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#include "precomp.hpp"
/****************************************************************************************\
* [scaled] Identity matrix initialization *
\****************************************************************************************/
namespace cv {
class StdMatAllocator : public MatAllocator
{
public:
UMatData* allocate(int dims, const int* sizes, int type, size_t* step) const
{
size_t total = CV_ELEM_SIZE(type);
for( int i = dims-1; i >= 0; i-- )
{
if( step )
step[i] = total;
total *= sizes[i];
}
uchar* data = (uchar*)fastMalloc(total);
UMatData* u = new UMatData(this);
u->data = u->origdata = data;
u->size = total;
u->refcount = 1;
return u;
}
bool allocate(UMatData* u, int accessFlags) const
{
if(!u) return false;
if(u->handle != 0)
return true;
return UMat::getStdAllocator()->allocate(u, accessFlags);
}
void deallocate(UMatData* u) const
{
if(u)
fastFree(u->origdata);
delete u;
}
void map(UMatData*, int) const
{
}
void unmap(UMatData* u) const
{
if(u->urefcount == 0)
deallocate(u);
}
void download(UMatData* u, void* dstptr,
int dims, const size_t sz[],
const size_t srcofs[], const size_t srcstep[],
const size_t dststep[]) const
{
if(!u)
return;
int isz[CV_MAX_DIM];
uchar* srcptr = u->data;
for( int i = 0; i < dims; i++ )
{
CV_Assert( sz[i] <= (size_t)INT_MAX );
if( sz[i] == 0 )
return;
if( srcofs )
srcptr += srcofs[i]*(i <= dims-2 ? srcstep[i] : 1);
isz[i] = (int)sz[i];
}
Mat src(dims, isz, CV_8U, srcptr, srcstep);
Mat dst(dims, isz, CV_8U, dstptr, dststep);
const Mat* arrays[] = { &src, &dst };
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs, 2);
size_t j, planesz = it.size;
for( j = 0; j < it.nplanes; j++, ++it )
memcpy(ptrs[1], ptrs[0], planesz);
}
void upload(UMatData* u, const void* srcptr, int dims, const size_t sz[],
const size_t dstofs[], const size_t dststep[],
const size_t srcstep[]) const
{
if(!u)
return;
int isz[CV_MAX_DIM];
uchar* dstptr = u->data;
for( int i = 0; i < dims; i++ )
{
CV_Assert( sz[i] <= (size_t)INT_MAX );
if( sz[i] == 0 )
return;
if( dstofs )
dstptr += dstofs[i]*(i <= dims-2 ? dststep[i] : 1);
isz[i] = (int)sz[i];
}
Mat src(dims, isz, CV_8U, (void*)srcptr, srcstep);
Mat dst(dims, isz, CV_8U, dstptr, dststep);
const Mat* arrays[] = { &src, &dst };
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs, 2);
size_t j, planesz = it.size;
for( j = 0; j < it.nplanes; j++, ++it )
memcpy(ptrs[1], ptrs[0], planesz);
}
void copy(UMatData* usrc, UMatData* udst, int dims, const size_t sz[],
const size_t srcofs[], const size_t srcstep[],
const size_t dstofs[], const size_t dststep[], bool) const
{
if(!usrc || !udst)
return;
int isz[CV_MAX_DIM];
uchar* srcptr = usrc->data;
uchar* dstptr = udst->data;
for( int i = 0; i < dims; i++ )
{
CV_Assert( sz[i] <= (size_t)INT_MAX );
if( sz[i] == 0 )
return;
if( srcofs )
srcptr += srcofs[i]*(i <= dims-2 ? srcstep[i] : 1);
if( dstofs )
dstptr += dstofs[i]*(i <= dims-2 ? dststep[i] : 1);
isz[i] = (int)sz[i];
}
Mat src(dims, isz, CV_8U, srcptr, srcstep);
Mat dst(dims, isz, CV_8U, dstptr, dststep);
const Mat* arrays[] = { &src, &dst };
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs, 2);
size_t j, planesz = it.size;
for( j = 0; j < it.nplanes; j++, ++it )
memcpy(ptrs[1], ptrs[0], planesz);
}
};
MatAllocator* Mat::getStdAllocator()
{
static StdMatAllocator allocator;
return &allocator;
}
void swap( Mat& a, Mat& b )
{
std::swap(a.flags, b.flags);
std::swap(a.dims, b.dims);
std::swap(a.rows, b.rows);
std::swap(a.cols, b.cols);
std::swap(a.data, b.data);
std::swap(a.refcount, b.refcount);
std::swap(a.datastart, b.datastart);
std::swap(a.dataend, b.dataend);
std::swap(a.datalimit, b.datalimit);
std::swap(a.allocator, b.allocator);
std::swap(a.u, b.u);
std::swap(a.size.p, b.size.p);
std::swap(a.step.p, b.step.p);
std::swap(a.step.buf[0], b.step.buf[0]);
std::swap(a.step.buf[1], b.step.buf[1]);
if( a.step.p == b.step.buf )
{
a.step.p = a.step.buf;
a.size.p = &a.rows;
}
if( b.step.p == a.step.buf )
{
b.step.p = b.step.buf;
b.size.p = &b.rows;
}
}
static inline void setSize( Mat& m, int _dims, const int* _sz,
const size_t* _steps, bool autoSteps=false )
{
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), total = esz;
int i;
for( i = _dims-1; i >= 0; i-- )
{
int s = _sz[i];
CV_Assert( s >= 0 );
m.size.p[i] = s;
if( _steps )
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;
}
}
static void updateContinuityFlag(Mat& m)
{
int i, j;
for( i = 0; i < m.dims; i++ )
{
if( m.size[i] > 1 )
break;
}
for( j = m.dims-1; j > i; j-- )
{
if( m.step[j]*m.size[j] < m.step[j-1] )
break;
}
uint64 t = (uint64)m.step[0]*m.size[0];
if( j <= i && t == (size_t)t )
m.flags |= Mat::CONTINUOUS_FLAG;
else
m.flags &= ~Mat::CONTINUOUS_FLAG;
}
static void finalizeHdr(Mat& m)
{
updateContinuityFlag(m);
int d = m.dims;
if( d > 2 )
m.rows = m.cols = -1;
if(m.u)
m.data = m.datastart = m.u->data;
if( m.data )
{
m.datalimit = m.datastart + m.size[0]*m.step[0];
if( m.size[0] > 0 )
{
m.dataend = m.data + m.size[d-1]*m.step[d-1];
for( int i = 0; i < d-1; i++ )
m.dataend += (m.size[i] - 1)*m.step[i];
}
else
m.dataend = m.datalimit;
}
else
m.dataend = m.datalimit = 0;
}
void Mat::create(int d, const int* _sizes, int _type)
{
int i;
CV_Assert(0 <= d && d <= CV_MAX_DIM && _sizes);
_type = CV_MAT_TYPE(_type);
if( data && (d == dims || (d == 1 && dims <= 2)) && _type == type() )
{
if( d == 2 && rows == _sizes[0] && cols == _sizes[1] )
return;
for( i = 0; i < d; i++ )
if( size[i] != _sizes[i] )
break;
if( i == d && (d > 1 || size[1] == 1))
return;
}
release();
if( d == 0 )
return;
flags = (_type & CV_MAT_TYPE_MASK) | MAGIC_VAL;
setSize(*this, d, _sizes, 0, true);
if( total() > 0 )
{
MatAllocator *a = allocator, *a0 = getStdAllocator();
#ifdef HAVE_TGPU
if( !a || a == tegra::getAllocator() )
a = tegra::getAllocator(d, _sizes, _type);
#endif
if(!a)
a = a0;
try
{
u = a->allocate(dims, size, _type, step.p);
CV_Assert(u != 0);
}
catch(...)
{
if(a != a0)
u = a0->allocate(dims, size, _type, step.p);
CV_Assert(u != 0);
}
CV_Assert( step[dims-1] == (size_t)CV_ELEM_SIZE(flags) );
}
finalizeHdr(*this);
}
void Mat::copySize(const Mat& m)
{
setSize(*this, m.dims, 0, 0);
for( int i = 0; i < dims; i++ )
{
size[i] = m.size[i];
step[i] = m.step[i];
}
}
void Mat::deallocate()
{
if(u)
(u->currAllocator ? u->currAllocator : allocator ? allocator : getStdAllocator())->unmap(u);
}
Mat::Mat(const Mat& m, const Range& _rowRange, const Range& _colRange)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0),
datalimit(0), allocator(0), size(&rows)
{
CV_Assert( m.dims >= 2 );
if( m.dims > 2 )
{
AutoBuffer<Range> rs(m.dims);
rs[0] = _rowRange;
rs[1] = _colRange;
for( int i = 2; i < m.dims; i++ )
rs[i] = Range::all();
*this = m(rs);
return;
}
*this = m;
if( _rowRange != Range::all() && _rowRange != Range(0,rows) )
{
CV_Assert( 0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows );
rows = _rowRange.size();
data += step*_rowRange.start;
flags |= SUBMATRIX_FLAG;
}
if( _colRange != Range::all() && _colRange != Range(0,cols) )
{
CV_Assert( 0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols );
cols = _colRange.size();
data += _colRange.start*elemSize();
flags &= cols < m.cols ? ~CONTINUOUS_FLAG : -1;
flags |= SUBMATRIX_FLAG;
}
if( rows == 1 )
flags |= CONTINUOUS_FLAG;
if( rows <= 0 || cols <= 0 )
{
release();
rows = cols = 0;
}
}
Mat::Mat(const Mat& m, const Rect& roi)
: flags(m.flags), dims(2), rows(roi.height), cols(roi.width),
data(m.data + roi.y*m.step[0]), refcount(m.refcount),
datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit),
allocator(m.allocator), size(&rows)
{
CV_Assert( m.dims <= 2 );
flags &= roi.width < m.cols ? ~CONTINUOUS_FLAG : -1;
flags |= roi.height == 1 ? CONTINUOUS_FLAG : 0;
size_t esz = CV_ELEM_SIZE(flags);
data += roi.x*esz;
CV_Assert( 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols &&
0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows );
if( refcount )
CV_XADD(refcount, 1);
if( roi.width < m.cols || roi.height < m.rows )
flags |= SUBMATRIX_FLAG;
step[0] = m.step[0]; step[1] = esz;
if( rows <= 0 || cols <= 0 )
{
release();
rows = cols = 0;
}
}
Mat::Mat(int _dims, const int* _sizes, int _type, void* _data, const size_t* _steps)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0),
datalimit(0), allocator(0), size(&rows)
{
flags |= CV_MAT_TYPE(_type);
data = datastart = (uchar*)_data;
setSize(*this, _dims, _sizes, _steps, true);
finalizeHdr(*this);
}
Mat::Mat(const Mat& m, const Range* ranges)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0),
datalimit(0), allocator(0), size(&rows)
{
int i, d = m.dims;
CV_Assert(ranges);
for( i = 0; i < d; i++ )
{
Range r = ranges[i];
CV_Assert( r == Range::all() || (0 <= r.start && r.start < r.end && r.end <= m.size[i]) );
}
*this = m;
for( i = 0; i < d; i++ )
{
Range r = ranges[i];
if( r != Range::all() && r != Range(0, size.p[i]))
{
size.p[i] = r.end - r.start;
data += r.start*step.p[i];
flags |= SUBMATRIX_FLAG;
}
}
updateContinuityFlag(*this);
}
static Mat cvMatNDToMat(const CvMatND* m, bool copyData)
{
Mat thiz;
if( !m )
return thiz;
thiz.data = thiz.datastart = m->data.ptr;
thiz.flags |= CV_MAT_TYPE(m->type);
int _sizes[CV_MAX_DIM];
size_t _steps[CV_MAX_DIM];
int i, d = m->dims;
for( i = 0; i < d; i++ )
{
_sizes[i] = m->dim[i].size;
_steps[i] = m->dim[i].step;
}
setSize(thiz, d, _sizes, _steps);
finalizeHdr(thiz);
if( copyData )
{
Mat temp(thiz);
thiz.release();
temp.copyTo(thiz);
}
return thiz;
}
static Mat cvMatToMat(const CvMat* m, bool copyData)
{
Mat thiz;
if( !m )
return thiz;
if( !copyData )
{
thiz.flags = Mat::MAGIC_VAL + (m->type & (CV_MAT_TYPE_MASK|CV_MAT_CONT_FLAG));
thiz.dims = 2;
thiz.rows = m->rows;
thiz.cols = m->cols;
thiz.data = thiz.datastart = m->data.ptr;
size_t esz = CV_ELEM_SIZE(m->type), minstep = thiz.cols*esz, _step = m->step;
if( _step == 0 )
_step = minstep;
thiz.datalimit = thiz.datastart + _step*thiz.rows;
thiz.dataend = thiz.datalimit - _step + minstep;
thiz.step[0] = _step; thiz.step[1] = esz;
}
else
{
thiz.data = thiz.datastart = thiz.dataend = 0;
Mat(m->rows, m->cols, m->type, m->data.ptr, m->step).copyTo(thiz);
}
return thiz;
}
static Mat iplImageToMat(const IplImage* img, bool copyData)
{
Mat m;
if( !img )
return m;
m.dims = 2;
CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0);
int imgdepth = IPL2CV_DEPTH(img->depth);
size_t esz;
m.step[0] = img->widthStep;
if(!img->roi)
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL);
m.flags = Mat::MAGIC_VAL + CV_MAKETYPE(imgdepth, img->nChannels);
m.rows = img->height;
m.cols = img->width;
m.datastart = m.data = (uchar*)img->imageData;
esz = CV_ELEM_SIZE(m.flags);
}
else
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0);
bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE;
m.flags = Mat::MAGIC_VAL + CV_MAKETYPE(imgdepth, selectedPlane ? 1 : img->nChannels);
m.rows = img->roi->height;
m.cols = img->roi->width;
esz = CV_ELEM_SIZE(m.flags);
m.data = m.datastart = (uchar*)img->imageData +
(selectedPlane ? (img->roi->coi - 1)*m.step*img->height : 0) +
img->roi->yOffset*m.step[0] + img->roi->xOffset*esz;
}
m.datalimit = m.datastart + m.step.p[0]*m.rows;
m.dataend = m.datastart + m.step.p[0]*(m.rows-1) + esz*m.cols;
m.flags |= (m.cols*esz == m.step.p[0] || m.rows == 1 ? Mat::CONTINUOUS_FLAG : 0);
m.step[1] = esz;
if( copyData )
{
Mat m2 = m;
m.release();
if( !img->roi || !img->roi->coi ||
img->dataOrder == IPL_DATA_ORDER_PLANE)
m2.copyTo(m);
else
{
int ch[] = {img->roi->coi - 1, 0};
