Fixed float.h issue. OpenCV with built libraries working for linux x64

35 files changed, 0 insertions, 13216 deletions

diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/block.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/block.hpp
deleted file mode 100644
index 6cc00ae..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/block.hpp
+++ /dev/null
@@ -1,203 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_DEVICE_BLOCK_HPP__
-#define __OPENCV_GPU_DEVICE_BLOCK_HPP__
-
-namespace cv { namespace gpu { namespace device
-{
-    struct Block
-    {
-        static __device__ __forceinline__ unsigned int id()
-        {
-            return blockIdx.x;
-        }
-
-        static __device__ __forceinline__ unsigned int stride()
-        {
-            return blockDim.x * blockDim.y * blockDim.z;
-        }
-
-        static __device__ __forceinline__ void sync()
-        {
-            __syncthreads();
-        }
-
-        static __device__ __forceinline__ int flattenedThreadId()
-        {
-            return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
-        }
-
-        template<typename It, typename T>
-        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
-        {
-            int STRIDE = stride();
-            It t = beg + flattenedThreadId();
-
-            for(; t < end; t += STRIDE)
-                *t = value;
-        }
-
-        template<typename OutIt, typename T>
-        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
-        {
-            int STRIDE = stride();
-            int tid = flattenedThreadId();
-            value += tid;
-
-            for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
-                *t = value;
-        }
-
-        template<typename InIt, typename OutIt>
-        static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
-        {
-            int STRIDE = stride();
-            InIt  t = beg + flattenedThreadId();
-            OutIt o = out + (t - beg);
-
-            for(; t < end; t += STRIDE, o += STRIDE)
-                *o = *t;
-        }
-
-        template<typename InIt, typename OutIt, class UnOp>
-        static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op)
-        {
-            int STRIDE = stride();
-            InIt  t = beg + flattenedThreadId();
-            OutIt o = out + (t - beg);
-
-            for(; t < end; t += STRIDE, o += STRIDE)
-                *o = op(*t);
-        }
-
-        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
-        static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
-        {
-            int STRIDE = stride();
-            InIt1 t1 = beg1 + flattenedThreadId();
-            InIt2 t2 = beg2 + flattenedThreadId();
-            OutIt o  = out + (t1 - beg1);
-
-            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
-                *o = op(*t1, *t2);
-        }
-
-        template<int CTA_SIZE, typename T, class BinOp>
-        static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
-        {
-            int tid = flattenedThreadId();
-            T val =  buffer[tid];
-
-            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
-            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
-            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
-            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }
-
-            if (tid < 32)
-            {
-                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
-                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
-                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
-                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
-                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
-                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
-            }
-        }
-
-        template<int CTA_SIZE, typename T, class BinOp>
-        static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
-        {
-            int tid = flattenedThreadId();
-            T val =  buffer[tid] = init;
-            __syncthreads();
-
-            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
-            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
-            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
-            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }
-
-            if (tid < 32)
-            {
-                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
-                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
-                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
-                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
-                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
-                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
-            }
-            __syncthreads();
-            return buffer[0];
-        }
-
-        template <typename T, class BinOp>
-        static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
-        {
-            int ftid = flattenedThreadId();
-            int sft = stride();
-
-            if (sft < n)
-            {
-                for (unsigned int i = sft + ftid; i < n; i += sft)
-                    data[ftid] = op(data[ftid], data[i]);
-
-                __syncthreads();
-
-                n = sft;
-            }
-
-            while (n > 1)
-            {
-                unsigned int half = n/2;
-
-                if (ftid < half)
-                    data[ftid] = op(data[ftid], data[n - ftid - 1]);
-
-                __syncthreads();
-
-                n = n - half;
-            }
-        }
-    };
-}}}
-
-#endif /* __OPENCV_GPU_DEVICE_BLOCK_HPP__ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/border_interpolate.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/border_interpolate.hpp
deleted file mode 100644
index 693ba21..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/border_interpolate.hpp
+++ /dev/null
@@ -1,714 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
-#define __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
-
-#include "saturate_cast.hpp"
-#include "vec_traits.hpp"
-#include "vec_math.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    //////////////////////////////////////////////////////////////
-    // BrdConstant
-
-    template <typename D> struct BrdRowConstant
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits<D>::all(0)) : width(width_), val(val_) {}
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return x >= 0 ? saturate_cast<D>(data[x]) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return x < width ? saturate_cast<D>(data[x]) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return (x >= 0 && x < width) ? saturate_cast<D>(data[x]) : val;
-        }
-
-        const int width;
-        const D val;
-    };
-
-    template <typename D> struct BrdColConstant
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits<D>::all(0)) : height(height_), val(val_) {}
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return y >= 0 ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return y < height ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return (y >= 0 && y < height) ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
-        }
-
-        const int height;
-        const D val;
-    };
-
-    template <typename D> struct BrdConstant
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits<D>::all(0)) : height(height_), width(width_), val(val_)
-        {
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(((const T*)((const uchar*)data + y * step))[x]) : val;
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
-        }
-
-        const int height;
-        const int width;
-        const D val;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdReplicate
-
-    template <typename D> struct BrdRowReplicate
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::max(x, 0);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::min(x, last_col);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        const int last_col;
-    };
-
-    template <typename D> struct BrdColReplicate
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::max(y, 0);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::min(y, last_row);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const T*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const T*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const T*)((const char*)data + idx_row(y) * step));
-        }
-
-        const int last_row;
-    };
-
-    template <typename D> struct BrdReplicate
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::max(y, 0);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::min(y, last_row);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::max(x, 0);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::min(x, last_col);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        const int last_row;
-        const int last_col;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdReflect101
-
-    template <typename D> struct BrdRowReflect101
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::abs(x) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        const int last_col;
-    };
-
-    template <typename D> struct BrdColReflect101
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::abs(y) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
-        }
-
-        const int last_row;
-    };
-
-    template <typename D> struct BrdReflect101
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::abs(y) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::abs(x) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        const int last_row;
-        const int last_col;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdReflect
-
-    template <typename D> struct BrdRowReflect
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (::abs(x) - (x < 0)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_high(::abs(x) - (x < 0));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        const int last_col;
-    };
-
-    template <typename D> struct BrdColReflect
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (::abs(y) - (y < 0)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_high(::abs(y) - (y < 0));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
-        }
-
-        const int last_row;
-    };
-
-    template <typename D> struct BrdReflect
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (::abs(y) - (y < 0)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/;
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (::abs(x) - (x < 0)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return (last_col - ::abs(last_col - x) + (x > last_col));
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        const int last_row;
-        const int last_col;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdWrap
-
-    template <typename D> struct BrdRowWrap
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return (x < width) * x + (x >= width) * (x % width);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_high(idx_col_low(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        const int width;
-    };
-
-    template <typename D> struct BrdColWrap
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return (y < height) * y + (y >= height) * (y % height);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_high(idx_row_low(y));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
-        }
-
-        const int height;
-    };
-
-    template <typename D> struct BrdWrap
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) :
-            height(height_), width(width_)
-        {
-        }
-        template <typename U>
-        __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) :
-            height(height_), width(width_)
-        {
-        }
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (y >= 0) ? y : (y - ((y - height + 1) / height) * height);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return (y < height) ? y : (y % height);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_high(idx_row_low(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (x >= 0) ? x : (x - ((x - width + 1) / width) * width);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return (x < width) ? x : (x % width);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_high(idx_col_low(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        const int height;
-        const int width;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BorderReader
-
-    template <typename Ptr2D, typename B> struct BorderReader
-    {
-        typedef typename B::result_type elem_type;
-        typedef typename Ptr2D::index_type index_type;
-
-        __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {}
-
-        __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const
-        {
-            return b.at(y, x, ptr);
-        }
-
-        const Ptr2D ptr;
-        const B b;
-    };
-
-    // under win32 there is some bug with templated types that passed as kernel parameters
-    // with this specialization all works fine
-    template <typename Ptr2D, typename D> struct BorderReader< Ptr2D, BrdConstant<D> >
-    {
-        typedef typename BrdConstant<D>::result_type elem_type;
-        typedef typename Ptr2D::index_type index_type;
-
-        __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant<D>& b) :
-            src(src_), height(b.height), width(b.width), val(b.val)
-        {
-        }
-
-        __device__ __forceinline__ D operator ()(index_type y, index_type x) const
-        {
-            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
-        }
-
-        const Ptr2D src;
-        const int height;
-        const int width;
-        const D val;
-    };
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/color.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/color.hpp
deleted file mode 100644
index 5af64bf..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/color.hpp
+++ /dev/null
@@ -1,301 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_COLOR_HPP__
-#define __OPENCV_GPU_COLOR_HPP__
-
-#include "detail/color_detail.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    // All OPENCV_GPU_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements
-    // template <typename T> class ColorSpace1_to_ColorSpace2_traits
-    // {
-    //     typedef ... functor_type;
-    //     static __host__ __device__ functor_type create_functor();
-    // };
-
-    OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5)
-    OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5)
-    OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3)
-    OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4)
-
-    #undef OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5)
-    OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6)
-
-    #undef OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5)
-    OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0)
-    OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0)
-
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0)
-
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0)
-    OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0)
-
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0)
-    OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS
-
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0)
-
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0)
-    OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0)
-
-    #undef OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_BORDER_INTERPOLATE_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/common.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/common.hpp
deleted file mode 100644
index 26a349f..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/common.hpp
+++ /dev/null
@@ -1,118 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_COMMON_HPP__
-#define __OPENCV_GPU_COMMON_HPP__
-
-#include <cuda_runtime.h>
-#include "opencv2/core/cuda_devptrs.hpp"
-
-#ifndef CV_PI
-    #define CV_PI   3.1415926535897932384626433832795
-#endif
-
-#ifndef CV_PI_F
-    #ifndef CV_PI
-        #define CV_PI_F 3.14159265f
-    #else
-        #define CV_PI_F ((float)CV_PI)
-    #endif
-#endif
-
-#if defined(__GNUC__)
-    #define cudaSafeCall(expr)  ___cudaSafeCall(expr, __FILE__, __LINE__, __func__)
-#else /* defined(__CUDACC__) || defined(__MSVC__) */
-    #define cudaSafeCall(expr)  ___cudaSafeCall(expr, __FILE__, __LINE__)
-#endif
-
-namespace cv { namespace gpu
-{
-    void error(const char *error_string, const char *file, const int line, const char *func);
-
-    template <typename T> static inline bool isAligned(const T* ptr, size_t size)
-    {
-        return reinterpret_cast<size_t>(ptr) % size == 0;
-    }
-
-    static inline bool isAligned(size_t step, size_t size)
-    {
-        return step % size == 0;
-    }
-}}
-
-static inline void ___cudaSafeCall(cudaError_t err, const char *file, const int line, const char *func = "")
-{
-    if (cudaSuccess != err)
-        cv::gpu::error(cudaGetErrorString(err), file, line, func);
-}
-
-namespace cv { namespace gpu
-{
-    __host__ __device__ __forceinline__ int divUp(int total, int grain)
-    {
-        return (total + grain - 1) / grain;
-    }
-
-    namespace device
-    {
-        using cv::gpu::divUp;
-
-#ifdef __CUDACC__
-        typedef unsigned char uchar;
-        typedef unsigned short ushort;
-        typedef signed char schar;
-        #if defined (_WIN32) || defined (__APPLE__) || defined (__QNX__)
-            typedef unsigned int uint;
-        #endif
-
-        template<class T> inline void bindTexture(const textureReference* tex, const PtrStepSz<T>& img)
-        {
-            cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>();
-            cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) );
-        }
-#endif // __CUDACC__
-    }
-}}
-
-
-
-#endif // __OPENCV_GPU_COMMON_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/datamov_utils.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/datamov_utils.hpp
deleted file mode 100644
index a3f62fb..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/datamov_utils.hpp
+++ /dev/null
@@ -1,105 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_DATAMOV_UTILS_HPP__
-#define __OPENCV_GPU_DATAMOV_UTILS_HPP__
-
-#include "common.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200
-
-        // for Fermi memory space is detected automatically
-        template <typename T> struct ForceGlob
-        {
-            __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val)  { val = ptr[offset];  }
-        };
-
-    #else // __CUDA_ARCH__ >= 200
-
-        #if defined(_WIN64) || defined(__LP64__)
-            // 64-bit register modifier for inlined asm
-            #define OPENCV_GPU_ASM_PTR "l"
-        #else
-            // 32-bit register modifier for inlined asm
-            #define OPENCV_GPU_ASM_PTR "r"
-        #endif
-
-        template<class T> struct ForceGlob;
-
-        #define OPENCV_GPU_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \
-            template <> struct ForceGlob<base_type> \
-            { \
-                __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
-                { \
-                    asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_GPU_ASM_PTR(ptr + offset)); \
-                } \
-            };
-
-        #define OPENCV_GPU_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \
-            template <> struct ForceGlob<base_type> \
-            { \
-                __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
-                { \
-                    asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast<uint*>(&val)) : OPENCV_GPU_ASM_PTR(ptr + offset)); \
-                } \
-            };
-
-            OPENCV_GPU_DEFINE_FORCE_GLOB_B(uchar,  u8)
-            OPENCV_GPU_DEFINE_FORCE_GLOB_B(schar,  s8)
-            OPENCV_GPU_DEFINE_FORCE_GLOB_B(char,   b8)
-            OPENCV_GPU_DEFINE_FORCE_GLOB  (ushort, u16, h)
-            OPENCV_GPU_DEFINE_FORCE_GLOB  (short,  s16, h)
-            OPENCV_GPU_DEFINE_FORCE_GLOB  (uint,   u32, r)
-            OPENCV_GPU_DEFINE_FORCE_GLOB  (int,    s32, r)
-            OPENCV_GPU_DEFINE_FORCE_GLOB  (float,  f32, f)
-            OPENCV_GPU_DEFINE_FORCE_GLOB  (double, f64, d)
-
-        #undef OPENCV_GPU_DEFINE_FORCE_GLOB
-        #undef OPENCV_GPU_DEFINE_FORCE_GLOB_B
-        #undef OPENCV_GPU_ASM_PTR
-
-    #endif // __CUDA_ARCH__ >= 200
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_DATAMOV_UTILS_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/color_detail.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/color_detail.hpp
deleted file mode 100644
index c4ec64b..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/color_detail.hpp
+++ /dev/null
@@ -1,2219 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_COLOR_DETAIL_HPP__
-#define __OPENCV_GPU_COLOR_DETAIL_HPP__
-
-#include "../common.hpp"
-#include "../vec_traits.hpp"
-#include "../saturate_cast.hpp"
-#include "../limits.hpp"
-#include "../functional.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    #ifndef CV_DESCALE
-        #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))
-    #endif
-
-    namespace color_detail
-    {
-        template<typename T> struct ColorChannel
-        {
-            typedef float worktype_f;
-            static __device__ __forceinline__ T max() { return numeric_limits<T>::max(); }
-            static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); }
-        };
-
-        template<> struct ColorChannel<float>
-        {
-            typedef float worktype_f;
-            static __device__ __forceinline__ float max() { return 1.f; }
-            static __device__ __forceinline__ float half() { return 0.5f; }
-        };
-
-        template <typename T> static __device__ __forceinline__ void setAlpha(typename TypeVec<T, 3>::vec_type& vec, T val)
-        {
-        }
-
-        template <typename T> static __device__ __forceinline__ void setAlpha(typename TypeVec<T, 4>::vec_type& vec, T val)
-        {
-            vec.w = val;
-        }
-
-        template <typename T> static __device__ __forceinline__ T getAlpha(const typename TypeVec<T, 3>::vec_type& vec)
-        {
-            return ColorChannel<T>::max();
-        }
-
-        template <typename T> static __device__ __forceinline__ T getAlpha(const typename TypeVec<T, 4>::vec_type& vec)
-        {
-            return vec.w;
-        }
-
-        enum
-        {
-            yuv_shift  = 14,
-            xyz_shift  = 12,
-            R2Y        = 4899,
-            G2Y        = 9617,
-            B2Y        = 1868,
-            BLOCK_SIZE = 256
-        };
-    }
-
-////////////////// Various 3/4-channel to 3/4-channel RGB transformations /////////////////
-
-    namespace color_detail
-    {
-        template <typename T, int scn, int dcn, int bidx> struct RGB2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                dst.x = bidx == 0 ? src.x : src.z;
-                dst.y = src.y;
-                dst.z = bidx == 0 ? src.z : src.x;
-                setAlpha(dst, getAlpha<T>(src));
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB() {}
-            __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {}
-        };
-
-        template <> struct RGB2RGB<uchar, 4, 4, 2> : unary_function<uint, uint>
-        {
-            __device__ uint operator()(uint src) const
-            {
-                uint dst = 0;
-
-                dst |= (0xffu & (src >> 16));
-                dst |= (0xffu & (src >> 8)) << 8;
-                dst |= (0xffu & (src)) << 16;
-                dst |= (0xffu & (src >> 24)) << 24;
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB() {}
-            __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB //////////
-
-    namespace color_detail
-    {
-        template <int green_bits, int bidx> struct RGB2RGB5x5Converter;
-
-        template<int bidx> struct RGB2RGB5x5Converter<6, bidx>
-        {
-            template <typename T>
-            static __device__ __forceinline__ ushort cvt(const T& src)
-            {
-                uint b = bidx == 0 ? src.x : src.z;
-                uint g = src.y;
-                uint r = bidx == 0 ? src.z : src.x;
-                return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8));
-            }
-        };
-
-        template<int bidx> struct RGB2RGB5x5Converter<5, bidx>
-        {
-            static __device__ __forceinline__ ushort cvt(const uchar3& src)
-            {
-                uint b = bidx == 0 ? src.x : src.z;
-                uint g = src.y;
-                uint r = bidx == 0 ? src.z : src.x;
-                return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7));
-            }
-
-            static __device__ __forceinline__ ushort cvt(const uchar4& src)
-            {
-                uint b = bidx == 0 ? src.x : src.z;
-                uint g = src.y;
-                uint r = bidx == 0 ? src.z : src.x;
-                uint a = src.w;
-                return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000));
-            }
-        };
-
-        template<int scn, int bidx, int green_bits> struct RGB2RGB5x5:
-            unary_function<typename TypeVec<uchar, scn>::vec_type, ushort>
-        {
-            __device__ __forceinline__ ushort operator()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                return RGB2RGB5x5Converter<green_bits, bidx>::cvt(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB5x5() {}
-            __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2RGB5x5<scn, bidx, green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <int green_bits, int bidx> struct RGB5x52RGBConverter;
-
-        template <int bidx> struct RGB5x52RGBConverter<5, bidx>
-        {
-            static __device__ __forceinline__ void cvt(uint src, uchar3& dst)
-            {
-                (bidx == 0 ? dst.x : dst.z) = src << 3;
-                dst.y                       = (src >> 2) & ~7;
-                (bidx == 0 ? dst.z : dst.x) = (src >> 7) & ~7;
-            }
-
-            static __device__ __forceinline__ void cvt(uint src, uint& dst)
-            {
-                dst = 0;
-
-                dst |= (0xffu & (src << 3)) << (bidx * 8);
-                dst |= (0xffu & ((src >> 2) & ~7)) << 8;
-                dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8);
-                dst |= ((src & 0x8000) * 0xffu) << 24;
-            }
-        };
-
-        template <int bidx> struct RGB5x52RGBConverter<6, bidx>
-        {
-            static __device__ __forceinline__ void cvt(uint src, uchar3& dst)
-            {
-                (bidx == 0 ? dst.x : dst.z) = src << 3;
-                dst.y                       = (src >> 3) & ~3;
-                (bidx == 0 ? dst.z : dst.x) = (src >> 8) & ~7;
-            }
-
-            static __device__ __forceinline__ void cvt(uint src, uint& dst)
-            {
-                dst = 0xffu << 24;
-
-                dst |= (0xffu & (src << 3)) << (bidx * 8);
-                dst |= (0xffu &((src >> 3) & ~3)) << 8;
-                dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8);
-            }
-        };
-
-        template <int dcn, int bidx, int green_bits> struct RGB5x52RGB;
-
-        template <int bidx, int green_bits> struct RGB5x52RGB<3, bidx, green_bits> : unary_function<ushort, uchar3>
-        {
-            __device__ __forceinline__ uchar3 operator()(ushort src) const
-            {
-                uchar3 dst;
-                RGB5x52RGBConverter<green_bits, bidx>::cvt(src, dst);
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB5x52RGB() {}
-            __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {}
-        };
-
-        template <int bidx, int green_bits> struct RGB5x52RGB<4, bidx, green_bits> : unary_function<ushort, uint>
-        {
-            __device__ __forceinline__ uint operator()(ushort src) const
-            {
-                uint dst;
-                RGB5x52RGBConverter<green_bits, bidx>::cvt(src, dst);
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB5x52RGB() {}
-            __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB5x52RGB<dcn, bidx, green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////// Grayscale to Color ////////////////////////////////
-
-    namespace color_detail
-    {
-        template <typename T, int dcn> struct Gray2RGB : unary_function<T, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(T src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                dst.z = dst.y = dst.x = src;
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ Gray2RGB() {}
-            __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {}
-        };
-
-        template <> struct Gray2RGB<uchar, 4> : unary_function<uchar, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                uint dst = 0xffu << 24;
-
-                dst |= src;
-                dst |= src << 8;
-                dst |= src << 16;
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ Gray2RGB() {}
-            __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::Gray2RGB<T, dcn> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <int green_bits> struct Gray2RGB5x5Converter;
-
-        template<> struct Gray2RGB5x5Converter<6>
-        {
-            static __device__ __forceinline__ ushort cvt(uint t)
-            {
-                return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8));
-            }
-        };
-
-        template<> struct Gray2RGB5x5Converter<5>
-        {
-            static __device__ __forceinline__ ushort cvt(uint t)
-            {
-                t >>= 3;
-                return (ushort)(t | (t << 5) | (t << 10));
-            }
-        };
-
-        template<int green_bits> struct Gray2RGB5x5 : unary_function<uchar, ushort>
-        {
-            __device__ __forceinline__ ushort operator()(uint src) const
-            {
-                return Gray2RGB5x5Converter<green_bits>::cvt(src);
-            }
-
-            __host__ __device__ __forceinline__ Gray2RGB5x5() {}
-            __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::Gray2RGB5x5<green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////// Color to Grayscale ////////////////////////////////
-
-    namespace color_detail
-    {
-        template <int green_bits> struct RGB5x52GrayConverter;
-
-        template <> struct RGB5x52GrayConverter<6>
-        {
-            static __device__ __forceinline__ uchar cvt(uint t)
-            {
-                return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift);
-            }
-        };
-
-        template <> struct RGB5x52GrayConverter<5>
-        {
-            static __device__ __forceinline__ uchar cvt(uint t)
-            {
-                return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift);
-            }
-        };
-
-        template<int green_bits> struct RGB5x52Gray : unary_function<ushort, uchar>
-        {
-            __device__ __forceinline__ uchar operator()(uint src) const
-            {
-                return RGB5x52GrayConverter<green_bits>::cvt(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB5x52Gray() {}
-            __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB5x52Gray<green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <int bidx, typename T> static __device__ __forceinline__ uint RGB2GrayConvert_8U(const T& src)
-        {
-            uint b = bidx == 0 ? src.x : src.z;
-            uint g = src.y;
-            uint r = bidx == 0 ? src.z : src.x;
-            return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift);
-        }
-
-        template <int bidx> static __device__ __forceinline__ uchar RGB2GrayConvert_8UC4(uint src)
-        {
-            uint b = 0xffu & (src >> (bidx * 8));
-            uint g = 0xffu & (src >> 8);
-            uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
-            return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift);
-        }
-
-        template <int bidx, typename T> static __device__ __forceinline__ float RGB2GrayConvert_32F(const T& src)
-        {
-            float b = bidx == 0 ? src.x : src.z;
-            float g = src.y;
-            float r = bidx == 0 ? src.z : src.x;
-            return b * 0.114f + g * 0.587f + r * 0.299f;
-        }
-
-        template <typename T, int scn, int bidx> struct RGB2Gray : unary_function<typename TypeVec<T, scn>::vec_type, T>
-        {
-            __device__ __forceinline__ T operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                return RGB2GrayConvert_8U<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2Gray() {}
-            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
-        };
-
-        template <int scn, int bidx> struct RGB2Gray<float, scn, bidx> :
-            unary_function<typename TypeVec<float, scn>::vec_type, float>
-        {
-            __device__ __forceinline__ float operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                return RGB2GrayConvert_32F<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2Gray() {}
-            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
-        };
-
-        template <int bidx> struct RGB2Gray<uchar, 4, bidx> : unary_function<uint, uchar>
-        {
-            __device__ __forceinline__ uchar operator()(uint src) const
-            {
-                return RGB2GrayConvert_8UC4<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2Gray() {}
-            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2Gray<T, scn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> YUV //////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f };
-        __constant__ int   c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 };
-
-        template <int bidx, typename T, typename D> static __device__ void RGB2YUVConvert(const T& src, D& dst)
-        {
-            const int delta = ColorChannel<typename VecTraits<T>::elem_type>::half() * (1 << yuv_shift);
-
-            const int b = bidx == 0 ? src.x : src.z;
-            const int g = src.y;
-            const int r = bidx == 0 ? src.z : src.x;
-
-            const int Y = CV_DESCALE(b * c_RGB2YUVCoeffs_i[2] + g * c_RGB2YUVCoeffs_i[1] + r * c_RGB2YUVCoeffs_i[0], yuv_shift);
-            const int Cr = CV_DESCALE((r - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift);
-            const int Cb = CV_DESCALE((b - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift);
-
-            dst.x = saturate_cast<typename VecTraits<T>::elem_type>(Y);
-            dst.y = saturate_cast<typename VecTraits<T>::elem_type>(Cr);
-            dst.z = saturate_cast<typename VecTraits<T>::elem_type>(Cb);
-        }
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void RGB2YUVConvert_32F(const T& src, D& dst)
-        {
-            const float b = bidx == 0 ? src.x : src.z;
-            const float g = src.y;
-            const float r = bidx == 0 ? src.z : src.x;
-
-            dst.x = b * c_RGB2YUVCoeffs_f[2] + g * c_RGB2YUVCoeffs_f[1] + r * c_RGB2YUVCoeffs_f[0];
-            dst.y = (r - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel<float>::half();
-            dst.z = (b - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel<float>::half();
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct RGB2YUV
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-                RGB2YUVConvert<bidx>(src, dst);
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2YUV() {}
-            __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {}
-        };
-
-        template <int scn, int dcn, int bidx> struct RGB2YUV<float, scn, dcn, bidx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-                RGB2YUVConvert_32F<bidx>(src, dst);
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2YUV() {}
-            __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2YUV<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f };
-        __constant__ int   c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 };
-
-        template <int bidx, typename T, typename D> static __device__ void YUV2RGBConvert(const T& src, D& dst)
-        {
-            const int b = src.x + CV_DESCALE((src.z - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift);
-
-            const int g = src.x + CV_DESCALE((src.z - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YUV2RGBCoeffs_i[2]
-                                             + (src.y - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift);
-
-            const int r = src.x + CV_DESCALE((src.y - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift);
-
-            (bidx == 0 ? dst.x : dst.z) = saturate_cast<typename VecTraits<D>::elem_type>(b);
-            dst.y                       = saturate_cast<typename VecTraits<D>::elem_type>(g);
-            (bidx == 0 ? dst.z : dst.x) = saturate_cast<typename VecTraits<D>::elem_type>(r);
-        }
-
-        template <int bidx> static __device__ uint YUV2RGBConvert_8UC4(uint src)
-        {
-            const int x = 0xff & (src);
-            const int y = 0xff & (src >> 8);
-            const int z = 0xff & (src >> 16);
-
-            const int b = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift);
-
-            const int g = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[2]
-                                         + (y - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift);
-
-            const int r = x + CV_DESCALE((y - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(b) << (bidx * 8);
-            dst |= saturate_cast<uchar>(g) << 8;
-            dst |= saturate_cast<uchar>(r) << ((bidx ^ 2) * 8);
-
-            return dst;
-        }
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void YUV2RGBConvert_32F(const T& src, D& dst)
-        {
-            (bidx == 0 ? dst.x : dst.z) = src.x + (src.z - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[3];
-
-            dst.y                       = src.x + (src.z - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[2]
-                                          + (src.y - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[1];
-
-            (bidx == 0 ? dst.z : dst.x) = src.x + (src.y - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[0];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct YUV2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                YUV2RGBConvert<bidx>(src, dst);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ YUV2RGB() {}
-            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
-        };
-
-        template <int scn, int dcn, int bidx> struct YUV2RGB<float, scn, dcn, bidx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                YUV2RGBConvert_32F<bidx>(src, dst);
-                setAlpha(dst, ColorChannel<float>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ YUV2RGB() {}
-            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
-        };
-
-        template <int bidx> struct YUV2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator ()(uint src) const
-            {
-                return YUV2RGBConvert_8UC4<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ YUV2RGB() {}
-            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::YUV2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> YCrCb //////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f};
-        __constant__ int   c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241};
-
-        template <int bidx, typename T, typename D> static __device__ void RGB2YCrCbConvert(const T& src, D& dst)
-        {
-            const int delta = ColorChannel<typename VecTraits<T>::elem_type>::half() * (1 << yuv_shift);
-
-            const int b = bidx == 0 ? src.x : src.z;
-            const int g = src.y;
-            const int r = bidx == 0 ? src.z : src.x;
-
-            const int Y = CV_DESCALE(b * c_RGB2YCrCbCoeffs_i[2] + g * c_RGB2YCrCbCoeffs_i[1] + r * c_RGB2YCrCbCoeffs_i[0], yuv_shift);
-            const int Cr = CV_DESCALE((r - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift);
-            const int Cb = CV_DESCALE((b - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift);
-
