Smooth Code

拼搏现实的明天。 2022-09-25 05:26 160阅读 0赞

/*M///
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    #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. */