opencv/modules/core/src/matrix.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
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//M*/
#include "precomp.hpp"
/****************************************************************************************\
* [scaled] Identity matrix initialization *
\****************************************************************************************/
namespace cv {
Mat::Mat(const IplImage* img, bool copyData)
{
CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0);
int depth = IPL2CV_DEPTH(img->depth);
size_t esz;
step = img->widthStep;
refcount = 0;
if(!img->roi)
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL);
flags = MAGIC_VAL + CV_MAKETYPE(depth, img->nChannels);
rows = img->height; cols = img->width;
datastart = data = (uchar*)img->imageData;
esz = elemSize();
}
else
{
CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0);
bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE;
flags = MAGIC_VAL + CV_MAKETYPE(depth, selectedPlane ? 1 : img->nChannels);
rows = img->roi->height; cols = img->roi->width;
esz = elemSize();
data = datastart = (uchar*)img->imageData +
(selectedPlane ? (img->roi->coi - 1)*step*img->height : 0) +
img->roi->yOffset*step + img->roi->xOffset*esz;
}
dataend = datastart + step*(rows-1) + esz*cols;
flags |= (cols*esz == step || rows == 1 ? CONTINUOUS_FLAG : 0);
if( copyData )
{
Mat m = *this;
rows = cols = 0;
if( !img->roi || !img->roi->coi ||
img->dataOrder == IPL_DATA_ORDER_PLANE)
m.copyTo(*this);
else
{
int ch[] = {img->roi->coi - 1, 0};
create(m.rows, m.cols, m.type());
mixChannels(&m, 1, this, 1, ch, 1);
}
}
}
Mat::operator IplImage() const
{
IplImage img;
cvInitImageHeader(&img, size(), cvIplDepth(flags), channels());
cvSetData(&img, data, (int)step);
return img;
}
Mat cvarrToMat(const CvArr* arr, bool copyData,
bool allowND, int coiMode)
{
if( CV_IS_MAT(arr) )
return Mat((const CvMat*)arr, copyData );
else 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 Mat(iplimg, copyData);
}
else if( CV_IS_SEQ(arr) )
{
CvSeq* seq = (CvSeq*)arr;
CV_Assert(seq->total > 0 && CV_ELEM_SIZE(seq->flags) == seq->elem_size);
if(!copyData && seq->first->next == seq->first)
return Mat(seq->total, 1, CV_MAT_TYPE(seq->flags), seq->first->data);
Mat buf(seq->total, 1, CV_MAT_TYPE(seq->flags));
cvCvtSeqToArray(seq, buf.data, CV_WHOLE_SEQ);
return buf;
}
else
{
CvMat hdr, *cvmat = cvGetMat( arr, &hdr, 0, allowND ? 1 : 0 );
if( cvmat )
return Mat(cvmat, copyData);
}
return Mat();
}
void extractImageCOI(const CvArr* arr, Mat& ch, int coi)
{
Mat mat = cvarrToMat(arr, false, true, 1);
ch.create(mat.size(), mat.depth());
if(coi < 0)
CV_Assert( CV_IS_IMAGE(arr) && (coi = cvGetImageCOI((const IplImage*)arr)-1) >= 0 );
CV_Assert(0 <= coi && coi < mat.channels());
int _pairs[] = { coi, 0 };
mixChannels( &mat, 1, &ch, 1, _pairs, 1 );
}
void insertImageCOI(const Mat& ch, CvArr* arr, int coi)
{
Mat mat = cvarrToMat(arr, false, true, 1);
if(coi < 0)
CV_Assert( CV_IS_IMAGE(arr) && (coi = cvGetImageCOI((const IplImage*)arr)-1) >= 0 );
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 );
}
Mat Mat::reshape(int new_cn, int new_rows) const
{
Mat hdr = *this;
int cn = channels();
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 = 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);
return hdr;
}
void
setIdentity( Mat& m, const Scalar& s )
{
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;
}
}
Scalar trace( const Mat& m )
{
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 *
\****************************************************************************************/
template<typename T> static void
transposeI_( Mat& mat )
{
int rows = mat.rows, cols = mat.cols;
uchar* data = mat.data;
size_t step = mat.step;
for( int i = 0; i < rows; i++ )
{
T* row = (T*)(data + step*i);
uchar* data1 = data + i*sizeof(T);
for( int j = i+1; j < cols; j++ )
std::swap( row[j], *(T*)(data1 + step*j) );
}
}
template<typename T> static void
transpose_( const Mat& src, Mat& dst )
{
int rows = dst.rows, cols = dst.cols;
uchar* data = src.data;
size_t step = src.step;
for( int i = 0; i < rows; i++ )
{
T* row = (T*)(dst.data + dst.step*i);
uchar* data1 = data + i*sizeof(T);
for( int j = 0; j < cols; j++ )
row[j] = *(T*)(data1 + step*j);
}
}
typedef void (*TransposeInplaceFunc)( Mat& mat );
typedef void (*TransposeFunc)( const Mat& src, Mat& dst );
void transpose( const Mat& src, Mat& dst )
{
TransposeInplaceFunc itab[] =
{
0,
transposeI_<uchar>, // 1
transposeI_<ushort>, // 2
transposeI_<Vec<uchar,3> >, // 3
transposeI_<int>, // 4
0,
transposeI_<Vec<ushort,3> >, // 6
0,
transposeI_<int64>, // 8
0, 0, 0,
transposeI_<Vec<int,3> >, // 12
0, 0, 0,
