Smooth Code

/*M/// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" /* * This file includes the code, contributed by Simon Perreault * (the function icvMedianBlur_8u_O1) * * Constant-time median filtering -- http://nomis80.org/ctmf.html * Copyright (C) 2006 Simon Perreault * * Contact: * Laboratoire de vision et systemes numeriques * Pavillon Adrien-Pouliot * Universite Laval * Sainte-Foy, Quebec, Canada * G1K 7P4 * * perreaul@gel.ulaval.ca */ namespace cv { /****************************************************************************************\ Box Filter \****************************************************************************************/ template<typename T, typename ST> struct RowSum : public BaseRowFilter { RowSum( int _ksize, int _anchor ) { 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++ ) { ST s = 0; for( i = 0; i < ksz_cn; i += cn ) s += S[i]; D[0] = s; for( i = 0; i < width; i += cn ) { s += S[i + ksz_cn] - S[i]; D[i+cn] = s; } } } }; template<typename ST, typename T> struct ColumnSum : public BaseColumnFilter { ColumnSum( int _ksize, int _anchor, double _scale ) { ksize = _ksize; anchor = _anchor; scale = _scale; sumCount = 0; } void reset() { sumCount = 0; } void operator()(const uchar** src, uchar* dst, int dststep, int count, int width) { int i; ST* SUM; bool haveScale = scale != 1; double _scale = scale; if( width != (int)sum.size() ) { sum.resize(width); sumCount = 0; } SUM = &sum[0]; if( sumCount == 0 ) { for( i = 0; i < width; i++ ) SUM[i] = 0; for( ; sumCount < ksize - 1; sumCount++, src++ ) { const ST* Sp = (const ST*)src[0]; for( i = 0; i <= width - 2; i += 2 ) { ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1]; SUM[i] = s0; SUM[i+1] = s1; } for( ; i < width; i++ ) SUM[i] += Sp[i]; } } else { CV_Assert( sumCount == ksize-1 ); src += ksize-1; } for( ; count--; src++ ) { const ST* Sp = (const ST*)src[0]; const ST* Sm = (const ST*)src[1-ksize]; T* D = (T*)dst; if( haveScale ) { for( i = 0; i <= width - 2; i += 2 ) { ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1]; D[i] = saturate_cast<T>(s0*_scale); D[i+1] = saturate_cast<T>(s1*_scale); s0 -= Sm[i]; s1 -= Sm[i+1]; SUM[i] = s0; SUM[i+1] = s1; } for( ; i < width; i++ ) { ST s0 = SUM[i] + Sp[i]; D[i] = saturate_cast<T>(s0*_scale); SUM[i] = s0 - Sm[i]; } } else { for( i = 0; i <= width - 2; i += 2 ) { ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1]; D[i] = saturate_cast<T>(s0); D[i+1] = saturate_cast<T>(s1); s0 -= Sm[i]; s1 -= Sm[i+1]; SUM[i] = s0; SUM[i+1] = s1; } for( ; i < width; i++ ) { ST s0 = SUM[i] + Sp[i]; D[i] = saturate_cast<T>(s0); SUM[i] = s0 - Sm[i]; } } dst += dststep; } } double scale; int sumCount; vector<ST> sum; }; Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType, int ksize, int anchor) { int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType); CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(srcType) ); if( anchor < 0 ) anchor = ksize/2; if( sdepth == CV_8U && ddepth == CV_32S ) return Ptr<BaseRowFilter>(new RowSum<uchar, int>(ksize, anchor)); if( sdepth == CV_8U && ddepth == CV_64F ) return Ptr<BaseRowFilter>(new RowSum<uchar, double>(ksize, anchor)); if( sdepth == CV_16U && ddepth == CV_32S ) return Ptr<BaseRowFilter>(new RowSum<ushort, int>(ksize, anchor)); if( sdepth == CV_16U && ddepth == CV_64F ) return Ptr<BaseRowFilter>(new RowSum<ushort, double>(ksize, anchor)); if( sdepth == CV_16S && ddepth == CV_32S ) return Ptr<BaseRowFilter>(new RowSum<short, int>(ksize, anchor)); if( sdepth == CV_32S && ddepth == CV_32S ) return Ptr<BaseRowFilter>(new RowSum<int, int>(ksize, anchor)); if( sdepth == CV_16S && ddepth == CV_64F ) return Ptr<BaseRowFilter>(new RowSum<short, double>(ksize, anchor)); if( sdepth == CV_32F && ddepth == CV_64F ) return Ptr<BaseRowFilter>(new RowSum<float, double>(ksize, anchor)); if( sdepth == CV_64F && ddepth == CV_64F ) return Ptr<BaseRowFilter>(new RowSum<double, double>(ksize, anchor)); CV_Error_( CV_StsNotImplemented, ("Unsupported combination of source format (=%d), and buffer format (=%d)", srcType, sumType)); return Ptr<BaseRowFilter>(0); } Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType, int ksize, int anchor, double scale) { int sdepth = CV_MAT_DEPTH(sumType), ddepth = CV_MAT_DEPTH(dstType); CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(dstType) ); if( anchor < 0 ) anchor = ksize/2; if( ddepth == CV_8U && sdepth == CV_32S ) return Ptr<BaseColumnFilter>(new ColumnSum<int, uchar>(ksize, anchor, scale)); if( ddepth == CV_8U && sdepth == CV_64F ) return Ptr<BaseColumnFilter>(new ColumnSum<double, uchar>(ksize, anchor, scale)); if( ddepth == CV_16U && sdepth == CV_32S ) return Ptr<BaseColumnFilter>(new ColumnSum<int, ushort>(ksize, anchor, scale)); if( ddepth == CV_16U && sdepth == CV_64F ) return Ptr<BaseColumnFilter>(new ColumnSum<double, ushort>(ksize, anchor, scale)); if( ddepth == CV_16S && sdepth == CV_32S ) return Ptr<BaseColumnFilter>(new ColumnSum<int, short>(ksize, anchor, scale)); if( ddepth == CV_16S && sdepth == CV_64F ) return Ptr<BaseColumnFilter>(new ColumnSum<double, short>(ksize, anchor, scale)); if( ddepth == CV_32S && sdepth == CV_32S ) return Ptr<BaseColumnFilter>(new ColumnSum<int, int>(ksize, anchor, scale)); if( ddepth == CV_32F && sdepth == CV_32S ) return Ptr<BaseColumnFilter>(new ColumnSum<int, float>(ksize, anchor, scale)); if( ddepth == CV_32F && sdepth == CV_64F ) return Ptr<BaseColumnFilter>(new ColumnSum<double, float>(ksize, anchor, scale)); if( ddepth == CV_64F && sdepth == CV_32S ) return Ptr<BaseColumnFilter>(new ColumnSum<int, double>(ksize, anchor, scale)); if( ddepth == CV_64F && sdepth == CV_64F ) return Ptr<BaseColumnFilter>(new ColumnSum<double, double>(ksize, anchor, scale)); CV_Error_( CV_StsNotImplemented, ("Unsupported combination of sum format (=%d), and destination format (=%d)", sumType, dstType)); return Ptr<BaseColumnFilter>(0); } Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize, Point anchor, bool normalize, int borderType ) { int sdepth = CV_MAT_DEPTH(srcType); int cn = CV_MAT_CN(srcType), sumType = CV_64F; if( sdepth < CV_32S && (!normalize || ksize.width*ksize.height <= (sdepth == CV_8U ? (1<<23) : sdepth == CV_16U ? (1 << 15) : (1 << 16))) ) sumType = CV_32S; sumType = CV_MAKETYPE( sumType, cn ); Ptr<BaseRowFilter> rowFilter = getRowSumFilter(srcType, sumType, ksize.width, anchor.x ); Ptr<BaseColumnFilter> columnFilter = getColumnSumFilter(sumType, dstType, ksize.height, anchor.y, normalize ? 1./(ksize.width*ksize.height) : 1); return Ptr<FilterEngine>(new FilterEngine(Ptr<BaseFilter>(0), rowFilter, columnFilter, srcType, dstType, sumType, borderType )); } void boxFilter( const Mat& src, Mat& dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType ) { int sdepth = src.depth(), cn = src.channels(); if( ddepth < 0 ) ddepth = sdepth; dst.create( src.size(), CV_MAKETYPE(ddepth, cn) ); if( borderType != BORDER_CONSTANT && normalize ) { if( src.rows == 1 ) ksize.height = 1; if( src.cols == 1 ) ksize.width = 1; } Ptr<FilterEngine> f = createBoxFilter( src.type(), dst.type(), ksize, anchor, normalize, borderType ); f->apply( src, dst ); } void blur( const Mat& src, CV_OUT Mat& dst, Size ksize, Point anchor, int borderType ) { boxFilter( src, dst, -1, ksize, anchor, true, borderType ); } /****************************************************************************************\ Gaussian Blur \****************************************************************************************/ Mat getGaussianKernel( int n, double sigma, int ktype ) { const int SMALL_GAUSSIAN_SIZE = 7; static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] = { {1.f}, {0.25f, 0.5f, 0.25f}, {0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f}, {0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f} }; const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ? small_gaussian_tab[n>>1] : 0; CV_Assert( ktype == CV_32F || ktype == CV_64F ); Mat kernel(n, 1, ktype); float* cf = (float*)kernel.data; double* cd = (double*)kernel.data; double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8; double scale2X = -0.5/(sigmaX*sigmaX); double sum = 0; int i; for( i = 0; i < n; i++ ) { double x = i - (n-1)*0.5; double t = fixed_kernel ? (double)fixed_kernel[i] : std::exp(scale2X*x*x); if( ktype == CV_32F ) { cf[i] = (float)t; sum += cf[i]; } else { cd[i] = t; sum += cd[i]; } } sum = 1./sum; for( i = 0; i < n; i++ ) { if( ktype == CV_32F ) cf[i] = (float)(cf[i]*sum); else cd[i] *= sum; } return kernel; } Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigma1, double sigma2, int borderType ) { int depth = CV_MAT_DEPTH(type); if( sigma2 <= 0 ) sigma2 = sigma1; // automatic detection of kernel size from sigma if( ksize.width <= 0 && sigma1 > 0 ) ksize.width = cvRound(sigma1*(depth == CV_8U ? 3 : 4)*2 + 1)|1; if( ksize.height <= 0 && sigma2 > 0 ) ksize.height = cvRound(sigma2*(depth == CV_8U ? 