-            dst.x = saturate_cast<typename VecTraits<T>::elem_type>(Y);
-            dst.y = saturate_cast<typename VecTraits<T>::elem_type>(Cr);
-            dst.z = saturate_cast<typename VecTraits<T>::elem_type>(Cb);
-        }
-
-        template <int bidx> static __device__ uint RGB2YCrCbConvert_8UC4(uint src)
-        {
-            const int delta = ColorChannel<uchar>::half() * (1 << yuv_shift);
-
-            const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift);
-            const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift);
-            const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift);
-
-            uint dst = 0;
-
-            dst |= saturate_cast<uchar>(Y);
-            dst |= saturate_cast<uchar>(Cr) << 8;
-            dst |= saturate_cast<uchar>(Cb) << 16;
-
-            return dst;
-        }
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void RGB2YCrCbConvert_32F(const T& src, D& dst)
-        {
-            const float b = bidx == 0 ? src.x : src.z;
-            const float g = src.y;
-            const float r = bidx == 0 ? src.z : src.x;
-
-            dst.x = b * c_RGB2YCrCbCoeffs_f[2] + g * c_RGB2YCrCbCoeffs_f[1] + r * c_RGB2YCrCbCoeffs_f[0];
-            dst.y = (r - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel<float>::half();
-            dst.z = (b - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel<float>::half();
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct RGB2YCrCb
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-                RGB2YCrCbConvert<bidx>(src, dst);
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2YCrCb() {}
-            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
-        };
-
-        template <int scn, int dcn, int bidx> struct RGB2YCrCb<float, scn, dcn, bidx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-                RGB2YCrCbConvert_32F<bidx>(src, dst);
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2YCrCb() {}
-            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
-        };
-
-        template <int bidx> struct RGB2YCrCb<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator ()(uint src) const
-            {
-                return RGB2YCrCbConvert_8UC4<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2YCrCb() {}
-            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2YCrCb<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f};
-        __constant__ int   c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049};
-
-        template <int bidx, typename T, typename D> static __device__ void YCrCb2RGBConvert(const T& src, D& dst)
-        {
-            const int b = src.x + CV_DESCALE((src.z - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift);
-            const int g = src.x + CV_DESCALE((src.z - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YCrCb2RGBCoeffs_i[2] +
-                    (src.y - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift);
-            const int r = src.x + CV_DESCALE((src.y - ColorChannel<typename VecTraits<D>::elem_type>::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift);
-
-            (bidx == 0 ? dst.x : dst.z) = saturate_cast<typename VecTraits<D>::elem_type>(b);
-            dst.y                       = saturate_cast<typename VecTraits<D>::elem_type>(g);
-            (bidx == 0 ? dst.z : dst.x) = saturate_cast<typename VecTraits<D>::elem_type>(r);
-        }
-
-        template <int bidx> static __device__ uint YCrCb2RGBConvert_8UC4(uint src)
-        {
-            const int x = 0xff & (src);
-            const int y = 0xff & (src >> 8);
-            const int z = 0xff & (src >> 16);
-
-            const int b = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift);
-            const int g = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift);
-            const int r = x + CV_DESCALE((y - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(b) << (bidx * 8);
-            dst |= saturate_cast<uchar>(g) << 8;
-            dst |= saturate_cast<uchar>(r) << ((bidx ^ 2) * 8);
-
-            return dst;
-        }
-
-        template <int bidx, typename T, typename D> __device__ __forceinline__ void YCrCb2RGBConvert_32F(const T& src, D& dst)
-        {
-            (bidx == 0 ? dst.x : dst.z) = src.x + (src.z - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[3];
-            dst.y                       = src.x + (src.z - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[1];
-            (bidx == 0 ? dst.z : dst.x) = src.x + (src.y - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[0];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct YCrCb2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                YCrCb2RGBConvert<bidx>(src, dst);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ YCrCb2RGB() {}
-            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
-        };
-
-        template <int scn, int dcn, int bidx> struct YCrCb2RGB<float, scn, dcn, bidx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                YCrCb2RGBConvert_32F<bidx>(src, dst);
-                setAlpha(dst, ColorChannel<float>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ YCrCb2RGB() {}
-            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
-        };
-
-        template <int bidx> struct YCrCb2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator ()(uint src) const
-            {
-                return YCrCb2RGBConvert_8UC4<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ YCrCb2RGB() {}
-            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::YCrCb2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-////////////////////////////////////// RGB <-> XYZ ///////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f };
-        __constant__ int   c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 };
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void RGB2XYZConvert_8U(const T& src, D& dst)
-        {
-            const uint b = bidx == 0 ? src.x : src.z;
-            const uint g = src.y;
-            const uint r = bidx == 0 ? src.z : src.x;
-
-            dst.x = saturate_cast<typename VecTraits<D>::elem_type>(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift));
-            dst.y = saturate_cast<typename VecTraits<D>::elem_type>(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift));
-            dst.z = saturate_cast<typename VecTraits<D>::elem_type>(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift));
-        }
-
-        template <int bidx> static __device__ __forceinline__ uint RGB2XYZConvert_8UC4(uint src)
-        {
-            const uint b = 0xffu & (src >> (bidx * 8));
-            const uint g = 0xffu & (src >> 8);
-            const uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
-
-            const uint x = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift));
-            const uint y = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift));
-            const uint z = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift));
-
-            uint dst = 0;
-
-            dst |= x;
-            dst |= y << 8;
-            dst |= z << 16;
-
-            return dst;
-        }
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void RGB2XYZConvert_32F(const T& src, D& dst)
-        {
-            const float b = bidx == 0 ? src.x : src.z;
-            const float g = src.y;
-            const float r = bidx == 0 ? src.z : src.x;
-
-            dst.x = r * c_RGB2XYZ_D65f[0] + g * c_RGB2XYZ_D65f[1] + b * c_RGB2XYZ_D65f[2];
-            dst.y = r * c_RGB2XYZ_D65f[3] + g * c_RGB2XYZ_D65f[4] + b * c_RGB2XYZ_D65f[5];
-            dst.z = r * c_RGB2XYZ_D65f[6] + g * c_RGB2XYZ_D65f[7] + b * c_RGB2XYZ_D65f[8];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct RGB2XYZ
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                RGB2XYZConvert_8U<bidx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2XYZ() {}
-            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
-        };
-
-        template <int scn, int dcn, int bidx> struct RGB2XYZ<float, scn, dcn, bidx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2XYZConvert_32F<bidx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2XYZ() {}
-            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
-        };
-
-        template <int bidx> struct RGB2XYZ<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return RGB2XYZConvert_8UC4<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2XYZ() {}
-            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2XYZ<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f };
-        __constant__ int   c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 };
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void XYZ2RGBConvert_8U(const T& src, D& dst)
-        {
-            (bidx == 0 ? dst.z : dst.x) = saturate_cast<typename VecTraits<D>::elem_type>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift));
-            dst.y                       = saturate_cast<typename VecTraits<D>::elem_type>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift));
-            (bidx == 0 ? dst.x : dst.z) = saturate_cast<typename VecTraits<D>::elem_type>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift));
-        }
-
-        template <int bidx> static __device__ __forceinline__ uint XYZ2RGBConvert_8UC4(uint src)
-        {
-            const int x = 0xff & src;
-            const int y = 0xff & (src >> 8);
-            const int z = 0xff & (src >> 16);
-
-            const uint r = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift));
-            const uint g = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift));
-            const uint b = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift));
-
-            uint dst = 0xffu << 24;
-
-            dst |= b << (bidx * 8);
-            dst |= g << 8;
-            dst |= r << ((bidx ^ 2) * 8);
-
-            return dst;
-        }
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void XYZ2RGBConvert_32F(const T& src, D& dst)
-        {
-            (bidx == 0 ? dst.z : dst.x) = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2];
-            dst.y                       = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5];
-            (bidx == 0 ? dst.x : dst.z) = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct XYZ2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                XYZ2RGBConvert_8U<bidx>(src, dst);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ XYZ2RGB() {}
-            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
-        };
-
-        template <int scn, int dcn, int bidx> struct XYZ2RGB<float, scn, dcn, bidx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                XYZ2RGBConvert_32F<bidx>(src, dst);
-                setAlpha(dst, ColorChannel<float>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ XYZ2RGB() {}
-            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
-        };
-
-        template <int bidx> struct XYZ2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return XYZ2RGBConvert_8UC4<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ XYZ2RGB() {}
-            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::XYZ2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-////////////////////////////////////// RGB <-> HSV ///////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ int c_HsvDivTable   [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096};
-        __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482};
-        __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685};
-
-        template <int bidx, int hr, typename T, typename D> static __device__ void RGB2HSVConvert_8U(const T& src, D& dst)
-        {
-            const int hsv_shift = 12;
-            const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256;
-
-            int b = bidx == 0 ? src.x : src.z;
-            int g = src.y;
-            int r = bidx == 0 ? src.z : src.x;
-
-            int h, s, v = b;
-            int vmin = b, diff;
-            int vr, vg;
-
-            v = ::max(v, g);
-            v = ::max(v, r);
-            vmin = ::min(vmin, g);
-            vmin = ::min(vmin, r);
-
-            diff = v - vmin;
-            vr = (v == r) * -1;
-            vg = (v == g) * -1;
-
-            s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
-            h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h += (h < 0) * hr;
-
-            dst.x = saturate_cast<uchar>(h);
-            dst.y = (uchar)s;
-            dst.z = (uchar)v;
-        }
-
-        template <int bidx, int hr> static __device__ uint RGB2HSVConvert_8UC4(uint src)
-        {
-            const int hsv_shift = 12;
-            const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256;
-
-            const int b = 0xff & (src >> (bidx * 8));
-            const int g = 0xff & (src >> 8);
-            const int r = 0xff & (src >> ((bidx ^ 2) * 8));
-
-            int h, s, v = b;
-            int vmin = b, diff;
-            int vr, vg;
-
-            v = ::max(v, g);
-            v = ::max(v, r);
-            vmin = ::min(vmin, g);
-            vmin = ::min(vmin, r);
-
-            diff = v - vmin;
-            vr = (v == r) * -1;
-            vg = (v == g) * -1;
-
-            s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
-            h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h += (h < 0) * hr;
-
-            uint dst = 0;
-
-            dst |= saturate_cast<uchar>(h);
-            dst |= (0xffu & s) << 8;
-            dst |= (0xffu & v) << 16;
-
-            return dst;
-        }
-
-        template <int bidx, int hr, typename T, typename D> static __device__ void RGB2HSVConvert_32F(const T& src, D& dst)
-        {
-            const float hscale = hr * (1.f / 360.f);
-
-            float b = bidx == 0 ? src.x : src.z;
-            float g = src.y;
-            float r = bidx == 0 ? src.z : src.x;
-
-            float h, s, v;
-
-            float vmin, diff;
-
-            v = vmin = r;
-            v = fmax(v, g);
-            v = fmax(v, b);
-            vmin = fmin(vmin, g);
-            vmin = fmin(vmin, b);
-
-            diff = v - vmin;
-            s = diff / (float)(::fabs(v) + numeric_limits<float>::epsilon());
-            diff = (float)(60. / (diff + numeric_limits<float>::epsilon()));
-
-            h  = (v == r) * (g - b) * diff;
-            h += (v != r && v == g) * ((b - r) * diff + 120.f);
-            h += (v != r && v != g) * ((r - g) * diff + 240.f);
-            h += (h < 0) * 360.f;
-
-            dst.x = h * hscale;
-            dst.y = s;
-            dst.z = v;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct RGB2HSV
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                RGB2HSVConvert_8U<bidx, hr>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2HSV() {}
-            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
-        };
-
-        template <int scn, int dcn, int bidx, int hr> struct RGB2HSV<float, scn, dcn, bidx, hr>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2HSVConvert_32F<bidx, hr>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2HSV() {}
-            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
-        };
-
-        template <int bidx, int hr> struct RGB2HSV<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return RGB2HSVConvert_8UC4<bidx, hr>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2HSV() {}
-            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HSV<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HSV<T, scn, dcn, bidx, 256> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HSV<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HSV<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} };
-
-        template <int bidx, int hr, typename T, typename D> static __device__ void HSV2RGBConvert_32F(const T& src, D& dst)
-        {
-            const float hscale = 6.f / hr;
-
-            float h = src.x, s = src.y, v = src.z;
-            float b = v, g = v, r = v;
-
-            if (s != 0)
-            {
-                h *= hscale;
-
-                if( h < 0 )
-                    do h += 6; while( h < 0 );
-                else if( h >= 6 )
-                    do h -= 6; while( h >= 6 );
-
-                int sector = __float2int_rd(h);
-                h -= sector;
-
-                if ( (unsigned)sector >= 6u )
-                {
-                    sector = 0;
-                    h = 0.f;
-                }
-
-                float tab[4];
-                tab[0] = v;
-                tab[1] = v * (1.f - s);
-                tab[2] = v * (1.f - s * h);
-                tab[3] = v * (1.f - s * (1.f - h));
-
-                b = tab[c_HsvSectorData[sector][0]];
-                g = tab[c_HsvSectorData[sector][1]];
-                r = tab[c_HsvSectorData[sector][2]];
-            }
-
-            dst.x = (bidx == 0 ? b : r);
-            dst.y = g;
-            dst.z = (bidx == 0 ? r : b);
-        }
-
-        template <int bidx, int HR, typename T, typename D> static __device__ void HSV2RGBConvert_8U(const T& src, D& dst)
-        {
-            float3 buf;
-
-            buf.x = src.x;
-            buf.y = src.y * (1.f / 255.f);
-            buf.z = src.z * (1.f / 255.f);
-
-            HSV2RGBConvert_32F<bidx, HR>(buf, buf);
-
-            dst.x = saturate_cast<uchar>(buf.x * 255.f);
-            dst.y = saturate_cast<uchar>(buf.y * 255.f);
-            dst.z = saturate_cast<uchar>(buf.z * 255.f);
-        }
-
-        template <int bidx, int hr> static __device__ uint HSV2RGBConvert_8UC4(uint src)
-        {
-            float3 buf;
-
-            buf.x = src & 0xff;
-            buf.y = ((src >> 8) & 0xff) * (1.f/255.f);
-            buf.z = ((src >> 16) & 0xff) * (1.f/255.f);
-
-            HSV2RGBConvert_32F<bidx, hr>(buf, buf);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(buf.x * 255.f);
-            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
-            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
-
-            return dst;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct HSV2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                HSV2RGBConvert_8U<bidx, hr>(src, dst);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ HSV2RGB() {}
-            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
-        };
-
-        template <int scn, int dcn, int bidx, int hr> struct HSV2RGB<float, scn, dcn, bidx, hr>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                HSV2RGBConvert_32F<bidx, hr>(src, dst);
-                setAlpha(dst, ColorChannel<float>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ HSV2RGB() {}
-            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
-        };
-
-        template <int bidx, int hr> struct HSV2RGB<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return HSV2RGBConvert_8UC4<bidx, hr>(src);
-            }
-
-            __host__ __device__ __forceinline__ HSV2RGB() {}
-            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::HSV2RGB<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::HSV2RGB<T, scn, dcn, bidx, 255> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::HSV2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::HSV2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-/////////////////////////////////////// RGB <-> HLS ////////////////////////////////////////
-
-    namespace color_detail
-    {
-        template <int bidx, int hr, typename T, typename D> static __device__ void RGB2HLSConvert_32F(const T& src, D& dst)
-        {
-            const float hscale = hr * (1.f / 360.f);
-
-            float b = bidx == 0 ? src.x : src.z;
-            float g = src.y;
-            float r = bidx == 0 ? src.z : src.x;
-
-            float h = 0.f, s = 0.f, l;
-            float vmin, vmax, diff;
-
-            vmax = vmin = r;
-            vmax = fmax(vmax, g);
-            vmax = fmax(vmax, b);
-            vmin = fmin(vmin, g);
-            vmin = fmin(vmin, b);
-
-            diff = vmax - vmin;
-            l = (vmax + vmin) * 0.5f;
-
-            if (diff > numeric_limits<float>::epsilon())
-            {
-                s = (l < 0.5f) * diff / (vmax + vmin);
-                s += (l >= 0.5f) * diff / (2.0f - vmax - vmin);
-
-                diff = 60.f / diff;
-
-                h  = (vmax == r) * (g - b) * diff;
-                h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f);
-                h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f);
-                h += (h < 0.f) * 360.f;
-            }
-
-            dst.x = h * hscale;
-            dst.y = l;
-            dst.z = s;
-        }
-
-        template <int bidx, int hr, typename T, typename D> static __device__ void RGB2HLSConvert_8U(const T& src, D& dst)
-        {
-            float3 buf;
-
-            buf.x = src.x * (1.f / 255.f);
-            buf.y = src.y * (1.f / 255.f);
-            buf.z = src.z * (1.f / 255.f);
-
-            RGB2HLSConvert_32F<bidx, hr>(buf, buf);
-
-            dst.x = saturate_cast<uchar>(buf.x);
-            dst.y = saturate_cast<uchar>(buf.y*255.f);
-            dst.z = saturate_cast<uchar>(buf.z*255.f);
-        }
-
-        template <int bidx, int hr> static __device__ uint RGB2HLSConvert_8UC4(uint src)
-        {
-            float3 buf;
-
-            buf.x = (0xff & src) * (1.f / 255.f);
-            buf.y = (0xff & (src >> 8)) * (1.f / 255.f);
-            buf.z = (0xff & (src >> 16)) * (1.f / 255.f);
-
-            RGB2HLSConvert_32F<bidx, hr>(buf, buf);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(buf.x);
-            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
-            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
-
-            return dst;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct RGB2HLS
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                RGB2HLSConvert_8U<bidx, hr>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2HLS() {}
-            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
-        };
-
-        template <int scn, int dcn, int bidx, int hr> struct RGB2HLS<float, scn, dcn, bidx, hr>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2HLSConvert_32F<bidx, hr>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2HLS() {}
-            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
-        };
-
-        template <int bidx, int hr> struct RGB2HLS<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return RGB2HLSConvert_8UC4<bidx, hr>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2HLS() {}
-            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HLS<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HLS<T, scn, dcn, bidx, 256> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HLS<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2HLS<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} };
-
-        template <int bidx, int hr, typename T, typename D> static __device__ void HLS2RGBConvert_32F(const T& src, D& dst)
-        {
-            const float hscale = 6.0f / hr;
-
-            float h = src.x, l = src.y, s = src.z;
-            float b = l, g = l, r = l;
-
-            if (s != 0)
-            {
-                float p2  = (l <= 0.5f) * l * (1 + s);
-                      p2 += (l > 0.5f) * (l + s - l * s);
-                float p1 = 2 * l - p2;
-
-                h *= hscale;
-
-                if( h < 0 )
-                    do h += 6; while( h < 0 );
-                else if( h >= 6 )
-                    do h -= 6; while( h >= 6 );
-
-                int sector;
-                sector = __float2int_rd(h);
-
-                h -= sector;
-
-                float tab[4];
-                tab[0] = p2;
-                tab[1] = p1;
-                tab[2] = p1 + (p2 - p1) * (1 - h);
-                tab[3] = p1 + (p2 - p1) * h;
-
-                b = tab[c_HlsSectorData[sector][0]];
-                g = tab[c_HlsSectorData[sector][1]];
-                r = tab[c_HlsSectorData[sector][2]];
-            }
-
-            dst.x = bidx == 0 ? b : r;
-            dst.y = g;
-            dst.z = bidx == 0 ? r : b;
-        }
-
-        template <int bidx, int hr, typename T, typename D> static __device__ void HLS2RGBConvert_8U(const T& src, D& dst)
-        {
-            float3 buf;
-
-            buf.x = src.x;
-            buf.y = src.y * (1.f / 255.f);
-            buf.z = src.z * (1.f / 255.f);
-
-            HLS2RGBConvert_32F<bidx, hr>(buf, buf);
-
-            dst.x = saturate_cast<uchar>(buf.x * 255.f);
-            dst.y = saturate_cast<uchar>(buf.y * 255.f);
-            dst.z = saturate_cast<uchar>(buf.z * 255.f);
-        }
-
-        template <int bidx, int hr> static __device__ uint HLS2RGBConvert_8UC4(uint src)
-        {
-            float3 buf;
-
-            buf.x = 0xff & src;
-            buf.y = (0xff & (src >> 8)) * (1.f / 255.f);
-            buf.z = (0xff & (src >> 16)) * (1.f / 255.f);
-
-            HLS2RGBConvert_32F<bidx, hr>(buf, buf);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(buf.x * 255.f);
-            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
-            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
-
-            return dst;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct HLS2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                HLS2RGBConvert_8U<bidx, hr>(src, dst);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ HLS2RGB() {}
-            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
-        };
-
-        template <int scn, int dcn, int bidx, int hr> struct HLS2RGB<float, scn, dcn, bidx, hr>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                HLS2RGBConvert_32F<bidx, hr>(src, dst);
-                setAlpha(dst, ColorChannel<float>::max());
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ HLS2RGB() {}
-            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
-        };
-
-        template <int bidx, int hr> struct HLS2RGB<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return HLS2RGBConvert_8UC4<bidx, hr>(src);
-            }
-
-            __host__ __device__ __forceinline__ HLS2RGB() {}
-            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::HLS2RGB<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::HLS2RGB<T, scn, dcn, bidx, 255> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::HLS2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::gpu::device::color_detail::HLS2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> Lab /////////////////////////////////////
-
-    namespace color_detail
-    {
-        enum
-        {
-            LAB_CBRT_TAB_SIZE = 1024,
-            GAMMA_TAB_SIZE = 1024,
-            lab_shift = xyz_shift,
-            gamma_shift = 3,
-            lab_shift2 = (lab_shift + gamma_shift),
-            LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift))
-        };
-
-        __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040};
-
-        __device__ __forceinline__ int LabCbrt_b(int i)
-        {
-            float x = i * (1.f / (255.f * (1 << gamma_shift)));
-            return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x));
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LabConvert_8U(const T& src, D& dst)
-        {
-            const int Lscale = (116 * 255 + 50) / 100;
-            const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100);
-
-            int B = blueIdx == 0 ? src.x : src.z;
-            int G = src.y;
-            int R = blueIdx == 0 ? src.z : src.x;
-
-            if (srgb)
-            {
-                B = c_sRGBGammaTab_b[B];
-                G = c_sRGBGammaTab_b[G];
-                R = c_sRGBGammaTab_b[R];
-            }
-            else
-            {
-                B <<= 3;
-                G <<= 3;
-                R <<= 3;
-            }
-
-            int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift));
-            int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift));
-            int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift));
-
-            int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2);
-            int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2);
-            int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2);
-
-            dst.x = saturate_cast<uchar>(L);
-            dst.y = saturate_cast<uchar>(a);
-            dst.z = saturate_cast<uchar>(b);
-        }
-
-        __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n)
-        {
-            int ix = ::min(::max(int(x), 0), n-1);
-            x -= ix;
-            tab += ix * 4;
-            return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0];
-        }
-
-        __constant__ float c_sRGBGammaTab[] = 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-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LabConvert_32F(const T& src, D& dst)
-        {
-            const float _1_3 = 1.0f / 3.0f;
-            const float _a = 16.0f / 116.0f;
-
-            float B = blueIdx == 0 ? src.x : src.z;
-            float G = src.y;
-            float R = blueIdx == 0 ? src.z : src.x;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            float X = B * 0.189828f + G * 0.376219f + R * 0.433953f;
-            float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f;
-            float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f;
-
-            float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a);
-            float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a);
-            float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a);
-
-            float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y);
-            float a = 500.f * (FX - FY);
-            float b = 200.f * (FY - FZ);
-
-            dst.x = L;
-            dst.y = a;
-            dst.z = b;
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct RGB2Lab;
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Lab<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                RGB2LabConvert_8U<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2Lab() {}
-            __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {}
-        };
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Lab<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2LabConvert_32F<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2Lab() {}
-            __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2Lab<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_sRGBInvGammaTab[] = 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-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Lab2RGBConvert_32F(const T& src, D& dst)
-        {
-            const float lThresh = 0.008856f * 903.3f;
-            const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f;
-
-            float Y, fy;
-
-            if (src.x <= lThresh)
-            {
-                Y = src.x / 903.3f;
-                fy = 7.787f * Y + 16.0f / 116.0f;
-            }
-            else
-            {
-                fy = (src.x + 16.0f) / 116.0f;
-                Y = fy * fy * fy;
-            }
-
-            float X = src.y / 500.0f + fy;
-            float Z = fy - src.z / 200.0f;
-
-            if (X <= fThresh)
-                X = (X - 16.0f / 116.0f) / 7.787f;
-            else
-                X = X * X * X;
-
-            if (Z <= fThresh)
-                Z = (Z - 16.0f / 116.0f) / 7.787f;
-            else
-                Z = Z * Z * Z;
-
-            float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z;
-            float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z;
-            float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            dst.x = blueIdx == 0 ? B : R;
-            dst.y = G;
-            dst.z = blueIdx == 0 ? R : B;
-            setAlpha(dst, ColorChannel<float>::max());
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Lab2RGBConvert_8U(const T& src, D& dst)
-        {
-            float3 srcf, dstf;
-
-            srcf.x = src.x * (100.f / 255.f);
-            srcf.y = src.y - 128;
-            srcf.z = src.z - 128;
-
-            Lab2RGBConvert_32F<srgb, blueIdx>(srcf, dstf);
-
-            dst.x = saturate_cast<uchar>(dstf.x * 255.f);
-            dst.y = saturate_cast<uchar>(dstf.y * 255.f);
-            dst.z = saturate_cast<uchar>(dstf.z * 255.f);
-            setAlpha(dst, ColorChannel<uchar>::max());
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct Lab2RGB;
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Lab2RGB<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                Lab2RGBConvert_8U<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ Lab2RGB() {}
-            __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {}
-        };
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Lab2RGB<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                Lab2RGBConvert_32F<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ Lab2RGB() {}
-            __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::Lab2RGB<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> Luv /////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_LabCbrtTab[] = 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-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LuvConvert_32F(const T& src, D& dst)
-        {
-            const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3);
-            const float _un = 13 * (4 * 0.950456f * _d);
-            const float _vn = 13 * (9 * _d);
-
-            float B = blueIdx == 0 ? src.x : src.z;
-            float G = src.y;
-            float R = blueIdx == 0 ? src.z : src.x;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            float X = R * 0.412453f + G * 0.357580f + B * 0.180423f;
-            float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f;
-            float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f;
-
-            float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE);
-            L = 116.f * L - 16.f;
-
-            const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits<float>::epsilon());
-            float u = L * (X * d - _un);
-            float v = L * ((9 * 0.25f) * Y * d - _vn);
-
-            dst.x = L;
-            dst.y = u;
-            dst.z = v;
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LuvConvert_8U(const T& src, D& dst)
-        {
-            float3 srcf, dstf;
-
-            srcf.x = src.x * (1.f / 255.f);
-            srcf.y = src.y * (1.f / 255.f);
-            srcf.z = src.z * (1.f / 255.f);
-
-            RGB2LuvConvert_32F<srgb, blueIdx>(srcf, dstf);
-
-            dst.x = saturate_cast<uchar>(dstf.x * 2.55f);
-            dst.y = saturate_cast<uchar>(dstf.y * 0.72033898305084743f + 96.525423728813564f);
-            dst.z = saturate_cast<uchar>(dstf.z * 0.9732824427480916f + 136.259541984732824f);
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct RGB2Luv;
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Luv<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                RGB2LuvConvert_8U<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2Luv() {}
-            __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {}
-        };
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Luv<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2LuvConvert_32F<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2Luv() {}
-            __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::RGB2Luv<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Luv2RGBConvert_32F(const T& src, D& dst)
-        {
-            const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3);
-            const float _un = 4 * 0.950456f * _d;
-            const float _vn = 9 * _d;
-
-            float L = src.x;
-            float u = src.y;
-            float v = src.z;
-
-            float Y = (L + 16.f) * (1.f / 116.f);
-            Y = Y * Y * Y;
-
-            float d = (1.f / 13.f) / L;
-            u = u * d + _un;
-            v = v * d + _vn;
-
-            float iv = 1.f / v;
-            float X = 2.25f * u * Y * iv;
-            float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv;
-
-            float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z;
-            float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z;
-            float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            dst.x = blueIdx == 0 ? B : R;
-            dst.y = G;
-            dst.z = blueIdx == 0 ? R : B;
-            setAlpha(dst, ColorChannel<float>::max());
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Luv2RGBConvert_8U(const T& src, D& dst)
-        {
-            float3 srcf, dstf;
-
-            srcf.x = src.x * (100.f / 255.f);
-            srcf.y = src.y * 1.388235294117647f - 134.f;
-            srcf.z = src.z * 1.027450980392157f - 140.f;
-
-            Luv2RGBConvert_32F<srgb, blueIdx>(srcf, dstf);
-
-            dst.x = saturate_cast<uchar>(dstf.x * 255.f);
-            dst.y = saturate_cast<uchar>(dstf.y * 255.f);
-            dst.z = saturate_cast<uchar>(dstf.z * 255.f);
-            setAlpha(dst, ColorChannel<uchar>::max());
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct Luv2RGB;
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Luv2RGB<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                Luv2RGBConvert_8U<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ Luv2RGB() {}
-            __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {}
-        };
-
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Luv2RGB<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                Luv2RGBConvert_32F<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ Luv2RGB() {}
-            __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {}
-        };
-    }
-
-#define OPENCV_GPU_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::gpu::device::color_detail::Luv2RGB<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    #undef CV_DESCALE
-
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_COLOR_DETAIL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/reduce.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/reduce.hpp
deleted file mode 100644
index 091a160..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/reduce.hpp
+++ /dev/null
@@ -1,361 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_REDUCE_DETAIL_HPP__
-#define __OPENCV_GPU_REDUCE_DETAIL_HPP__
-
-#include <thrust/tuple.h>
-#include "../warp.hpp"
-#include "../warp_shuffle.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    namespace reduce_detail
-    {
-        template <typename T> struct GetType;
-        template <typename T> struct GetType<T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<volatile T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<T&>
-        {
-            typedef T type;
-        };
-
-        template <unsigned int I, unsigned int N>
-        struct For
-        {
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
-            {
-                thrust::get<I>(smem)[tid] = thrust::get<I>(val);
-
-                For<I + 1, N>::loadToSmem(smem, val, tid);
-            }
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
-            {
-                thrust::get<I>(val) = thrust::get<I>(smem)[tid];
-
-                For<I + 1, N>::loadFromSmem(smem, val, tid);
-            }
-
-            template <class PointerTuple, class ValTuple, class OpTuple>
-            static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op)
-            {
-                typename GetType<typename thrust::tuple_element<I, PointerTuple>::type>::type reg = thrust::get<I>(smem)[tid + delta];
-                thrust::get<I>(smem)[tid] = thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
-
-                For<I + 1, N>::merge(smem, val, tid, delta, op);
-            }
-            template <class ValTuple, class OpTuple>
-            static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op)
-            {
-                typename GetType<typename thrust::tuple_element<I, ValTuple>::type>::type reg = shfl_down(thrust::get<I>(val), delta, width);
-                thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
