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added to gpu module linear filters for int and float source types.

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refactored gpu module.
This commit is contained in:
Vladislav Vinogradov 2010-10-20 08:50:14 +00:00
parent ea040ce71a
commit b08f60828b
19 changed files with 1511 additions and 945 deletions
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13
modules/gpu/include/opencv2/gpu/devmem2d.hpp
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@ -50,7 +50,7 @@ namespace cv
// Simple lightweight structure that encapsulates image ptr on device, its pitch and its sizes.
// It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile
template<typename T = unsigned char>
template <typename T>
struct DevMem2D_
{
typedef T elem_t;
@ -60,16 +60,21 @@ namespace cv
int rows;
T* ptr;
size_t step;
size_t elem_step;
DevMem2D_() : cols(0), rows(0), ptr(0), step(0) {}
DevMem2D_() : cols(0), rows(0), ptr(0), step(0), elem_step(0) {}
DevMem2D_(int rows_, int cols_, T *ptr_, size_t step_)
: cols(cols_), rows(rows_), ptr(ptr_), step(step_) {}
: cols(cols_), rows(rows_), ptr(ptr_), step(step_), elem_step(step_ / sizeof(T)) {}
template <typename U>
explicit DevMem2D_(const DevMem2D_<U>& d)
: cols(d.cols), rows(d.rows), ptr((T*)d.ptr), step(d.step), elem_step(d.step / sizeof(T)) {}
size_t elemSize() const { return elem_size; }
};
typedef DevMem2D_<> DevMem2D;
typedef DevMem2D_<unsigned char> DevMem2D;
typedef DevMem2D_<float> DevMem2Df;
typedef DevMem2D_<int> DevMem2Di;
}

21
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -636,7 +636,7 @@ namespace cv
//! 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, bool rowFilterFirst = true);
const Ptr<BaseColumnFilter_GPU>& columnFilter);
//! returns horizontal 1D box filter
//! supports only CV_8UC1 source type and CV_32FC1 sum type
@ -658,7 +658,7 @@ namespace cv
//! 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 GpuMat& kernel, const Size& ksize,
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.
@ -667,25 +667,24 @@ namespace cv
//! returns 2D filter with the specified kernel
//! 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> getLinearFilter_GPU(int srcType, int dstType, const GpuMat& kernel, const Size& ksize,
Point anchor = Point(-1, -1), int nDivisor = 1);
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
Point anchor = Point(-1, -1));
//! returns the non-separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
const Point& anchor = Point(-1,-1));
//! returns the primitive row filter with the specified kernel
CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const GpuMat& rowKernel,
int anchor = -1, int nDivisor = 1);
CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
int anchor = -1);
//! returns the primitive column filter with the specified kernel
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const GpuMat& columnKernel,
int anchor = -1, int nDivisor = 1);
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
int anchor = -1);
//! 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), bool rowFilterFirst = true);
const Mat& columnKernel, const Point& anchor = Point(-1,-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);
@ -720,7 +719,7 @@ namespace cv
//! 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), bool rowFilterFirst = true);
Point anchor = Point(-1,-1));
//! 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);

8
modules/gpu/src/arithm.cpp
View File

@ -316,9 +316,9 @@ void cv::gpu::absdiff(const GpuMat& src, const Scalar& s, GpuMat& dst)
////////////////////////////////////////////////////////////////////////
// compare
namespace cv { namespace gpu { namespace matrix_operations
namespace cv { namespace gpu { namespace mathfunc
{
void compare_ne_8u(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
}}}
@ -346,7 +346,7 @@ void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int c
}
else
{
matrix_operations::compare_ne_8u(src1, src2, dst);
mathfunc::compare_ne_8uc4(src1, src2, dst);
}
}
else
@ -359,7 +359,7 @@ void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int c
}
else
{
matrix_operations::compare_ne_32f(src1, src2, dst);
mathfunc::compare_ne_32f(src1, src2, dst);
}
}
}

233
modules/gpu/src/cuda/bilateral_filter.cu
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@ -1,233 +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/gpu/devmem2d.hpp"
#include "saturate_cast.hpp"
#include "safe_call.hpp"
using namespace cv::gpu;
#ifndef FLT_MAX
#define FLT_MAX 3.402823466e+30F
#endif
namespace bf_krnls
{
__constant__ float* ctable_color;
__constant__ float* ctable_space;
__constant__ size_t ctable_space_step;
__constant__ int cndisp;
__constant__ int cradius;
__constant__ short cedge_disc;
__constant__ short cmax_disc;
}
namespace cv { namespace gpu { namespace bf
{
void load_constants(float* table_color, const DevMem2Df& table_space, int ndisp, int radius, short edge_disc, short max_disc)
{
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::ctable_color, &table_color, sizeof(table_color)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::ctable_space, &table_space.ptr, sizeof(table_space.ptr)) );
size_t table_space_step = table_space.step / sizeof(float);
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::ctable_space_step, &table_space_step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cndisp, &ndisp, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cradius, &radius, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cedge_disc, &edge_disc, sizeof(short)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cmax_disc, &max_disc, sizeof(short)) );
}
}}}
namespace bf_krnls
{
template <int channels>
struct DistRgbMax
{
static __device__ uchar calc(const uchar* a, const uchar* b)
{
uchar x = abs(a[0] - b[0]);
uchar y = abs(a[1] - b[1]);
uchar z = abs(a[2] - b[2]);
return (max(max(x, y), z));
}
};
template <>
struct DistRgbMax<1>
{
static __device__ uchar calc(const uchar* a, const uchar* b)
{
return abs(a[0] - b[0]);
}
};
template <int channels, typename T>
__global__ void bilateral_filter(int t, T* disp, size_t disp_step, const uchar* img, size_t img_step, int h, int w)
{
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = ((blockIdx.x * blockDim.x + threadIdx.x) << 1) + ((y + t) & 1);
T dp[5];
if (y > 0 && y < h - 1 && x > 0 && x < w - 1)
{
dp[0] = *(disp + (y ) * disp_step + x + 0);
dp[1] = *(disp + (y-1) * disp_step + x + 0);
dp[2] = *(disp + (y ) * disp_step + x - 1);
dp[3] = *(disp + (y+1) * disp_step + x + 0);
dp[4] = *(disp + (y ) * disp_step + x + 1);
if(abs(dp[1] - dp[0]) >= cedge_disc || abs(dp[2] - dp[0]) >= cedge_disc || abs(dp[3] - dp[0]) >= cedge_disc || abs(dp[4] - dp[0]) >= cedge_disc)
{
const int ymin = max(0, y - cradius);
const int xmin = max(0, x - cradius);
const int ymax = min(h - 1, y + cradius);
const int xmax = min(w - 1, x + cradius);
float cost[] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
const uchar* ic = img + y * img_step + channels * x;
for(int yi = ymin; yi <= ymax; yi++)
{
const T* disp_y = disp + yi * disp_step;
for(int xi = xmin; xi <= xmax; xi++)
{
const uchar* in = img + yi * img_step + channels * xi;
uchar dist_rgb = DistRgbMax<channels>::calc(in, ic);
const float weight = ctable_color[dist_rgb] * (ctable_space + abs(y-yi)* ctable_space_step)[abs(x-xi)];
const T disp_reg = disp_y[xi];
cost[0] += min(cmax_disc, abs(disp_reg - dp[0])) * weight;
cost[1] += min(cmax_disc, abs(disp_reg - dp[1])) * weight;
cost[2] += min(cmax_disc, abs(disp_reg - dp[2])) * weight;
cost[3] += min(cmax_disc, abs(disp_reg - dp[3])) * weight;
cost[4] += min(cmax_disc, abs(disp_reg - dp[4])) * weight;
}
}
float minimum = FLT_MAX;
int id = 0;
if (cost[0] < minimum)
{
minimum = cost[0];
id = 0;
}
if (cost[1] < minimum)
{
minimum = cost[1];
id = 1;
}
if (cost[2] < minimum)
{
minimum = cost[2];
id = 2;
}
if (cost[3] < minimum)
{
minimum = cost[3];
id = 3;
}
if (cost[4] < minimum)
{
minimum = cost[4];
id = 4;
}
*(disp + y * disp_step + x) = dp[id];
}
}
}
}
namespace cv { namespace gpu { namespace bf
{
template <typename T>
void bilateral_filter_caller(const DevMem2D_<T>& disp, const DevMem2D& img, int channels, int iters, cudaStream_t stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(disp.cols, threads.x << 1);
grid.y = divUp(disp.rows, threads.y);
switch (channels)
{
case 1:
for (int i = 0; i < iters; ++i)
{
bf_krnls::bilateral_filter<1><<<grid, threads, 0, stream>>>(0, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
bf_krnls::bilateral_filter<1><<<grid, threads, 0, stream>>>(1, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
}
break;
case 3:
for (int i = 0; i < iters; ++i)
{
bf_krnls::bilateral_filter<3><<<grid, threads, 0, stream>>>(0, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
bf_krnls::bilateral_filter<3><<<grid, threads, 0, stream>>>(1, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
}
break;
default:
cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
}
if (stream != 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void bilateral_filter_gpu(const DevMem2D& disp, const DevMem2D& img, int channels, int iters, cudaStream_t stream)
{
bilateral_filter_caller(disp, img, channels, iters, stream);
}
void bilateral_filter_gpu(const DevMem2D_<short>& disp, const DevMem2D& img, int channels, int iters, cudaStream_t stream)
{
bilateral_filter_caller(disp, img, channels, iters, stream);
}
}}}

