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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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5
modules/imgproc/src/_list.h
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@@ -98,6 +98,7 @@ typedef struct _list _CVLIST;
_LIST_INLINE CVPOS prefix##get_tail_pos_##type(_CVLIST*);\
_LIST_INLINE type* prefix##get_next_##type(CVPOS*);\
_LIST_INLINE type* prefix##get_prev_##type(CVPOS*);\
_LIST_INLINE int prefix##is_pos_##type(CVPOS pos);\
/* Modification functions*/\
_LIST_INLINE void prefix##clear_list_##type(_CVLIST*);\
_LIST_INLINE CVPOS prefix##add_head_##type(_CVLIST*, type*);\
@@ -151,8 +152,8 @@ typedef struct _list _CVLIST;
}\
element->m_next = ((element_type*)l->m_head_free.m_pos);\
l->m_head_free.m_pos = element;
/*#define GET_FIRST_FREE(l) ((ELEMENT_##type*)(l->m_head_free.m_pos))*/
#define IMPLEMENT_LIST(type, prefix)\

12
modules/imgproc/src/floodfill.cpp
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@@ -233,7 +233,7 @@ typedef DiffC3<cv::Vec3i> Diff32sC3;
typedef DiffC1<float> Diff32fC1;
typedef DiffC3<cv::Vec3f> Diff32fC3;
cv::Vec3i& operator += (cv::Vec3i& a, const cv::Vec3b& b)
static cv::Vec3i& operator += (cv::Vec3i& a, const cv::Vec3b& b)
{
a[0] += b[0];
a[1] += b[1];
@@ -440,7 +440,7 @@ cvFloodFill( CvArr* arr, CvPoint seed_point,
{
cv::Ptr<CvMat> tempMask;
cv::AutoBuffer<CvFFillSegment> buffer;
if( comp )
memset( comp, 0, sizeof(*comp) );
@@ -491,16 +491,16 @@ cvFloodFill( CvArr* arr, CvPoint seed_point,
{
/*int elem_size = CV_ELEM_SIZE(type);
const uchar* seed_ptr = img->data.ptr + img->step*seed_point.y + elem_size*seed_point.x;
// check if the new value is different from the current value at the seed point.
// if they are exactly the same, use the generic version with mask to avoid infinite loops.
for( i = 0; i < elem_size; i++ )
if( seed_ptr[i] != ((uchar*)nv_buf)[i] )
break;
if( i == elem_size )
return;*/
if( type == CV_8UC1 )
icvFloodFill_CnIR(img->data.ptr, img->step, size, seed_point, nv_buf.b[0],
comp, flags, buffer, buffer_size);
@@ -632,7 +632,7 @@ int cv::floodFill( InputOutputArray _image, Point seedPoint,
}
int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
Point seedPoint, Scalar newVal, Rect* rect,
Point seedPoint, Scalar newVal, Rect* rect,
Scalar loDiff, Scalar upDiff, int flags )
{
CvConnectedComp ccomp;

20
modules/imgproc/src/grabcut.cpp
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@@ -230,7 +230,7 @@ void GMM::calcInverseCovAndDeterm( int ci )
Calculate beta - parameter of GrabCut algorithm.
beta = 1/(2*avg(sqr(||color[i] - color[j]||)))
*/
double calcBeta( const Mat& img )
static double calcBeta( const Mat& img )
{
double beta = 0;
for( int y = 0; y < img.rows; y++ )
@@ -272,7 +272,7 @@ double calcBeta( const Mat& img )
Calculate weights of noterminal vertices of graph.
beta and gamma - parameters of GrabCut algorithm.
*/
void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& uprightW, double beta, double gamma )
static void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& uprightW, double beta, double gamma )
{
const double gammaDivSqrt2 = gamma / std::sqrt(2.0f);
leftW.create( img.rows, img.cols, CV_64FC1 );
@@ -319,7 +319,7 @@ void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& upri
/*
Check size, type and element values of mask matrix.
*/
void checkMask( const Mat& img, const Mat& mask )
static void checkMask( const Mat& img, const Mat& mask )
{
if( mask.empty() )
CV_Error( CV_StsBadArg, "mask is empty" );
@@ -342,7 +342,7 @@ void checkMask( const Mat& img, const Mat& mask )
/*
Initialize mask using rectangular.
*/
void initMaskWithRect( Mat& mask, Size imgSize, Rect rect )
static void initMaskWithRect( Mat& mask, Size imgSize, Rect rect )
{
mask.create( imgSize, CV_8UC1 );
mask.setTo( GC_BGD );
@@ -358,7 +358,7 @@ void initMaskWithRect( Mat& mask, Size imgSize, Rect rect )
/*
Initialize GMM background and foreground models using kmeans algorithm.
*/
void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM )
static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM )
{
const int kMeansItCount = 10;
const int kMeansType = KMEANS_PP_CENTERS;
@@ -398,7 +398,7 @@ void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM )
/*
Assign GMMs components for each pixel.
*/
void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, Mat& compIdxs )
static void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, Mat& compIdxs )
{
Point p;
for( p.y = 0; p.y < img.rows; p.y++ )
@@ -415,7 +415,7 @@ void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, c
/*
Learn GMMs parameters.
*/
void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGMM, GMM& fgdGMM )
static void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGMM, GMM& fgdGMM )
{
bgdGMM.initLearning();
fgdGMM.initLearning();
@@ -443,7 +443,7 @@ void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGM
/*
Construct GCGraph
*/
void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, double lambda,
static void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, double lambda,
const Mat& leftW, const Mat& upleftW, const Mat& upW, const Mat& uprightW,
GCGraph<double>& graph )
{
@@ -506,7 +506,7 @@ void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const
/*
Estimate segmentation using MaxFlow algorithm
*/
void estimateSegmentation( GCGraph<double>& graph, Mat& mask )
static void estimateSegmentation( GCGraph<double>& graph, Mat& mask )
{
graph.maxFlow();
Point p;
@@ -533,7 +533,7 @@ void cv::grabCut( InputArray _img, InputOutputArray _mask, Rect rect,
Mat& mask = _mask.getMatRef();
Mat& bgdModel = _bgdModel.getMatRef();
Mat& fgdModel = _fgdModel.getMatRef();
if( img.empty() )
CV_Error( CV_StsBadArg, "image is empty" );
if( img.type() != CV_8UC3 )