transposeI_<Vec<int64,2> >, // 16
0, 0, 0, 0, 0, 0, 0,
transposeI_<Vec<int64,3> >, // 24
0, 0, 0, 0, 0, 0, 0,
transposeI_<Vec<int64,4> > // 32
};
TransposeFunc tab[] =
{
0,
transpose_<uchar>, // 1
transpose_<ushort>, // 2
transpose_<Vec<uchar,3> >, // 3
transpose_<int>, // 4
0,
transpose_<Vec<ushort,3> >, // 6
0,
transpose_<int64>, // 8
0, 0, 0,
transpose_<Vec<int,3> >, // 12
0, 0, 0,
transpose_<Vec<int64,2> >, // 16
0, 0, 0, 0, 0, 0, 0,
transpose_<Vec<int64,3> >, // 24
0, 0, 0, 0, 0, 0, 0,
transpose_<Vec<int64,4> > // 32
};
size_t esz = src.elemSize();
CV_Assert( esz <= (size_t)32 );
if( dst.data == src.data && dst.cols == dst.rows )
{
TransposeInplaceFunc func = itab[esz];
CV_Assert( func != 0 );
func( dst );
}
else
{
dst.create( src.cols, src.rows, src.type() );
TransposeFunc func = tab[esz];
CV_Assert( func != 0 );
func( src, dst );
}
}
void completeSymm( Mat& matrix, bool LtoR )
{
int i, j, nrows = matrix.rows, type = matrix.type();
int j0 = 0, j1 = nrows;
CV_Assert( matrix.rows == matrix.cols );
if( type == CV_32FC1 || type == CV_32SC1 )
{
int* data = (int*)matrix.data;
size_t step = matrix.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*)matrix.data;
size_t step = matrix.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, "" );
}
Mat Mat::cross(const Mat& m) const
{
int t = type(), d = CV_MAT_DEPTH(t);
CV_Assert( size() == m.size() && t == m.type() &&
((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1)));
Mat result(rows, cols, t);
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 Mat to vector *
\****************************************************************************************/
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;
for( i = 0; 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;
}
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]);
}
a0 = op(a0, a1);
dst[k] = (ST)a0;
}
}
}
}
typedef void (*ReduceFunc)( const Mat& src, Mat& dst );
void reduce(const Mat& src, Mat& dst, int dim, int op, int dtype)
{
int op0 = op;
int stype = src.type(), sdepth = src.depth();
if( dtype < 0 )
dtype = stype;
int ddepth = CV_MAT_DEPTH(dtype);
dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, dtype >= 0 ? dtype : stype);
Mat 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(src.channels()));
}
ReduceFunc func = 0;
if( dim == 0 )
{
if( op == CV_REDUCE_SUM )
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = reduceR_<uchar,int,OpAdd<int> >;
if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceR_<uchar,float,OpAdd<int> >;
if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceR_<uchar,double,OpAdd<int> >;
if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceR_<ushort,float,OpAdd<float> >;
if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceR_<ushort,double,OpAdd<double> >;
if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceR_<short,float,OpAdd<float> >;
if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceR_<short,double,OpAdd<double> >;
if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceR_<float,float,OpAdd<float> >;
if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceR_<float,double,OpAdd<double> >;
if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceR_<double,double,OpAdd<double> >;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = reduceR_<uchar, uchar, OpMax<uchar> >;
if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceR_<float, float, OpMax<float> >;
if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceR_<double, double, OpMax<double> >;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = reduceR_<uchar, uchar, OpMin<uchar> >;
if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceR_<float, float, OpMin<float> >;
if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceR_<double, double, OpMin<double> >;
}
}
else
{
if(op == CV_REDUCE_SUM)
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = reduceC_<uchar,int,OpAdd<int> >;
if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceC_<uchar,float,OpAdd<int> >;
if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceC_<uchar,double,OpAdd<int> >;
if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceC_<ushort,float,OpAdd<float> >;
if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceC_<ushort,double,OpAdd<double> >;
if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceC_<short,float,OpAdd<float> >;
if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceC_<short,double,OpAdd<double> >;
if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceC_<float,float,OpAdd<float> >;
if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceC_<float,double,OpAdd<double> >;