115
modules/gpu/src/cuda/color.cu
View File

@ -42,6 +42,7 @@
#include "cuda_shared.hpp"
#include "saturate_cast.hpp"
#include "vecmath.hpp"
using namespace cv::gpu;
@ -53,16 +54,8 @@ using namespace cv::gpu;
#define FLT_EPSILON 1.192092896e-07F
#endif
namespace imgproc
namespace imgproc_krnls
{
template<typename T, int N> struct TypeVec {};
template<> struct TypeVec<uchar, 3> { typedef uchar3 vec_t; };
template<> struct TypeVec<uchar, 4> { typedef uchar4 vec_t; };
template<> struct TypeVec<ushort, 3> { typedef ushort3 vec_t; };
template<> struct TypeVec<ushort, 4> { typedef ushort4 vec_t; };
template<> struct TypeVec<float, 3> { typedef float3 vec_t; };
template<> struct TypeVec<float, 4> { typedef float4 vec_t; };
template<typename T> struct ColorChannel {};
template<> struct ColorChannel<uchar>
{
@ -106,7 +99,7 @@ namespace imgproc
////////////////// Various 3/4-channel to 3/4-channel RGB transformations /////////////////
namespace imgproc
namespace imgproc_krnls
{
template <int SRCCN, int DSTCN, typename T>
__global__ void RGB2RGB(const uchar* src_, size_t src_step, uchar* dst_, size_t dst_step, int rows, int cols, int bidx)
@ -132,7 +125,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int SRCCN, int DSTCN>
void RGB2RGB_caller(const DevMem2D& src, const DevMem2D& dst, int bidx, cudaStream_t stream)
@ -143,7 +136,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB2RGB<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2RGB<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -189,7 +182,7 @@ namespace cv { namespace gpu { namespace improc
/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB //////////
namespace imgproc
namespace imgproc_krnls
{
template <int GREEN_BITS, int DSTCN> struct RGB5x52RGBConverter {};
template <int DSTCN> struct RGB5x52RGBConverter<5, DSTCN>
@ -281,7 +274,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <int GREEN_BITS, int DSTCN>
void RGB5x52RGB_caller(const DevMem2D& src, const DevMem2D& dst, int bidx, cudaStream_t stream)
@ -292,7 +285,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB5x52RGB<GREEN_BITS, DSTCN><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB5x52RGB<GREEN_BITS, DSTCN><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -320,7 +313,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB2RGB5x5<SRCCN, GREEN_BITS><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2RGB5x5<SRCCN, GREEN_BITS><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -342,7 +335,7 @@ namespace cv { namespace gpu { namespace improc
///////////////////////////////// Grayscale to Color ////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
template <int DSTCN, typename T>
__global__ void Gray2RGB(const uchar* src_, size_t src_step, uchar* dst_, size_t dst_step, int rows, int cols)
@ -396,7 +389,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int DSTCN>
void Gray2RGB_caller(const DevMem2D& src, const DevMem2D& dst, cudaStream_t stream)
@ -407,7 +400,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::Gray2RGB<DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::Gray2RGB<DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols);
if (stream == 0)
@ -447,7 +440,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::Gray2RGB5x5<GREEN_BITS><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::Gray2RGB5x5<GREEN_BITS><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols);
if (stream == 0)
@ -468,7 +461,7 @@ namespace cv { namespace gpu { namespace improc
///////////////////////////////// Color to Grayscale ////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
#undef R2Y
#undef G2Y
@ -550,7 +543,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int SRCCN>
void RGB2Gray_caller(const DevMem2D& src, const DevMem2D& dst, int bidx, cudaStream_t stream)
@ -561,7 +554,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB2Gray<SRCCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2Gray<SRCCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -601,7 +594,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB5x52Gray<GREEN_BITS><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB5x52Gray<GREEN_BITS><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols);
if (stream == 0)
@ -622,7 +615,7 @@ namespace cv { namespace gpu { namespace improc
///////////////////////////////////// RGB <-> YCrCb //////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
__constant__ float cYCrCbCoeffs_f[5];
__constant__ int cYCrCbCoeffs_i[5];
@ -721,7 +714,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int SRCCN, int DSTCN>
void RGB2YCrCb_caller(const DevMem2D& src, const DevMem2D& dst, int bidx, cudaStream_t stream)
@ -732,7 +725,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB2YCrCb<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2YCrCb<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -748,7 +741,7 @@ namespace cv { namespace gpu { namespace improc
{RGB2YCrCb_caller<uchar, 4, 3>, RGB2YCrCb_caller<uchar, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cYCrCbCoeffs_i, coeffs, 5 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cYCrCbCoeffs_i, coeffs, 5 * sizeof(int)) );
RGB2YCrCb_callers[srccn-3][dstcn-3](src, dst, bidx, stream);
}
@ -762,7 +755,7 @@ namespace cv { namespace gpu { namespace improc
{RGB2YCrCb_caller<ushort, 4, 3>, RGB2YCrCb_caller<ushort, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cYCrCbCoeffs_i, coeffs, 5 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cYCrCbCoeffs_i, coeffs, 5 * sizeof(int)) );
RGB2YCrCb_callers[srccn-3][dstcn-3](src, dst, bidx, stream);
}
@ -776,7 +769,7 @@ namespace cv { namespace gpu { namespace improc
{RGB2YCrCb_caller<float, 4, 3>, RGB2YCrCb_caller<float, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cYCrCbCoeffs_f, coeffs, 5 * sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cYCrCbCoeffs_f, coeffs, 5 * sizeof(float)) );
RGB2YCrCb_callers[srccn-3][dstcn-3](src, dst, bidx, stream);
}
@ -790,7 +783,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::YCrCb2RGB<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::YCrCb2RGB<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -806,7 +799,7 @@ namespace cv { namespace gpu { namespace improc
{YCrCb2RGB_caller<uchar, 4, 3>, YCrCb2RGB_caller<uchar, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cYCrCbCoeffs_i, coeffs, 4 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cYCrCbCoeffs_i, coeffs, 4 * sizeof(int)) );
YCrCb2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, stream);
}
@ -820,7 +813,7 @@ namespace cv { namespace gpu { namespace improc
{YCrCb2RGB_caller<ushort, 4, 3>, YCrCb2RGB_caller<ushort, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cYCrCbCoeffs_i, coeffs, 4 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cYCrCbCoeffs_i, coeffs, 4 * sizeof(int)) );
YCrCb2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, stream);
}
@ -834,7 +827,7 @@ namespace cv { namespace gpu { namespace improc
{YCrCb2RGB_caller<float, 4, 3>, YCrCb2RGB_caller<float, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cYCrCbCoeffs_f, coeffs, 4 * sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cYCrCbCoeffs_f, coeffs, 4 * sizeof(float)) );
YCrCb2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, stream);
}
@ -842,7 +835,7 @@ namespace cv { namespace gpu { namespace improc
////////////////////////////////////// RGB <-> XYZ ///////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
__constant__ float cXYZ_D65f[9];
__constant__ int cXYZ_D65i[9];
@ -931,7 +924,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int SRCCN, int DSTCN>
void RGB2XYZ_caller(const DevMem2D& src, const DevMem2D& dst, cudaStream_t stream)
@ -942,7 +935,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::RGB2XYZ<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2XYZ<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols);
if (stream == 0)
@ -958,7 +951,7 @@ namespace cv { namespace gpu { namespace improc
{RGB2XYZ_caller<uchar, 4, 3>, RGB2XYZ_caller<uchar, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
RGB2XYZ_callers[srccn-3][dstcn-3](src, dst, stream);
}
@ -972,7 +965,7 @@ namespace cv { namespace gpu { namespace improc
{RGB2XYZ_caller<ushort, 4, 3>, RGB2XYZ_caller<ushort, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
RGB2XYZ_callers[srccn-3][dstcn-3](src, dst, stream);
}
@ -986,7 +979,7 @@ namespace cv { namespace gpu { namespace improc
{RGB2XYZ_caller<float, 4, 3>, RGB2XYZ_caller<float, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cXYZ_D65f, coeffs, 9 * sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cXYZ_D65f, coeffs, 9 * sizeof(float)) );
RGB2XYZ_callers[srccn-3][dstcn-3](src, dst, stream);
}
@ -1000,7 +993,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
imgproc::XYZ2RGB<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::XYZ2RGB<SRCCN, DSTCN, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols);
if (stream == 0)
@ -1016,7 +1009,7 @@ namespace cv { namespace gpu { namespace improc
{XYZ2RGB_caller<uchar, 4, 3>, XYZ2RGB_caller<uchar, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
XYZ2RGB_callers[srccn-3][dstcn-3](src, dst, stream);
}
@ -1030,7 +1023,7 @@ namespace cv { namespace gpu { namespace improc
{XYZ2RGB_caller<ushort, 4, 3>, XYZ2RGB_caller<ushort, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cXYZ_D65i, coeffs, 9 * sizeof(int)) );
XYZ2RGB_callers[srccn-3][dstcn-3](src, dst, stream);
}
@ -1044,7 +1037,7 @@ namespace cv { namespace gpu { namespace improc
{XYZ2RGB_caller<float, 4, 3>, XYZ2RGB_caller<float, 4, 4>}
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cXYZ_D65f, coeffs, 9 * sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cXYZ_D65f, coeffs, 9 * sizeof(float)) );
XYZ2RGB_callers[srccn-3][dstcn-3](src, dst, stream);
}
@ -1052,7 +1045,7 @@ namespace cv { namespace gpu { namespace improc
////////////////////////////////////// RGB <-> HSV ///////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
__constant__ int cHsvDivTable[256];
@ -1229,7 +1222,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int SRCCN, int DSTCN>
void RGB2HSV_caller(const DevMem2D& src, const DevMem2D& dst, int bidx, int hrange, cudaStream_t stream)
@ -1241,10 +1234,10 @@ namespace cv { namespace gpu { namespace improc
grid.y = divUp(src.rows, threads.y);
if (hrange == 180)
imgproc::RGB2HSV<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2HSV<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
else
imgproc::RGB2HSV<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2HSV<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -1295,7 +1288,7 @@ namespace cv { namespace gpu { namespace improc
4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229,
4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096
};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cHsvDivTable, div_table, sizeof(div_table)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cHsvDivTable, div_table, sizeof(div_table)) );
RGB2HSV_callers[srccn-3][dstcn-3](src, dst, bidx, hrange, stream);
}
@ -1323,10 +1316,10 @@ namespace cv { namespace gpu { namespace improc
grid.y = divUp(src.rows, threads.y);
if (hrange == 180)
imgproc::HSV2RGB<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::HSV2RGB<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
else
imgproc::HSV2RGB<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::HSV2RGB<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -1345,7 +1338,7 @@ namespace cv { namespace gpu { namespace improc
static const int sector_data[][3] =
{{1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0}};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cHsvSectorData, sector_data, sizeof(sector_data)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cHsvSectorData, sector_data, sizeof(sector_data)) );
HSV2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, hrange, stream);
}
@ -1362,7 +1355,7 @@ namespace cv { namespace gpu { namespace improc
static const int sector_data[][3] =
{{1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0}};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cHsvSectorData, sector_data, sizeof(sector_data)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cHsvSectorData, sector_data, sizeof(sector_data)) );
HSV2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, hrange, stream);
}
@ -1370,7 +1363,7 @@ namespace cv { namespace gpu { namespace improc
/////////////////////////////////////// RGB <-> HLS ////////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
template<typename T, int HR> struct RGB2HLSConvertor;
template<int HR> struct RGB2HLSConvertor<float, HR>
@ -1541,7 +1534,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T, int SRCCN, int DSTCN>
void RGB2HLS_caller(const DevMem2D& src, const DevMem2D& dst, int bidx, int hrange, cudaStream_t stream)
@ -1553,10 +1546,10 @@ namespace cv { namespace gpu { namespace improc
grid.y = divUp(src.rows, threads.y);
if (hrange == 180)
imgproc::RGB2HLS<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2HLS<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
else
imgproc::RGB2HLS<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::RGB2HLS<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -1598,10 +1591,10 @@ namespace cv { namespace gpu { namespace improc
grid.y = divUp(src.rows, threads.y);
if (hrange == 180)
imgproc::HLS2RGB<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::HLS2RGB<SRCCN, DSTCN, 180, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
else
imgproc::HLS2RGB<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
imgproc_krnls::HLS2RGB<SRCCN, DSTCN, 255, T><<<grid, threads, 0, stream>>>(src.ptr, src.step,
dst.ptr, dst.step, src.rows, src.cols, bidx);
if (stream == 0)
@ -1620,7 +1613,7 @@ namespace cv { namespace gpu { namespace improc
static const int sector_data[][3]=
{{1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0}};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cHlsSectorData, sector_data, sizeof(sector_data)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cHlsSectorData, sector_data, sizeof(sector_data)) );
HLS2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, hrange, stream);
}
@ -1637,7 +1630,7 @@ namespace cv { namespace gpu { namespace improc
static const int sector_data[][3]=
{{1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0}};
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cHlsSectorData, sector_data, sizeof(sector_data)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cHlsSectorData, sector_data, sizeof(sector_data)) );
HLS2RGB_callers[srccn-3][dstcn-3](src, dst, bidx, hrange, stream);
}

24
modules/gpu/src/cuda/constantspacebp.cu
View File

@ -54,20 +54,18 @@ using namespace cv::gpu;
#define SHRT_MAX 32767
#endif
template <typename T>
struct TypeLimits {};
template <>
struct TypeLimits<short>
namespace csbp_krnls
{
static __device__ short max() {return SHRT_MAX;}
};
template <>
struct TypeLimits<float>
{
static __device__ float max() {return FLT_MAX;}
};
template <typename T> struct TypeLimits;
template <> struct TypeLimits<short>
{
static __device__ short max() {return SHRT_MAX;}
};
template <> struct TypeLimits<float>
{
static __device__ float max() {return FLT_MAX;}
};
}
///////////////////////////////////////////////////////////////
/////////////////////// load constants ////////////////////////

13
modules/gpu/src/cuda/cuda_shared.hpp
View File

@ -58,19 +58,8 @@ namespace cv
static inline int divUp(int a, int b) { return (a % b == 0) ? a/b : a/b + 1; }
namespace matrix_operations
{
extern "C" void copy_to_with_mask(const DevMem2D& src, DevMem2D dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
extern "C" void set_to_without_mask (DevMem2D dst, int depth, const double *scalar, int channels, const cudaStream_t & stream = 0);
extern "C" void set_to_with_mask (DevMem2D dst, int depth, const double *scalar, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
extern "C" void convert_to(const DevMem2D& src, int sdepth, DevMem2D dst, int ddepth, int channels, double alpha, double beta, const cudaStream_t & stream = 0);
}
template<class T>
inline void uploadConstant(const char* name, const T& value) { cudaSafeCall( cudaMemcpyToSymbol(name, &value, sizeof(T)) ); }
static inline void uploadConstant(const char* name, const T& value) { cudaSafeCall( cudaMemcpyToSymbol(name, &value, sizeof(T)) ); }
}
}

