generic-library/opencv
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Several type of formal refactoring:

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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
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76
modules/core/src/matmul.cpp
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@@ -822,7 +822,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
break;
}
if( C.data )
if( !C.empty() )
{
CV_Assert( C.type() == type &&
(((flags&GEMM_3_T) == 0 && C.rows == d_size.height && C.cols == d_size.width) ||
@@ -841,9 +841,9 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
{
if( type == CV_32F )
{
float* d = (float*)D.data;
const float *a = (const float*)A.data,
*b = (const float*)B.data,
float* d = D.ptr<float>();
const float *a = A.ptr<float>(),
*b = B.ptr<float>(),
*c = (const float*)C.data;
size_t d_step = D.step/sizeof(d[0]),
a_step = A.step/sizeof(a[0]),
@@ -969,9 +969,9 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
if( type == CV_64F )
{
double* d = (double*)D.data;
const double *a = (const double*)A.data,
*b = (const double*)B.data,
double* d = D.ptr<double>();
const double *a = A.ptr<double>(),
*b = B.ptr<double>(),
*c = (const double*)C.data;
size_t d_step = D.step/sizeof(d[0]),
a_step = A.step/sizeof(a[0]),
@@ -1211,8 +1211,8 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
(d_size.width <= block_lin_size &&
d_size.height <= block_lin_size && len <= block_lin_size) )
{
singleMulFunc( A.data, A.step, B.data, b_step, Cdata, Cstep,
matD->data, matD->step, a_size, d_size, alpha, beta, flags );
singleMulFunc( A.ptr(), A.step, B.ptr(), b_step, Cdata, Cstep,
matD->ptr(), matD->step, a_size, d_size, alpha, beta, flags );
}
else
{
@@ -1239,7 +1239,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
else
b_step0 = elem_size, b_step1 = b_step;
if( !C.data )
if( C.empty() )
{
c_step0 = c_step1 = 0;
flags &= ~GEMM_3_T;
@@ -1285,7 +1285,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
for( j = 0; j < d_size.width; j += dj )
{
uchar* _d = matD->data + i*matD->step + j*elem_size;
uchar* _d = matD->ptr() + i*matD->step + j*elem_size;
const uchar* _c = Cdata + i*c_step0 + j*c_step1;
size_t _d_step = matD->step;
dj = dn0;
@@ -1302,9 +1302,9 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
for( k = 0; k < len; k += dk )
{
const uchar* _a = A.data + i*a_step0 + k*a_step1;
const uchar* _a = A.ptr() + i*a_step0 + k*a_step1;
size_t _a_step = A.step;
const uchar* _b = B.data + k*b_step0 + j*b_step1;
const uchar* _b = B.ptr() + k*b_step0 + j*b_step1;
size_t _b_step = b_step;
Size a_bl_size;
@@ -1349,7 +1349,7 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
if( dk0 < len )
storeFunc( _c, Cstep, _d, _d_step,
matD->data + i*matD->step + j*elem_size,
matD->ptr(i) + j*elem_size,
matD->step, Size(dj,di), alpha, beta, flags );
}
}
@@ -1858,7 +1858,7 @@ void cv::transform( InputArray _src, OutputArray _dst, InputArray _mtx )
_mbuf.allocate(dcn*(scn+1));
mbuf = (double*)_mbuf;
Mat tmp(dcn, scn+1, mtype, mbuf);
memset(tmp.data, 0, tmp.total()*tmp.elemSize());
memset(tmp.ptr(), 0, tmp.total()*tmp.elemSize());
if( m.cols == scn+1 )
m.convertTo(tmp, mtype);
else
@@ -1869,7 +1869,7 @@ void cv::transform( InputArray _src, OutputArray _dst, InputArray _mtx )
m = tmp;
}
else
mbuf = (double*)m.data;
mbuf = m.ptr<double>();
if( scn == dcn )
{
@@ -2039,7 +2039,7 @@ void cv::perspectiveTransform( InputArray _src, OutputArray _dst, InputArray _mt
m = tmp;
}
else
mbuf = (double*)m.data;
mbuf = m.ptr<double>();
TransformFunc func = depth == CV_32F ?