-
-                For<I + 1, N>::mergeShfl(val, delta, width, op);
-            }
-        };
-        template <unsigned int N>
-        struct For<N, N>
-        {
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int)
-            {
-            }
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int)
-            {
-            }
-
-            template <class PointerTuple, class ValTuple, class OpTuple>
-            static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&)
-            {
-            }
-            template <class ValTuple, class OpTuple>
-            static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&)
-            {
-            }
-        };
-
-        template <typename T>
-        __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid)
-        {
-            smem[tid] = val;
-        }
-        template <typename T>
-        __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid)
-        {
-            val = smem[tid];
-        }
-        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
-        __device__ __forceinline__ void loadToSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                                       const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                                       unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadToSmem(smem, val, tid);
-        }
-        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
-        __device__ __forceinline__ void loadFromSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                                         const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                                         unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadFromSmem(smem, val, tid);
-        }
-
-        template <typename T, class Op>
-        __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op)
-        {
-            T reg = smem[tid + delta];
-            smem[tid] = val = op(val, reg);
-        }
-        template <typename T, class Op>
-        __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op)
-        {
-            T reg = shfl_down(val, delta, width);
-            val = op(val, reg);
-        }
-        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
-                  class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
-        __device__ __forceinline__ void merge(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                              const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                              unsigned int tid,
-                                              unsigned int delta,
-                                              const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::merge(smem, val, tid, delta, op);
-        }
-        template <typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
-                  class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
-        __device__ __forceinline__ void mergeShfl(const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                                  unsigned int delta,
-                                                  unsigned int width,
-                                                  const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9> >::value>::mergeShfl(val, delta, width, op);
-        }
-
-        template <unsigned int N> struct Generic
-        {
-            template <typename Pointer, typename Reference, class Op>
-            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-                loadToSmem(smem, val, tid);
-                if (N >= 32)
-                    __syncthreads();
-
-                if (N >= 2048)
-                {
-                    if (tid < 1024)
-                        merge(smem, val, tid, 1024, op);
-
-                    __syncthreads();
-                }
-                if (N >= 1024)
-                {
-                    if (tid < 512)
-                        merge(smem, val, tid, 512, op);
-
-                    __syncthreads();
-                }
-                if (N >= 512)
-                {
-                    if (tid < 256)
-                        merge(smem, val, tid, 256, op);
-
-                    __syncthreads();
-                }
-                if (N >= 256)
-                {
-                    if (tid < 128)
-                        merge(smem, val, tid, 128, op);
-
-                    __syncthreads();
-                }
-                if (N >= 128)
-                {
-                    if (tid < 64)
-                        merge(smem, val, tid, 64, op);
-
-                    __syncthreads();
-                }
-                if (N >= 64)
-                {
-                    if (tid < 32)
-                        merge(smem, val, tid, 32, op);
-                }
-
-                if (tid < 16)
-                {
-                    merge(smem, val, tid, 16, op);
-                    merge(smem, val, tid, 8, op);
-                    merge(smem, val, tid, 4, op);
-                    merge(smem, val, tid, 2, op);
-                    merge(smem, val, tid, 1, op);
-                }
-            }
-        };
-
-        template <unsigned int I, typename Pointer, typename Reference, class Op>
-        struct Unroll
-        {
-            static __device__ void loopShfl(Reference val, Op op, unsigned int N)
-            {
-                mergeShfl(val, I, N, op);
-                Unroll<I / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
-            }
-            static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-                merge(smem, val, tid, I, op);
-                Unroll<I / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
-            }
-        };
-        template <typename Pointer, typename Reference, class Op>
-        struct Unroll<0, Pointer, Reference, Op>
-        {
-            static __device__ void loopShfl(Reference, Op, unsigned int)
-            {
-            }
-            static __device__ void loop(Pointer, Reference, unsigned int, Op)
-            {
-            }
-        };
-
-        template <unsigned int N> struct WarpOptimized
-        {
-            template <typename Pointer, typename Reference, class Op>
-            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-            #if __CUDA_ARCH__ >= 300
-                (void) smem;
-                (void) tid;
-
-                Unroll<N / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
-            #else
-                loadToSmem(smem, val, tid);
-
-                if (tid < N / 2)
-                    Unroll<N / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
-            #endif
-            }
-        };
-
-        template <unsigned int N> struct GenericOptimized32
-        {
-            enum { M = N / 32 };
-
-            template <typename Pointer, typename Reference, class Op>
-            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-                const unsigned int laneId = Warp::laneId();
-
-            #if __CUDA_ARCH__ >= 300
-                Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize);
-
-                if (laneId == 0)
-                    loadToSmem(smem, val, tid / 32);
-            #else
-                loadToSmem(smem, val, tid);
-
-                if (laneId < 16)
-                    Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op);
-
-                __syncthreads();
-
-                if (laneId == 0)
-                    loadToSmem(smem, val, tid / 32);
-            #endif
-
-                __syncthreads();
-
-                loadFromSmem(smem, val, tid);
-
-                if (tid < 32)
-                {
-                #if __CUDA_ARCH__ >= 300
-                    Unroll<M / 2, Pointer, Reference, Op>::loopShfl(val, op, M);
-                #else
-                    Unroll<M / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
-                #endif
-                }
-            }
-        };
-
-        template <bool val, class T1, class T2> struct StaticIf;
-        template <class T1, class T2> struct StaticIf<true, T1, T2>
-        {
-            typedef T1 type;
-        };
-        template <class T1, class T2> struct StaticIf<false, T1, T2>
-        {
-            typedef T2 type;
-        };
-
-        template <unsigned int N> struct IsPowerOf2
-        {
-            enum { value = ((N != 0) && !(N & (N - 1))) };
-        };
-
-        template <unsigned int N> struct Dispatcher
-        {
-            typedef typename StaticIf<
-                (N <= 32) && IsPowerOf2<N>::value,
-                WarpOptimized<N>,
-                typename StaticIf<
-                    (N <= 1024) && IsPowerOf2<N>::value,
-                    GenericOptimized32<N>,
-                    Generic<N>
-                >::type
-            >::type reductor;
-        };
-    }
-}}}
-
-#endif // __OPENCV_GPU_REDUCE_DETAIL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/reduce_key_val.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/reduce_key_val.hpp
deleted file mode 100644
index a84e0c2..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/reduce_key_val.hpp
+++ /dev/null
@@ -1,498 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_PRED_VAL_REDUCE_DETAIL_HPP__
-#define __OPENCV_GPU_PRED_VAL_REDUCE_DETAIL_HPP__
-
-#include <thrust/tuple.h>
-#include "../warp.hpp"
-#include "../warp_shuffle.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    namespace reduce_key_val_detail
-    {
-        template <typename T> struct GetType;
-        template <typename T> struct GetType<T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<volatile T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<T&>
-        {
-            typedef T type;
-        };
-
-        template <unsigned int I, unsigned int N>
-        struct For
-        {
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
-            {
-                thrust::get<I>(smem)[tid] = thrust::get<I>(data);
-
-                For<I + 1, N>::loadToSmem(smem, data, tid);
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
-            {
-                thrust::get<I>(data) = thrust::get<I>(smem)[tid];
-
-                For<I + 1, N>::loadFromSmem(smem, data, tid);
-            }
-
-            template <class ReferenceTuple>
-            static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width)
-            {
-                thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
-
-                For<I + 1, N>::copyShfl(val, delta, width);
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta)
-            {
-                thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
-
-                For<I + 1, N>::copy(svals, val, tid, delta);
-            }
-
-            template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width)
-            {
-                typename GetType<typename thrust::tuple_element<I, KeyReferenceTuple>::type>::type reg = shfl_down(thrust::get<I>(key), delta, width);
-
-                if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
-                {
-                    thrust::get<I>(key) = reg;
-                    thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
-                }
-
-                For<I + 1, N>::mergeShfl(key, val, cmp, delta, width);
-            }
-            template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key,
-                                         const ValPointerTuple& svals, const ValReferenceTuple& val,
-                                         const CmpTuple& cmp,
-                                         unsigned int tid, unsigned int delta)
-            {
-                typename GetType<typename thrust::tuple_element<I, KeyPointerTuple>::type>::type reg = thrust::get<I>(skeys)[tid + delta];
-
-                if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
-                {
-                    thrust::get<I>(skeys)[tid] = thrust::get<I>(key) = reg;
-                    thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
-                }
-
-                For<I + 1, N>::merge(skeys, key, svals, val, cmp, tid, delta);
-            }
-        };
-        template <unsigned int N>
-        struct For<N, N>
-        {
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
-            {
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
-            {
-            }
-
-            template <class ReferenceTuple>
-            static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int)
-            {
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int)
-            {
-            }
-
-            template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int)
-            {
-            }
-            template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&,
-                                         const ValPointerTuple&, const ValReferenceTuple&,
-                                         const CmpTuple&,
-                                         unsigned int, unsigned int)
-            {
-            }
-        };
-
-        //////////////////////////////////////////////////////
-        // loadToSmem
-
-        template <typename T>
-        __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid)
-        {
-            smem[tid] = data;
-        }
-        template <typename T>
-        __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid)
-        {
-            data = smem[tid];
-        }
-        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void loadToSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
-                                                   const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
-                                                   unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadToSmem(smem, data, tid);
-        }
-        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void loadFromSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
-                                                     const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
-                                                     unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadFromSmem(smem, data, tid);
-        }
-
-        //////////////////////////////////////////////////////
-        // copyVals
-
-        template <typename V>
-        __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width)
-        {
-            val = shfl_down(val, delta, width);
-        }
-        template <typename V>
-        __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta)
-        {
-            svals[tid] = val = svals[tid + delta];
-        }
-        template <typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void copyValsShfl(const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                     unsigned int delta,
-                                                     int width)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9> >::value>::copyShfl(val, delta, width);
-        }
-        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void copyVals(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                 unsigned int tid, unsigned int delta)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::copy(svals, val, tid, delta);
-        }
-
-        //////////////////////////////////////////////////////
-        // merge
-
-        template <typename K, typename V, class Cmp>
-        __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width)
-        {
-            K reg = shfl_down(key, delta, width);
-
-            if (cmp(reg, key))
-            {
-                key = reg;
-                copyValsShfl(val, delta, width);
-            }
-        }
-        template <typename K, typename V, class Cmp>
-        __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta)
-        {
-            K reg = skeys[tid + delta];
-
-            if (cmp(reg, key))
-            {
-                skeys[tid] = key = reg;
-                copyVals(svals, val, tid, delta);
-            }
-        }
-        template <typename K,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp>
-        __device__ __forceinline__ void mergeShfl(K& key,
-                                                  const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                  const Cmp& cmp,
-                                                  unsigned int delta, int width)
-        {
-            K reg = shfl_down(key, delta, width);
-
-            if (cmp(reg, key))
-            {
-                key = reg;
-                copyValsShfl(val, delta, width);
-            }
-        }
-        template <typename K,
-                  typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp>
-        __device__ __forceinline__ void merge(volatile K* skeys, K& key,
-                                              const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                              const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                              const Cmp& cmp, unsigned int tid, unsigned int delta)
-        {
-            K reg = skeys[tid + delta];
-
-            if (cmp(reg, key))
-            {
-                skeys[tid] = key = reg;
-                copyVals(svals, val, tid, delta);
-            }
-        }
-        template <typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
-        __device__ __forceinline__ void mergeShfl(const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
-                                                  const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                  const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
-                                                  unsigned int delta, int width)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9> >::value>::mergeShfl(key, val, cmp, delta, width);
-        }
-        template <typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
-                  typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
-                  typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
-        __device__ __forceinline__ void merge(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
-                                              const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
-                                              const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                              const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                              const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
-                                              unsigned int tid, unsigned int delta)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::merge(skeys, key, svals, val, cmp, tid, delta);
-        }
-
-        //////////////////////////////////////////////////////
-        // Generic
-
-        template <unsigned int N> struct Generic
-        {
-            template <class KP, class KR, class VP, class VR, class Cmp>
-            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-                loadToSmem(skeys, key, tid);
-                loadValsToSmem(svals, val, tid);
-                if (N >= 32)
-                    __syncthreads();
-
-                if (N >= 2048)
-                {
-                    if (tid < 1024)
-                        merge(skeys, key, svals, val, cmp, tid, 1024);
-
-                    __syncthreads();
-                }
-                if (N >= 1024)
-                {
-                    if (tid < 512)
-                        merge(skeys, key, svals, val, cmp, tid, 512);
-
-                    __syncthreads();
-                }
-                if (N >= 512)
-                {
-                    if (tid < 256)
-                        merge(skeys, key, svals, val, cmp, tid, 256);
-
-                    __syncthreads();
-                }
-                if (N >= 256)
-                {
-                    if (tid < 128)
-                        merge(skeys, key, svals, val, cmp, tid, 128);
-
-                    __syncthreads();
-                }
-                if (N >= 128)
-                {
-                    if (tid < 64)
-                        merge(skeys, key, svals, val, cmp, tid, 64);
-
-                    __syncthreads();
-                }
-                if (N >= 64)
-                {
-                    if (tid < 32)
-                        merge(skeys, key, svals, val, cmp, tid, 32);
-                }
-
-                if (tid < 16)
-                {
-                    merge(skeys, key, svals, val, cmp, tid, 16);
-                    merge(skeys, key, svals, val, cmp, tid, 8);
-                    merge(skeys, key, svals, val, cmp, tid, 4);
-                    merge(skeys, key, svals, val, cmp, tid, 2);
-                    merge(skeys, key, svals, val, cmp, tid, 1);
-                }
-            }
-        };
-
-        template <unsigned int I, class KP, class KR, class VP, class VR, class Cmp>
-        struct Unroll
-        {
-            static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N)
-            {
-                mergeShfl(key, val, cmp, I, N);
-                Unroll<I / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
-            }
-            static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-                merge(skeys, key, svals, val, cmp, tid, I);
-                Unroll<I / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-            }
-        };
-        template <class KP, class KR, class VP, class VR, class Cmp>
-        struct Unroll<0, KP, KR, VP, VR, Cmp>
-        {
-            static __device__ void loopShfl(KR, VR, Cmp, unsigned int)
-            {
-            }
-            static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp)
-            {
-            }
-        };
-
-        template <unsigned int N> struct WarpOptimized
-        {
-            template <class KP, class KR, class VP, class VR, class Cmp>
-            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-            #if 0 // __CUDA_ARCH__ >= 300
-                (void) skeys;
-                (void) svals;
-                (void) tid;
-
-                Unroll<N / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
-            #else
-                loadToSmem(skeys, key, tid);
-                loadToSmem(svals, val, tid);
-
-                if (tid < N / 2)
-                    Unroll<N / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-            #endif
-            }
-        };
-
-        template <unsigned int N> struct GenericOptimized32
-        {
-            enum { M = N / 32 };
-
-            template <class KP, class KR, class VP, class VR, class Cmp>
-            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-                const unsigned int laneId = Warp::laneId();
-
-            #if 0 // __CUDA_ARCH__ >= 300
-                Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize);
-
-                if (laneId == 0)
-                {
-                    loadToSmem(skeys, key, tid / 32);
-                    loadToSmem(svals, val, tid / 32);
-                }
-            #else
-                loadToSmem(skeys, key, tid);
-                loadToSmem(svals, val, tid);
-
-                if (laneId < 16)
-                    Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-
-                __syncthreads();
-
-                if (laneId == 0)
-                {
-                    loadToSmem(skeys, key, tid / 32);
-                    loadToSmem(svals, val, tid / 32);
-                }
-            #endif
-
-                __syncthreads();
-
-                loadFromSmem(skeys, key, tid);
-
-                if (tid < 32)
-                {
-                #if 0 // __CUDA_ARCH__ >= 300
-                    loadFromSmem(svals, val, tid);
-
-                    Unroll<M / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, M);
-                #else
-                    Unroll<M / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-                #endif
-                }
-            }
-        };
-
-        template <bool val, class T1, class T2> struct StaticIf;
-        template <class T1, class T2> struct StaticIf<true, T1, T2>
-        {
-            typedef T1 type;
-        };
-        template <class T1, class T2> struct StaticIf<false, T1, T2>
-        {
-            typedef T2 type;
-        };
-
-        template <unsigned int N> struct IsPowerOf2
-        {
-            enum { value = ((N != 0) && !(N & (N - 1))) };
-        };
-
-        template <unsigned int N> struct Dispatcher
-        {
-            typedef typename StaticIf<
-                (N <= 32) && IsPowerOf2<N>::value,
-                WarpOptimized<N>,
-                typename StaticIf<
-                    (N <= 1024) && IsPowerOf2<N>::value,
-                    GenericOptimized32<N>,
-                    Generic<N>
-                >::type
-            >::type reductor;
-        };
-    }
-}}}
-
-#endif // __OPENCV_GPU_PRED_VAL_REDUCE_DETAIL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/transform_detail.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/transform_detail.hpp
deleted file mode 100644
index 10da593..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/transform_detail.hpp
+++ /dev/null
@@ -1,395 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_TRANSFORM_DETAIL_HPP__
-#define __OPENCV_GPU_TRANSFORM_DETAIL_HPP__
-
-#include "../common.hpp"
-#include "../vec_traits.hpp"
-#include "../functional.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    namespace transform_detail
-    {
-        //! Read Write Traits
-
-        template <typename T, typename D, int shift> struct UnaryReadWriteTraits
-        {
-            typedef typename TypeVec<T, shift>::vec_type read_type;
-            typedef typename TypeVec<D, shift>::vec_type write_type;
-        };
-
-        template <typename T1, typename T2, typename D, int shift> struct BinaryReadWriteTraits
-        {
-            typedef typename TypeVec<T1, shift>::vec_type read_type1;
-            typedef typename TypeVec<T2, shift>::vec_type read_type2;
-            typedef typename TypeVec<D, shift>::vec_type write_type;
-        };
-
-        //! Transform kernels
-
-        template <int shift> struct OpUnroller;
-        template <> struct OpUnroller<1>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-            }
-        };
-        template <> struct OpUnroller<2>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src.y);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src1.y, src2.y);
-            }
-        };
-        template <> struct OpUnroller<3>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src.z);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src1.y, src2.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src1.z, src2.z);
-            }
-        };
-        template <> struct OpUnroller<4>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src.z);
-                if (mask(y, x_shifted + 3))
-                    dst.w = op(src.w);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src1.y, src2.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src1.z, src2.z);
-                if (mask(y, x_shifted + 3))
-                    dst.w = op(src1.w, src2.w);
-            }
-        };
-        template <> struct OpUnroller<8>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.a0 = op(src.a0);
-                if (mask(y, x_shifted + 1))
-                    dst.a1 = op(src.a1);
-                if (mask(y, x_shifted + 2))
-                    dst.a2 = op(src.a2);
-                if (mask(y, x_shifted + 3))
-                    dst.a3 = op(src.a3);
-                if (mask(y, x_shifted + 4))
-                    dst.a4 = op(src.a4);
-                if (mask(y, x_shifted + 5))
-                    dst.a5 = op(src.a5);
-                if (mask(y, x_shifted + 6))
-                    dst.a6 = op(src.a6);
-                if (mask(y, x_shifted + 7))
-                    dst.a7 = op(src.a7);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.a0 = op(src1.a0, src2.a0);
-                if (mask(y, x_shifted + 1))
-                    dst.a1 = op(src1.a1, src2.a1);
-                if (mask(y, x_shifted + 2))
-                    dst.a2 = op(src1.a2, src2.a2);
-                if (mask(y, x_shifted + 3))
-                    dst.a3 = op(src1.a3, src2.a3);
-                if (mask(y, x_shifted + 4))
-                    dst.a4 = op(src1.a4, src2.a4);
-                if (mask(y, x_shifted + 5))
-                    dst.a5 = op(src1.a5, src2.a5);
-                if (mask(y, x_shifted + 6))
-                    dst.a6 = op(src1.a6, src2.a6);
-                if (mask(y, x_shifted + 7))
-                    dst.a7 = op(src1.a7, src2.a7);
-            }
-        };
-
-        template <typename T, typename D, typename UnOp, typename Mask>
-        static __global__ void transformSmart(const PtrStepSz<T> src_, PtrStep<D> dst_, const Mask mask, const UnOp op)
-        {
-            typedef TransformFunctorTraits<UnOp> ft;
-            typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::read_type read_type;
-            typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::write_type write_type;
-
-            const int x = threadIdx.x + blockIdx.x * blockDim.x;
-            const int y = threadIdx.y + blockIdx.y * blockDim.y;
-            const int x_shifted = x * ft::smart_shift;
-
-            if (y < src_.rows)
-            {
-                const T* src = src_.ptr(y);
-                D* dst = dst_.ptr(y);
-
-                if (x_shifted + ft::smart_shift - 1 < src_.cols)
-                {
-                    const read_type src_n_el = ((const read_type*)src)[x];
-                    write_type dst_n_el = ((const write_type*)dst)[x];
-
-                    OpUnroller<ft::smart_shift>::unroll(src_n_el, dst_n_el, mask, op, x_shifted, y);
-
-                    ((write_type*)dst)[x] = dst_n_el;
-                }
-                else
-                {
-                    for (int real_x = x_shifted; real_x < src_.cols; ++real_x)
-                    {
-                        if (mask(y, real_x))
-                            dst[real_x] = op(src[real_x]);
-                    }
-                }
-            }
-        }
-
-        template <typename T, typename D, typename UnOp, typename Mask>
-        __global__ static void transformSimple(const PtrStepSz<T> src, PtrStep<D> dst, const Mask mask, const UnOp op)
-        {
-            const int x = blockDim.x * blockIdx.x + threadIdx.x;
-            const int y = blockDim.y * blockIdx.y + threadIdx.y;
-
-            if (x < src.cols && y < src.rows && mask(y, x))
-            {
-                dst.ptr(y)[x] = op(src.ptr(y)[x]);
-            }
-        }
-
-        template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-        static __global__ void transformSmart(const PtrStepSz<T1> src1_, const PtrStep<T2> src2_, PtrStep<D> dst_,
-            const Mask mask, const BinOp op)
-        {
-            typedef TransformFunctorTraits<BinOp> ft;
-            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type1 read_type1;
-            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type2 read_type2;
-            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::write_type write_type;
-
-            const int x = threadIdx.x + blockIdx.x * blockDim.x;
-            const int y = threadIdx.y + blockIdx.y * blockDim.y;
-            const int x_shifted = x * ft::smart_shift;
-
-            if (y < src1_.rows)
-            {
-                const T1* src1 = src1_.ptr(y);
-                const T2* src2 = src2_.ptr(y);
-                D* dst = dst_.ptr(y);
-
-                if (x_shifted + ft::smart_shift - 1 < src1_.cols)
-                {
-                    const read_type1 src1_n_el = ((const read_type1*)src1)[x];
-                    const read_type2 src2_n_el = ((const read_type2*)src2)[x];
-                    write_type dst_n_el = ((const write_type*)dst)[x];
-
-                    OpUnroller<ft::smart_shift>::unroll(src1_n_el, src2_n_el, dst_n_el, mask, op, x_shifted, y);
-
-                    ((write_type*)dst)[x] = dst_n_el;
-                }
-                else
-                {
-                    for (int real_x = x_shifted; real_x < src1_.cols; ++real_x)
-                    {
-                        if (mask(y, real_x))
-                            dst[real_x] = op(src1[real_x], src2[real_x]);
-                    }
-                }
-            }
-        }
-
-        template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-        static __global__ void transformSimple(const PtrStepSz<T1> src1, const PtrStep<T2> src2, PtrStep<D> dst,
-            const Mask mask, const BinOp op)
-        {
-            const int x = blockDim.x * blockIdx.x + threadIdx.x;
-            const int y = blockDim.y * blockIdx.y + threadIdx.y;
-
-            if (x < src1.cols && y < src1.rows && mask(y, x))
-            {
-                const T1 src1_data = src1.ptr(y)[x];
-                const T2 src2_data = src2.ptr(y)[x];
-                dst.ptr(y)[x] = op(src1_data, src2_data);
-            }
-        }
-
-        template <bool UseSmart> struct TransformDispatcher;
-        template<> struct TransformDispatcher<false>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<UnOp> ft;
-
-                const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
-                const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1);
-
-                transformSimple<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<BinOp> ft;
-
-                const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
-                const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1);
-
-                transformSimple<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-        };
-        template<> struct TransformDispatcher<true>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<UnOp> ft;
-
-                StaticAssert<ft::smart_shift != 1>::check();
-
-                if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) ||
-                    !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
-                {
-                    TransformDispatcher<false>::call(src, dst, op, mask, stream);
-                    return;
-                }
-
-                const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
-                const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1);
-
-                transformSmart<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<BinOp> ft;
-
-                StaticAssert<ft::smart_shift != 1>::check();
-
-                if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) ||
-                    !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) ||
-                    !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
-                {
-                    TransformDispatcher<false>::call(src1, src2, dst, op, mask, stream);
-                    return;
-                }
-
-                const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
-                const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1);
-
-                transformSmart<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-        };
-    } // namespace transform_detail
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_TRANSFORM_DETAIL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/type_traits_detail.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/type_traits_detail.hpp
deleted file mode 100644
index 97ff00d..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/type_traits_detail.hpp
+++ /dev/null
@@ -1,187 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_TYPE_TRAITS_DETAIL_HPP__
-#define __OPENCV_GPU_TYPE_TRAITS_DETAIL_HPP__
-
-#include "../common.hpp"
-#include "../vec_traits.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    namespace type_traits_detail
-    {
-        template <bool, typename T1, typename T2> struct Select { typedef T1 type; };
-        template <typename T1, typename T2> struct Select<false, T1, T2> { typedef T2 type; };
-
-        template <typename T> struct IsSignedIntergral { enum {value = 0}; };
-        template <> struct IsSignedIntergral<schar> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<char1> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<short> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<short1> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<int> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<int1> { enum {value = 1}; };
-
-        template <typename T> struct IsUnsignedIntegral { enum {value = 0}; };
-        template <> struct IsUnsignedIntegral<uchar> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<uchar1> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<ushort> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<ushort1> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<uint> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<uint1> { enum {value = 1}; };
-
-        template <typename T> struct IsIntegral { enum {value = IsSignedIntergral<T>::value || IsUnsignedIntegral<T>::value}; };
-        template <> struct IsIntegral<char> { enum {value = 1}; };
-        template <> struct IsIntegral<bool> { enum {value = 1}; };
-
-        template <typename T> struct IsFloat { enum {value = 0}; };
-        template <> struct IsFloat<float> { enum {value = 1}; };
-        template <> struct IsFloat<double> { enum {value = 1}; };
-
-        template <typename T> struct IsVec { enum {value = 0}; };
-        template <> struct IsVec<uchar1> { enum {value = 1}; };
-        template <> struct IsVec<uchar2> { enum {value = 1}; };
-        template <> struct IsVec<uchar3> { enum {value = 1}; };
-        template <> struct IsVec<uchar4> { enum {value = 1}; };
-        template <> struct IsVec<uchar8> { enum {value = 1}; };
-        template <> struct IsVec<char1> { enum {value = 1}; };
-        template <> struct IsVec<char2> { enum {value = 1}; };
-        template <> struct IsVec<char3> { enum {value = 1}; };
-        template <> struct IsVec<char4> { enum {value = 1}; };
-        template <> struct IsVec<char8> { enum {value = 1}; };
-        template <> struct IsVec<ushort1> { enum {value = 1}; };
-        template <> struct IsVec<ushort2> { enum {value = 1}; };
-        template <> struct IsVec<ushort3> { enum {value = 1}; };
-        template <> struct IsVec<ushort4> { enum {value = 1}; };
-        template <> struct IsVec<ushort8> { enum {value = 1}; };
-        template <> struct IsVec<short1> { enum {value = 1}; };
-        template <> struct IsVec<short2> { enum {value = 1}; };
-        template <> struct IsVec<short3> { enum {value = 1}; };
-        template <> struct IsVec<short4> { enum {value = 1}; };
-        template <> struct IsVec<short8> { enum {value = 1}; };
-        template <> struct IsVec<uint1> { enum {value = 1}; };
-        template <> struct IsVec<uint2> { enum {value = 1}; };
-        template <> struct IsVec<uint3> { enum {value = 1}; };
-        template <> struct IsVec<uint4> { enum {value = 1}; };
-        template <> struct IsVec<uint8> { enum {value = 1}; };
-        template <> struct IsVec<int1> { enum {value = 1}; };
-        template <> struct IsVec<int2> { enum {value = 1}; };
-        template <> struct IsVec<int3> { enum {value = 1}; };
-        template <> struct IsVec<int4> { enum {value = 1}; };
-        template <> struct IsVec<int8> { enum {value = 1}; };
-        template <> struct IsVec<float1> { enum {value = 1}; };
-        template <> struct IsVec<float2> { enum {value = 1}; };
-        template <> struct IsVec<float3> { enum {value = 1}; };
-        template <> struct IsVec<float4> { enum {value = 1}; };
-        template <> struct IsVec<float8> { enum {value = 1}; };
-        template <> struct IsVec<double1> { enum {value = 1}; };
-        template <> struct IsVec<double2> { enum {value = 1}; };
-        template <> struct IsVec<double3> { enum {value = 1}; };
-        template <> struct IsVec<double4> { enum {value = 1}; };
-        template <> struct IsVec<double8> { enum {value = 1}; };
-
-        template <class U> struct AddParameterType { typedef const U& type; };
-        template <class U> struct AddParameterType<U&> { typedef U& type; };
