core: keep history of mean.cpp

modules/core/src/mean.simd.hpp

+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+
+#include "precomp.hpp"
+#include "opencl_kernels_core.hpp"
+#include "opencv2/core/openvx/ovx_defs.hpp"
+#include "stat.hpp"
+
+#if defined HAVE_IPP
+namespace cv
+{
+static bool ipp_mean( Mat &src, Mat &mask, Scalar &ret )
+{
+    CV_INSTRUMENT_REGION_IPP();
+
+#if IPP_VERSION_X100 >= 700
+    size_t total_size = src.total();
+    int cn = src.channels();
+    if (cn > 4)
+        return false;
+    int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
+    if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
+    {
+        IppiSize sz = { cols, rows };
+        int type = src.type();
+        if( !mask.empty() )
+        {
+            typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC1)(const void *, int, const void *, int, IppiSize, Ipp64f *);
+            ippiMaskMeanFuncC1 ippiMean_C1MR =
+            type == CV_8UC1 ? (ippiMaskMeanFuncC1)ippiMean_8u_C1MR :
+            type == CV_16UC1 ? (ippiMaskMeanFuncC1)ippiMean_16u_C1MR :
+            type == CV_32FC1 ? (ippiMaskMeanFuncC1)ippiMean_32f_C1MR :
+            0;
+            if( ippiMean_C1MR )
+            {
+                Ipp64f res;
+                if( CV_INSTRUMENT_FUN_IPP(ippiMean_C1MR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &res) >= 0 )
+                {
+                    ret = Scalar(res);
+                    return true;
+                }
+            }
+            typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC3)(const void *, int, const void *, int, IppiSize, int, Ipp64f *);
+            ippiMaskMeanFuncC3 ippiMean_C3MR =
+            type == CV_8UC3 ? (ippiMaskMeanFuncC3)ippiMean_8u_C3CMR :
+            type == CV_16UC3 ? (ippiMaskMeanFuncC3)ippiMean_16u_C3CMR :
+            type == CV_32FC3 ? (ippiMaskMeanFuncC3)ippiMean_32f_C3CMR :
+            0;
+            if( ippiMean_C3MR )
+            {
+                Ipp64f res1, res2, res3;
+                if( CV_INSTRUMENT_FUN_IPP(ippiMean_C3MR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 1, &res1) >= 0 &&
+                    CV_INSTRUMENT_FUN_IPP(ippiMean_C3MR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 2, &res2) >= 0 &&
+                    CV_INSTRUMENT_FUN_IPP(ippiMean_C3MR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 3, &res3) >= 0 )
+                {
+                    ret = Scalar(res1, res2, res3);
+                    return true;
+                }
+            }
+        }
+        else
+        {
+            typedef IppStatus (CV_STDCALL* ippiMeanFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm);
+            typedef IppStatus (CV_STDCALL* ippiMeanFuncNoHint)(const void*, int, IppiSize, double *);
+            ippiMeanFuncHint ippiMeanHint =
+                type == CV_32FC1 ? (ippiMeanFuncHint)ippiMean_32f_C1R :
+                type == CV_32FC3 ? (ippiMeanFuncHint)ippiMean_32f_C3R :
+                type == CV_32FC4 ? (ippiMeanFuncHint)ippiMean_32f_C4R :
+                0;
+            ippiMeanFuncNoHint ippiMean =
+                type == CV_8UC1 ? (ippiMeanFuncNoHint)ippiMean_8u_C1R :
+                type == CV_8UC3 ? (ippiMeanFuncNoHint)ippiMean_8u_C3R :
+                type == CV_8UC4 ? (ippiMeanFuncNoHint)ippiMean_8u_C4R :
+                type == CV_16UC1 ? (ippiMeanFuncNoHint)ippiMean_16u_C1R :
+                type == CV_16UC3 ? (ippiMeanFuncNoHint)ippiMean_16u_C3R :
+                type == CV_16UC4 ? (ippiMeanFuncNoHint)ippiMean_16u_C4R :
+                type == CV_16SC1 ? (ippiMeanFuncNoHint)ippiMean_16s_C1R :
+                type == CV_16SC3 ? (ippiMeanFuncNoHint)ippiMean_16s_C3R :
+                type == CV_16SC4 ? (ippiMeanFuncNoHint)ippiMean_16s_C4R :
+                0;
+            // Make sure only zero or one version of the function pointer is valid
+            CV_Assert(!ippiMeanHint || !ippiMean);
+            if( ippiMeanHint || ippiMean )
+            {
+                Ipp64f res[4];
+                IppStatus status = ippiMeanHint ? CV_INSTRUMENT_FUN_IPP(ippiMeanHint, src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