32
modules/imgproc/src/hough.cpp
View File

@@ -114,7 +114,7 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta,
_tabCos.allocate(numangle);
int *accum = _accum, *sort_buf = _sort_buf;
float *tabSin = _tabSin, *tabCos = _tabCos;
memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) );
for( ang = 0, n = 0; n < numangle; ang += theta, n++ )
@@ -249,7 +249,7 @@ icvHoughLinesSDiv( const CvMat* img,
/* Precalculating sin */
_sinTable.resize( 5 * tn * stn );
sinTable = &_sinTable[0];
for( index = 0; index < 5 * tn * stn; index++ )
sinTable[index] = (float)cos( stheta * index * 0.2f );
@@ -449,7 +449,7 @@ icvHoughLinesSDiv( const CvMat* img,
h_get_next__index( &pos );
}
}
h_destroy_list__index(list);
}
@@ -756,7 +756,7 @@ cvHoughLines2( CvArr* src_image, void* lineStorage, int method,
}
else
CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
iparam1 = cvRound(param1);
iparam2 = cvRound(param2);
@@ -842,7 +842,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
acols = accum->cols - 2;
adata = accum->data.i;
astep = accum->step/sizeof(adata[0]);
// Accumulate circle evidence for each edge pixel
// Accumulate circle evidence for each edge pixel
for( y = 0; y < rows; y++ )
{
const uchar* edges_row = edges->data.ptr + y*edges->step;
@@ -868,7 +868,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
x0 = cvRound((x*idp)*ONE);
y0 = cvRound((y*idp)*ONE);
// Step from min_radius to max_radius in both directions of the gradient
// Step from min_radius to max_radius in both directions of the gradient
for( k = 0; k < 2; k++ )
{
x1 = x0 + min_radius * sx;
@@ -894,7 +894,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
nz_count = nz->total;
if( !nz_count )
return;
//Find possible circle centers
//Find possible circle centers
for( y = 1; y < arows - 1; y++ )
{
for( x = 1; x < acols - 1; x++ )
@@ -924,19 +924,19 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
dr = dp;
min_dist = MAX( min_dist, dp );
min_dist *= min_dist;
// For each found possible center
// Estimate radius and check support
// For each found possible center
// Estimate radius and check support
for( i = 0; i < centers->total; i++ )
{
int ofs = *(int*)cvGetSeqElem( centers, i );
y = ofs/(acols+2);
x = ofs - (y)*(acols+2);
//Calculate circle's center in pixels
//Calculate circle's center in pixels
float cx = (float)((x + 0.5f)*dp), cy = (float)(( y + 0.5f )*dp);
float start_dist, dist_sum;
float r_best = 0, c[3];
int max_count = 0;
// Check distance with previously detected circles
// Check distance with previously detected circles
for( j = 0; j < circles->total; j++ )
{
float* c = (float*)cvGetSeqElem( circles, j );
@@ -946,7 +946,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
if( j < circles->total )
continue;
// Estimate best radius
// Estimate best radius
cvStartReadSeq( nz, &reader );
for( j = k = 0; j < nz_count; j++ )
{
@@ -982,7 +982,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
{
float r_cur = ddata[sort_buf[(j + start_idx)/2]];
if( (start_idx - j)*r_best >= max_count*r_cur ||
(r_best < FLT_EPSILON && start_idx - j >= max_count) )
(r_best < FLT_EPSILON && start_idx - j >= max_count) )
{
r_best = r_cur;
max_count = start_idx - j;
@@ -993,7 +993,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
}
dist_sum += d;
}
// Check if the circle has enough support
// Check if the circle has enough support
if( max_count > acc_threshold )
{
c[0] = cx;
@@ -1103,9 +1103,9 @@ static void seqToMat(const CvSeq* seq, OutputArray _arr)
else
_arr.release();
}
}
void cv::HoughLines( InputArray _image, OutputArray _lines,
double rho, double theta, int threshold,
double srn, double stn )

