || lut->width*lut->height != 256 )
CV_ERROR( CV_StsBadArg, "The LUT must be continuous array \n"
"with 256 elements of the same type as destination" );
size = cvGetMatSize( src );
if( lut_cn == 1 )
{
size.width *= cn;
cn = 1;
}
if( CV_IS_MAT_CONT( src->type & dst->type ))
{
size.width *= size.height;
size.height = 1;
}
lut_data = lut->data.ptr;
if( CV_MAT_DEPTH( src->type ) == CV_8S )
{
int half_size = CV_ELEM_SIZE1(depth)*cn*128;
/*zeng
shuffled_lut = (uchar*)cvStackAlloc(half_size*2);
*/
shuffled_lut = (uchar*)cvAlloc(half_size*2);
// shuffle lut
memcpy( shuffled_lut, lut_data + half_size, half_size );
memcpy( shuffled_lut + half_size, lut_data, half_size );
lut_data = shuffled_lut;
}
if( lut_cn == 1 || lut_cn <= 4 && depth == CV_8U )
{
CvLUT_TransformFunc func = depth == CV_8U ? lut_8u_tab[cn-1] :
(CvLUT_TransformFunc)(lut_c1_tab.fn_2d[depth]);
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
IPPI_CALL( func( src->data.ptr, src->step, dst->data.ptr,
dst->step, size, lut_data ));
}
else
{
CvLUT_TransformCnFunc func =
(CvLUT_TransformCnFunc)(lut_cn_tab.fn_2d[depth]);
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
IPPI_CALL( func( src->data.ptr, src->step, dst->data.ptr,
dst->step, size, lut_data, cn ));
}
if(shuffled_lut)
{
cvFree(&shuffled_lut);
}
__END__;
}
bool numpy_to_mat(const PyObject* o, cv::Mat& m, const char* name, bool allowND)
{
if(!o || o == Py_None)
{
if( !m.data )
m.allocator = &g_numpyAllocator;
return true;
}
if( !PyArray_Check(o) )
{
failmsg("%s is not a numpy array", name);
return false;
}
int typenum = PyArray_TYPE(o);
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U : typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT || typenum == NPY_LONG ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if( type < 0 )
{
failmsg("%s data type = %d is not supported", name, typenum);
return false;
}
int ndims = PyArray_NDIM(o);
if(ndims >= CV_MAX_DIM)
{
failmsg("%s dimensionality (=%d) is too high", name, ndims);
return false;
}
int size[CV_MAX_DIM+1];
size_t step[CV_MAX_DIM+1], elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(o);
const npy_intp* _strides = PyArray_STRIDES(o);
for(int i = 0; i < ndims; i++)
{
size[i] = (int)_sizes[i];
step[i] = (size_t)_strides[i];
}
if( ndims == 0 || step[ndims-1] > elemsize ) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if( ndims == 3 && size[2] <= CV_CN_MAX && step[1] == elemsize*size[2] )
{
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if( ndims > 2 && !allowND )
{
failmsg("%s has more than 2 dimensions", name);
return false;
}
m = Mat(ndims, size, type, PyArray_DATA(o), step);
if( m.data )
{
m.refcount = refcountFromPyObject(o);
m.addref(); // protect the original numpy array from deallocation
// (since Mat destructor will decrement the reference counter)
};
m.allocator = &g_numpyAllocator;
return true;
}