m.create(m2.rows, m2.cols, m2.type());
mixChannels(&m2, 1, &m, 1, ch, 1);
}
}
return m;
}
Mat Mat::diag(int d) const
{
CV_Assert( dims <= 2 );
Mat m = *this;
size_t esz = elemSize();
int len;
if( d >= 0 )
{
len = std::min(cols - d, rows);
m.data += esz*d;
}
else
{
len = std::min(rows + d, cols);
m.data -= step[0]*d;
}
CV_DbgAssert( len > 0 );
m.size[0] = m.rows = len;
m.size[1] = m.cols = 1;
m.step[0] += (len > 1 ? esz : 0);
if( m.rows > 1 )
m.flags &= ~CONTINUOUS_FLAG;
else
m.flags |= CONTINUOUS_FLAG;
if( size() != Size(1,1) )
m.flags |= SUBMATRIX_FLAG;
return m;
}
void Mat::pop_back(size_t nelems)
{
CV_Assert( nelems <= (size_t)size.p[0] );
if( isSubmatrix() )
*this = rowRange(0, size.p[0] - (int)nelems);
else
{
size.p[0] -= (int)nelems;
dataend -= nelems*step.p[0];
/*if( size.p[0] <= 1 )
{
if( dims <= 2 )
flags |= CONTINUOUS_FLAG;
else
updateContinuityFlag(*this);
}*/
}
}
void Mat::push_back_(const void* elem)
{
int r = size.p[0];
if( isSubmatrix() || dataend + step.p[0] > datalimit )
reserve( std::max(r + 1, (r*3+1)/2) );
size_t esz = elemSize();
memcpy(data + r*step.p[0], elem, esz);
size.p[0] = r + 1;
dataend += step.p[0];
if( esz < step.p[0] )
flags &= ~CONTINUOUS_FLAG;
}
void Mat::reserve(size_t nelems)
{
const size_t MIN_SIZE = 64;
CV_Assert( (int)nelems >= 0 );
if( !isSubmatrix() && data + step.p[0]*nelems <= datalimit )
return;
int r = size.p[0];
if( (size_t)r >= nelems )
return;
size.p[0] = std::max((int)nelems, 1);
size_t newsize = total()*elemSize();
if( newsize < MIN_SIZE )
size.p[0] = (int)((MIN_SIZE + newsize - 1)*nelems/newsize);
Mat m(dims, size.p, type());
size.p[0] = r;
if( r > 0 )
{
Mat mpart = m.rowRange(0, r);
copyTo(mpart);
}
*this = m;
size.p[0] = r;
dataend = data + step.p[0]*r;
}
void Mat::resize(size_t nelems)
{
int saveRows = size.p[0];
if( saveRows == (int)nelems )
return;
CV_Assert( (int)nelems >= 0 );
if( isSubmatrix() || data + step.p[0]*nelems > datalimit )
reserve(nelems);
size.p[0] = (int)nelems;
dataend += (size.p[0] - saveRows)*step.p[0];
//updateContinuityFlag(*this);
}
void Mat::resize(size_t nelems, const Scalar& s)
{
int saveRows = size.p[0];
resize(nelems);
if( size.p[0] > saveRows )
{
Mat part = rowRange(saveRows, size.p[0]);
part = s;
}
}
void Mat::push_back(const Mat& elems)
{
int r = size.p[0], delta = elems.size.p[0];
if( delta == 0 )
return;
if( this == &elems )
{
Mat tmp = elems;
push_back(tmp);
return;
}
if( !data )
{
*this = elems.clone();
return;
}
size.p[0] = elems.size.p[0];
bool eq = size == elems.size;
size.p[0] = r;
if( !eq )
CV_Error(CV_StsUnmatchedSizes, "");
if( type() != elems.type() )
CV_Error(CV_StsUnmatchedFormats, "");
if( isSubmatrix() || dataend + step.p[0]*delta > datalimit )
reserve( std::max(r + delta, (r*3+1)/2) );
size.p[0] += delta;
dataend += step.p[0]*delta;
//updateContinuityFlag(*this);
if( isContinuous() && elems.isContinuous() )
memcpy(data + r*step.p[0], elems.data, elems.total()*elems.elemSize());
else
{
Mat part = rowRange(r, r + delta);
elems.copyTo(part);
}
}
Mat cvarrToMat(const CvArr* arr, bool copyData,
bool /*allowND*/, int coiMode, AutoBuffer<double>* abuf )
{
if( !arr )
return Mat();
if( CV_IS_MAT_HDR_Z(arr) )
return cvMatToMat((const CvMat*)arr, copyData);
if( CV_IS_MATND(arr) )
return cvMatNDToMat((const CvMatND*)arr, copyData );
if( CV_IS_IMAGE(arr) )
{
const IplImage* iplimg = (const IplImage*)arr;
if( coiMode == 0 && iplimg->roi && iplimg->roi->coi > 0 )
CV_Error(CV_BadCOI, "COI is not supported by the function");
return iplImageToMat(iplimg, copyData);
}
if( CV_IS_SEQ(arr) )
{
CvSeq* seq = (CvSeq*)arr;
int total = seq->total, type = CV_MAT_TYPE(seq->flags), esz = seq->elem_size;
if( total == 0 )
return Mat();
CV_Assert(total > 0 && CV_ELEM_SIZE(seq->flags) == esz);
if(!copyData && seq->first->next == seq->first)
return Mat(total, 1, type, seq->first->data);
if( abuf )
{
abuf->allocate(((size_t)total*esz + sizeof(double)-1)/sizeof(double));
double* bufdata = *abuf;
cvCvtSeqToArray(seq, bufdata, CV_WHOLE_SEQ);
return Mat(total, 1, type, bufdata);
}
Mat buf(total, 1, type);
cvCvtSeqToArray(seq, buf.data, CV_WHOLE_SEQ);
return buf;
}
CV_Error(CV_StsBadArg, "Unknown array type");
return Mat();
}
void Mat::locateROI( Size& wholeSize, Point& ofs ) const
{
CV_Assert( dims <= 2 && step[0] > 0 );
size_t esz = elemSize(), minstep;
ptrdiff_t delta1 = data - datastart, delta2 = dataend - datastart;
if( delta1 == 0 )
ofs.x = ofs.y = 0;
else
{
ofs.y = (int)(delta1/step[0]);
ofs.x = (int)((delta1 - step[0]*ofs.y)/esz);
CV_DbgAssert( data == datastart + ofs.y*step[0] + ofs.x*esz );
}
minstep = (ofs.x + cols)*esz;
wholeSize.height = (int)((delta2 - minstep)/step[0] + 1);
wholeSize.height = std::max(wholeSize.height, ofs.y + rows);
wholeSize.width = (int)((delta2 - step*(wholeSize.height-1))/esz);
wholeSize.width = std::max(wholeSize.width, ofs.x + cols);
}
Mat& Mat::adjustROI( int dtop, int dbottom, int dleft, int dright )
{
CV_Assert( dims <= 2 && step[0] > 0 );
Size wholeSize; Point ofs;
size_t esz = elemSize();
locateROI( wholeSize, ofs );
int row1 = std::max(ofs.y - dtop, 0), row2 = std::min(ofs.y + rows + dbottom, wholeSize.height);
int col1 = std::max(ofs.x - dleft, 0), col2 = std::min(ofs.x + cols + dright, wholeSize.width);
data += (row1 - ofs.y)*step + (col1 - ofs.x)*esz;
rows = row2 - row1; cols = col2 - col1;
size.p[0] = rows; size.p[1] = cols;
if( esz*cols == step[0] || rows == 1 )
flags |= CONTINUOUS_FLAG;
else
flags &= ~CONTINUOUS_FLAG;
return *this;
}
}
void cv::extractImageCOI(const CvArr* arr, OutputArray _ch, int coi)
{
Mat mat = cvarrToMat(arr, false, true, 1);
_ch.create(mat.dims, mat.size, mat.depth());
Mat ch = _ch.getMat();
if(coi < 0)
{
CV_Assert( CV_IS_IMAGE(arr) );
coi = cvGetImageCOI((const IplImage*)arr)-1;
}
CV_Assert(0 <= coi && coi < mat.channels());
int _pairs[] = { coi, 0 };
mixChannels( &mat, 1, &ch, 1, _pairs, 1 );
}
void cv::insertImageCOI(InputArray _ch, CvArr* arr, int coi)
{
Mat ch = _ch.getMat(), mat = cvarrToMat(arr, false, true, 1);
if(coi < 0)
{
CV_Assert( CV_IS_IMAGE(arr) );
coi = cvGetImageCOI((const IplImage*)arr)-1;
}
CV_Assert(ch.size == mat.size && ch.depth() == mat.depth() && 0 <= coi && coi < mat.channels());
int _pairs[] = { 0, coi };
mixChannels( &ch, 1, &mat, 1, _pairs, 1 );
}
namespace cv
{
Mat Mat::reshape(int new_cn, int new_rows) const
{
int cn = channels();
Mat hdr = *this;
if( dims > 2 && new_rows == 0 && new_cn != 0 && size[dims-1]*cn % new_cn == 0 )
{
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT);
hdr.step[dims-1] = CV_ELEM_SIZE(hdr.flags);
hdr.size[dims-1] = hdr.size[dims-1]*cn / new_cn;
return hdr;
}
CV_Assert( dims <= 2 );
if( new_cn == 0 )
new_cn = cn;
int total_width = cols * cn;
if( (new_cn > total_width || total_width % new_cn != 0) && new_rows == 0 )
new_rows = rows * total_width / new_cn;
if( new_rows != 0 && new_rows != rows )
{
int total_size = total_width * rows;
if( !isContinuous() )
CV_Error( CV_BadStep,
"The matrix is not continuous, thus its number of rows can not be changed" );
if( (unsigned)new_rows > (unsigned)total_size )
CV_Error( CV_StsOutOfRange, "Bad new number of rows" );
total_width = total_size / new_rows;
if( total_width * new_rows != total_size )
CV_Error( CV_StsBadArg, "The total number of matrix elements "
"is not divisible by the new number of rows" );
hdr.rows = new_rows;
hdr.step[0] = total_width * elemSize1();
}
int new_width = total_width / new_cn;
if( new_width * new_cn != total_width )
CV_Error( CV_BadNumChannels,
"The total width is not divisible by the new number of channels" );
hdr.cols = new_width;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT);
hdr.step[1] = CV_ELEM_SIZE(hdr.flags);
return hdr;
}
Mat Mat::diag(const Mat& d)
{
CV_Assert( d.cols == 1 || d.rows == 1 );
int len = d.rows + d.cols - 1;
Mat m(len, len, d.type(), Scalar(0));
Mat md = m.diag();
if( d.cols == 1 )
d.copyTo(md);
else
transpose(d, md);
return m;
}
int Mat::checkVector(int _elemChannels, int _depth, bool _requireContinuous) const
{
return (depth() == _depth || _depth <= 0) &&
(isContinuous() || !_requireContinuous) &&
((dims == 2 && (((rows == 1 || cols == 1) && channels() == _elemChannels) ||
(cols == _elemChannels && channels() == 1))) ||
(dims == 3 && channels() == 1 && size.p[2] == _elemChannels && (size.p[0] == 1 || size.p[1] == 1) &&
(isContinuous() || step.p[1] == step.p[2]*size.p[2])))
? (int)(total()*channels()/_elemChannels) : -1;
}
void scalarToRawData(const Scalar& s, void* _buf, int type, int unroll_to)
{
int i, depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
CV_Assert(cn <= 4);
switch(depth)
{
case CV_8U:
{
uchar* buf = (uchar*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<uchar>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
}
break;
case CV_8S:
{
schar* buf = (schar*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<schar>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
}
break;
case CV_16U:
{
ushort* buf = (ushort*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<ushort>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
}
break;
case CV_16S:
{
short* buf = (short*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<short>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
}
break;
case CV_32S:
{
int* buf = (int*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<int>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
}
break;
case CV_32F:
{
float* buf = (float*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<float>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
}
break;
case CV_64F:
{
double* buf = (double*)_buf;
for(i = 0; i < cn; i++)
buf[i] = saturate_cast<double>(s.val[i]);
for(; i < unroll_to; i++)
buf[i] = buf[i-cn];
break;
}
default:
CV_Error(CV_StsUnsupportedFormat,"");
}
}
/*************************************************************************************************\
Input/Output Array
\*************************************************************************************************/
Mat _InputArray::getMat(int i) const
{
int k = kind();
int accessFlags = flags & ACCESS_MASK;
if( k == MAT )
{
const Mat* m = (const Mat*)obj;
if( i < 0 )
return *m;
return m->row(i);
}
if( k == UMAT )
{
const UMat* m = (const UMat*)obj;
if( i < 0 )
return m->getMat(accessFlags);
return m->getMat(accessFlags).row(i);
}
if( k == EXPR )
{
CV_Assert( i < 0 );
return (Mat)*((const MatExpr*)obj);
}
if( k == MATX )
{
CV_Assert( i < 0 );
return Mat(sz, flags, obj);
}
if( k == STD_VECTOR )
{
CV_Assert( i < 0 );
int t = CV_MAT_TYPE(flags);
const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
return !v.empty() ? Mat(size(), t, (void*)&v[0]) : Mat();
}
if( k == NONE )
return Mat();
if( k == STD_VECTOR_VECTOR )
{
int t = type(i);
const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
CV_Assert( 0 <= i && i < (int)vv.size() );
const std::vector<uchar>& v = vv[i];
return !v.empty() ? Mat(size(i), t, (void*)&v[0]) : Mat();
}
if( k == STD_VECTOR_MAT )
{
const std::vector<Mat>& v = *(const std::vector<Mat>*)obj;
CV_Assert( 0 <= i && i < (int)v.size() );
return v[i];
}
if( k == STD_VECTOR_UMAT )
{
const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
CV_Assert( 0 <= i && i < (int)v.size() );
return v[i].getMat(accessFlags);
}
if( k == OPENGL_BUFFER )
{
CV_Assert( i < 0 );
CV_Error(cv::Error::StsNotImplemented, "You should explicitly call mapHost/unmapHost methods for ogl::Buffer object");
return Mat();
}
if( k == GPU_MAT )
{
CV_Assert( i < 0 );
CV_Error(cv::Error::StsNotImplemented, "You should explicitly call download method for cuda::GpuMat object");