3 : 4)*2 + 1)|1; CV_Assert( ksize.width > 0 && ksize.width % 2 == 1 && ksize.height > 0 && ksize.height % 2 == 1 ); sigma1 = std::max( sigma1, 0. ); sigma2 = std::max( sigma2, 0. ); Mat kx = getGaussianKernel( ksize.width, sigma1, std::max(depth, CV_32F) ); Mat ky; if( ksize.height == ksize.width && std::abs(sigma1 - sigma2) < DBL_EPSILON ) ky = kx; else ky = getGaussianKernel( ksize.height, sigma2, std::max(depth, CV_32F) ); return createSeparableLinearFilter( type, type, kx, ky, Point(-1,-1), 0, borderType ); } void GaussianBlur( const Mat& src, Mat& dst, Size ksize, double sigma1, double sigma2, int borderType ) { if( ksize.width == 1 && ksize.height == 1 ) { src.copyTo(dst); return; } dst.create( src.size(), src.type() ); if( borderType != BORDER_CONSTANT ) { if( src.rows == 1 ) ksize.height = 1; if( src.cols == 1 ) ksize.width = 1; } Ptr<FilterEngine> f = createGaussianFilter( src.type(), ksize, sigma1, sigma2, borderType ); f->apply( src, dst ); } /****************************************************************************************\ Median Filter \****************************************************************************************/ #if _MSC_VER >= 1200 #pragma warning( disable: 4244 ) #endif typedef ushort HT; /** * This structure represents a two-tier histogram. The first tier (known as the * "coarse" level) is 4 bit wide and the second tier (known as the "fine" level) * is 8 bit wide. Pixels inserted in the fine level also get inserted into the * coarse bucket designated by the 4 MSBs of the fine bucket value. * * The structure is aligned on 16 bits, which is a prerequisite for SIMD * instructions. Each bucket is 16 bit wide, which means that extra care must be * taken to prevent overflow. */ typedef struct { HT coarse[16]; HT fine[16][16]; } Histogram; #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; __m128i* ry = (__m128i*)y; __m128i r0 = _mm_add_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0)); __m128i r1 = _mm_add_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1)); _mm_store_si128(ry+0, r0); _mm_store_si128(ry+1, r1); } static inline void histogram_sub_simd( const HT x[16], HT y[16] ) { const __m128i* rx = (const __m128i*)x; __m128i* ry = (__m128i*)y; __m128i r0 = _mm_sub_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0)); __m128i r1 = _mm_sub_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1)); _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; for( i = 0; i < 16; ++i ) y[i] = (HT)(y[i] + x[i]); } static inline void histogram_sub( const HT x[16], HT y[16] ) { int i; for( i = 0; i < 16; ++i ) y[i] = (HT)(y[i] - x[i]); } static inline void histogram_muladd( int a, const HT x[16], HT y[16] ) { for( int i = 0; i < 16; ++i ) y[i] = (HT)(y[i] + a * x[i]); } static void medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) { /** * HOP is short for Histogram OPeration. This macro makes an operation \a op on * histogram \a h for pixel value \a x. It takes care of handling both levels. */ #define HOP(h,x,op) \ h.coarse[x>>4] op, \ *((HT*)h.fine + x) op #define COP(c,j,x,op) \ h_coarse[ 16*(n*c+j) + (x>>4) ] op, \ h_fine[ 16 * (n*(16*c+(x>>4)) + j) + (x & 0xF) ] op int cn = _dst.channels(), m = _dst.rows, r = (ksize-1)/2; size_t sstep = _src.step, dstep = _dst.step; Histogram CV_DECL_ALIGNED(16) H[4]; HT CV_DECL_ALIGNED(16) luc[4][16]; int STRIPE_SIZE = std::min( _dst.cols, 512/cn ); vector<HT> _h_coarse(1 * 16 * (STRIPE_SIZE + 2*r) * cn + 16); vector<HT> _h_fine(16 * 16 * (STRIPE_SIZE + 2*r) * cn + 16); HT* h_coarse = alignPtr(&_h_coarse[0], 16); HT* h_fine = alignPtr(&_h_fine[0], 16); #if MEDIAN_HAVE_SIMD volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); #endif for( int x = 0; x < _dst.cols; x += STRIPE_SIZE ) { int i, j, k, c, n = std::min(_dst.cols - x, STRIPE_SIZE) + r*2; const uchar* src = _src.data + x*cn; uchar* dst = _dst.data + (x - r)*cn; memset( h_coarse, 0, 16*n*cn*sizeof(h_coarse[0]) ); memset( h_fine, 0, 16*16*n*cn*sizeof(h_fine[0]) ); // First row initialization for( c = 0; c < cn; c++ ) { for( j = 0; j < n; j++ ) COP( c, j, src[cn*j+c], += r+2 ); for( i = 1; i < r; i++ ) { const uchar* p = src + sstep*std::min(i, m-1); for ( j = 0; j < n; j++ ) COP( c, j, p[cn*j+c], ++ ); } } for( i = 0; i < m; i++ ) { const uchar* p0 = src + sstep * std::max( 0, i-r-1 ); const uchar* p1 = src + sstep * std::min( m-1, i+r ); memset( H, 0, cn*sizeof(H[0]) ); memset( luc, 0, cn*sizeof(luc[0]) ); for( c = 0; c < cn; c++ ) { // Update column histograms for the entire row. for( j = 0; j < n; j++ ) { COP( c, j, p0[j*cn + c], -- ); COP( c, j, p1[j*cn + c], ++ ); } // First column initialization for( k = 0; k < 16; ++k ) histogram_muladd( 2*r+1, &h_fine[16*n*(16*c+k)], &H[c].fine[k][0] ); #if MEDIAN_HAVE_SIMD if( useSIMD ) { for( j = 0; j < 2*r; ++j ) histogram_add_simd( &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_simd( &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 ) { sum += H[c].coarse[k]; if ( sum > t ) { sum -= H[c].coarse[k]; break; } } 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_simd( &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] ); luc[c][k] = (HT)(j+r+1); } } else { for ( ; luc[c][k] < j+r+1; ++luc[c][k] ) { histogram_sub_simd( &h_fine[16*(n*(16*c+k)+MAX(luc[c][k]-2*r-1,0))], H[c].fine[k] ); histogram_add_simd( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] ); } } histogram_sub_simd( &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++ ) { sum += segment[b]; if ( sum > t ) { dst[dstep*i+cn*j+c] = (uchar)(16*k + b); break; } } assert( b < 16 ); } } else #endif { 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 ) { sum += H[c].coarse[k]; if ( sum > t ) { sum -= H[c].coarse[k]; break; } } 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] ); luc[c][k] = (HT)(j+r+1); } } else { for ( ; luc[c][k] < j+r+1; ++luc[c][k] ) { histogram_sub( &h_fine[16*(n*(16*c+k)+MAX(luc[c][k]-2*r-1,0))], H[c].fine[k] ); 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++ ) { sum += segment[b]; if ( sum > t ) { dst[dstep*i+cn*j+c] = (uchar)(16*k + b); break; } } assert( b < 16 ); } } } } } #undef HOP #undef COP } #if _MSC_VER >= 1200 #pragma warning( default: 4244 ) #endif static void medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m ) { #define N 16 int zone0[4][N]; int zone1[4][N*N]; int x, y; int n2 = m*m/2; Size size = _dst.size(); const uchar* src = _src.data; uchar* dst = _dst.data; int src_step = (int)_src.step, dst_step = (int)_dst.step; int cn = _src.channels(); const uchar* src_max = src + size.height*src_step; #define UPDATE_ACC01( pix, cn, op ) \ { \ int p = (pix); \ zone1[cn][p] op; \ zone0[cn][p >> 4] op; \ } //CV_Assert( size.height >= nx && size.width >= nx ); for( x = 0; x < size.width; x++, src += cn, dst += cn ) { uchar* dst_cur = dst; const uchar* src_top = src; const uchar* src_bottom = src; int k, c; int src_step1 = src_step, dst_step1 = dst_step; if( x % 2 != 0 ) { src_bottom = src_top += src_step*(size.height-1); dst_cur += dst_step*(size.height-1); src_step1 = -src_step1; dst_step1 = -dst_step1; } // init accumulator memset( zone0, 0, sizeof(zone0[0])*cn ); memset( zone1, 0, sizeof(zone1[0])*cn ); for( y = 0; y <= m/2; y++ ) { for( c = 0; c < cn; c++ ) { if( y > 0 ) { for( k = 0; k < m*cn; k += cn ) UPDATE_ACC01( src_bottom[k+c], c, ++ ); } else { for( k = 0; k < m*cn; k += cn ) UPDATE_ACC01( src_bottom[k+c], c, += m/2+1 ); } } if( (src_step1 > 0 && y < size.height-1) || (src_step1 < 0 && size.height-y-1 > 0) ) src_bottom += src_step1; } for( y = 0; y < size.height; y++, dst_cur += dst_step1 ) { // find median for( c = 0; c < cn; c++ ) { int s = 0; for( k = 0; ; k++ ) { int t = s + zone0[c][k]; if( t > n2 ) break; s = t; } for( k *= N; ;k++ ) { s += zone1[c][k]; if( s > n2 ) break; } dst_cur[c] = (uchar)k; } if( y+1 == size.height ) break; if( cn == 1 ) { for( k = 0; k < m; k++ ) { int p = src_top[k]; int q = src_bottom[k]; zone1[0][p]--; zone0[0][p>>4]--; zone1[0][q]++; zone0[0][q>>4]++; } } else if( cn == 3 ) { for( k = 0; k < m*3; k += 3 ) { UPDATE_ACC01( src_top[k], 0, -- ); UPDATE_ACC01( src_top[k+1], 1, -- ); UPDATE_ACC01( src_top[k+2], 2, -- ); UPDATE_ACC01( src_bottom[k], 0, ++ ); UPDATE_ACC01( src_bottom[k+1], 1, ++ ); UPDATE_ACC01( src_bottom[k+2], 2, ++ ); } } else { assert( cn == 4 ); for( k = 0; k < m*4; k += 4 ) { UPDATE_ACC01( src_top[k], 0, -- ); UPDATE_ACC01( src_top[k+1], 1, -- ); UPDATE_ACC01( src_top[k+2], 2, -- ); UPDATE_ACC01( src_top[k+3], 3, -- ); UPDATE_ACC01( src_bottom[k], 0, ++ ); UPDATE_ACC01( src_bottom[k+1], 1, ++ ); UPDATE_ACC01( src_bottom[k+2], 2, ++ ); UPDATE_ACC01( src_bottom[k+3], 3, ++ ); } } if( (src_step1 > 0 && src_bottom + src_step1 < src_max) || (src_step1 < 0 && src_bottom + src_step1 >= src) ) src_bottom += src_step1; if( y >= m/2 ) src_top += src_step1; } } #undef N #undef UPDATE_ACC } struct MinMax8u { typedef uchar value_type; typedef int arg_type; enum { SIZE = 1 }; arg_type load(const uchar* ptr) { return *ptr; } void store(uchar* ptr, arg_type val) { *ptr = (uchar)val; } void operator()(arg_type& a, arg_type& b) const { int t = CV_FAST_CAST_8U(a - b); b += t; a -= t; } }; struct MinMax16u { typedef ushort