-        template <> struct AddParameterType<void> { typedef void type; };
-
-        template <class U> struct ReferenceTraits
-        {
-            enum { value = false };
-            typedef U type;
-        };
-        template <class U> struct ReferenceTraits<U&>
-        {
-            enum { value = true };
-            typedef U type;
-        };
-
-        template <class U> struct PointerTraits
-        {
-            enum { value = false };
-            typedef void type;
-        };
-        template <class U> struct PointerTraits<U*>
-        {
-            enum { value = true };
-            typedef U type;
-        };
-        template <class U> struct PointerTraits<U*&>
-        {
-            enum { value = true };
-            typedef U type;
-        };
-
-        template <class U> struct UnConst
-        {
-            typedef U type;
-            enum { value = 0 };
-        };
-        template <class U> struct UnConst<const U>
-        {
-            typedef U type;
-            enum { value = 1 };
-        };
-        template <class U> struct UnConst<const U&>
-        {
-            typedef U& type;
-            enum { value = 1 };
-        };
-
-        template <class U> struct UnVolatile
-        {
-            typedef U type;
-            enum { value = 0 };
-        };
-        template <class U> struct UnVolatile<volatile U>
-        {
-            typedef U type;
-            enum { value = 1 };
-        };
-        template <class U> struct UnVolatile<volatile U&>
-        {
-            typedef U& type;
-            enum { value = 1 };
-        };
-    } // namespace type_traits_detail
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_TYPE_TRAITS_DETAIL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/vec_distance_detail.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/vec_distance_detail.hpp
deleted file mode 100644
index 78ab556..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/detail/vec_distance_detail.hpp
+++ /dev/null
@@ -1,117 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_VEC_DISTANCE_DETAIL_HPP__
-#define __OPENCV_GPU_VEC_DISTANCE_DETAIL_HPP__
-
-#include "../datamov_utils.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    namespace vec_distance_detail
-    {
-        template <int THREAD_DIM, int N> struct UnrollVecDiffCached
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind)
-            {
-                if (ind < len)
-                {
-                    T1 val1 = *vecCached++;
-
-                    T2 val2;
-                    ForceGlob<T2>::Load(vecGlob, ind, val2);
-
-                    dist.reduceIter(val1, val2);
-
-                    UnrollVecDiffCached<THREAD_DIM, N - 1>::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM);
-                }
-            }
-
-            template <typename Dist, typename T1, typename T2>
-            static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist)
-            {
-                T1 val1 = *vecCached++;
-
-                T2 val2;
-                ForceGlob<T2>::Load(vecGlob, 0, val2);
-                vecGlob += THREAD_DIM;
-
-                dist.reduceIter(val1, val2);
-
-                UnrollVecDiffCached<THREAD_DIM, N - 1>::calcWithoutCheck(vecCached, vecGlob, dist);
-            }
-        };
-        template <int THREAD_DIM> struct UnrollVecDiffCached<THREAD_DIM, 0>
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int)
-            {
-            }
-
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&)
-            {
-            }
-        };
-
-        template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN> struct VecDiffCachedCalculator;
-        template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, false>
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
-            {
-                UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcCheck(vecCached, vecGlob, len, dist, tid);
-            }
-        };
-        template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, true>
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
-            {
-                UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcWithoutCheck(vecCached, vecGlob + tid, dist);
-            }
-        };
-    } // namespace vec_distance_detail
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_VEC_DISTANCE_DETAIL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/dynamic_smem.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/dynamic_smem.hpp
deleted file mode 100644
index cf431d9..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/dynamic_smem.hpp
+++ /dev/null
@@ -1,80 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_DYNAMIC_SMEM_HPP__
-#define __OPENCV_GPU_DYNAMIC_SMEM_HPP__
-
-namespace cv { namespace gpu { namespace device
-{
-    template<class T> struct DynamicSharedMem
-    {
-        __device__ __forceinline__ operator T*()
-        {
-            extern __shared__ int __smem[];
-            return (T*)__smem;
-        }
-
-        __device__ __forceinline__ operator const T*() const
-        {
-            extern __shared__ int __smem[];
-            return (T*)__smem;
-        }
-    };
-
-    // specialize for double to avoid unaligned memory access compile errors
-    template<> struct DynamicSharedMem<double>
-    {
-        __device__ __forceinline__ operator double*()
-        {
-            extern __shared__ double __smem_d[];
-            return (double*)__smem_d;
-        }
-
-        __device__ __forceinline__ operator const double*() const
-        {
-            extern __shared__ double __smem_d[];
-            return (double*)__smem_d;
-        }
-    };
-}}}
-
-#endif // __OPENCV_GPU_DYNAMIC_SMEM_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/emulation.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/emulation.hpp
deleted file mode 100644
index bf47bc5..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/emulation.hpp
+++ /dev/null
@@ -1,138 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_GPU_EMULATION_HPP_
-#define OPENCV_GPU_EMULATION_HPP_
-
-#include "warp_reduce.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    struct Emulation
-    {
-
-        static __device__ __forceinline__ int syncthreadsOr(int pred)
-        {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
-                // just campilation stab
-                return 0;
-#else
-                return __syncthreads_or(pred);
-#endif
-        }
-
-        template<int CTA_SIZE>
-        static __forceinline__ __device__ int Ballot(int predicate)
-        {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
-            return __ballot(predicate);
-#else
-            __shared__ volatile int cta_buffer[CTA_SIZE];
-
-            int tid = threadIdx.x;
-            cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0;
-            return warp_reduce(cta_buffer);
-#endif
-        }
-
-        struct smem
-        {
-            enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U };
-
-            template<typename T>
-            static __device__ __forceinline__ T atomicInc(T* address, T val)
-            {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
-                T count;
-                unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
-                do
-                {
-                    count = *address & TAG_MASK;
-                    count = tag | (count + 1);
-                    *address = count;
-                } while (*address != count);
-
-                return (count & TAG_MASK) - 1;
-#else
-                return ::atomicInc(address, val);
-#endif
-            }
-
-            template<typename T>
-            static __device__ __forceinline__ T atomicAdd(T* address, T val)
-            {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
-                T count;
-                unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
-                do
-                {
-                    count = *address & TAG_MASK;
-                    count = tag | (count + val);
-                    *address = count;
-                } while (*address != count);
-
-                return (count & TAG_MASK) - val;
-#else
-                return ::atomicAdd(address, val);
-#endif
-            }
-
-            template<typename T>
-            static __device__ __forceinline__ T atomicMin(T* address, T val)
-            {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
-                T count = ::min(*address, val);
-                do
-                {
-                    *address = count;
-                } while (*address > count);
-
-                return count;
-#else
-                return ::atomicMin(address, val);
-#endif
-            }
-        };
-    };
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif /* OPENCV_GPU_EMULATION_HPP_ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/filters.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/filters.hpp
deleted file mode 100644
index d193969..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/filters.hpp
+++ /dev/null
@@ -1,278 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_FILTERS_HPP__
-#define __OPENCV_GPU_FILTERS_HPP__
-
-#include "saturate_cast.hpp"
-#include "vec_traits.hpp"
-#include "vec_math.hpp"
-#include "type_traits.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template <typename Ptr2D> struct PointFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
-        : src(src_)
-        {
-            (void)fx;
-            (void)fy;
-        }
-
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            return src(__float2int_rz(y), __float2int_rz(x));
-        }
-
-        const Ptr2D src;
-    };
-
-    template <typename Ptr2D> struct LinearFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
-        : src(src_)
-        {
-            (void)fx;
-            (void)fy;
-        }
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-
-            work_type out = VecTraits<work_type>::all(0);
-
-            const int x1 = __float2int_rd(x);
-            const int y1 = __float2int_rd(y);
-            const int x2 = x1 + 1;
-            const int y2 = y1 + 1;
-
-            elem_type src_reg = src(y1, x1);
-            out = out + src_reg * ((x2 - x) * (y2 - y));
-
-            src_reg = src(y1, x2);
-            out = out + src_reg * ((x - x1) * (y2 - y));
-
-            src_reg = src(y2, x1);
-            out = out + src_reg * ((x2 - x) * (y - y1));
-
-            src_reg = src(y2, x2);
-            out = out + src_reg * ((x - x1) * (y - y1));
-
-            return saturate_cast<elem_type>(out);
-        }
-
-        const Ptr2D src;
-    };
-
-    template <typename Ptr2D> struct CubicFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-        typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-
-        explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
-        : src(src_)
-        {
-            (void)fx;
-            (void)fy;
-        }
-
-        static __device__ __forceinline__ float bicubicCoeff(float x_)
-        {
-            float x = fabsf(x_);
-            if (x <= 1.0f)
-            {
-                return x * x * (1.5f * x - 2.5f) + 1.0f;
-            }
-            else if (x < 2.0f)
-            {
-                return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
-            }
-            else
-            {
-                return 0.0f;
-            }
-        }
-
-        __device__ elem_type operator ()(float y, float x) const
-        {
-            const float xmin = ::ceilf(x - 2.0f);
-            const float xmax = ::floorf(x + 2.0f);
-
-            const float ymin = ::ceilf(y - 2.0f);
-            const float ymax = ::floorf(y + 2.0f);
-
-            work_type sum = VecTraits<work_type>::all(0);
-            float wsum = 0.0f;
-
-            for (float cy = ymin; cy <= ymax; cy += 1.0f)
-            {
-                for (float cx = xmin; cx <= xmax; cx += 1.0f)
-                {
-                    const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy);
-                    sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx));
-                    wsum += w;
-                }
-            }
-
-            work_type res = (!wsum)? VecTraits<work_type>::all(0) : sum / wsum;
-
-            return saturate_cast<elem_type>(res);
-        }
-
-        const Ptr2D src;
-    };
-    // for integer scaling
-    template <typename Ptr2D> struct IntegerAreaFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
-            : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {}
-
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            float fsx1 = x * scale_x;
-            float fsx2 = fsx1 + scale_x;
-
-            int sx1 = __float2int_ru(fsx1);
-            int sx2 = __float2int_rd(fsx2);
-
-            float fsy1 = y * scale_y;
-            float fsy2 = fsy1 + scale_y;
-
-            int sy1 = __float2int_ru(fsy1);
-            int sy2 = __float2int_rd(fsy2);
-
-            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-            work_type out = VecTraits<work_type>::all(0.f);
-
-            for(int dy = sy1; dy < sy2; ++dy)
-                for(int dx = sx1; dx < sx2; ++dx)
-                {
-                    out = out + src(dy, dx) * scale;
-                }
-
-            return saturate_cast<elem_type>(out);
-        }
-
-        const Ptr2D src;
-        float scale_x, scale_y ,scale;
-    };
-
-    template <typename Ptr2D> struct AreaFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
-            : src(src_), scale_x(scale_x_), scale_y(scale_y_){}
-
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            float fsx1 = x * scale_x;
-            float fsx2 = fsx1 + scale_x;
-
-            int sx1 = __float2int_ru(fsx1);
-            int sx2 = __float2int_rd(fsx2);
-
-            float fsy1 = y * scale_y;
-            float fsy2 = fsy1 + scale_y;
-
-            int sy1 = __float2int_ru(fsy1);
-            int sy2 = __float2int_rd(fsy2);
-
-            float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1));
-
-            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-            work_type out = VecTraits<work_type>::all(0.f);
-
-            for (int dy = sy1; dy < sy2; ++dy)
-            {
-                for (int dx = sx1; dx < sx2; ++dx)
-                    out = out + src(dy, dx) * scale;
-
-                if (sx1 > fsx1)
-                    out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale);
-
-                if (sx2 < fsx2)
-                    out = out + src(dy, sx2) * ((fsx2 -sx2) * scale);
-            }
-
-            if (sy1 > fsy1)
-                for (int dx = sx1; dx < sx2; ++dx)
-                    out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale);
-
-            if (sy2 < fsy2)
-                for (int dx = sx1; dx < sx2; ++dx)
-                    out = out + src(sy2, dx) * ((fsy2 -sy2) * scale);
-
-            if ((sy1 > fsy1) &&  (sx1 > fsx1))
-                out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale);
-
-            if ((sy1 > fsy1) &&  (sx2 < fsx2))
-                out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale);
-
-            if ((sy2 < fsy2) &&  (sx2 < fsx2))
-                out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale);
-
-            if ((sy2 < fsy2) &&  (sx1 > fsx1))
-                out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale);
-
-            return saturate_cast<elem_type>(out);
-        }
-
-        const Ptr2D src;
-        float scale_x, scale_y;
-        int width, haight;
-    };
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_FILTERS_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/funcattrib.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/funcattrib.hpp
deleted file mode 100644
index 2ed7980..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/funcattrib.hpp
+++ /dev/null
@@ -1,71 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_DEVICE_FUNCATTRIB_HPP_
-#define __OPENCV_GPU_DEVICE_FUNCATTRIB_HPP_
-
-#include <cstdio>
-
-namespace cv { namespace gpu { namespace device
-{
-    template<class Func>
-    void printFuncAttrib(Func& func)
-    {
-
-        cudaFuncAttributes attrs;
-        cudaFuncGetAttributes(&attrs, func);
-
-        printf("=== Function stats ===\n");
-        printf("Name: \n");
-        printf("sharedSizeBytes    = %d\n", attrs.sharedSizeBytes);
-        printf("constSizeBytes     = %d\n", attrs.constSizeBytes);
-        printf("localSizeBytes     = %d\n", attrs.localSizeBytes);
-        printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock);
-        printf("numRegs            = %d\n", attrs.numRegs);
-        printf("ptxVersion         = %d\n", attrs.ptxVersion);
-        printf("binaryVersion      = %d\n", attrs.binaryVersion);
-        printf("\n");
-        fflush(stdout);
-    }
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif  /* __OPENCV_GPU_DEVICE_FUNCATTRIB_HPP_ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/functional.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/functional.hpp
deleted file mode 100644
index db26473..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/functional.hpp
+++ /dev/null
@@ -1,789 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_FUNCTIONAL_HPP__
-#define __OPENCV_GPU_FUNCTIONAL_HPP__
-
-#include <functional>
-#include "saturate_cast.hpp"
-#include "vec_traits.hpp"
-#include "type_traits.hpp"
-#include "device_functions.h"
-
-namespace cv { namespace gpu { namespace device
-{
-    // Function Objects
-    template<typename Argument, typename Result> struct unary_function : public std::unary_function<Argument, Result> {};
-    template<typename Argument1, typename Argument2, typename Result> struct binary_function : public std::binary_function<Argument1, Argument2, Result> {};
-
-    // Arithmetic Operations
-    template <typename T> struct plus : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a + b;
-        }
-        __host__ __device__ __forceinline__ plus() {}
-        __host__ __device__ __forceinline__ plus(const plus&) {}
-    };
-
-    template <typename T> struct minus : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a - b;
-        }
-        __host__ __device__ __forceinline__ minus() {}
-        __host__ __device__ __forceinline__ minus(const minus&) {}
-    };
-
-    template <typename T> struct multiplies : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a * b;
-        }
-        __host__ __device__ __forceinline__ multiplies() {}
-        __host__ __device__ __forceinline__ multiplies(const multiplies&) {}
-    };
-
-    template <typename T> struct divides : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a / b;
-        }
-        __host__ __device__ __forceinline__ divides() {}
-        __host__ __device__ __forceinline__ divides(const divides&) {}
-    };
-
-    template <typename T> struct modulus : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a % b;
-        }
-        __host__ __device__ __forceinline__ modulus() {}
-        __host__ __device__ __forceinline__ modulus(const modulus&) {}
-    };
-
-    template <typename T> struct negate : unary_function<T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a) const
-        {
-            return -a;
-        }
-        __host__ __device__ __forceinline__ negate() {}
-        __host__ __device__ __forceinline__ negate(const negate&) {}
-    };
-
-    // Comparison Operations
-    template <typename T> struct equal_to : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a == b;
-        }
-        __host__ __device__ __forceinline__ equal_to() {}
-        __host__ __device__ __forceinline__ equal_to(const equal_to&) {}
-    };
-
-    template <typename T> struct not_equal_to : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a != b;
-        }
-        __host__ __device__ __forceinline__ not_equal_to() {}
-        __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {}
-    };
-
-    template <typename T> struct greater : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a > b;
-        }
-        __host__ __device__ __forceinline__ greater() {}
-        __host__ __device__ __forceinline__ greater(const greater&) {}
-    };
-
-    template <typename T> struct less : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a < b;
-        }
-        __host__ __device__ __forceinline__ less() {}
-        __host__ __device__ __forceinline__ less(const less&) {}
-    };
-
-    template <typename T> struct greater_equal : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a >= b;
-        }
-        __host__ __device__ __forceinline__ greater_equal() {}
-        __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {}
-    };
-
-    template <typename T> struct less_equal : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a <= b;
-        }
-        __host__ __device__ __forceinline__ less_equal() {}
-        __host__ __device__ __forceinline__ less_equal(const less_equal&) {}
-    };
-
-    // Logical Operations
-    template <typename T> struct logical_and : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a && b;
-        }
-        __host__ __device__ __forceinline__ logical_and() {}
-        __host__ __device__ __forceinline__ logical_and(const logical_and&) {}
-    };
-
-    template <typename T> struct logical_or : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a || b;
-        }
-        __host__ __device__ __forceinline__ logical_or() {}
-        __host__ __device__ __forceinline__ logical_or(const logical_or&) {}
-    };
-
-    template <typename T> struct logical_not : unary_function<T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a) const
-        {
-            return !a;
-        }
-        __host__ __device__ __forceinline__ logical_not() {}
-        __host__ __device__ __forceinline__ logical_not(const logical_not&) {}
-    };
-
-    // Bitwise Operations
-    template <typename T> struct bit_and : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a & b;
-        }
-        __host__ __device__ __forceinline__ bit_and() {}
-        __host__ __device__ __forceinline__ bit_and(const bit_and&) {}
-    };
-
-    template <typename T> struct bit_or : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a | b;
-        }
-        __host__ __device__ __forceinline__ bit_or() {}
-        __host__ __device__ __forceinline__ bit_or(const bit_or&) {}
-    };
-
-    template <typename T> struct bit_xor : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a ^ b;
-        }
-        __host__ __device__ __forceinline__ bit_xor() {}
-        __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {}
-    };
-
-    template <typename T> struct bit_not : unary_function<T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType v) const
-        {
-            return ~v;
-        }
-        __host__ __device__ __forceinline__ bit_not() {}
-        __host__ __device__ __forceinline__ bit_not(const bit_not&) {}
-    };
-
-    // Generalized Identity Operations
-    template <typename T> struct identity : unary_function<T, T>
-    {
-        __device__ __forceinline__ typename TypeTraits<T>::ParameterType operator()(typename TypeTraits<T>::ParameterType x) const
-        {
-            return x;
-        }
-        __host__ __device__ __forceinline__ identity() {}
-        __host__ __device__ __forceinline__ identity(const identity&) {}
-    };
-
-    template <typename T1, typename T2> struct project1st : binary_function<T1, T2, T1>
-    {
-        __device__ __forceinline__ typename TypeTraits<T1>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
-        {
-            return lhs;
-        }
-        __host__ __device__ __forceinline__ project1st() {}
-        __host__ __device__ __forceinline__ project1st(const project1st&) {}
-    };
-
-    template <typename T1, typename T2> struct project2nd : binary_function<T1, T2, T2>
-    {
-        __device__ __forceinline__ typename TypeTraits<T2>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
-        {
-            return rhs;
-        }
-        __host__ __device__ __forceinline__ project2nd() {}
-        __host__ __device__ __forceinline__ project2nd(const project2nd&) {}
-    };
-
-    // Min/Max Operations
-
-#define OPENCV_GPU_IMPLEMENT_MINMAX(name, type, op) \
-    template <> struct name<type> : binary_function<type, type, type> \
-    { \
-        __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \
-        __host__ __device__ __forceinline__ name() {}\
-        __host__ __device__ __forceinline__ name(const name&) {}\
-    };
-
-    template <typename T> struct maximum : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
-        {
-            return max(lhs, rhs);
-        }
-        __host__ __device__ __forceinline__ maximum() {}
-        __host__ __device__ __forceinline__ maximum(const maximum&) {}
-    };
-
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, uchar, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, schar, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, char, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, ushort, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, short, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, int, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, uint, ::max)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, float, ::fmax)
-    OPENCV_GPU_IMPLEMENT_MINMAX(maximum, double, ::fmax)
-
-    template <typename T> struct minimum : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
-        {
-            return min(lhs, rhs);
-        }
-        __host__ __device__ __forceinline__ minimum() {}
-        __host__ __device__ __forceinline__ minimum(const minimum&) {}
-    };
-
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, uchar, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, schar, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, char, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, ushort, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, short, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, int, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, uint, ::min)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, float, ::fmin)
-    OPENCV_GPU_IMPLEMENT_MINMAX(minimum, double, ::fmin)
-
-#undef OPENCV_GPU_IMPLEMENT_MINMAX
-
-    // Math functions
-
-    template <typename T> struct abs_func : unary_function<T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType x) const
-        {
-            return abs(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<unsigned char> : unary_function<unsigned char, unsigned char>
-    {
-        __device__ __forceinline__ unsigned char operator ()(unsigned char x) const
-        {
-            return x;
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<signed char> : unary_function<signed char, signed char>
-    {
-        __device__ __forceinline__ signed char operator ()(signed char x) const
-        {
-            return ::abs((int)x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<char> : unary_function<char, char>
-    {
-        __device__ __forceinline__ char operator ()(char x) const
-        {
-            return ::abs((int)x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<unsigned short> : unary_function<unsigned short, unsigned short>
-    {
-        __device__ __forceinline__ unsigned short operator ()(unsigned short x) const
-        {
-            return x;
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<short> : unary_function<short, short>
-    {
-        __device__ __forceinline__ short operator ()(short x) const
-        {
-            return ::abs((int)x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<unsigned int> : unary_function<unsigned int, unsigned int>
-    {
-        __device__ __forceinline__ unsigned int operator ()(unsigned int x) const
-        {
-            return x;
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<int> : unary_function<int, int>
-    {
-        __device__ __forceinline__ int operator ()(int x) const
-        {
-            return ::abs(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<float> : unary_function<float, float>
-    {
-        __device__ __forceinline__ float operator ()(float x) const
-        {
-            return ::fabsf(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<double> : unary_function<double, double>
-    {
-        __device__ __forceinline__ double operator ()(double x) const
-        {
-            return ::fabs(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-
-#define OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(name, func) \
-    template <typename T> struct name ## _func : unary_function<T, float> \
-    { \
-        __device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v) const \
-        { \
-            return func ## f(v); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    }; \
-    template <> struct name ## _func<double> : unary_function<double, double> \
-    { \
-        __device__ __forceinline__ double operator ()(double v) const \
-        { \
-            return func(v); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    };
-
-#define OPENCV_GPU_IMPLEMENT_BIN_FUNCTOR(name, func) \
-    template <typename T> struct name ## _func : binary_function<T, T, float> \
-    { \
-        __device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v1, typename TypeTraits<T>::ParameterType v2) const \
-        { \
-            return func ## f(v1, v2); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    }; \
-    template <> struct name ## _func<double> : binary_function<double, double, double> \
-    { \
-        __device__ __forceinline__ double operator ()(double v1, double v2) const \
-        { \
-            return func(v1, v2); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    };
-
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(exp, ::exp)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(log, ::log)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(log2, ::log2)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(log10, ::log10)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(sin, ::sin)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(cos, ::cos)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(tan, ::tan)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(asin, ::asin)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(acos, ::acos)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(atan, ::atan)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh)
-    OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh)
-
-    OPENCV_GPU_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot)
-    OPENCV_GPU_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2)
-    OPENCV_GPU_IMPLEMENT_BIN_FUNCTOR(pow, ::pow)
-
-    #undef OPENCV_GPU_IMPLEMENT_UN_FUNCTOR
-    #undef OPENCV_GPU_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE
-    #undef OPENCV_GPU_IMPLEMENT_BIN_FUNCTOR
-
-    template<typename T> struct hypot_sqr_func : binary_function<T, T, float>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType src1, typename TypeTraits<T>::ParameterType src2) const
-        {
-            return src1 * src1 + src2 * src2;
-        }
-        __host__ __device__ __forceinline__ hypot_sqr_func() {}
-        __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {}
-    };
-
-    // Saturate Cast Functor
-    template <typename T, typename D> struct saturate_cast_func : unary_function<T, D>
-    {
-        __device__ __forceinline__ D operator ()(typename TypeTraits<T>::ParameterType v) const
-        {
-            return saturate_cast<D>(v);
-        }
-        __host__ __device__ __forceinline__ saturate_cast_func() {}
-        __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {}
-    };
-
-    // Threshold Functors
-    template <typename T> struct thresh_binary_func : unary_function<T, T>
-    {
-        __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src > thresh) * maxVal;
-        }
-
-        __host__ __device__ __forceinline__ thresh_binary_func() {}
-        __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other)
-            : thresh(other.thresh), maxVal(other.maxVal) {}
-
-        const T thresh;
-        const T maxVal;
-    };
-
-    template <typename T> struct thresh_binary_inv_func : unary_function<T, T>
-    {
-        __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src <= thresh) * maxVal;
-        }
-
-        __host__ __device__ __forceinline__ thresh_binary_inv_func() {}
-        __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other)
-            : thresh(other.thresh), maxVal(other.maxVal) {}
-
-        const T thresh;
-        const T maxVal;
-    };
-
-    template <typename T> struct thresh_trunc_func : unary_function<T, T>
-    {
-        explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return minimum<T>()(src, thresh);
-        }
-
-        __host__ __device__ __forceinline__ thresh_trunc_func() {}
-        __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other)
-            : thresh(other.thresh) {}
-
-        const T thresh;
-    };
-
-    template <typename T> struct thresh_to_zero_func : unary_function<T, T>
-    {
-        explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src > thresh) * src;
-        }
-
-        __host__ __device__ __forceinline__ thresh_to_zero_func() {}
-       __host__  __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other)
-            : thresh(other.thresh) {}
-
-        const T thresh;
-    };
-
-    template <typename T> struct thresh_to_zero_inv_func : unary_function<T, T>
-    {
-        explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src <= thresh) * src;
-        }
-
-        __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {}
-        __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other)
-            : thresh(other.thresh) {}
-
-        const T thresh;
-    };
-
-    // Function Object Adaptors
-    template <typename Predicate> struct unary_negate : unary_function<typename Predicate::argument_type, bool>
-    {
-      explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {}
-
-      __device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::argument_type>::ParameterType x) const
-      {
-          return !pred(x);
-      }
-
-      __host__ __device__ __forceinline__ unary_negate() {}
-      __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {}
-
-      const Predicate pred;
-    };
-
-    template <typename Predicate> __host__ __device__ __forceinline__ unary_negate<Predicate> not1(const Predicate& pred)
-    {
-        return unary_negate<Predicate>(pred);
-    }
-
-    template <typename Predicate> struct binary_negate : binary_function<typename Predicate::first_argument_type, typename Predicate::second_argument_type, bool>
-    {
-        explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {}
-
-        __device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::first_argument_type>::ParameterType x,
-                                                   typename TypeTraits<typename Predicate::second_argument_type>::ParameterType y) const
-        {
-            return !pred(x,y);
-        }
-
-        __host__ __device__ __forceinline__ binary_negate() {}
-        __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {}
-
-        const Predicate pred;
-    };
-
-    template <typename BinaryPredicate> __host__ __device__ __forceinline__ binary_negate<BinaryPredicate> not2(const BinaryPredicate& pred)
-    {
-        return binary_negate<BinaryPredicate>(pred);
-    }
-
-    template <typename Op> struct binder1st : unary_function<typename Op::second_argument_type, typename Op::result_type>
-    {
-        __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {}
-
-        __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits<typename Op::second_argument_type>::ParameterType a) const
-        {
-            return op(arg1, a);
-        }
-
-        __host__ __device__ __forceinline__ binder1st() {}
-        __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {}
-
-        const Op op;
-        const typename Op::first_argument_type arg1;
-    };
-
-    template <typename Op, typename T> __host__ __device__ __forceinline__ binder1st<Op> bind1st(const Op& op, const T& x)
-    {
-        return binder1st<Op>(op, typename Op::first_argument_type(x));
-    }
-
-    template <typename Op> struct binder2nd : unary_function<typename Op::first_argument_type, typename Op::result_type>
-    {
-        __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {}
-
-        __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits<typename Op::first_argument_type>::ParameterType a) const
-        {
-            return op(a, arg2);
-        }
-
-        __host__ __device__ __forceinline__ binder2nd() {}
-        __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {}
-
-        const Op op;
-        const typename Op::second_argument_type arg2;
-    };
-
-    template <typename Op, typename T> __host__ __device__ __forceinline__ binder2nd<Op> bind2nd(const Op& op, const T& x)
-    {
-        return binder2nd<Op>(op, typename Op::second_argument_type(x));
-    }
-
-    // Functor Traits
-    template <typename F> struct IsUnaryFunction
-    {
-        typedef char Yes;
-        struct No {Yes a[2];};
-
-        template <typename T, typename D> static Yes check(unary_function<T, D>);
-        static No check(...);
-
-        static F makeF();
-
-        enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
-    };
-
-    template <typename F> struct IsBinaryFunction
-    {
-        typedef char Yes;
-        struct No {Yes a[2];};
-
-        template <typename T1, typename T2, typename D> static Yes check(binary_function<T1, T2, D>);
-        static No check(...);
-
-        static F makeF();
-
-        enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
-    };
-
-    namespace functional_detail
-    {
-        template <size_t src_elem_size, size_t dst_elem_size> struct UnOpShift { enum { shift = 1 }; };
-        template <size_t src_elem_size> struct UnOpShift<src_elem_size, 1> { enum { shift = 4 }; };
-        template <size_t src_elem_size> struct UnOpShift<src_elem_size, 2> { enum { shift = 2 }; };
-
-        template <typename T, typename D> struct DefaultUnaryShift
-        {
-            enum { shift = UnOpShift<sizeof(T), sizeof(D)>::shift };
-        };
-
-        template <size_t src_elem_size1, size_t src_elem_size2, size_t dst_elem_size> struct BinOpShift { enum { shift = 1 }; };
-        template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 1> { enum { shift = 4 }; };
-        template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 2> { enum { shift = 2 }; };
-
-        template <typename T1, typename T2, typename D> struct DefaultBinaryShift
-        {
-            enum { shift = BinOpShift<sizeof(T1), sizeof(T2), sizeof(D)>::shift };
-        };
-
-        template <typename Func, bool unary = IsUnaryFunction<Func>::value> struct ShiftDispatcher;
-        template <typename Func> struct ShiftDispatcher<Func, true>
-        {
-            enum { shift = DefaultUnaryShift<typename Func::argument_type, typename Func::result_type>::shift };
-        };
-        template <typename Func> struct ShiftDispatcher<Func, false>
-        {
-            enum { shift = DefaultBinaryShift<typename Func::first_argument_type, typename Func::second_argument_type, typename Func::result_type>::shift };
-        };
-    }
-
-    template <typename Func> struct DefaultTransformShift
-    {
-        enum { shift = functional_detail::ShiftDispatcher<Func>::shift };
-    };
-
-    template <typename Func> struct DefaultTransformFunctorTraits
-    {
-        enum { simple_block_dim_x = 16 };
-        enum { simple_block_dim_y = 16 };
-
-        enum { smart_block_dim_x = 16 };
-        enum { smart_block_dim_y = 16 };
-        enum { smart_shift = DefaultTransformShift<Func>::shift };
-    };
-
-    template <typename Func> struct TransformFunctorTraits : DefaultTransformFunctorTraits<Func> {};
-
-#define OPENCV_GPU_TRANSFORM_FUNCTOR_TRAITS(type) \
-    template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type >
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_FUNCTIONAL_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/limits.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/limits.hpp
deleted file mode 100644