+                                CV_INSTRUMENT_FUN_IPP(ippiMean, src.ptr(), (int)src.step[0], sz, res);
+                if( status >= 0 )
+                {
+                    for( int i = 0; i < cn; i++ )
+                        ret[i] = res[i];
+                    return true;
+                }
+            }
+        }
+    }
+    return false;
+#else
+    return false;
+#endif
+}
+}
+#endif
+
+cv::Scalar cv::mean( InputArray _src, InputArray _mask )
+{
+    CV_INSTRUMENT_REGION();
+
+    Mat src = _src.getMat(), mask = _mask.getMat();
+    CV_Assert( mask.empty() || mask.type() == CV_8U );
+
+    int k, cn = src.channels(), depth = src.depth();
+    Scalar s;
+
+    CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_mean(src, mask, s), s)
+
+    SumFunc func = getSumFunc(depth);
+
+    CV_Assert( cn <= 4 && func != 0 );
+
+    const Mat* arrays[] = {&src, &mask, 0};
+    uchar* ptrs[2] = {};
+    NAryMatIterator it(arrays, ptrs);
+    int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
+    int j, count = 0;
+    AutoBuffer<int> _buf;
+    int* buf = (int*)&s[0];
+    bool blockSum = depth <= CV_16S;
+    size_t esz = 0, nz0 = 0;
+
+    if( blockSum )
+    {
+        intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
+        blockSize = std::min(blockSize, intSumBlockSize);
+        _buf.allocate(cn);
+        buf = _buf.data();
+
+        for( k = 0; k < cn; k++ )
+            buf[k] = 0;
+        esz = src.elemSize();
+    }
+
+    for( size_t i = 0; i < it.nplanes; i++, ++it )
+    {
+        for( j = 0; j < total; j += blockSize )
+        {
+            int bsz = std::min(total - j, blockSize);
+            int nz = func( ptrs[0], ptrs[1], (uchar*)buf, bsz, cn );
+            count += nz;
+            nz0 += nz;
+            if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
+            {
+                for( k = 0; k < cn; k++ )
+                {
+                    s[k] += buf[k];
+                    buf[k] = 0;
+                }
+                count = 0;
+            }
+            ptrs[0] += bsz*esz;
+            if( ptrs[1] )
+                ptrs[1] += bsz;
+        }
+    }
+    return s*(nz0 ? 1./nz0 : 0);
+}
+
+//==================================================================================================
+
+namespace cv {
+
+template <typename T, typename ST, typename SQT>
+struct SumSqr_SIMD
+{
+    int operator () (const T *, const uchar *, ST *, SQT *, int, int) const
+    {
+        return 0;
+    }
+};
+
+#if CV_SIMD
+
+template <>
+struct SumSqr_SIMD<uchar, int, int>
+{
+    int operator () (const uchar * src0, const uchar * mask, int * sum, int * sqsum, int len, int cn) const
+    {
+        if (mask || (cn != 1 && cn != 2 && cn != 4))
+            return 0;
+        len *= cn;
+
+        int x = 0;
+        v_int32 v_sum = vx_setzero_s32();
+        v_int32 v_sqsum = vx_setzero_s32();
+
+        const int len0 = len & -v_uint8::nlanes;
+        while(x < len0)
+        {
+            const int len_tmp = min(x + 256*v_uint16::nlanes, len0);
+            v_uint16 v_sum16 = vx_setzero_u16();
+            for ( ; x < len_tmp; x += v_uint8::nlanes)
+            {
+                v_uint16 v_src0 = vx_load_expand(src0 + x);
+                v_uint16 v_src1 = vx_load_expand(src0 + x + v_uint16::nlanes);
+                v_sum16 += v_src0 + v_src1;
+                v_int16 v_tmp0, v_tmp1;
+                v_zip(v_reinterpret_as_s16(v_src0), v_reinterpret_as_s16(v_src1), v_tmp0, v_tmp1);
+                v_sqsum += v_dotprod(v_tmp0, v_tmp0) + v_dotprod(v_tmp1, v_tmp1);
+            }
+            v_uint32 v_half0, v_half1;
+            v_expand(v_sum16, v_half0, v_half1);
+            v_sum += v_reinterpret_as_s32(v_half0 + v_half1);
+        }
+        if (x <= len - v_uint16::nlanes)
+        {
+            v_uint16 v_src = vx_load_expand(src0 + x);
+            v_uint16 v_half = v_combine_high(v_src, v_src);
+
+            v_uint32 v_tmp0, v_tmp1;
+            v_expand(v_src + v_half, v_tmp0, v_tmp1);
+            v_sum += v_reinterpret_as_s32(v_tmp0);
+
+            v_int16 v_tmp2, v_tmp3;