455
modules/gpu/src/cuda/filters.cu Normal file
View File

@ -0,0 +1,455 @@
/*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/gpu/devmem2d.hpp"
#include "saturate_cast.hpp"
#include "safe_call.hpp"
#include "cuda_shared.hpp"
using namespace cv::gpu;
#ifndef FLT_MAX
#define FLT_MAX 3.402823466e+30F
#endif
/////////////////////////////////////////////////////////////////////////////////////////////////
// Linear filters
#define MAX_KERNEL_SIZE 16
namespace filter_krnls
{
__constant__ float cLinearKernel[MAX_KERNEL_SIZE];
}
namespace cv { namespace gpu { namespace filters
{
void loadLinearKernel(const float kernel[], int ksize)
{
cudaSafeCall( cudaMemcpyToSymbol(filter_krnls::cLinearKernel, kernel, ksize * sizeof(float)) );
}
}}}
namespace filter_krnls
{
template <int BLOCK_DIM_X, int BLOCK_DIM_Y, int KERNEL_SIZE, typename T, typename D>
__global__ void linearRowFilter(const T* src, size_t src_step, D* dst, size_t dst_step, int anchor, int width, int height)
{
__shared__ T smem[BLOCK_DIM_Y * BLOCK_DIM_X * 3];
const int blockStartX = blockDim.x * blockIdx.x;
const int blockStartY = blockDim.y * blockIdx.y;
const int threadX = blockStartX + threadIdx.x;
const int prevThreadX = threadX - blockDim.x;
const int nextThreadX = threadX + blockDim.x;
const int threadY = blockStartY + threadIdx.y;
T* sDataRow = smem + threadIdx.y * blockDim.x * 3;
if (threadY < height)
{
const T* rowSrc = src + threadY * src_step;
sDataRow[threadIdx.x + blockDim.x] = threadX < width ? rowSrc[threadX] : 0;
sDataRow[threadIdx.x] = prevThreadX >= 0 ? rowSrc[prevThreadX] : 0;
sDataRow[(blockDim.x << 1) + threadIdx.x] = nextThreadX < width ? rowSrc[nextThreadX] : 0;
__syncthreads();
if (threadX < width)
{
float sum = 0;
sDataRow += threadIdx.x + blockDim.x - anchor;
#pragma unroll
for(int i = 0; i < KERNEL_SIZE; ++i)
sum += cLinearKernel[i] * sDataRow[i];
dst[threadY * dst_step + threadX] = saturate_cast<D>(sum);
}
}
}
}
namespace cv { namespace gpu { namespace filters
{
template <int KERNEL_SIZE, typename T, typename D>
void linearRowFilter_caller(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor)
{
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 16;
dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y);
dim3 blocks(divUp(src.cols, BLOCK_DIM_X), divUp(src.rows, BLOCK_DIM_Y));
filter_krnls::linearRowFilter<BLOCK_DIM_X, BLOCK_DIM_Y, KERNEL_SIZE><<<blocks, threads>>>(src.ptr, src.elem_step,
dst.ptr, dst.elem_step, anchor, src.cols, src.rows);
cudaSafeCall( cudaThreadSynchronize() );
}
template <typename T, typename D>
inline void linearRowFilter_gpu(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor);
static const caller_t callers[] =
{linearRowFilter_caller<0 , T, D>, linearRowFilter_caller<1 , T, D>,
linearRowFilter_caller<2 , T, D>, linearRowFilter_caller<3 , T, D>,
linearRowFilter_caller<4 , T, D>, linearRowFilter_caller<5 , T, D>,
linearRowFilter_caller<6 , T, D>, linearRowFilter_caller<7 , T, D>,
linearRowFilter_caller<8 , T, D>, linearRowFilter_caller<9 , T, D>,
linearRowFilter_caller<10, T, D>, linearRowFilter_caller<11, T, D>,
linearRowFilter_caller<12, T, D>, linearRowFilter_caller<13, T, D>,
linearRowFilter_caller<14, T, D>, linearRowFilter_caller<15, T, D>};
loadLinearKernel(kernel, ksize);
callers[ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor);
}
void linearRowFilter_gpu_32s32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearRowFilter_gpu<int, int>(src, dst, kernel, ksize, anchor);
}
void linearRowFilter_gpu_32s32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearRowFilter_gpu<int, float>(src, dst, kernel, ksize, anchor);
}
void linearRowFilter_gpu_32f32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearRowFilter_gpu<float, int>(src, dst, kernel, ksize, anchor);
}
void linearRowFilter_gpu_32f32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearRowFilter_gpu<float, float>(src, dst, kernel, ksize, anchor);
}
}}}
namespace filter_krnls
{
template <int BLOCK_DIM_X, int BLOCK_DIM_Y, int KERNEL_SIZE, typename T, typename D>
__global__ void linearColumnFilter(const T* src, size_t src_step, D* dst, size_t dst_step, int anchor, int width, int height)
{
__shared__ T smem[BLOCK_DIM_Y * BLOCK_DIM_X * 3];
const int blockStartX = blockDim.x * blockIdx.x;
const int blockStartY = blockDim.y * blockIdx.y;
const int threadX = blockStartX + threadIdx.x;
const int threadY = blockStartY + threadIdx.y;
const int prevThreadY = threadY - blockDim.y;
const int nextThreadY = threadY + blockDim.y;
const int smem_step = blockDim.x;
T* sDataColumn = smem + threadIdx.x;
if (threadX < width)
{
const T* colSrc = src + threadX;
sDataColumn[(threadIdx.y + blockDim.y) * smem_step] = threadY < height ? colSrc[threadY * src_step] : 0;
sDataColumn[threadIdx.y * smem_step] = prevThreadY >= 0 ? colSrc[prevThreadY * src_step] : 0;
sDataColumn[(threadIdx.y + (blockDim.y << 1)) * smem_step] = nextThreadY < height ? colSrc[nextThreadY * src_step] : 0;
__syncthreads();
if (threadY < height)
{
float sum = 0;
sDataColumn += (threadIdx.y + blockDim.y - anchor)* smem_step;
#pragma unroll
for(int i = 0; i < KERNEL_SIZE; ++i)
sum += cLinearKernel[i] * sDataColumn[i * smem_step];
dst[threadY * dst_step + threadX] = saturate_cast<D>(sum);
}
}
}
}
namespace cv { namespace gpu { namespace filters
{
template <int KERNEL_SIZE, typename T, typename D>
void linearColumnFilter_caller(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor)
{
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 16;
dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y);
dim3 blocks(divUp(src.cols, BLOCK_DIM_X), divUp(src.rows, BLOCK_DIM_Y));
filter_krnls::linearColumnFilter<BLOCK_DIM_X, BLOCK_DIM_Y, KERNEL_SIZE><<<blocks, threads>>>(src.ptr, src.elem_step,
dst.ptr, dst.elem_step, anchor, src.cols, src.rows);
cudaSafeCall( cudaThreadSynchronize() );
}
template <typename T, typename D>
inline void linearColumnFilter_gpu(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor);
static const caller_t callers[] =
{linearColumnFilter_caller<0 , T, D>, linearColumnFilter_caller<1 , T, D>,
linearColumnFilter_caller<2 , T, D>, linearColumnFilter_caller<3 , T, D>,
linearColumnFilter_caller<4 , T, D>, linearColumnFilter_caller<5 , T, D>,
linearColumnFilter_caller<6 , T, D>, linearColumnFilter_caller<7 , T, D>,
linearColumnFilter_caller<8 , T, D>, linearColumnFilter_caller<9 , T, D>,
linearColumnFilter_caller<10, T, D>, linearColumnFilter_caller<11, T, D>,
linearColumnFilter_caller<12, T, D>, linearColumnFilter_caller<13, T, D>,
linearColumnFilter_caller<14, T, D>, linearColumnFilter_caller<15, T, D>};
loadLinearKernel(kernel, ksize);
callers[ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor);
}
void linearColumnFilter_gpu_32s32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearColumnFilter_gpu<int, int>(src, dst, kernel, ksize, anchor);
}
void linearColumnFilter_gpu_32s32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearColumnFilter_gpu<int, float>(src, dst, kernel, ksize, anchor);
}
void linearColumnFilter_gpu_32f32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearColumnFilter_gpu<float, int>(src, dst, kernel, ksize, anchor);
}
void linearColumnFilter_gpu_32f32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor)
{
linearColumnFilter_gpu<float, float>(src, dst, kernel, ksize, anchor);
}
}}}
/////////////////////////////////////////////////////////////////////////////////////////////////
// Bilateral filters
namespace bf_krnls
{
__constant__ float* ctable_color;
__constant__ float* ctable_space;
__constant__ size_t ctable_space_step;
__constant__ int cndisp;
__constant__ int cradius;
__constant__ short cedge_disc;
__constant__ short cmax_disc;
}
namespace cv { namespace gpu { namespace bf
{
void load_constants(float* table_color, const DevMem2Df& table_space, int ndisp, int radius, short edge_disc, short max_disc)
{
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::ctable_color, &table_color, sizeof(table_color)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::ctable_space, &table_space.ptr, sizeof(table_space.ptr)) );
size_t table_space_step = table_space.step / sizeof(float);
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::ctable_space_step, &table_space_step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cndisp, &ndisp, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cradius, &radius, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cedge_disc, &edge_disc, sizeof(short)) );
cudaSafeCall( cudaMemcpyToSymbol(bf_krnls::cmax_disc, &max_disc, sizeof(short)) );
}
}}}
namespace bf_krnls
{
template <int channels>
struct DistRgbMax
{
static __device__ uchar calc(const uchar* a, const uchar* b)
{
uchar x = abs(a[0] - b[0]);
uchar y = abs(a[1] - b[1]);
uchar z = abs(a[2] - b[2]);
return (max(max(x, y), z));
}
};
template <>
struct DistRgbMax<1>
{
static __device__ uchar calc(const uchar* a, const uchar* b)
{
return abs(a[0] - b[0]);
}
};
template <int channels, typename T>
__global__ void bilateral_filter(int t, T* disp, size_t disp_step, const uchar* img, size_t img_step, int h, int w)
{
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = ((blockIdx.x * blockDim.x + threadIdx.x) << 1) + ((y + t) & 1);
T dp[5];
if (y > 0 && y < h - 1 && x > 0 && x < w - 1)
{
dp[0] = *(disp + (y ) * disp_step + x + 0);
dp[1] = *(disp + (y-1) * disp_step + x + 0);
dp[2] = *(disp + (y ) * disp_step + x - 1);
dp[3] = *(disp + (y+1) * disp_step + x + 0);
dp[4] = *(disp + (y ) * disp_step + x + 1);
if(abs(dp[1] - dp[0]) >= cedge_disc || abs(dp[2] - dp[0]) >= cedge_disc || abs(dp[3] - dp[0]) >= cedge_disc || abs(dp[4] - dp[0]) >= cedge_disc)
{
const int ymin = max(0, y - cradius);
const int xmin = max(0, x - cradius);
const int ymax = min(h - 1, y + cradius);
const int xmax = min(w - 1, x + cradius);
float cost[] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
const uchar* ic = img + y * img_step + channels * x;
for(int yi = ymin; yi <= ymax; yi++)
{
const T* disp_y = disp + yi * disp_step;
for(int xi = xmin; xi <= xmax; xi++)
{
const uchar* in = img + yi * img_step + channels * xi;
uchar dist_rgb = DistRgbMax<channels>::calc(in, ic);
const float weight = ctable_color[dist_rgb] * (ctable_space + abs(y-yi)* ctable_space_step)[abs(x-xi)];
const T disp_reg = disp_y[xi];
cost[0] += min(cmax_disc, abs(disp_reg - dp[0])) * weight;
cost[1] += min(cmax_disc, abs(disp_reg - dp[1])) * weight;
cost[2] += min(cmax_disc, abs(disp_reg - dp[2])) * weight;
cost[3] += min(cmax_disc, abs(disp_reg - dp[3])) * weight;
cost[4] += min(cmax_disc, abs(disp_reg - dp[4])) * weight;
}
}
float minimum = FLT_MAX;
int id = 0;
if (cost[0] < minimum)
{
minimum = cost[0];
id = 0;
}
if (cost[1] < minimum)
{
minimum = cost[1];
id = 1;
}
if (cost[2] < minimum)
{
minimum = cost[2];
id = 2;
}
if (cost[3] < minimum)
{
minimum = cost[3];
id = 3;
}
if (cost[4] < minimum)
{
minimum = cost[4];
id = 4;
}
*(disp + y * disp_step + x) = dp[id];
}
}
}
}
namespace cv { namespace gpu { namespace bf
{
template <typename T>
void bilateral_filter_caller(const DevMem2D_<T>& disp, const DevMem2D& img, int channels, int iters, cudaStream_t stream)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(disp.cols, threads.x << 1);
grid.y = divUp(disp.rows, threads.y);
switch (channels)
{
case 1:
for (int i = 0; i < iters; ++i)
{
bf_krnls::bilateral_filter<1><<<grid, threads, 0, stream>>>(0, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
bf_krnls::bilateral_filter<1><<<grid, threads, 0, stream>>>(1, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
}
break;
case 3:
for (int i = 0; i < iters; ++i)
{
bf_krnls::bilateral_filter<3><<<grid, threads, 0, stream>>>(0, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
bf_krnls::bilateral_filter<3><<<grid, threads, 0, stream>>>(1, disp.ptr, disp.step/sizeof(T), img.ptr, img.step, disp.rows, disp.cols);
}
break;
default:
cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
}
if (stream != 0)
cudaSafeCall( cudaThreadSynchronize() );
}
void bilateral_filter_gpu(const DevMem2D& disp, const DevMem2D& img, int channels, int iters, cudaStream_t stream)
{
bilateral_filter_caller(disp, img, channels, iters, stream);
}
void bilateral_filter_gpu(const DevMem2D_<short>& disp, const DevMem2D& img, int channels, int iters, cudaStream_t stream)
{
bilateral_filter_caller(disp, img, channels, iters, stream);
}
}}}

48
modules/gpu/src/cuda/imgproc.cu
View File

@ -45,7 +45,7 @@
using namespace cv::gpu;
/////////////////////////////////// Remap ///////////////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
texture<unsigned char, 2, cudaReadModeNormalizedFloat> tex_remap;
@ -123,7 +123,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
void remap_gpu_1c(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst)
{
@ -132,15 +132,15 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(dst.cols, threads.x);
grid.y = divUp(dst.rows, threads.y);
imgproc::tex_remap.filterMode = cudaFilterModeLinear;
imgproc::tex_remap.addressMode[0] = imgproc::tex_remap.addressMode[1] = cudaAddressModeWrap;
imgproc_krnls::tex_remap.filterMode = cudaFilterModeLinear;
imgproc_krnls::tex_remap.addressMode[0] = imgproc_krnls::tex_remap.addressMode[1] = cudaAddressModeWrap;
cudaChannelFormatDesc desc = cudaCreateChannelDesc<unsigned char>();
cudaSafeCall( cudaBindTexture2D(0, imgproc::tex_remap, src.ptr, desc, src.cols, src.rows, src.step) );
cudaSafeCall( cudaBindTexture2D(0, imgproc_krnls::tex_remap, src.ptr, desc, src.cols, src.rows, src.step) );
imgproc::remap_1c<<<grid, threads>>>(xmap.ptr, ymap.ptr, xmap.step, dst.ptr, dst.step, dst.cols, dst.rows);
imgproc_krnls::remap_1c<<<grid, threads>>>(xmap.ptr, ymap.ptr, xmap.step, dst.ptr, dst.step, dst.cols, dst.rows);
cudaSafeCall( cudaThreadSynchronize() );
cudaSafeCall( cudaUnbindTexture(imgproc::tex_remap) );
cudaSafeCall( cudaUnbindTexture(imgproc_krnls::tex_remap) );
}
void remap_gpu_3c(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst)
@ -150,7 +150,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(dst.cols, threads.x);
grid.y = divUp(dst.rows, threads.y);
imgproc::remap_3c<<<grid, threads>>>(src.ptr, src.step, xmap.ptr, ymap.ptr, xmap.step, dst.ptr, dst.step, dst.cols, dst.rows);
imgproc_krnls::remap_3c<<<grid, threads>>>(src.ptr, src.step, xmap.ptr, ymap.ptr, xmap.step, dst.ptr, dst.step, dst.cols, dst.rows);
cudaSafeCall( cudaThreadSynchronize() );
}
@ -159,7 +159,7 @@ namespace cv { namespace gpu { namespace improc
/////////////////////////////////// MeanShiftfiltering ///////////////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
texture<uchar4, 2> tex_meanshift;
@ -254,7 +254,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
extern "C" void meanShiftFiltering_gpu(const DevMem2D& src, DevMem2D dst, int sp, int sr, int maxIter, float eps)
{
@ -264,11 +264,11 @@ namespace cv { namespace gpu { namespace improc
grid.y = divUp(src.rows, threads.y);
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar4>();
cudaSafeCall( cudaBindTexture2D( 0, imgproc::tex_meanshift, src.ptr, desc, src.cols, src.rows, src.step ) );
cudaSafeCall( cudaBindTexture2D( 0, imgproc_krnls::tex_meanshift, src.ptr, desc, src.cols, src.rows, src.step ) );
imgproc::meanshift_kernel<<< grid, threads >>>( dst.ptr, dst.step, dst.cols, dst.rows, sp, sr, maxIter, eps );
imgproc_krnls::meanshift_kernel<<< grid, threads >>>( dst.ptr, dst.step, dst.cols, dst.rows, sp, sr, maxIter, eps );
cudaSafeCall( cudaThreadSynchronize() );
cudaSafeCall( cudaUnbindTexture( imgproc::tex_meanshift ) );
cudaSafeCall( cudaUnbindTexture( imgproc_krnls::tex_meanshift ) );
}
extern "C" void meanShiftProc_gpu(const DevMem2D& src, DevMem2D dstr, DevMem2D dstsp, int sp, int sr, int maxIter, float eps)
{
@ -278,17 +278,17 @@ namespace cv { namespace gpu { namespace improc
grid.y = divUp(src.rows, threads.y);
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar4>();
cudaSafeCall( cudaBindTexture2D( 0, imgproc::tex_meanshift, src.ptr, desc, src.cols, src.rows, src.step ) );
cudaSafeCall( cudaBindTexture2D( 0, imgproc_krnls::tex_meanshift, src.ptr, desc, src.cols, src.rows, src.step ) );
imgproc::meanshiftproc_kernel<<< grid, threads >>>( dstr.ptr, dstr.step, dstsp.ptr, dstsp.step, dstr.cols, dstr.rows, sp, sr, maxIter, eps );
imgproc_krnls::meanshiftproc_kernel<<< grid, threads >>>( dstr.ptr, dstr.step, dstsp.ptr, dstsp.step, dstr.cols, dstr.rows, sp, sr, maxIter, eps );
cudaSafeCall( cudaThreadSynchronize() );
cudaSafeCall( cudaUnbindTexture( imgproc::tex_meanshift ) );
cudaSafeCall( cudaUnbindTexture( imgproc_krnls::tex_meanshift ) );
}
}}}
/////////////////////////////////// drawColorDisp ///////////////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
template <typename T>
__device__ unsigned int cvtPixel(T d, int ndisp, float S = 1, float V = 1)
@ -391,7 +391,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
void drawColorDisp_gpu(const DevMem2D& src, const DevMem2D& dst, int ndisp, const cudaStream_t& stream)
{
@ -400,7 +400,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x << 2);
grid.y = divUp(src.rows, threads.y);
imgproc::drawColorDisp<<<grid, threads, 0, stream>>>(src.ptr, src.step, dst.ptr, dst.step, src.cols, src.rows, ndisp);
imgproc_krnls::drawColorDisp<<<grid, threads, 0, stream>>>(src.ptr, src.step, dst.ptr, dst.step, src.cols, src.rows, ndisp);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
@ -413,7 +413,7 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(src.cols, threads.x << 1);
grid.y = divUp(src.rows, threads.y);
imgproc::drawColorDisp<<<grid, threads, 0, stream>>>(src.ptr, src.step / sizeof(short), dst.ptr, dst.step, src.cols, src.rows, ndisp);
imgproc_krnls::drawColorDisp<<<grid, threads, 0, stream>>>(src.ptr, src.step / sizeof(short), dst.ptr, dst.step, src.cols, src.rows, ndisp);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
@ -422,7 +422,7 @@ namespace cv { namespace gpu { namespace improc
/////////////////////////////////// reprojectImageTo3D ///////////////////////////////////////////////
namespace imgproc
namespace imgproc_krnls
{
__constant__ float cq[16];
@ -457,7 +457,7 @@ namespace imgproc
}
}
namespace cv { namespace gpu { namespace improc
namespace cv { namespace gpu { namespace imgproc
{
template <typename T>
inline void reprojectImageTo3D_caller(const DevMem2D_<T>& disp, const DevMem2Df& xyzw, const float* q, const cudaStream_t& stream)
@ -467,9 +467,9 @@ namespace cv { namespace gpu { namespace improc
grid.x = divUp(disp.cols, threads.x);
grid.y = divUp(disp.rows, threads.y);
cudaSafeCall( cudaMemcpyToSymbol(imgproc::cq, q, 16 * sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(imgproc_krnls::cq, q, 16 * sizeof(float)) );
imgproc::reprojectImageTo3D<<<grid, threads, 0, stream>>>(disp.ptr, disp.step / sizeof(T), xyzw.ptr, xyzw.step / sizeof(float), disp.rows, disp.cols);
imgproc_krnls::reprojectImageTo3D<<<grid, threads, 0, stream>>>(disp.ptr, disp.step / sizeof(T), xyzw.ptr, xyzw.step / sizeof(float), disp.rows, disp.cols);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );

48
modules/gpu/src/cuda/mathfunc.cu
View File

@ -41,6 +41,9 @@
//M*/
#include "cuda_shared.hpp"
#include "saturate_cast.hpp"
#include "transform.hpp"
#include "vecmath.hpp"
using namespace cv::gpu;
@ -48,6 +51,9 @@ using namespace cv::gpu;
#define CV_PI 3.1415926535897932384626433832795f
#endif
//////////////////////////////////////////////////////////////////////////////////////
// Cart <-> Polar
namespace mathfunc_krnls
{
struct Nothing
@ -143,8 +149,8 @@ namespace cv { namespace gpu { namespace mathfunc
const float scale = angleInDegrees ? (float)(180.0f / CV_PI) : 1.f;
mathfunc_krnls::cartToPolar<Mag, Angle><<<grid, threads, 0, stream>>>(
x.ptr, x.step / sizeof(float), y.ptr, y.step / sizeof(float),
mag.ptr, mag.step / sizeof(float), angle.ptr, angle.step / sizeof(float), scale, x.cols, x.rows);
x.ptr, x.elem_step, y.ptr, y.elem_step,
mag.ptr, mag.elem_step, angle.ptr, angle.elem_step, scale, x.cols, x.rows);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
@ -191,8 +197,8 @@ namespace cv { namespace gpu { namespace mathfunc
const float scale = angleInDegrees ? (float)(CV_PI / 180.0f) : 1.0f;
mathfunc_krnls::polarToCart<Mag><<<grid, threads, 0, stream>>>(mag.ptr, mag.step / sizeof(float),
angle.ptr, angle.step / sizeof(float), scale, x.ptr, x.step / sizeof(float), y.ptr, y.step / sizeof(float), mag.cols, mag.rows);
mathfunc_krnls::polarToCart<Mag><<<grid, threads, 0, stream>>>(mag.ptr, mag.elem_step,
angle.ptr, angle.elem_step, scale, x.ptr, x.elem_step, y.ptr, y.elem_step, mag.cols, mag.rows);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
@ -210,3 +216,37 @@ namespace cv { namespace gpu { namespace mathfunc
callers[mag.ptr == 0](mag, angle, x, y, angleInDegrees, stream);
}
}}}
//////////////////////////////////////////////////////////////////////////////////////
// Compare
namespace mathfunc_krnls
{
template <typename T1, typename T2>
struct NotEqual
{
__device__ uchar operator()(const T1& src1, const T2& src2, int, int)
{
return static_cast<uchar>(static_cast<int>(src1 != src2) * 255);
}
};
}
namespace cv { namespace gpu { namespace mathfunc
{
template <typename T1, typename T2>
inline void compare_ne(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
mathfunc_krnls::NotEqual<T1, T2> op;
transform(static_cast< DevMem2D_<T1> >(src1), static_cast< DevMem2D_<T2> >(src2), dst, op, 0);
}
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<uint, uint>(src1, src2, dst);
}
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<float, float>(src1, src2, dst);
}
}}}

596
modules/gpu/src/cuda/matrix_operations.cu
View File

@ -47,121 +47,18 @@
#include "saturate_cast.hpp"
using namespace cv::gpu;
using namespace cv::gpu::matrix_operations;
namespace mat_operators
namespace matop_krnls
{
__constant__ double scalar_d[4];
template <typename T>
class shift_and_sizeof;
template <>
class shift_and_sizeof<char>
{
public:
enum { shift = 0 };
};
template <>
class shift_and_sizeof<unsigned char>
{
public:
enum { shift = 0 };
};
template <>
class shift_and_sizeof<short>
{
public:
enum { shift = 1 };
};
template <>
class shift_and_sizeof<unsigned short>
{
public:
enum { shift = 1 };
};
template <>
class shift_and_sizeof<int>
{
public:
enum { shift = 2 };
};
template <>
class shift_and_sizeof<float>
{
public:
enum { shift = 2 };
};
template <>
class shift_and_sizeof<double>
{
public:
enum { shift = 3 };
};
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// CopyTo /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ void kernel_copy_to_with_mask(T * mat_src, T * mat_dst, const unsigned char * mask, int cols, int rows, int step_mat, int step_mask, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * ( step_mat >> shift_and_sizeof<T>::shift ) + x;
mat_dst[idx] = mat_src[idx];
}
}
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// SetTo //////////////////////////////////
///////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ void kernel_set_to_without_mask(T * mat, int cols, int rows, int step, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = scalar_d[ x % channels ];
}
}
template<typename T>
__global__ void kernel_set_to_with_mask(T * mat, const unsigned char * mask, int cols, int rows, int step, int channels, int step_mask)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = scalar_d[ x % channels ];
}
}
///////////////////////////////////////////////////////////////////////////
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
template <typename T> struct shift_and_sizeof;
template <> struct shift_and_sizeof<char> { enum { shift = 0 }; };
template <> struct shift_and_sizeof<unsigned char> { enum { shift = 0 }; };
template <> struct shift_and_sizeof<short> { enum { shift = 1 }; };
template <> struct shift_and_sizeof<unsigned short> { enum { shift = 1 }; };
template <> struct shift_and_sizeof<int> { enum { shift = 2 }; };
template <> struct shift_and_sizeof<float> { enum { shift = 2 }; };
template <> struct shift_and_sizeof<double> { enum { shift = 3 }; };
template <typename T, typename DT, size_t src_elem_size, size_t dst_elem_size>
struct ReadWriteTraits
{
@ -218,9 +115,206 @@ namespace mat_operators
typedef int2 read_type;
typedef short2 write_type;
};
}
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// CopyTo /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
namespace matop_krnls
{
template<typename T>
__global__ void copy_to_with_mask(T * mat_src, T * mat_dst, const unsigned char * mask, int cols, int rows, int step_mat, int step_mask, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * ( step_mat >> shift_and_sizeof<T>::shift ) + x;
mat_dst[idx] = mat_src[idx];
}
}
}
namespace cv { namespace gpu { namespace matrix_operations
{
typedef void (*CopyToFunc)(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels, const cudaStream_t & stream);
template<typename T>
void copy_to_with_mask_run(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(16,16, 1);
dim3 numBlocks ( divUp(mat_src.cols * channels , threadsPerBlock.x) , divUp(mat_src.rows , threadsPerBlock.y), 1);
if (stream == 0)
{
::matop_krnls::copy_to_with_mask<T><<<numBlocks,threadsPerBlock>>>
((T*)mat_src.ptr, (T*)mat_dst.ptr, (unsigned char*)mask.ptr, mat_src.cols, mat_src.rows, mat_src.step, mask.step, channels);
cudaSafeCall ( cudaThreadSynchronize() );
}
else
{
::matop_krnls::copy_to_with_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>
((T*)mat_src.ptr, (T*)mat_dst.ptr, (unsigned char*)mask.ptr, mat_src.cols, mat_src.rows, mat_src.step, mask.step, channels);
}
}
void copy_to_with_mask(const DevMem2D& mat_src, DevMem2D mat_dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
static CopyToFunc tab[8] =
{
copy_to_with_mask_run<unsigned char>,
copy_to_with_mask_run<char>,
copy_to_with_mask_run<unsigned short>,
copy_to_with_mask_run<short>,
copy_to_with_mask_run<int>,
copy_to_with_mask_run<float>,
copy_to_with_mask_run<double>,
0
};
CopyToFunc func = tab[depth];
if (func == 0) cv::gpu::error("Unsupported copyTo operation", __FILE__, __LINE__);
func(mat_src, mat_dst, mask, channels, stream);
}
}}}
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// SetTo //////////////////////////////////
///////////////////////////////////////////////////////////////////////////
namespace matop_krnls
{
__constant__ double scalar_d[4];
template<typename T>
__global__ void set_to_without_mask(T * mat, int cols, int rows, int step, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = scalar_d[ x % channels ];
}
}
template<typename T>
__global__ void set_to_with_mask(T * mat, const unsigned char * mask, int cols, int rows, int step, int channels, int step_mask)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = scalar_d[ x % channels ];
}
}
}
namespace cv { namespace gpu { namespace matrix_operations
{
typedef void (*SetToFunc_with_mask)(const DevMem2D& mat, const DevMem2D& mask, int channels, const cudaStream_t & stream);
typedef void (*SetToFunc_without_mask)(const DevMem2D& mat, int channels, const cudaStream_t & stream);
template <typename T>
void set_to_with_mask_run(const DevMem2D& mat, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
if (stream == 0)
{
::matop_krnls::set_to_with_mask<T><<<numBlocks,threadsPerBlock>>>((T*)mat.ptr, (unsigned char *)mask.ptr, mat.cols, mat.rows, mat.step, channels, mask.step);
cudaSafeCall ( cudaThreadSynchronize() );
}
else
{
::matop_krnls::set_to_with_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>((T*)mat.ptr, (unsigned char *)mask.ptr, mat.cols, mat.rows, mat.step, channels, mask.step);
}
}
template <typename T>
void set_to_without_mask_run(const DevMem2D& mat, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
if (stream == 0)
{
matop_krnls::set_to_without_mask<T><<<numBlocks,threadsPerBlock>>>((T*)mat.ptr, mat.cols, mat.rows, mat.step, channels);
cudaSafeCall ( cudaThreadSynchronize() );
}
else
{
matop_krnls::set_to_without_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>((T*)mat.ptr, mat.cols, mat.rows, mat.step, channels);
}
}
void set_to_without_mask(DevMem2D mat, int depth, const double *scalar, int channels, const cudaStream_t & stream)
{
cudaSafeCall( cudaMemcpyToSymbol(matop_krnls::scalar_d, scalar, sizeof(double) * 4));
static SetToFunc_without_mask tab[8] =
{
set_to_without_mask_run<unsigned char>,
set_to_without_mask_run<char>,
set_to_without_mask_run<unsigned short>,
set_to_without_mask_run<short>,
set_to_without_mask_run<int>,
set_to_without_mask_run<float>,
set_to_without_mask_run<double>,
0
};
SetToFunc_without_mask func = tab[depth];
if (func == 0)
cv::gpu::error("Unsupported setTo operation", __FILE__, __LINE__);
func(mat, channels, stream);
}
void set_to_with_mask(DevMem2D mat, int depth, const double * scalar, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
cudaSafeCall( cudaMemcpyToSymbol(matop_krnls::scalar_d, scalar, sizeof(double) * 4));
static SetToFunc_with_mask tab[8] =
{
set_to_with_mask_run<unsigned char>,
set_to_with_mask_run<char>,
set_to_with_mask_run<unsigned short>,
set_to_with_mask_run<short>,
set_to_with_mask_run<int>,
set_to_with_mask_run<float>,
set_to_with_mask_run<double>,
0
};
SetToFunc_with_mask func = tab[depth];
if (func == 0)
cv::gpu::error("Unsupported setTo operation", __FILE__, __LINE__);
func(mat, mask, channels, stream);
}
}}}
///////////////////////////////////////////////////////////////////////////
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
namespace matop_krnls
{
template <typename T, typename DT>
__global__ static void kernel_convert_to(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
__global__ static void convert_to(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
typedef typename ReadWriteTraits<T, DT, sizeof(T), sizeof(DT)>::read_type read_type;
typedef typename ReadWriteTraits<T, DT, sizeof(T), sizeof(DT)>::write_type write_type;
@ -253,253 +347,63 @@ namespace mat_operators
dst[(x * shift) + i] = saturate_cast<DT>(alpha * src[(x * shift) + i] + beta);
}
}
}
}
}
///////////////////////////////////////////////////////////////////////////
/////////////////////////////// compare_ne ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
namespace cv { namespace gpu { namespace matrix_operations
{
typedef void (*CvtFunc)(const DevMem2D& src, DevMem2D& dst, size_t width, size_t height, double alpha, double beta, const cudaStream_t & stream);
template <typename T>
__global__ void kernel_compare_ne(uchar* src1, size_t src1_step, uchar* src2, size_t src2_step, uchar* dst, size_t dst_step, int cols, int rows)
template<typename T, typename DT>
void cvt_(const DevMem2D& src, DevMem2D& dst, size_t width, size_t height, double alpha, double beta, const cudaStream_t & stream)
{
const size_t x = threadIdx.x + blockIdx.x * blockDim.x;
const size_t y = threadIdx.y + blockIdx.y * blockDim.y;
const int shift = ::matop_krnls::ReadWriteTraits<T, DT, sizeof(T), sizeof(DT)>::shift;
if (x < cols && y < rows)
dim3 block(32, 8);
dim3 grid(divUp(width, block.x * shift), divUp(height, block.y));
if (stream == 0)
{
T src1_pix = ((T*)(src1 + y * src1_step))[x];
T src2_pix = ((T*)(src2 + y * src2_step))[x];
uchar res = (uchar)(src1_pix != src2_pix) * 255;
((dst + y * dst_step))[x] = res;
matop_krnls::convert_to<T, DT><<<grid, block>>>(src.ptr, src.step, dst.ptr, dst.step, width, height, alpha, beta);
cudaSafeCall( cudaThreadSynchronize() );
}
else
{
matop_krnls::convert_to<T, DT><<<grid, block, 0, stream>>>(src.ptr, src.step, dst.ptr, dst.step, width, height, alpha, beta);
}
}
} // namespace mat_operators
namespace cv
{
namespace gpu
void convert_to(const DevMem2D& src, int sdepth, DevMem2D dst, int ddepth, int channels, double alpha, double beta, const cudaStream_t & stream)
{
namespace matrix_operations
static CvtFunc tab[8][8] =
{
{cvt_<uchar, uchar>, cvt_<uchar, schar>, cvt_<uchar, ushort>, cvt_<uchar, short>,
cvt_<uchar, int>, cvt_<uchar, float>, cvt_<uchar, double>, 0},
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// CopyTo /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
{cvt_<schar, uchar>, cvt_<schar, schar>, cvt_<schar, ushort>, cvt_<schar, short>,
cvt_<schar, int>, cvt_<schar, float>, cvt_<schar, double>, 0},
typedef void (*CopyToFunc)(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels, const cudaStream_t & stream);
{cvt_<ushort, uchar>, cvt_<ushort, schar>, cvt_<ushort, ushort>, cvt_<ushort, short>,
cvt_<ushort, int>, cvt_<ushort, float>, cvt_<ushort, double>, 0},
template<typename T>
void copy_to_with_mask_run(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(16,16, 1);
dim3 numBlocks ( divUp(mat_src.cols * channels , threadsPerBlock.x) , divUp(mat_src.rows , threadsPerBlock.y), 1);
if (stream == 0)
{
::mat_operators::kernel_copy_to_with_mask<T><<<numBlocks,threadsPerBlock>>>
((T*)mat_src.ptr, (T*)mat_dst.ptr, (unsigned char*)mask.ptr, mat_src.cols, mat_src.rows, mat_src.step, mask.step, channels);
cudaSafeCall ( cudaThreadSynchronize() );
}
else
{
::mat_operators::kernel_copy_to_with_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>
((T*)mat_src.ptr, (T*)mat_dst.ptr, (unsigned char*)mask.ptr, mat_src.cols, mat_src.rows, mat_src.step, mask.step, channels);
}
}
{cvt_<short, uchar>, cvt_<short, schar>, cvt_<short, ushort>, cvt_<short, short>,
cvt_<short, int>, cvt_<short, float>, cvt_<short, double>, 0},
extern "C" void copy_to_with_mask(const DevMem2D& mat_src, DevMem2D mat_dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
static CopyToFunc tab[8] =
{
copy_to_with_mask_run<unsigned char>,
copy_to_with_mask_run<char>,
copy_to_with_mask_run<unsigned short>,
copy_to_with_mask_run<short>,
copy_to_with_mask_run<int>,
copy_to_with_mask_run<float>,
copy_to_with_mask_run<double>,
0
};
{cvt_<int, uchar>, cvt_<int, schar>, cvt_<int, ushort>,
cvt_<int, short>, cvt_<int, int>, cvt_<int, float>, cvt_<int, double>, 0},
CopyToFunc func = tab[depth];
{cvt_<float, uchar>, cvt_<float, schar>, cvt_<float, ushort>,
cvt_<float, short>, cvt_<float, int>, cvt_<float, float>, cvt_<float, double>, 0},
if (func == 0) cv::gpu::error("Unsupported copyTo operation", __FILE__, __LINE__);
{cvt_<double, uchar>, cvt_<double, schar>, cvt_<double, ushort>,
cvt_<double, short>, cvt_<double, int>, cvt_<double, float>, cvt_<double, double>, 0},
func(mat_src, mat_dst, mask, channels, stream);
}
{0,0,0,0,0,0,0,0}
};
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// SetTo //////////////////////////////////
///////////////////////////////////////////////////////////////////////////
typedef void (*SetToFunc_with_mask)(const DevMem2D& mat, const DevMem2D& mask, int channels, const cudaStream_t & stream);
typedef void (*SetToFunc_without_mask)(const DevMem2D& mat, int channels, const cudaStream_t & stream);
template <typename T>
void set_to_with_mask_run(const DevMem2D& mat, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
if (stream == 0)
{
::mat_operators::kernel_set_to_with_mask<T><<<numBlocks,threadsPerBlock>>>((T*)mat.ptr, (unsigned char *)mask.ptr, mat.cols, mat.rows, mat.step, channels, mask.step);
cudaSafeCall ( cudaThreadSynchronize() );
}
else
{
::mat_operators::kernel_set_to_with_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>((T*)mat.ptr, (unsigned char *)mask.ptr, mat.cols, mat.rows, mat.step, channels, mask.step);
}
}
template <typename T>
void set_to_without_mask_run(const DevMem2D& mat, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
if (stream == 0)
{
mat_operators::kernel_set_to_without_mask<T><<<numBlocks,threadsPerBlock>>>((T*)mat.ptr, mat.cols, mat.rows, mat.step, channels);
cudaSafeCall ( cudaThreadSynchronize() );
}
else
{
mat_operators::kernel_set_to_without_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>((T*)mat.ptr, mat.cols, mat.rows, mat.step, channels);
}
}
extern "C" void set_to_without_mask(DevMem2D mat, int depth, const double *scalar, int channels, const cudaStream_t & stream)
{
cudaSafeCall( cudaMemcpyToSymbol(mat_operators::scalar_d, scalar, sizeof(double) * 4));
static SetToFunc_without_mask tab[8] =
{
set_to_without_mask_run<unsigned char>,
set_to_without_mask_run<char>,
set_to_without_mask_run<unsigned short>,
set_to_without_mask_run<short>,
set_to_without_mask_run<int>,
set_to_without_mask_run<float>,
set_to_without_mask_run<double>,
0
};
SetToFunc_without_mask func = tab[depth];
if (func == 0)
cv::gpu::error("Unsupported setTo operation", __FILE__, __LINE__);
func(mat, channels, stream);
}
extern "C" void set_to_with_mask(DevMem2D mat, int depth, const double * scalar, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
cudaSafeCall( cudaMemcpyToSymbol(mat_operators::scalar_d, scalar, sizeof(double) * 4));
static SetToFunc_with_mask tab[8] =
{
set_to_with_mask_run<unsigned char>,
set_to_with_mask_run<char>,
set_to_with_mask_run<unsigned short>,
set_to_with_mask_run<short>,
set_to_with_mask_run<int>,
set_to_with_mask_run<float>,
set_to_with_mask_run<double>,
0
};
SetToFunc_with_mask func = tab[depth];
if (func == 0)
cv::gpu::error("Unsupported setTo operation", __FILE__, __LINE__);
func(mat, mask, channels, stream);
}
///////////////////////////////////////////////////////////////////////////
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
typedef void (*CvtFunc)(const DevMem2D& src, DevMem2D& dst, size_t width, size_t height, double alpha, double beta, const cudaStream_t & stream);
template<typename T, typename DT>
void cvt_(const DevMem2D& src, DevMem2D& dst, size_t width, size_t height, double alpha, double beta, const cudaStream_t & stream)
{
const int shift = ::mat_operators::ReadWriteTraits<T, DT, sizeof(T), sizeof(DT)>::shift;
dim3 block(32, 8);
dim3 grid(divUp(width, block.x * shift), divUp(height, block.y));
if (stream == 0)
{
mat_operators::kernel_convert_to<T, DT><<<grid, block>>>(src.ptr, src.step, dst.ptr, dst.step, width, height, alpha, beta);
cudaSafeCall( cudaThreadSynchronize() );
}
else
{
mat_operators::kernel_convert_to<T, DT><<<grid, block, 0, stream>>>(src.ptr, src.step, dst.ptr, dst.step, width, height, alpha, beta);
}
}
extern "C" void convert_to(const DevMem2D& src, int sdepth, DevMem2D dst, int ddepth, int channels, double alpha, double beta, const cudaStream_t & stream)
{
static CvtFunc tab[8][8] =
{
{cvt_<uchar, uchar>, cvt_<uchar, schar>, cvt_<uchar, ushort>, cvt_<uchar, short>,
cvt_<uchar, int>, cvt_<uchar, float>, cvt_<uchar, double>, 0},
{cvt_<schar, uchar>, cvt_<schar, schar>, cvt_<schar, ushort>, cvt_<schar, short>,
cvt_<schar, int>, cvt_<schar, float>, cvt_<schar, double>, 0},
{cvt_<ushort, uchar>, cvt_<ushort, schar>, cvt_<ushort, ushort>, cvt_<ushort, short>,
cvt_<ushort, int>, cvt_<ushort, float>, cvt_<ushort, double>, 0},
{cvt_<short, uchar>, cvt_<short, schar>, cvt_<short, ushort>, cvt_<short, short>,
cvt_<short, int>, cvt_<short, float>, cvt_<short, double>, 0},
{cvt_<int, uchar>, cvt_<int, schar>, cvt_<int, ushort>,
cvt_<int, short>, cvt_<int, int>, cvt_<int, float>, cvt_<int, double>, 0},
{cvt_<float, uchar>, cvt_<float, schar>, cvt_<float, ushort>,
cvt_<float, short>, cvt_<float, int>, cvt_<float, float>, cvt_<float, double>, 0},
{cvt_<double, uchar>, cvt_<double, schar>, cvt_<double, ushort>,
cvt_<double, short>, cvt_<double, int>, cvt_<double, float>, cvt_<double, double>, 0},
{0,0,0,0,0,0,0,0}
};
CvtFunc func = tab[sdepth][ddepth];
if (func == 0)
cv::gpu::error("Unsupported convert operation", __FILE__, __LINE__);
func(src, dst, src.cols * channels, src.rows, alpha, beta, stream);
}
///////////////////////////////////////////////////////////////////////////
/////////////////////////////// compare_ne ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
void compare_ne_8u(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
dim3 block(32, 8);
dim3 grid(divUp(src1.cols, block.x), divUp(src1.rows, block.y));
mat_operators::kernel_compare_ne<uint><<<grid, block>>>(src1.ptr, src1.step, src2.ptr, src2.step, dst.ptr, dst.step, src1.cols, src1.rows);
cudaSafeCall( cudaThreadSynchronize() );
}
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
dim3 block(32, 8);
dim3 grid(divUp(src1.cols, block.x), divUp(src1.rows, block.y));
mat_operators::kernel_compare_ne<float><<<grid, block>>>(src1.ptr, src1.step, src2.ptr, src2.step, dst.ptr, dst.step, src1.cols, src1.rows);
cudaSafeCall( cudaThreadSynchronize() );
}
} // namespace matrix_operations
} // namespace gpu
} // namespace cv
CvtFunc func = tab[sdepth][ddepth];
if (func == 0)
cv::gpu::error("Unsupported convert operation", __FILE__, __LINE__);
func(src, dst, src.cols * channels, src.rows, alpha, beta, stream);
}
}}}