value_type; typedef int arg_type; enum { SIZE = 1 }; arg_type load(const ushort* ptr) { return *ptr; } void store(ushort* ptr, arg_type val) { *ptr = (ushort)val; } void operator()(arg_type& a, arg_type& b) const { arg_type t = a; a = std::min(a, b); b = std::max(b, t); } }; struct MinMax16s { typedef short value_type; typedef int arg_type; enum { SIZE = 1 }; arg_type load(const short* ptr) { return *ptr; } void store(short* ptr, arg_type val) { *ptr = (short)val; } void operator()(arg_type& a, arg_type& b) const { arg_type t = a; a = std::min(a, b); b = std::max(b, t); } }; struct MinMax32f { typedef float value_type; typedef float arg_type; enum { SIZE = 1 }; arg_type load(const float* ptr) { return *ptr; } void store(float* ptr, arg_type val) { *ptr = val; } void operator()(arg_type& a, arg_type& b) const { arg_type t = a; a = std::min(a, b); b = std::max(b, t); } }; #if CV_SSE2 struct MinMaxVec8u { typedef uchar value_type; typedef __m128i arg_type; enum { SIZE = 16 }; arg_type load(const uchar* ptr) { return _mm_loadu_si128((const __m128i*)ptr); } void store(uchar* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); } void operator()(arg_type& a, arg_type& b) const { arg_type t = a; a = _mm_min_epu8(a, b); b = _mm_max_epu8(b, t); } }; struct MinMaxVec16u { typedef ushort value_type; typedef __m128i arg_type; enum { SIZE = 8 }; arg_type load(const ushort* ptr) { return _mm_loadu_si128((const __m128i*)ptr); } void store(ushort* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); } void operator()(arg_type& a, arg_type& b) const { arg_type t = _mm_subs_epu16(a, b); a = _mm_subs_epu16(a, t); b = _mm_adds_epu16(b, t); } }; struct MinMaxVec16s { typedef short value_type; typedef __m128i arg_type; enum { SIZE = 8 }; arg_type load(const short* ptr) { return _mm_loadu_si128((const __m128i*)ptr); } void store(short* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); } void operator()(arg_type& a, arg_type& b) const { arg_type t = a; a = _mm_min_epi16(a, b); b = _mm_max_epi16(b, t); } }; struct MinMaxVec32f { typedef float value_type; typedef __m128 arg_type; enum { SIZE = 4 }; arg_type load(const float* ptr) { return _mm_loadu_ps(ptr); } void store(float* ptr, arg_type val) { _mm_storeu_ps(ptr, val); } void operator()(arg_type& a, arg_type& b) const { arg_type t = a; a = _mm_min_ps(a, b); b = _mm_max_ps(b, t); } }; #else typedef MinMax8u MinMaxVec8u; typedef MinMax16u MinMaxVec16u; typedef MinMax16s MinMaxVec16s; typedef MinMax32f MinMaxVec32f; #endif template<class Op, class VecOp> static void medianBlur_SortNet( const Mat& _src, Mat& _dst, int m ) { typedef typename Op::value_type T; typedef typename Op::arg_type WT; typedef typename VecOp::arg_type VT; const T* src = (const T*)_src.data; T* dst = (T*)_dst.data; int sstep = (int)(_src.step/sizeof(T)); int dstep = (int)(_dst.step/sizeof(T)); Size size = _dst.size(); int i, j, k, cn = _src.channels(); Op op; VecOp vop; volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); if( m == 3 ) { if( size.width == 1 || size.height == 1 ) { int len = size.width + size.height - 1; int sdelta = size.height == 1 ? cn : sstep; int sdelta0 = size.height == 1 ? 0 : sstep - cn; int ddelta = size.height == 1 ? cn : dstep; for( i = 0; i < len; i++, src += sdelta0, dst += ddelta ) for( j = 0; j < cn; j++, src++ ) { WT p0 = src[i > 0 ? -sdelta : 0]; WT p1 = src[0]; WT p2 = src[i < len - 1 ? sdelta : 0]; op(p0, p1); op(p1, p2); op(p0, p1); dst[j] = (T)p1; } return; } size.width *= cn; for( i = 0; i < size.height; i++, dst += dstep ) { const T* row0 = src + std::max(i - 1, 0)*sstep; const T* row1 = src + i*sstep; const T* row2 = src + std::min(i + 1, size.height-1)*sstep; int limit = useSIMD ? cn : size.width; for(j = 0;; ) { for( ; j < limit; j++ ) { int j0 = j >= cn ? j - cn : j; int j2 = j < size.width - cn ? j + cn : j; WT p0 = row0[j0], p1 = row0[j], p2 = row0[j2]; WT p3 = row1[j0], p4 = row1[j], p5 = row1[j2]; WT p6 = row2[j0], p7 = row2[j], p8 = row2[j2]; op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p1); op(p3, p4); op(p6, p7); op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p3); op(p5, p8); op(p4, p7); op(p3, p6); op(p1, p4); op(p2, p5); op(p4, p7); op(p4, p2); op(p6, p4); op(p4, p2); dst[j] = (T)p4; } if( limit == size.width ) break; for( ; j <= size.width - VecOp::SIZE - cn; j += VecOp::SIZE ) { VT p0 = vop.load(row0+j-cn), p1 = vop.load(row0+j), p2 = vop.load(row0+j+cn); VT p3 = vop.load(row1+j-cn), p4 = vop.load(row1+j), p5 = vop.load(row1+j+cn); VT p6 = vop.load(row2+j-cn), p7 = vop.load(row2+j), p8 = vop.load(row2+j+cn); vop(p1, p2); vop(p4, p5); vop(p7, p8); vop(p0, p1); vop(p3, p4); vop(p6, p7); vop(p1, p2); vop(p4, p5); vop(p7, p8); vop(p0, p3); vop(p5, p8); vop(p4, p7); vop(p3, p6); vop(p1, p4); vop(p2, p5); vop(p4, p7); vop(p4, p2); vop(p6, p4); vop(p4, p2); vop.store(dst+j, p4); } limit = size.width; } } } else if( m == 5 ) { if( size.width == 1 || size.height == 1 ) { int len = size.width + size.height - 1; int sdelta = size.height == 1 ? cn : sstep; int sdelta0 = size.height == 1 ? 