if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceC_<double,double,OpAdd<double> >;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = reduceC_<uchar, uchar, OpMax<uchar> >;
if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceC_<float, float, OpMax<float> >;
if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceC_<double, double, OpMax<double> >;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = reduceC_<uchar, uchar, OpMin<uchar> >;
if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceC_<float, float, OpMin<float> >;
if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceC_<double, double, OpMin<double> >;
}
}
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));
}
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, LessThan<T>() );
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 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 sort( const Mat& src, Mat& dst, int flags )
{
static SortFunc tab[] =
{
sort_<uchar>, sort_<schar>, sort_<ushort>, sort_<short>,
sort_<int>, sort_<float>, sort_<double>, 0
};
SortFunc func = tab[src.depth()];
CV_Assert( src.channels() == 1 && func != 0 );
dst.create( src.size(), src.type() );
func( src, dst, flags );
}
void sortIdx( const Mat& src, Mat& dst, int flags )
{
static SortFunc tab[] =
{
sortIdx_<uchar>, sortIdx_<schar>, sortIdx_<ushort>, sortIdx_<short>,
sortIdx_<int>, sortIdx_<float>, sortIdx_<double>, 0
};
SortFunc func = tab[src.depth()];
CV_Assert( src.channels() == 1 && func != 0 );
if( dst.data == src.data )
dst.release();
dst.create( src.size(), CV_32S );
func( src, dst, flags );
}
static void generateRandomCenter(const 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];
}
static inline float distance(const float* a, const float* b, int n, bool simd)
{
int j = 0; float d = 0.f;
#if CV_SSE
if( simd )
{
float CV_DECL_ALIGNED(16) buf[4];
__m128 d0 = _mm_setzero_ps(), d1 = _mm_setzero_ps();
for( ; j <= n - 8; j += 8 )
{
__m128 t0 = _mm_sub_ps(_mm_loadu_ps(a + j), _mm_loadu_ps(b + j));
__m128 t1 = _mm_sub_ps(_mm_loadu_ps(a + j + 4), _mm_loadu_ps(b + j + 4));
d0 = _mm_add_ps(d0, _mm_mul_ps(t0, t0));
d1 = _mm_add_ps(d1, _mm_mul_ps(t1, t1));
}
_mm_store_ps(buf, _mm_add_ps(d0, d1));
d = buf[0] + buf[1] + buf[2] + buf[3];
}
else
#endif
{
for( ; j <= n - 4; j += 4 )
{
float t0 = a[j] - b[j], t1 = a[j+1] - b[j+1], t2 = a[j+2] - b[j+2], t3 = a[j+3] - b[j+3];
d += t0*t0 + t1*t1 + t2*t2 + t3*t3;
}
}
for( ; j < n; j++ )
{
float t = a[j] - b[j];
d += t*t;
}
return d;
}
/*
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);
int step = _data.step/sizeof(data[0]);
vector<int> _centers(K);
int* centers = &_centers[0];
vector<float> _dist(N*3);
float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
double sum0 = 0;
bool simd = checkHardwareSupport(CV_CPU_SSE);
centers[0] = (unsigned)rng % N;
for( i = 0; i < N; i++ )
{
dist[i] = distance(data + step*i, data + step*centers[0], dims, simd);
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;
for( i = 0; i < N; i++ )
{
tdist2[i] = std::min(distance(data + step*i, data + step*ci, dims, simd), dist[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];
}
}
double kmeans( const Mat& data, int K, Mat& best_labels,
TermCriteria criteria, int attempts,
int flags, Mat* _centers )
{
const int SPP_TRIALS = 3;
int N = data.rows > 1 ? data.rows : data.cols;
int dims = (data.rows > 1 ? data.cols : 1)*data.channels();
int type = data.depth();
bool simd = checkHardwareSupport(CV_CPU_SSE);
attempts = std::max(attempts, 1);
CV_Assert( type == CV_32F && K > 0 );
Mat _labels;
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);
vector<int> counters(K);
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; iter < criteria.maxCount && max_center_shift > criteria.epsilon; iter++ )
{
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);
for( j = 0; 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;
}
for( ; j < dims; j++ )
center[j] += sample[j];
counters[k]++;
}
if( iter > 0 )
max_center_shift = 0;
for( k = 0; k < K; k++ )
{
float* center = centers.ptr<float>(k);
if( counters[k] != 0 )
{
float scale = 1.f/counters[k];
for( j = 0; j < dims; j++ )
center[j] *= scale;
}
else
generateRandomCenter(_box, center, rng);
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);
}
}
}
// assign labels
compactness = 0;
for( i = 0; i < N; i++ )
{
sample = data.ptr<float>(i);
int k_best = 0;
double min_dist = DBL_MAX;
for( k = 0; k < K; k++ )
{
const float* center = centers.ptr<float>(k);
double dist = distance(sample, center, dims, simd);
if( min_dist > dist )
{
min_dist = dist;
k_best = k;
}
}
compactness += min_dist;
labels[i] = k_best;
}
}
if( compactness < best_compactness )
{
best_compactness = compactness;
if( _centers )