return Mat();
}
if( k == CUDA_MEM )
{
CV_Assert( i < 0 );
const cuda::CudaMem* cuda_mem = (const cuda::CudaMem*)obj;
return cuda_mem->createMatHeader();
}
CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
return Mat();
}
UMat _InputArray::getUMat(int i) const
{
int k = kind();
int accessFlags = flags & ACCESS_MASK;
if( k == UMAT )
{
const UMat* m = (const UMat*)obj;
if( i < 0 )
return *m;
return m->row(i);
}
if( k == STD_VECTOR_UMAT )
{
const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
CV_Assert( 0 <= i && i < (int)v.size() );
return v[i];
}
if( k == MAT )
{
const Mat* m = (const Mat*)obj;
if( i < 0 )
return m->getUMat(accessFlags);
return m->row(i).getUMat(accessFlags);
}
return getMat(i).getUMat(accessFlags);
}
void _InputArray::getMatVector(std::vector<Mat>& mv) const
{
int k = kind();
int accessFlags = flags & ACCESS_MASK;
if( k == MAT )
{
const Mat& m = *(const Mat*)obj;
int i, n = (int)m.size[0];
mv.resize(n);
for( i = 0; i < n; i++ )
mv[i] = m.dims == 2 ? Mat(1, m.cols, m.type(), (void*)m.ptr(i)) :
Mat(m.dims-1, &m.size[1], m.type(), (void*)m.ptr(i), &m.step[1]);
return;
}
if( k == EXPR )
{
Mat m = *(const MatExpr*)obj;
int i, n = m.size[0];
mv.resize(n);
for( i = 0; i < n; i++ )
mv[i] = m.row(i);
return;
}
if( k == MATX )
{
size_t i, n = sz.height, esz = CV_ELEM_SIZE(flags);
mv.resize(n);
for( i = 0; i < n; i++ )
mv[i] = Mat(1, sz.width, CV_MAT_TYPE(flags), (uchar*)obj + esz*sz.width*i);
return;
}
if( k == STD_VECTOR )
{
const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
size_t i, n = v.size(), esz = CV_ELEM_SIZE(flags);
int t = CV_MAT_DEPTH(flags), cn = CV_MAT_CN(flags);
mv.resize(n);
for( i = 0; i < n; i++ )
mv[i] = Mat(1, cn, t, (void*)(&v[0] + esz*i));
return;
}
if( k == NONE )
{
mv.clear();
return;
}
if( k == STD_VECTOR_VECTOR )
{
const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
int i, n = (int)vv.size();
int t = CV_MAT_TYPE(flags);
mv.resize(n);
for( i = 0; i < n; i++ )
{
const std::vector<uchar>& v = vv[i];
mv[i] = Mat(size(i), t, (void*)&v[0]);
}
return;
}
if( k == STD_VECTOR_UMAT )
{
const std::vector<UMat>& v = *(const std::vector<UMat>*)obj;
size_t i, n = v.size();
mv.resize(n);
for( i = 0; i < n; i++ )
mv[i] = v[i].getMat(accessFlags);
return;
}
CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
}
cuda::GpuMat _InputArray::getGpuMat() const
{
int k = kind();
if (k == GPU_MAT)
{
const cuda::GpuMat* d_mat = (const cuda::GpuMat*)obj;
return *d_mat;
}
if (k == CUDA_MEM)
{
const cuda::CudaMem* cuda_mem = (const cuda::CudaMem*)obj;
return cuda_mem->createGpuMatHeader();
}
if (k == OPENGL_BUFFER)
{
CV_Error(cv::Error::StsNotImplemented, "You should explicitly call mapDevice/unmapDevice methods for ogl::Buffer object");
return cuda::GpuMat();
}
if (k == NONE)
return cuda::GpuMat();
CV_Error(cv::Error::StsNotImplemented, "getGpuMat is available only for cuda::GpuMat and cuda::CudaMem");
return cuda::GpuMat();
}
ogl::Buffer _InputArray::getOGlBuffer() const
{
int k = kind();
CV_Assert(k == OPENGL_BUFFER);
const ogl::Buffer* gl_buf = (const ogl::Buffer*)obj;
return *gl_buf;
}
int _InputArray::kind() const
{
return flags & KIND_MASK;
}
Size _InputArray::size(int i) const
{
int k = kind();
if( k == MAT )
{
CV_Assert( i < 0 );
return ((const Mat*)obj)->size();
}
if( k == EXPR )
{
CV_Assert( i < 0 );
return ((const MatExpr*)obj)->size();
}
if( k == UMAT )
{
CV_Assert( i < 0 );
return ((const UMat*)obj)->size();
}
if( k == MATX )
{
CV_Assert( i < 0 );
return sz;
}
if( k == STD_VECTOR )
{
CV_Assert( i < 0 );
const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
const std::vector<int>& iv = *(const std::vector<int>*)obj;
size_t szb = v.size(), szi = iv.size();
return szb == szi ? Size((int)szb, 1) : Size((int)(szb/CV_ELEM_SIZE(flags)), 1);
}
if( k == NONE )
return Size();
if( k == STD_VECTOR_VECTOR )
{
const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
if( i < 0 )
return vv.empty() ? Size() : Size((int)vv.size(), 1);
CV_Assert( i < (int)vv.size() );
const std::vector<std::vector<int> >& ivv = *(const std::vector<std::vector<int> >*)obj;
size_t szb = vv[i].size(), szi = ivv[i].size();
return szb == szi ? Size((int)szb, 1) : Size((int)(szb/CV_ELEM_SIZE(flags)), 1);
}
if( k == STD_VECTOR_MAT )
{
const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
if( i < 0 )
return vv.empty() ? Size() : Size((int)vv.size(), 1);
CV_Assert( i < (int)vv.size() );
return vv[i].size();
}
if( k == OPENGL_BUFFER )
{
CV_Assert( i < 0 );
const ogl::Buffer* buf = (const ogl::Buffer*)obj;
return buf->size();
}
if( k == GPU_MAT )
{
CV_Assert( i < 0 );
const cuda::GpuMat* d_mat = (const cuda::GpuMat*)obj;
return d_mat->size();
}
if( k == OCL_MAT )
{
CV_Error(CV_StsNotImplemented, "This method is not implemented for oclMat yet");
}
CV_Assert( k == CUDA_MEM );
//if( k == CUDA_MEM )
{
CV_Assert( i < 0 );
const cuda::CudaMem* cuda_mem = (const cuda::CudaMem*)obj;
return cuda_mem->size();
}
}
size_t _InputArray::total(int i) const
{
int k = kind();
if( k == MAT )
{
CV_Assert( i < 0 );
return ((const Mat*)obj)->total();
}
if( k == UMAT )
{
CV_Assert( i < 0 );
return ((const UMat*)obj)->total();
}
if( k == STD_VECTOR_MAT )
{
const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
if( i < 0 )
return vv.size();
CV_Assert( i < (int)vv.size() );
return vv[i].total();
}
return size(i).area();
}
int _InputArray::type(int i) const
{
int k = kind();
if( k == MAT )
return ((const Mat*)obj)->type();
if( k == UMAT )
return ((const UMat*)obj)->type();
if( k == EXPR )
return ((const MatExpr*)obj)->type();
if( k == MATX || k == STD_VECTOR || k == STD_VECTOR_VECTOR )
return CV_MAT_TYPE(flags);
if( k == NONE )
return -1;
if( k == STD_VECTOR_MAT )
{
const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
CV_Assert( i < (int)vv.size() );
return vv[i >= 0 ? i : 0].type();
}
if( k == OPENGL_BUFFER )
return ((const ogl::Buffer*)obj)->type();
if( k == GPU_MAT )
return ((const cuda::GpuMat*)obj)->type();
CV_Assert( k == CUDA_MEM );
//if( k == CUDA_MEM )
return ((const cuda::CudaMem*)obj)->type();
}
int _InputArray::depth(int i) const
{
return CV_MAT_DEPTH(type(i));
}
int _InputArray::channels(int i) const
{
return CV_MAT_CN(type(i));
}
bool _InputArray::empty() const
{
int k = kind();
if( k == MAT )
return ((const Mat*)obj)->empty();
if( k == UMAT )
return ((const UMat*)obj)->empty();
if( k == EXPR )
return false;
if( k == MATX )
return false;
if( k == STD_VECTOR )
{
const std::vector<uchar>& v = *(const std::vector<uchar>*)obj;
return v.empty();
}
if( k == NONE )
return true;
if( k == STD_VECTOR_VECTOR )
{
const std::vector<std::vector<uchar> >& vv = *(const std::vector<std::vector<uchar> >*)obj;
return vv.empty();
}
if( k == STD_VECTOR_MAT )
{
const std::vector<Mat>& vv = *(const std::vector<Mat>*)obj;
return vv.empty();
}
if( k == OPENGL_BUFFER )
return ((const ogl::Buffer*)obj)->empty();
if( k == OCL_MAT )
{
CV_Error(CV_StsNotImplemented, "This method is not implemented for oclMat yet");
}
if( k == GPU_MAT )
return ((const cuda::GpuMat*)obj)->empty();
CV_Assert( k == CUDA_MEM );
//if( k == CUDA_MEM )
return ((const cuda::CudaMem*)obj)->empty();
}
bool _OutputArray::fixedSize() const
{
return (flags & FIXED_SIZE) == FIXED_SIZE;
}
bool _OutputArray::fixedType() const
{
return (flags & FIXED_TYPE) == FIXED_TYPE;
}
void _OutputArray::create(Size _sz, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
{
int k = kind();
if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == _sz);
CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype);
((Mat*)obj)->create(_sz, mtype);
return;
}
if( k == UMAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((UMat*)obj)->size.operator()() == _sz);
CV_Assert(!fixedType() || ((UMat*)obj)->type() == mtype);
((UMat*)obj)->create(_sz, mtype);
return;
}
if( k == GPU_MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((cuda::GpuMat*)obj)->size() == _sz);
CV_Assert(!fixedType() || ((cuda::GpuMat*)obj)->type() == mtype);
((cuda::GpuMat*)obj)->create(_sz, mtype);
return;
}
if( k == OPENGL_BUFFER && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((ogl::Buffer*)obj)->size() == _sz);
CV_Assert(!fixedType() || ((ogl::Buffer*)obj)->type() == mtype);
((ogl::Buffer*)obj)->create(_sz, mtype);
return;
}
if( k == CUDA_MEM && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((cuda::CudaMem*)obj)->size() == _sz);
CV_Assert(!fixedType() || ((cuda::CudaMem*)obj)->type() == mtype);
((cuda::CudaMem*)obj)->create(_sz, mtype);
return;
}
int sizes[] = {_sz.height, _sz.width};
create(2, sizes, mtype, i, allowTransposed, fixedDepthMask);
}
void _OutputArray::create(int rows, int cols, int mtype, int i, bool allowTransposed, int fixedDepthMask) const
{
int k = kind();
if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == Size(cols, rows));
CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype);
((Mat*)obj)->create(rows, cols, mtype);
return;
}
if( k == UMAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((UMat*)obj)->size.operator()() == Size(cols, rows));
CV_Assert(!fixedType() || ((UMat*)obj)->type() == mtype);
((UMat*)obj)->create(rows, cols, mtype);
return;
}
if( k == GPU_MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((cuda::GpuMat*)obj)->size() == Size(cols, rows));
CV_Assert(!fixedType() || ((cuda::GpuMat*)obj)->type() == mtype);
((cuda::GpuMat*)obj)->create(rows, cols, mtype);
return;
}
if( k == OPENGL_BUFFER && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((ogl::Buffer*)obj)->size() == Size(cols, rows));
CV_Assert(!fixedType() || ((ogl::Buffer*)obj)->type() == mtype);
((ogl::Buffer*)obj)->create(rows, cols, mtype);
return;
}
if( k == CUDA_MEM && i < 0 && !allowTransposed && fixedDepthMask == 0 )
{
CV_Assert(!fixedSize() || ((cuda::CudaMem*)obj)->size() == Size(cols, rows));
CV_Assert(!fixedType() || ((cuda::CudaMem*)obj)->type() == mtype);
((cuda::CudaMem*)obj)->create(rows, cols, mtype);
return;
}
int sizes[] = {rows, cols};
create(2, sizes, mtype, i, allowTransposed, fixedDepthMask);
}
void _OutputArray::create(int dims, const int* sizes, int mtype, int i,
bool allowTransposed, int fixedDepthMask) const
{
int k = kind();
mtype = CV_MAT_TYPE(mtype);
if( k == MAT )
{
CV_Assert( i < 0 );
Mat& m = *(Mat*)obj;
if( allowTransposed )
{
if( !m.isContinuous() )
{
CV_Assert(!fixedType() && !fixedSize());
m.release();
}
if( dims == 2 && m.dims == 2 && m.data &&
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
return;
}
if(fixedType())
{
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_Assert(CV_MAT_TYPE(mtype) == m.type());
}
if(fixedSize())
{
CV_Assert(m.dims == dims);
for(int j = 0; j < dims; ++j)
CV_Assert(m.size[j] == sizes[j]);
}
m.create(dims, sizes, mtype);
return;
}
if( k == UMAT )
{
CV_Assert( i < 0 );
UMat& m = *(UMat*)obj;
if( allowTransposed )
{
if( !m.isContinuous() )
{
CV_Assert(!fixedType() && !fixedSize());
m.release();
}
if( dims == 2 && m.dims == 2 && !m.empty() &&
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
return;
}
if(fixedType())
{
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_Assert(CV_MAT_TYPE(mtype) == m.type());
}
if(fixedSize())
{
CV_Assert(m.dims == dims);
for(int j = 0; j < dims; ++j)
CV_Assert(m.size[j] == sizes[j]);
}
m.create(dims, sizes, mtype);
return;
}
if( k == MATX )
{
CV_Assert( i < 0 );
int type0 = CV_MAT_TYPE(flags);
CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0) );
CV_Assert( dims == 2 && ((sizes[0] == sz.height && sizes[1] == sz.width) ||
