/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // 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. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // 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. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" /****************************************************************************************\ * [scaled] Identity matrix initialization * \****************************************************************************************/ namespace cv { void swap( Mat& a, Mat& b ) { /*int *ap = (int*)&a, *bp = (int*)&b; size_t i, n = sizeof(Mat)/sizeof(ap[0]); for( i = 0; i < n; i++ ) std::swap(ap[i], bp[i]);*/ 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.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.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; } int64 t = (int64)(m.step[0]/CV_ELEM_SIZE(m.flags))*m.size[0]; if( j <= i && t == (int)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.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 && _sizes && 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, allocator == 0); if( total() > 0 ) { if( !allocator ) { size_t total = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); data = datastart = (uchar*)fastMalloc(total + (int)sizeof(*refcount)); refcount = (int*)(data + total); *refcount = 1; } else { allocator->allocate(dims, size, _type, refcount, datastart, data, step.p); 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( allocator ) allocator->deallocate(refcount, datastart, data); else { CV_DbgAssert(refcount != 0); fastFree(datastart); } } Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : flags(0), 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 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|CV_MAT_TYPE(_type)), dims(0), rows(0), cols(0), data((uchar*)_data), refcount(0), datastart((uchar*)_data), dataend((uchar*)_data), datalimit((uchar*)_data), allocator(0), size(&rows) { setSize(*this, _dims, _sizes, _steps, true); finalizeHdr(*this); } Mat::Mat(const Mat& m, const Range* ranges) : flags(m.flags), 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); } Mat::Mat(const CvMatND* m, bool copyData) : flags(MAGIC_VAL|CV_MAT_TYPE(m->type)), dims(0), rows(0), cols(0), data((uchar*)m->data.ptr), refcount(0), datastart((uchar*)m->data.ptr), allocator(0), size(&rows) { 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(*this, d, _sizes, _steps); finalizeHdr(*this); if( copyData ) { Mat temp(*this); temp.copyTo(*this); } } 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; } Mat::Mat(const IplImage* img, bool copyData) : flags(MAGIC_VAL), dims(2), rows(0), cols(0), data(0), refcount(0), datastart(0), dataend(0), allocator(0), size(&rows) { CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0); int depth = IPL2CV_DEPTH(img->depth); size_t esz; step[0] = img->widthStep; 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 = CV_ELEM_SIZE(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; flags = MAGIC_VAL + CV_MAKETYPE(depth, selectedPlane ? 1 : img->nChannels); rows = img->roi->height; cols = img->roi->width; esz = CV_ELEM_SIZE(flags); data = datastart = (uchar*)img->imageData + (selectedPlane ? (img->roi->coi - 1)*step*img->height : 0) + img->roi->yOffset*step[0] + img->roi->xOffset*esz; } datalimit = datastart + step.p[0]*rows; dataend = datastart + step.p[0]*(rows-1) + esz*cols; flags |= (cols*esz == step.p[0] || rows == 1 ? CONTINUOUS_FLAG : 0); step[1] = esz; if( copyData ) { Mat m = *this; release(); 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 { CV_Assert( dims <= 2 ); IplImage img; cvInitImageHeader(&img, size(), cvIplDepth(flags), channels()); cvSetData(&img, data, (int)step[0]); return img; } 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 ) { 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) { if( !arr ) return Mat(); if( CV_IS_MAT(arr) ) return Mat((const CvMat*)arr, copyData ); if( CV_IS_MATND(arr) ) return Mat((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 Mat(iplimg, copyData); } 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; } 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 extractImageCOI(const CvArr* arr, Mat& ch, int coi) { Mat mat = cvarrToMat(arr, false, true, 1); ch.create(mat.dims, mat.size, mat.depth()); 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 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; } 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 { CV_Assert( dims <= 2 ); 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[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; } 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))) || (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(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(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(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(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(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(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(s.val[i]); for(; i < unroll_to; i++) buf[i] = buf[i-cn]; break; } default: CV_Error(CV_StsUnsupportedFormat,""); } } /*************************************************************************************************\ Matrix Operations \*************************************************************************************************/ void hconcat(const Mat* src, size_t