potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 15:23:05 +04:00
Code Packages Releases Wiki Activity

Several type of formal refactoring:

Browse Source 
1. someMatrix.data -> someMatrix.prt()
2. someMatrix.data + someMatrix.step * lineIndex -> someMatrix.ptr( lineIndex )
3. (SomeType*) someMatrix.data -> someMatrix.ptr<SomeType>()
4. someMatrix.data -> !someMatrix.empty() ( or !someMatrix.data -> someMatrix.empty() ) in logical expressions
This commit is contained in:
Adil Ibragimov
2014-08-13 15:08:27 +04:00
parent 30111a786a
commit 8a4a1bb018
134 changed files with 988 additions and 986 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/core/src/matrix.cpp
+37 -37
View File
@@ -352,7 +352,7 @@ static void finalizeHdr(Mat& m)
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];
m.dataend = m.ptr() + 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];
}
@@ -871,7 +871,7 @@ Mat cvarrToMat(const CvArr* arr, bool copyData,
}
Mat buf(total, 1, type);
cvCvtSeqToArray(seq, buf.data, CV_WHOLE_SEQ);
cvCvtSeqToArray(seq, buf.ptr(), CV_WHOLE_SEQ);
return buf;
}
CV_Error(CV_StsBadArg, "Unknown array type");
@@ -1941,7 +1941,7 @@ size_t _InputArray::offset(int i) const
{
CV_Assert( i < 0 );
const Mat * const m = ((const Mat*)obj);
return (size_t)(m->data - m->datastart);
return (size_t)(m->ptr() - m->datastart);
}
if( k == UMAT )
@@ -1960,7 +1960,7 @@ size_t _InputArray::offset(int i) const
return 1;
CV_Assert( i < (int)vv.size() );
return (size_t)(vv[i].data - vv[i].datastart);
return (size_t)(vv[i].ptr() - vv[i].datastart);
}
if( k == STD_VECTOR_UMAT )
@@ -2618,7 +2618,7 @@ void _OutputArray::setTo(const _InputArray& arr, const _InputArray & mask) const
{
Mat value = arr.getMat();
CV_Assert( checkScalar(value, type(), arr.kind(), _InputArray::GPU_MAT) );
((cuda::GpuMat*)obj)->setTo(Scalar(Vec<double, 4>((double *)value.data)), mask);
((cuda::GpuMat*)obj)->setTo(Scalar(Vec<double, 4>(value.ptr<double>())), mask);
}
else
CV_Error(Error::StsNotImplemented, "");
@@ -2804,7 +2804,7 @@ void cv::setIdentity( InputOutputArray _m, const Scalar& s )
if( type == CV_32FC1 )
{
float* data = (float*)m.data;
float* data = m.ptr<float>();
float val = (float)s[0];
size_t step = m.step/sizeof(data[0]);
@@ -2818,7 +2818,7 @@ void cv::setIdentity( InputOutputArray _m, const Scalar& s )
}
else if( type == CV_64FC1 )
{
double* data = (double*)m.data;
double* data = m.ptr<double>();
double val = s[0];
size_t step = m.step/sizeof(data[0]);
@@ -2846,7 +2846,7 @@ cv::Scalar cv::trace( InputArray _m )
if( type == CV_32FC1 )
{
const float* ptr = (const float*)m.data;
const float* ptr = m.ptr<float>();
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( i = 0; i < nm; i++ )
@@ -2856,7 +2856,7 @@ cv::Scalar cv::trace( InputArray _m )
if( type == CV_64FC1 )
{
const double* ptr = (const double*)m.data;
const double* ptr = m.ptr<double>();
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( i = 0; i < nm; i++ )
@@ -3115,13 +3115,13 @@ void cv::transpose( InputArray _src, OutputArray _dst )