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(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), dst, idx;
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);
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 ? &centers : 0 );
if( _compactness )
*_compactness = compactness;
return 1;
}
///////////////////////////// n-dimensional matrices ////////////////////////////
namespace cv
{
//////////////////////////////// MatND ///////////////////////////////////
MatND::MatND(const MatND& m, const Range* ranges)
: flags(MAGIC_VAL), dims(0), refcount(0), data(0), datastart(0), dataend(0)
{
int i, j, 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() )
{
size[i] = r.end - r.start;
data += r.start*step[i];
}
}
for( i = 0; i < d; i++ )
{
if( size[i] != 1 )
break;
}
CV_Assert( step[d-1] == elemSize() );
for( j = d-1; j > i; j-- )
{
if( step[j]*size[j] < step[j-1] )
break;
}
flags = (flags & ~CONTINUOUS_FLAG) | (j <= i ? CONTINUOUS_FLAG : 0);
}
void MatND::create(int d, const int* _sizes, int _type)
{
CV_Assert(d > 0 && _sizes);
int i;
_type = CV_MAT_TYPE(_type);
if( data && d == dims && _type == type() )
{
for( i = 0; i < d; i++ )
if( size[i] != _sizes[i] )
break;
if( i == d )
return;
}
release();
flags = (_type & CV_MAT_TYPE_MASK) | MAGIC_VAL | CONTINUOUS_FLAG;
size_t total = elemSize();
int64 total1;
for( i = d-1; i >= 0; i-- )
{
int sz = _sizes[i];
size[i] = sz;
step[i] = total;
total1 = (int64)total*sz;
CV_Assert( sz > 0 );
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;
}
total = alignSize(total, (int)sizeof(*refcount));
data = datastart = (uchar*)fastMalloc(total + (int)sizeof(*refcount));
dataend = datastart + step[0]*size[0];
refcount = (int*)(data + total);
*refcount = 1;
dims = d;
}
void MatND::copyTo( MatND& m ) const
{
m.create( dims, size, type() );
NAryMatNDIterator it(*this, m);
for( int i = 0; i < it.nplanes; i++, ++it )
it.planes[0].copyTo(it.planes[1]);
}
void MatND::copyTo( MatND& m, const MatND& mask ) const
{
m.create( dims, size, type() );
NAryMatNDIterator it(*this, m, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
it.planes[0].copyTo(it.planes[1], it.planes[2]);
}
void MatND::convertTo( MatND& m, int rtype, double alpha, double beta ) const
{
rtype = rtype < 0 ? type() : CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels());
m.create( dims, size, rtype );
NAryMatNDIterator it(*this, m);
for( int i = 0; i < it.nplanes; i++, ++it )
it.planes[0].convertTo(it.planes[1], rtype, alpha, beta);
}
MatND& MatND::operator = (const Scalar& s)
{
NAryMatNDIterator it(*this);
for( int i = 0; i < it.nplanes; i++, ++it )
it.planes[0] = s;
return *this;
}
MatND& MatND::setTo(const Scalar& s, const MatND& mask)
{
NAryMatNDIterator it(*this, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
it.planes[0].setTo(s, it.planes[1]);
return *this;
}
MatND MatND::reshape(int, int, const int*) const
{
CV_Error(CV_StsNotImplemented, "");
// TBD
return MatND();
}
MatND::operator Mat() const
{
int i, d = dims, d1, rows, cols;
size_t _step = Mat::AUTO_STEP;
if( d <= 2 )
{
rows = size[0];
cols = d == 2 ? size[1] : 1;
if( d == 2 )
_step = step[0];
}
else
{
rows = 1;
cols = size[d-1];
for( d1 = 0; d1 < d; d1++ )
if( size[d1] > 1 )
break;
for( i = d-1; i > d1; i-- )
{
int64 cols1 = (int64)cols*size[i-1];
if( cols1 != (int)cols1 || size[i]*step[i] != step[i-1] )
break;
cols = (int)cols1;
}
if( i > d1 )
{
--i;
_step = step[i];
rows = size[i];
for( ; i > d1; i-- )
{
int64 rows1 = (int64)rows*size[i-1];
if( rows1 != (int)rows1 || size[i]*step[i] != step[i-1] )
break;
rows = (int)rows1;
}
if( i > d1 )
CV_Error( CV_StsBadArg,
"The nD matrix can not be represented as 2D matrix due "
"to its layout in memory; you may use (Mat)the_matnd.clone() instead" );
}
}
Mat m(rows, cols, type(), data, _step);
m.datastart = datastart;
m.dataend = dataend;
m.refcount = refcount;
m.addref();
return m;
}
MatND::operator CvMatND() const
{
CvMatND mat;
cvInitMatNDHeader( &mat, dims, size, type(), data );
int i, d = dims;
for( i = 0; i < d; i++ )
mat.dim[i].step = (int)step[i];
mat.type |= flags & CONTINUOUS_FLAG;
return mat;
}
NAryMatNDIterator::NAryMatNDIterator(const MatND** _arrays, size_t count)
{
init(_arrays, count);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND* _arrays, size_t count)
{
AutoBuffer<const MatND*, 32> buf(count);
for( size_t i = 0; i < count; i++ )
buf[i] = _arrays + i;
init(buf, count);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND& m1)
{
const MatND* mm[] = {&m1};
init(mm, 1);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND& m1, const MatND& m2)
{
const MatND* mm[] = {&m1, &m2};
init(mm, 2);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND& m1, const MatND& m2, const MatND& m3)