(allowTransposed && sizes[0] == sz.width && sizes[1] == sz.height)));
return;
}
if( k == STD_VECTOR || k == STD_VECTOR_VECTOR )
{
CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0;
std::vector<uchar>* v = (std::vector<uchar>*)obj;
if( k == STD_VECTOR_VECTOR )
{
std::vector<std::vector<uchar> >& vv = *(std::vector<std::vector<uchar> >*)obj;
if( i < 0 )
{
CV_Assert(!fixedSize() || len == vv.size());
vv.resize(len);
return;
}
CV_Assert( i < (int)vv.size() );
v = &vv[i];
}
else
CV_Assert( i < 0 );
int type0 = CV_MAT_TYPE(flags);
CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) );
int esz = CV_ELEM_SIZE(type0);
CV_Assert(!fixedSize() || len == ((std::vector<uchar>*)v)->size() / esz);
switch( esz )
{
case 1:
((std::vector<uchar>*)v)->resize(len);
break;
case 2:
((std::vector<Vec2b>*)v)->resize(len);
break;
case 3:
((std::vector<Vec3b>*)v)->resize(len);
break;
case 4:
((std::vector<int>*)v)->resize(len);
break;
case 6:
((std::vector<Vec3s>*)v)->resize(len);
break;
case 8:
((std::vector<Vec2i>*)v)->resize(len);
break;
case 12:
((std::vector<Vec3i>*)v)->resize(len);
break;
case 16:
((std::vector<Vec4i>*)v)->resize(len);
break;
case 24:
((std::vector<Vec6i>*)v)->resize(len);
break;
case 32:
((std::vector<Vec8i>*)v)->resize(len);
break;
case 36:
((std::vector<Vec<int, 9> >*)v)->resize(len);
break;
case 48:
((std::vector<Vec<int, 12> >*)v)->resize(len);
break;
case 64:
((std::vector<Vec<int, 16> >*)v)->resize(len);
break;
case 128:
((std::vector<Vec<int, 32> >*)v)->resize(len);
break;
case 256:
((std::vector<Vec<int, 64> >*)v)->resize(len);
break;
case 512:
((std::vector<Vec<int, 128> >*)v)->resize(len);
break;
default:
CV_Error_(CV_StsBadArg, ("Vectors with element size %d are not supported. Please, modify OutputArray::create()\n", esz));
}
return;
}
if( k == NONE )
{
CV_Error(CV_StsNullPtr, "create() called for the missing output array" );
return;
}
if( k == STD_VECTOR_MAT )
{
std::vector<Mat>& v = *(std::vector<Mat>*)obj;
if( i < 0 )
{
CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) );
size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = v.size();
CV_Assert(!fixedSize() || len == len0);
v.resize(len);
if( fixedType() )
{
int _type = CV_MAT_TYPE(flags);
for( size_t j = len0; j < len; j++ )
{
if( v[j].type() == _type )
continue;
CV_Assert( v[j].empty() );
v[j].flags = (v[j].flags & ~CV_MAT_TYPE_MASK) | _type;
}
}
return;
}
CV_Assert( i < (int)v.size() );
Mat& m = v[i];
if( allowTransposed )
{
if( !m.isContinuous() )
{
CV_Assert(!fixedType() && !fixedSize());
m.release();
}
if( dims == 2 && m.dims == 2 && m.data &&
m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] )
return;
}
if(fixedType())
{
if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
mtype = m.type();
else
CV_Assert(!fixedType() || (CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0));
}
if(fixedSize())
{
CV_Assert(m.dims == dims);
for(int j = 0; j < dims; ++j)
CV_Assert(m.size[j] == sizes[j]);
}
m.create(dims, sizes, mtype);
}
CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
}
void _OutputArray::release() const
{
CV_Assert(!fixedSize());
int k = kind();
if( k == MAT )
{
((Mat*)obj)->release();
return;
}
if( k == GPU_MAT )
{
((cuda::GpuMat*)obj)->release();
return;
}
if( k == CUDA_MEM )
{
((cuda::CudaMem*)obj)->release();
return;
}
if( k == OPENGL_BUFFER )
{
((ogl::Buffer*)obj)->release();
return;
}
if( k == NONE )
return;
if( k == STD_VECTOR )
{
create(Size(), CV_MAT_TYPE(flags));
return;
}
if( k == STD_VECTOR_VECTOR )
{
((std::vector<std::vector<uchar> >*)obj)->clear();
return;
}
if( k == STD_VECTOR_MAT )
{
((std::vector<Mat>*)obj)->clear();
}
CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
}
void _OutputArray::clear() const
{
int k = kind();
if( k == MAT )
{
CV_Assert(!fixedSize());
((Mat*)obj)->resize(0);
return;
}
release();
}
bool _OutputArray::needed() const
{
return kind() != NONE;
}
Mat& _OutputArray::getMatRef(int i) const
{
int k = kind();
if( i < 0 )
{
CV_Assert( k == MAT );
return *(Mat*)obj;
}
else
{
CV_Assert( k == STD_VECTOR_MAT );
std::vector<Mat>& v = *(std::vector<Mat>*)obj;
CV_Assert( i < (int)v.size() );
return v[i];
}
}
cuda::GpuMat& _OutputArray::getGpuMatRef() const
{
int k = kind();
CV_Assert( k == GPU_MAT );
return *(cuda::GpuMat*)obj;
}
ogl::Buffer& _OutputArray::getOGlBufferRef() const
{
int k = kind();
CV_Assert( k == OPENGL_BUFFER );
return *(ogl::Buffer*)obj;
}
cuda::CudaMem& _OutputArray::getCudaMemRef() const
{
int k = kind();
CV_Assert( k == CUDA_MEM );
return *(cuda::CudaMem*)obj;
}
static _InputOutputArray _none;
InputOutputArray noArray() { return _none; }
}
/*************************************************************************************************\
Matrix Operations
\*************************************************************************************************/
void cv::hconcat(const Mat* src, size_t nsrc, OutputArray _dst)
{
if( nsrc == 0 || !src )
{
_dst.release();
return;
}
int totalCols = 0, cols = 0;
size_t i;
for( i = 0; i < nsrc; i++ )
{
CV_Assert( !src[i].empty() && src[i].dims <= 2 &&
src[i].rows == src[0].rows &&
src[i].type() == src[0].type());
totalCols += src[i].cols;
}
_dst.create( src[0].rows, totalCols, src[0].type());
Mat dst = _dst.getMat();
for( i = 0; i < nsrc; i++ )
{
Mat dpart = dst(Rect(cols, 0, src[i].cols, src[i].rows));
src[i].copyTo(dpart);
cols += src[i].cols;
}
}
void cv::hconcat(InputArray src1, InputArray src2, OutputArray dst)
{
Mat src[] = {src1.getMat(), src2.getMat()};
hconcat(src, 2, dst);
}
void cv::hconcat(InputArray _src, OutputArray dst)
{
std::vector<Mat> src;
_src.getMatVector(src);
hconcat(!src.empty() ? &src[0] : 0, src.size(), dst);
}
void cv::vconcat(const Mat* src, size_t nsrc, OutputArray _dst)
{
if( nsrc == 0 || !src )
{
_dst.release();
return;
}
int totalRows = 0, rows = 0;
size_t i;
for( i = 0; i < nsrc; i++ )
{
CV_Assert( !src[i].empty() && src[i].dims <= 2 &&
src[i].cols == src[0].cols &&
src[i].type() == src[0].type());
totalRows += src[i].rows;
}
_dst.create( totalRows, src[0].cols, src[0].type());
Mat dst = _dst.getMat();
for( i = 0; i < nsrc; i++ )
{
Mat dpart(dst, Rect(0, rows, src[i].cols, src[i].rows));
src[i].copyTo(dpart);
rows += src[i].rows;
}
}
void cv::vconcat(InputArray src1, InputArray src2, OutputArray dst)
{
Mat src[] = {src1.getMat(), src2.getMat()};
vconcat(src, 2, dst);
}
void cv::vconcat(InputArray _src, OutputArray dst)
{
std::vector<Mat> src;
_src.getMatVector(src);
vconcat(!src.empty() ? &src[0] : 0, src.size(), dst);
}
//////////////////////////////////////// set identity ////////////////////////////////////////////
void cv::setIdentity( InputOutputArray _m, const Scalar& s )
{
Mat m = _m.getMat();
CV_Assert( m.dims <= 2 );
int i, j, rows = m.rows, cols = m.cols, type = m.type();
if( type == CV_32FC1 )
{
float* data = (float*)m.data;
float val = (float)s[0];
size_t step = m.step/sizeof(data[0]);
for( i = 0; i < rows; i++, data += step )
{
for( j = 0; j < cols; j++ )
data[j] = 0;
if( i < cols )
data[i] = val;
}
}
else if( type == CV_64FC1 )
{
double* data = (double*)m.data;
double val = s[0];
size_t step = m.step/sizeof(data[0]);
for( i = 0; i < rows; i++, data += step )
{
for( j = 0; j < cols; j++ )
data[j] = j == i ? val : 0;
}
}
else
{
m = Scalar(0);
m.diag() = s;
}
}
//////////////////////////////////////////// trace ///////////////////////////////////////////
cv::Scalar cv::trace( InputArray _m )
{
Mat m = _m.getMat();
CV_Assert( m.dims <= 2 );
int i, type = m.type();
int nm = std::min(m.rows, m.cols);
if( type == CV_32FC1 )
{
const float* ptr = (const float*)m.data;
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( i = 0; i < nm; i++ )
_s += ptr[i*step];
return _s;
}
if( type == CV_64FC1 )
{
const double* ptr = (const double*)m.data;
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( i = 0; i < nm; i++ )
_s += ptr[i*step];
return _s;
}
return cv::sum(m.diag());
}
////////////////////////////////////// transpose /////////////////////////////////////////
namespace cv
{
template<typename T> static void
transpose_( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz )
{
int i=0, j, m = sz.width, n = sz.height;
#if CV_ENABLE_UNROLLED
for(; i <= m - 4; i += 4 )
{
T* d0 = (T*)(dst + dstep*i);
T* d1 = (T*)(dst + dstep*(i+1));
T* d2 = (T*)(dst + dstep*(i+2));
T* d3 = (T*)(dst + dstep*(i+3));
for( j = 0; j <= n - 4; j += 4 )
{
const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j);
const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1));
const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2));
const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3));
d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0];
d1[j] = s0[1]; d1[j+1] = s1[1]; d1[j+2] = s2[1]; d1[j+3] = s3[1];
d2[j] = s0[2]; d2[j+1] = s1[2]; d2[j+2] = s2[2]; d2[j+3] = s3[2];
d3[j] = s0[3]; d3[j+1] = s1[3]; d3[j+2] = s2[3]; d3[j+3] = s3[3];
}
for( ; j < n; j++ )
{
const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep);
d0[j] = s0[0]; d1[j] = s0[1]; d2[j] = s0[2]; d3[j] = s0[3];
}
}
#endif
for( ; i < m; i++ )
{
T* d0 = (T*)(dst + dstep*i);
j = 0;
#if CV_ENABLE_UNROLLED
for(; j <= n - 4; j += 4 )
{
const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j);
const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1));
const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2));
const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3));
d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0];
}
#endif
for( ; j < n; j++ )
{
const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep);
d0[j] = s0[0];
}
}
}
template<typename T> static void
transposeI_( uchar* data, size_t step, int n )
{
int i, j;
for( i = 0; i < n; i++ )
{
T* row = (T*)(data + step*i);
uchar* data1 = data + i*sizeof(T);
for( j = i+1; j < n; j++ )
std::swap( row[j], *(T*)(data1 + step*j) );
}
}
typedef void (*TransposeFunc)( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz );
typedef void (*TransposeInplaceFunc)( uchar* data, size_t step, int n );
#define DEF_TRANSPOSE_FUNC(suffix, type) \
static void transpose_##suffix( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ) \
{ transpose_<type>(src, sstep, dst, dstep, sz); } \
\
static void transposeI_##suffix( uchar* data, size_t step, int n ) \
{ transposeI_<type>(data, step, n); }
DEF_TRANSPOSE_FUNC(8u, uchar)
DEF_TRANSPOSE_FUNC(16u, ushort)
DEF_TRANSPOSE_FUNC(8uC3, Vec3b)
DEF_TRANSPOSE_FUNC(32s, int)
DEF_TRANSPOSE_FUNC(16uC3, Vec3s)
DEF_TRANSPOSE_FUNC(32sC2, Vec2i)
DEF_TRANSPOSE_FUNC(32sC3, Vec3i)
DEF_TRANSPOSE_FUNC(32sC4, Vec4i)
DEF_TRANSPOSE_FUNC(32sC6, Vec6i)
DEF_TRANSPOSE_FUNC(32sC8, Vec8i)
static TransposeFunc transposeTab[] =
{
0, transpose_8u, transpose_16u, transpose_8uC3, transpose_32s, 0, transpose_16uC3, 0,
transpose_32sC2, 0, 0, 0, transpose_32sC3, 0, 0, 0, transpose_32sC4,
0, 0, 0, 0, 0, 0, 0, transpose_32sC6, 0, 0, 0, 0, 0, 0, 0, transpose_32sC8
};
static TransposeInplaceFunc transposeInplaceTab[] =
{
0, transposeI_8u, transposeI_16u, transposeI_8uC3, transposeI_32s, 0, transposeI_16uC3, 0,
transposeI_32sC2, 0, 0, 0, transposeI_32sC3, 0, 0, 0, transposeI_32sC4,
0, 0, 0, 0, 0, 0, 0, transposeI_32sC6, 0, 0, 0, 0, 0, 0, 0, transposeI_32sC8
};
}
void cv::transpose( InputArray _src, OutputArray _dst )
{
Mat src = _src.getMat();
if( src.empty() )
{
_dst.release();
return;
}
size_t esz = src.elemSize();
CV_Assert( src.dims <= 2 && esz <= (size_t)32 );
_dst.create(src.cols, src.rows, src.type());
Mat dst = _dst.getMat();
// handle the case of single-column/single-row matrices, stored in STL vectors.