314
modules/gpu/src/cuda/saturate_cast.hpp
View File

@ -49,124 +49,206 @@ namespace cv
{
namespace gpu
{
// To fix link error: this func already defined in other obj file
namespace
template<typename _Tp> static __device__ _Tp saturate_cast(uchar v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(schar v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(ushort v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(short v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(uint v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(int v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(float v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(double v) { return _Tp(v); }
template<> static __device__ uchar saturate_cast<uchar>(schar v)
{ return (uchar)max((int)v, 0); }
template<> static __device__ uchar saturate_cast<uchar>(ushort v)
{ return (uchar)min((uint)v, (uint)UCHAR_MAX); }
template<> static __device__ uchar saturate_cast<uchar>(int v)
{ return (uchar)((uint)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> static __device__ uchar saturate_cast<uchar>(uint v)
{ return (uchar)min(v, (uint)UCHAR_MAX); }
template<> static __device__ uchar saturate_cast<uchar>(short v)
{ return saturate_cast<uchar>((uint)v); }
template<> static __device__ uchar saturate_cast<uchar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<uchar>(iv); }
template<> static __device__ uchar saturate_cast<uchar>(double v)
{
template<typename _Tp> __device__ _Tp saturate_cast(uchar v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(schar v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(ushort v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(short v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(uint v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(int v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(float v) { return _Tp(v); }
template<typename _Tp> __device__ _Tp saturate_cast(double v) { return _Tp(v); }
template<> __device__ uchar saturate_cast<uchar>(schar v)
{ return (uchar)max((int)v, 0); }
template<> __device__ uchar saturate_cast<uchar>(ushort v)
{ return (uchar)min((uint)v, (uint)UCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(int v)
{ return (uchar)((uint)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> __device__ uchar saturate_cast<uchar>(uint v)
{ return (uchar)min(v, (uint)UCHAR_MAX); }
template<> __device__ uchar saturate_cast<uchar>(short v)
{ return saturate_cast<uchar>((uint)v); }
template<> __device__ uchar saturate_cast<uchar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<uchar>(iv); }
template<> __device__ uchar saturate_cast<uchar>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<uchar>(iv);
#else
return saturate_cast<uchar>((float)v);
#endif
}
template<> __device__ schar saturate_cast<schar>(uchar v)
{ return (schar)min((int)v, SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(ushort v)
{ return (schar)min((uint)v, (uint)SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(int v)
{
return (schar)((uint)(v-SCHAR_MIN) <= (uint)UCHAR_MAX ?
v : v > 0 ? SCHAR_MAX : SCHAR_MIN);
}
template<> __device__ schar saturate_cast<schar>(short v)
{ return saturate_cast<schar>((int)v); }
template<> __device__ schar saturate_cast<schar>(uint v)
{ return (schar)min(v, (uint)SCHAR_MAX); }
template<> __device__ schar saturate_cast<schar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<schar>(iv); }
template<> __device__ schar saturate_cast<schar>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<schar>(iv);
#else
return saturate_cast<schar>((float)v);
#endif
}
template<> __device__ ushort saturate_cast<ushort>(schar v)
{ return (ushort)max((int)v, 0); }
template<> __device__ ushort saturate_cast<ushort>(short v)
{ return (ushort)max((int)v, 0); }
template<> __device__ ushort saturate_cast<ushort>(int v)
{ return (ushort)((uint)v <= (uint)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> __device__ ushort saturate_cast<ushort>(uint v)
{ return (ushort)min(v, (uint)USHRT_MAX); }
template<> __device__ ushort saturate_cast<ushort>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<ushort>(iv); }
template<> __device__ ushort saturate_cast<ushort>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<ushort>(iv);
#else
return saturate_cast<ushort>((float)v);
#endif
}
template<> __device__ short saturate_cast<short>(ushort v)
{ return (short)min((int)v, SHRT_MAX); }
template<> __device__ short saturate_cast<short>(int v)
{
return (short)((uint)(v - SHRT_MIN) <= (uint)USHRT_MAX ?
v : v > 0 ? SHRT_MAX : SHRT_MIN);
}
template<> __device__ short saturate_cast<short>(uint v)
{ return (short)min(v, (uint)SHRT_MAX); }
template<> __device__ short saturate_cast<short>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<short>(iv); }
template<> __device__ short saturate_cast<short>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<short>(iv);
#else
return saturate_cast<short>((float)v);
#endif
}
template<> __device__ int saturate_cast<int>(float v) { return __float2int_rn(v); }
template<> __device__ 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__ uint saturate_cast<uint>(float v){ return __float2uint_rn(v); }
template<> __device__ 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
}
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<uchar>(iv);
#else
return saturate_cast<uchar>((float)v);
#endif
}
template<> static __device__ schar saturate_cast<schar>(uchar v)
{ return (schar)min((int)v, SCHAR_MAX); }
template<> static __device__ schar saturate_cast<schar>(ushort v)
{ return (schar)min((uint)v, (uint)SCHAR_MAX); }
template<> static __device__ schar saturate_cast<schar>(int v)
{
return (schar)((uint)(v-SCHAR_MIN) <= (uint)UCHAR_MAX ?
v : v > 0 ? SCHAR_MAX : SCHAR_MIN);
}
template<> static __device__ schar saturate_cast<schar>(short v)
{ return saturate_cast<schar>((int)v); }
template<> static __device__ schar saturate_cast<schar>(uint v)
{ return (schar)min(v, (uint)SCHAR_MAX); }
template<> static __device__ schar saturate_cast<schar>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<schar>(iv); }
template<> static __device__ schar saturate_cast<schar>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<schar>(iv);
#else
return saturate_cast<schar>((float)v);
#endif
}
template<> static __device__ ushort saturate_cast<ushort>(schar v)
{ return (ushort)max((int)v, 0); }
template<> static __device__ ushort saturate_cast<ushort>(short v)
{ return (ushort)max((int)v, 0); }
template<> static __device__ ushort saturate_cast<ushort>(int v)
{ return (ushort)((uint)v <= (uint)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> static __device__ ushort saturate_cast<ushort>(uint v)
{ return (ushort)min(v, (uint)USHRT_MAX); }
template<> static __device__ ushort saturate_cast<ushort>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<ushort>(iv); }
template<> static __device__ ushort saturate_cast<ushort>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<ushort>(iv);
#else
return saturate_cast<ushort>((float)v);
#endif
}
template<> static __device__ short saturate_cast<short>(ushort v)
{ return (short)min((int)v, SHRT_MAX); }
template<> static __device__ short saturate_cast<short>(int v)
{
return (short)((uint)(v - SHRT_MIN) <= (uint)USHRT_MAX ?
v : v > 0 ? SHRT_MAX : SHRT_MIN);
}
template<> static __device__ short saturate_cast<short>(uint v)
{ return (short)min(v, (uint)SHRT_MAX); }
template<> static __device__ short saturate_cast<short>(float v)
{ int iv = __float2int_rn(v); return saturate_cast<short>(iv); }
template<> static __device__ short saturate_cast<short>(double v)
{
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 130
int iv = __double2int_rn(v); return saturate_cast<short>(iv);
#else
return saturate_cast<short>((float)v);
#endif
}
template<> static __device__ int saturate_cast<int>(float v) { return __float2int_rn(v); }
template<> static __device__ 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<> static __device__ uint saturate_cast<uint>(float v){ return __float2uint_rn(v); }
template<> static __device__ 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
}
template<typename _Tp> static __device__ _Tp saturate_cast(uchar4 v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(char4 v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(ushort4 v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(short4 v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(uint4 v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(int4 v) { return _Tp(v); }
template<typename _Tp> static __device__ _Tp saturate_cast(float4 v) { return _Tp(v); }
template<> static __device__ uchar4 saturate_cast<uchar4>(char4 v)
{ return make_uchar4(saturate_cast<uchar>(v.x), saturate_cast<uchar>(v.y), saturate_cast<uchar>(v.z), saturate_cast<uchar>(v.w)); }
template<> static __device__ uchar4 saturate_cast<uchar4>(ushort4 v)
{ return make_uchar4(saturate_cast<uchar>(v.x), saturate_cast<uchar>(v.y), saturate_cast<uchar>(v.z), saturate_cast<uchar>(v.w)); }
template<> static __device__ uchar4 saturate_cast<uchar4>(short4 v)
{ return make_uchar4(saturate_cast<uchar>(v.x), saturate_cast<uchar>(v.y), saturate_cast<uchar>(v.z), saturate_cast<uchar>(v.w)); }
template<> static __device__ uchar4 saturate_cast<uchar4>(uint4 v)
{ return make_uchar4(saturate_cast<uchar>(v.x), saturate_cast<uchar>(v.y), saturate_cast<uchar>(v.z), saturate_cast<uchar>(v.w)); }
template<> static __device__ uchar4 saturate_cast<uchar4>(int4 v)
{ return make_uchar4(saturate_cast<uchar>(v.x), saturate_cast<uchar>(v.y), saturate_cast<uchar>(v.z), saturate_cast<uchar>(v.w)); }
template<> static __device__ uchar4 saturate_cast<uchar4>(float4 v)
{ return make_uchar4(saturate_cast<uchar>(v.x), saturate_cast<uchar>(v.y), saturate_cast<uchar>(v.z), saturate_cast<uchar>(v.w)); }
template<> static __device__ char4 saturate_cast<char4>(uchar4 v)
{ return make_char4(saturate_cast<char>(v.x), saturate_cast<char>(v.y), saturate_cast<char>(v.z), saturate_cast<char>(v.w)); }
template<> static __device__ char4 saturate_cast<char4>(ushort4 v)
{ return make_char4(saturate_cast<char>(v.x), saturate_cast<char>(v.y), saturate_cast<char>(v.z), saturate_cast<char>(v.w)); }
template<> static __device__ char4 saturate_cast<char4>(short4 v)
{ return make_char4(saturate_cast<char>(v.x), saturate_cast<char>(v.y), saturate_cast<char>(v.z), saturate_cast<char>(v.w)); }
template<> static __device__ char4 saturate_cast<char4>(uint4 v)
{ return make_char4(saturate_cast<char>(v.x), saturate_cast<char>(v.y), saturate_cast<char>(v.z), saturate_cast<char>(v.w)); }
template<> static __device__ char4 saturate_cast<char4>(int4 v)
{ return make_char4(saturate_cast<char>(v.x), saturate_cast<char>(v.y), saturate_cast<char>(v.z), saturate_cast<char>(v.w)); }
template<> static __device__ char4 saturate_cast<char4>(float4 v)
{ return make_char4(saturate_cast<char>(v.x), saturate_cast<char>(v.y), saturate_cast<char>(v.z), saturate_cast<char>(v.w)); }
template<> static __device__ ushort4 saturate_cast<ushort4>(uchar4 v)
{ return make_ushort4(v.x, v.y, v.z, v.w); }
template<> static __device__ ushort4 saturate_cast<ushort4>(char4 v)
{ return make_ushort4(saturate_cast<ushort>(v.x), saturate_cast<ushort>(v.y), saturate_cast<ushort>(v.z), saturate_cast<ushort>(v.w)); }
template<> static __device__ ushort4 saturate_cast<ushort4>(short4 v)
{ return make_ushort4(saturate_cast<ushort>(v.x), saturate_cast<ushort>(v.y), saturate_cast<ushort>(v.z), saturate_cast<ushort>(v.w)); }
template<> static __device__ ushort4 saturate_cast<ushort4>(uint4 v)
{ return make_ushort4(saturate_cast<ushort>(v.x), saturate_cast<ushort>(v.y), saturate_cast<ushort>(v.z), saturate_cast<ushort>(v.w)); }
template<> static __device__ ushort4 saturate_cast<ushort4>(int4 v)
{ return make_ushort4(saturate_cast<ushort>(v.x), saturate_cast<ushort>(v.y), saturate_cast<ushort>(v.z), saturate_cast<ushort>(v.w)); }
template<> static __device__ ushort4 saturate_cast<ushort4>(float4 v)
{ return make_ushort4(saturate_cast<ushort>(v.x), saturate_cast<ushort>(v.y), saturate_cast<ushort>(v.z), saturate_cast<ushort>(v.w)); }
template<> static __device__ short4 saturate_cast<short4>(uchar4 v)
{ return make_short4(v.x, v.y, v.z, v.w); }
template<> static __device__ short4 saturate_cast<short4>(char4 v)
{ return make_short4(v.x, v.y, v.z, v.w); }
template<> static __device__ short4 saturate_cast<short4>(ushort4 v)
{ return make_short4(saturate_cast<short>(v.x), saturate_cast<short>(v.y), saturate_cast<short>(v.z), saturate_cast<short>(v.w)); }
template<> static __device__ short4 saturate_cast<short4>(uint4 v)
{ return make_short4(saturate_cast<short>(v.x), saturate_cast<short>(v.y), saturate_cast<short>(v.z), saturate_cast<short>(v.w)); }
template<> static __device__ short4 saturate_cast<short4>(int4 v)
{ return make_short4(saturate_cast<short>(v.x), saturate_cast<short>(v.y), saturate_cast<short>(v.z), saturate_cast<short>(v.w)); }
template<> static __device__ short4 saturate_cast<short4>(float4 v)
{ return make_short4(saturate_cast<short>(v.x), saturate_cast<short>(v.y), saturate_cast<short>(v.z), saturate_cast<short>(v.w)); }
template<> static __device__ uint4 saturate_cast<uint4>(uchar4 v)
{ return make_uint4(v.x, v.y, v.z, v.w); }
template<> static __device__ uint4 saturate_cast<uint4>(char4 v)
{ return make_uint4(saturate_cast<uint>(v.x), saturate_cast<uint>(v.y), saturate_cast<uint>(v.z), saturate_cast<uint>(v.w)); }
template<> static __device__ uint4 saturate_cast<uint4>(ushort4 v)
{ return make_uint4(v.x, v.y, v.z, v.w); }
template<> static __device__ uint4 saturate_cast<uint4>(short4 v)
{ return make_uint4(saturate_cast<uint>(v.x), saturate_cast<uint>(v.y), saturate_cast<uint>(v.z), saturate_cast<uint>(v.w)); }
template<> static __device__ uint4 saturate_cast<uint4>(int4 v)
{ return make_uint4(saturate_cast<uint>(v.x), saturate_cast<uint>(v.y), saturate_cast<uint>(v.z), saturate_cast<uint>(v.w)); }
template<> static __device__ uint4 saturate_cast<uint4>(float4 v)
{ return make_uint4(saturate_cast<uint>(v.x), saturate_cast<uint>(v.y), saturate_cast<uint>(v.z), saturate_cast<uint>(v.w)); }
template<> static __device__ int4 saturate_cast<int4>(uchar4 v)
{ return make_int4(v.x, v.y, v.z, v.w); }
template<> static __device__ int4 saturate_cast<int4>(char4 v)
{ return make_int4(v.x, v.y, v.z, v.w); }
template<> static __device__ int4 saturate_cast<int4>(ushort4 v)
{ return make_int4(v.x, v.y, v.z, v.w); }
template<> static __device__ int4 saturate_cast<int4>(short4 v)
{ return make_int4(v.x, v.y, v.z, v.w); }
template<> static __device__ int4 saturate_cast<int4>(uint4 v)
{ return make_int4(saturate_cast<int>(v.x), saturate_cast<int>(v.y), saturate_cast<int>(v.z), saturate_cast<int>(v.w)); }
template<> static __device__ int4 saturate_cast<int4>(float4 v)
{ return make_int4(saturate_cast<int>(v.x), saturate_cast<int>(v.y), saturate_cast<int>(v.z), saturate_cast<int>(v.w)); }
}
}