IppiSize roiSize = { src.cols, src.rows };
if (ippFunc != 0)
{
if (ippFunc(src.data, (int)src.step, dst.data, (int)dst.step, roiSize) >= 0)
if (ippFunc(src.ptr(), (int)src.step, dst.ptr(), (int)dst.step, roiSize) >= 0)
return;
setIppErrorStatus();
}
else if (ippFuncI != 0)
{
if (ippFuncI(dst.data, (int)dst.step, roiSize) >= 0)
if (ippFuncI(dst.ptr(), (int)dst.step, roiSize) >= 0)
return;
setIppErrorStatus();
}
@@ -3132,13 +3132,13 @@ void cv::transpose( InputArray _src, OutputArray _dst )
TransposeInplaceFunc func = transposeInplaceTab[esz];
CV_Assert( func != 0 );
CV_Assert( dst.cols == dst.rows );
func( dst.data, dst.step, dst.rows );
func( dst.ptr(), dst.step, dst.rows );
}
else
{
TransposeFunc func = transposeTab[esz];
CV_Assert( func != 0 );
func( src.data, src.step, dst.data, dst.step, src.size() );
func( src.ptr(), src.step, dst.ptr(), dst.step, src.size() );
}
}
@@ -3154,7 +3154,7 @@ void cv::completeSymm( InputOutputArray _m, bool LtoR )
int rows = m.rows;
int j0 = 0, j1 = rows;
uchar* data = m.data;
uchar* data = m.ptr();
for( int i = 0; i < rows; i++ )
{
if( !LtoR ) j1 = i; else j0 = i+1;
@@ -3212,8 +3212,8 @@ reduceR_( const Mat& srcmat, Mat& dstmat )
size.width *= srcmat.channels();
AutoBuffer<WT> buffer(size.width);
WT* buf = buffer;
ST* dst = (ST*)dstmat.data;
const T* src = (const T*)srcmat.data;
ST* dst = dstmat.ptr<ST>();
const T* src = srcmat.ptr<T>();
size_t srcstep = srcmat.step/sizeof(src[0]);
int i;
Op op;
@@ -3258,8 +3258,8 @@ reduceC_( const Mat& srcmat, Mat& dstmat )
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);
const T* src = srcmat.ptr<T>(y);
ST* dst = dstmat.ptr<ST>(y);
if( size.width == cn )
for( k = 0; k < cn; k++ )
dst[k] = src[k];
@@ -3356,7 +3356,7 @@ static inline void reduceSumC_8u16u16s32f_64f(const cv::Mat& srcmat, cv::Mat& ds
if (ippFunc)
{
for (int y = 0; y < size.height; ++y)
if (ippFunc(srcmat.data + sstep * y, sstep, roisize, dstmat.ptr<Ipp64f>(y)) < 0)
if (ippFunc(srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y)) < 0)
{
setIppErrorStatus();
cv::Mat dstroi = dstmat.rowRange(y, y + 1);
@@ -3367,7 +3367,7 @@ static inline void reduceSumC_8u16u16s32f_64f(const cv::Mat& srcmat, cv::Mat& ds
else if (ippFuncHint)
{
for (int y = 0; y < size.height; ++y)
if (ippFuncHint(srcmat.data + sstep * y, sstep, roisize, dstmat.ptr<Ipp64f>(y), ippAlgHintAccurate) < 0)
if (ippFuncHint(srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y), ippAlgHintAccurate) < 0)
{
setIppErrorStatus();
cv::Mat dstroi = dstmat.rowRange(y, y + 1);
@@ -3780,10 +3780,10 @@ template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
T* ptr = bptr;
if( sortRows )
{
T* dptr = (T*)(dst.data + dst.step*i);
T* dptr = dst.ptr<T>(i);
if( !inplace )
{
const T* sptr = (const T*)(src.data + src.step*i);
const T* sptr = src.ptr<T>(i);
memcpy(dptr, sptr, sizeof(T) * len);
}
ptr = dptr;
@@ -3791,7 +3791,7 @@ template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
else
{
for( j = 0; j < len; j++ )
ptr[j] = ((const T*)(src.data + src.step*j))[i];