if( src.rows != dst.cols || src.cols != dst.rows )
{
CV_Assert( src.size() == dst.size() && (src.cols == 1 || src.rows == 1) );
src.copyTo(dst);
return;
}
if( dst.data == src.data )
{
TransposeInplaceFunc func = transposeInplaceTab[esz];
CV_Assert( func != 0 );
func( dst.data, dst.step, dst.rows );
}
else
{
TransposeFunc func = transposeTab[esz];
CV_Assert( func != 0 );
func( src.data, src.step, dst.data, dst.step, src.size() );
}
}
void cv::completeSymm( InputOutputArray _m, bool LtoR )
{
Mat m = _m.getMat();
CV_Assert( m.dims <= 2 );
int i, j, nrows = m.rows, type = m.type();
int j0 = 0, j1 = nrows;
CV_Assert( m.rows == m.cols );
if( type == CV_32FC1 || type == CV_32SC1 )
{
int* data = (int*)m.data;
size_t step = m.step/sizeof(data[0]);
for( i = 0; i < nrows; i++ )
{
if( !LtoR ) j1 = i; else j0 = i+1;
for( j = j0; j < j1; j++ )
data[i*step + j] = data[j*step + i];
}
}
else if( type == CV_64FC1 )
{
double* data = (double*)m.data;
size_t step = m.step/sizeof(data[0]);
for( i = 0; i < nrows; i++ )
{
if( !LtoR ) j1 = i; else j0 = i+1;
for( j = j0; j < j1; j++ )
data[i*step + j] = data[j*step + i];
}
}
else
CV_Error( CV_StsUnsupportedFormat, "" );
}
cv::Mat cv::Mat::cross(InputArray _m) const
{
Mat m = _m.getMat();
int tp = type(), d = CV_MAT_DEPTH(tp);
CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() &&
((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1)));
Mat result(rows, cols, tp);
if( d == CV_32F )
{
const float *a = (const float*)data, *b = (const float*)m.data;
float* c = (float*)result.data;
size_t lda = rows > 1 ? step/sizeof(a[0]) : 1;
size_t ldb = rows > 1 ? m.step/sizeof(b[0]) : 1;
c[0] = a[lda] * b[ldb*2] - a[lda*2] * b[ldb];
c[1] = a[lda*2] * b[0] - a[0] * b[ldb*2];
c[2] = a[0] * b[ldb] - a[lda] * b[0];
}
else if( d == CV_64F )
{
const double *a = (const double*)data, *b = (const double*)m.data;
double* c = (double*)result.data;
size_t lda = rows > 1 ? step/sizeof(a[0]) : 1;
size_t ldb = rows > 1 ? m.step/sizeof(b[0]) : 1;
c[0] = a[lda] * b[ldb*2] - a[lda*2] * b[ldb];
c[1] = a[lda*2] * b[0] - a[0] * b[ldb*2];
c[2] = a[0] * b[ldb] - a[lda] * b[0];
}
return result;
}
////////////////////////////////////////// reduce ////////////////////////////////////////////
namespace cv
{
template<typename T, typename ST, class Op> static void
reduceR_( const Mat& srcmat, Mat& dstmat )
{
typedef typename Op::rtype WT;
Size size = srcmat.size();
size.width *= srcmat.channels();
AutoBuffer<WT> buffer(size.width);
WT* buf = buffer;
ST* dst = (ST*)dstmat.data;
const T* src = (const T*)srcmat.data;
size_t srcstep = srcmat.step/sizeof(src[0]);
int i;
Op op;
for( i = 0; i < size.width; i++ )
buf[i] = src[i];
for( ; --size.height; )
{
src += srcstep;
i = 0;
#if CV_ENABLE_UNROLLED
for(; i <= size.width - 4; i += 4 )
{
WT s0, s1;
s0 = op(buf[i], (WT)src[i]);
s1 = op(buf[i+1], (WT)src[i+1]);
buf[i] = s0; buf[i+1] = s1;
s0 = op(buf[i+2], (WT)src[i+2]);
s1 = op(buf[i+3], (WT)src[i+3]);
buf[i+2] = s0; buf[i+3] = s1;
}
#endif
for( ; i < size.width; i++ )
buf[i] = op(buf[i], (WT)src[i]);
}
for( i = 0; i < size.width; i++ )
dst[i] = (ST)buf[i];
}
template<typename T, typename ST, class Op> static void
reduceC_( const Mat& srcmat, Mat& dstmat )
{
typedef typename Op::rtype WT;
Size size = srcmat.size();
int i, k, cn = srcmat.channels();
size.width *= cn;
Op op;
for( int y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
ST* dst = (ST*)(dstmat.data + dstmat.step*y);
if( size.width == cn )
for( k = 0; k < cn; k++ )
dst[k] = src[k];
else
{
for( k = 0; k < cn; k++ )
{
WT a0 = src[k], a1 = src[k+cn];
for( i = 2*cn; i <= size.width - 4*cn; i += 4*cn )
{
a0 = op(a0, (WT)src[i+k]);
a1 = op(a1, (WT)src[i+k+cn]);
a0 = op(a0, (WT)src[i+k+cn*2]);
a1 = op(a1, (WT)src[i+k+cn*3]);
}
for( ; i < size.width; i += cn )
{
a0 = op(a0, (WT)src[i+k]);
}
a0 = op(a0, a1);
dst[k] = (ST)a0;
}
}
}
}
typedef void (*ReduceFunc)( const Mat& src, Mat& dst );
}
#define reduceSumR8u32s reduceR_<uchar, int, OpAdd<int> >
#define reduceSumR8u32f reduceR_<uchar, float, OpAdd<int> >
#define reduceSumR8u64f reduceR_<uchar, double,OpAdd<int> >
#define reduceSumR16u32f reduceR_<ushort,float, OpAdd<float> >
#define reduceSumR16u64f reduceR_<ushort,double,OpAdd<double> >
#define reduceSumR16s32f reduceR_<short, float, OpAdd<float> >
#define reduceSumR16s64f reduceR_<short, double,OpAdd<double> >
#define reduceSumR32f32f reduceR_<float, float, OpAdd<float> >
#define reduceSumR32f64f reduceR_<float, double,OpAdd<double> >
#define reduceSumR64f64f reduceR_<double,double,OpAdd<double> >
#define reduceMaxR8u reduceR_<uchar, uchar, OpMax<uchar> >
#define reduceMaxR16u reduceR_<ushort,ushort,OpMax<ushort> >
#define reduceMaxR16s reduceR_<short, short, OpMax<short> >
#define reduceMaxR32f reduceR_<float, float, OpMax<float> >
#define reduceMaxR64f reduceR_<double,double,OpMax<double> >
#define reduceMinR8u reduceR_<uchar, uchar, OpMin<uchar> >
#define reduceMinR16u reduceR_<ushort,ushort,OpMin<ushort> >
#define reduceMinR16s reduceR_<short, short, OpMin<short> >
#define reduceMinR32f reduceR_<float, float, OpMin<float> >
#define reduceMinR64f reduceR_<double,double,OpMin<double> >
#define reduceSumC8u32s reduceC_<uchar, int, OpAdd<int> >
#define reduceSumC8u32f reduceC_<uchar, float, OpAdd<int> >
#define reduceSumC8u64f reduceC_<uchar, double,OpAdd<int> >
#define reduceSumC16u32f reduceC_<ushort,float, OpAdd<float> >
#define reduceSumC16u64f reduceC_<ushort,double,OpAdd<double> >
#define reduceSumC16s32f reduceC_<short, float, OpAdd<float> >
#define reduceSumC16s64f reduceC_<short, double,OpAdd<double> >
#define reduceSumC32f32f reduceC_<float, float, OpAdd<float> >
#define reduceSumC32f64f reduceC_<float, double,OpAdd<double> >
#define reduceSumC64f64f reduceC_<double,double,OpAdd<double> >
#define reduceMaxC8u reduceC_<uchar, uchar, OpMax<uchar> >
#define reduceMaxC16u reduceC_<ushort,ushort,OpMax<ushort> >
#define reduceMaxC16s reduceC_<short, short, OpMax<short> >
#define reduceMaxC32f reduceC_<float, float, OpMax<float> >
#define reduceMaxC64f reduceC_<double,double,OpMax<double> >
#define reduceMinC8u reduceC_<uchar, uchar, OpMin<uchar> >
#define reduceMinC16u reduceC_<ushort,ushort,OpMin<ushort> >
#define reduceMinC16s reduceC_<short, short, OpMin<short> >
#define reduceMinC32f reduceC_<float, float, OpMin<float> >
#define reduceMinC64f reduceC_<double,double,OpMin<double> >
void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
{
Mat src = _src.getMat();
CV_Assert( src.dims <= 2 );
int op0 = op;
int stype = src.type(), sdepth = src.depth(), cn = src.channels();
if( dtype < 0 )
dtype = _dst.fixedType() ? _dst.type() : stype;
int ddepth = CV_MAT_DEPTH(dtype);
_dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1,
CV_MAKETYPE(dtype >= 0 ? dtype : stype, cn));
Mat dst = _dst.getMat(), temp = dst;
CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX ||
op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
CV_Assert( src.channels() == dst.channels() );
if( op == CV_REDUCE_AVG )
{
op = CV_REDUCE_SUM;
if( sdepth < CV_32S && ddepth < CV_32S )
{
temp.create(dst.rows, dst.cols, CV_32SC(cn));
ddepth = CV_32S;
}
}
ReduceFunc func = 0;
if( dim == 0 )
{
if( op == CV_REDUCE_SUM )
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = GET_OPTIMIZED(reduceSumR8u32s);
else if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceSumR8u32f;
else if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceSumR8u64f;
else if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceSumR16u32f;
else if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceSumR16u64f;
else if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceSumR16s32f;
else if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceSumR16s64f;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceSumR32f32f);
else if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceSumR32f64f;
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceSumR64f64f;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMaxR8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMaxR16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMaxR16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMaxR32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxR64f;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMinR8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMinR16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMinR16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMinR32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMinR64f;
}
}
else
{
if(op == CV_REDUCE_SUM)
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = GET_OPTIMIZED(reduceSumC8u32s);
else if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceSumC8u32f;
else if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceSumC8u64f;
else if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceSumC16u32f;
else if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceSumC16u64f;
else if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceSumC16s32f;
else if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceSumC16s64f;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceSumC32f32f);
else if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceSumC32f64f;
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceSumC64f64f;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMaxC8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMaxC16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMaxC16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMaxC32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxC64f;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMinC8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMinC16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMinC16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMinC32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMinC64f;
}
}
if( !func )
CV_Error( CV_StsUnsupportedFormat,
"Unsupported combination of input and output array formats" );
func( src, temp );
if( op0 == CV_REDUCE_AVG )
temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols));
}
//////////////////////////////////////// sort ///////////////////////////////////////////
namespace cv
{
template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
{
AutoBuffer<T> buf;
T* bptr;
int i, j, n, len;
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool inplace = src.data == dst.data;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
if( sortRows )
n = src.rows, len = src.cols;
else
{
n = src.cols, len = src.rows;
buf.allocate(len);
}
bptr = (T*)buf;
for( i = 0; i < n; i++ )
{
T* ptr = bptr;
if( sortRows )
{
T* dptr = (T*)(dst.data + dst.step*i);
if( !inplace )
{
const T* sptr = (const T*)(src.data + src.step*i);
for( j = 0; j < len; j++ )
dptr[j] = sptr[j];
}
ptr = dptr;
}
else
{
for( j = 0; j < len; j++ )
ptr[j] = ((const T*)(src.data + src.step*j))[i];
}
std::sort( ptr, ptr + len );
if( sortDescending )
for( j = 0; j < len/2; j++ )
std::swap(ptr[j], ptr[len-1-j]);
if( !sortRows )
for( j = 0; j < len; j++ )
((T*)(dst.data + dst.step*j))[i] = ptr[j];
}
}
template<typename _Tp> class LessThanIdx
{
public:
LessThanIdx( const _Tp* _arr ) : arr(_arr) {}
bool operator()(int a, int b) const { return arr[a] < arr[b]; }
const _Tp* arr;
};
template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
{
AutoBuffer<T> buf;
AutoBuffer<int> ibuf;
T* bptr;
int* _iptr;
int i, j, n, len;
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
CV_Assert( src.data != dst.data );
if( sortRows )
n = src.rows, len = src.cols;
else
{
n = src.cols, len = src.rows;
buf.allocate(len);
ibuf.allocate(len);
}
bptr = (T*)buf;
_iptr = (int*)ibuf;
for( i = 0; i < n; i++ )
{
T* ptr = bptr;
int* iptr = _iptr;
if( sortRows )
{
ptr = (T*)(src.data + src.step*i);
iptr = (int*)(dst.data + dst.step*i);
}
else
{
for( j = 0; j < len; j++ )
ptr[j] = ((const T*)(src.data + src.step*j))[i];
}
for( j = 0; j < len; j++ )
iptr[j] = j;
std::sort( iptr, iptr + len, LessThanIdx<T>(ptr) );
if( sortDescending )
for( j = 0; j < len/2; j++ )
std::swap(iptr[j], iptr[len-1-j]);
if( !sortRows )
for( j = 0; j < len; j++ )
((int*)(dst.data + dst.step*j))[i] = iptr[j];
}
}
typedef void (*SortFunc)(const Mat& src, Mat& dst, int flags);
}
void cv::sort( InputArray _src, OutputArray _dst, int flags )
{
static SortFunc tab[] =
{
sort_<uchar>, sort_<schar>, sort_<ushort>, sort_<short>,
sort_<int>, sort_<float>, sort_<double>, 0
};
Mat src = _src.getMat();
SortFunc func = tab[src.depth()];
CV_Assert( src.dims <= 2 && src.channels() == 1 && func != 0 );