+            v_zip(v_reinterpret_as_s16(v_src), v_reinterpret_as_s16(v_half), v_tmp2, v_tmp3);
+            v_sqsum += v_dotprod(v_tmp2, v_tmp2);
+            x += v_uint16::nlanes;
+        }
+
+        if (cn == 1)
+        {
+            *sum += v_reduce_sum(v_sum);
+            *sqsum += v_reduce_sum(v_sqsum);
+        }
+        else
+        {
+            int CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[2 * v_int32::nlanes];
+            v_store(ar, v_sum);
+            v_store(ar + v_int32::nlanes, v_sqsum);
+            for (int i = 0; i < v_int32::nlanes; ++i)
+            {
+                sum[i % cn] += ar[i];
+                sqsum[i % cn] += ar[v_int32::nlanes + i];
+            }
+        }
+        v_cleanup();
+        return x / cn;
+    }
+};
+
+template <>
+struct SumSqr_SIMD<schar, int, int>
+{
+    int operator () (const schar * src0, const uchar * mask, int * sum, int * sqsum, int len, int cn) const
+    {
+        if (mask || (cn != 1 && cn != 2 && cn != 4))
+            return 0;
+        len *= cn;
+
+        int x = 0;
+        v_int32 v_sum = vx_setzero_s32();
+        v_int32 v_sqsum = vx_setzero_s32();
+
+        const int len0 = len & -v_int8::nlanes;
+        while (x < len0)
+        {
+            const int len_tmp = min(x + 256 * v_int16::nlanes, len0);
+            v_int16 v_sum16 = vx_setzero_s16();
+            for (; x < len_tmp; x += v_int8::nlanes)
+            {
+                v_int16 v_src0 = vx_load_expand(src0 + x);
+                v_int16 v_src1 = vx_load_expand(src0 + x + v_int16::nlanes);
+                v_sum16 += v_src0 + v_src1;
+                v_int16 v_tmp0, v_tmp1;
+                v_zip(v_src0, v_src1, v_tmp0, v_tmp1);
+                v_sqsum += v_dotprod(v_tmp0, v_tmp0) + v_dotprod(v_tmp1, v_tmp1);
+            }
+            v_int32 v_half0, v_half1;
+            v_expand(v_sum16, v_half0, v_half1);
+            v_sum += v_half0 + v_half1;
+        }
+        if (x <= len - v_int16::nlanes)
+        {
+            v_int16 v_src = vx_load_expand(src0 + x);
+            v_int16 v_half = v_combine_high(v_src, v_src);
+
+            v_int32 v_tmp0, v_tmp1;
+            v_expand(v_src + v_half, v_tmp0, v_tmp1);
+            v_sum += v_tmp0;
+
+            v_int16 v_tmp2, v_tmp3;
+            v_zip(v_src, v_half, v_tmp2, v_tmp3);
+            v_sqsum += v_dotprod(v_tmp2, v_tmp2);
+            x += v_int16::nlanes;
+        }
+
+        if (cn == 1)
+        {
+            *sum += v_reduce_sum(v_sum);
+            *sqsum += v_reduce_sum(v_sqsum);
+        }
+        else
+        {
+            int CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[2 * v_int32::nlanes];
+            v_store(ar, v_sum);
+            v_store(ar + v_int32::nlanes, v_sqsum);
+            for (int i = 0; i < v_int32::nlanes; ++i)
+            {
+                sum[i % cn] += ar[i];
+                sqsum[i % cn] += ar[v_int32::nlanes + i];
+            }
+        }
+        v_cleanup();
+        return x / cn;
+    }
+};
+
+#endif
+
+template<typename T, typename ST, typename SQT>
+static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int len, int cn )
+{
+    const T* src = src0;
+
+    if( !mask )
+    {
+        SumSqr_SIMD<T, ST, SQT> vop;
+        int x = vop(src0, mask, sum, sqsum, len, cn), k = cn % 4;
+        src = src0 + x * cn;
+
+        if( k == 1 )
+        {
+            ST s0 = sum[0];
+            SQT sq0 = sqsum[0];
+            for(int i = x; i < len; i++, src += cn )
+            {
+                T v = src[0];
+                s0 += v; sq0 += (SQT)v*v;
+            }
+            sum[0] = s0;
+            sqsum[0] = sq0;
+        }
+        else if( k == 2 )
+        {
+            ST s0 = sum[0], s1 = sum[1];
+            SQT sq0 = sqsum[0], sq1 = sqsum[1];
+            for(int i = x; i < len; i++, src += cn )
+            {
+                T v0 = src[0], v1 = src[1];
+                s0 += v0; sq0 += (SQT)v0*v0;
+                s1 += v1; sq1 += (SQT)v1*v1;
+            }
+            sum[0] = s0; sum[1] = s1;
+            sqsum[0] = sq0; sqsum[1] = sq1;
+        }
+        else if( k == 3 )
+        {