0 : sstep - cn; int ddelta = size.height == 1 ? cn : dstep; for( i = 0; i < len; i++, src += sdelta0, dst += ddelta ) for( j = 0; j < cn; j++, src++ ) { int i1 = i > 0 ? -sdelta : 0; int i0 = i > 1 ? -sdelta*2 : i1; int i3 = i < len-1 ? sdelta : 0; int i4 = i < len-2 ? sdelta*2 : i3; WT p0 = src[i0], p1 = src[i1], p2 = src[0], p3 = src[i3], p4 = src[i4]; op(p0, p1); op(p3, p4); op(p2, p3); op(p3, p4); op(p0, p2); op(p2, p4); op(p1, p3); op(p1, p2); dst[j] = (T)p2; } return; } size.width *= cn; for( i = 0; i < size.height; i++, dst += dstep ) { const T* row[5]; row[0] = src + std::max(i - 2, 0)*sstep; row[1] = src + std::max(i - 1, 0)*sstep; row[2] = src + i*sstep; row[3] = src + std::min(i + 1, size.height-1)*sstep; row[4] = src + std::min(i + 2, size.height-1)*sstep; int limit = useSIMD ? cn*2 : size.width; for(j = 0;; ) { for( ; j < limit; j++ ) { WT p[25]; int j1 = j >= cn ? j - cn : j; int j0 = j >= cn*2 ? j - cn*2 : j1; int j3 = j < size.width - cn ? j + cn : j; int j4 = j < size.width - cn*2 ? j + cn*2 : j3; for( k = 0; k < 5; k++ ) { const T* rowk = row[k]; p[k*5] = rowk[j0]; p[k*5+1] = rowk[j1]; 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]); op(p[10], p[11]); op(p[9], p[10]); op(p[10], p[11]); op(p[6], p[9]); op(p[8], p[11]); op(p[8], p[9]); op(p[7], p[10]); op(p[7], p[8]); op(p[9], p[10]); op(p[0], p[6]); op(p[4], p[10]); op(p[4], p[6]); op(p[2], p[8]); op(p[2], p[4]); op(p[6], p[8]); op(p[1], p[7]); op(p[5], p[11]); op(p[5], p[7]); op(p[3], p[9]); op(p[3], p[5]); op(p[7], p[9]); op(p[1], p[2]); op(p[3], p[4]); op(p[5], p[6]); op(p[7], p[8]); op(p[9], p[10]); op(p[13], p[14]); op(p[12], p[13]); op(p[13], p[14]); op(p[16], p[17]); op(p[15], p[16]); op(p[16], p[17]); op(p[12], p[15]); op(p[14], p[17]); op(p[14], p[15]); op(p[13], p[16]); op(p[13], p[14]); op(p[15], p[16]); op(p[19], p[20]); op(p[18], p[19]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[21], p[23]); op(p[22], p[24]); op(p[22], p[23]); op(p[18], p[21]); op(p[20], p[23]); op(p[20], p[21]); op(p[19], p[22]); op(p[22], p[24]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[12], p[18]); op(p[16], p[22]); op(p[16], p[18]); op(p[14], p[20]); op(p[20], p[24]); op(p[14], p[16]); op(p[18], p[20]); op(p[22], p[24]); op(p[13], p[19]); op(p[17], p[23]); op(p[17], p[19]); op(p[15], p[21]); op(p[15], p[17]); op(p[19], p[21]); op(p[13], p[14]); op(p[15], p[16]); op(p[17], p[18]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[0], p[12]); op(p[8], p[20]); op(p[8], p[12]); op(p[4], p[16]); op(p[16], p[24]); op(p[12], p[16]); op(p[2], p[14]); op(p[10], p[22]); op(p[10], p[14]); op(p[6], p[18]); op(p[6], p[10]); op(p[10], p[12]); op(p[1], p[13]); op(p[9], p[21]); op(p[9], p[13]); op(p[5], p[17]); op(p[13], p[17]); op(p[3], p[15]); op(p[11], p[23]); op(p[11], p[15]); op(p[7], p[19]); op(p[7], p[11]); op(p[11], p[13]); op(p[11], p[12]); dst[j] = (T)p[12]; } if( limit == size.width ) break; for( ; j <= size.width - VecOp::SIZE - cn*2; j += VecOp::SIZE ) { VT p[25]; for( k = 0; k < 5; k++ ) { const T* rowk = row[k]; p[k*5] = vop.load(rowk+j-cn*2); p[k*5+1] = vop.load(rowk+j-cn); 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]); vop(p[10], p[11]); vop(p[9], p[10]); vop(p[10], p[11]); vop(p[6], p[9]); vop(p[8], p[11]); vop(p[8], p[9]); vop(p[7], p[10]); vop(p[7], p[8]); vop(p[9], p[10]); vop(p[0], p[6]); vop(p[4], p[10]); vop(p[4], p[6]); vop(p[2], p[8]); vop(p[2], p[4]); vop(p[6], p[8]); vop(p[1], p[7]); vop(p[5], p[11]); vop(p[5], p[7]); vop(p[3], p[9]); vop(p[3], p[5]); vop(p[7], p[9]); vop(p[1], p[2]); vop(p[3], p[4]); vop(p[5], p[6]); vop(p[7], p[8]); vop(p[9], p[10]); vop(p[13], p[14]); vop(p[12], p[13]); vop(p[13], p[14]); vop(p[16], p[17]); vop(p[15], p[16]); vop(p[16], p[17]); vop(p[12], p[15]); vop(p[14], p[17]); vop(p[14], p[15]); vop(p[13], p[16]); vop(p[13], p[14]); vop(p[15], p[16]); vop(p[19], p[20]); vop(p[18], p[19]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[21], p[23]); vop(p[22], p[24]