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
func( src, dst, flags );
}
void cv::sortIdx( InputArray _src, OutputArray _dst, int flags )
{
static SortFunc tab[] =
{
sortIdx_<uchar>, sortIdx_<schar>, sortIdx_<ushort>, sortIdx_<short>,
sortIdx_<int>, sortIdx_<float>, sortIdx_<double>, 0
};
Mat src = _src.getMat();
SortFunc func = tab[src.depth()];
CV_Assert( src.dims <= 2 && src.channels() == 1 && func != 0 );
Mat dst = _dst.getMat();
if( dst.data == src.data )
_dst.release();
_dst.create( src.size(), CV_32S );
dst = _dst.getMat();
func( src, dst, flags );
}
////////////////////////////////////////// kmeans ////////////////////////////////////////////
namespace cv
{
static void generateRandomCenter(const std::vector<Vec2f>& box, float* center, RNG& rng)
{
size_t j, dims = box.size();
float margin = 1.f/dims;
for( j = 0; j < dims; j++ )
center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
}
class KMeansPPDistanceComputer : public ParallelLoopBody
{
public:
KMeansPPDistanceComputer( float *_tdist2,
const float *_data,
const float *_dist,
int _dims,
size_t _step,
size_t _stepci )
: tdist2(_tdist2),
data(_data),
dist(_dist),
dims(_dims),
step(_step),
stepci(_stepci) { }
void operator()( const cv::Range& range ) const
{
const int begin = range.start;
const int end = range.end;
for ( int i = begin; i<end; i++ )
{
tdist2[i] = std::min(normL2Sqr_(data + step*i, data + stepci, dims), dist[i]);
}
}
private:
KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
float *tdist2;
const float *data;
const float *dist;
const int dims;
const size_t step;
const size_t stepci;
};
/*
k-means center initialization using the following algorithm:
Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
*/
static void generateCentersPP(const Mat& _data, Mat& _out_centers,
int K, RNG& rng, int trials)
{
int i, j, k, dims = _data.cols, N = _data.rows;
const float* data = _data.ptr<float>(0);
size_t step = _data.step/sizeof(data[0]);
std::vector<int> _centers(K);
int* centers = &_centers[0];
std::vector<float> _dist(N*3);
float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
double sum0 = 0;
centers[0] = (unsigned)rng % N;
for( i = 0; i < N; i++ )
{
dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
sum0 += dist[i];
}
for( k = 1; k < K; k++ )
{
double bestSum = DBL_MAX;
int bestCenter = -1;
for( j = 0; j < trials; j++ )
{
double p = (double)rng*sum0, s = 0;
for( i = 0; i < N-1; i++ )
if( (p -= dist[i]) <= 0 )
break;
int ci = i;
parallel_for_(Range(0, N),
KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
for( i = 0; i < N; i++ )
{
s += tdist2[i];
}
if( s < bestSum )
{
bestSum = s;
bestCenter = ci;
std::swap(tdist, tdist2);
}
}
centers[k] = bestCenter;
sum0 = bestSum;
std::swap(dist, tdist);
}
for( k = 0; k < K; k++ )
{
const float* src = data + step*centers[k];
float* dst = _out_centers.ptr<float>(k);
for( j = 0; j < dims; j++ )
dst[j] = src[j];
}
}
class KMeansDistanceComputer : public ParallelLoopBody
{
public:
KMeansDistanceComputer( double *_distances,
int *_labels,
const Mat& _data,
const Mat& _centers )
: distances(_distances),
labels(_labels),
data(_data),
centers(_centers)
{
}
void operator()( const Range& range ) const
{
const int begin = range.start;
const int end = range.end;
const int K = centers.rows;
const int dims = centers.cols;
const float *sample;
for( int i = begin; i<end; ++i)
{
sample = data.ptr<float>(i);
int k_best = 0;
double min_dist = DBL_MAX;
for( int k = 0; k < K; k++ )
{
const float* center = centers.ptr<float>(k);
const double dist = normL2Sqr_(sample, center, dims);
if( min_dist > dist )
{
min_dist = dist;
k_best = k;
}
}
distances[i] = min_dist;
labels[i] = k_best;
}
}
private:
KMeansDistanceComputer& operator=(const KMeansDistanceComputer&); // to quiet MSVC
double *distances;
int *labels;
const Mat& data;
const Mat& centers;
};
}
double cv::kmeans( InputArray _data, int K,
InputOutputArray _bestLabels,
TermCriteria criteria, int attempts,
int flags, OutputArray _centers )
{
const int SPP_TRIALS = 3;
Mat data = _data.getMat();
bool isrow = data.rows == 1 && data.channels() > 1;
int N = !isrow ? data.rows : data.cols;
int dims = (!isrow ? data.cols : 1)*data.channels();
int type = data.depth();
attempts = std::max(attempts, 1);
CV_Assert( data.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( N >= K );
_bestLabels.create(N, 1, CV_32S, -1, true);
Mat _labels, best_labels = _bestLabels.getMat();
if( flags & CV_KMEANS_USE_INITIAL_LABELS )
{
CV_Assert( (best_labels.cols == 1 || best_labels.rows == 1) &&
best_labels.cols*best_labels.rows == N &&
best_labels.type() == CV_32S &&
best_labels.isContinuous());
best_labels.copyTo(_labels);
}
else
{
if( !((best_labels.cols == 1 || best_labels.rows == 1) &&
best_labels.cols*best_labels.rows == N &&
best_labels.type() == CV_32S &&
best_labels.isContinuous()))
best_labels.create(N, 1, CV_32S);
_labels.create(best_labels.size(), best_labels.type());
}
int* labels = _labels.ptr<int>();
Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
std::vector<int> counters(K);
std::vector<Vec2f> _box(dims);
Vec2f* box = &_box[0];
double best_compactness = DBL_MAX, compactness = 0;
RNG& rng = theRNG();
int a, iter, i, j, k;
if( criteria.type & TermCriteria::EPS )
criteria.epsilon = std::max(criteria.epsilon, 0.);
else
criteria.epsilon = FLT_EPSILON;
criteria.epsilon *= criteria.epsilon;
if( criteria.type & TermCriteria::COUNT )
criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
else
criteria.maxCount = 100;
if( K == 1 )
{
attempts = 1;
criteria.maxCount = 2;
}
const float* sample = data.ptr<float>(0);
for( j = 0; j < dims; j++ )
box[j] = Vec2f(sample[j], sample[j]);
for( i = 1; i < N; i++ )
{
sample = data.ptr<float>(i);
for( j = 0; j < dims; j++ )
{
float v = sample[j];
box[j][0] = std::min(box[j][0], v);
box[j][1] = std::max(box[j][1], v);
}
}
for( a = 0; a < attempts; a++ )
{
double max_center_shift = DBL_MAX;
for( iter = 0;; )
{
swap(centers, old_centers);
if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
{
if( flags & KMEANS_PP_CENTERS )
generateCentersPP(data, centers, K, rng, SPP_TRIALS);
else
{
for( k = 0; k < K; k++ )
generateRandomCenter(_box, centers.ptr<float>(k), rng);
}
}
else
{
if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
{
for( i = 0; i < N; i++ )
CV_Assert( (unsigned)labels[i] < (unsigned)K );
}
// compute centers
centers = Scalar(0);
for( k = 0; k < K; k++ )
counters[k] = 0;
for( i = 0; i < N; i++ )
{
sample = data.ptr<float>(i);
k = labels[i];
float* center = centers.ptr<float>(k);
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= dims - 4; j += 4 )
{
float t0 = center[j] + sample[j];
float t1 = center[j+1] + sample[j+1];
center[j] = t0;
center[j+1] = t1;
t0 = center[j+2] + sample[j+2];
t1 = center[j+3] + sample[j+3];
center[j+2] = t0;
center[j+3] = t1;
}
#endif
for( ; j < dims; j++ )
center[j] += sample[j];
counters[k]++;
}
if( iter > 0 )
max_center_shift = 0;
for( k = 0; k < K; k++ )
{
if( counters[k] != 0 )
continue;
// if some cluster appeared to be empty then:
// 1. find the biggest cluster
// 2. find the farthest from the center point in the biggest cluster
// 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
int max_k = 0;
for( int k1 = 1; k1 < K; k1++ )
{
if( counters[max_k] < counters[k1] )
max_k = k1;
}
double max_dist = 0;
int farthest_i = -1;
float* new_center = centers.ptr<float>(k);
float* old_center = centers.ptr<float>(max_k);
float* _old_center = temp.ptr<float>(); // normalized
float scale = 1.f/counters[max_k];
for( j = 0; j < dims; j++ )
_old_center[j] = old_center[j]*scale;
for( i = 0; i < N; i++ )
{
if( labels[i] != max_k )
continue;
sample = data.ptr<float>(i);
double dist = normL2Sqr_(sample, _old_center, dims);
if( max_dist <= dist )
{
max_dist = dist;
farthest_i = i;
}
}
counters[max_k]--;
counters[k]++;
labels[farthest_i] = k;
sample = data.ptr<float>(farthest_i);
for( j = 0; j < dims; j++ )
{
old_center[j] -= sample[j];
new_center[j] += sample[j];
}
}
for( k = 0; k < K; k++ )
{
float* center = centers.ptr<float>(k);
CV_Assert( counters[k] != 0 );
float scale = 1.f/counters[k];
for( j = 0; j < dims; j++ )
center[j] *= scale;
if( iter > 0 )
{
double dist = 0;
const float* old_center = old_centers.ptr<float>(k);
for( j = 0; j < dims; j++ )
{
double t = center[j] - old_center[j];
dist += t*t;
}
max_center_shift = std::max(max_center_shift, dist);
}
}
}
if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
break;
// assign labels
Mat dists(1, N, CV_64F);
double* dist = dists.ptr<double>(0);
parallel_for_(Range(0, N),
KMeansDistanceComputer(dist, labels, data, centers));
compactness = 0;
for( i = 0; i < N; i++ )
{
compactness += dist[i];
}
}
if( compactness < best_compactness )
{
best_compactness = compactness;
if( _centers.needed() )
centers.copyTo(_centers);
_labels.copyTo(best_labels);
}
}
return best_compactness;
}
CV_IMPL void cvSetIdentity( CvArr* arr, CvScalar value )
{
cv::Mat m = cv::cvarrToMat(arr);
cv::setIdentity(m, value);
}
CV_IMPL CvScalar cvTrace( const CvArr* arr )
{
return cv::trace(cv::cvarrToMat(arr));
}
CV_IMPL void cvTranspose( const CvArr* srcarr, CvArr* dstarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.rows == dst.cols && src.cols == dst.rows && src.type() == dst.type() );
transpose( src, dst );
}
CV_IMPL void cvCompleteSymm( CvMat* matrix, int LtoR )
{
cv::Mat m = cv::cvarrToMat(matrix);
cv::completeSymm( m, LtoR != 0 );
}
CV_IMPL void cvCrossProduct( const CvArr* srcAarr, const CvArr* srcBarr, CvArr* dstarr )
{
cv::Mat srcA = cv::cvarrToMat(srcAarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( srcA.size() == dst.size() && srcA.type() == dst.type() );
srcA.cross(cv::cvarrToMat(srcBarr)).copyTo(dst);
}
CV_IMPL void
cvReduce( const CvArr* srcarr, CvArr* dstarr, int dim, int op )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
if( dim < 0 )
dim = src.rows > dst.rows ? 0 : src.cols > dst.cols ? 1 : dst.cols == 1;
if( dim > 1 )
CV_Error( CV_StsOutOfRange, "The reduced dimensionality index is out of range" );
if( (dim == 0 && (dst.cols != src.cols || dst.rows != 1)) ||
(dim == 1 && (dst.rows != src.rows || dst.cols != 1)) )
CV_Error( CV_StsBadSize, "The output array size is incorrect" );
if( src.channels() != dst.channels() )
CV_Error( CV_StsUnmatchedFormats, "Input and output arrays must have the same number of channels" );
cv::reduce(src, dst, dim, op, dst.type());
}
CV_IMPL CvArr*
cvRange( CvArr* arr, double start, double end )
{
int ok = 0;
CvMat stub, *mat = (CvMat*)arr;
double delta;
int type, step;
double val = start;
int i, j;
int rows, cols;
if( !CV_IS_MAT(mat) )
mat = cvGetMat( mat, &stub);
rows = mat->rows;
cols = mat->cols;
type = CV_MAT_TYPE(mat->type);
delta = (end-start)/(rows*cols);
if( CV_IS_MAT_CONT(mat->type) )
{
cols *= rows;
rows = 1;
step = 1;
}
else
step = mat->step / CV_ELEM_SIZE(type);
if( type == CV_32SC1 )
{
int* idata = mat->data.i;
int ival = cvRound(val), idelta = cvRound(delta);
if( fabs(val - ival) < DBL_EPSILON &&
fabs(delta - idelta) < DBL_EPSILON )
{
for( i = 0; i < rows; i++, idata += step )
for( j = 0; j < cols; j++, ival += idelta )
idata[j] = ival;
}
else
{
for( i = 0; i < rows; i++, idata += step )
for( j = 0; j < cols; j++, val += delta )
idata[j] = cvRound(val);
}
}
else if( type == CV_32FC1 )
{
float* fdata = mat->data.fl;
for( i = 0; i < rows; i++, fdata += step )
for( j = 0; j < cols; j++, val += delta )
fdata[j] = (float)val;
}
else
CV_Error( CV_StsUnsupportedFormat, "The function only supports 32sC1 and 32fC1 datatypes" );
ok = 1;
return ok ? arr : 0;
}
CV_IMPL void
cvSort( const CvArr* _src, CvArr* _dst, CvArr* _idx, int flags )
{
cv::Mat src = cv::cvarrToMat(_src);
if( _idx )
{
cv::Mat idx0 = cv::cvarrToMat(_idx), idx = idx0;
CV_Assert( src.size() == idx.size() && idx.type() == CV_32S && src.data != idx.data );
cv::sortIdx( src, idx, flags );
CV_Assert( idx0.data == idx.data );
}
if( _dst )
{
cv::Mat dst0 = cv::cvarrToMat(_dst), dst = dst0;
CV_Assert( src.size() == dst.size() && src.type() == dst.type() );
cv::sort( src, dst, flags );
CV_Assert( dst0.data == dst.data );
}
}
CV_IMPL int
cvKMeans2( const CvArr* _samples, int cluster_count, CvArr* _labels,
CvTermCriteria termcrit, int attempts, CvRNG*,
int flags, CvArr* _centers, double* _compactness )
{
cv::Mat data = cv::cvarrToMat(_samples), labels = cv::cvarrToMat(_labels), centers;
if( _centers )
{
centers = cv::cvarrToMat(_centers);
centers = centers.reshape(1);
data = data.reshape(1);
CV_Assert( !centers.empty() );
CV_Assert( centers.rows == cluster_count );
CV_Assert( centers.cols == data.cols );
CV_Assert( centers.depth() == data.depth() );
}