+            ST s0 = sum[0], s1 = sum[1], s2 = sum[2];
+            SQT sq0 = sqsum[0], sq1 = sqsum[1], sq2 = sqsum[2];
+            for(int i = x; i < len; i++, src += cn )
+            {
+                T v0 = src[0], v1 = src[1], v2 = src[2];
+                s0 += v0; sq0 += (SQT)v0*v0;
+                s1 += v1; sq1 += (SQT)v1*v1;
+                s2 += v2; sq2 += (SQT)v2*v2;
+            }
+            sum[0] = s0; sum[1] = s1; sum[2] = s2;
+            sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2;
+        }
+
+        for( ; k < cn; k += 4 )
+        {
+            src = src0 + x * cn + k;
+            ST s0 = sum[k], s1 = sum[k+1], s2 = sum[k+2], s3 = sum[k+3];
+            SQT sq0 = sqsum[k], sq1 = sqsum[k+1], sq2 = sqsum[k+2], sq3 = sqsum[k+3];
+            for(int i = x; i < len; i++, src += cn )
+            {
+                T v0, v1;
+                v0 = src[0], v1 = src[1];
+                s0 += v0; sq0 += (SQT)v0*v0;
+                s1 += v1; sq1 += (SQT)v1*v1;
+                v0 = src[2], v1 = src[3];
+                s2 += v0; sq2 += (SQT)v0*v0;
+                s3 += v1; sq3 += (SQT)v1*v1;
+            }
+            sum[k] = s0; sum[k+1] = s1;
+            sum[k+2] = s2; sum[k+3] = s3;
+            sqsum[k] = sq0; sqsum[k+1] = sq1;
+            sqsum[k+2] = sq2; sqsum[k+3] = sq3;
+        }
+        return len;
+    }
+
+    int i, nzm = 0;
+
+    if( cn == 1 )
+    {
+        ST s0 = sum[0];
+        SQT sq0 = sqsum[0];
+        for( i = 0; i < len; i++ )
+            if( mask[i] )
+            {
+                T v = src[i];
+                s0 += v; sq0 += (SQT)v*v;
+                nzm++;
+            }
+        sum[0] = s0;
+        sqsum[0] = sq0;
+    }
+    else if( cn == 3 )
+    {
+        ST s0 = sum[0], s1 = sum[1], s2 = sum[2];
+        SQT sq0 = sqsum[0], sq1 = sqsum[1], sq2 = sqsum[2];
+        for( i = 0; i < len; i++, src += 3 )
+            if( mask[i] )
+            {
+                T v0 = src[0], v1 = src[1], v2 = src[2];
+                s0 += v0; sq0 += (SQT)v0*v0;
+                s1 += v1; sq1 += (SQT)v1*v1;
+                s2 += v2; sq2 += (SQT)v2*v2;
+                nzm++;
+            }
+        sum[0] = s0; sum[1] = s1; sum[2] = s2;
+        sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2;
+    }
+    else
+    {
+        for( i = 0; i < len; i++, src += cn )
+            if( mask[i] )
+            {
+                for( int k = 0; k < cn; k++ )
+                {
+                    T v = src[k];
+                    ST s = sum[k] + v;
+                    SQT sq = sqsum[k] + (SQT)v*v;
+                    sum[k] = s; sqsum[k] = sq;
+                }
+                nzm++;
+            }
+    }
+    return nzm;
+}
+
+
+static int sqsum8u( const uchar* src, const uchar* mask, int* sum, int* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+static int sqsum8s( const schar* src, const uchar* mask, int* sum, int* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+static int sqsum16u( const ushort* src, const uchar* mask, int* sum, double* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+static int sqsum16s( const short* src, const uchar* mask, int* sum, double* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+static int sqsum32s( const int* src, const uchar* mask, double* sum, double* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+static int sqsum32f( const float* src, const uchar* mask, double* sum, double* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+static int sqsum64f( const double* src, const uchar* mask, double* sum, double* sqsum, int len, int cn )
+{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
+
+typedef int (*SumSqrFunc)(const uchar*, const uchar* mask, uchar*, uchar*, int, int);
+
+static SumSqrFunc getSumSqrTab(int depth)
+{
+    static SumSqrFunc sumSqrTab[] =
+    {
+        (SumSqrFunc)GET_OPTIMIZED(sqsum8u), (SumSqrFunc)sqsum8s, (SumSqrFunc)sqsum16u, (SumSqrFunc)sqsum16s,
+        (SumSqrFunc)sqsum32s, (SumSqrFunc)GET_OPTIMIZED(sqsum32f), (SumSqrFunc)sqsum64f, 0
+    };
+
+    return sumSqrTab[depth];
+}
+
+#ifdef HAVE_OPENCL