nsrc, Mat& 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()); 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 hconcat(const Mat& src1, const Mat& src2, Mat& dst) { Mat src[] = {src1, src2}; hconcat(src, 2, dst); } void hconcat(const vector& src, CV_OUT Mat& dst) { hconcat(!src.empty() ? &src[0] : 0, src.size(), dst); } void vconcat(const Mat* src, size_t nsrc, Mat& 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()); 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 vconcat(const Mat& src1, const Mat& src2, Mat& dst) { Mat src[] = {src1, src2}; vconcat(src, 2, dst); } void vconcat(const vector& src, CV_OUT Mat& dst) { vconcat(!src.empty() ? &src[0] : 0, src.size(), dst); } //////////////////////////////////////// set identity //////////////////////////////////////////// void setIdentity( Mat& m, const Scalar& s ) { 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 /////////////////////////////////////////// Scalar trace( const Mat& m ) { 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 ///////////////////////////////////////// template 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 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_, // 1 transposeI_, // 2 transposeI_ >, // 3 transposeI_, // 4 0, transposeI_ >, // 6 0, transposeI_ >, // 8 0, 0, 0, transposeI_ >, // 12 0, 0, 0, transposeI_ >, // 16 0, 0, 0, 0, 0, 0, 0, transposeI_ >, // 24 0, 0, 0, 0, 0, 0, 0, transposeI_ > // 32 }; TransposeFunc tab[] = { 0, transpose_, // 1 transpose_, // 2 transpose_ >, // 3 transpose_, // 4 0, transpose_ >, // 6 0, transpose_ >, // 8 0, 0, 0, transpose_ >, // 12 0, 0, 0, transpose_ >, // 16 0, 0, 0, 0, 0, 0, 0, transpose_ >, // 24 0, 0, 0, 0, 0, 0, 0, transpose_ > // 32 }; size_t esz = src.elemSize(); CV_Assert( src.dims <= 2 && 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& m, bool LtoR ) { 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, "" ); } Mat Mat::cross(const Mat& m) const { int t = type(), d = CV_MAT_DEPTH(t); CV_Assert( dims <= 2 && m.dims <= 2 && 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 //////////////////////////////////////////// template static void reduceR_( const Mat& srcmat, Mat& dstmat ) { typedef typename Op::rtype WT; Size size = srcmat.size(); size.width *= srcmat.channels(); AutoBuffer 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 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) { CV_Assert( src.dims <= 2 ); 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_ >; if(sdepth == CV_8U && ddepth == CV_32F) func = reduceR_ >; if(sdepth == CV_8U && ddepth == CV_64F) func = reduceR_ >; if(sdepth == CV_16U && ddepth == CV_32F) func = reduceR_ >; if(sdepth == CV_16U && ddepth == CV_64F) func = reduceR_ >; if(sdepth == CV_16S && ddepth == CV_32F) func = reduceR_ >; if(sdepth == CV_16S && ddepth == CV_64F) func = reduceR_ >; if(sdepth == CV_32F && ddepth == CV_32F) func = reduceR_ >; if(sdepth == CV_32F && ddepth == CV_64F) func = reduceR_ >; if(sdepth == CV_64F && ddepth == CV_64F) func = reduceR_ >; } else if(op == CV_REDUCE_MAX) { if(sdepth == CV_8U && ddepth == CV_8U) func = reduceR_ >; if(sdepth == CV_32F && ddepth == CV_32F) func = reduceR_ >; if(sdepth == CV_64F && ddepth == CV_64F) func = reduceR_ >; } else if(op == CV_REDUCE_MIN) { if(sdepth == CV_8U && ddepth == CV_8U) func = reduceR_ >; if(sdepth == CV_32F && ddepth == CV_32F) func = reduceR_ >; if(sdepth == CV_64F && ddepth == CV_64F) func = reduceR_ >; } } else { if(op == CV_REDUCE_SUM) { if(sdepth == CV_8U && ddepth == CV_32S) func = reduceC_ >; if(sdepth == CV_8U && ddepth == CV_32F) func = reduceC_ >; if(sdepth == CV_8U && ddepth == CV_64F) func = reduceC_ >; if(sdepth == CV_16U && ddepth == CV_32F) func = reduceC_ >; if(sdepth == CV_16U && ddepth == CV_64F) func = reduceC_ >; if(sdepth == CV_16S && ddepth == CV_32F) func = reduceC_ >; if(sdepth == CV_16S && ddepth == CV_64F) func = reduceC_ >; if(sdepth == CV_32F && ddepth == CV_32F) func = reduceC_ >; if(sdepth == CV_32F && ddepth == CV_64F) func = reduceC_ >; if(sdepth == CV_64F && ddepth == CV_64F) func = reduceC_ >; } else if(op == CV_REDUCE_MAX) { if(sdepth == CV_8U && ddepth == CV_8U) func = reduceC_ >; if(sdepth == CV_32F && ddepth == CV_32F) func = reduceC_ >; if(sdepth == CV_64F && ddepth == CV_64F) func = reduceC_ >; } else if(op == CV_REDUCE_MIN) { if(sdepth == CV_8U && ddepth == CV_8U) func = reduceC_ >; if(sdepth == CV_32F && ddepth == CV_32F) func = reduceC_ >; if(sdepth == CV_64F && ddepth == CV_64F) func = reduceC_ >; } } 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 /////////////////////////////////////////// template static void sort_( const Mat& src, Mat& dst, int flags ) { AutoBuffer 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() ); 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 static void sortIdx_( const Mat& src, Mat& dst, int flags ) { AutoBuffer buf; AutoBuffer 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(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_, sort_, sort_, sort_, sort_, sort_, sort_, 0 }; SortFunc func = tab[src.depth()]; CV_Assert( src.dims <= 2 && 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_, sortIdx_, sortIdx_, sortIdx_, sortIdx_, sortIdx_, sortIdx_, 0 }; SortFunc func = tab[src.depth()]; CV_Assert( src.dims <= 2 && src.channels() == 1 && func != 0 ); if( dst.data == src.data ) dst.release(); dst.create( src.size(), CV_32S ); func( src, dst, flags ); } ////////////////////////////////////////// kmeans //////////////////////////////////////////// static void generateRandomCenter(const vector& 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(0); int step = (int)(_data.step/sizeof(data[0])); vector _centers(K); int* centers = &_centers[0]; vector _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(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( data.dims <= 2 && 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(); Mat centers(K, dims, type), old_centers(K, dims, type); vector counters(K); vector _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(0); for( j = 0; j < dims; j++ ) box[j] = Vec2f(sample[j], sample[j]); for( i = 1; i < N; i++ ) { sample = data.ptr(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(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(i); k = labels[i]; float* center = centers.ptr(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(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(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(i); int k_best = 0; double min_dist = DBL_MAX; for( k = 0; k < K; k++ ) { const float* center = centers.ptr(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 ? ¢ers : 0 ); if( _compactness ) *_compactness = compactness; return 1; } ///////////////////////////// n-dimensional matrices //////////////////////////// namespace cv { Mat Mat::reshape(int, int, const int*) const { CV_Error(CV_StsNotImplemented, ""); // TBD return Mat(); } Mat::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; } NAryMatIterator::NAryMatIterator() : arrays(0), planes(0), narrays(0), nplanes(0), iterdepth(0), idx(0) { } NAryMatIterator::NAryMatIterator(const Mat** _arrays, Mat* _planes, int _narrays) : arrays(0), planes(0), narrays(0), nplanes(0), iterdepth(0), idx(0) { init(_arrays, _planes, _narrays); } void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, int _narrays) { CV_Assert( _arrays && _planes ); int i, j, d1=0, i0 = -1, d = -1, total = 0; arrays = _arrays; planes = _planes; narrays = _narrays; nplanes = 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); if( !arrays[i]->data ) continue; const Mat& A = *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" 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 ) { 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; nplanes = 1; for( j = iterdepth-1; j >= 0; j-- ) nplanes *= arrays[i0]->size[j]; } else iterdepth = nplanes = 0; for( i = 0; i < narrays; 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; } idx = 0; } NAryMatIterator& NAryMatIterator::operator ++() { if( idx >= nplanes-1 ) return *this; ++idx; for( int i = 0; i < narrays; i++ ) { const Mat& 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; } 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-1)*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); } ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) { if( a.m != b.m ) return INT_MAX; if( a.sliceEnd == b.sliceEnd ) return (b.ptr - a.ptr)/b.elemSize; return b.lpos() - a.lpos(); } //////////////////////////////// SparseMat //////////////////////////////// template 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(*from); else for( int i = 0; i < cn; i++ ) to[i] = saturate_cast(from[i]); } template 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(*from*alpha + beta); else for( int i = 0; i < cn; i++ ) to[i] = saturate_cast(from[i]*alpha + beta); } ConvertData getConvertData(int fromType, int toType) { static ConvertData tab[][8] = {{ convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 0 }, { convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 0 }, { convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 0 }, { convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 0 }, { convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 0 }, { convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 0 }, { convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, convertData_, 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_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 0 }, { convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 0 }, { convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 0 }, { convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 0 }, { convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 0 }, { convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 0 }, { convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, convertScaleData_, 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) : 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 ); 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 ); 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(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(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(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(); vector _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(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 vector& 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; } inline Rect RotatedRect::boundingRect() const { Point2f pt[4]; points(pt); Rect r(cvFloor(min(min(min(pt[0].x, pt[1].x), pt[2].x), pt[3].x)), cvFloor(min(min(min(pt[0].y, pt[1].y), pt[2].y), pt[3].y)), cvCeil(max(max(max(pt[0].x, pt[1].x), pt[2].x), pt[3].x)), cvCeil(max(max(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; } } /* End of file. */