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/*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 "cuda_shared.hpp"
#include "saturate_cast.hpp"
#include "vecmath.hpp"
namespace cv { namespace gpu { namespace algo_krnls
{
template <typename T, typename D, typename UnOp>
static __global__ void transform(const T* src, size_t src_step,
D* dst, size_t dst_step, int width, int height, UnOp op)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < width && y < height)
{
T src_data = src[y * src_step + x];
dst[y * dst_step + x] = op(src_data, x, y);
}
}
template <typename T1, typename T2, typename D, typename BinOp>
static __global__ void transform(const T1* src1, size_t src1_step, const T2* src2, size_t src2_step,
D* dst, size_t dst_step, int width, int height, BinOp op)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < width && y < height)
{
T1 src1_data = src1[y * src1_step + x];
T2 src2_data = src2[y * src2_step + x];
dst[y * dst_step + x] = op(src1_data, src2_data, x, y);
}
}
}}}
namespace cv
{
namespace gpu
{
template <typename T, typename D, typename UnOp>
static void transform(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, UnOp op, cudaStream_t stream)
{
dim3 threads(16, 16, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
algo_krnls::transform<<<grid, threads, 0, stream>>>(src.ptr, src.elem_step,
dst.ptr, dst.elem_step, src.cols, src.rows, op);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
template <typename T1, typename T2, typename D, typename BinOp>
static void transform(const DevMem2D_<T1>& src1, const DevMem2D_<T2>& src2, const DevMem2D_<D>& dst, BinOp op, cudaStream_t stream)
{
dim3 threads(16, 16, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(src1.cols, threads.x);
grid.y = divUp(src1.rows, threads.y);
algo_krnls::transform<<<grid, threads, 0, stream>>>(src1.ptr, src1.elem_step,
src2.ptr, src2.elem_step, dst.ptr, dst.elem_step, src1.cols, src1.rows, op);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
}
}
#endif // __OPENCV_GPU_TRANSFORM_HPP__

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/*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 "cuda_shared.hpp"
namespace cv
{
namespace gpu
{
template<typename T, int N> struct TypeVec;
template<typename T> struct TypeVec<T, 1> { typedef T vec_t; };
template<> struct TypeVec<unsigned char, 2> { typedef uchar2 vec_t; };
template<> struct TypeVec<uchar2, 2> { typedef uchar2 vec_t; };
template<> struct TypeVec<unsigned char, 3> { typedef uchar3 vec_t; };;
template<> struct TypeVec<uchar3, 3> { typedef uchar3 vec_t; };
template<> struct TypeVec<unsigned char, 4> { typedef uchar4 vec_t; };;
template<> struct TypeVec<uchar4, 4> { typedef uchar4 vec_t; };
template<> struct TypeVec<char, 2> { typedef char2 vec_t; };
template<> struct TypeVec<char2, 2> { typedef char2 vec_t; };
template<> struct TypeVec<char, 3> { typedef char3 vec_t; };
template<> struct TypeVec<char3, 3> { typedef char3 vec_t; };
template<> struct TypeVec<char, 4> { typedef char4 vec_t; };
template<> struct TypeVec<char4, 4> { typedef char4 vec_t; };
template<> struct TypeVec<unsigned short, 2> { typedef ushort2 vec_t; };
template<> struct TypeVec<ushort2, 2> { typedef ushort2 vec_t; };
template<> struct TypeVec<unsigned short, 3> { typedef ushort3 vec_t; };
template<> struct TypeVec<ushort3, 3> { typedef ushort3 vec_t; };
template<> struct TypeVec<unsigned short, 4> { typedef ushort4 vec_t; };
template<> struct TypeVec<ushort4, 4> { typedef ushort4 vec_t; };
template<> struct TypeVec<short, 2> { typedef short2 vec_t; };
template<> struct TypeVec<short2, 2> { typedef short2 vec_t; };
template<> struct TypeVec<short, 3> { typedef short3 vec_t; };
template<> struct TypeVec<short3, 3> { typedef short3 vec_t; };
template<> struct TypeVec<short, 4> { typedef short4 vec_t; };
template<> struct TypeVec<short4, 4> { typedef short4 vec_t; };
template<> struct TypeVec<unsigned int, 2> { typedef uint2 vec_t; };
template<> struct TypeVec<uint2, 2> { typedef uint2 vec_t; };
template<> struct TypeVec<unsigned int, 3> { typedef uint3 vec_t; };
template<> struct TypeVec<uint3, 3> { typedef uint3 vec_t; };
template<> struct TypeVec<unsigned int, 4> { typedef uint4 vec_t; };
template<> struct TypeVec<uint4, 4> { typedef uint4 vec_t; };
template<> struct TypeVec<int, 2> { typedef int2 vec_t; };
template<> struct TypeVec<int2, 2> { typedef int2 vec_t; };
template<> struct TypeVec<int, 3> { typedef int3 vec_t; };
template<> struct TypeVec<int3, 3> { typedef int3 vec_t; };
template<> struct TypeVec<int, 4> { typedef int4 vec_t; };
template<> struct TypeVec<int4, 4> { typedef int4 vec_t; };
template<> struct TypeVec<float, 2> { typedef float2 vec_t; };
template<> struct TypeVec<float2, 2> { typedef float2 vec_t; };
template<> struct TypeVec<float, 3> { typedef float3 vec_t; };
template<> struct TypeVec<float3, 3> { typedef float3 vec_t; };
template<> struct TypeVec<float, 4> { typedef float4 vec_t; };
template<> struct TypeVec<float4, 4> { typedef float4 vec_t; };
static __device__ uchar4 operator+(const uchar4& a, const uchar4& b)
{
return make_uchar4(a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w);
}
static __device__ uchar4 operator-(const uchar4& a, const uchar4& b)
{
return make_uchar4(a.x - b.x, a.y - b.y, a.z - b.z, a.w - b.w);
}
static __device__ uchar4 operator*(const uchar4& a, const uchar4& b)
{
return make_uchar4(a.x * b.x, a.y * b.y, a.z * b.z, a.w * b.w);
}
static __device__ uchar4 operator/(const uchar4& a, const uchar4& b)
{
return make_uchar4(a.x / b.x, a.y / b.y, a.z / b.z, a.w / b.w);
}
template <typename T>
static __device__ uchar4 operator*(const uchar4& a, T s)
{
return make_uchar4(a.x * s, a.y * s, a.z * s, a.w * s);
}
template <typename T>
static __device__ uchar4 operator*(T s, const uchar4& a)
{
return a * s;
}
}
}
#endif // __OPENCV_GPU_VECMATH_HPP__

16
modules/gpu/src/cudastream.cpp
View File

@ -69,6 +69,22 @@ void cv::gpu::Stream::enqueueConvert(const GpuMat& /*src*/, GpuMat& /*dst*/, int
#include "opencv2/gpu/stream_accessor.hpp"
namespace cv
{
namespace gpu
{
namespace matrix_operations
{
void copy_to_with_mask(const DevMem2D& src, DevMem2D dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
void set_to_without_mask (DevMem2D dst, int depth, const double *scalar, int channels, const cudaStream_t & stream = 0);
void set_to_with_mask (DevMem2D dst, int depth, const double *scalar, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
void convert_to(const DevMem2D& src, int sdepth, DevMem2D dst, int ddepth, int channels, double alpha, double beta, const cudaStream_t & stream = 0);
}
}
}
struct Stream::Impl
{
cudaStream_t stream;