+static bool ocl_meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
+{
+    CV_INSTRUMENT_REGION_OPENCL();
+
+    bool haveMask = _mask.kind() != _InputArray::NONE;
+    int nz = haveMask ? -1 : (int)_src.total();
+    Scalar mean(0), stddev(0);
+    const int cn = _src.channels();
+    if (cn > 4)
+        return false;
+
+    {
+        int type = _src.type(), depth = CV_MAT_DEPTH(type);
+        bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0,
+                isContinuous = _src.isContinuous(),
+                isMaskContinuous = _mask.isContinuous();
+        const ocl::Device &defDev = ocl::Device::getDefault();
+        int groups = defDev.maxComputeUnits();
+        if (defDev.isIntel())
+        {
+            static const int subSliceEUCount = 10;
+            groups = (groups / subSliceEUCount) * 2;
+        }
+        size_t wgs = defDev.maxWorkGroupSize();
+
+        int ddepth = std::max(CV_32S, depth), sqddepth = std::max(CV_32F, depth),
+                dtype = CV_MAKE_TYPE(ddepth, cn),
+                sqdtype = CV_MAKETYPE(sqddepth, cn);
+        CV_Assert(!haveMask || _mask.type() == CV_8UC1);
+
+        int wgs2_aligned = 1;
+        while (wgs2_aligned < (int)wgs)
+            wgs2_aligned <<= 1;
+        wgs2_aligned >>= 1;
+
+        if ( (!doubleSupport && depth == CV_64F) )
+            return false;
+
+        char cvt[2][40];
+        String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstT1=%s -D sqddepth=%d"
+                             " -D sqdstT=%s -D sqdstT1=%s -D convertToSDT=%s -D cn=%d%s%s"
+                             " -D convertToDT=%s -D WGS=%d -D WGS2_ALIGNED=%d%s%s",
+                             ocl::typeToStr(type), ocl::typeToStr(depth),
+                             ocl::typeToStr(dtype), ocl::typeToStr(ddepth), sqddepth,
+                             ocl::typeToStr(sqdtype), ocl::typeToStr(sqddepth),
+                             ocl::convertTypeStr(depth, sqddepth, cn, cvt[0]),
+                             cn, isContinuous ? " -D HAVE_SRC_CONT" : "",
+                             isMaskContinuous ? " -D HAVE_MASK_CONT" : "",
+                             ocl::convertTypeStr(depth, ddepth, cn, cvt[1]),
+                             (int)wgs, wgs2_aligned, haveMask ? " -D HAVE_MASK" : "",
+                             doubleSupport ? " -D DOUBLE_SUPPORT" : "");
+
+        ocl::Kernel k("meanStdDev", ocl::core::meanstddev_oclsrc, opts);
+        if (k.empty())
+            return false;
+
+        int dbsize = groups * ((haveMask ? CV_ELEM_SIZE1(CV_32S) : 0) +
+                               CV_ELEM_SIZE(sqdtype) + CV_ELEM_SIZE(dtype));
+        UMat src = _src.getUMat(), db(1, dbsize, CV_8UC1), mask = _mask.getUMat();
+
+        ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
+                dbarg = ocl::KernelArg::PtrWriteOnly(db),
+                maskarg = ocl::KernelArg::ReadOnlyNoSize(mask);
+
+        if (haveMask)
+            k.args(srcarg, src.cols, (int)src.total(), groups, dbarg, maskarg);
+        else
+            k.args(srcarg, src.cols, (int)src.total(), groups, dbarg);
+
+        size_t globalsize = groups * wgs;
+
+        if(!k.run(1, &globalsize, &wgs, false))
+            return false;
+
+        typedef Scalar (* part_sum)(Mat m);
+        part_sum funcs[3] = { ocl_part_sum<int>, ocl_part_sum<float>, ocl_part_sum<double> };
+        Mat dbm = db.getMat(ACCESS_READ);
+
+        mean = funcs[ddepth - CV_32S](Mat(1, groups, dtype, dbm.ptr()));
+        stddev = funcs[sqddepth - CV_32S](Mat(1, groups, sqdtype, dbm.ptr() + groups * CV_ELEM_SIZE(dtype)));
+
+        if (haveMask)
+            nz = saturate_cast<int>(funcs[0](Mat(1, groups, CV_32SC1, dbm.ptr() +
+                                                 groups * (CV_ELEM_SIZE(dtype) +
+                                                           CV_ELEM_SIZE(sqdtype))))[0]);
+    }
+
+    double total = nz != 0 ? 1.0 / nz : 0;
+    int k, j;
+    for (int i = 0; i < cn; ++i)
+    {
+        mean[i] *= total;
+        stddev[i] = std::sqrt(std::max(stddev[i] * total - mean[i] * mean[i] , 0.));
+    }
+
+    for( j = 0; j < 2; j++ )
+    {