{
const MatND* mm[] = {&m1, &m2, &m3};
init(mm, 3);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND& m1, const MatND& m2,
const MatND& m3, const MatND& m4)
{
const MatND* mm[] = {&m1, &m2, &m3, &m4};
init(mm, 4);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND& m1, const MatND& m2,
const MatND& m3, const MatND& m4,
const MatND& m5)
{
const MatND* mm[] = {&m1, &m2, &m3, &m4, &m5};
init(mm, 5);
}
NAryMatNDIterator::NAryMatNDIterator(const MatND& m1, const MatND& m2,
const MatND& m3, const MatND& m4,
const MatND& m5, const MatND& m6)
{
const MatND* mm[] = {&m1, &m2, &m3, &m4, &m5, &m6};
init(mm, 6);
}
void NAryMatNDIterator::init(const MatND** _arrays, size_t count)
{
CV_Assert( _arrays && count > 0 );
arrays.resize(count);
int i, j, d1=0, i0 = -1, d = -1, n = (int)count;
iterdepth = 0;
for( i = 0; i < n; i++ )
{
if( !_arrays[i] || !_arrays[i]->data )
{
arrays[i] = MatND();
continue;
}
const MatND& A = arrays[i] = *_arrays[i];
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" steps do not affect the continuity
for( d1 = 0; d1 < d; d1++ )
if( A.size[d1] > 1 )
break;
}
else
{
CV_Assert( A.dims == d );
for( j = 0; j < d; j++ )
CV_Assert( A.size[j] == arrays[i0].size[j] );
}
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 )
CV_Error( CV_StsBadArg, "All the input arrays are empty" );
int total = arrays[i0].size[d-1];
for( j = d-1; j > iterdepth; j-- )
{
int64 total1 = (int64)total*arrays[i0].size[j-1];
if( total1 != (int)total1 )
break;
total = (int)total1;
}
iterdepth = j;
if( iterdepth == d1 )
iterdepth = 0;
planes.resize(n);
for( i = 0; i < n; i++ )
{
if( !arrays[i].data )
{
planes[i] = Mat();
continue;
}
planes[i] = Mat( 1, total, arrays[i].type(), arrays[i].data );
planes[i].datastart = arrays[i].datastart;
planes[i].dataend = arrays[i].dataend;
planes[i].refcount = arrays[i].refcount;
planes[i].addref();
}
idx = 0;
nplanes = 1;
for( j = iterdepth-1; j >= 0; j-- )
nplanes *= arrays[i0].size[j];
}
NAryMatNDIterator& NAryMatNDIterator::operator ++()
{
if( idx >= nplanes-1 )
return *this;
++idx;
for( size_t i = 0; i < arrays.size(); i++ )
{
const MatND& A = arrays[i];
Mat& M = planes[i];
if( !A.data )
continue;
int _idx = 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;
}
M.data = data;
}
return *this;
}
NAryMatNDIterator NAryMatNDIterator::operator ++(int)
{
NAryMatNDIterator it = *this;
++*this;
return it;
}
void add(const MatND& a, const MatND& b, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
add( it.planes[0], it.planes[1], it.planes[2], it.planes[3] );
}
void subtract(const MatND& a, const MatND& b, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
subtract( it.planes[0], it.planes[1], it.planes[2], it.planes[3] );
}
void add(const MatND& a, const MatND& b, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
add( it.planes[0], it.planes[1], it.planes[2] );
}
void subtract(const MatND& a, const MatND& b, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
subtract( it.planes[0], it.planes[1], it.planes[2] );
}
void add(const MatND& a, const Scalar& s, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
add( it.planes[0], s, it.planes[1], it.planes[2] );
}
void subtract(const Scalar& s, const MatND& a, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
subtract( s, it.planes[0], it.planes[1], it.planes[2] );
}
void multiply(const MatND& a, const MatND& b, MatND& c, double scale)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
multiply( it.planes[0], it.planes[1], it.planes[2], scale );
}
void divide(const MatND& a, const MatND& b, MatND& c, double scale)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
divide( it.planes[0], it.planes[1], it.planes[2], scale );
}
void divide(double scale, const MatND& b, MatND& c)
{
c.create(b.dims, b.size, b.type());
NAryMatNDIterator it(b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
divide( scale, it.planes[0], it.planes[1] );
}
void scaleAdd(const MatND& a, double alpha, const MatND& b, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
scaleAdd( it.planes[0], alpha, it.planes[1], it.planes[2] );
}
void addWeighted(const MatND& a, double alpha, const MatND& b,
double beta, double gamma, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
addWeighted( it.planes[0], alpha, it.planes[1], beta, gamma, it.planes[2] );
}
Scalar sum(const MatND& m)
{
NAryMatNDIterator it(m);
Scalar s;
for( int i = 0; i < it.nplanes; i++, ++it )
s += sum(it.planes[0]);
return s;
}
int countNonZero( const MatND& m )
{
NAryMatNDIterator it(m);
int nz = 0;
for( int i = 0; i < it.nplanes; i++, ++it )
nz += countNonZero(it.planes[0]);
return nz;
}
Scalar mean(const MatND& m)
{
NAryMatNDIterator it(m);
double total = 1;