CV_Assert( labels.isContinuous() && labels.type() == CV_32S &&
(labels.cols == 1 || labels.rows == 1) &&
labels.cols + labels.rows - 1 == data.rows );
double compactness = cv::kmeans(data, cluster_count, labels, termcrit, attempts,
flags, _centers ? cv::_OutputArray(centers) : cv::_OutputArray() );
if( _compactness )
*_compactness = compactness;
return 1;
}
///////////////////////////// n-dimensional matrices ////////////////////////////
namespace cv
{
Mat Mat::reshape(int _cn, int _newndims, const int* _newsz) const
{
if(_newndims == dims)
{
if(_newsz == 0)
return reshape(_cn);
if(_newndims == 2)
return reshape(_cn, _newsz[0]);
}
CV_Error(CV_StsNotImplemented, "");
// TBD
return Mat();
}
NAryMatIterator::NAryMatIterator()
: arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0)
{
}
NAryMatIterator::NAryMatIterator(const Mat** _arrays, Mat* _planes, int _narrays)
: arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0)
{
init(_arrays, _planes, 0, _narrays);
}
NAryMatIterator::NAryMatIterator(const Mat** _arrays, uchar** _ptrs, int _narrays)
: arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0)
{
init(_arrays, 0, _ptrs, _narrays);
}
void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, uchar** _ptrs, int _narrays)
{
CV_Assert( _arrays && (_ptrs || _planes) );
int i, j, d1=0, i0 = -1, d = -1;
arrays = _arrays;
ptrs = _ptrs;
planes = _planes;
narrays = _narrays;
nplanes = 0;
size = 0;
if( narrays < 0 )
{
for( i = 0; _arrays[i] != 0; i++ )
;
narrays = i;
CV_Assert(narrays <= 1000);
}
iterdepth = 0;
for( i = 0; i < narrays; i++ )
{
CV_Assert(arrays[i] != 0);
const Mat& A = *arrays[i];
if( ptrs )
ptrs[i] = A.data;
if( !A.data )
continue;
if( i0 < 0 )
{
i0 = i;
d = A.dims;
// find the first dimensionality which is different from 1;
// in any of the arrays the first "d1" step do not affect the continuity
for( d1 = 0; d1 < d; d1++ )
if( A.size[d1] > 1 )
break;
}
else
CV_Assert( A.size == arrays[i0]->size );
if( !A.isContinuous() )
{
CV_Assert( A.step[d-1] == A.elemSize() );
for( j = d-1; j > d1; j-- )
if( A.step[j]*A.size[j] < A.step[j-1] )
break;
iterdepth = std::max(iterdepth, j);
}
}
if( i0 >= 0 )
{
size = arrays[i0]->size[d-1];
for( j = d-1; j > iterdepth; j-- )
{
int64 total1 = (int64)size*arrays[i0]->size[j-1];
if( total1 != (int)total1 )
break;
size = (int)total1;
}
iterdepth = j;
if( iterdepth == d1 )
iterdepth = 0;
nplanes = 1;
for( j = iterdepth-1; j >= 0; j-- )
nplanes *= arrays[i0]->size[j];
}
else
iterdepth = 0;
idx = 0;
if( !planes )
return;
for( i = 0; i < narrays; i++ )
{
CV_Assert(arrays[i] != 0);
const Mat& A = *arrays[i];
if( !A.data )
{
planes[i] = Mat();
continue;
}
planes[i] = Mat(1, (int)size, A.type(), A.data);
}
}
NAryMatIterator& NAryMatIterator::operator ++()
{
if( idx >= nplanes-1 )
return *this;
++idx;
if( iterdepth == 1 )
{
if( ptrs )
{
for( int i = 0; i < narrays; i++ )
{
if( !ptrs[i] )
continue;
ptrs[i] = arrays[i]->data + arrays[i]->step[0]*idx;
}
}
if( planes )
{
for( int i = 0; i < narrays; i++ )
{
if( !planes[i].data )
continue;
planes[i].data = arrays[i]->data + arrays[i]->step[0]*idx;
}
}
}
else
{
for( int i = 0; i < narrays; i++ )
{
const Mat& A = *arrays[i];
if( !A.data )
continue;
int _idx = (int)idx;
uchar* data = A.data;
for( int j = iterdepth-1; j >= 0 && _idx > 0; j-- )
{
int szi = A.size[j], t = _idx/szi;
data += (_idx - t * szi)*A.step[j];
_idx = t;
}
if( ptrs )
ptrs[i] = data;
if( planes )
planes[i].data = data;
}
}
return *this;
}
NAryMatIterator NAryMatIterator::operator ++(int)
{
NAryMatIterator it = *this;
++*this;
return it;
}
///////////////////////////////////////////////////////////////////////////
// MatConstIterator //
///////////////////////////////////////////////////////////////////////////
Point MatConstIterator::pos() const
{
if( !m )
return Point();
CV_DbgAssert(m->dims <= 2);
ptrdiff_t ofs = ptr - m->data;
int y = (int)(ofs/m->step[0]);
return Point((int)((ofs - y*m->step[0])/elemSize), y);
}
void MatConstIterator::pos(int* _idx) const
{
CV_Assert(m != 0 && _idx);
ptrdiff_t ofs = ptr - m->data;
for( int i = 0; i < m->dims; i++ )
{
size_t s = m->step[i], v = ofs/s;
ofs -= v*s;
_idx[i] = (int)v;
}
}
ptrdiff_t MatConstIterator::lpos() const
{
if(!m)
return 0;
if( m->isContinuous() )
return (ptr - sliceStart)/elemSize;
ptrdiff_t ofs = ptr - m->data;
int i, d = m->dims;
if( d == 2 )
{
ptrdiff_t y = ofs/m->step[0];
return y*m->cols + (ofs - y*m->step[0])/elemSize;
}
ptrdiff_t result = 0;
for( i = 0; i < d; i++ )
{
size_t s = m->step[i], v = ofs/s;
ofs -= v*s;
result = result*m->size[i] + v;
}
return result;
}
void MatConstIterator::seek(ptrdiff_t ofs, bool relative)
{
if( m->isContinuous() )
{
ptr = (relative ? ptr : sliceStart) + ofs*elemSize;
if( ptr < sliceStart )
ptr = sliceStart;
else if( ptr > sliceEnd )
ptr = sliceEnd;
return;
}
int d = m->dims;
if( d == 2 )
{
ptrdiff_t ofs0, y;
if( relative )
{
ofs0 = ptr - m->data;
y = ofs0/m->step[0];
ofs += y*m->cols + (ofs0 - y*m->step[0])/elemSize;
}
y = ofs/m->cols;
int y1 = std::min(std::max((int)y, 0), m->rows-1);
sliceStart = m->data + y1*m->step[0];
sliceEnd = sliceStart + m->cols*elemSize;
ptr = y < 0 ? sliceStart : y >= m->rows ? sliceEnd :
sliceStart + (ofs - y*m->cols)*elemSize;
return;
}
if( relative )
ofs += lpos();
if( ofs < 0 )
ofs = 0;
int szi = m->size[d-1];
ptrdiff_t t = ofs/szi;
int v = (int)(ofs - t*szi);
ofs = t;
ptr = m->data + v*elemSize;
sliceStart = m->data;
for( int i = d-2; i >= 0; i-- )
{
szi = m->size[i];
t = ofs/szi;
v = (int)(ofs - t*szi);
ofs = t;
sliceStart += v*m->step[i];
}
sliceEnd = sliceStart + m->size[d-1]*elemSize;
if( ofs > 0 )
ptr = sliceEnd;
else
ptr = sliceStart + (ptr - m->data);
}
void MatConstIterator::seek(const int* _idx, bool relative)
{
int i, d = m->dims;
ptrdiff_t ofs = 0;
if( !_idx )
;
else if( d == 2 )
ofs = _idx[0]*m->size[1] + _idx[1];
else
{
for( i = 0; i < d; i++ )
ofs = ofs*m->size[i] + _idx[i];
}
seek(ofs, relative);
}
//////////////////////////////// SparseMat ////////////////////////////////
template<typename T1, typename T2> void
convertData_(const void* _from, void* _to, int cn)
{
const T1* from = (const T1*)_from;
T2* to = (T2*)_to;
if( cn == 1 )
*to = saturate_cast<T2>(*from);
else
for( int i = 0; i < cn; i++ )
to[i] = saturate_cast<T2>(from[i]);
}
template<typename T1, typename T2> void
convertScaleData_(const void* _from, void* _to, int cn, double alpha, double beta)
{
const T1* from = (const T1*)_from;
T2* to = (T2*)_to;
if( cn == 1 )
*to = saturate_cast<T2>(*from*alpha + beta);
else
for( int i = 0; i < cn; i++ )
to[i] = saturate_cast<T2>(from[i]*alpha + beta);
}
typedef void (*ConvertData)(const void* from, void* to, int cn);
typedef void (*ConvertScaleData)(const void* from, void* to, int cn, double alpha, double beta);
static ConvertData getConvertElem(int fromType, int toType)
{
static ConvertData tab[][8] =
{{ convertData_<uchar, uchar>, convertData_<uchar, schar>,
convertData_<uchar, ushort>, convertData_<uchar, short>,
convertData_<uchar, int>, convertData_<uchar, float>,
convertData_<uchar, double>, 0 },
{ convertData_<schar, uchar>, convertData_<schar, schar>,
convertData_<schar, ushort>, convertData_<schar, short>,
convertData_<schar, int>, convertData_<schar, float>,
convertData_<schar, double>, 0 },
{ convertData_<ushort, uchar>, convertData_<ushort, schar>,
convertData_<ushort, ushort>, convertData_<ushort, short>,
convertData_<ushort, int>, convertData_<ushort, float>,
convertData_<ushort, double>, 0 },
{ convertData_<short, uchar>, convertData_<short, schar>,
convertData_<short, ushort>, convertData_<short, short>,
convertData_<short, int>, convertData_<short, float>,
convertData_<short, double>, 0 },
{ convertData_<int, uchar>, convertData_<int, schar>,
convertData_<int, ushort>, convertData_<int, short>,
convertData_<int, int>, convertData_<int, float>,
convertData_<int, double>, 0 },
{ convertData_<float, uchar>, convertData_<float, schar>,
convertData_<float, ushort>, convertData_<float, short>,
convertData_<float, int>, convertData_<float, float>,
convertData_<float, double>, 0 },
{ convertData_<double, uchar>, convertData_<double, schar>,
convertData_<double, ushort>, convertData_<double, short>,
convertData_<double, int>, convertData_<double, float>,
convertData_<double, double>, 0 },
{ 0, 0, 0, 0, 0, 0, 0, 0 }};
ConvertData func = tab[CV_MAT_DEPTH(fromType)][CV_MAT_DEPTH(toType)];
CV_Assert( func != 0 );
return func;
}
static ConvertScaleData getConvertScaleElem(int fromType, int toType)
{
static ConvertScaleData tab[][8] =
{{ convertScaleData_<uchar, uchar>, convertScaleData_<uchar, schar>,
convertScaleData_<uchar, ushort>, convertScaleData_<uchar, short>,
convertScaleData_<uchar, int>, convertScaleData_<uchar, float>,
convertScaleData_<uchar, double>, 0 },
{ convertScaleData_<schar, uchar>, convertScaleData_<schar, schar>,
convertScaleData_<schar, ushort>, convertScaleData_<schar, short>,
convertScaleData_<schar, int>, convertScaleData_<schar, float>,
convertScaleData_<schar, double>, 0 },
{ convertScaleData_<ushort, uchar>, convertScaleData_<ushort, schar>,
convertScaleData_<ushort, ushort>, convertScaleData_<ushort, short>,
convertScaleData_<ushort, int>, convertScaleData_<ushort, float>,
convertScaleData_<ushort, double>, 0 },
{ convertScaleData_<short, uchar>, convertScaleData_<short, schar>,
convertScaleData_<short, ushort>, convertScaleData_<short, short>,
convertScaleData_<short, int>, convertScaleData_<short, float>,
convertScaleData_<short, double>, 0 },
{ convertScaleData_<int, uchar>, convertScaleData_<int, schar>,
convertScaleData_<int, ushort>, convertScaleData_<int, short>,
convertScaleData_<int, int>, convertScaleData_<int, float>,
convertScaleData_<int, double>, 0 },
{ convertScaleData_<float, uchar>, convertScaleData_<float, schar>,
convertScaleData_<float, ushort>, convertScaleData_<float, short>,
convertScaleData_<float, int>, convertScaleData_<float, float>,
convertScaleData_<float, double>, 0 },
{ convertScaleData_<double, uchar>, convertScaleData_<double, schar>,
convertScaleData_<double, ushort>, convertScaleData_<double, short>,
convertScaleData_<double, int>, convertScaleData_<double, float>,
convertScaleData_<double, double>, 0 },
{ 0, 0, 0, 0, 0, 0, 0, 0 }};
ConvertScaleData func = tab[CV_MAT_DEPTH(fromType)][CV_MAT_DEPTH(toType)];
CV_Assert( func != 0 );
return func;
}
enum { HASH_SIZE0 = 8 };
static inline void copyElem(const uchar* from, uchar* to, size_t elemSize)
{
size_t i;
for( i = 0; i + sizeof(int) <= elemSize; i += sizeof(int) )
*(int*)(to + i) = *(const int*)(from + i);
for( ; i < elemSize; i++ )
to[i] = from[i];
}
static inline bool isZeroElem(const uchar* data, size_t elemSize)
{
size_t i;
for( i = 0; i + sizeof(int) <= elemSize; i += sizeof(int) )
if( *(int*)(data + i) != 0 )
return false;
for( ; i < elemSize; i++ )
if( data[i] != 0 )
return false;
return true;
}
SparseMat::Hdr::Hdr( int _dims, const int* _sizes, int _type )
{
refcount = 1;
dims = _dims;
valueOffset = (int)alignSize(sizeof(SparseMat::Node) +
sizeof(int)*std::max(dims - CV_MAX_DIM, 0), CV_ELEM_SIZE1(_type));
nodeSize = alignSize(valueOffset +
CV_ELEM_SIZE(_type), (int)sizeof(size_t));
int i;
for( i = 0; i < dims; i++ )
size[i] = _sizes[i];
for( ; i < CV_MAX_DIM; i++ )
size[i] = 0;
clear();
}
void SparseMat::Hdr::clear()
{
hashtab.clear();
hashtab.resize(HASH_SIZE0);
pool.clear();
pool.resize(nodeSize);
nodeCount = freeList = 0;
}
SparseMat::SparseMat(const Mat& m)
: flags(MAGIC_VAL), hdr(0)
{
create( m.dims, m.size, m.type() );
int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1];
size_t esz = m.elemSize();
uchar* dptr = m.data;
for(;;)
{
for( i = 0; i < lastSize; i++, dptr += esz )
{
if( isZeroElem(dptr, esz) )
continue;
idx[d-1] = i;
uchar* to = newNode(idx, hash(idx));
copyElem( dptr, to, esz );
}
for( i = d - 2; i >= 0; i-- )
{
dptr += m.step[i] - m.size[i+1]*m.step[i+1];
if( ++idx[i] < m.size[i] )
break;
idx[i] = 0;
}
if( i < 0 )
break;
}
}
void SparseMat::create(int d, const int* _sizes, int _type)
{
int i;
CV_Assert( _sizes && 0 < d && d <= CV_MAX_DIM );
for( i = 0; i < d; i++ )
CV_Assert( _sizes[i] > 0 );
_type = CV_MAT_TYPE(_type);
if( hdr && _type == type() && hdr->dims == d && hdr->refcount == 1 )
{
for( i = 0; i < d; i++ )
if( _sizes[i] != hdr->size[i] )
break;
if( i == d )
{
clear();
return;
}
}
release();
flags = MAGIC_VAL | _type;
hdr = new Hdr(d, _sizes, _type);
}
void SparseMat::copyTo( SparseMat& m ) const
{
if( hdr == m.hdr )
return;
if( !hdr )
{
m.release();
return;
}
m.create( hdr->dims, hdr->size, type() );
SparseMatConstIterator from = begin();
size_t i, N = nzcount(), esz = elemSize();