+        const double * const sptr = j == 0 ? &mean[0] : &stddev[0];
+        _OutputArray _dst = j == 0 ? _mean : _sdv;
+        if( !_dst.needed() )
+            continue;
+
+        if( !_dst.fixedSize() )
+            _dst.create(cn, 1, CV_64F, -1, true);
+        Mat dst = _dst.getMat();
+        int dcn = (int)dst.total();
+        CV_Assert( dst.type() == CV_64F && dst.isContinuous() &&
+                   (dst.cols == 1 || dst.rows == 1) && dcn >= cn );
+        double* dptr = dst.ptr<double>();
+        for( k = 0; k < cn; k++ )
+            dptr[k] = sptr[k];
+        for( ; k < dcn; k++ )
+            dptr[k] = 0;
+    }
+
+    return true;
+}
+#endif
+
+#ifdef HAVE_OPENVX
+    static bool openvx_meanStdDev(Mat& src, OutputArray _mean, OutputArray _sdv, Mat& mask)
+    {
+        size_t total_size = src.total();
+        int rows = src.size[0], cols = rows ? (int)(total_size / rows) : 0;
+        if (src.type() != CV_8UC1|| !mask.empty() ||
+               (src.dims != 2 && !(src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size))
+           )
+        return false;
+
+        try
+        {
+            ivx::Context ctx = ovx::getOpenVXContext();
+#ifndef VX_VERSION_1_1
+            if (ctx.vendorID() == VX_ID_KHRONOS)
+                return false; // Do not use OpenVX meanStdDev estimation for sample 1.0.1 implementation due to lack of accuracy
+#endif
+
+            ivx::Image
+                ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
+                    ivx::Image::createAddressing(cols, rows, 1, (vx_int32)(src.step[0])), src.ptr());
+
+            vx_float32 mean_temp, stddev_temp;
+            ivx::IVX_CHECK_STATUS(vxuMeanStdDev(ctx, ia, &mean_temp, &stddev_temp));
+
+            if (_mean.needed())
+            {
+                if (!_mean.fixedSize())
+                    _mean.create(1, 1, CV_64F, -1, true);
+                Mat mean = _mean.getMat();
+                CV_Assert(mean.type() == CV_64F && mean.isContinuous() &&
+                    (mean.cols == 1 || mean.rows == 1) && mean.total() >= 1);
+                double *pmean = mean.ptr<double>();
+                pmean[0] = mean_temp;
+                for (int c = 1; c < (int)mean.total(); c++)
+                    pmean[c] = 0;
+            }
+
+            if (_sdv.needed())
+            {
+                if (!_sdv.fixedSize())
+                    _sdv.create(1, 1, CV_64F, -1, true);
+                Mat stddev = _sdv.getMat();
+                CV_Assert(stddev.type() == CV_64F && stddev.isContinuous() &&
+                    (stddev.cols == 1 || stddev.rows == 1) && stddev.total() >= 1);
+                double *pstddev = stddev.ptr<double>();
+                pstddev[0] = stddev_temp;
+                for (int c = 1; c < (int)stddev.total(); c++)
+                    pstddev[c] = 0;
+            }
+        }
+        catch (const ivx::RuntimeError & e)
+        {
+            VX_DbgThrow(e.what());
+        }
+        catch (const ivx::WrapperError & e)
+        {
+            VX_DbgThrow(e.what());
+        }
+
+        return true;
+    }
+#endif
+
+#ifdef HAVE_IPP
+static bool ipp_meanStdDev(Mat& src, OutputArray _mean, OutputArray _sdv, Mat& mask)
+{
+    CV_INSTRUMENT_REGION_IPP();
+
+#if IPP_VERSION_X100 >= 700
+    int cn = src.channels();
+
+#if IPP_VERSION_X100 < 201801
+    // IPP_DISABLE: C3C functions can read outside of allocated memory
+    if (cn > 1)
+        return false;
+#endif
+#if IPP_VERSION_X100 >= 201900 && IPP_VERSION_X100 < 201901
+    // IPP_DISABLE: 32f C3C functions can read outside of allocated memory
+    if (cn > 1 && src.depth() == CV_32F)
+        return false;
+
+    // SSE4.2 buffer overrun
+#if defined(_WIN32) && !defined(_WIN64)
+    // IPPICV doesn't have AVX2 in 32-bit builds
+    // However cv::ipp::getIppTopFeatures() may return AVX2 value on AVX2 capable H/W
+    // details #12959
+#else
+    if (cv::ipp::getIppTopFeatures() == ippCPUID_SSE42) // Linux x64 + OPENCV_IPP=SSE42 is affected too
+#endif
+    {
+        if (src.depth() == CV_32F && src.dims > 1 && src.size[src.dims - 1] == 6)
+            return false;
+    }
+#endif
+