for( int i = 0; i < m.dims; i++ )
total *= m.size[i];
return sum(m)*(1./total);
}
Scalar mean(const MatND& m, const MatND& mask)
{
if( !mask.data )
return mean(m);
NAryMatNDIterator it(m, mask);
double total = 0;
Scalar s;
for( int i = 0; i < it.nplanes; i++, ++it )
{
int n = countNonZero(it.planes[1]);
s += mean(it.planes[0], it.planes[1])*(double)n;
total += n;
}
return s *= 1./std::max(total, 1.);
}
void meanStdDev(const MatND& m, Scalar& mean, Scalar& stddev, const MatND& mask)
{
NAryMatNDIterator it(m, mask);
double total = 0;
Scalar s, sq;
int k, cn = m.channels();
for( int i = 0; i < it.nplanes; i++, ++it )
{
Scalar _mean, _stddev;
meanStdDev(it.planes[0], _mean, _stddev, it.planes[1]);
double nz = mask.data ? countNonZero(it.planes[1]) :
(double)it.planes[0].rows*it.planes[0].cols;
for( k = 0; k < cn; k++ )
{
s[k] += _mean[k]*nz;
sq[k] += (_stddev[k]*_stddev[k] + _mean[k]*_mean[k])*nz;
}
total += nz;
}
mean = stddev = Scalar();
total = 1./std::max(total, 1.);
for( k = 0; k < cn; k++ )
{
mean[k] = s[k]*total;
stddev[k] = std::sqrt(std::max(sq[k]*total - mean[k]*mean[k], 0.));
}
}
double norm(const MatND& a, int normType, const MatND& mask)
{
NAryMatNDIterator it(a, mask);
double total = 0;
for( int i = 0; i < it.nplanes; i++, ++it )
{
double n = norm(it.planes[0], normType, it.planes[1]);
if( normType == NORM_INF )
total = std::max(total, n);
else if( normType == NORM_L1 )
total += n;
else
total += n*n;
}
return normType != NORM_L2 ? total : std::sqrt(total);
}
double norm(const MatND& a, const MatND& b,
int normType, const MatND& mask)
{
bool isRelative = (normType & NORM_RELATIVE) != 0;
normType &= 7;
NAryMatNDIterator it(a, b, mask);
double num = 0, denom = 0;
for( int i = 0; i < it.nplanes; i++, ++it )
{
double n = norm(it.planes[0], it.planes[1], normType, it.planes[2]);
double d = !isRelative ? 0 : norm(it.planes[1], normType, it.planes[2]);
if( normType == NORM_INF )
{
num = std::max(num, n);
denom = std::max(denom, d);
}
else if( normType == NORM_L1 )
{
num += n;
denom += d;
}
else
{
num += n*n;
denom += d*d;
}
}
if( normType == NORM_L2 )
{
num = std::sqrt(num);
denom = std::sqrt(denom);
}
return !isRelative ? num : num/std::max(denom,DBL_EPSILON);
}
void normalize( const MatND& src, MatND& dst, double a, double b,
int norm_type, int rtype, const MatND& mask )
{
double scale = 1, shift = 0;
if( norm_type == CV_MINMAX )
{
double smin = 0, smax = 0;
double dmin = std::min( a, b ), dmax = std::max( a, b );
minMaxLoc( src, &smin, &smax, 0, 0, mask );
scale = (dmax - dmin)*(smax - smin > DBL_EPSILON ? 1./(smax - smin) : 0);
shift = dmin - smin*scale;
}
else if( norm_type == CV_L2 || norm_type == CV_L1 || norm_type == CV_C )
{
scale = norm( src, norm_type, mask );
scale = scale > DBL_EPSILON ? a/scale : 0.;
shift = 0;
}
else
CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" );
if( !mask.data )
src.convertTo( dst, rtype, scale, shift );
else
{
MatND temp;
src.convertTo( temp, rtype, scale, shift );
temp.copyTo( dst, mask );
}
}
static void ofs2idx(const MatND& a, size_t ofs, int* idx)
{
int i, d = a.dims;
for( i = 0; i < d; i++ )
{
idx[i] = (int)(ofs / a.step[i]);
ofs %= a.step[i];
}
}
void minMaxLoc(const MatND& a, double* minVal,
double* maxVal, int* minLoc, int* maxLoc,
const MatND& mask)
{
NAryMatNDIterator it(a, mask);
double minval = DBL_MAX, maxval = -DBL_MAX;
size_t minofs = 0, maxofs = 0, esz = a.elemSize();
for( int i = 0; i < it.nplanes; i++, ++it )
{
double val0 = 0, val1 = 0;
Point pt0, pt1;
minMaxLoc( it.planes[0], &val0, &val1, &pt0, &pt1, it.planes[1] );
if( val0 < minval )
{
minval = val0;
minofs = (it.planes[0].data - a.data) + pt0.x*esz;
}
if( val1 > maxval )
{
maxval = val1;
maxofs = (it.planes[0].data - a.data) + pt1.x*esz;
}
}
if( minVal )
*minVal = minval;
if( maxVal )
*maxVal = maxval;
if( minLoc )
ofs2idx(a, minofs, minLoc);
if( maxLoc )
ofs2idx(a, maxofs, maxLoc);
}
void merge(const MatND* mv, size_t n, MatND& dst)
{
size_t k;
CV_Assert( n > 0 );
vector<MatND> v(n + 1);
int total_cn = 0;
for( k = 0; k < n; k++ )
{
total_cn += mv[k].channels();
v[k] = mv[k];
}
dst.create( mv[0].dims, mv[0].size, CV_MAKETYPE(mv[0].depth(), total_cn) );
v[n] = dst;
NAryMatNDIterator it(&v[0], v.size());
for( int i = 0; i < it.nplanes; i++, ++it )
merge( &it.planes[0], n, it.planes[n] );
}
void split(const MatND& m, MatND* mv)
{
size_t k, n = m.channels();
CV_Assert( n > 0 );
vector<MatND> v(n + 1);
for( k = 0; k < n; k++ )
{
mv[k].create( m.dims, m.size, CV_MAKETYPE(m.depth(), 1) );
v[k] = mv[k];
}
v[n] = m;
NAryMatNDIterator it(&v[0], v.size());
for( int i = 0; i < it.nplanes; i++, ++it )
split( it.planes[n], &it.planes[0] );
}