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.newNode(n->idx, n->hashval);
copyElem( from.ptr, to, esz );
}
}
void SparseMat::copyTo( Mat& m ) const
{
CV_Assert( hdr );
m.create( dims(), hdr->size, type() );
m = Scalar(0);
SparseMatConstIterator from = begin();
size_t i, N = nzcount(), esz = elemSize();
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
copyElem( from.ptr, m.ptr(n->idx), esz);
}
}
void SparseMat::convertTo( SparseMat& m, int rtype, double alpha ) const
{
int cn = channels();
if( rtype < 0 )
rtype = type();
rtype = CV_MAKETYPE(rtype, cn);
if( hdr == m.hdr && rtype != type() )
{
SparseMat temp;
convertTo(temp, rtype, alpha);
m = temp;
return;
}
CV_Assert(hdr != 0);
if( hdr != m.hdr )
m.create( hdr->dims, hdr->size, rtype );
SparseMatConstIterator from = begin();
size_t i, N = nzcount();
if( alpha == 1 )
{
ConvertData cvtfunc = getConvertElem(type(), rtype);
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = hdr == m.hdr ? from.ptr : m.newNode(n->idx, n->hashval);
cvtfunc( from.ptr, to, cn );
}
}
else
{
ConvertScaleData cvtfunc = getConvertScaleElem(type(), rtype);
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = hdr == m.hdr ? from.ptr : m.newNode(n->idx, n->hashval);
cvtfunc( from.ptr, to, cn, alpha, 0 );
}
}
}
void SparseMat::convertTo( Mat& m, int rtype, double alpha, double beta ) const
{
int cn = channels();
if( rtype < 0 )
rtype = type();
rtype = CV_MAKETYPE(rtype, cn);
CV_Assert( hdr );
m.create( dims(), hdr->size, rtype );
m = Scalar(beta);
SparseMatConstIterator from = begin();
size_t i, N = nzcount();
if( alpha == 1 && beta == 0 )
{
ConvertData cvtfunc = getConvertElem(type(), rtype);
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.ptr(n->idx);
cvtfunc( from.ptr, to, cn );
}
}
else
{
ConvertScaleData cvtfunc = getConvertScaleElem(type(), rtype);
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.ptr(n->idx);
cvtfunc( from.ptr, to, cn, alpha, beta );
}
}
}
void SparseMat::clear()
{
if( hdr )
hdr->clear();
}
uchar* SparseMat::ptr(int i0, bool createMissing, size_t* hashval)
{
CV_Assert( hdr && hdr->dims == 1 );
size_t h = hashval ? *hashval : hash(i0);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h && elem->idx[0] == i0 )
return &value<uchar>(elem);
nidx = elem->next;
}
if( createMissing )
{
int idx[] = { i0 };
return newNode( idx, h );
}
return 0;
}
uchar* SparseMat::ptr(int i0, int i1, bool createMissing, size_t* hashval)
{
CV_Assert( hdr && hdr->dims == 2 );
size_t h = hashval ? *hashval : hash(i0, i1);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h && elem->idx[0] == i0 && elem->idx[1] == i1 )
return &value<uchar>(elem);
nidx = elem->next;
}
if( createMissing )
{
int idx[] = { i0, i1 };
return newNode( idx, h );
}
return 0;
}
uchar* SparseMat::ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval)
{
CV_Assert( hdr && hdr->dims == 3 );
size_t h = hashval ? *hashval : hash(i0, i1, i2);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h && elem->idx[0] == i0 &&
elem->idx[1] == i1 && elem->idx[2] == i2 )
return &value<uchar>(elem);
nidx = elem->next;
}
if( createMissing )
{
int idx[] = { i0, i1, i2 };
return newNode( idx, h );
}
return 0;
}
uchar* SparseMat::ptr(const int* idx, bool createMissing, size_t* hashval)
{
CV_Assert( hdr );
int i, d = hdr->dims;
size_t h = hashval ? *hashval : hash(idx);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx];
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h )
{
for( i = 0; i < d; i++ )
if( elem->idx[i] != idx[i] )
break;
if( i == d )
return &value<uchar>(elem);
}
nidx = elem->next;
}
return createMissing ? newNode(idx, h) : 0;
}
void SparseMat::erase(int i0, int i1, size_t* hashval)
{
CV_Assert( hdr && hdr->dims == 2 );
size_t h = hashval ? *hashval : hash(i0, i1);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx], previdx=0;
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h && elem->idx[0] == i0 && elem->idx[1] == i1 )
break;
previdx = nidx;
nidx = elem->next;
}
if( nidx )
removeNode(hidx, nidx, previdx);
}
void SparseMat::erase(int i0, int i1, int i2, size_t* hashval)
{
CV_Assert( hdr && hdr->dims == 3 );
size_t h = hashval ? *hashval : hash(i0, i1, i2);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx], previdx=0;
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h && elem->idx[0] == i0 &&
elem->idx[1] == i1 && elem->idx[2] == i2 )
break;
previdx = nidx;
nidx = elem->next;
}
if( nidx )
removeNode(hidx, nidx, previdx);
}
void SparseMat::erase(const int* idx, size_t* hashval)
{
CV_Assert( hdr );
int i, d = hdr->dims;
size_t h = hashval ? *hashval : hash(idx);
size_t hidx = h & (hdr->hashtab.size() - 1), nidx = hdr->hashtab[hidx], previdx=0;
uchar* pool = &hdr->pool[0];
while( nidx != 0 )
{
Node* elem = (Node*)(pool + nidx);
if( elem->hashval == h )
{
for( i = 0; i < d; i++ )
if( elem->idx[i] != idx[i] )
break;
if( i == d )
break;
}
previdx = nidx;
nidx = elem->next;
}
if( nidx )
removeNode(hidx, nidx, previdx);
}
void SparseMat::resizeHashTab(size_t newsize)
{
newsize = std::max(newsize, (size_t)8);
if((newsize & (newsize-1)) != 0)
newsize = (size_t)1 << cvCeil(std::log((double)newsize)/CV_LOG2);
size_t i, hsize = hdr->hashtab.size();
std::vector<size_t> _newh(newsize);
size_t* newh = &_newh[0];
for( i = 0; i < newsize; i++ )
newh[i] = 0;
uchar* pool = &hdr->pool[0];
for( i = 0; i < hsize; i++ )
{
size_t nidx = hdr->hashtab[i];
while( nidx )
{
Node* elem = (Node*)(pool + nidx);
size_t next = elem->next;
size_t newhidx = elem->hashval & (newsize - 1);
elem->next = newh[newhidx];
newh[newhidx] = nidx;
nidx = next;
}
}
hdr->hashtab = _newh;
}
uchar* SparseMat::newNode(const int* idx, size_t hashval)
{
const int HASH_MAX_FILL_FACTOR=3;
assert(hdr);
size_t hsize = hdr->hashtab.size();
if( ++hdr->nodeCount > hsize*HASH_MAX_FILL_FACTOR )
{
resizeHashTab(std::max(hsize*2, (size_t)8));
hsize = hdr->hashtab.size();
}
if( !hdr->freeList )
{
size_t i, nsz = hdr->nodeSize, psize = hdr->pool.size(),
newpsize = std::max(psize*2, 8*nsz);
hdr->pool.resize(newpsize);
uchar* pool = &hdr->pool[0];
hdr->freeList = std::max(psize, nsz);
for( i = hdr->freeList; i < newpsize - nsz; i += nsz )
((Node*)(pool + i))->next = i + nsz;
((Node*)(pool + i))->next = 0;
}
size_t nidx = hdr->freeList;
Node* elem = (Node*)&hdr->pool[nidx];
hdr->freeList = elem->next;
elem->hashval = hashval;
size_t hidx = hashval & (hsize - 1);
elem->next = hdr->hashtab[hidx];
hdr->hashtab[hidx] = nidx;
int i, d = hdr->dims;
for( i = 0; i < d; i++ )
elem->idx[i] = idx[i];
size_t esz = elemSize();
uchar* p = &value<uchar>(elem);
if( esz == sizeof(float) )
*((float*)p) = 0.f;
else if( esz == sizeof(double) )
*((double*)p) = 0.;
else
memset(p, 0, esz);
return p;
}
void SparseMat::removeNode(size_t hidx, size_t nidx, size_t previdx)
{
Node* n = node(nidx);
if( previdx )
{
Node* prev = node(previdx);
prev->next = n->next;
}
else
hdr->hashtab[hidx] = n->next;
n->next = hdr->freeList;
hdr->freeList = nidx;
--hdr->nodeCount;
}
SparseMatConstIterator::SparseMatConstIterator(const SparseMat* _m)
: m((SparseMat*)_m), hashidx(0), ptr(0)
{
if(!_m || !_m->hdr)
return;
SparseMat::Hdr& hdr = *m->hdr;
const std::vector<size_t>& htab = hdr.hashtab;
size_t i, hsize = htab.size();
for( i = 0; i < hsize; i++ )
{
size_t nidx = htab[i];
if( nidx )
{
hashidx = i;
ptr = &hdr.pool[nidx] + hdr.valueOffset;
return;
}
}
}
SparseMatConstIterator& SparseMatConstIterator::operator ++()
{
if( !ptr || !m || !m->hdr )
return *this;
SparseMat::Hdr& hdr = *m->hdr;
size_t next = ((const SparseMat::Node*)(ptr - hdr.valueOffset))->next;
if( next )
{
ptr = &hdr.pool[next] + hdr.valueOffset;
return *this;
}
size_t i = hashidx + 1, sz = hdr.hashtab.size();
for( ; i < sz; i++ )
{
size_t nidx = hdr.hashtab[i];
if( nidx )
{
hashidx = i;
ptr = &hdr.pool[nidx] + hdr.valueOffset;
return *this;
}
}
hashidx = sz;
ptr = 0;
return *this;
}
double norm( const SparseMat& src, int normType )
{
SparseMatConstIterator it = src.begin();
size_t i, N = src.nzcount();
normType &= NORM_TYPE_MASK;
int type = src.type();
double result = 0;
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
if( type == CV_32F )
{
if( normType == NORM_INF )
for( i = 0; i < N; i++, ++it )
result = std::max(result, std::abs((double)*(const float*)it.ptr));
else if( normType == NORM_L1 )
for( i = 0; i < N; i++, ++it )
result += std::abs(*(const float*)it.ptr);
else
for( i = 0; i < N; i++, ++it )
{
double v = *(const float*)it.ptr;
result += v*v;
}
}
else if( type == CV_64F )
{
if( normType == NORM_INF )
for( i = 0; i < N; i++, ++it )
result = std::max(result, std::abs(*(const double*)it.ptr));
else if( normType == NORM_L1 )
for( i = 0; i < N; i++, ++it )
result += std::abs(*(const double*)it.ptr);
else
for( i = 0; i < N; i++, ++it )
{
double v = *(const double*)it.ptr;
result += v*v;
}
}
else
CV_Error( CV_StsUnsupportedFormat, "Only 32f and 64f are supported" );
if( normType == NORM_L2 )
result = std::sqrt(result);
return result;
}
void minMaxLoc( const SparseMat& src, double* _minval, double* _maxval, int* _minidx, int* _maxidx )
{
SparseMatConstIterator it = src.begin();
size_t i, N = src.nzcount(), d = src.hdr ? src.hdr->dims : 0;
int type = src.type();
const int *minidx = 0, *maxidx = 0;
if( type == CV_32F )
{
float minval = FLT_MAX, maxval = -FLT_MAX;
for( i = 0; i < N; i++, ++it )
{
float v = *(const float*)it.ptr;
if( v < minval )
{
minval = v;
minidx = it.node()->idx;
}
if( v > maxval )
{
maxval = v;
maxidx = it.node()->idx;
}
}
if( _minval )
*_minval = minval;
if( _maxval )
*_maxval = maxval;
}
else if( type == CV_64F )
{
double minval = DBL_MAX, maxval = -DBL_MAX;
for( i = 0; i < N; i++, ++it )
{
double v = *(const double*)it.ptr;
if( v < minval )
{
minval = v;
minidx = it.node()->idx;
}
if( v > maxval )
{
maxval = v;
maxidx = it.node()->idx;
}
}
if( _minval )
*_minval = minval;
if( _maxval )
*_maxval = maxval;
}
else
CV_Error( CV_StsUnsupportedFormat, "Only 32f and 64f are supported" );
if( _minidx )
for( i = 0; i < d; i++ )
_minidx[i] = minidx[i];
if( _maxidx )
for( i = 0; i < d; i++ )
_maxidx[i] = maxidx[i];
}
void normalize( const SparseMat& src, SparseMat& dst, double a, int norm_type )
{
double scale = 1;
if( norm_type == CV_L2 || norm_type == CV_L1 || norm_type == CV_C )
{
scale = norm( src, norm_type );
scale = scale > DBL_EPSILON ? a/scale : 0.;
}
else
CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" );
src.convertTo( dst, -1, scale );
}
////////////////////// RotatedRect //////////////////////
void RotatedRect::points(Point2f pt[]) const
{
double _angle = angle*CV_PI/180.;
float b = (float)cos(_angle)*0.5f;
float a = (float)sin(_angle)*0.5f;
pt[0].x = center.x - a*size.height - b*size.width;
pt[0].y = center.y + b*size.height - a*size.width;
pt[1].x = center.x + a*size.height - b*size.width;
pt[1].y = center.y - b*size.height - a*size.width;
pt[2].x = 2*center.x - pt[0].x;
pt[2].y = 2*center.y - pt[0].y;
pt[3].x = 2*center.x - pt[1].x;
pt[3].y = 2*center.y - pt[1].y;
}
Rect RotatedRect::boundingRect() const
{
Point2f pt[4];
points(pt);
Rect r(cvFloor(std::min(std::min(std::min(pt[0].x, pt[1].x), pt[2].x), pt[3].x)),
cvFloor(std::min(std::min(std::min(pt[0].y, pt[1].y), pt[2].y), pt[3].y)),
cvCeil(std::max(std::max(std::max(pt[0].x, pt[1].x), pt[2].x), pt[3].x)),
cvCeil(std::max(std::max(std::max(pt[0].y, pt[1].y), pt[2].y), pt[3].y)));
r.width -= r.x - 1;
r.height -= r.y - 1;
return r;
}
}
// glue
CvMatND::CvMatND(const cv::Mat& m)
{
cvInitMatNDHeader(this, m.dims, m.size, m.type(), m.data );
int i, d = m.dims;
for( i = 0; i < d; i++ )
dim[i].step = (int)m.step[i];
type |= m.flags & cv::Mat::CONTINUOUS_FLAG;
}
_IplImage::_IplImage(const cv::Mat& m)
{
CV_Assert( m.dims <= 2 );
cvInitImageHeader(this, m.size(), cvIplDepth(m.flags), m.channels());
cvSetData(this, m.data, (int)m.step[0]);
}
CvSparseMat* cvCreateSparseMat(const cv::SparseMat& sm)
{
if( !sm.hdr )
return 0;
CvSparseMat* m = cvCreateSparseMat(sm.hdr->dims, sm.hdr->size, sm.type());
cv::SparseMatConstIterator from = sm.begin();
size_t i, N = sm.nzcount(), esz = sm.elemSize();
for( i = 0; i < N; i++, ++from )
{
const cv::SparseMat::Node* n = from.node();
uchar* to = cvPtrND(m, n->idx, 0, -2, 0);
cv::copyElem(from.ptr, to, esz);
}
return m;
}
void CvSparseMat::copyToSparseMat(cv::SparseMat& m) const
{
m.create( dims, &size[0], type );
CvSparseMatIterator it;
CvSparseNode* n = cvInitSparseMatIterator(this, &it);
size_t esz = m.elemSize();
for( ; n != 0; n = cvGetNextSparseNode(&it) )
{
const int* idx = CV_NODE_IDX(this, n);
uchar* to = m.newNode(idx, m.hash(idx));
cv::copyElem((const uchar*)CV_NODE_VAL(this, n), to, esz);
}
}
/* End of file. */