+    size_t total_size = src.total();
+    int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
+    if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
+    {
+        Ipp64f mean_temp[3];
+        Ipp64f stddev_temp[3];
+        Ipp64f *pmean = &mean_temp[0];
+        Ipp64f *pstddev = &stddev_temp[0];
+        Mat mean, stddev;
+        int dcn_mean = -1;
+        if( _mean.needed() )
+        {
+            if( !_mean.fixedSize() )
+                _mean.create(cn, 1, CV_64F, -1, true);
+            mean = _mean.getMat();
+            dcn_mean = (int)mean.total();
+            pmean = mean.ptr<Ipp64f>();
+        }
+        int dcn_stddev = -1;
+        if( _sdv.needed() )
+        {
+            if( !_sdv.fixedSize() )
+                _sdv.create(cn, 1, CV_64F, -1, true);
+            stddev = _sdv.getMat();
+            dcn_stddev = (int)stddev.total();
+            pstddev = stddev.ptr<Ipp64f>();
+        }
+        for( int c = cn; c < dcn_mean; c++ )
+            pmean[c] = 0;
+        for( int c = cn; c < dcn_stddev; c++ )
+            pstddev[c] = 0;
+        IppiSize sz = { cols, rows };
+        int type = src.type();
+        if( !mask.empty() )
+        {
+            typedef IppStatus (CV_STDCALL* ippiMaskMeanStdDevFuncC1)(const void *, int, const void *, int, IppiSize, Ipp64f *, Ipp64f *);
+            ippiMaskMeanStdDevFuncC1 ippiMean_StdDev_C1MR =
+            type == CV_8UC1 ? (ippiMaskMeanStdDevFuncC1)ippiMean_StdDev_8u_C1MR :
+            type == CV_16UC1 ? (ippiMaskMeanStdDevFuncC1)ippiMean_StdDev_16u_C1MR :
+            type == CV_32FC1 ? (ippiMaskMeanStdDevFuncC1)ippiMean_StdDev_32f_C1MR :
+            0;
+            if( ippiMean_StdDev_C1MR )
+            {
+                if( CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C1MR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, pmean, pstddev) >= 0 )
+                {
+                    return true;
+                }
+            }
+            typedef IppStatus (CV_STDCALL* ippiMaskMeanStdDevFuncC3)(const void *, int, const void *, int, IppiSize, int, Ipp64f *, Ipp64f *);
+            ippiMaskMeanStdDevFuncC3 ippiMean_StdDev_C3CMR =
+            type == CV_8UC3 ? (ippiMaskMeanStdDevFuncC3)ippiMean_StdDev_8u_C3CMR :
+            type == CV_16UC3 ? (ippiMaskMeanStdDevFuncC3)ippiMean_StdDev_16u_C3CMR :
+            type == CV_32FC3 ? (ippiMaskMeanStdDevFuncC3)ippiMean_StdDev_32f_C3CMR :
+            0;
+            if( ippiMean_StdDev_C3CMR )
+            {
+                if( CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C3CMR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
+                    CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C3CMR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
+                    CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C3CMR, src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
+                {
+                    return true;
+                }
+            }
+        }
+        else
+        {
+            typedef IppStatus (CV_STDCALL* ippiMeanStdDevFuncC1)(const void *, int, IppiSize, Ipp64f *, Ipp64f *);
+            ippiMeanStdDevFuncC1 ippiMean_StdDev_C1R =
+            type == CV_8UC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_8u_C1R :
+            type == CV_16UC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_16u_C1R :
+#if (IPP_VERSION_X100 >= 810)
+            type == CV_32FC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_32f_C1R ://Aug 2013: bug in IPP 7.1, 8.0
+#endif
+            0;
+            if( ippiMean_StdDev_C1R )
+            {
+                if( CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C1R, src.ptr(), (int)src.step[0], sz, pmean, pstddev) >= 0 )
+                {
+                    return true;
+                }
+            }
+            typedef IppStatus (CV_STDCALL* ippiMeanStdDevFuncC3)(const void *, int, IppiSize, int, Ipp64f *, Ipp64f *);
+            ippiMeanStdDevFuncC3 ippiMean_StdDev_C3CR =
+            type == CV_8UC3 ? (ippiMeanStdDevFuncC3)ippiMean_StdDev_8u_C3CR :
+            type == CV_16UC3 ? (ippiMeanStdDevFuncC3)ippiMean_StdDev_16u_C3CR :