index 5959780..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/limits.hpp
+++ /dev/null
@@ -1,122 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_LIMITS_GPU_HPP__
-#define __OPENCV_GPU_LIMITS_GPU_HPP__
-
-#include <limits.h>
-#include <float.h>
-#include "common.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-
-template <class T> struct numeric_limits;
-
-template <> struct numeric_limits<bool>
-{
-    __device__ __forceinline__ static bool min() { return false; }
-    __device__ __forceinline__ static bool max() { return true;  }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<signed char>
-{
-    __device__ __forceinline__ static signed char min() { return SCHAR_MIN; }
-    __device__ __forceinline__ static signed char max() { return SCHAR_MAX; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<unsigned char>
-{
-    __device__ __forceinline__ static unsigned char min() { return 0; }
-    __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<short>
-{
-    __device__ __forceinline__ static short min() { return SHRT_MIN; }
-    __device__ __forceinline__ static short max() { return SHRT_MAX; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<unsigned short>
-{
-    __device__ __forceinline__ static unsigned short min() { return 0; }
-    __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<int>
-{
-    __device__ __forceinline__ static int min() { return INT_MIN; }
-    __device__ __forceinline__ static int max() { return INT_MAX; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<unsigned int>
-{
-    __device__ __forceinline__ static unsigned int min() { return 0; }
-    __device__ __forceinline__ static unsigned int max() { return UINT_MAX; }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<float>
-{
-    __device__ __forceinline__ static float min() { return FLT_MIN; }
-    __device__ __forceinline__ static float max() { return FLT_MAX; }
-    __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<double>
-{
-    __device__ __forceinline__ static double min() { return DBL_MIN; }
-    __device__ __forceinline__ static double max() { return DBL_MAX; }
-    __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; }
-    static const bool is_signed = true;
-};
-
-}}} // namespace cv { namespace gpu { namespace device {
-
-#endif // __OPENCV_GPU_LIMITS_GPU_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/reduce.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/reduce.hpp
deleted file mode 100644
index 2161b06..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/reduce.hpp
+++ /dev/null
@@ -1,197 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_REDUCE_HPP__
-#define __OPENCV_GPU_REDUCE_HPP__
-
-#include <thrust/tuple.h>
-#include "detail/reduce.hpp"
-#include "detail/reduce_key_val.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template <int N, typename T, class Op>
-    __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op)
-    {
-        reduce_detail::Dispatcher<N>::reductor::template reduce<volatile T*, T&, const Op&>(smem, val, tid, op);
-    }
-    template <int N,
-              typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-              typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
-              class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
-    __device__ __forceinline__ void reduce(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                           const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                           unsigned int tid,
-                                           const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
-    {
-        reduce_detail::Dispatcher<N>::reductor::template reduce<
-                const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>&,
-                const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>&,
-                const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>&>(smem, val, tid, op);
-    }
-
-    template <unsigned int N, typename K, typename V, class Cmp>
-    __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp)
-    {
-        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&, volatile V*, V&, const Cmp&>(skeys, key, svals, val, tid, cmp);
-    }
-    template <unsigned int N,
-              typename K,
-              typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-              typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-              class Cmp>
-    __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key,
-                                                 const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                 unsigned int tid, const Cmp& cmp)
-    {
-        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&,
-                const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
-                const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
-                const Cmp&>(skeys, key, svals, val, tid, cmp);
-    }
-    template <unsigned int N,
-              typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
-              typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
-              typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-              typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-              class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
-    __device__ __forceinline__ void reduceKeyVal(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
-                                                 const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
-                                                 const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                 unsigned int tid,
-                                                 const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp)
-    {
-        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<
-                const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>&,
-                const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>&,
-                const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
-                const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
-                const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>&
-                >(skeys, key, svals, val, tid, cmp);
-    }
-
-    // smem_tuple
-
-    template <typename T0>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*>
-    smem_tuple(T0* t0)
-    {
-        return thrust::make_tuple((volatile T0*) t0);
-    }
-
-    template <typename T0, typename T1>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*>
-    smem_tuple(T0* t0, T1* t1)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1);
-    }
-
-    template <typename T0, typename T1, typename T2>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*>
-    smem_tuple(T0* t0, T1* t1, T2* t2)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8, typename T9>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*, volatile T9*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9);
-    }
-}}}
-
-#endif // __OPENCV_GPU_UTILITY_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/saturate_cast.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/saturate_cast.hpp
deleted file mode 100644
index 7a2799f..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/saturate_cast.hpp
+++ /dev/null
@@ -1,284 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_SATURATE_CAST_HPP__
-#define __OPENCV_GPU_SATURATE_CAST_HPP__
-
-#include "common.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); }
-
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(schar v)
-    {
-        uint res = 0;
-        int vi = v;
-        asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(short v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(ushort v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(int v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(uint v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(float v)
-    {
-        uint res = 0;
-        asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(double v)
-    {
-    #if __CUDA_ARCH__ >= 130
-        uint res = 0;
-        asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<uchar>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(uchar v)
-    {
-        uint res = 0;
-        uint vi = v;
-        asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(short v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(ushort v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(int v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(uint v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(float v)
-    {
-        uint res = 0;
-        asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(double v)
-    {
-    #if __CUDA_ARCH__ >= 130
-        uint res = 0;
-        asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<schar>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(schar v)
-    {
-        ushort res = 0;
-        int vi = v;
-        asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(short v)
-    {
-        ushort res = 0;
-        asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(int v)
-    {
-        ushort res = 0;
-        asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(uint v)
-    {
-        ushort res = 0;
-        asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(float v)
-    {
-        ushort res = 0;
-        asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(double v)
-    {
-    #if __CUDA_ARCH__ >= 130
-        ushort res = 0;
-        asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<ushort>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ short saturate_cast<short>(ushort v)
-    {
-        short res = 0;
-        asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(int v)
-    {
-        short res = 0;
-        asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(uint v)
-    {
-        short res = 0;
-        asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(float v)
-    {
-        short res = 0;
-        asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(double v)
-    {
-    #if __CUDA_ARCH__ >= 130
-        short res = 0;
-        asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<short>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ int saturate_cast<int>(uint v)
-    {
-        int res = 0;
-        asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ int saturate_cast<int>(float v)
-    {
-        return __float2int_rn(v);
-    }
-    template<> __device__ __forceinline__ int saturate_cast<int>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        return __double2int_rn(v);
-    #else
-        return saturate_cast<int>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(schar v)
-    {
-        uint res = 0;
-        int vi = v;
-        asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(short v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(int v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(float v)
-    {
-        return __float2uint_rn(v);
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        return __double2uint_rn(v);
-    #else
-        return saturate_cast<uint>((float)v);
-    #endif
-    }
-}}}
-
-#endif /* __OPENCV_GPU_SATURATE_CAST_HPP__ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/scan.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/scan.hpp
deleted file mode 100644
index 3d8da16..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/scan.hpp
+++ /dev/null
@@ -1,250 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_SCAN_HPP__
-#define __OPENCV_GPU_SCAN_HPP__
-
-#include "opencv2/gpu/device/common.hpp"
-#include "opencv2/gpu/device/utility.hpp"
-#include "opencv2/gpu/device/warp.hpp"
-#include "opencv2/gpu/device/warp_shuffle.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    enum ScanKind { EXCLUSIVE = 0,  INCLUSIVE = 1 };
-
-    template <ScanKind Kind, typename T, typename F> struct WarpScan
-    {
-        __device__ __forceinline__ WarpScan() {}
-        __device__ __forceinline__ WarpScan(const WarpScan& other) { (void)other; }
-
-        __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
-        {
-            const unsigned int lane = idx & 31;
-            F op;
-
-            if ( lane >=  1) ptr [idx ] = op(ptr [idx -  1], ptr [idx]);
-            if ( lane >=  2) ptr [idx ] = op(ptr [idx -  2], ptr [idx]);
-            if ( lane >=  4) ptr [idx ] = op(ptr [idx -  4], ptr [idx]);
-            if ( lane >=  8) ptr [idx ] = op(ptr [idx -  8], ptr [idx]);
-            if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
-
-            if( Kind == INCLUSIVE )
-                return ptr [idx];
-            else
-                return (lane > 0) ? ptr [idx - 1] : 0;
-        }
-
-        __device__ __forceinline__ unsigned int index(const unsigned int tid)
-        {
-            return tid;
-        }
-
-        __device__ __forceinline__ void init(volatile T *ptr){}
-
-        static const int warp_offset      = 0;
-
-        typedef WarpScan<INCLUSIVE, T, F>  merge;
-    };
-
-    template <ScanKind Kind , typename T, typename F> struct WarpScanNoComp
-    {
-        __device__ __forceinline__ WarpScanNoComp() {}
-        __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { (void)other; }
-
-        __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
-        {
-            const unsigned int lane = threadIdx.x & 31;
-            F op;
-
-            ptr [idx ] = op(ptr [idx -  1], ptr [idx]);
-            ptr [idx ] = op(ptr [idx -  2], ptr [idx]);
-            ptr [idx ] = op(ptr [idx -  4], ptr [idx]);
-            ptr [idx ] = op(ptr [idx -  8], ptr [idx]);
-            ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
-
-            if( Kind == INCLUSIVE )
-                return ptr [idx];
-            else
-                return (lane > 0) ? ptr [idx - 1] : 0;
-        }
-
-        __device__ __forceinline__ unsigned int index(const unsigned int tid)
-        {
-            return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask);
-        }
-
-        __device__ __forceinline__ void init(volatile T *ptr)
-        {
-            ptr[threadIdx.x] = 0;
-        }
-
-        static const int warp_smem_stride = 32 + 16 + 1;
-        static const int warp_offset      = 16;
-        static const int warp_log         = 5;
-        static const int warp_mask        = 31;
-
-        typedef WarpScanNoComp<INCLUSIVE, T, F> merge;
-    };
-
-    template <ScanKind Kind , typename T, typename Sc, typename F> struct BlockScan
-    {
-        __device__ __forceinline__ BlockScan() {}
-        __device__ __forceinline__ BlockScan(const BlockScan& other) { (void)other; }
-
-        __device__ __forceinline__ T operator()(volatile T *ptr)
-        {
-            const unsigned int tid  = threadIdx.x;
-            const unsigned int lane = tid & warp_mask;
-            const unsigned int warp = tid >> warp_log;
-
-            Sc scan;
-            typename Sc::merge merge_scan;
-            const unsigned int idx = scan.index(tid);
-
-            T val = scan(ptr, idx);
-            __syncthreads ();
-
-            if( warp == 0)
-                scan.init(ptr);
-            __syncthreads ();
-
-            if( lane == 31 )
-                ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx];
-            __syncthreads ();
-
-            if( warp == 0 )
-                merge_scan(ptr, idx);
-            __syncthreads();
-
-            if ( warp > 0)
-                val = ptr [scan.warp_offset + warp - 1] + val;
-            __syncthreads ();
-
-            ptr[idx] = val;
-            __syncthreads ();
-
-            return val ;
-        }
-
-        static const int warp_log  = 5;
-        static const int warp_mask = 31;
-    };
-
-    template <typename T>
-    __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
-    {
-    #if __CUDA_ARCH__ >= 300
-        const unsigned int laneId = cv::gpu::device::Warp::laneId();
-
-        // scan on shuffl functions
-        #pragma unroll
-        for (int i = 1; i <= (OPENCV_GPU_WARP_SIZE / 2); i *= 2)
-        {
-            const T n = cv::gpu::device::shfl_up(idata, i);
-            if (laneId >= i)
-                  idata += n;
-        }
-
-        return idata;
-    #else
-        unsigned int pos = 2 * tid - (tid & (OPENCV_GPU_WARP_SIZE - 1));
-        s_Data[pos] = 0;
-        pos += OPENCV_GPU_WARP_SIZE;
-        s_Data[pos] = idata;
-
-        s_Data[pos] += s_Data[pos - 1];
-        s_Data[pos] += s_Data[pos - 2];
-        s_Data[pos] += s_Data[pos - 4];
-        s_Data[pos] += s_Data[pos - 8];
-        s_Data[pos] += s_Data[pos - 16];
-
-        return s_Data[pos];
-    #endif
-    }
-
-    template <typename T>
-    __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid)
-    {
-        return warpScanInclusive(idata, s_Data, tid) - idata;
-    }
-
-    template <int tiNumScanThreads, typename T>
-    __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
-    {
-        if (tiNumScanThreads > OPENCV_GPU_WARP_SIZE)
-        {
-            //Bottom-level inclusive warp scan
-            T warpResult = warpScanInclusive(idata, s_Data, tid);
-
-            //Save top elements of each warp for exclusive warp scan
-            //sync to wait for warp scans to complete (because s_Data is being overwritten)
-            __syncthreads();
-            if ((tid & (OPENCV_GPU_WARP_SIZE - 1)) == (OPENCV_GPU_WARP_SIZE - 1))
-            {
-                s_Data[tid >> OPENCV_GPU_LOG_WARP_SIZE] = warpResult;
-            }
-
-            //wait for warp scans to complete
-            __syncthreads();
-
-            if (tid < (tiNumScanThreads / OPENCV_GPU_WARP_SIZE) )
-            {
-                //grab top warp elements
-                T val = s_Data[tid];
-                //calculate exclusive scan and write back to shared memory
-                s_Data[tid] = warpScanExclusive(val, s_Data, tid);
-            }
-
-            //return updated warp scans with exclusive scan results
-            __syncthreads();
-
-            return warpResult + s_Data[tid >> OPENCV_GPU_LOG_WARP_SIZE];
-        }
-        else
-        {
-            return warpScanInclusive(idata, s_Data, tid);
-        }
-    }
-}}}
-
-#endif // __OPENCV_GPU_SCAN_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/simd_functions.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/simd_functions.hpp
deleted file mode 100644
index b0377e5..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/simd_functions.hpp
+++ /dev/null
@@ -1,909 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-/*
- * Copyright (c) 2013 NVIDIA Corporation. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- *   Redistributions of source code must retain the above copyright notice,
- *   this list of conditions and the following disclaimer.
- *
- *   Redistributions in binary form must reproduce the above copyright notice,
- *   this list of conditions and the following disclaimer in the documentation
- *   and/or other materials provided with the distribution.
- *
- *   Neither the name of NVIDIA Corporation nor the names of its contributors
- *   may be used to endorse or promote products derived from this software
- *   without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- * POSSIBILITY OF SUCH DAMAGE.
- */
-
-#ifndef __OPENCV_GPU_SIMD_FUNCTIONS_HPP__
-#define __OPENCV_GPU_SIMD_FUNCTIONS_HPP__
-
-#include "common.hpp"
-
-/*
-  This header file contains inline functions that implement intra-word SIMD
-  operations, that are hardware accelerated on sm_3x (Kepler) GPUs. Efficient
-  emulation code paths are provided for earlier architectures (sm_1x, sm_2x)
-  to make the code portable across all GPUs supported by CUDA. The following
-  functions are currently implemented:
-
-  vadd2(a,b)      per-halfword unsigned addition, with wrap-around: a + b
-  vsub2(a,b)      per-halfword unsigned subtraction, with wrap-around: a - b
-  vabsdiff2(a,b)  per-halfword unsigned absolute difference: |a - b|
-  vavg2(a,b)      per-halfword unsigned average: (a + b) / 2
-  vavrg2(a,b)     per-halfword unsigned rounded average: (a + b + 1) / 2
-  vseteq2(a,b)    per-halfword unsigned comparison: a == b ? 1 : 0
-  vcmpeq2(a,b)    per-halfword unsigned comparison: a == b ? 0xffff : 0
-  vsetge2(a,b)    per-halfword unsigned comparison: a >= b ? 1 : 0
-  vcmpge2(a,b)    per-halfword unsigned comparison: a >= b ? 0xffff : 0
-  vsetgt2(a,b)    per-halfword unsigned comparison: a > b ? 1 : 0
-  vcmpgt2(a,b)    per-halfword unsigned comparison: a > b ? 0xffff : 0
-  vsetle2(a,b)    per-halfword unsigned comparison: a <= b ? 1 : 0
-  vcmple2(a,b)    per-halfword unsigned comparison: a <= b ? 0xffff : 0
-  vsetlt2(a,b)    per-halfword unsigned comparison: a < b ? 1 : 0
-  vcmplt2(a,b)    per-halfword unsigned comparison: a < b ? 0xffff : 0
-  vsetne2(a,b)    per-halfword unsigned comparison: a != b ? 1 : 0
-  vcmpne2(a,b)    per-halfword unsigned comparison: a != b ? 0xffff : 0
-  vmax2(a,b)      per-halfword unsigned maximum: max(a, b)
-  vmin2(a,b)      per-halfword unsigned minimum: min(a, b)
-
-  vadd4(a,b)      per-byte unsigned addition, with wrap-around: a + b
-  vsub4(a,b)      per-byte unsigned subtraction, with wrap-around: a - b
-  vabsdiff4(a,b)  per-byte unsigned absolute difference: |a - b|
-  vavg4(a,b)      per-byte unsigned average: (a + b) / 2
-  vavrg4(a,b)     per-byte unsigned rounded average: (a + b + 1) / 2
-  vseteq4(a,b)    per-byte unsigned comparison: a == b ? 1 : 0
-  vcmpeq4(a,b)    per-byte unsigned comparison: a == b ? 0xff : 0
-  vsetge4(a,b)    per-byte unsigned comparison: a >= b ? 1 : 0
-  vcmpge4(a,b)    per-byte unsigned comparison: a >= b ? 0xff : 0
-  vsetgt4(a,b)    per-byte unsigned comparison: a > b ? 1 : 0
-  vcmpgt4(a,b)    per-byte unsigned comparison: a > b ? 0xff : 0
-  vsetle4(a,b)    per-byte unsigned comparison: a <= b ? 1 : 0
-  vcmple4(a,b)    per-byte unsigned comparison: a <= b ? 0xff : 0
-  vsetlt4(a,b)    per-byte unsigned comparison: a < b ? 1 : 0
-  vcmplt4(a,b)    per-byte unsigned comparison: a < b ? 0xff : 0
-  vsetne4(a,b)    per-byte unsigned comparison: a != b ? 1: 0
-  vcmpne4(a,b)    per-byte unsigned comparison: a != b ? 0xff: 0
-  vmax4(a,b)      per-byte unsigned maximum: max(a, b)
-  vmin4(a,b)      per-byte unsigned minimum: min(a, b)
-*/
-
-namespace cv { namespace gpu { namespace device
-{
-    // 2
-
-    static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = a ^ b;          // sum bits
-        r = a + b;          // actual sum
-        s = s ^ r;          // determine carry-ins for each bit position
-        s = s & 0x00010000; // carry-in to high word (= carry-out from low word)
-        r = r - s;          // subtract out carry-out from low word
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = a ^ b;          // sum bits
-        r = a - b;          // actual sum
-        s = s ^ r;          // determine carry-ins for each bit position
-        s = s & 0x00010000; // borrow to high word
-        r = r + s;          // compensate for borrow from low word
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t, u, v;
-        s = a & 0x0000ffff; // extract low halfword
-        r = b & 0x0000ffff; // extract low halfword
-        u = ::max(r, s);    // maximum of low halfwords
-        v = ::min(r, s);    // minimum of low halfwords
-        s = a & 0xffff0000; // extract high halfword
-        r = b & 0xffff0000; // extract high halfword
-        t = ::max(r, s);    // maximum of high halfwords
-        s = ::min(r, s);    // minimum of high halfwords
-        r = u | t;          // maximum of both halfwords
-        s = v | s;          // minimum of both halfwords
-        r = r - s;          // |a - b| = max(a,b) - min(a,b);
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, s;
-
-        // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
-        // (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
-        s = a ^ b;
-        r = a & b;
-        s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries
-        s = s >> 1;
-        s = r + s;
-
-        return s;
-    }
-
-    static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
-        // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
-        unsigned int s;
-        s = a ^ b;
-        r = a | b;
-        s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries
-        s = s >> 1;
-        r = r - s;
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        r = r ^ c;          // extract msbs, msb = 1 if r < 0x8000
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r & ~c;         // msb = 1, if r was 0x0000
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vseteq2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        r = r ^ c;          // extract msbs, msb = 1 if r < 0x8000
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r & ~c;         // msb = 1, if r was 0x0000
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavrg2(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetge2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavrg2(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg2(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80008000; // msbs = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetgt2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg2(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80008000; // msbs = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg2(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetle2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg2(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg2(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetlt2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg2(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r | c;          // msb = 1, if r was not 0x0000
-        c = c & 0x80008000; // extract msbs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetne2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r | c;          // msb = 1, if r was not 0x0000
-        c = c & 0x80008000; // extract msbs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t, u;
-        r = a & 0x0000ffff; // extract low halfword
-        s = b & 0x0000ffff; // extract low halfword
-        t = ::max(r, s);    // maximum of low halfwords
-        r = a & 0xffff0000; // extract high halfword
-        s = b & 0xffff0000; // extract high halfword
-        u = ::max(r, s);    // maximum of high halfwords
-        r = t | u;          // combine halfword maximums
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t, u;
-        r = a & 0x0000ffff; // extract low halfword
-        s = b & 0x0000ffff; // extract low halfword
-        t = ::min(r, s);    // minimum of low halfwords
-        r = a & 0xffff0000; // extract high halfword
-        s = b & 0xffff0000; // extract high halfword
-        u = ::min(r, s);    // minimum of high halfwords
-        r = t | u;          // combine halfword minimums
-    #endif
-
-        return r;
-    }
-
-    // 4
-
-    static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t;
-        s = a ^ b;          // sum bits
-        r = a & 0x7f7f7f7f; // clear msbs
-        t = b & 0x7f7f7f7f; // clear msbs
-        s = s & 0x80808080; // msb sum bits
-        r = r + t;          // add without msbs, record carry-out in msbs
-        r = r ^ s;          // sum of msb sum and carry-in bits, w/o carry-out
-    #endif /* __CUDA_ARCH__ >= 300 */
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t;
-        s = a ^ ~b;         // inverted sum bits
-        r = a | 0x80808080; // set msbs
-        t = b & 0x7f7f7f7f; // clear msbs
-        s = s & 0x80808080; // inverted msb sum bits
-        r = r - t;          // subtract w/o msbs, record inverted borrows in msb
-        r = r ^ s;          // combine inverted msb sum bits and borrows
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, s;
-
-        // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
-        // (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
-        s = a ^ b;
-        r = a & b;
-        s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
-        s = s >> 1;
-        s = r + s;
-
-        return s;
-    }
-
-    static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
-        // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
-        unsigned int c;
-        c = a ^ b;
-        r = a | b;
-        c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
-        c = c >> 1;
-        r = r - c;
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x00 if a == b
-        c = r | 0x80808080; // set msbs, to catch carry out
-        r = r ^ c;          // extract msbs, msb = 1 if r < 0x80
-        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
-        c = r & ~c;         // msb = 1, if r was 0x00
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, t;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vseteq4(a, b);
-        t = r << 8;         // convert bool
-        r = t - r;          //  to mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        t = a ^ b;          // 0x00 if a == b
-        r = t | 0x80808080; // set msbs, to catch carry out
-        t = t ^ r;          // extract msbs, msb = 1 if t < 0x80
-        r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80
-        r = t & ~r;         // msb = 1, if t was 0x00
-        t = r >> 7;         // build mask
-        t = r - t;          //  from
-        r = t | r;          //   msbs
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg4(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetle4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg4(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80808080; // msbs = carry-outs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg4(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetlt4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg4(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80808080; // msbs = carry-outs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavrg4(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, s;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetge4(a, b);
-        s = r << 8;         // convert bool
-        r = s - r;          //  to mask
-    #else
-        asm ("not.b32 %0,%0;" : "+r"(b));
-        r = vavrg4 (a, b);  // (a + ~b + 1) / 2 = (a - b) / 2
-        r = r & 0x80808080; // msb = carry-outs
-        s = r >> 7;         // build mask
-        s = r - s;          //  from
-        r = s | r;          //   msbs
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg4(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetgt4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg4(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x00 if a == b
-        c = r | 0x80808080; // set msbs, to catch carry out
-        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
-        c = r | c;          // msb = 1, if r was not 0x00
-        c = c & 0x80808080; // extract msbs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetne4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        r = a ^ b;          // 0x00 if a == b
-        c = r | 0x80808080; // set msbs, to catch carry out
-        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
-        c = r | c;          // msb = 1, if r was not 0x00
-        c = c & 0x80808080; // extract msbs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = vcmpge4(a, b);  // mask = 0xff if a >= b
-        r = a ^ b;          //
-        s = (r &  s) ^ b;   // select a when a >= b, else select b => max(a,b)
-        r = s ^ r;          // select a when b >= a, else select b => min(a,b)
-        r = s - r;          // |a - b| = max(a,b) - min(a,b);
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = vcmpge4(a, b);  // mask = 0xff if a >= b
-        r = a & s;          // select a when b >= a
-        s = b & ~s;         // select b when b < a
-        r = r | s;          // combine byte selections
-    #endif
-
-        return r;           // byte-wise unsigned maximum
-    }
-
-    static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = vcmpge4(b, a);  // mask = 0xff if a >= b
-        r = a & s;          // select a when b >= a
-        s = b & ~s;         // select b when b < a
-        r = r | s;          // combine byte selections
-    #endif
-
-        return r;
-    }
-}}}
-
-#endif // __OPENCV_GPU_SIMD_FUNCTIONS_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/static_check.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/static_check.hpp
deleted file mode 100644
index e77691b..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/static_check.hpp
+++ /dev/null
@@ -1,67 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_GPU_DEVICE_STATIC_CHECK_HPP__
-#define __OPENCV_GPU_GPU_DEVICE_STATIC_CHECK_HPP__
-
-#if defined(__CUDACC__)
-    #define __OPENCV_GPU_HOST_DEVICE__ __host__ __device__ __forceinline__
-#else
-    #define __OPENCV_GPU_HOST_DEVICE__
-#endif
-
-namespace cv { namespace gpu
-{
-    namespace device
-    {
-        template<bool expr> struct Static {};
-
-        template<> struct Static<true>
-        {
-            __OPENCV_GPU_HOST_DEVICE__ static void check() {};
-        };
-    }
-}}
-
-#undef __OPENCV_GPU_HOST_DEVICE__
-
-#endif /* __OPENCV_GPU_GPU_DEVICE_STATIC_CHECK_HPP__ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/transform.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/transform.hpp
deleted file mode 100644
index 636caac..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/transform.hpp
+++ /dev/null
@@ -1,67 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_TRANSFORM_HPP__
-#define __OPENCV_GPU_TRANSFORM_HPP__
-
-#include "common.hpp"
-#include "utility.hpp"
-#include "detail/transform_detail.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template <typename T, typename D, typename UnOp, typename Mask>
-    static inline void transform(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, const Mask& mask, cudaStream_t stream)
-    {
-        typedef TransformFunctorTraits<UnOp> ft;
-        transform_detail::TransformDispatcher<VecTraits<T>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream);
-    }
-
-    template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-    static inline void transform(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, const Mask& mask, cudaStream_t stream)
-    {
-        typedef TransformFunctorTraits<BinOp> ft;
-        transform_detail::TransformDispatcher<VecTraits<T1>::cn == 1 && VecTraits<T2>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream);
-    }
-}}}
-
-#endif // __OPENCV_GPU_TRANSFORM_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/type_traits.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/type_traits.hpp
deleted file mode 100644
index 1b36acc..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/type_traits.hpp
+++ /dev/null
@@ -1,82 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_TYPE_TRAITS_HPP__
-#define __OPENCV_GPU_TYPE_TRAITS_HPP__
-
-#include "detail/type_traits_detail.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template <typename T> struct IsSimpleParameter
-    {
-        enum {value = type_traits_detail::IsIntegral<T>::value || type_traits_detail::IsFloat<T>::value ||
-            type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<T>::type>::value};
-    };
-
-    template <typename T> struct TypeTraits
-    {
-        typedef typename type_traits_detail::UnConst<T>::type                                                NonConstType;
-        typedef typename type_traits_detail::UnVolatile<T>::type                                             NonVolatileType;
-        typedef typename type_traits_detail::UnVolatile<typename type_traits_detail::UnConst<T>::type>::type UnqualifiedType;
-        typedef typename type_traits_detail::PointerTraits<UnqualifiedType>::type                            PointeeType;
-        typedef typename type_traits_detail::ReferenceTraits<T>::type                                        ReferredType;
-
-        enum { isConst          = type_traits_detail::UnConst<T>::value };
-        enum { isVolatile       = type_traits_detail::UnVolatile<T>::value };
-
-        enum { isReference      = type_traits_detail::ReferenceTraits<UnqualifiedType>::value };
-        enum { isPointer        = type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<UnqualifiedType>::type>::value };
-
-        enum { isUnsignedInt    = type_traits_detail::IsUnsignedIntegral<UnqualifiedType>::value };
-        enum { isSignedInt      = type_traits_detail::IsSignedIntergral<UnqualifiedType>::value };
-        enum { isIntegral       = type_traits_detail::IsIntegral<UnqualifiedType>::value };
-        enum { isFloat          = type_traits_detail::IsFloat<UnqualifiedType>::value };
-        enum { isArith          = isIntegral || isFloat };
-        enum { isVec            = type_traits_detail::IsVec<UnqualifiedType>::value };
-
-        typedef typename type_traits_detail::Select<IsSimpleParameter<UnqualifiedType>::value,
-            T, typename type_traits_detail::AddParameterType<T>::type>::type ParameterType;
-    };
-}}}
-
-#endif // __OPENCV_GPU_TYPE_TRAITS_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/utility.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/utility.hpp
deleted file mode 100644
index 85e81ac..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/utility.hpp
+++ /dev/null
@@ -1,213 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_UTILITY_HPP__
-#define __OPENCV_GPU_UTILITY_HPP__
-
-#include "saturate_cast.hpp"
-#include "datamov_utils.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    #define OPENCV_GPU_LOG_WARP_SIZE        (5)
-    #define OPENCV_GPU_WARP_SIZE            (1 << OPENCV_GPU_LOG_WARP_SIZE)
-    #define OPENCV_GPU_LOG_MEM_BANKS        ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla
-    #define OPENCV_GPU_MEM_BANKS            (1 << OPENCV_GPU_LOG_MEM_BANKS)
-
-    ///////////////////////////////////////////////////////////////////////////////
-    // swap
-
-    template <typename T> void __device__ __host__ __forceinline__ swap(T& a, T& b)
-    {
-        const T temp = a;
-        a = b;
-        b = temp;
-    }
-
-    ///////////////////////////////////////////////////////////////////////////////
-    // Mask Reader
-
-    struct SingleMask
-    {
-        explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {}
-        __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){}
-
-        __device__ __forceinline__ bool operator()(int y, int x) const
-        {
-            return mask.ptr(y)[x] != 0;
-        }
-
-        PtrStepb mask;
-    };
-
-    struct SingleMaskChannels
-    {
-        __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_)
-        : mask(mask_), channels(channels_) {}
-        __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_)
-            :mask(mask_.mask), channels(mask_.channels){}
-
-        __device__ __forceinline__ bool operator()(int y, int x) const
-        {
-            return mask.ptr(y)[x / channels] != 0;
-        }
-
-        PtrStepb mask;
-        int channels;
-    };
-
-    struct MaskCollection
-    {
-        explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_)
-            : maskCollection(maskCollection_) {}
-
-        __device__ __forceinline__ MaskCollection(const MaskCollection& masks_)
-            : maskCollection(masks_.maskCollection), curMask(masks_.curMask){}
-
-        __device__ __forceinline__ void next()
-        {
-            curMask = *maskCollection++;
-        }
-        __device__ __forceinline__ void setMask(int z)
-        {
-            curMask = maskCollection[z];
-        }
-
-        __device__ __forceinline__ bool operator()(int y, int x) const
-        {
-            uchar val;
-            return curMask.data == 0 || (ForceGlob<uchar>::Load(curMask.ptr(y), x, val), (val != 0));
-        }
-
-        const PtrStepb* maskCollection;
-        PtrStepb curMask;
-    };
-
-    struct WithOutMask
-    {
-        __host__ __device__ __forceinline__ WithOutMask(){}
-        __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){}
-
-        __device__ __forceinline__ void next() const
-        {
-        }
-        __device__ __forceinline__ void setMask(int) const
-        {
-        }
-
-        __device__ __forceinline__ bool operator()(int, int) const