void mixChannels(const MatND* src, int nsrcs, MatND* dst, int ndsts,
const int* fromTo, size_t npairs)
{
size_t k, m = nsrcs, n = ndsts;
CV_Assert( n > 0 && m > 0 );
vector<MatND> v(m + n);
for( k = 0; k < m; k++ )
v[k] = src[k];
for( k = 0; k < n; k++ )
v[m + k] = dst[k];
NAryMatNDIterator it(&v[0], v.size());
for( int i = 0; i < it.nplanes; i++, ++it )
{
Mat* pptr = &it.planes[0];
mixChannels( pptr, m, pptr + m, n, fromTo, npairs );
}
}
void bitwise_and(const MatND& a, const MatND& b, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_and( it.planes[0], it.planes[1], it.planes[2], it.planes[3] );
}
void bitwise_or(const MatND& a, const MatND& b, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_or( it.planes[0], it.planes[1], it.planes[2], it.planes[3] );
}
void bitwise_xor(const MatND& a, const MatND& b, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_xor( it.planes[0], it.planes[1], it.planes[2], it.planes[3] );
}
void bitwise_and(const MatND& a, const Scalar& s, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_and( it.planes[0], s, it.planes[1], it.planes[2] );
}
void bitwise_or(const MatND& a, const Scalar& s, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_or( it.planes[0], s, it.planes[1], it.planes[2] );
}
void bitwise_xor(const MatND& a, const Scalar& s, MatND& c, const MatND& mask)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c, mask);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_xor( it.planes[0], s, it.planes[1], it.planes[2] );
}
void bitwise_not(const MatND& a, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c);
for( int i = 0; i < it.nplanes; i++, ++it )
bitwise_not( it.planes[0], it.planes[1] );
}
void absdiff(const MatND& a, const MatND& b, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
absdiff( it.planes[0], it.planes[1], it.planes[2] );
}
void absdiff(const MatND& a, const Scalar& s, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c);
for( int i = 0; i < it.nplanes; i++, ++it )
absdiff( it.planes[0], s, it.planes[1] );
}
void inRange(const MatND& src, const MatND& lowerb,
const MatND& upperb, MatND& dst)
{
dst.create(src.dims, src.size, CV_8UC1);
NAryMatNDIterator it(src, lowerb, upperb, dst);
for( int i = 0; i < it.nplanes; i++, ++it )
inRange( it.planes[0], it.planes[1], it.planes[2], it.planes[3] );
}
void inRange(const MatND& src, const Scalar& lowerb,
const Scalar& upperb, MatND& dst)
{
dst.create(src.dims, src.size, CV_8UC1);
NAryMatNDIterator it(src, dst);
for( int i = 0; i < it.nplanes; i++, ++it )
inRange( it.planes[0], lowerb, upperb, it.planes[1] );
}
void compare(const MatND& a, const MatND& b, MatND& c, int cmpop)
{
c.create(a.dims, a.size, CV_8UC1);
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
compare( it.planes[0], it.planes[1], it.planes[2], cmpop );
}
void compare(const MatND& a, double s, MatND& c, int cmpop)
{
c.create(a.dims, a.size, CV_8UC1);
NAryMatNDIterator it(a, c);
for( int i = 0; i < it.nplanes; i++, ++it )
compare( it.planes[0], s, it.planes[1], cmpop );
}
void min(const MatND& a, const MatND& b, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
min( it.planes[0], it.planes[1], it.planes[2] );
}
void min(const MatND& a, double alpha, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c);
for( int i = 0; i < it.nplanes; i++, ++it )
min( it.planes[0], alpha, it.planes[1] );
}
void max(const MatND& a, const MatND& b, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b, c);
for( int i = 0; i < it.nplanes; i++, ++it )
max( it.planes[0], it.planes[1], it.planes[2] );
}
void max(const MatND& a, double alpha, MatND& c)
{
c.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, c);
for( int i = 0; i < it.nplanes; i++, ++it )
max( it.planes[0], alpha, it.planes[1] );
}
void sqrt(const MatND& a, MatND& b)
{
b.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b);
for( int i = 0; i < it.nplanes; i++, ++it )
sqrt( it.planes[0], it.planes[1] );
}
void pow(const MatND& a, double power, MatND& b)
{
b.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b);
for( int i = 0; i < it.nplanes; i++, ++it )
pow( it.planes[0], power, it.planes[1] );
}
void exp(const MatND& a, MatND& b)
{
b.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b);
for( int i = 0; i < it.nplanes; i++, ++it )
exp( it.planes[0], it.planes[1] );
}
void log(const MatND& a, MatND& b)
{
b.create(a.dims, a.size, a.type());
NAryMatNDIterator it(a, b);
for( int i = 0; i < it.nplanes; i++, ++it )
log( it.planes[0], it.planes[1] );
}
bool checkRange(const MatND& a, bool quiet, int*,
double minVal, double maxVal)
{
NAryMatNDIterator it(a);
for( int i = 0; i < it.nplanes; i++, ++it )
{
Point pt;
if( !checkRange( it.planes[0], quiet, &pt, minVal, maxVal ))
{