+            type == CV_32FC3 ? (ippiMeanStdDevFuncC3)ippiMean_StdDev_32f_C3CR :
+            0;
+            if( ippiMean_StdDev_C3CR )
+            {
+                if( CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C3CR, src.ptr(), (int)src.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
+                    CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C3CR, src.ptr(), (int)src.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
+                    CV_INSTRUMENT_FUN_IPP(ippiMean_StdDev_C3CR, src.ptr(), (int)src.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
+                {
+                    return true;
+                }
+            }
+        }
+    }
+#else
+    CV_UNUSED(src); CV_UNUSED(_mean); CV_UNUSED(_sdv); CV_UNUSED(mask);
+#endif
+    return false;
+}
+#endif
+
+} // cv::
+
+void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_Assert(!_src.empty());
+    CV_Assert( _mask.empty() || _mask.type() == CV_8UC1 );
+
+    CV_OCL_RUN(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
+               ocl_meanStdDev(_src, _mean, _sdv, _mask))
+
+    Mat src = _src.getMat(), mask = _mask.getMat();
+
+    CV_OVX_RUN(!ovx::skipSmallImages<VX_KERNEL_MEAN_STDDEV>(src.cols, src.rows),
+               openvx_meanStdDev(src, _mean, _sdv, mask))
+
+    CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_meanStdDev(src, _mean, _sdv, mask));
+
+    int k, cn = src.channels(), depth = src.depth();
+
+    SumSqrFunc func = getSumSqrTab(depth);
+
+    CV_Assert( func != 0 );
+
+    const Mat* arrays[] = {&src, &mask, 0};
+    uchar* ptrs[2] = {};
+    NAryMatIterator it(arrays, ptrs);
+    int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
+    int j, count = 0, nz0 = 0;
+    AutoBuffer<double> _buf(cn*4);
+    double *s = (double*)_buf.data(), *sq = s + cn;
+    int *sbuf = (int*)s, *sqbuf = (int*)sq;
+    bool blockSum = depth <= CV_16S, blockSqSum = depth <= CV_8S;
+    size_t esz = 0;
+
+    for( k = 0; k < cn; k++ )
+        s[k] = sq[k] = 0;
+
+    if( blockSum )
+    {
+        intSumBlockSize = 1 << 15;
+        blockSize = std::min(blockSize, intSumBlockSize);
+        sbuf = (int*)(sq + cn);
+        if( blockSqSum )
+            sqbuf = sbuf + cn;
+        for( k = 0; k < cn; k++ )
+            sbuf[k] = sqbuf[k] = 0;
+        esz = src.elemSize();
+    }
+
+    for( size_t i = 0; i < it.nplanes; i++, ++it )
+    {
+        for( j = 0; j < total; j += blockSize )
+        {
+            int bsz = std::min(total - j, blockSize);
+            int nz = func( ptrs[0], ptrs[1], (uchar*)sbuf, (uchar*)sqbuf, bsz, cn );
+            count += nz;
+            nz0 += nz;
+            if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
+            {
+                for( k = 0; k < cn; k++ )
+                {
+                    s[k] += sbuf[k];
+                    sbuf[k] = 0;
+                }
+                if( blockSqSum )
+                {
+                    for( k = 0; k < cn; k++ )
+                    {
+                        sq[k] += sqbuf[k];
+                        sqbuf[k] = 0;
+                    }
+                }
+                count = 0;
+            }
+            ptrs[0] += bsz*esz;
+            if( ptrs[1] )
+                ptrs[1] += bsz;
+        }
+    }
+
+    double scale = nz0 ? 1./nz0 : 0.;
+    for( k = 0; k < cn; k++ )
+    {
+        s[k] *= scale;
+        sq[k] = std::sqrt(std::max(sq[k]*scale - s[k]*s[k], 0.));
+    }
+
+    for( j = 0; j < 2; j++ )
+    {
+        const double* sptr = j == 0 ? s : sq;
+        _OutputArray _dst = j == 0 ? _mean : _sdv;
+        if( !_dst.needed() )
+            continue;
+
+        if( !_dst.fixedSize() )
+            _dst.create(cn, 1, CV_64F, -1, true);
+        Mat dst = _dst.getMat();
+        int dcn = (int)dst.total();
+        CV_Assert( dst.type() == CV_64F && dst.isContinuous() &&
+                   (dst.cols == 1 || dst.rows == 1) && dcn >= cn );
+        double* dptr = dst.ptr<double>();
+        for( k = 0; k < cn; k++ )
+            dptr[k] = sptr[k];
+        for( ; k < dcn; k++ )
+            dptr[k] = 0;
+    }
+}