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Warning fixes continued

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This commit is contained in:
Andrey Kamaev
2012-06-09 15:00:04 +00:00
parent f6b451c607
commit f2d3b9b4a1
127 changed files with 6298 additions and 6277 deletions
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58
modules/core/src/matmul.cpp
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@@ -63,7 +63,7 @@ GEMM_CopyBlock( const uchar* src, size_t src_step,
for( ; size.height--; src += src_step, dst += dst_step )
{
j=0;
j=0;
#if CV_ENABLE_UNROLLED
for( ; j <= size.width - 4; j += 4 )
{
@@ -345,7 +345,7 @@ GEMMSingleMul( const T* a_data, size_t a_step,
for( k = 0; k < n; k++, b_data += b_step )
{
WT al(a_data[k]);
j=0;
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= m - 4; j += 4 )
{
@@ -513,8 +513,8 @@ GEMMStore( const T* c_data, size_t c_step,
if( _c_data )
{
c_data = _c_data;
j=0;
#if CV_ENABLE_UNROLLED
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= d_size.width - 4; j += 4, c_data += 4*c_step1 )
{
WT t0 = alpha*d_buf[j];
@@ -539,8 +539,8 @@ GEMMStore( const T* c_data, size_t c_step,
}
else
{
j = 0;
#if CV_ENABLE_UNROLLED
j = 0;
#if CV_ENABLE_UNROLLED
for( ; j <= d_size.width - 4; j += 4 )
{
WT t0 = alpha*d_buf[j];
@@ -552,7 +552,7 @@ GEMMStore( const T* c_data, size_t c_step,
d_data[j+2] = T(t0);
d_data[j+3] = T(t1);
}
#endif
#endif
for( ; j < d_size.width; j++ )
d_data[j] = T(alpha*d_buf[j]);
}
@@ -597,7 +597,7 @@ static void GEMMSingleMul_64f( const double* a_data, size_t a_step,
alpha, beta, flags);
}
static void GEMMSingleMul_32fc( const Complexf* a_data, size_t a_step,
const Complexf* b_data, size_t b_step,
const Complexf* c_data, size_t c_step,
@@ -620,7 +620,7 @@ static void GEMMSingleMul_64fc( const Complexd* a_data, size_t a_step,
GEMMSingleMul<Complexd,Complexd>(a_data, a_step, b_data, b_step, c_data,
c_step, d_data, d_step, a_size, d_size,
alpha, beta, flags);
}
}
static void GEMMBlockMul_32f( const float* a_data, size_t a_step,
const float* b_data, size_t b_step,
@@ -696,7 +696,7 @@ static void GEMMStore_64fc( const Complexd* c_data, size_t c_step,
}
void cv::gemm( InputArray matA, InputArray matB, double alpha,
InputArray matC, double beta, OutputArray matD, int flags )
InputArray matC, double beta, OutputArray _matD, int flags )
{
const int block_lin_size = 128;
const int block_size = block_lin_size * block_lin_size;
@@ -741,8 +741,8 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha,
((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height)));
}
matD.create( d_size.height, d_size.width, type );
Mat D = matD.getMat();
_matD.create( d_size.height, d_size.width, type );
Mat D = _matD.getMat();
if( (flags & GEMM_3_T) != 0 && C.data == D.data )
{
transpose( C, C );
@@ -2008,7 +2008,7 @@ static void scaleAdd_32f(const float* src1, const float* src2, float* dst,
t1 = src1[i+3]*alpha + src2[i+3];
dst[i+2] = t0; dst[i+3] = t1;
}
for(; i < len; i++ )
for(; i < len; i++ )
dst[i] = src1[i]*alpha + src2[i];
}
@@ -2035,7 +2035,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst,
}
else
#endif
//vz why do we need unroll here?
//vz why do we need unroll here?
for( ; i <= len - 4; i += 4 )
{
double t0, t1;
@@ -2046,7 +2046,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst,
t1 = src1[i+3]*alpha + src2[i+3];
dst[i+2] = t0; dst[i+3] = t1;
}
for(; i < len; i++ )
for(; i < len; i++ )
dst[i] = src1[i]*alpha + src2[i];
}
@@ -2072,7 +2072,7 @@ void cv::scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray
float falpha = (float)alpha;
void* palpha = depth == CV_32F ? (void*)&falpha : (void*)&alpha;
ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f;
ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f;
if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() )
{
@@ -2134,12 +2134,12 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
_mean = mean.reshape(1, size.height);
}
void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray _mean, int flags, int ctype )
void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mean, int flags, int ctype )
{
if(_data.kind() == _InputArray::STD_VECTOR_MAT)
if(_src.kind() == _InputArray::STD_VECTOR_MAT)
{
std::vector<cv::Mat> src;
_data.getMatVector(src);
_src.getMatVector(src);
CV_Assert( src.size() > 0 );
@@ -2185,7 +2185,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray
return;
}
Mat data = _data.getMat(), mean;
Mat data = _src.getMat(), mean;
CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) );
bool takeRows = (flags & CV_COVAR_ROWS) != 0;
int type = data.type();
@@ -2209,7 +2209,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray
else
{
ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F);
reduce( _data, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
mean = _mean.getMat();
}
@@ -2223,7 +2223,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray
double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat();
Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat();
int type = v1.type(), depth = v1.depth();
Size sz = v1.size();
int i, j, len = sz.width*sz.height*v1.channels();
@@ -2261,7 +2261,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
double row_sum = 0;
j = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for(; j <= len - 4; j += 4 )
row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] +
diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3];
@@ -2292,7 +2292,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
double row_sum = 0;
j = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for(; j <= len - 4; j += 4 )
row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] +
diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3];
@@ -2642,7 +2642,7 @@ dotProd_(const T* src1, const T* src2, int len)
{
int i = 0;
double result = 0;
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; i <= len - 4; i += 4 )
result += (double)src1[i]*src2[i] + (double)src1[i+1]*src2[i+1] +
(double)src1[i+2]*src2[i+2] + (double)src1[i+3]*src2[i+3];
@@ -2674,7 +2674,7 @@ static double dotProd_8u(const uchar* src1, const uchar* src2, int len)
{
blockSize = std::min(len0 - i, blockSize0);
__m128i s = _mm_setzero_si128();
j = 0;
j = 0;
for( ; j <= blockSize - 16; j += 16 )
{
__m128i b0 = _mm_loadu_si128((const __m128i*)(src1 + j));
@@ -2806,9 +2806,9 @@ double Mat::dot(InputArray _mat) const
PCA::PCA() {}
PCA::PCA(InputArray data, InputArray mean, int flags, int maxComponents)
PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents)
{
operator()(data, mean, flags, maxComponents);
operator()(data, _mean, flags, maxComponents);
}
PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents)
@@ -2964,7 +2964,7 @@ void cv::PCACompute(InputArray data, InputOutputArray mean,
pca.mean.copyTo(mean);
pca.eigenvectors.copyTo(eigenvectors);
}
void cv::PCAProject(InputArray data, InputArray mean,
InputArray eigenvectors, OutputArray result)
{