); vop(p[22], p[23]); vop(p[18], p[21]); vop(p[20], p[23]); vop(p[20], p[21]); vop(p[19], p[22]); vop(p[22], p[24]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[12], p[18]); vop(p[16], p[22]); vop(p[16], p[18]); vop(p[14], p[20]); vop(p[20], p[24]); vop(p[14], p[16]); vop(p[18], p[20]); vop(p[22], p[24]); vop(p[13], p[19]); vop(p[17], p[23]); vop(p[17], p[19]); vop(p[15], p[21]); vop(p[15], p[17]); vop(p[19], p[21]); vop(p[13], p[14]); vop(p[15], p[16]); vop(p[17], p[18]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[0], p[12]); vop(p[8], p[20]); vop(p[8], p[12]); vop(p[4], p[16]); vop(p[16], p[24]); vop(p[12], p[16]); vop(p[2], p[14]); vop(p[10], p[22]); vop(p[10], p[14]); vop(p[6], p[18]); vop(p[6], p[10]); vop(p[10], p[12]); vop(p[1], p[13]); vop(p[9], p[21]); vop(p[9], p[13]); vop(p[5], p[17]); vop(p[13], p[17]); vop(p[3], p[15]); vop(p[11], p[23]); vop(p[11], p[15]); vop(p[7], p[19]); vop(p[7], p[11]); vop(p[11], p[13]); vop(p[11], p[12]); vop.store(dst+j, p[12]); } limit = size.width; } } } } void medianBlur( const Mat& src0, Mat& dst, int ksize ) { if( ksize <= 1 ) { src0.copyTo(dst); return; } CV_Assert( ksize % 2 == 1 ); Size size = src0.size(); int cn = src0.channels(); bool useSortNet = ksize == 3 || (ksize == 5 #if !CV_SSE2 && src0.depth() > CV_8U #endif ); dst.create( src0.size(), src0.type() ); Mat src; if( useSortNet ) { if( dst.data != src0.data ) src = src0; else src0.copyTo(src); } else cv::copyMakeBorder( src0, src, 0, 0, ksize/2, ksize/2, BORDER_REPLICATE ); if( useSortNet ) { if( src.depth() == CV_8U ) medianBlur_SortNet<MinMax8u, MinMaxVec8u>( src, dst, ksize ); else if( src.depth() == CV_16U ) medianBlur_SortNet<MinMax16u, MinMaxVec16u>( src, dst, ksize ); else if( src.depth() == CV_16S ) medianBlur_SortNet<MinMax16s, MinMaxVec16s>( src, dst, ksize ); else if( src.depth() == CV_32F ) medianBlur_SortNet<MinMax32f, MinMaxVec32f>( src, dst, ksize ); else CV_Error(CV_StsUnsupportedFormat, ""); return; } CV_Assert( src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4) ); double img_size_mp = (double)(size.width*size.height)/(1 << 20); if( ksize <= 3 + (img_size_mp < 1 ? 12 : img_size_mp < 4 ? 6 : 2)*(MEDIAN_HAVE_SIMD && checkHardwareSupport(CV_CPU_SSE2) ? 1 : 3)) medianBlur_8u_Om( src, dst, ksize ); else medianBlur_8u_O1( src, dst, ksize ); } /****************************************************************************************\ Bilateral Filtering \****************************************************************************************/ static void bilateralFilter_8u( const Mat& src, Mat& dst, int d, double sigma_color, double sigma_space, int borderType ) { double gauss_color_coeff = -0.5/(sigma_color*sigma_color); double gauss_space_coeff = -0.5/(sigma_space*sigma_space); int cn = src.channels(); int i, j, k, maxk, radius; Size size = src.size(); CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.data ); if( sigma_color <= 0 ) sigma_color = 1; if( sigma_space <= 0 ) sigma_space = 1; if( d <= 0 ) radius = cvRound(sigma_space*1.5); else radius = d/2; radius = MAX(radius, 1); d = radius*2 + 1; Mat temp; copyMakeBorder( src, temp, radius, radius, radius, radius, borderType ); vector<float> _color_weight(cn*256); vector<float> _space_weight(d*d); vector<int> _space_ofs(d*d); float* color_weight = &_color_weight[0]; float* space_weight = &_space_weight[0]; int* space_ofs = &_space_ofs[0]; // initialize color-related bilateral filter coefficients for( i = 0; i < 256*cn; i++ ) color_weight[i] = (float)std::exp(i*i*gauss_color_coeff); // initialize space-related bilateral filter coefficients for( i = -radius, maxk = 0; i <= radius; i++ ) for( j = -radius; j <= radius; j++ ) { double r = std::sqrt((double)i*i + (double)j*j); if( r > radius ) continue; space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff); space_ofs[maxk++] = (int)(i*temp.step + j*cn); } for( i = 0; i < size.height; i++ ) { const uchar* sptr = temp.data + (i+radius)*temp.step + radius*cn; uchar* dptr = dst.data + i*dst.step; if( cn == 1 ) { for( j = 0; j < size.width; j++ ) { float sum = 0, wsum = 0; int val0 = sptr[j]; for( k = 0; k < maxk; k++ ) { int val = sptr[j + space_ofs[k]]; float w = space_weight[k]*color_weight[std::abs(val - val0)]; sum += val*w; wsum += w; } // overflow is not possible here => there is no need to use CV_CAST_8U dptr[j] = (uchar)cvRound(sum/wsum); } } else { assert( cn == 3 ); for( j = 0; j < size.width*3; j += 3 ) { float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0; int