// todo: set index properly
return false;
}
}
return true;
}
//////////////////////////////// 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);
}
ConvertData getConvertData(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;
}
ConvertScaleData getConvertScaleData(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; (int)i <= (int)(elemSize - sizeof(int)); 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 <= elemSize - sizeof(int); 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, bool try1d)
: flags(MAGIC_VAL), hdr(0)
{
bool is1d = try1d && m.cols == 1;
if( is1d )
{
int i, M = m.rows;
const uchar* data = m.data;
size_t step = m.step, esz = m.elemSize();
create( 1, &M, m.type() );
for( i = 0; i < M; i++ )
{
const uchar* from = data + step*i;
if( isZeroElem(from, esz) )
continue;
uchar* to = newNode(&i, hash(i));
copyElem(from, to, esz);
}
}
else
{
int i, j, size[] = {m.rows, m.cols};
const uchar* data = m.data;
size_t step = m.step, esz = m.elemSize();
create( 2, size, m.type() );
for( i = 0; i < m.rows; i++ )
{
for( j = 0; j < m.cols; j++ )
{
const uchar* from = data + step*i + esz*j;
if( isZeroElem(from, esz) )
continue;
int idx[] = {i, j};
uchar* to = newNode(idx, hash(i, j));
copyElem(from, to, esz);
}
}
}
}
SparseMat::SparseMat(const MatND& 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* ptr = m.data;
for(;;)
{
for( i = 0; i < lastSize; i++, ptr += esz )
{
if( isZeroElem(ptr, esz) )
continue;
idx[d-1] = i;
uchar* to = newNode(idx, hash(idx));
copyElem( ptr, to, esz );
}
for( i = d - 2; i >= 0; i-- )
{
ptr += 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;
}
}
SparseMat::SparseMat(const CvSparseMat* m)
: flags(MAGIC_VAL), hdr(0)
{
CV_Assert(m);
create( m->dims, &m->size[0], m->type );
CvSparseMatIterator it;
CvSparseNode* n = cvInitSparseMatIterator(m, &it);
size_t esz = elemSize();
for( ; n != 0; n = cvGetNextSparseNode(&it) )
{
const int* idx = CV_NODE_IDX(m, n);
uchar* to = newNode(idx, hash(idx));
copyElem((const uchar*)CV_NODE_VAL(m, n), to, esz);
}
}
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 && hdr->dims <= 2 );
m.create( hdr->size[0], hdr->dims == 2 ? hdr->size[1] : 1, type() );
m = Scalar(0);
SparseMatConstIterator from = begin();
size_t i, N = nzcount(), esz = elemSize();
if( hdr->dims == 2 )
{
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.data + m.step*n->idx[0] + esz*n->idx[1];
copyElem( from.ptr, to, esz );
}
}
else
{
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.data + esz*n->idx[0];
copyElem( from.ptr, to, esz );
}
}
}
void SparseMat::copyTo( MatND& 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 = getConvertData(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 = getConvertScaleData(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 && hdr->dims <= 2 );
m.create( hdr->size[0], hdr->dims == 2 ? hdr->size[1] : 1, type() );
m = Scalar(beta);
SparseMatConstIterator from = begin();
size_t i, N = nzcount(), esz = CV_ELEM_SIZE(rtype);
if( alpha == 1 && beta == 0 )
{
ConvertData cvtfunc = getConvertData(type(), rtype);
if( hdr->dims == 2 )
{
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.data + m.step*n->idx[0] + esz*n->idx[1];
cvtfunc( from.ptr, to, cn );
}
}
else
{
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.data + esz*n->idx[0];
cvtfunc( from.ptr, to, cn );
}
}
}
else
{
ConvertScaleData cvtfunc = getConvertScaleData(type(), rtype);
if( hdr->dims == 2 )
{
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.data + m.step*n->idx[0] + esz*n->idx[1];
cvtfunc( from.ptr, to, cn, alpha, beta );
}
}
else
{
for( i = 0; i < N; i++, ++from )
{
const Node* n = from.node();
uchar* to = m.data + esz*n->idx[0];
cvtfunc( from.ptr, to, cn, alpha, beta );
}
}
}
}
void SparseMat::convertTo( MatND& 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 = getConvertData(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 = getConvertScaleData(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();
}
SparseMat::operator CvSparseMat*() const
{
if( !hdr )
return 0;
CvSparseMat* m = cvCreateSparseMat(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 = cvPtrND(m, n->idx, 0, -2, 0);
copyElem(from.ptr, to, esz);
}
return m;
}
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 = 1 << cvCeil(std::log((double)newsize)/CV_LOG2);
size_t i, hsize = hdr->hashtab.size();
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];
d = elemSize();
uchar* p = &value<uchar>(elem);
if( d == sizeof(float) )
*((float*)p) = 0.f;
else if( d == sizeof(double) )
*((double*)p) = 0.;
else
memset(p, 0, d);
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 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 );
}
}
/* End of file. */