-        {
-            return true;
-        }
-
-        __device__ __forceinline__ bool operator()(int, int, int) const
-        {
-            return true;
-        }
-
-        static __device__ __forceinline__ bool check(int, int)
-        {
-            return true;
-        }
-
-        static __device__ __forceinline__ bool check(int, int, int)
-        {
-            return true;
-        }
-    };
-
-    ///////////////////////////////////////////////////////////////////////////////
-    // Solve linear system
-
-    // solve 2x2 linear system Ax=b
-    template <typename T> __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2])
-    {
-        T det = A[0][0] * A[1][1] - A[1][0] * A[0][1];
-
-        if (det != 0)
-        {
-            double invdet = 1.0 / det;
-
-            x[0] = saturate_cast<T>(invdet * (b[0] * A[1][1] - b[1] * A[0][1]));
-
-            x[1] = saturate_cast<T>(invdet * (A[0][0] * b[1] - A[1][0] * b[0]));
-
-            return true;
-        }
-
-        return false;
-    }
-
-    // solve 3x3 linear system Ax=b
-    template <typename T> __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3])
-    {
-        T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1])
-              - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0])
-              + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]);
-
-        if (det != 0)
-        {
-            double invdet = 1.0 / det;
-
-            x[0] = saturate_cast<T>(invdet *
-                (b[0]    * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) -
-                 A[0][1] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) +
-                 A[0][2] * (b[1]    * A[2][1] - A[1][1] * b[2]   )));
-
-            x[1] = saturate_cast<T>(invdet *
-                (A[0][0] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) -
-                 b[0]    * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) +
-                 A[0][2] * (A[1][0] * b[2]    - b[1]    * A[2][0])));
-
-            x[2] = saturate_cast<T>(invdet *
-                (A[0][0] * (A[1][1] * b[2]    - b[1]    * A[2][1]) -
-                 A[0][1] * (A[1][0] * b[2]    - b[1]    * A[2][0]) +
-                 b[0]    * (A[1][0] * A[2][1] - A[1][1] * A[2][0])));
-
-            return true;
-        }
-
-        return false;
-    }
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_UTILITY_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_distance.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_distance.hpp
deleted file mode 100644
index d5b4bb2..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_distance.hpp
+++ /dev/null
@@ -1,224 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_VEC_DISTANCE_HPP__
-#define __OPENCV_GPU_VEC_DISTANCE_HPP__
-
-#include "reduce.hpp"
-#include "functional.hpp"
-#include "detail/vec_distance_detail.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template <typename T> struct L1Dist
-    {
-        typedef int value_type;
-        typedef int result_type;
-
-        __device__ __forceinline__ L1Dist() : mySum(0) {}
-
-        __device__ __forceinline__ void reduceIter(int val1, int val2)
-        {
-            mySum = __sad(val1, val2, mySum);
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
-        }
-
-        __device__ __forceinline__ operator int() const
-        {
-            return mySum;
-        }
-
-        int mySum;
-    };
-    template <> struct L1Dist<float>
-    {
-        typedef float value_type;
-        typedef float result_type;
-
-        __device__ __forceinline__ L1Dist() : mySum(0.0f) {}
-
-        __device__ __forceinline__ void reduceIter(float val1, float val2)
-        {
-            mySum += ::fabs(val1 - val2);
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
-        }
-
-        __device__ __forceinline__ operator float() const
-        {
-            return mySum;
-        }
-
-        float mySum;
-    };
-
-    struct L2Dist
-    {
-        typedef float value_type;
-        typedef float result_type;
-
-        __device__ __forceinline__ L2Dist() : mySum(0.0f) {}
-
-        __device__ __forceinline__ void reduceIter(float val1, float val2)
-        {
-            float reg = val1 - val2;
-            mySum += reg * reg;
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
-        }
-
-        __device__ __forceinline__ operator float() const
-        {
-            return sqrtf(mySum);
-        }
-
-        float mySum;
-    };
-
-    struct HammingDist
-    {
-        typedef int value_type;
-        typedef int result_type;
-
-        __device__ __forceinline__ HammingDist() : mySum(0) {}
-
-        __device__ __forceinline__ void reduceIter(int val1, int val2)
-        {
-            mySum += __popc(val1 ^ val2);
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
-        }
-
-        __device__ __forceinline__ operator int() const
-        {
-            return mySum;
-        }
-
-        int mySum;
-    };
-
-    // calc distance between two vectors in global memory
-    template <int THREAD_DIM, typename Dist, typename T1, typename T2>
-    __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
-    {
-        for (int i = tid; i < len; i += THREAD_DIM)
-        {
-            T1 val1;
-            ForceGlob<T1>::Load(vec1, i, val1);
-
-            T2 val2;
-            ForceGlob<T2>::Load(vec2, i, val2);
-
-            dist.reduceIter(val1, val2);
-        }
-
-        dist.reduceAll<THREAD_DIM>(smem, tid);
-    }
-
-    // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
-    template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
-    __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
-    {
-        vec_distance_detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
-
-        dist.reduceAll<THREAD_DIM>(smem, tid);
-    }
-
-    // calc distance between two vectors in global memory
-    template <int THREAD_DIM, typename T1> struct VecDiffGlobal
-    {
-        explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
-        {
-            vec1 = vec1_;
-        }
-
-        template <typename T2, typename Dist>
-        __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
-        {
-            calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
-        }
-
-        const T1* vec1;
-    };
-
-    // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
-    template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
-    {
-        template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
-        {
-            if (glob_tid < len)
-                smem[glob_tid] = vec1[glob_tid];
-            __syncthreads();
-
-            U* vec1ValsPtr = vec1Vals;
-
-            #pragma unroll
-            for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
-                *vec1ValsPtr++ = smem[i];
-
-            __syncthreads();
-        }
-
-        template <typename T2, typename Dist>
-        __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
-        {
-            calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
-        }
-
-        U vec1Vals[MAX_LEN / THREAD_DIM];
-    };
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_VEC_DISTANCE_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_math.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_math.hpp
deleted file mode 100644
index a6cb43a..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_math.hpp
+++ /dev/null
@@ -1,922 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_VECMATH_HPP__
-#define __OPENCV_GPU_VECMATH_HPP__
-
-#include "vec_traits.hpp"
-#include "saturate_cast.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-
-// saturate_cast
-
-namespace vec_math_detail
-{
-    template <int cn, typename VecD> struct SatCastHelper;
-    template <typename VecD> struct SatCastHelper<1, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x));
-        }
-    };
-    template <typename VecD> struct SatCastHelper<2, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y));
-        }
-    };
-    template <typename VecD> struct SatCastHelper<3, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z));
-        }
-    };
-    template <typename VecD> struct SatCastHelper<4, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z), saturate_cast<D>(v.w));
-        }
-    };
-
-    template <typename VecD, typename VecS> static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v)
-    {
-        return SatCastHelper<VecTraits<VecD>::cn, VecD>::cast(v);
-    }
-}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-// unary operators
-
-#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \
-    { \
-        return VecTraits<output_type ## 1>::make(op (a.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \
-    { \
-        return VecTraits<output_type ## 2>::make(op (a.x), op (a.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \
-    { \
-        return VecTraits<output_type ## 3>::make(op (a.x), op (a.y), op (a.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \
-    { \
-        return VecTraits<output_type ## 4>::make(op (a.x), op (a.y), op (a.z), op (a.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP
-
-// unary functions
-
-#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \
-    { \
-        return VecTraits<output_type ## 1>::make(func (a.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \
-    { \
-        return VecTraits<output_type ## 2>::make(func (a.x), func (a.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \
-    { \
-        return VecTraits<output_type ## 3>::make(func (a.x), func (a.y), func (a.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \
-    { \
-        return VecTraits<output_type ## 4>::make(func (a.x), func (a.y), func (a.z), func (a.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC
-
-// binary operators (vec & vec)
-
-#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(a.x op b.x); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(a.x op b.x, a.y op b.y); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(a.x op b.x, a.y op b.y, a.z op b.z); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP
-
-// binary operators (vec & scalar)
-
-#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 1>::make(a.x op s); \
-    } \
-    __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(s op b.x); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 2>::make(a.x op s, a.y op s); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(s op b.x, s op b.y); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 3>::make(a.x op s, a.y op s, a.z op s); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(s op b.x, s op b.y, s op b.z); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 4>::make(a.x op s, a.y op s, a.z op s, a.w op s); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(s op b.x, s op b.y, s op b.z, s op b.w); \
-    }
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint)
-
-#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP
-
-// binary function (vec & vec)
-
-#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(func (a.x, b.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(func (a.x, b.x), func (a.y, b.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC
-
-// binary function (vec & scalar)
-
-#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 1>::make(func ((output_type) a.x, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(func ((output_type) s, (output_type) b.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 2>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 3>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 4>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double)
-
-#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC
-
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_VECMATH_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_traits.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_traits.hpp
deleted file mode 100644
index 8d179c8..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/vec_traits.hpp
+++ /dev/null
@@ -1,280 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_VEC_TRAITS_HPP__
-#define __OPENCV_GPU_VEC_TRAITS_HPP__
-
-#include "common.hpp"
-
-namespace cv { namespace gpu { namespace device
-{
-    template<typename T, int N> struct TypeVec;
-
-    struct __align__(8) uchar8
-    {
-        uchar a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7)
-    {
-        uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(8) char8
-    {
-        schar a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7)
-    {
-        char8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(16) ushort8
-    {
-        ushort a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7)
-    {
-        ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(16) short8
-    {
-        short a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7)
-    {
-        short8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(32) uint8
-    {
-        uint a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7)
-    {
-        uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(32) int8
-    {
-        int a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7)
-    {
-        int8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(32) float8
-    {
-        float a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7)
-    {
-        float8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct double8
-    {
-        double a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7)
-    {
-        double8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-
-#define OPENCV_GPU_IMPLEMENT_TYPE_VEC(type) \
-    template<> struct TypeVec<type, 1> { typedef type vec_type; }; \
-    template<> struct TypeVec<type ## 1, 1> { typedef type ## 1 vec_type; }; \
-    template<> struct TypeVec<type, 2> { typedef type ## 2 vec_type; }; \
-    template<> struct TypeVec<type ## 2, 2> { typedef type ## 2 vec_type; }; \
-    template<> struct TypeVec<type, 3> { typedef type ## 3 vec_type; }; \
-    template<> struct TypeVec<type ## 3, 3> { typedef type ## 3 vec_type; }; \
-    template<> struct TypeVec<type, 4> { typedef type ## 4 vec_type; }; \
-    template<> struct TypeVec<type ## 4, 4> { typedef type ## 4 vec_type; }; \
-    template<> struct TypeVec<type, 8> { typedef type ## 8 vec_type; }; \
-    template<> struct TypeVec<type ## 8, 8> { typedef type ## 8 vec_type; };
-
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(uchar)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(char)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(ushort)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(short)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(int)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(uint)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(float)
-    OPENCV_GPU_IMPLEMENT_TYPE_VEC(double)
-
-    #undef OPENCV_GPU_IMPLEMENT_TYPE_VEC
-
-    template<> struct TypeVec<schar, 1> { typedef schar vec_type; };
-    template<> struct TypeVec<schar, 2> { typedef char2 vec_type; };
-    template<> struct TypeVec<schar, 3> { typedef char3 vec_type; };
-    template<> struct TypeVec<schar, 4> { typedef char4 vec_type; };
-    template<> struct TypeVec<schar, 8> { typedef char8 vec_type; };
-
-    template<> struct TypeVec<bool, 1> { typedef uchar vec_type; };
-    template<> struct TypeVec<bool, 2> { typedef uchar2 vec_type; };
-    template<> struct TypeVec<bool, 3> { typedef uchar3 vec_type; };
-    template<> struct TypeVec<bool, 4> { typedef uchar4 vec_type; };
-    template<> struct TypeVec<bool, 8> { typedef uchar8 vec_type; };
-
-    template<typename T> struct VecTraits;
-
-#define OPENCV_GPU_IMPLEMENT_VEC_TRAITS(type) \
-    template<> struct VecTraits<type> \
-    { \
-        typedef type elem_type; \
-        enum {cn=1}; \
-        static __device__ __host__ __forceinline__ type all(type v) {return v;} \
-        static __device__ __host__ __forceinline__ type make(type x) {return x;} \
-        static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \
-    }; \
-    template<> struct VecTraits<type ## 1> \
-    { \
-        typedef type elem_type; \
-        enum {cn=1}; \
-        static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \
-        static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \
-        static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \
-    }; \
-    template<> struct VecTraits<type ## 2> \
-    { \
-        typedef type elem_type; \
-        enum {cn=2}; \
-        static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \
-        static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \
-        static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \
-    }; \
-    template<> struct VecTraits<type ## 3> \
-    { \
-        typedef type elem_type; \
-        enum {cn=3}; \
-        static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \
-        static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \
-        static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \
-    }; \
-    template<> struct VecTraits<type ## 4> \
-    { \
-        typedef type elem_type; \
-        enum {cn=4}; \
-        static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \
-        static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \
-        static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \
-    }; \
-    template<> struct VecTraits<type ## 8> \
-    { \
-        typedef type elem_type; \
-        enum {cn=8}; \
-        static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \
-        static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \
-        static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \
-    };
-
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(uchar)
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(ushort)
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(short)
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(int)
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(uint)
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(float)
-    OPENCV_GPU_IMPLEMENT_VEC_TRAITS(double)
-
-    #undef OPENCV_GPU_IMPLEMENT_VEC_TRAITS
-
-    template<> struct VecTraits<char>
-    {
-        typedef char elem_type;
-        enum {cn=1};
-        static __device__ __host__ __forceinline__ char all(char v) {return v;}
-        static __device__ __host__ __forceinline__ char make(char x) {return x;}
-        static __device__ __host__ __forceinline__ char make(const char* x) {return *x;}
-    };
-    template<> struct VecTraits<schar>
-    {
-        typedef schar elem_type;
-        enum {cn=1};
-        static __device__ __host__ __forceinline__ schar all(schar v) {return v;}
-        static __device__ __host__ __forceinline__ schar make(schar x) {return x;}
-        static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;}
-    };
-    template<> struct VecTraits<char1>
-    {
-        typedef schar elem_type;
-        enum {cn=1};
-        static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);}
-        static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);}
-        static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);}
-    };
-    template<> struct VecTraits<char2>
-    {
-        typedef schar elem_type;
-        enum {cn=2};
-        static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);}
-        static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);}
-        static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);}
-    };
-    template<> struct VecTraits<char3>
-    {
-        typedef schar elem_type;
-        enum {cn=3};
-        static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);}
-        static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);}
-        static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);}
-    };
-    template<> struct VecTraits<char4>
-    {
-        typedef schar elem_type;
-        enum {cn=4};
-        static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);}
-        static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);}
-        static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);}
-    };
-    template<> struct VecTraits<char8>
-    {
-        typedef schar elem_type;
-        enum {cn=8};
-        static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);}
-        static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);}
-        static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);}
-    };
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_VEC_TRAITS_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp.hpp
deleted file mode 100644
index 0f1dc79..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp.hpp
+++ /dev/null
@@ -1,131 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_DEVICE_WARP_HPP__
-#define __OPENCV_GPU_DEVICE_WARP_HPP__
-
-namespace cv { namespace gpu { namespace device
-{
-    struct Warp
-    {
-        enum
-        {
-            LOG_WARP_SIZE = 5,
-            WARP_SIZE     = 1 << LOG_WARP_SIZE,
-            STRIDE        = WARP_SIZE
-        };
-
-        /** \brief Returns the warp lane ID of the calling thread. */
-        static __device__ __forceinline__ unsigned int laneId()
-        {
-            unsigned int ret;
-            asm("mov.u32 %0, %laneid;" : "=r"(ret) );
-            return ret;
-        }
-
-        template<typename It, typename T>
-        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
-        {
-            for(It t = beg + laneId(); t < end; t += STRIDE)
-                *t = value;
-        }
-
-        template<typename InIt, typename OutIt>
-        static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out)
-        {
-            for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
-                *out = *t;
-            return out;
-        }
-
-        template<typename InIt, typename OutIt, class UnOp>
-        static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op)
-        {
-            for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
-                *out = op(*t);
-            return out;
-        }
-
-        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
-        static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
-        {
-            unsigned int lane = laneId();
-
-            InIt1 t1 = beg1 + lane;
-            InIt2 t2 = beg2 + lane;
-            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE)
-                *out = op(*t1, *t2);
-            return out;
-        }
-
-        template <class T, class BinOp>
-        static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op)
-        {
-            const unsigned int lane = laneId();
-
-            if (lane < 16)
-            {
-                T partial = ptr[lane];
-
-                ptr[lane] = partial = op(partial, ptr[lane + 16]);
-                ptr[lane] = partial = op(partial, ptr[lane + 8]);
-                ptr[lane] = partial = op(partial, ptr[lane + 4]);
-                ptr[lane] = partial = op(partial, ptr[lane + 2]);
-                ptr[lane] = partial = op(partial, ptr[lane + 1]);
-            }
-
-            return *ptr;
-        }
-
-        template<typename OutIt, typename T>
-        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
-        {
-            unsigned int lane = laneId();
-            value += lane;
-
-            for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE)
-                *t = value;
-        }
-    };
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif /* __OPENCV_GPU_DEVICE_WARP_HPP__ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp_reduce.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp_reduce.hpp
deleted file mode 100644
index d4e64c4..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp_reduce.hpp
+++ /dev/null
@@ -1,68 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_GPU_WARP_REDUCE_HPP__
-#define OPENCV_GPU_WARP_REDUCE_HPP__
-
-namespace cv { namespace gpu { namespace device
-{
-    template <class T>
-    __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x)
-    {
-        const unsigned int lane = tid & 31; // index of thread in warp (0..31)
-
-        if (lane < 16)
-        {
-            T partial = ptr[tid];
-
-            ptr[tid] = partial = partial + ptr[tid + 16];
-            ptr[tid] = partial = partial + ptr[tid + 8];
-            ptr[tid] = partial = partial + ptr[tid + 4];
-            ptr[tid] = partial = partial + ptr[tid + 2];
-            ptr[tid] = partial = partial + ptr[tid + 1];
-        }
-
-        return ptr[tid - lane];
-    }
-}}} // namespace cv { namespace gpu { namespace device {
-
-#endif /* OPENCV_GPU_WARP_REDUCE_HPP__ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp_shuffle.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp_shuffle.hpp
deleted file mode 100644
index 8b4479a..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/device/warp_shuffle.hpp
+++ /dev/null
@@ -1,145 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_WARP_SHUFFLE_HPP__
-#define __OPENCV_GPU_WARP_SHUFFLE_HPP__
-
-namespace cv { namespace gpu { namespace device
-{
-    template <typename T>
-    __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        return __shfl(val, srcLane, width);
-    #else
-        return T();
-    #endif
-    }
-    __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        return (unsigned int) __shfl((int) val, srcLane, width);
-    #else
-        return 0;
-    #endif
-    }
-    __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        int lo = __double2loint(val);
-        int hi = __double2hiint(val);
-
-        lo = __shfl(lo, srcLane, width);
-        hi = __shfl(hi, srcLane, width);
-
-        return __hiloint2double(hi, lo);
-    #else
-        return 0.0;
-    #endif
-    }
-
-    template <typename T>
-    __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        return __shfl_down(val, delta, width);
-    #else
-        return T();
-    #endif
-    }
-    __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        return (unsigned int) __shfl_down((int) val, delta, width);
-    #else
-        return 0;
-    #endif
-    }
-    __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        int lo = __double2loint(val);
-        int hi = __double2hiint(val);
-
-        lo = __shfl_down(lo, delta, width);
-        hi = __shfl_down(hi, delta, width);
-
-        return __hiloint2double(hi, lo);
-    #else
-        return 0.0;
-    #endif
-    }
-
-    template <typename T>
-    __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        return __shfl_up(val, delta, width);
-    #else
-        return T();
-    #endif
-    }
-    __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        return (unsigned int) __shfl_up((int) val, delta, width);
-    #else
-        return 0;
-    #endif
-    }
-    __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize)
-    {
-    #if __CUDA_ARCH__ >= 300
-        int lo = __double2loint(val);
-        int hi = __double2hiint(val);
-
-        lo = __shfl_up(lo, delta, width);
-        hi = __shfl_up(hi, delta, width);
-
-        return __hiloint2double(hi, lo);
-    #else
-        return 0.0;
-    #endif
-    }
-}}}
-
-#endif // __OPENCV_GPU_WARP_SHUFFLE_HPP__
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/devmem2d.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/devmem2d.hpp
deleted file mode 100644
index 18dfcd8..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/devmem2d.hpp
+++ /dev/null
@@ -1,43 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "opencv2/core/cuda_devptrs.hpp"
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp
deleted file mode 100644
index de16982..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/gpu.hpp
+++ /dev/null
@@ -1,2530 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_HPP__
-#define __OPENCV_GPU_HPP__
-
-#ifndef SKIP_INCLUDES
-#include <vector>
-#include <memory>
-#include <iosfwd>
-#endif
-
-#include "opencv2/core/gpumat.hpp"
-#include "opencv2/imgproc/imgproc.hpp"
-#include "opencv2/objdetect/objdetect.hpp"
-#include "opencv2/features2d/features2d.hpp"
-
-namespace cv { namespace gpu {
-
-//////////////////////////////// CudaMem ////////////////////////////////
-// CudaMem is limited cv::Mat with page locked memory allocation.
-// Page locked memory is only needed for async and faster coping to GPU.
-// It is convertable to cv::Mat header without reference counting
-// so you can use it with other opencv functions.
-
-// Page-locks the matrix m memory and maps it for the device(s)
-CV_EXPORTS void registerPageLocked(Mat& m);
-// Unmaps the memory of matrix m, and makes it pageable again.
-CV_EXPORTS void unregisterPageLocked(Mat& m);
-
-class CV_EXPORTS CudaMem
-{
-public:
-    enum  { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
-
-    CudaMem();
-    CudaMem(const CudaMem& m);
-
-    CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
-    CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
-
-
-    //! creates from cv::Mat with coping data
-    explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);
-
-    ~CudaMem();
-
-    CudaMem& operator = (const CudaMem& m);
-
-    //! returns deep copy of the matrix, i.e. the data is copied
-    CudaMem clone() const;
-
-    //! allocates new matrix data unless the matrix already has specified size and type.
-    void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
-    void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);
-
-    //! decrements reference counter and released memory if needed.
-    void release();
-
-    //! returns matrix header with disabled reference counting for CudaMem data.
-    Mat createMatHeader() const;
-    operator Mat() const;
-
-    //! maps host memory into device address space and returns GpuMat header for it. Throws exception if not supported by hardware.
-    GpuMat createGpuMatHeader() const;
-    operator GpuMat() const;
-
-    //returns if host memory can be mapperd to gpu address space;
-    static bool canMapHostMemory();
-
-    // Please see cv::Mat for descriptions
-    bool isContinuous() const;
-    size_t elemSize() const;
-    size_t elemSize1() const;
-    int type() const;
-    int depth() const;
-    int channels() const;
-    size_t step1() const;
-    Size size() const;
-    bool empty() const;
-
-
-    // Please see cv::Mat for descriptions
-    int flags;
-    int rows, cols;
-    size_t step;
-
-    uchar* data;
-    int* refcount;
-
-    uchar* datastart;
-    uchar* dataend;
-
-    int alloc_type;
-};
-
-//////////////////////////////// CudaStream ////////////////////////////////
-// Encapculates Cuda Stream. Provides interface for async coping.
-// Passed to each function that supports async kernel execution.
-// Reference counting is enabled
-
-class CV_EXPORTS Stream
-{
-public:
-    Stream();
-    ~Stream();
-
-    Stream(const Stream&);
-    Stream& operator =(const Stream&);
-
-    bool queryIfComplete();
-    void waitForCompletion();
-
-    //! downloads asynchronously
-    // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
-    void enqueueDownload(const GpuMat& src, CudaMem& dst);
-    void enqueueDownload(const GpuMat& src, Mat& dst);
-
-    //! uploads asynchronously
-    // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
-    void enqueueUpload(const CudaMem& src, GpuMat& dst);
-    void enqueueUpload(const Mat& src, GpuMat& dst);
-
-    //! copy asynchronously
-    void enqueueCopy(const GpuMat& src, GpuMat& dst);
-
-    //! memory set asynchronously
-    void enqueueMemSet(GpuMat& src, Scalar val);
-    void enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask);
-
-    //! converts matrix type, ex from float to uchar depending on type
-    void enqueueConvert(const GpuMat& src, GpuMat& dst, int dtype, double a = 1, double b = 0);
-
-    //! adds a callback to be called on the host after all currently enqueued items in the stream have completed
-    typedef void (*StreamCallback)(Stream& stream, int status, void* userData);
-    void enqueueHostCallback(StreamCallback callback, void* userData);
-
-    static Stream& Null();
-
-    operator bool() const;
-
-private:
-    struct Impl;
-
-    explicit Stream(Impl* impl);
-    void create();
-    void release();
-
-    Impl *impl;
-
-    friend struct StreamAccessor;
-};
-
-
-//////////////////////////////// Filter Engine ////////////////////////////////
-
-/*!
-The Base Class for 1D or Row-wise Filters
-
-This is the base class for linear or non-linear filters that process 1D data.
-In particular, such filters are used for the "horizontal" filtering parts in separable filters.
-*/
-class CV_EXPORTS BaseRowFilter_GPU
-{
-public:
-    BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
-    virtual ~BaseRowFilter_GPU() {}
-    virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
-    int ksize, anchor;
-};
-
-/*!
-The Base Class for Column-wise Filters
-
-This is the base class for linear or non-linear filters that process columns of 2D arrays.
-Such filters are used for the "vertical" filtering parts in separable filters.
-*/
-class CV_EXPORTS BaseColumnFilter_GPU
-{
-public:
-    BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
-    virtual ~BaseColumnFilter_GPU() {}
-    virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
-    int ksize, anchor;
-};
-
-/*!
-The Base Class for Non-Separable 2D Filters.
-
-This is the base class for linear or non-linear 2D filters.
-*/
-class CV_EXPORTS BaseFilter_GPU
-{
-public:
-    BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
-    virtual ~BaseFilter_GPU() {}
-    virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
-    Size ksize;
-    Point anchor;
-};
-
-/*!
-The Base Class for Filter Engine.
-
-The class can be used to apply an arbitrary filtering operation to an image.
-It contains all the necessary intermediate buffers.
-*/
-class CV_EXPORTS FilterEngine_GPU
-{
-public:
-    virtual ~FilterEngine_GPU() {}
-
-    virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
-};
-
-//! returns the non-separable filter engine with the specified filter
-CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);
-
-//! returns the separable filter engine with the specified filters
-CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
-    const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
-CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
-    const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf);
-
-//! returns horizontal 1D box filter
-//! supports only CV_8UC1 source type and CV_32FC1 sum type
-CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
-
-//! returns vertical 1D box filter
-//! supports only CV_8UC1 sum type and CV_32FC1 dst type
-CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
-
-//! returns 2D box filter
-//! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
-CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
-
-//! returns box filter engine
-CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
-    const Point& anchor = Point(-1,-1));
-
-//! returns 2D morphological filter
-//! only MORPH_ERODE and MORPH_DILATE are supported
-//! supports CV_8UC1 and CV_8UC4 types
-//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
-CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
-    Point anchor=Point(-1,-1));
-
-//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
-CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
-    const Point& anchor = Point(-1,-1), int iterations = 1);
-CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf,
-    const Point& anchor = Point(-1,-1), int iterations = 1);
-
-//! returns 2D filter with the specified kernel
-//! supports CV_8U, CV_16U and CV_32F one and four channel image
-CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
-
-//! returns the non-separable linear filter engine
-CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
-    Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT);
-
-//! returns the primitive row filter with the specified kernel.
-//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
-//! there are two version of algorithm: NPP and OpenCV.
-//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
-//! otherwise calls OpenCV version.
-//! NPP supports only BORDER_CONSTANT border type.
-//! OpenCV version supports only CV_32F as buffer depth and
-//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
-CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
-    int anchor = -1, int borderType = BORDER_DEFAULT);
-
-//! returns the primitive column filter with the specified kernel.
-//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type.
-//! there are two version of algorithm: NPP and OpenCV.
-//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
-//! otherwise calls OpenCV version.
-//! NPP supports only BORDER_CONSTANT border type.
-//! OpenCV version supports only CV_32F as buffer depth and
-//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
-CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
-    int anchor = -1, int borderType = BORDER_DEFAULT);
-
-//! returns the separable linear filter engine
-CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
-    const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
-    int columnBorderType = -1);
-CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
-    const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
-    int columnBorderType = -1);
-
-//! returns filter engine for the generalized Sobel operator
-CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
-                                                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf,
-                                                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-
-//! returns the Gaussian filter engine
-CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
-                                                          int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
-                                                          int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-
-//! returns maximum filter
-CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
-
-//! returns minimum filter
-CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
-
-//! smooths the image using the normalized box filter
-//! supports CV_8UC1, CV_8UC4 types
-CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null());
-
-//! a synonym for normalized box filter
-static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
-{
-    boxFilter(src, dst, -1, ksize, anchor, stream);
-}
-
-//! erodes the image (applies the local minimum operator)
-CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
-CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
-                      Point anchor = Point(-1, -1), int iterations = 1,
-                      Stream& stream = Stream::Null());
-
-//! dilates the image (applies the local maximum operator)
-CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
-CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
-                       Point anchor = Point(-1, -1), int iterations = 1,
-                       Stream& stream = Stream::Null());
-
-//! applies an advanced morphological operation to the image
-CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
-CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2,
-                             Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
-
-//! applies non-separable 2D linear filter to the image
-CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
-
-//! applies separable 2D linear filter to the image
-CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
-                            Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf,
-                            Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1,
-                            Stream& stream = Stream::Null());
-
-//! applies generalized Sobel operator to the image
-CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
-                      int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1,
-                      int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
-
-//! applies the vertical or horizontal Scharr operator to the image
-CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
-                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1,
-                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
-
-//! smooths the image using Gaussian filter.
-CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
-                             int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
-CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
-                             int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
-
-//! applies Laplacian operator to the image
-//! supports only ksize = 1 and ksize = 3
-CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
-
-
-////////////////////////////// Arithmetics ///////////////////////////////////
-
-//! implements generalized matrix product algorithm GEMM from BLAS
-CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
-    const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
-
-//! transposes the matrix
-//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
-CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! reverses the order of the rows, columns or both in a matrix
-//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
-CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
-
-//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
-//! destination array will have the depth type as lut and the same channels number as source
-//! supports CV_8UC1, CV_8UC3 types
-CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! makes multi-channel array out of several single-channel arrays
-CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! makes multi-channel array out of several single-channel arrays
-CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! copies each plane of a multi-channel array to a dedicated array
-CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());
-
-//! copies each plane of a multi-channel array to a dedicated array
-CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null());
-
-//! computes magnitude of complex (x(i).re, x(i).im) vector
-//! supports only CV_32FC2 type
-CV_EXPORTS void magnitude(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());
-
-//! computes squared magnitude of complex (x(i).re, x(i).im) vector
-//! supports only CV_32FC2 type
-CV_EXPORTS void magnitudeSqr(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());
-
-//! computes magnitude of each (x(i), y(i)) vector
-//! supports only floating-point source
-CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
-
-//! computes squared magnitude of each (x(i), y(i)) vector
-//! supports only floating-point source
-CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
-
-//! computes angle (angle(i)) of each (x(i), y(i)) vector
-//! supports only floating-point source
-CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
-
-//! converts Cartesian coordinates to polar
-//! supports only floating-point source
-CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
-
-//! converts polar coordinates to Cartesian
-//! supports only floating-point source
-CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null());
-
-//! scales and shifts array elements so that either the specified norm (alpha) or the minimum (alpha) and maximum (beta) array values get the specified values
-CV_EXPORTS void normalize(const GpuMat& src, GpuMat& dst, double alpha = 1, double beta = 0,
-                          int norm_type = NORM_L2, int dtype = -1, const GpuMat& mask = GpuMat());
-CV_EXPORTS void normalize(const GpuMat& src, GpuMat& dst, double a, double b,
-                          int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf);
-
-
-//////////////////////////// Per-element operations ////////////////////////////////////
-
-//! adds one matrix to another (c = a + b)
-CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
-//! adds scalar to a matrix (c = a + s)
-CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
-
-//! subtracts one matrix from another (c = a - b)
-CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
-//! subtracts scalar from a matrix (c = a - s)
-CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
-
-//! computes element-wise weighted product of the two arrays (c = scale * a * b)
-CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
-//! weighted multiplies matrix to a scalar (c = scale * a * s)
-CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
-
-//! computes element-wise weighted quotient of the two arrays (c = a / b)
-CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
-//! computes element-wise weighted quotient of matrix and scalar (c = a / s)
-CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
-//! computes element-wise weighted reciprocal of an array (dst = scale/src2)
-CV_EXPORTS void divide(double scale, const GpuMat& b, GpuMat& c, int dtype = -1, Stream& stream = Stream::Null());
-
-//! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
-CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
-                            int dtype = -1, Stream& stream = Stream::Null());
-
-//! adds scaled array to another one (dst = alpha*src1 + src2)
-static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
-{
-    addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
-}
-
-//! computes element-wise absolute difference of two arrays (c = abs(a - b))
-CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
-//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
-CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
-
-//! computes absolute value of each matrix element
-//! supports CV_16S and CV_32F depth
-CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes square of each pixel in an image
-//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
-CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes square root of each pixel in an image
-//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
-CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes exponent of each matrix element (b = e**a)
-//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
-CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
-
-//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
-//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
-CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
-
-//! computes power of each matrix element:
-//    (dst(i,j) = pow(     src(i,j) , power), if src.type() is integer
-//    (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
-//! supports all, except depth == CV_64F
-CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! compares elements of two arrays (c = a \<cmpop\> b)
-CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
-CV_EXPORTS void compare(const GpuMat& a, Scalar sc, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
-
-//! performs per-elements bit-wise inversion
-CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
-
-//! calculates per-element bit-wise disjunction of two arrays
-CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
-//! calculates per-element bit-wise disjunction of array and scalar
-//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
-CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! calculates per-element bit-wise conjunction of two arrays
-CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
-//! calculates per-element bit-wise conjunction of array and scalar
-//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
-CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! calculates per-element bit-wise "exclusive or" operation
-CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
-//! calculates per-element bit-wise "exclusive or" of array and scalar
-//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
-CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! pixel by pixel right shift of an image by a constant value
-//! supports 1, 3 and 4 channels images with integers elements
-CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! pixel by pixel left shift of an image by a constant value
-//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
-CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes per-element minimum of two arrays (dst = min(src1, src2))
-CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes per-element minimum of array and scalar (dst = min(src1, src2))
-CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes per-element maximum of two arrays (dst = max(src1, src2))
-CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! computes per-element maximum of array and scalar (dst = max(src1, src2))
-CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
-
-enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
-       ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};
-
-//! Composite two images using alpha opacity values contained in each image
-//! Supports CV_8UC4, CV_16UC4, CV_32SC4 and CV_32FC4 types
-CV_EXPORTS void alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null());
-
-
-////////////////////////////// Image processing //////////////////////////////
-
-//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]]
-//! supports only CV_32FC1 map type
-CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap,
-                      int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(),
-                      Stream& stream = Stream::Null());
-
-//! Does mean shift filtering on GPU.
-CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
-                                   TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
-                                   Stream& stream = Stream::Null());
-
-//! Does mean shift procedure on GPU.
-CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
-                              TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
-                              Stream& stream = Stream::Null());
-
-//! Does mean shift segmentation with elimination of small regions.
-CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
-                                      TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
-
-//! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV.
-//! Supported types of input disparity: CV_8U, CV_16S.
-//! Output disparity has CV_8UC4 type in BGRA format (alpha = 255).
-CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null());
-
-//! Reprojects disparity image to 3D space.
-//! Supports CV_8U and CV_16S types of input disparity.
-//! The output is a 3- or 4-channel floating-point matrix.
-//! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map.
-//! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify.
-CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, int dst_cn = 4, Stream& stream = Stream::Null());
-
-//! converts image from one color space to another
-CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null());
-
-enum
-{
-    // Bayer Demosaicing (Malvar, He, and Cutler)
-    COLOR_BayerBG2BGR_MHT = 256,
-    COLOR_BayerGB2BGR_MHT = 257,
-    COLOR_BayerRG2BGR_MHT = 258,
-    COLOR_BayerGR2BGR_MHT = 259,
-
-    COLOR_BayerBG2RGB_MHT = COLOR_BayerRG2BGR_MHT,
-    COLOR_BayerGB2RGB_MHT = COLOR_BayerGR2BGR_MHT,
-    COLOR_BayerRG2RGB_MHT = COLOR_BayerBG2BGR_MHT,
-    COLOR_BayerGR2RGB_MHT = COLOR_BayerGB2BGR_MHT,
-
-    COLOR_BayerBG2GRAY_MHT = 260,
-    COLOR_BayerGB2GRAY_MHT = 261,
-    COLOR_BayerRG2GRAY_MHT = 262,
-    COLOR_BayerGR2GRAY_MHT = 263
-};
-CV_EXPORTS void demosaicing(const GpuMat& src, GpuMat& dst, int code, int dcn = -1, Stream& stream = Stream::Null());
-
-//! swap channels
-//! dstOrder - Integer array describing how channel values are permutated. The n-th entry
-//!            of the array contains the number of the channel that is stored in the n-th channel of
-//!            the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR
-//!            channel order.
-CV_EXPORTS void swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null());
-
-//! Routines for correcting image color gamma
-CV_EXPORTS void gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward = true, Stream& stream = Stream::Null());
-
-//! applies fixed threshold to the image
-CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
-
-//! resizes the image
-//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA
-CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
-
-//! warps the image using affine transformation
-//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
-CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
-    int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
-
-CV_EXPORTS void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
-
-//! warps the image using perspective transformation
-//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
-CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
-    int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());
-
-CV_EXPORTS void buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
-
-//! builds plane warping maps
-CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T, float scale,
-                                   GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
-
-//! builds cylindrical warping maps
-CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
-                                         GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
-
-//! builds spherical warping maps
-CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
-                                       GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());
-
-//! rotates an image around the origin (0,0) and then shifts it
-//! supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
-//! supports 1, 3 or 4 channels images with CV_8U, CV_16U or CV_32F depth
-CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0,
-                       int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
-
-//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
-CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType,
-                               const Scalar& value = Scalar(), Stream& stream = Stream::Null());
-
-//! computes the integral image
-//! sum will have CV_32S type, but will contain unsigned int values
-//! supports only CV_8UC1 source type
-CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null());
-//! buffered version
-CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
-
-//! computes squared integral image
-//! result matrix will have 64F type, but will contain 64U values
-//! supports source images of 8UC1 type only
-CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null());
-
-//! computes vertical sum, supports only CV_32FC1 images
-CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum);
-
-//! computes the standard deviation of integral images
-//! supports only CV_32SC1 source type and CV_32FC1 sqr type
-//! output will have CV_32FC1 type
-CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null());
-
-//! computes Harris cornerness criteria at each image pixel
-CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
-CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
-CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k,
-                             int borderType = BORDER_REFLECT101, Stream& stream = Stream::Null());
-
-//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
-CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
-CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
-CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize,
-    int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null());
-
-//! performs per-element multiplication of two full (not packed) Fourier spectrums
-//! supports 32FC2 matrices only (interleaved format)
-CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream = Stream::Null());
-
-//! performs per-element multiplication of two full (not packed) Fourier spectrums
-//! supports 32FC2 matrices only (interleaved format)
-CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());
-
-//! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
-//! Param dft_size is the size of DFT transform.
-//!
-//! If the source matrix is not continous, then additional copy will be done,
-//! so to avoid copying ensure the source matrix is continous one. If you want to use
-//! preallocated output ensure it is continuous too, otherwise it will be reallocated.
-//!
-//! Being implemented via CUFFT real-to-complex transform result contains only non-redundant values
-//! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved.
-//!
-//! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format.
-CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
-
-struct CV_EXPORTS ConvolveBuf
-{
-    Size result_size;
-    Size block_size;
-    Size user_block_size;
-    Size dft_size;
-    int spect_len;
-
-    GpuMat image_spect, templ_spect, result_spect;
-    GpuMat image_block, templ_block, result_data;
-
-    void create(Size image_size, Size templ_size);
-    static Size estimateBlockSize(Size result_size, Size templ_size);
-};
-
-
-//! computes convolution (or cross-correlation) of two images using discrete Fourier transform
-//! supports source images of 32FC1 type only
-//! result matrix will have 32FC1 type
-CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
-CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
-
-struct CV_EXPORTS MatchTemplateBuf
-{
-    Size user_block_size;
-    GpuMat imagef, templf;
-    std::vector<GpuMat> images;
-    std::vector<GpuMat> image_sums;
-    std::vector<GpuMat> image_sqsums;
-};
-
-//! computes the proximity map for the raster template and the image where the template is searched for
-CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null());
-
-//! computes the proximity map for the raster template and the image where the template is searched for
-CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null());
-
-//! smoothes the source image and downsamples it
-CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! upsamples the source image and then smoothes it
-CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
-
-//! performs linear blending of two images
-//! to avoid accuracy errors sum of weigths shouldn't be very close to zero
-CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
-                            GpuMat& result, Stream& stream = Stream::Null());
-
-//! Performa bilateral filtering of passsed image
-CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
-                                int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
-
-//! Brute force non-local means algorith (slow but universal)
-CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
-
-//! Fast (but approximate)version of non-local means algorith similar to CPU function (running sums technique)
-class CV_EXPORTS FastNonLocalMeansDenoising
-{
-public:
-    //! Simple method, recommended for grayscale images (though it supports multichannel images)
-    void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
-
-    //! Processes luminance and color components separatelly
-    void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
-
-private:
-
-    GpuMat buffer, extended_src_buffer;
-    GpuMat lab, l, ab;
-};
-
-struct CV_EXPORTS CannyBuf
-{
-    void create(const Size& image_size, int apperture_size = 3);
-    void release();
-
-    GpuMat dx, dy;
-    GpuMat mag;
-    GpuMat map;
-    GpuMat st1, st2;
-    GpuMat unused;
-    Ptr<FilterEngine_GPU> filterDX, filterDY;
-
-    CannyBuf() {}
-    explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);}
-    CannyBuf(const GpuMat& dx_, const GpuMat& dy_);
-};
-
-CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
-CV_EXPORTS void Canny(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
-CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
-CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
-
-class CV_EXPORTS ImagePyramid
-{
-public:
-    inline ImagePyramid() : nLayers_(0) {}
-    inline ImagePyramid(const GpuMat& img, int nLayers, Stream& stream = Stream::Null())
-    {
-        build(img, nLayers, stream);
-    }
-
-    void build(const GpuMat& img, int nLayers, Stream& stream = Stream::Null());
-
-    void getLayer(GpuMat& outImg, Size outRoi, Stream& stream = Stream::Null()) const;
-
-    inline void release()
-    {
-        layer0_.release();
-        pyramid_.clear();
-        nLayers_ = 0;
-    }
-
-private:
-    GpuMat layer0_;
-    std::vector<GpuMat> pyramid_;
-    int nLayers_;
-};
-
-//! HoughLines
-
-struct HoughLinesBuf
-{
-    GpuMat accum;
-    GpuMat list;
-};
-
-CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
-CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
-CV_EXPORTS void HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes = noArray());
-
-//! HoughLinesP
-
-//! finds line segments in the black-n-white image using probabalistic Hough transform
-CV_EXPORTS void HoughLinesP(const GpuMat& image, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096);
-
-//! HoughCircles
-
-struct HoughCirclesBuf
-{
-    GpuMat edges;
-    GpuMat accum;
-    GpuMat list;
-    CannyBuf cannyBuf;
-};
-
-CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
-CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
-CV_EXPORTS void HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circles);
-
-//! finds arbitrary template in the grayscale image using Generalized Hough Transform
-//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
-//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
-class CV_EXPORTS GeneralizedHough_GPU : public Algorithm
-{
-public:
-    static Ptr<GeneralizedHough_GPU> create(int method);
-
-    virtual ~GeneralizedHough_GPU();
-
-    //! set template to search
-    void setTemplate(const GpuMat& templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1));
-    void setTemplate(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter = Point(-1, -1));
-
-    //! find template on image
-    void detect(const GpuMat& image, GpuMat& positions, int cannyThreshold = 100);
-    void detect(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions);
-
-    void download(const GpuMat& d_positions, OutputArray h_positions, OutputArray h_votes = noArray());
-
-    void release();
-
-protected:
-    virtual void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter) = 0;
-    virtual void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions) = 0;
-    virtual void releaseImpl() = 0;
-
-private:
-    GpuMat edges_;
-    CannyBuf cannyBuf_;
-};
-
-////////////////////////////// Matrix reductions //////////////////////////////
-
-//! computes mean value and standard deviation of all or selected array elements
-//! supports only CV_8UC1 type
-CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
-//! buffered version
-CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
-
-//! computes norm of array
-//! supports NORM_INF, NORM_L1, NORM_L2
-//! supports all matrices except 64F
-CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
-CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf);
-CV_EXPORTS double norm(const GpuMat& src1, int normType, const GpuMat& mask, GpuMat& buf);
-
-//! computes norm of the difference between two arrays
-//! supports NORM_INF, NORM_L1, NORM_L2
-//! supports only CV_8UC1 type
-CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);
-
-//! computes sum of array elements
-//! supports only single channel images
-CV_EXPORTS Scalar sum(const GpuMat& src);
-CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
-CV_EXPORTS Scalar sum(const GpuMat& src, const GpuMat& mask, GpuMat& buf);
-
-//! computes sum of array elements absolute values
-//! supports only single channel images
-CV_EXPORTS Scalar absSum(const GpuMat& src);
-CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf);
-CV_EXPORTS Scalar absSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf);
-
-//! computes squared sum of array elements
-//! supports only single channel images
-CV_EXPORTS Scalar sqrSum(const GpuMat& src);
-CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf);
-CV_EXPORTS Scalar sqrSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf);
-
-//! finds global minimum and maximum array elements and returns their values
-CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());
-CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);
-
-//! finds global minimum and maximum array elements and returns their values with locations
-CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
-                          const GpuMat& mask=GpuMat());
-CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
-                          const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);
-
-//! counts non-zero array elements
-CV_EXPORTS int countNonZero(const GpuMat& src);
-CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
-
-//! reduces a matrix to a vector
-CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
-
-
-///////////////////////////// Calibration 3D //////////////////////////////////
-
-CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
-                                GpuMat& dst, Stream& stream = Stream::Null());
-
-CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
-                              const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
-                              Stream& stream = Stream::Null());
-
-CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
-                               const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
-                               int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
-                               std::vector<int>* inliers=NULL);
-
-//////////////////////////////// Image Labeling ////////////////////////////////
-
-//!performs labeling via graph cuts of a 2D regular 4-connected graph.
-CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
-                         GpuMat& buf, Stream& stream = Stream::Null());
-
-//!performs labeling via graph cuts of a 2D regular 8-connected graph.
-CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
-                         GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
-                         GpuMat& labels,
-                         GpuMat& buf, Stream& stream = Stream::Null());
-
-//! compute mask for Generalized Flood fill componetns labeling.
-CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null());
-
-//! performs connected componnents labeling.
-CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null());
-
-////////////////////////////////// Histograms //////////////////////////////////
-
-//! Compute levels with even distribution. levels will have 1 row and nLevels cols and CV_32SC1 type.
-CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel);
-//! Calculates histogram with evenly distributed bins for signle channel source.
-//! Supports CV_8UC1, CV_16UC1 and CV_16SC1 source types.
-//! Output hist will have one row and histSize cols and CV_32SC1 type.
-CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
-CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
-//! Calculates histogram with evenly distributed bins for four-channel source.
-//! All channels of source are processed separately.
-//! Supports CV_8UC4, CV_16UC4 and CV_16SC4 source types.
-//! Output hist[i] will have one row and histSize[i] cols and CV_32SC1 type.
-CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
-CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
-//! Calculates histogram with bins determined by levels array.
-//! levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
-//! Supports CV_8UC1, CV_16UC1, CV_16SC1 and CV_32FC1 source types.
-//! Output hist will have one row and (levels.cols-1) cols and CV_32SC1 type.
-CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null());
-CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
-//! Calculates histogram with bins determined by levels array.
-//! All levels must have one row and CV_32SC1 type if source has integer type or CV_32FC1 otherwise.
-//! All channels of source are processed separately.
-//! Supports CV_8UC4, CV_16UC4, CV_16SC4 and CV_32FC4 source types.
-//! Output hist[i] will have one row and (levels[i].cols-1) cols and CV_32SC1 type.
-CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
-CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());
-
-//! Calculates histogram for 8u one channel image
-//! Output hist will have one row, 256 cols and CV32SC1 type.
-CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
-CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
-
-//! normalizes the grayscale image brightness and contrast by normalizing its histogram
-CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
-CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
-CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
-
-class CV_EXPORTS CLAHE : public cv::CLAHE
-{
-public:
-    using cv::CLAHE::apply;
-    virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0;
-};
-CV_EXPORTS Ptr<cv::gpu::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
-
-//////////////////////////////// StereoBM_GPU ////////////////////////////////
-
-class CV_EXPORTS StereoBM_GPU
-{
-public:
-    enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
-
-    enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
-
-    //! the default constructor
-    StereoBM_GPU();
-    //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
-    StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
-
-    //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
-    //! Output disparity has CV_8U type.
-    void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
-
-    //! Some heuristics that tries to estmate
-    // if current GPU will be faster than CPU in this algorithm.
-    // It queries current active device.
-    static bool checkIfGpuCallReasonable();
-
-    int preset;
-    int ndisp;
-    int winSize;
-
-    // If avergeTexThreshold  == 0 => post procesing is disabled
-    // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
-    // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
-    // i.e. input left image is low textured.
-    float avergeTexThreshold;
-
-private:
-    GpuMat minSSD, leBuf, riBuf;
-};
-
-////////////////////////// StereoBeliefPropagation ///////////////////////////
-// "Efficient Belief Propagation for Early Vision"
-// P.Felzenszwalb
-
-class CV_EXPORTS StereoBeliefPropagation
-{
-public:
-    enum { DEFAULT_NDISP  = 64 };
-    enum { DEFAULT_ITERS  = 5  };
-    enum { DEFAULT_LEVELS = 5  };
-
-    static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);
-
-    //! the default constructor
-    explicit StereoBeliefPropagation(int ndisp  = DEFAULT_NDISP,
-                                     int iters  = DEFAULT_ITERS,
-                                     int levels = DEFAULT_LEVELS,
-                                     int msg_type = CV_32F);
-
-    //! the full constructor taking the number of disparities, number of BP iterations on each level,
-    //! number of levels, truncation of data cost, data weight,
-    //! truncation of discontinuity cost and discontinuity single jump
-    //! DataTerm = data_weight * min(fabs(I2-I1), max_data_term)
-    //! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term)
-    //! please see paper for more details
-    StereoBeliefPropagation(int ndisp, int iters, int levels,
-        float max_data_term, float data_weight,
-        float max_disc_term, float disc_single_jump,
-        int msg_type = CV_32F);
-
-    //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
-    //! if disparity is empty output type will be CV_16S else output type will be disparity.type().
-    void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
-
-
-    //! version for user specified data term
-    void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());
-
-    int ndisp;
-
-    int iters;
-    int levels;
-
-    float max_data_term;
-    float data_weight;
-    float max_disc_term;
-    float disc_single_jump;
-
-    int msg_type;
-private:
-    GpuMat u, d, l, r, u2, d2, l2, r2;
-    std::vector<GpuMat> datas;
-    GpuMat out;
-};
-
-/////////////////////////// StereoConstantSpaceBP ///////////////////////////
-// "A Constant-Space Belief Propagation Algorithm for Stereo Matching"
-// Qingxiong Yang, Liang Wang, Narendra Ahuja
-// http://vision.ai.uiuc.edu/~qyang6/
-
-class CV_EXPORTS StereoConstantSpaceBP
-{
-public:
-    enum { DEFAULT_NDISP    = 128 };
-    enum { DEFAULT_ITERS    = 8   };
-    enum { DEFAULT_LEVELS   = 4   };
-    enum { DEFAULT_NR_PLANE = 4   };
-
-    static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);
-
-    //! the default constructor
-    explicit StereoConstantSpaceBP(int ndisp    = DEFAULT_NDISP,
-                                   int iters    = DEFAULT_ITERS,
-                                   int levels   = DEFAULT_LEVELS,
-                                   int nr_plane = DEFAULT_NR_PLANE,
-                                   int msg_type = CV_32F);
-
-    //! the full constructor taking the number of disparities, number of BP iterations on each level,
-    //! number of levels, number of active disparity on the first level, truncation of data cost, data weight,
-    //! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold
-    StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
-        float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
-        int min_disp_th = 0,
-        int msg_type = CV_32F);
-
-    //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
-    //! if disparity is empty output type will be CV_16S else output type will be disparity.type().
-    void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
-
-    int ndisp;
-
-    int iters;
-    int levels;
-
-    int nr_plane;
-
-    float max_data_term;
-    float data_weight;
-    float max_disc_term;
-    float disc_single_jump;
-
-    int min_disp_th;
-
-    int msg_type;
-
-    bool use_local_init_data_cost;
-private:
-    GpuMat messages_buffers;
-
-    GpuMat temp;
-    GpuMat out;
-};
-
-/////////////////////////// DisparityBilateralFilter ///////////////////////////
-// Disparity map refinement using joint bilateral filtering given a single color image.
-// Qingxiong Yang, Liang Wang, Narendra Ahuja
-// http://vision.ai.uiuc.edu/~qyang6/
-
-class CV_EXPORTS DisparityBilateralFilter
-{
-public:
-    enum { DEFAULT_NDISP  = 64 };
-    enum { DEFAULT_RADIUS = 3 };
-    enum { DEFAULT_ITERS  = 1 };
-
-    //! the default constructor
-    explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);
-
-    //! the full constructor taking the number of disparities, filter radius,
-    //! number of iterations, truncation of data continuity, truncation of disparity continuity
-    //! and filter range sigma
-    DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);
-
-    //! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image.
-    //! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type.
-    void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());
-
-private:
-    int ndisp;
-    int radius;
-    int iters;
-
-    float edge_threshold;
-    float max_disc_threshold;
-    float sigma_range;
-
-    GpuMat table_color;
-    GpuMat table_space;
-};
-
-
-//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
-struct CV_EXPORTS HOGConfidence
-{
-   double scale;
-   vector<Point> locations;
-   vector<double> confidences;
-   vector<double> part_scores[4];
-};
-
-struct CV_EXPORTS HOGDescriptor
-{
-    enum { DEFAULT_WIN_SIGMA = -1 };
-    enum { DEFAULT_NLEVELS = 64 };
-    enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
-
-    HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
-                  Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
-                  int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
-                  double threshold_L2hys=0.2, bool gamma_correction=true,
-                  int nlevels=DEFAULT_NLEVELS);
-
-    size_t getDescriptorSize() const;
-    size_t getBlockHistogramSize() const;
-
-    void setSVMDetector(const vector<float>& detector);
-
-    static vector<float> getDefaultPeopleDetector();
-    static vector<float> getPeopleDetector48x96();
-    static vector<float> getPeopleDetector64x128();
-
-    void detect(const GpuMat& img, vector<Point>& found_locations,
-                double hit_threshold=0, Size win_stride=Size(),
-                Size padding=Size());
-
-    void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
-                          double hit_threshold=0, Size win_stride=Size(),
-                          Size padding=Size(), double scale0=1.05,
-                          int group_threshold=2);
-
-    void computeConfidence(const GpuMat& img, vector<Point>& hits, double hit_threshold,
-                                                Size win_stride, Size padding, vector<Point>& locations, vector<double>& confidences);
-
-    void computeConfidenceMultiScale(const GpuMat& img, vector<Rect>& found_locations,
-                                                                    double hit_threshold, Size win_stride, Size padding,
-                                                                    vector<HOGConfidence> &conf_out, int group_threshold);
-
-    void getDescriptors(const GpuMat& img, Size win_stride,
-                        GpuMat& descriptors,
-                        int descr_format=DESCR_FORMAT_COL_BY_COL);
-
-    Size win_size;
-    Size block_size;
-    Size block_stride;
-    Size cell_size;
-    int nbins;
-    double win_sigma;
-    double threshold_L2hys;
-    bool gamma_correction;
-    int nlevels;
-
-protected:
-    void computeBlockHistograms(const GpuMat& img);
-    void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
-
-    double getWinSigma() const;
-    bool checkDetectorSize() const;
-
-    static int numPartsWithin(int size, int part_size, int stride);
-    static Size numPartsWithin(Size size, Size part_size, Size stride);
-
-    // Coefficients of the separating plane
-    float free_coef;
-    GpuMat detector;
-
-    // Results of the last classification step
-    GpuMat labels, labels_buf;
-    Mat labels_host;
-
-    // Results of the last histogram evaluation step
-    GpuMat block_hists, block_hists_buf;
-
-    // Gradients conputation results
-    GpuMat grad, qangle, grad_buf, qangle_buf;
-
-    // returns subbuffer with required size, reallocates buffer if nessesary.
-    static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
-    static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
-
-    std::vector<GpuMat> image_scales;
-};
-
-
-////////////////////////////////// BruteForceMatcher //////////////////////////////////
-
-class CV_EXPORTS BruteForceMatcher_GPU_base
-{
-public:
-    enum DistType {L1Dist = 0, L2Dist, HammingDist};
-
-    explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist);
-
-    // Add descriptors to train descriptor collection
-    void add(const std::vector<GpuMat>& descCollection);
-
-    // Get train descriptors collection
-    const std::vector<GpuMat>& getTrainDescriptors() const;
-
-    // Clear train descriptors collection
-    void clear();
-
-    // Return true if there are not train descriptors in collection
-    bool empty() const;
-
-    // Return true if the matcher supports mask in match methods
-    bool isMaskSupported() const;
-
-    // Find one best match for each query descriptor
-    void matchSingle(const GpuMat& query, const GpuMat& train,
-        GpuMat& trainIdx, GpuMat& distance,
-        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
-
-    // Download trainIdx and distance and convert it to CPU vector with DMatch
-    static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
-    // Convert trainIdx and distance to vector with DMatch
-    static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
-
-    // Find one best match for each query descriptor
-    void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
-
-    // Make gpu collection of trains and masks in suitable format for matchCollection function
-    void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
-
-    // Find one best match from train collection for each query descriptor
-    void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
-        GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
-        const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());
-
-    // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
-    static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
-    // Convert trainIdx, imgIdx and distance to vector with DMatch
-    static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
-
-    // Find one best match from train collection for each query descriptor.
-    void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
-
-    // Find k best matches for each query descriptor (in increasing order of distances)
-    void knnMatchSingle(const GpuMat& query, const GpuMat& train,
-        GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
-        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
-
-    // Download trainIdx and distance and convert it to vector with DMatch
-    // compactResult is used when mask is not empty. If compactResult is false matches
-    // vector will have the same size as queryDescriptors rows. If compactResult is true
-    // matches vector will not contain matches for fully masked out query descriptors.
-    static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-    // Convert trainIdx and distance to vector with DMatch
-    static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-
-    // Find k best matches for each query descriptor (in increasing order of distances).
-    // compactResult is used when mask is not empty. If compactResult is false matches
-    // vector will have the same size as queryDescriptors rows. If compactResult is true
-    // matches vector will not contain matches for fully masked out query descriptors.
-    void knnMatch(const GpuMat& query, const GpuMat& train,
-        std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
-        bool compactResult = false);
-
-    // Find k best matches from train collection for each query descriptor (in increasing order of distances)
-    void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
-        GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
-        const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
-
-    // Download trainIdx and distance and convert it to vector with DMatch
-    // compactResult is used when mask is not empty. If compactResult is false matches
-    // vector will have the same size as queryDescriptors rows. If compactResult is true
-    // matches vector will not contain matches for fully masked out query descriptors.
-    static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-    // Convert trainIdx and distance to vector with DMatch
-    static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-
-    // Find k best matches  for each query descriptor (in increasing order of distances).
-    // compactResult is used when mask is not empty. If compactResult is false matches
-    // vector will have the same size as queryDescriptors rows. If compactResult is true
-    // matches vector will not contain matches for fully masked out query descriptors.
-    void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
-        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
-
-    // Find best matches for each query descriptor which have distance less than maxDistance.