b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2]; for( k = 0; k < maxk; k++ ) { const uchar* sptr_k = sptr + j + space_ofs[k]; int b = sptr_k[0], g = sptr_k[1], r = sptr_k[2]; float w = space_weight[k]*color_weight[std::abs(b - b0) + std::abs(g - g0) + std::abs(r - r0)]; sum_b += b*w; sum_g += g*w; sum_r += r*w; wsum += w; } wsum = 1.f/wsum; b0 = cvRound(sum_b*wsum); g0 = cvRound(sum_g*wsum); r0 = cvRound(sum_r*wsum); dptr[j] = (uchar)b0; dptr[j+1] = (uchar)g0; dptr[j+2] = (uchar)r0; } } } } static void bilateralFilter_32f( const Mat& src, Mat& dst, int d, double sigma_color, double sigma_space, int borderType ) { double gauss_color_coeff = -0.5/(sigma_color*sigma_color); double gauss_space_coeff = -0.5/(sigma_space*sigma_space); int cn = src.channels(); int i, j, k, maxk, radius; double minValSrc=-1, maxValSrc=1; const int kExpNumBinsPerChannel = 1 << 12; int kExpNumBins = 0; float lastExpVal = 1.f; float len, scale_index; Size size = src.size(); CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.data ); if( sigma_color <= 0 ) sigma_color = 1; if( sigma_space <= 0 ) sigma_space = 1; if( d <= 0 ) radius = cvRound(sigma_space*1.5); else radius = d/2; 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 ); // allocate lookup tables vector<float> _space_weight(d*d); vector<int> _space_ofs(d*d); float* space_weight = &_space_weight[0]; int* space_ofs = &_space_ofs[0]; // assign a length which is slightly more than needed len = (float)(maxValSrc - minValSrc) * cn; kExpNumBins = kExpNumBinsPerChannel * cn; vector<float> _expLUT(kExpNumBins+2); float* expLUT = &_expLUT[0]; scale_index = kExpNumBins/len; // initialize the exp LUT for( i = 0; i < kExpNumBins+2; i++ ) { if( lastExpVal > 0.f ) { double val = i / scale_index; expLUT[i] = (float)std::exp(val * val * gauss_color_coeff); lastExpVal = expLUT[i]; } 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++ ) { double r = std::sqrt((double)i*i + (double)j*j); if( r > radius ) continue; space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff); space_ofs[maxk++] = (int)(i*(temp.step/sizeof(float)) + j*cn); } for( i = 0; i < size.height; i++ ) { const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn; float* dptr = (float*)(dst.data + i*dst.step); if( cn == 1 ) { for( j = 0; j < size.width; j++ ) { float sum = 0, wsum = 0; float val0 = sptr[j]; for( k = 0; k < maxk; k++ ) { float val = sptr[j + space_ofs[k]]; 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; wsum += w; } dptr[j] = (float)(sum/wsum); } } else { assert( cn == 3 ); for( j = 0; j < size.width*3; j += 3 ) { float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0; float b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2]; for( k = 0; k < maxk; k++ ) { 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) + std::abs(g - g0) + std::abs(r - r0))*scale_index); int idx = cvFloor(alpha); alpha -= idx; float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx])); sum_b += b*w; sum_g += g*w; sum_r += r*w; wsum += w; } wsum = 1.f/wsum; b0 = sum_b*wsum; g0 = sum_g*wsum; r0 = sum_r*wsum; dptr[j] = b0; dptr[j+1] = g0; dptr[j+2] = r0; } } } } void bilateralFilter( const Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType ) { dst.create( src.size(), src.type() ); if( src.depth() == CV_8U ) bilateralFilter_8u( src, dst, d, sigmaColor, sigmaSpace, borderType ); else if( src.depth() == CV_32F ) bilateralFilter_32f( src, dst, d, sigmaColor, sigmaSpace, borderType ); else CV_Error( CV_StsUnsupportedFormat, "Bilateral filtering is only implemented for 8u and 32f images" ); } } // CV_IMPL void cvSmooth( const void* srcarr, void* dstarr, int smooth_type, int param1, int param2, double param3, double param4 ) { cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0; CV_Assert( dst.size() == src.size() && (smooth_type == CV_BLUR_NO_SCALE || dst.type() == src.type()) ); if( param2 <= 0 ) param2 = param1; if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE ) cv::boxFilter( src, dst, dst.depth(), cv::Size(param1, param2), cv::Point(-1,-1), smooth_type == CV_BLUR, cv::BORDER_REPLICATE ); else if( smooth_type == CV_GAUSSIAN ) cv::GaussianBlur( src, dst, cv::Size(param1, param2), param3, param4, cv::BORDER_REPLICATE ); else if( smooth_type == CV_MEDIAN ) cv::medianBlur( src, dst, param1 ); else cv::bilateralFilter( src, dst, param1, param3, param4, cv::BORDER_REPLICATE ); if( dst.data != dst0.data ) CV_Error( CV_StsUnmatchedFormats, "The destination image does not have the proper type" ); } /* End of file. */