245
modules/gpu/src/filtering_npp.cpp → modules/gpu/src/filtering.cpp
View File

@ -49,18 +49,18 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA)
Ptr<FilterEngine_GPU> cv::gpu::createFilter2D_GPU(const Ptr<BaseFilter_GPU>) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>&, const Ptr<BaseColumnFilter_GPU>&, bool) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>&, const Ptr<BaseColumnFilter_GPU>&) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseRowFilter_GPU> cv::gpu::getRowSumFilter_GPU(int, int, int, int) { throw_nogpu(); return Ptr<BaseRowFilter_GPU>(0); }
Ptr<BaseColumnFilter_GPU> cv::gpu::getColumnSumFilter_GPU(int, int, int, int) { throw_nogpu(); return Ptr<BaseColumnFilter_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getBoxFilter_GPU(int, int, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createBoxFilter_GPU(int, int, const Size&, const Point&) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getMorphologyFilter_GPU(int, int, const GpuMat&, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getMorphologyFilter_GPU(int, int, const Mat&, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createMorphologyFilter_GPU(int, int, const Mat&, const Point&, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int, int, const GpuMat&, const Size&, Point, int) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int, int, const Mat&, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int, int, const Mat&, const Point&) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int, int, const GpuMat&, int, int) { throw_nogpu(); return Ptr<BaseRowFilter_GPU>(0); }
Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int, int, const GpuMat&, int, int) { throw_nogpu(); return Ptr<BaseColumnFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableLinearFilter_GPU(int, int, const Mat&, const Mat&, const Point&, bool) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int, int, const Mat&, int) { throw_nogpu(); return Ptr<BaseRowFilter_GPU>(0); }
Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int, int, const Mat&, int) { throw_nogpu(); return Ptr<BaseColumnFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableLinearFilter_GPU(int, int, const Mat&, const Mat&, const Point&) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createDerivFilter_GPU(int, int, int, int, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createGaussianFilter_GPU(int, Size, double, double) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getMaxFilter_GPU(int, int, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
@ -71,7 +71,7 @@ void cv::gpu::erode( const GpuMat&, GpuMat&, const Mat&, Point, int) { throw_nog
void cv::gpu::dilate( const GpuMat&, GpuMat&, const Mat&, Point, int) { throw_nogpu(); }
void cv::gpu::morphologyEx( const GpuMat&, GpuMat&, int, const Mat&, Point, int) { throw_nogpu(); }
void cv::gpu::filter2D(const GpuMat&, GpuMat&, int, const Mat&, Point) { throw_nogpu(); }
void cv::gpu::sepFilter2D(const GpuMat&, GpuMat&, int, const Mat&, const Mat&, Point, bool) { throw_nogpu(); }
void cv::gpu::sepFilter2D(const GpuMat&, GpuMat&, int, const Mat&, const Mat&, Point) { throw_nogpu(); }
void cv::gpu::Sobel(const GpuMat&, GpuMat&, int, int, int, int, double) { throw_nogpu(); }
void cv::gpu::Scharr(const GpuMat&, GpuMat&, int, int, int, double) { throw_nogpu(); }
void cv::gpu::GaussianBlur(const GpuMat&, GpuMat&, Size, double, double) { throw_nogpu(); }
@ -164,28 +164,10 @@ Ptr<FilterEngine_GPU> cv::gpu::createFilter2D_GPU(const Ptr<BaseFilter_GPU> filt
namespace
{
struct RowColumnFilterApply
{
void operator()(Ptr<BaseRowFilter_GPU>& rowFilter, Ptr<BaseColumnFilter_GPU>& columnFilter,
GpuMat& srcROI, GpuMat& dstROI, GpuMat& dstBufROI)
{
(*rowFilter)(srcROI, dstBufROI);
(*columnFilter)(dstBufROI, dstROI);
}
};
struct ColumnRowFilterApply
{
void operator()(Ptr<BaseRowFilter_GPU>& rowFilter, Ptr<BaseColumnFilter_GPU>& columnFilter,
GpuMat& srcROI, GpuMat& dstROI, GpuMat& dstBufROI)
{
(*columnFilter)(srcROI, dstBufROI);
(*rowFilter)(dstBufROI, dstROI);
}
};
class SeparableFilterEngine_GPU_base : public FilterEngine_GPU
class SeparableFilterEngine_GPU : public FilterEngine_GPU
{
public:
SeparableFilterEngine_GPU_base(const Ptr<BaseRowFilter_GPU>& rowFilter_,
SeparableFilterEngine_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter_,
const Ptr<BaseColumnFilter_GPU>& columnFilter_) :
rowFilter(rowFilter_), columnFilter(columnFilter_)
{
@ -208,6 +190,9 @@ namespace
srcROI = src(roi);
dstROI = dst(roi);
dstBufROI = dstBuf(roi);
(*rowFilter)(srcROI, dstBufROI);
(*columnFilter)(dstBufROI, dstROI);
}
Ptr<BaseRowFilter_GPU> rowFilter;
@ -219,32 +204,12 @@ namespace
GpuMat dstROI;
GpuMat dstBufROI;
};
template <typename FA>
class SeparableFilterEngine_GPU : public SeparableFilterEngine_GPU_base
{
public:
SeparableFilterEngine_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter_,
const Ptr<BaseColumnFilter_GPU>& columnFilter_, FA fa_) :
SeparableFilterEngine_GPU_base(rowFilter_, columnFilter_), fa(fa_)
{
}
virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1))
{
SeparableFilterEngine_GPU_base::apply(src, dst, roi);
fa(rowFilter, columnFilter, srcROI, dstROI, dstBufROI);
}
FA fa;
};
}
Ptr<FilterEngine_GPU> cv::gpu::createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
const Ptr<BaseColumnFilter_GPU>& columnFilter, bool rowFilterFirst)
const Ptr<BaseColumnFilter_GPU>& columnFilter)
{
if (rowFilterFirst)
return Ptr<FilterEngine_GPU>(new SeparableFilterEngine_GPU<RowColumnFilterApply>(rowFilter, columnFilter, RowColumnFilterApply()));
return Ptr<FilterEngine_GPU>(new SeparableFilterEngine_GPU<ColumnRowFilterApply>(rowFilter, columnFilter, ColumnRowFilterApply()));
return Ptr<FilterEngine_GPU>(new SeparableFilterEngine_GPU(rowFilter, columnFilter));
}
////////////////////////////////////////////////////////////////////////////////////////////////////
@ -398,7 +363,7 @@ namespace
};
}
Ptr<BaseFilter_GPU> cv::gpu::getMorphologyFilter_GPU(int op, int type, const GpuMat& kernel, const Size& ksize, Point anchor)
Ptr<BaseFilter_GPU> cv::gpu::getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize, Point anchor)
{
static const nppMorfFilter_t nppMorfFilter_callers[2][5] =
{
@ -408,11 +373,12 @@ Ptr<BaseFilter_GPU> cv::gpu::getMorphologyFilter_GPU(int op, int type, const Gpu
CV_Assert(op == MORPH_ERODE || op == MORPH_DILATE);
CV_Assert(type == CV_8UC1 || type == CV_8UC4);
CV_Assert(kernel.type() == CV_8UC1 && kernel.rows == 1 && kernel.cols == ksize.area());
GpuMat gpu_krnl;
normalizeKernel(kernel, gpu_krnl);
normalizeAnchor(anchor, ksize);
return Ptr<BaseFilter_GPU>(new NPPMorphFilter(ksize, anchor, kernel, nppMorfFilter_callers[op][CV_MAT_CN(type)]));
return Ptr<BaseFilter_GPU>(new NPPMorphFilter(ksize, anchor, gpu_krnl, nppMorfFilter_callers[op][CV_MAT_CN(type)]));
}
namespace
@ -447,10 +413,7 @@ Ptr<FilterEngine_GPU> cv::gpu::createMorphologyFilter_GPU(int op, int type, cons
Size ksize = kernel.size();
GpuMat gpu_krnl;
normalizeKernel(kernel, gpu_krnl);
Ptr<BaseFilter_GPU> filter2D = getMorphologyFilter_GPU(op, type, gpu_krnl, ksize, anchor);
Ptr<BaseFilter_GPU> filter2D = getMorphologyFilter_GPU(op, type, kernel, ksize, anchor);
return Ptr<FilterEngine_GPU>(new MorphologyFilterEngine_GPU(filter2D, iterations));
}
@ -575,27 +538,25 @@ namespace
};
}
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int srcType, int dstType, const GpuMat& kernel, const Size& ksize, Point anchor, int nDivisor)
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize, Point anchor)
{
static const nppFilter2D_t cppFilter2D_callers[] = {0, nppiFilter_8u_C1R, 0, 0, nppiFilter_8u_C4R};
CV_Assert((srcType == CV_8UC1 || srcType == CV_8UC4) && dstType == srcType);
CV_Assert(kernel.type() == CV_32SC1 && kernel.rows == 1 && kernel.cols == ksize.area());
CV_Assert((srcType == CV_8UC1 || srcType == CV_8UC4) && dstType == srcType);
GpuMat gpu_krnl;
int nDivisor;
normalizeKernel(kernel, gpu_krnl, CV_32S, &nDivisor, true);
normalizeAnchor(anchor, ksize);
return Ptr<BaseFilter_GPU>(new NPPLinearFilter(ksize, anchor, kernel, nDivisor, cppFilter2D_callers[CV_MAT_CN(srcType)]));
return Ptr<BaseFilter_GPU>(new NPPLinearFilter(ksize, anchor, gpu_krnl, nDivisor, cppFilter2D_callers[CV_MAT_CN(srcType)]));
}
Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Point& anchor)
{
Size ksize = kernel.size();
GpuMat gpu_krnl;
int nDivisor;
normalizeKernel(kernel, gpu_krnl, CV_32S, &nDivisor, true);
Ptr<BaseFilter_GPU> linearFilter = getLinearFilter_GPU(srcType, dstType, gpu_krnl, ksize, anchor, nDivisor);
Ptr<BaseFilter_GPU> linearFilter = getLinearFilter_GPU(srcType, dstType, kernel, ksize, anchor);
return createFilter2D_GPU(linearFilter);
}
@ -614,11 +575,26 @@ void cv::gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& ke
////////////////////////////////////////////////////////////////////////////////////////////////////
// Separable Linear Filter
namespace cv { namespace gpu { namespace filters
{
void linearRowFilter_gpu_32s32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearRowFilter_gpu_32s32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearRowFilter_gpu_32f32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearRowFilter_gpu_32f32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearColumnFilter_gpu_32s32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearColumnFilter_gpu_32s32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearColumnFilter_gpu_32f32s(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
void linearColumnFilter_gpu_32f32f(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
}}}
namespace
{
typedef NppStatus (*nppFilter1D_t)(const Npp8u * pSrc, Npp32s nSrcStep, Npp8u * pDst, Npp32s nDstStep, NppiSize oROI,
const Npp32s * pKernel, Npp32s nMaskSize, Npp32s nAnchor, Npp32s nDivisor);
typedef void (*gpuFilter1D_t)(const DevMem2D& src, const DevMem2D& dst, const float kernel[], int ksize, int anchor);
class NppLinearRowFilter : public BaseRowFilter_GPU
{
public:
@ -638,20 +614,64 @@ namespace
Npp32s nDivisor;
nppFilter1D_t func;
};
class GpuLinearRowFilter : public BaseRowFilter_GPU
{
public:
GpuLinearRowFilter(int ksize_, int anchor_, const Mat& kernel_, gpuFilter1D_t func_) :
BaseRowFilter_GPU(ksize_, anchor_), kernel(kernel_), func(func_) {}
virtual void operator()(const GpuMat& src, GpuMat& dst)
{
func(src, dst, kernel.ptr<float>(), ksize, anchor);
}
Mat kernel;
gpuFilter1D_t func;
};
}
Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int srcType, int bufType, const GpuMat& rowKernel, int anchor, int nDivisor)
Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel, int anchor)
{
using namespace cv::gpu::filters;
static const nppFilter1D_t nppFilter1D_callers[] = {0, nppiFilterRow_8u_C1R, 0, 0, nppiFilterRow_8u_C4R};
static const gpuFilter1D_t gpuFilter1D_callers[6][6] =
{
{0,0,0,0,0,0},
{0,0,0,0,0,0},
{0,0,0,0,0,0},
{0,0,0,0,0,0},
{0,0,0,0,linearRowFilter_gpu_32s32s, linearRowFilter_gpu_32s32f},
{0,0,0,0,linearRowFilter_gpu_32f32s, linearRowFilter_gpu_32f32f}
};
if ((srcType == CV_8UC1 || srcType == CV_8UC4) && bufType == srcType)
{
GpuMat gpu_row_krnl;
int nDivisor;
normalizeKernel(rowKernel, gpu_row_krnl, CV_32S, &nDivisor, true);
CV_Assert((srcType == CV_8UC1 || srcType == CV_8UC4) && bufType == srcType);
CV_Assert(rowKernel.type() == CV_32SC1 && rowKernel.rows == 1);
int ksize = gpu_row_krnl.cols;
normalizeAnchor(anchor, ksize);
int ksize = rowKernel.cols;
return Ptr<BaseRowFilter_GPU>(new NppLinearRowFilter(ksize, anchor, gpu_row_krnl, nDivisor,
nppFilter1D_callers[CV_MAT_CN(srcType)]));
}
else if ((srcType == CV_32SC1 || srcType == CV_32FC1) && (bufType == CV_32SC1 || bufType == CV_32FC1))
{
Mat temp(rowKernel.size(), CV_32FC1);
rowKernel.convertTo(temp, CV_32FC1);
Mat cont_krnl = temp.reshape(1, 1);
normalizeAnchor(anchor, ksize);
int ksize = cont_krnl.cols;
normalizeAnchor(anchor, ksize);
return Ptr<BaseRowFilter_GPU>(new NppLinearRowFilter(ksize, anchor, rowKernel, nDivisor, nppFilter1D_callers[CV_MAT_CN(srcType)]));
return Ptr<BaseRowFilter_GPU>(new GpuLinearRowFilter(ksize, anchor, cont_krnl,
gpuFilter1D_callers[CV_MAT_DEPTH(srcType)][CV_MAT_DEPTH(bufType)]));
}
CV_Assert(!"Unsupported types");
return Ptr<BaseRowFilter_GPU>(0);
}
namespace
@ -675,49 +695,88 @@ namespace
Npp32s nDivisor;
nppFilter1D_t func;
};
class GpuLinearColumnFilter : public BaseColumnFilter_GPU
{
public:
GpuLinearColumnFilter(int ksize_, int anchor_, const Mat& kernel_, gpuFilter1D_t func_) :
BaseColumnFilter_GPU(ksize_, anchor_), kernel(kernel_), func(func_) {}
virtual void operator()(const GpuMat& src, GpuMat& dst)
{
func(src, dst, kernel.ptr<float>(), ksize, anchor);
}
Mat kernel;
gpuFilter1D_t func;
};
}
Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int bufType, int dstType, const GpuMat& columnKernel, int anchor, int nDivisor)
Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel, int anchor)
{
using namespace cv::gpu::filters;
static const nppFilter1D_t nppFilter1D_callers[] = {0, nppiFilterColumn_8u_C1R, 0, 0, nppiFilterColumn_8u_C4R};
static const gpuFilter1D_t gpuFilter1D_callers[6][6] =
{
{0,0,0,0,0,0},
{0,0,0,0,0,0},
{0,0,0,0,0,0},
{0,0,0,0,0,0},
{0,0,0,0,linearColumnFilter_gpu_32s32s, linearColumnFilter_gpu_32s32f},
{0,0,0,0,linearColumnFilter_gpu_32f32s, linearColumnFilter_gpu_32f32f}
};
if ((bufType == CV_8UC1 || bufType == CV_8UC4) && dstType == bufType)
{
GpuMat gpu_col_krnl;
int nDivisor;
normalizeKernel(columnKernel, gpu_col_krnl, CV_32S, &nDivisor, true);
CV_Assert((bufType == CV_8UC1 || bufType == CV_8UC4) && dstType == bufType);
CV_Assert(columnKernel.type() == CV_32SC1 && columnKernel.rows == 1);
int ksize = gpu_col_krnl.cols;
normalizeAnchor(anchor, ksize);
int ksize = columnKernel.cols;
return Ptr<BaseColumnFilter_GPU>(new NppLinearColumnFilter(ksize, anchor, gpu_col_krnl, nDivisor,
nppFilter1D_callers[CV_MAT_CN(bufType)]));
}
else if ((bufType == CV_32SC1 || bufType == CV_32FC1) && (dstType == CV_32SC1 || dstType == CV_32FC1))
{
Mat temp(columnKernel.size(), CV_32FC1);
columnKernel.convertTo(temp, CV_32FC1);
Mat cont_krnl = temp.reshape(1, 1);
normalizeAnchor(anchor, ksize);
int ksize = cont_krnl.cols;
normalizeAnchor(anchor, ksize);
return Ptr<BaseColumnFilter_GPU>(new NppLinearColumnFilter(ksize, anchor, columnKernel, nDivisor, nppFilter1D_callers[CV_MAT_CN(bufType)]));
return Ptr<BaseColumnFilter_GPU>(new GpuLinearColumnFilter(ksize, anchor, cont_krnl,
gpuFilter1D_callers[CV_MAT_DEPTH(bufType)][CV_MAT_DEPTH(dstType)]));
}
CV_Assert(!"Unsupported types");
return Ptr<BaseColumnFilter_GPU>(0);
}
Ptr<FilterEngine_GPU> cv::gpu::createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel, const Mat& columnKernel,
const Point& anchor, bool rowFilterFirst)
const Point& anchor)
{
int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(dstType);
int cn = CV_MAT_CN(srcType);
int bdepth = std::max(sdepth, ddepth);
int bufType = CV_MAKETYPE(bdepth, cn);
GpuMat gpu_row_krnl, gpu_col_krnl;
int nRowDivisor, nColDivisor;
normalizeKernel(rowKernel, gpu_row_krnl, CV_32S, &nRowDivisor, true);
normalizeKernel(columnKernel, gpu_col_krnl, CV_32S, &nColDivisor, true);
Ptr<BaseRowFilter_GPU> rowFilter = getLinearRowFilter_GPU(srcType, bufType, rowKernel, anchor.x);
Ptr<BaseColumnFilter_GPU> columnFilter = getLinearColumnFilter_GPU(bufType, dstType, columnKernel, anchor.y);
Ptr<BaseRowFilter_GPU> rowFilter = getLinearRowFilter_GPU(srcType, bufType, gpu_row_krnl, anchor.x, nRowDivisor);
Ptr<BaseColumnFilter_GPU> columnFilter = getLinearColumnFilter_GPU(bufType, dstType, gpu_col_krnl, anchor.y, nColDivisor);
return createSeparableFilter_GPU(rowFilter, columnFilter, rowFilterFirst);
return createSeparableFilter_GPU(rowFilter, columnFilter);
}
void cv::gpu::sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, Point anchor, bool rowFilterFirst)
void cv::gpu::sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, Point anchor)
{
if( ddepth < 0 )
ddepth = src.depth();
dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));
Ptr<FilterEngine_GPU> f = createSeparableLinearFilter_GPU(src.type(), dst.type(), kernelX, kernelY, anchor, rowFilterFirst);
Ptr<FilterEngine_GPU> f = createSeparableLinearFilter_GPU(src.type(), dst.type(), kernelX, kernelY, anchor);
f->apply(src, dst);
}
@ -728,7 +787,7 @@ Ptr<FilterEngine_GPU> cv::gpu::createDerivFilter_GPU(int srcType, int dstType, i
{
Mat kx, ky;
getDerivKernels(kx, ky, dx, dy, ksize, false, CV_32F);
return createSeparableLinearFilter_GPU(srcType, dstType, kx, ky, Point(-1,-1), dx >= dy);
return createSeparableLinearFilter_GPU(srcType, dstType, kx, ky, Point(-1,-1));
}
void cv::gpu::Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize, double scale)
@ -746,7 +805,7 @@ void cv::gpu::Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy,
ky *= scale;
}
sepFilter2D(src, dst, ddepth, kx, ky, Point(-1,-1), dx >= dy);
sepFilter2D(src, dst, ddepth, kx, ky, Point(-1,-1));
}
void cv::gpu::Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale)
@ -764,7 +823,7 @@ void cv::gpu::Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy,
ky *= scale;
}
sepFilter2D(src, dst, ddepth, kx, ky, Point(-1,-1), dx >= dy);
sepFilter2D(src, dst, ddepth, kx, ky, Point(-1,-1));
}
void cv::gpu::Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize, double scale)

