opencv/modules/core/src/matrix_sparse.cpp at master · pythonmjs/opencv · GitHub

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// This file is part of OpenCV project.

// It is subject to the license terms in the LICENSE file found in the top-level directory

// of this distribution and at http://opencv.org/license.html

#include "opencv2/core/mat.hpp"

#include "opencv2/core/types_c.h"

#include "precomp.hpp"

namespace cv {

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) - MAX_DIM*sizeof(int) +

dims*sizeof(int), 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();

const uchar* dptr = m.ptr();

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)

{

CV_Assert( _sizes && 0 < d && d <= CV_MAX_DIM );

for( int 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 )

{

int i;

for( i = 0; i < d; i++ )

if( _sizes[i] != hdr->size[i] )

break;

if( i == d )

{

clear();

return;

}

int _sizes_backup[CV_MAX_DIM]; // #5991

if (_sizes == hdr->size)

{

for(int i = 0; i < d; i++ )

_sizes_backup[i] = _sizes[i];

_sizes = _sizes_backup;

}

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 N = nzcount(), esz = elemSize();

for( size_t 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 );

int ndims = dims();

m.create( ndims, hdr->size, type() );

m = Scalar(0);

SparseMatConstIterator from = begin();

size_t N = nzcount(), esz = elemSize();

for( size_t i = 0; i < N; i++, ++from )

{

const Node* n = from.node();

copyElem( from.ptr, (ndims > 1 ? m.ptr(n->idx) : m.ptr(n->idx[0])), 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 N = nzcount();

if( alpha == 1 )

{

ConvertData cvtfunc = getConvertElem(type(), rtype);

for( size_t 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( size_t 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 N = nzcount();

if( alpha == 1 && beta == 0 )

{

ConvertData cvtfunc = getConvertElem(type(), rtype);

for( size_t 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( size_t 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 NULL;

}

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 NULL;

}

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 NULL;

}

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) : NULL;

}

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 hsize = hdr->hashtab.size();

std::vector<size_t> _newh(newsize);

size_t* newh = &_newh[0];

for( size_t i = 0; i < newsize; i++ )

newh[i] = 0;

uchar* pool = &hdr->pool[0];

for( size_t 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*3/2, 8*nsz);

newpsize = (newpsize/nsz)*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;

}

// Operations

double norm( const SparseMat& src, int normType )

{

CV_INSTRUMENT_REGION()

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 )

{

CV_Assert(it.ptr);

result = std::max(result, std::abs((double)it.value<float>()));

}

else if( normType == NORM_L1 )

for( i = 0; i < N; i++, ++it )

{

CV_Assert(it.ptr);

result += std::abs(it.value<float>());

}

else

for( i = 0; i < N; i++, ++it )

{

CV_Assert(it.ptr);

double v = it.value<float>();

result += v*v;

}

else if( type == CV_64F )

{

if( normType == NORM_INF )

for( i = 0; i < N; i++, ++it )

{

CV_Assert(it.ptr);

result = std::max(result, std::abs(it.value<double>()));

}

else if( normType == NORM_L1 )

for( i = 0; i < N; i++, ++it )

{

CV_Assert(it.ptr);

result += std::abs(it.value<double>());

}

else

for( i = 0; i < N; i++, ++it )

{

CV_Assert(it.ptr);

double v = it.value<double>();

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 )

{

CV_INSTRUMENT_REGION()

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 )

{

CV_Assert(it.ptr);

float v = it.value<float>();

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 )

{

CV_Assert(it.ptr);

double v = it.value<double>();

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 && minidx )

for( i = 0; i < d; i++ )

_minidx[i] = minidx[i];

if( _maxidx && maxidx )

for( i = 0; i < d; i++ )

_maxidx[i] = maxidx[i];

}

void normalize( const SparseMat& src, SparseMat& dst, double a, int norm_type )

{

CV_INSTRUMENT_REGION()

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 );

}

} // cv::

// C-API glue

CvSparseMat* cvCreateSparseMat(const cv::SparseMat& sm)

{

if( !sm.hdr || sm.hdr->dims > (int)cv::SparseMat::MAX_DIM)

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);

}

opencv/modules/core/src/matrix_sparse.cpp at master · pythonmjs/opencv · GitHub

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