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Set stricter warning rules for gcc

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This commit is contained in:
Andrey Kamaev
2012-06-07 17:21:29 +00:00
parent 0395f7c63f
commit 49a1ba6038
241 changed files with 9054 additions and 8947 deletions
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82
modules/imgproc/src/smooth.cpp
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@@ -73,13 +73,13 @@ template<typename T, typename ST> struct RowSum : public BaseRowFilter
ksize = _ksize;
anchor = _anchor;
}
void operator()(const uchar* src, uchar* dst, int width, int cn)
{
const T* S = (const T*)src;
ST* D = (ST*)dst;
int i = 0, k, ksz_cn = ksize*cn;
width = (width - 1)*cn;
for( k = 0; k < cn; k++, S++, D++ )
{
@@ -108,7 +108,7 @@ template<typename ST, typename T> struct ColumnSum : public BaseColumnFilter
}
void reset() { sumCount = 0; }
void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int i;
@@ -198,7 +198,7 @@ template<typename ST, typename T> struct ColumnSum : public BaseColumnFilter
}
cv::Ptr<cv::BaseRowFilter> cv::getRowSumFilter(int srcType, int sumType, int ksize, int anchor)
{
int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType);
@@ -325,7 +325,7 @@ void cv::blur( InputArray src, OutputArray dst,
Size ksize, Point anchor, int borderType )
{
boxFilter( src, dst, -1, ksize, anchor, true, borderType );
}
}
/****************************************************************************************\
Gaussian Blur
@@ -422,7 +422,7 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
Mat src = _src.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
if( borderType != BORDER_CONSTANT )
{
if( src.rows == 1 )
@@ -454,7 +454,7 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
namespace cv
{
#if _MSC_VER >= 1200
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4244 )
#endif
@@ -479,7 +479,7 @@ typedef struct
#if CV_SSE2
#define MEDIAN_HAVE_SIMD 1
static inline void histogram_add_simd( const HT x[16], HT y[16] )
{
const __m128i* rx = (const __m128i*)x;
@@ -499,12 +499,12 @@ static inline void histogram_sub_simd( const HT x[16], HT y[16] )
_mm_store_si128(ry+0, r0);
_mm_store_si128(ry+1, r1);
}
#else
#define MEDIAN_HAVE_SIMD 0
#endif
static inline void histogram_add( const HT x[16], HT y[16] )
{
int i;
@@ -667,14 +667,14 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
{
for( j = 0; j < 2*r; ++j )
histogram_add( &h_coarse[16*(n*c+j)], H[c].coarse );
for( j = r; j < n-r; j++ )
{
int t = 2*r*r + 2*r, b, sum = 0;
HT* segment;
histogram_add( &h_coarse[16*(n*c + std::min(j+r,n-1))], H[c].coarse );
// Find median at coarse level
for ( k = 0; k < 16 ; ++k )
{
@@ -686,14 +686,14 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
}
}
assert( k < 16 );
/* Update corresponding histogram segment */
if ( luc[c][k] <= j-r )
{
memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
histogram_add( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
if ( luc[c][k] < j+r+1 )
{
histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] );
@@ -708,9 +708,9 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
histogram_add( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] );
}
}
histogram_sub( &h_coarse[16*(n*c+MAX(j-r,0))], H[c].coarse );
/* Find median in segment */
segment = H[c].fine[k];
for ( b = 0; b < 16 ; b++ )
@@ -734,7 +734,7 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
}
#if _MSC_VER >= 1200
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( default: 4244 )
#endif
@@ -910,7 +910,7 @@ struct MinMax16u
b = std::max(b, t);
}
};
struct MinMax16s
{
typedef short value_type;
@@ -974,7 +974,7 @@ struct MinMaxVec16u
}
};
struct MinMaxVec16s
{
typedef short value_type;
@@ -988,9 +988,9 @@ struct MinMaxVec16s
a = _mm_min_epi16(a, b);
b = _mm_max_epi16(b, t);
}
};
};
struct MinMaxVec32f
{
typedef float value_type;