(TransformFunc)perspectiveTransform_32f :
@@ -2227,7 +2227,7 @@ void cv::scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray
if (src1.isContinuous() && src2.isContinuous() && dst.isContinuous())
{
size_t len = src1.total()*cn;
func(src1.data, src2.data, dst.data, (int)len, palpha);
func(src1.ptr(), src2.ptr(), dst.ptr(), (int)len, palpha);
return;
}
@@ -2271,7 +2271,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
{
CV_Assert( data[i].size() == size && data[i].type() == type );
if( data[i].isContinuous() )
memcpy( _data.ptr(i), data[i].data, sz*esz );
memcpy( _data.ptr(i), data[i].ptr(), sz*esz );
else
{
Mat dataRow(size.height, size.width, type, _data.ptr(i));
@@ -2392,12 +2392,12 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
if( depth == CV_32F )
{
const float* src1 = (const float*)v1.data;
const float* src2 = (const float*)v2.data;
const float* src1 = v1.ptr<float>();
const float* src2 = v2.ptr<float>();
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = buf;
const float* mat = (const float*)icovar.data;
const float* mat = icovar.ptr<float>();
size_t matstep = icovar.step/sizeof(mat[0]);
for( ; sz.height--; src1 += step1, src2 += step2, diff += sz.width )
@@ -2423,12 +2423,12 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
}
else if( depth == CV_64F )
{
const double* src1 = (const double*)v1.data;
const double* src2 = (const double*)v2.data;
const double* src1 = v1.ptr<double>();
const double* src2 = v2.ptr<double>();
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = buf;
const double* mat = (const double*)icovar.data;
const double* mat = icovar.ptr<double>();
size_t matstep = icovar.step/sizeof(mat[0]);
for( ; sz.height--; src1 += step1, src2 += step2, diff += sz.width )
@@ -2469,9 +2469,9 @@ template<typename sT, typename dT> static void
MulTransposedR( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scale )
{
int i, j, k;
const sT* src = (const sT*)srcmat.data;
dT* dst = (dT*)dstmat.data;
const dT* delta = (const dT*)deltamat.data;
const sT* src = srcmat.ptr<sT>();
dT* dst = dstmat.ptr<dT>();
const dT* delta = deltamat.ptr<dT>();
size_t srcstep = srcmat.step/sizeof(src[0]);
size_t dststep = dstmat.step/sizeof(dst[0]);
size_t deltastep = deltamat.rows > 1 ? deltamat.step/sizeof(delta[0]) : 0;
@@ -2588,9 +2588,9 @@ template<typename sT, typename dT> static void
MulTransposedL( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scale )
{
int i, j, k;
const sT* src = (const sT*)srcmat.data;
dT* dst = (dT*)dstmat.data;
const dT* delta = (const dT*)deltamat.data;
const sT* src = srcmat.ptr<sT>();
dT* dst = dstmat.ptr<dT>();
const dT* delta = deltamat.ptr<dT>();
size_t srcstep = srcmat.step/sizeof(src[0]);
size_t dststep = dstmat.step/sizeof(dst[0]);
size_t deltastep = deltamat.rows > 1 ? deltamat.step/sizeof(delta[0]) : 0;
@@ -2669,7 +2669,7 @@ void cv::mulTransposed( InputArray _src, OutputArray _dst, bool ata,
dtype = std::max(std::max(CV_MAT_DEPTH(dtype >= 0 ? dtype : stype), delta.depth()), CV_32F);
CV_Assert( src.channels() == 1 );
if( delta.data )
if( !delta.empty() )
{
CV_Assert( delta.channels() == 1 &&
(delta.rows == src.rows || delta.rows == 1) &&
@@ -2688,7 +2688,7 @@ void cv::mulTransposed( InputArray _src, OutputArray _dst, bool ata,
{
Mat src2;
const Mat* tsrc = &src;
if( delta.data )
if( !delta.empty() )
{
if( delta.size() == src.size() )
subtract( src, delta, src2 );
@@ -3012,7 +3012,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
Mat covar( count, count, ctype );
if( _mean.data )
if( !_mean.empty() )
{
CV_Assert( _mean.size() == mean_sz );
_mean.convertTo(mean, ctype);
@@ -3148,7 +3148,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double reta
Mat covar( count, count, ctype );
if( _mean.data )
if( !_mean.empty() )
{
CV_Assert( _mean.size() == mean_sz );
_mean.convertTo(mean, ctype);
@@ -3203,7 +3203,7 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double reta
void PCA::project(InputArray _data, OutputArray result) const
{
Mat data = _data.getMat();
CV_Assert( mean.data && eigenvectors.data &&
CV_Assert( !mean.empty() && !eigenvectors.empty() &&
((mean.rows == 1 && mean.cols == data.cols) || (mean.cols == 1 && mean.rows == data.rows)));
Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
int ctype = mean.type();
@@ -3233,7 +3233,7 @@ Mat PCA::project(InputArray data) const
void PCA::backProject(InputArray _data, OutputArray result) const
{
Mat data = _data.getMat();
CV_Assert( mean.data && eigenvectors.data &&
CV_Assert( !mean.empty() && !eigenvectors.empty() &&
((mean.rows == 1 && eigenvectors.rows == data.cols) ||
(mean.cols == 1 && eigenvectors.rows == data.rows)));
@@ -3427,7 +3427,7 @@ cvCalcPCA( const CvArr* data_arr, CvArr* avg_arr, CvArr* eigenvals, CvArr* eigen
pca.eigenvectors = evects;
pca(data, (flags & CV_PCA_USE_AVG) ? mean : cv::Mat(),
flags, evals.data ? evals.rows + evals.cols - 1 : 0);
flags, !evals.empty() ? evals.rows + evals.cols - 1 : 0);
if( pca.mean.size() == mean.size() )
pca.mean.convertTo( mean, mean.type() );