30
modules/imgproc/src/phasecorr.cpp
View File

@@ -406,42 +406,42 @@ static void fftShift(InputOutputArray _out)
merge(planes, out);
}
Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weightBoxSize)
static Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weightBoxSize)
{
Mat src = _src.getMat();
int type = src.type();
CV_Assert( type == CV_32FC1 || type == CV_64FC1 );
int minr = peakLocation.y - (weightBoxSize.height >> 1);
int maxr = peakLocation.y + (weightBoxSize.height >> 1);
int minc = peakLocation.x - (weightBoxSize.width >> 1);
int maxc = peakLocation.x + (weightBoxSize.width >> 1);
Point2d centroid;
double sumIntensity = 0.0;
// clamp the values to min and max if needed.
if(minr < 0)
{
minr = 0;
}
if(minc < 0)
{
minc = 0;
}
if(maxr > src.rows - 1)
{
maxr = src.rows - 1;
}
if(maxc > src.cols - 1)
{
maxc = src.cols - 1;
}
if(type == CV_32FC1)
{
const float* dataIn = (const float*)src.data;
@@ -454,7 +454,7 @@ Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weigh
centroid.y += (double)y*dataIn[x];
sumIntensity += (double)dataIn[x];
}
dataIn += src.cols;
}
}
@@ -470,19 +470,19 @@ Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weigh
centroid.y += (double)y*dataIn[x];
sumIntensity += dataIn[x];
}
dataIn += src.cols;
}
}
sumIntensity += DBL_EPSILON; // prevent div0 problems...
centroid.x /= sumIntensity;
centroid.y /= sumIntensity;
return centroid;
}
}
cv::Point2d cv::phaseCorrelate(InputArray _src1, InputArray _src2, InputArray _window)

82
modules/imgproc/src/smooth.cpp
View File

@@ -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 )

22
modules/imgproc/src/sumpixels.cpp
View File

@@ -134,7 +134,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep,
if( size.width == cn )
buf[cn] = 0;
if( sqsum )
{
sqsum[-cn] = 0;
@@ -148,7 +148,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep,
sum += sumstep - cn;
tilted += tiltedstep - cn;
buf += -cn;
if( sqsum )
sqsum += sqsumstep - cn;
@@ -197,7 +197,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep,
tilted[x] = t0 + t1 + tilted[x - tiltedstep - cn];
buf[x] = t0;
}
if( sqsum )
sqsum++;
}
@@ -205,10 +205,10 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep,
}
}
#define DEF_INTEGRAL_FUNC(suffix, T, ST, QT) \
void integral_##suffix( T* src, size_t srcstep, ST* sum, size_t sumstep, QT* sqsum, size_t sqsumstep, \
ST* tilted, size_t tiltedstep, Size size, int cn ) \
static void integral_##suffix( T* src, size_t srcstep, ST* sum, size_t sumstep, QT* sqsum, size_t sqsumstep, \
ST* tilted, size_t tiltedstep, Size size, int cn ) \
{ integral_(src, srcstep, sum, sumstep, sqsum, sqsumstep, tilted, tiltedstep, size, cn); }
DEF_INTEGRAL_FUNC(8u32s, uchar, int, double)
@@ -217,7 +217,7 @@ DEF_INTEGRAL_FUNC(8u64f, uchar, double, double)
DEF_INTEGRAL_FUNC(32f, float, float, double)
DEF_INTEGRAL_FUNC(32f64f, float, double, double)
DEF_INTEGRAL_FUNC(64f, double, double, double)
typedef void (*IntegralFunc)(const uchar* src, size_t srcstep, uchar* sum, size_t sumstep,
uchar* sqsum, size_t sqsumstep, uchar* tilted, size_t tstep,
Size size, int cn );
@@ -236,19 +236,19 @@ void cv::integral( InputArray _src, OutputArray _sum, OutputArray _sqsum, Output
sdepth = CV_MAT_DEPTH(sdepth);
_sum.create( isize, CV_MAKETYPE(sdepth, cn) );
sum = _sum.getMat();
if( _tilted.needed() )
{
_tilted.create( isize, CV_MAKETYPE(sdepth, cn) );
tilted = _tilted.getMat();
}
if( _sqsum.needed() )
{
_sqsum.create( isize, CV_MAKETYPE(CV_64F, cn) );
sqsum = _sqsum.getMat();
}
IntegralFunc func = 0;
if( depth == CV_8U && sdepth == CV_32S )
@@ -269,7 +269,7 @@ void cv::integral( InputArray _src, OutputArray _sum, OutputArray _sqsum, Output
func( src.data, src.step, sum.data, sum.step, sqsum.data, sqsum.step,
tilted.data, tilted.step, src.size(), cn );
}
void cv::integral( InputArray src, OutputArray sum, int sdepth )
{
integral( src, sum, noArray(), noArray(), sdepth );