ptr[j] = src.ptr<T>(j)[i];
}
#ifdef USE_IPP_SORT
@@ -3820,7 +3820,7 @@ template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
if( !sortRows )
for( j = 0; j < len; j++ )
((T*)(dst.data + dst.step*j))[i] = ptr[j];
dst.ptr<T>(j)[i] = ptr[j];
}
}
@@ -3893,12 +3893,12 @@ template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
if( sortRows )
{
ptr = (T*)(src.data + src.step*i);
iptr = (int*)(dst.data + dst.step*i);
iptr = dst.ptr<int>(i);
}
else
{
for( j = 0; j < len; j++ )
ptr[j] = ((const T*)(src.data + src.step*j))[i];
ptr[j] = src.ptr<T>(j)[i];
}
for( j = 0; j < len; j++ )
iptr[j] = j;
@@ -3928,7 +3928,7 @@ template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
if( !sortRows )
for( j = 0; j < len; j++ )
((int*)(dst.data + dst.step*j))[i] = iptr[j];
dst.ptr<int>(j)[i] = iptr[j];
}
}
@@ -4159,7 +4159,7 @@ double cv::kmeans( InputArray _data, int K,
CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( N >= K );
Mat data(N, dims, CV_32F, data0.data, isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
Mat data(N, dims, CV_32F, data0.ptr(), isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
_bestLabels.create(N, 1, CV_32S, -1, true);
@@ -4765,7 +4765,7 @@ Point MatConstIterator::pos() const
return Point();
CV_DbgAssert(m->dims <= 2);
ptrdiff_t ofs = ptr - m->data;
ptrdiff_t ofs = ptr - m->ptr();
int y = (int)(ofs/m->step[0]);
return Point((int)((ofs - y*m->step[0])/elemSize), y);
}
@@ -4773,7 +4773,7 @@ Point MatConstIterator::pos() const
void MatConstIterator::pos(int* _idx) const
{
CV_Assert(m != 0 && _idx);
ptrdiff_t ofs = ptr - m->data;
ptrdiff_t ofs = ptr - m->ptr();
for( int i = 0; i < m->dims; i++ )
{
size_t s = m->step[i], v = ofs/s;
@@ -4788,7 +4788,7 @@ ptrdiff_t MatConstIterator::lpos() const
return 0;
if( m->isContinuous() )
return (ptr - sliceStart)/elemSize;
ptrdiff_t ofs = ptr - m->data;
ptrdiff_t ofs = ptr - m->ptr();
int i, d = m->dims;
if( d == 2 )
{
@@ -4823,13 +4823,13 @@ void MatConstIterator::seek(ptrdiff_t ofs, bool relative)
ptrdiff_t ofs0, y;
if( relative )
{
ofs0 = ptr - m->data;
ofs0 = ptr - m->ptr();
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];
sliceStart = m->ptr(y1);
sliceEnd = sliceStart + m->cols*elemSize;
ptr = y < 0 ? sliceStart : y >= m->rows ? sliceEnd :
sliceStart + (ofs - y*m->cols)*elemSize;
@@ -4846,8 +4846,8 @@ void MatConstIterator::seek(ptrdiff_t ofs, bool relative)
ptrdiff_t t = ofs/szi;
int v = (int)(ofs - t*szi);
ofs = t;
ptr = m->data + v*elemSize;
sliceStart = m->data;
ptr = m->ptr() + v*elemSize;
sliceStart = m->ptr();
for( int i = d-2; i >= 0; i-- )
{
@@ -4862,7 +4862,7 @@ void MatConstIterator::seek(ptrdiff_t ofs, bool relative)
if( ofs > 0 )
ptr = sliceEnd;
else
ptr = sliceStart + (ptr - m->data);
ptr = sliceStart + (ptr - m->ptr());
}
void MatConstIterator::seek(const int* _idx, bool relative)
@@ -5058,7 +5058,7 @@ SparseMat::SparseMat(const Mat& m)
int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1];
size_t esz = m.elemSize();
uchar* dptr = m.data;
const uchar* dptr = m.ptr();
for(;;)
{