42
modules/gpu/src/imgproc_gpu.cpp
View File

@ -75,7 +75,7 @@ void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*) { throw_nogpu();
namespace cv { namespace gpu
{
namespace improc
namespace imgproc
{
void remap_gpu_1c(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst);
void remap_gpu_3c(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst);
@ -142,7 +142,7 @@ namespace cv { namespace gpu
void cv::gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap)
{
typedef void (*remap_gpu_t)(const DevMem2D& src, const DevMem2Df& xmap, const DevMem2Df& ymap, DevMem2D dst);
static const remap_gpu_t callers[] = {improc::remap_gpu_1c, 0, improc::remap_gpu_3c};
static const remap_gpu_t callers[] = {imgproc::remap_gpu_1c, 0, imgproc::remap_gpu_3c};
CV_Assert((src.type() == CV_8U || src.type() == CV_8UC3) && xmap.type() == CV_32F && ymap.type() == CV_32F);
@ -180,7 +180,7 @@ void cv::gpu::meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
eps = 1.f;
eps = (float)std::max(criteria.epsilon, 0.0);
improc::meanShiftFiltering_gpu(src, dst, sp, sr, maxIter, eps);
imgproc::meanShiftFiltering_gpu(src, dst, sp, sr, maxIter, eps);
}
////////////////////////////////////////////////////////////////////////
@ -207,7 +207,7 @@ void cv::gpu::meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int
eps = 1.f;
eps = (float)std::max(criteria.epsilon, 0.0);
improc::meanShiftProc_gpu(src, dstr, dstsp, sp, sr, maxIter, eps);
imgproc::meanShiftProc_gpu(src, dstr, dstsp, sp, sr, maxIter, eps);
}
////////////////////////////////////////////////////////////////////////
@ -223,7 +223,7 @@ namespace
out = dst;
out.create(src.size(), CV_8UC4);
improc::drawColorDisp_gpu((DevMem2D_<T>)src, out, ndisp, stream);
imgproc::drawColorDisp_gpu((DevMem2D_<T>)src, out, ndisp, stream);
dst = out;
}
@ -256,7 +256,7 @@ namespace
void reprojectImageTo3D_caller(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const cudaStream_t& stream)
{
xyzw.create(disp.rows, disp.cols, CV_32FC4);
improc::reprojectImageTo3D_gpu((DevMem2D_<T>)disp, xyzw, Q.ptr<float>(), stream);
imgproc::reprojectImageTo3D_gpu((DevMem2D_<T>)disp, xyzw, Q.ptr<float>(), stream);
}
typedef void (*reprojectImageTo3D_caller_t)(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, const cudaStream_t& stream);
@ -313,7 +313,7 @@ namespace
case CV_RGBA2BGR: case CV_RGB2BGR: case CV_BGRA2RGBA:
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn, int bidx, cudaStream_t stream);
static const func_t funcs[] = {improc::RGB2RGB_gpu_8u, 0, improc::RGB2RGB_gpu_16u, 0, 0, improc::RGB2RGB_gpu_32f};
static const func_t funcs[] = {imgproc::RGB2RGB_gpu_8u, 0, imgproc::RGB2RGB_gpu_16u, 0, 0, imgproc::RGB2RGB_gpu_32f};
CV_Assert(scn == 3 || scn == 4);
@ -338,7 +338,7 @@ namespace
dst.create(sz, CV_8UC2);
improc::RGB2RGB5x5_gpu(src, scn, dst, green_bits, bidx, stream);
imgproc::RGB2RGB5x5_gpu(src, scn, dst, green_bits, bidx, stream);
break;
}
@ -356,14 +356,14 @@ namespace
dst.create(sz, CV_MAKETYPE(depth, dcn));
improc::RGB5x52RGB_gpu(src, green_bits, dst, dcn, bidx, stream);
imgproc::RGB5x52RGB_gpu(src, green_bits, dst, dcn, bidx, stream);
break;
}
case CV_BGR2GRAY: case CV_BGRA2GRAY: case CV_RGB2GRAY: case CV_RGBA2GRAY:
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int bidx, cudaStream_t stream);
static const func_t funcs[] = {improc::RGB2Gray_gpu_8u, 0, improc::RGB2Gray_gpu_16u, 0, 0, improc::RGB2Gray_gpu_32f};
static const func_t funcs[] = {imgproc::RGB2Gray_gpu_8u, 0, imgproc::RGB2Gray_gpu_16u, 0, 0, imgproc::RGB2Gray_gpu_32f};
CV_Assert(scn == 3 || scn == 4);
@ -383,14 +383,14 @@ namespace
dst.create(sz, CV_8UC1);
improc::RGB5x52Gray_gpu(src, green_bits, dst, stream);
imgproc::RGB5x52Gray_gpu(src, green_bits, dst, stream);
break;
}
case CV_GRAY2BGR: case CV_GRAY2BGRA:
{
typedef void (*func_t)(const DevMem2D& src, const DevMem2D& dst, int dstcn, cudaStream_t stream);
static const func_t funcs[] = {improc::Gray2RGB_gpu_8u, 0, improc::Gray2RGB_gpu_16u, 0, 0, improc::Gray2RGB_gpu_32f};
static const func_t funcs[] = {imgproc::Gray2RGB_gpu_8u, 0, imgproc::Gray2RGB_gpu_16u, 0, 0, imgproc::Gray2RGB_gpu_32f};
if (dcn <= 0) dcn = 3;
@ -410,7 +410,7 @@ namespace
dst.create(sz, CV_8UC2);
improc::Gray2RGB5x5_gpu(src, dst, green_bits, stream);
imgproc::Gray2RGB5x5_gpu(src, dst, green_bits, stream);
break;
}
@ -419,7 +419,7 @@ namespace
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn, int bidx,
const void* coeffs, cudaStream_t stream);
static const func_t funcs[] = {improc::RGB2YCrCb_gpu_8u, 0, improc::RGB2YCrCb_gpu_16u, 0, 0, improc::RGB2YCrCb_gpu_32f};
static const func_t funcs[] = {imgproc::RGB2YCrCb_gpu_8u, 0, imgproc::RGB2YCrCb_gpu_16u, 0, 0, imgproc::RGB2YCrCb_gpu_32f};
if (dcn <= 0) dcn = 3;
CV_Assert((scn == 3 || scn == 4) && (dcn == 3 || dcn == 4));
@ -456,7 +456,7 @@ namespace
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn, int bidx,
const void* coeffs, cudaStream_t stream);
static const func_t funcs[] = {improc::YCrCb2RGB_gpu_8u, 0, improc::YCrCb2RGB_gpu_16u, 0, 0, improc::YCrCb2RGB_gpu_32f};
static const func_t funcs[] = {imgproc::YCrCb2RGB_gpu_8u, 0, imgproc::YCrCb2RGB_gpu_16u, 0, 0, imgproc::YCrCb2RGB_gpu_32f};
if (dcn <= 0) dcn = 3;
@ -485,7 +485,7 @@ namespace
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn,
const void* coeffs, cudaStream_t stream);
static const func_t funcs[] = {improc::RGB2XYZ_gpu_8u, 0, improc::RGB2XYZ_gpu_16u, 0, 0, improc::RGB2XYZ_gpu_32f};
static const func_t funcs[] = {imgproc::RGB2XYZ_gpu_8u, 0, imgproc::RGB2XYZ_gpu_16u, 0, 0, imgproc::RGB2XYZ_gpu_32f};
if (dcn <= 0) dcn = 3;
@ -534,7 +534,7 @@ namespace
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn,
const void* coeffs, cudaStream_t stream);
static const func_t funcs[] = {improc::XYZ2RGB_gpu_8u, 0, improc::XYZ2RGB_gpu_16u, 0, 0, improc::XYZ2RGB_gpu_32f};
static const func_t funcs[] = {imgproc::XYZ2RGB_gpu_8u, 0, imgproc::XYZ2RGB_gpu_16u, 0, 0, imgproc::XYZ2RGB_gpu_32f};
if (dcn <= 0) dcn = 3;
@ -584,8 +584,8 @@ namespace
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn, int bidx,
int hrange, cudaStream_t stream);
static const func_t funcs_hsv[] = {improc::RGB2HSV_gpu_8u, 0, 0, 0, 0, improc::RGB2HSV_gpu_32f};
static const func_t funcs_hls[] = {improc::RGB2HLS_gpu_8u, 0, 0, 0, 0, improc::RGB2HLS_gpu_32f};
static const func_t funcs_hsv[] = {imgproc::RGB2HSV_gpu_8u, 0, 0, 0, 0, imgproc::RGB2HSV_gpu_32f};
static const func_t funcs_hls[] = {imgproc::RGB2HLS_gpu_8u, 0, 0, 0, 0, imgproc::RGB2HLS_gpu_32f};
if (dcn <= 0) dcn = 3;
@ -610,8 +610,8 @@ namespace
{
typedef void (*func_t)(const DevMem2D& src, int srccn, const DevMem2D& dst, int dstcn, int bidx,
int hrange, cudaStream_t stream);
static const func_t funcs_hsv[] = {improc::HSV2RGB_gpu_8u, 0, 0, 0, 0, improc::HSV2RGB_gpu_32f};
static const func_t funcs_hls[] = {improc::HLS2RGB_gpu_8u, 0, 0, 0, 0, improc::HLS2RGB_gpu_32f};
static const func_t funcs_hsv[] = {imgproc::HSV2RGB_gpu_8u, 0, 0, 0, 0, imgproc::HSV2RGB_gpu_32f};
static const func_t funcs_hls[] = {imgproc::HLS2RGB_gpu_8u, 0, 0, 0, 0, imgproc::HLS2RGB_gpu_32f};
if (dcn <= 0) dcn = 3;

16
modules/gpu/src/matrix_operations.cpp
View File

@ -77,6 +77,22 @@ namespace cv
#else /* !defined (HAVE_CUDA) */
namespace cv
{
namespace gpu
{
namespace matrix_operations
{
void copy_to_with_mask(const DevMem2D& src, DevMem2D dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
void set_to_without_mask (DevMem2D dst, int depth, const double *scalar, int channels, const cudaStream_t & stream = 0);
void set_to_with_mask (DevMem2D dst, int depth, const double *scalar, const DevMem2D& mask, int channels, const cudaStream_t & stream = 0);
void convert_to(const DevMem2D& src, int sdepth, DevMem2D dst, int ddepth, int channels, double alpha, double beta, const cudaStream_t & stream = 0);
}
}
}
void cv::gpu::GpuMat::upload(const Mat& m)
{
CV_DbgAssert(!m.empty());

5
tests/gpu/src/gputest_main.cpp
View File

@ -53,7 +53,6 @@ const char* blacklist[] =
//"GPU-NppImageMeanStdDev", // different precision
//"GPU-NppImageExp", // different precision
//"GPU-NppImageLog", // different precision
//"GPU-NppImageMagnitude", // different precision
"GPU-NppImageCanny", // NPP_TEXTURE_BIND_ERROR
//"GPU-NppImageResize", // different precision
@ -61,8 +60,8 @@ const char* blacklist[] =
//"GPU-NppImageWarpPerspective", // different precision
//"GPU-NppImageIntegral", // different precision
//"GPU-NppImageSobel", // ???
//"GPU-NppImageScharr", // ???
//"GPU-NppImageSobel", // sign error
//"GPU-NppImageScharr", // sign error
//"GPU-NppImageGaussianBlur", // different precision
0
};