@@ -1033,7 +1033,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
Op op;
VecOp vop;
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
if( m == 3 )
{
if( size.width == 1 || size.height == 1 )
@@ -1055,7 +1055,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
}
return;
}
size.width *= cn;
for( i = 0; i < size.height; i++, dst += dstep )
{
@@ -1155,7 +1155,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
p[k*5+2] = rowk[j]; p[k*5+3] = rowk[j3];
p[k*5+4] = rowk[j4];
}
op(p[1], p[2]); op(p[0], p[1]); op(p[1], p[2]); op(p[4], p[5]); op(p[3], p[4]);
op(p[4], p[5]); op(p[0], p[3]); op(p[2], p[5]); op(p[2], p[3]); op(p[1], p[4]);
op(p[1], p[2]); op(p[3], p[4]); op(p[7], p[8]); op(p[6], p[7]); op(p[7], p[8]);
@@ -1195,7 +1195,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
p[k*5+2] = vop.load(rowk+j); p[k*5+3] = vop.load(rowk+j+cn);
p[k*5+4] = vop.load(rowk+j+cn*2);
}
vop(p[1], p[2]); vop(p[0], p[1]); vop(p[1], p[2]); vop(p[4], p[5]); vop(p[3], p[4]);
vop(p[4], p[5]); vop(p[0], p[3]); vop(p[2], p[5]); vop(p[2], p[3]); vop(p[1], p[4]);
vop(p[1], p[2]); vop(p[3], p[4]); vop(p[7], p[8]); vop(p[6], p[7]); vop(p[7], p[8]);
@@ -1229,13 +1229,13 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
}
}
void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize )
{
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
if( ksize <= 1 )
{
src0.copyTo(dst);
@@ -1248,13 +1248,13 @@ void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize )
if (tegra::medianBlur(src0, dst, ksize))
return;
#endif
bool useSortNet = ksize == 3 || (ksize == 5
#if !CV_SSE2
&& src0.depth() > CV_8U
#endif
);
Mat src;
if( useSortNet )
{
@@ -1315,7 +1315,7 @@ bilateralFilter_8u( const Mat& src, Mat& dst, int d,
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
@@ -1422,7 +1422,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
@@ -1433,9 +1433,9 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
radius = MAX(radius, 1);
d = radius*2 + 1;
// compute the min/max range for the input image (even if multichannel)
minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc );
// temporary copy of the image with borders for easy processing
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
@@ -1454,7 +1454,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
float* expLUT = &_expLUT[0];
scale_index = kExpNumBins/len;
// initialize the exp LUT
for( i = 0; i < kExpNumBins+2; i++ )
{
@@ -1467,7 +1467,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
else
expLUT[i] = 0.f;
}
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
@@ -1481,7 +1481,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
for( i = 0; i < size.height; i++ )
{
const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn;
const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn;
float* dptr = (float*)(dst.data + i*dst.step);
if( cn == 1 )
@@ -1493,11 +1493,11 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
for( k = 0; k < maxk; k++ )
{
float val = sptr[j + space_ofs[k]];
float alpha = (float)(std::abs(val - val0)*scale_index);
float alpha = (float)(std::abs(val - val0)*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
sum += val*w;
sum += val*w;
wsum += w;
}
dptr[j] = (float)(sum/wsum);
@@ -1514,7 +1514,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d,
{
const float* sptr_k = sptr + j + space_ofs[k];
float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
float alpha = (float)((std::abs(b - b0) +
float alpha = (float)((std::abs(b - b0) +
std::abs(g - g0) + std::abs(r - r0))*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
@@ -1541,7 +1541,7 @@ void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
Mat src = _src.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
if( src.depth() == CV_8U )
bilateralFilter_8u( src, dst, d, sigmaColor, sigmaSpace, borderType );
else if( src.depth() == CV_32F )