-    // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
-    // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
-    // because it didn't have enough memory.
-    // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
-    // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
-    // Matches doesn't sorted.
-    void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
-        GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
-        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
-
-    // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
-    // matches will be sorted in increasing order of distances.
-    // compactResult is used when mask is not empty. If compactResult is false matches
-    // vector will have the same size as queryDescriptors rows. If compactResult is true
-    // matches vector will not contain matches for fully masked out query descriptors.
-    static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-    // Convert trainIdx, nMatches and distance to vector with DMatch.
-    static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-
-    // Find best matches for each query descriptor which have distance less than maxDistance
-    // in increasing order of distances).
-    void radiusMatch(const GpuMat& query, const GpuMat& train,
-        std::vector< std::vector<DMatch> >& matches, float maxDistance,
-        const GpuMat& mask = GpuMat(), bool compactResult = false);
-
-    // Find best matches for each query descriptor which have distance less than maxDistance.
-    // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
-    // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
-    // Matches doesn't sorted.
-    void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
-        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
-
-    // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
-    // matches will be sorted in increasing order of distances.
-    // compactResult is used when mask is not empty. If compactResult is false matches
-    // vector will have the same size as queryDescriptors rows. If compactResult is true
-    // matches vector will not contain matches for fully masked out query descriptors.
-    static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-    // Convert trainIdx, nMatches and distance to vector with DMatch.
-    static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
-        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
-
-    // Find best matches from train collection for each query descriptor which have distance less than
-    // maxDistance (in increasing order of distances).
-    void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
-        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
-
-    DistType distType;
-
-private:
-    std::vector<GpuMat> trainDescCollection;
-};
-
-template <class Distance>
-class CV_EXPORTS BruteForceMatcher_GPU;
-
-template <typename T>
-class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base
-{
-public:
-    explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L1Dist) {}
-    explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BruteForceMatcher_GPU_base(L1Dist) {}
-};
-template <typename T>
-class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base
-{
-public:
-    explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L2Dist) {}
-    explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BruteForceMatcher_GPU_base(L2Dist) {}
-};
-template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BruteForceMatcher_GPU_base
-{
-public:
-    explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {}
-    explicit BruteForceMatcher_GPU(Hamming /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {}
-};
-
-class CV_EXPORTS BFMatcher_GPU : public BruteForceMatcher_GPU_base
-{
-public:
-    explicit BFMatcher_GPU(int norm = NORM_L2) : BruteForceMatcher_GPU_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
-};
-
-////////////////////////////////// CascadeClassifier_GPU //////////////////////////////////////////
-// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny.
-class CV_EXPORTS CascadeClassifier_GPU
-{
-public:
-    CascadeClassifier_GPU();
-    CascadeClassifier_GPU(const std::string& filename);
-    ~CascadeClassifier_GPU();
-
-    bool empty() const;
-    bool load(const std::string& filename);
-    void release();
-
-    /* returns number of detected objects */
-    int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
-    int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
-
-    bool findLargestObject;
-    bool visualizeInPlace;
-
-    Size getClassifierSize() const;
-
-private:
-    struct CascadeClassifierImpl;
-    CascadeClassifierImpl* impl;
-    struct HaarCascade;
-    struct LbpCascade;
-    friend class CascadeClassifier_GPU_LBP;
-};
-
-////////////////////////////////// FAST //////////////////////////////////////////
-
-class CV_EXPORTS FAST_GPU
-{
-public:
-    enum
-    {
-        LOCATION_ROW = 0,
-        RESPONSE_ROW,
-        ROWS_COUNT
-    };
-
-    // all features have same size
-    static const int FEATURE_SIZE = 7;
-
-    explicit FAST_GPU(int threshold, bool nonmaxSuppression = true, double keypointsRatio = 0.05);
-
-    //! finds the keypoints using FAST detector
-    //! supports only CV_8UC1 images
-    void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
-    void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
-
-    //! download keypoints from device to host memory
-    void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
-
-    //! convert keypoints to KeyPoint vector
-    void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);
-
-    //! release temporary buffer's memory
-    void release();
-
-    bool nonmaxSuppression;
-
-    int threshold;
-
-    //! max keypoints = keypointsRatio * img.size().area()
-    double keypointsRatio;
-
-    //! find keypoints and compute it's response if nonmaxSuppression is true
-    //! return count of detected keypoints
-    int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
-
-    //! get final array of keypoints
-    //! performs nonmax suppression if needed
-    //! return final count of keypoints
-    int getKeyPoints(GpuMat& keypoints);
-
-private:
-    GpuMat kpLoc_;
-    int count_;
-
-    GpuMat score_;
-
-    GpuMat d_keypoints_;
-};
-
-////////////////////////////////// ORB //////////////////////////////////////////
-
-class CV_EXPORTS ORB_GPU
-{
-public:
-    enum
-    {
-        X_ROW = 0,
-        Y_ROW,
-        RESPONSE_ROW,
-        ANGLE_ROW,
-        OCTAVE_ROW,
-        SIZE_ROW,
-        ROWS_COUNT
-    };
-
-    enum
-    {
-        DEFAULT_FAST_THRESHOLD = 20
-    };
-
-    //! Constructor
-    explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
-                     int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);
-
-    //! Compute the ORB features on an image
-    //! image - the image to compute the features (supports only CV_8UC1 images)
-    //! mask - the mask to apply
-    //! keypoints - the resulting keypoints
-    void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
-    void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
-
-    //! Compute the ORB features and descriptors on an image
-    //! image - the image to compute the features (supports only CV_8UC1 images)
-    //! mask - the mask to apply
-    //! keypoints - the resulting keypoints
-    //! descriptors - descriptors array
-    void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
-    void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);
-
-    //! download keypoints from device to host memory
-    void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
-
-    //! convert keypoints to KeyPoint vector
-    void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
-
-    //! returns the descriptor size in bytes
-    inline int descriptorSize() const { return kBytes; }
-
-    inline void setFastParams(int threshold, bool nonmaxSuppression = true)
-    {
-        fastDetector_.threshold = threshold;
-        fastDetector_.nonmaxSuppression = nonmaxSuppression;
-    }
-
-    //! release temporary buffer's memory
-    void release();
-
-    //! if true, image will be blurred before descriptors calculation
-    bool blurForDescriptor;
-
-private:
-    enum { kBytes = 32 };
-
-    void buildScalePyramids(const GpuMat& image, const GpuMat& mask);
-
-    void computeKeyPointsPyramid();
-
-    void computeDescriptors(GpuMat& descriptors);
-
-    void mergeKeyPoints(GpuMat& keypoints);
-
-    int nFeatures_;
-    float scaleFactor_;
-    int nLevels_;
-    int edgeThreshold_;
-    int firstLevel_;
-    int WTA_K_;
-    int scoreType_;
-    int patchSize_;
-
-    // The number of desired features per scale
-    std::vector<size_t> n_features_per_level_;
-
-    // Points to compute BRIEF descriptors from
-    GpuMat pattern_;
-
-    std::vector<GpuMat> imagePyr_;
-    std::vector<GpuMat> maskPyr_;
-
-    GpuMat buf_;
-
-    std::vector<GpuMat> keyPointsPyr_;
-    std::vector<int> keyPointsCount_;
-
-    FAST_GPU fastDetector_;
-
-    Ptr<FilterEngine_GPU> blurFilter;
-
-    GpuMat d_keypoints_;
-};
-
-////////////////////////////////// Optical Flow //////////////////////////////////////////
-
-class CV_EXPORTS BroxOpticalFlow
-{
-public:
-    BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
-        alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
-        inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
-    {
-    }
-
-    //! Compute optical flow
-    //! frame0 - source frame (supports only CV_32FC1 type)
-    //! frame1 - frame to track (with the same size and type as frame0)
-    //! u      - flow horizontal component (along x axis)
-    //! v      - flow vertical component (along y axis)
-    void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
-
-    //! flow smoothness
-    float alpha;
-
-    //! gradient constancy importance
-    float gamma;
-
-    //! pyramid scale factor
-    float scale_factor;
-
-    //! number of lagged non-linearity iterations (inner loop)
-    int inner_iterations;
-
-    //! number of warping iterations (number of pyramid levels)
-    int outer_iterations;
-
-    //! number of linear system solver iterations
-    int solver_iterations;
-
-    GpuMat buf;
-};
-
-class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
-{
-public:
-    explicit GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
-        int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);
-
-    //! return 1 rows matrix with CV_32FC2 type
-    void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
-
-    int maxCorners;
-    double qualityLevel;
-    double minDistance;
-
-    int blockSize;
-    bool useHarrisDetector;
-    double harrisK;
-
-    void releaseMemory()
-    {
-        Dx_.release();
-        Dy_.release();
-        buf_.release();
-        eig_.release();
-        minMaxbuf_.release();
-        tmpCorners_.release();
-    }
-
-private:
-    GpuMat Dx_;
-    GpuMat Dy_;
-    GpuMat buf_;
-    GpuMat eig_;
-    GpuMat minMaxbuf_;
-    GpuMat tmpCorners_;
-};
-
-inline GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_,
-        int blockSize_, bool useHarrisDetector_, double harrisK_)
-{
-    maxCorners = maxCorners_;
-    qualityLevel = qualityLevel_;
-    minDistance = minDistance_;
-    blockSize = blockSize_;
-    useHarrisDetector = useHarrisDetector_;
-    harrisK = harrisK_;
-}
-
-
-class CV_EXPORTS PyrLKOpticalFlow
-{
-public:
-    PyrLKOpticalFlow();
-
-    void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
-        GpuMat& status, GpuMat* err = 0);
-
-    void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
-
-    void releaseMemory();
-
-    Size winSize;
-    int maxLevel;
-    int iters;
-    double derivLambda; //unused
-    bool useInitialFlow;
-    float minEigThreshold; //unused
-    bool getMinEigenVals;  //unused
-
-private:
-    GpuMat uPyr_[2];
-    vector<GpuMat> prevPyr_;
-    vector<GpuMat> nextPyr_;
-    GpuMat vPyr_[2];
-    vector<GpuMat> buf_;
-    vector<GpuMat> unused;
-    bool isDeviceArch11_;
-};
-
-
-class CV_EXPORTS FarnebackOpticalFlow
-{
-public:
-    FarnebackOpticalFlow()
-    {
-        numLevels = 5;
-        pyrScale = 0.5;
-        fastPyramids = false;
-        winSize = 13;
-        numIters = 10;
-        polyN = 5;
-        polySigma = 1.1;
-        flags = 0;
-        isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
-    }
-
-    int numLevels;
-    double pyrScale;
-    bool fastPyramids;
-    int winSize;
-    int numIters;
-    int polyN;
-    double polySigma;
-    int flags;
-
-    void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
-
-    void releaseMemory()
-    {
-        frames_[0].release();
-        frames_[1].release();
-        pyrLevel_[0].release();
-        pyrLevel_[1].release();
-        M_.release();
-        bufM_.release();
-        R_[0].release();
-        R_[1].release();
-        blurredFrame_[0].release();
-        blurredFrame_[1].release();
-        pyramid0_.clear();
-        pyramid1_.clear();
-    }
-
-private:
-    void prepareGaussian(
-            int n, double sigma, float *g, float *xg, float *xxg,
-            double &ig11, double &ig03, double &ig33, double &ig55);
-
-    void setPolynomialExpansionConsts(int n, double sigma);
-
-    void updateFlow_boxFilter(
-            const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
-            GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
-
-    void updateFlow_gaussianBlur(
-            const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
-            GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
-
-    GpuMat frames_[2];
-    GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
-    std::vector<GpuMat> pyramid0_, pyramid1_;
-
-    bool isDeviceArch11_;
-};
-
-
-// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
-//
-// see reference:
-//   [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
-//   [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
-class CV_EXPORTS OpticalFlowDual_TVL1_GPU
-{
-public:
-    OpticalFlowDual_TVL1_GPU();
-
-    void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy);
-
-    void collectGarbage();
-
-    /**
-     * Time step of the numerical scheme.
-     */
-    double tau;
-
-    /**
-     * Weight parameter for the data term, attachment parameter.
-     * This is the most relevant parameter, which determines the smoothness of the output.
-     * The smaller this parameter is, the smoother the solutions we obtain.
-     * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
-     */
-    double lambda;
-
-    /**
-     * Weight parameter for (u - v)^2, tightness parameter.
-     * It serves as a link between the attachment and the regularization terms.
-     * In theory, it should have a small value in order to maintain both parts in correspondence.
-     * The method is stable for a large range of values of this parameter.
-     */
-    double theta;
-
-    /**
-     * Number of scales used to create the pyramid of images.
-     */
-    int nscales;
-
-    /**
-     * Number of warpings per scale.
-     * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
-     * This is a parameter that assures the stability of the method.
-     * It also affects the running time, so it is a compromise between speed and accuracy.
-     */
-    int warps;
-
-    /**
-     * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
-     * A small value will yield more accurate solutions at the expense of a slower convergence.
-     */
-    double epsilon;
-
-    /**
-     * Stopping criterion iterations number used in the numerical scheme.
-     */
-    int iterations;
-
-    bool useInitialFlow;
-
-private:
-    void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2);
-
-    std::vector<GpuMat> I0s;
-    std::vector<GpuMat> I1s;
-    std::vector<GpuMat> u1s;
-    std::vector<GpuMat> u2s;
-
-    GpuMat I1x_buf;
-    GpuMat I1y_buf;
-
-    GpuMat I1w_buf;
-    GpuMat I1wx_buf;
-    GpuMat I1wy_buf;
-
-    GpuMat grad_buf;
-    GpuMat rho_c_buf;
-
-    GpuMat p11_buf;
-    GpuMat p12_buf;
-    GpuMat p21_buf;
-    GpuMat p22_buf;
-
-    GpuMat diff_buf;
-    GpuMat norm_buf;
-};
-
-
-//! Calculates optical flow for 2 images using block matching algorithm */
-CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
-                                  Size block_size, Size shift_size, Size max_range, bool use_previous,
-                                  GpuMat& velx, GpuMat& vely, GpuMat& buf,
-                                  Stream& stream = Stream::Null());
-
-class CV_EXPORTS FastOpticalFlowBM
-{
-public:
-    void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
-
-private:
-    GpuMat buffer;
-    GpuMat extended_I0;
-    GpuMat extended_I1;
-};
-
-
-//! Interpolate frames (images) using provided optical flow (displacement field).
-//! frame0   - frame 0 (32-bit floating point images, single channel)
-//! frame1   - frame 1 (the same type and size)
-//! fu       - forward horizontal displacement
-//! fv       - forward vertical displacement
-//! bu       - backward horizontal displacement
-//! bv       - backward vertical displacement
-//! pos      - new frame position
-//! newFrame - new frame
-//! buf      - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
-//!            occlusion masks            0, occlusion masks            1,
-//!            interpolated forward flow  0, interpolated forward flow  1,
-//!            interpolated backward flow 0, interpolated backward flow 1
-//!
-CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
-                                  const GpuMat& fu, const GpuMat& fv,
-                                  const GpuMat& bu, const GpuMat& bv,
-                                  float pos, GpuMat& newFrame, GpuMat& buf,
-                                  Stream& stream = Stream::Null());
-
-CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
-
-
-//////////////////////// Background/foreground segmentation ////////////////////////
-
-// Foreground Object Detection from Videos Containing Complex Background.
-// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
-// ACM MM2003 9p
-class CV_EXPORTS FGDStatModel
-{
-public:
-    struct CV_EXPORTS Params
-    {
-        int Lc;  // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
-        int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
-        int N2c; // Number of color vectors retained at given pixel.  Must be > N1c, typically ~ 5/3 of N1c.
-        // Used to allow the first N1c vectors to adapt over time to changing background.
-
-        int Lcc;  // Quantized levels per 'color co-occurrence' component.  Power of two, typically 16, 32 or 64.
-        int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
-        int N2cc; // Number of color co-occurrence vectors retained at given pixel.  Must be > N1cc, typically ~ 5/3 of N1cc.
-        // Used to allow the first N1cc vectors to adapt over time to changing background.
-
-        bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
-        int perform_morphing;     // Number of erode-dilate-erode foreground-blob cleanup iterations.
-        // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
-
-        float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
-        float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
-        float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
-
-        float delta;   // Affects color and color co-occurrence quantization, typically set to 2.
-        float T;       // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
-        float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
-
-        // default Params
-        Params();
-    };
-
-    // out_cn - channels count in output result (can be 3 or 4)
-    // 4-channels require more memory, but a bit faster
-    explicit FGDStatModel(int out_cn = 3);
-    explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
-
-    ~FGDStatModel();
-
-    void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
-    void release();
-
-    int update(const cv::gpu::GpuMat& curFrame);
-
-    //8UC3 or 8UC4 reference background image
-    cv::gpu::GpuMat background;
-
-    //8UC1 foreground image
-    cv::gpu::GpuMat foreground;
-
-    std::vector< std::vector<cv::Point> > foreground_regions;
-
-private:
-    FGDStatModel(const FGDStatModel&);
-    FGDStatModel& operator=(const FGDStatModel&);
-
-    class Impl;
-    std::auto_ptr<Impl> impl_;
-};
-
-/*!
- Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
-
- The class implements the following algorithm:
- "An improved adaptive background mixture model for real-time tracking with shadow detection"
- P. KadewTraKuPong and R. Bowden,
- Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
- http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
-*/
-class CV_EXPORTS MOG_GPU
-{
-public:
-    //! the default constructor
-    MOG_GPU(int nmixtures = -1);
-
-    //! re-initiaization method
-    void initialize(Size frameSize, int frameType);
-
-    //! the update operator
-    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());
-
-    //! computes a background image which are the mean of all background gaussians
-    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
-
-    //! releases all inner buffers
-    void release();
-
-    int history;
-    float varThreshold;
-    float backgroundRatio;
-    float noiseSigma;
-
-private:
-    int nmixtures_;
-
-    Size frameSize_;
-    int frameType_;
-    int nframes_;
-
-    GpuMat weight_;
-    GpuMat sortKey_;
-    GpuMat mean_;
-    GpuMat var_;
-};
-
-/*!
- The class implements the following algorithm:
- "Improved adaptive Gausian mixture model for background subtraction"
- Z.Zivkovic
- International Conference Pattern Recognition, UK, August, 2004.
- http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
-*/
-class CV_EXPORTS MOG2_GPU
-{
-public:
-    //! the default constructor
-    MOG2_GPU(int nmixtures = -1);
-
-    //! re-initiaization method
-    void initialize(Size frameSize, int frameType);
-
-    //! the update operator
-    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
-
-    //! computes a background image which are the mean of all background gaussians
-    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
-
-    //! releases all inner buffers
-    void release();
-
-    // parameters
-    // you should call initialize after parameters changes
-
-    int history;
-
-    //! here it is the maximum allowed number of mixture components.
-    //! Actual number is determined dynamically per pixel
-    float varThreshold;
-    // threshold on the squared Mahalanobis distance to decide if it is well described
-    // by the background model or not. Related to Cthr from the paper.
-    // This does not influence the update of the background. A typical value could be 4 sigma
-    // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
-
-    /////////////////////////
-    // less important parameters - things you might change but be carefull
-    ////////////////////////
-
-    float backgroundRatio;
-    // corresponds to fTB=1-cf from the paper
-    // TB - threshold when the component becomes significant enough to be included into
-    // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
-    // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
-    // it is considered foreground
-    // float noiseSigma;
-    float varThresholdGen;
-
-    //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
-    //when a sample is close to the existing components. If it is not close
-    //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
-    //Smaller Tg leads to more generated components and higher Tg might make
-    //lead to small number of components but they can grow too large
-    float fVarInit;
-    float fVarMin;
-    float fVarMax;
-
-    //initial variance  for the newly generated components.
-    //It will will influence the speed of adaptation. A good guess should be made.
-    //A simple way is to estimate the typical standard deviation from the images.
-    //I used here 10 as a reasonable value
-    // min and max can be used to further control the variance
-    float fCT; //CT - complexity reduction prior
-    //this is related to the number of samples needed to accept that a component
-    //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
-    //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
-
-    //shadow detection parameters
-    bool bShadowDetection; //default 1 - do shadow detection
-    unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
-    float fTau;
-    // Tau - shadow threshold. The shadow is detected if the pixel is darker
-    //version of the background. Tau is a threshold on how much darker the shadow can be.
-    //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
-    //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
-
-private:
-    int nmixtures_;
-
-    Size frameSize_;
-    int frameType_;
-    int nframes_;
-
-    GpuMat weight_;
-    GpuMat variance_;
-    GpuMat mean_;
-
-    GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
-};
-
-/**
- * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
- * images of the same size, where 255 indicates Foreground and 0 represents Background.
- * This class implements an algorithm described in "Visual Tracking of Human Visitors under
- * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
- * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
- */
-class CV_EXPORTS GMG_GPU
-{
-public:
-    GMG_GPU();
-
-    /**
-     * Validate parameters and set up data structures for appropriate frame size.
-     * @param frameSize Input frame size
-     * @param min       Minimum value taken on by pixels in image sequence. Usually 0
-     * @param max       Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
-     */
-    void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);
-
-    /**
-     * Performs single-frame background subtraction and builds up a statistical background image
-     * model.
-     * @param frame        Input frame
-     * @param fgmask       Output mask image representing foreground and background pixels
-     * @param learningRate determines how quickly features are “forgotten” from histograms
-     * @param stream       Stream for the asynchronous version
-     */
-    void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
-
-    //! Releases all inner buffers
-    void release();
-
-    //! Total number of distinct colors to maintain in histogram.
-    int maxFeatures;
-
-    //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms.
-    float learningRate;
-
-    //! Number of frames of video to use to initialize histograms.
-    int numInitializationFrames;
-
-    //! Number of discrete levels in each channel to be used in histograms.
-    int quantizationLevels;
-
-    //! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
-    float backgroundPrior;
-
-    //! Value above which pixel is determined to be FG.
-    float decisionThreshold;
-
-    //! Smoothing radius, in pixels, for cleaning up FG image.
-    int smoothingRadius;
-
-    //! Perform background model update.
-    bool updateBackgroundModel;
-
-private:
-    float maxVal_, minVal_;
-
-    Size frameSize_;
-
-    int frameNum_;
-
-    GpuMat nfeatures_;
-    GpuMat colors_;
-    GpuMat weights_;
-
-    Ptr<FilterEngine_GPU> boxFilter_;
-    GpuMat buf_;
-};
-
-////////////////////////////////// Video Encoding //////////////////////////////////
-
-// Works only under Windows
-// Supports olny H264 video codec and AVI files
-class CV_EXPORTS VideoWriter_GPU
-{
-public:
-    struct EncoderParams;
-
-    // Callbacks for video encoder, use it if you want to work with raw video stream
-    class EncoderCallBack;
-
-    enum SurfaceFormat
-    {
-        SF_UYVY = 0,
-        SF_YUY2,
-        SF_YV12,
-        SF_NV12,
-        SF_IYUV,
-        SF_BGR,
-        SF_GRAY = SF_BGR
-    };
-
-    VideoWriter_GPU();
-    VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
-    VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
-    VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
-    VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
-    ~VideoWriter_GPU();
-
-    // all methods throws cv::Exception if error occurs
-    void open(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
-    void open(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
-    void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
-    void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
-
-    bool isOpened() const;
-    void close();
-
-    void write(const cv::gpu::GpuMat& image, bool lastFrame = false);
-
-    struct CV_EXPORTS EncoderParams
-    {
-        int       P_Interval;      //    NVVE_P_INTERVAL,
-        int       IDR_Period;      //    NVVE_IDR_PERIOD,
-        int       DynamicGOP;      //    NVVE_DYNAMIC_GOP,
-        int       RCType;          //    NVVE_RC_TYPE,
-        int       AvgBitrate;      //    NVVE_AVG_BITRATE,
-        int       PeakBitrate;     //    NVVE_PEAK_BITRATE,
-        int       QP_Level_Intra;  //    NVVE_QP_LEVEL_INTRA,
-        int       QP_Level_InterP; //    NVVE_QP_LEVEL_INTER_P,
-        int       QP_Level_InterB; //    NVVE_QP_LEVEL_INTER_B,
-        int       DeblockMode;     //    NVVE_DEBLOCK_MODE,
-        int       ProfileLevel;    //    NVVE_PROFILE_LEVEL,
-        int       ForceIntra;      //    NVVE_FORCE_INTRA,
-        int       ForceIDR;        //    NVVE_FORCE_IDR,
-        int       ClearStat;       //    NVVE_CLEAR_STAT,
-        int       DIMode;          //    NVVE_SET_DEINTERLACE,
-        int       Presets;         //    NVVE_PRESETS,
-        int       DisableCabac;    //    NVVE_DISABLE_CABAC,
-        int       NaluFramingType; //    NVVE_CONFIGURE_NALU_FRAMING_TYPE
-        int       DisableSPSPPS;   //    NVVE_DISABLE_SPS_PPS
-
-        EncoderParams();
-        explicit EncoderParams(const std::string& configFile);
-
-        void load(const std::string& configFile);
-        void save(const std::string& configFile) const;
-    };
-
-    EncoderParams getParams() const;
-
-    class CV_EXPORTS EncoderCallBack
-    {
-    public:
-        enum PicType
-        {
-            IFRAME = 1,
-            PFRAME = 2,
-            BFRAME = 3
-        };
-
-        virtual ~EncoderCallBack() {}
-
-        // callback function to signal the start of bitstream that is to be encoded
-        // must return pointer to buffer
-        virtual uchar* acquireBitStream(int* bufferSize) = 0;
-
-        // callback function to signal that the encoded bitstream is ready to be written to file
-        virtual void releaseBitStream(unsigned char* data, int size) = 0;
-
-        // callback function to signal that the encoding operation on the frame has started
-        virtual void onBeginFrame(int frameNumber, PicType picType) = 0;
-
-        // callback function signals that the encoding operation on the frame has finished
-        virtual void onEndFrame(int frameNumber, PicType picType) = 0;
-    };
-
-private:
-    VideoWriter_GPU(const VideoWriter_GPU&);
-    VideoWriter_GPU& operator=(const VideoWriter_GPU&);
-
-    class Impl;
-    std::auto_ptr<Impl> impl_;
-};
-
-
-////////////////////////////////// Video Decoding //////////////////////////////////////////
-
-namespace detail
-{
-    class FrameQueue;
-    class VideoParser;
-}
-
-class CV_EXPORTS VideoReader_GPU
-{
-public:
-    enum Codec
-    {
-        MPEG1 = 0,
-        MPEG2,
-        MPEG4,
-        VC1,
-        H264,
-        JPEG,
-        H264_SVC,
-        H264_MVC,
-
-        Uncompressed_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')),   // Y,U,V (4:2:0)
-        Uncompressed_YV12   = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,V,U (4:2:0)
-        Uncompressed_NV12   = (('N'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,UV  (4:2:0)
-        Uncompressed_YUYV   = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')),   // YUYV/YUY2 (4:2:2)
-        Uncompressed_UYVY   = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y'))    // UYVY (4:2:2)
-    };
-
-    enum ChromaFormat
-    {
-        Monochrome=0,
-        YUV420,
-        YUV422,
-        YUV444
-    };
-
-    struct FormatInfo
-    {
-        Codec codec;
-        ChromaFormat chromaFormat;
-        int width;
-        int height;
-    };
-
-    class VideoSource;
-
-    VideoReader_GPU();
-    explicit VideoReader_GPU(const std::string& filename);
-    explicit VideoReader_GPU(const cv::Ptr<VideoSource>& source);
-
-    ~VideoReader_GPU();
-
-    void open(const std::string& filename);
-    void open(const cv::Ptr<VideoSource>& source);
-    bool isOpened() const;
-
-    void close();
-
-    bool read(GpuMat& image);
-
-    FormatInfo format() const;
-    void dumpFormat(std::ostream& st);
-
-    class CV_EXPORTS VideoSource
-    {
-    public:
-        VideoSource() : frameQueue_(0), videoParser_(0) {}
-        virtual ~VideoSource() {}
-
-        virtual FormatInfo format() const = 0;
-        virtual void start() = 0;
-        virtual void stop() = 0;
-        virtual bool isStarted() const = 0;
-        virtual bool hasError() const = 0;
-
-        void setFrameQueue(detail::FrameQueue* frameQueue) { frameQueue_ = frameQueue; }
-        void setVideoParser(detail::VideoParser* videoParser) { videoParser_ = videoParser; }
-
-    protected:
-        bool parseVideoData(const uchar* data, size_t size, bool endOfStream = false);
-
-    private:
-        VideoSource(const VideoSource&);
-        VideoSource& operator =(const VideoSource&);
-
-        detail::FrameQueue* frameQueue_;
-        detail::VideoParser* videoParser_;
-    };
-
-private:
-    VideoReader_GPU(const VideoReader_GPU&);
-    VideoReader_GPU& operator =(const VideoReader_GPU&);
-
-    class Impl;
-    std::auto_ptr<Impl> impl_;
-};
-
-//! removes points (CV_32FC2, single row matrix) with zero mask value
-CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);
-
-CV_EXPORTS void calcWobbleSuppressionMaps(
-        int left, int idx, int right, Size size, const Mat &ml, const Mat &mr,
-        GpuMat &mapx, GpuMat &mapy);
-
-} // namespace gpu
-
-} // namespace cv
-
-#endif /* __OPENCV_GPU_HPP__ */
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/gpumat.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/gpumat.hpp
deleted file mode 100644
index 840398b..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/gpumat.hpp
+++ /dev/null
@@ -1,43 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "opencv2/core/gpumat.hpp"
diff --git a/thirdparty/raspberrypi/includes/opencv2/gpu/stream_accessor.hpp b/thirdparty/raspberrypi/includes/opencv2/gpu/stream_accessor.hpp
deleted file mode 100644
index bcd58ba..0000000
--- a/thirdparty/raspberrypi/includes/opencv2/gpu/stream_accessor.hpp
+++ /dev/null
@@ -1,65 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_GPU_STREAM_ACCESSOR_HPP__
-#define __OPENCV_GPU_STREAM_ACCESSOR_HPP__
-
-#include "opencv2/gpu/gpu.hpp"
-#include "cuda_runtime_api.h"
-
-namespace cv
-{
-    namespace gpu
-    {
-        // This is only header file that depends on Cuda. All other headers are independent.
-        // So if you use OpenCV binaries you do noot need to install Cuda Toolkit.
-        // But of you wanna use GPU by yourself, may get cuda stream instance using the class below.
-        // In this case you have to install Cuda Toolkit.
-        struct StreamAccessor
-        {
-            CV_EXPORTS static cudaStream_t getStream(const Stream& stream);
-            CV_EXPORTS static Stream wrapStream(cudaStream_t stream);
-        };
-    }
-}
-
-#endif /* __OPENCV_GPU_STREAM_ACCESSOR_HPP__ */