--- /dev/null
+/*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) 2014, Intel Corporation, 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*/
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+/////////////////////////////////Macro for border type////////////////////////////////////////////
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+#ifdef BORDER_CONSTANT
+// CCCCCC|abcdefgh|CCCCCCC
+#define EXTRAPOLATE(x, maxV)
+#elif defined BORDER_REPLICATE
+// aaaaaa|abcdefgh|hhhhhhh
+#define EXTRAPOLATE(x, maxV) \
+ { \
+ (x) = max(min((x), (maxV) - 1), 0); \
+ }
+#elif defined BORDER_WRAP
+// cdefgh|abcdefgh|abcdefg
+#define EXTRAPOLATE(x, maxV) \
+ { \
+ (x) = ( (x) + (maxV) ) % (maxV); \
+ }
+#elif defined BORDER_REFLECT
+// fedcba|abcdefgh|hgfedcb
+#define EXTRAPOLATE(x, maxV) \
+ { \
+ (x) = min(((maxV)-1)*2-(x)+1, max((x),-(x)-1) ); \
+ }
+#elif defined BORDER_REFLECT_101 || defined BORDER_REFLECT101
+// gfedcb|abcdefgh|gfedcba
+#define EXTRAPOLATE(x, maxV) \
+ { \
+ (x) = min(((maxV)-1)*2-(x), max((x),-(x)) ); \
+ }
+#else
+#error No extrapolation method
+#endif
+
+#if CN != 3
+#define loadpix(addr) *(__global const srcT *)(addr)
+#define storepix(val, addr) *(__global dstT *)(addr) = val
+#define SRCSIZE (int)sizeof(srcT)
+#define DSTSIZE (int)sizeof(dstT)
+#else
+#define loadpix(addr) vload3(0, (__global const srcT1 *)(addr))
+#define storepix(val, addr) vstore3(val, 0, (__global dstT1 *)(addr))
+#define SRCSIZE (int)sizeof(srcT1)*3
+#define DSTSIZE (int)sizeof(dstT1)*3
+#endif
+
+#define SRC(_x,_y) convertToWT(loadpix(Src + mad24(_y, src_step, SRCSIZE * _x)))
+
+#ifdef BORDER_CONSTANT
+// CCCCCC|abcdefgh|CCCCCCC
+#define ELEM(_x,_y,r_edge,t_edge,const_v) (_x)<0 | (_x) >= (r_edge) | (_y)<0 | (_y) >= (t_edge) ? (const_v) : SRC((_x),(_y))
+#else
+#define ELEM(_x,_y,r_edge,t_edge,const_v) SRC((_x),(_y))
+#endif
+
+#define noconvert
+
+// horizontal and vertical filter kernels
+// should be defined on host during compile time to avoid overhead
+#define DIG(a) a,
+__constant int mat_kernelX[] = { KERNEL_MATRIX_X };
+__constant int mat_kernelY[] = { KERNEL_MATRIX_Y };
+
+__kernel void gaussian_blur_8u(__global uchar* Src, int src_step, int srcOffsetX, int srcOffsetY, int height, int width,
+ __global uchar* Dst, int dst_step, int dst_offset, int dst_rows, int dst_cols)
+{
+ // RADIUSX, RADIUSY are filter dimensions
+ // BLK_X, BLK_Y are local wrogroup sizes
+ // all these should be defined on host during compile time
+ // first lsmem array for source pixels used in first pass,
+ // second lsmemDy for storing first pass results
+ __local WT lsmem[BLK_Y + 2 * RADIUSY][BLK_X + 2 * RADIUSX];
+ __local WT lsmemDy[BLK_Y][BLK_X + 2 * RADIUSX];
+
+ // get local and global ids - used as image and local memory array indexes
+ int lix = get_local_id(0);
+ int liy = get_local_id(1);
+
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+
+ // calculate pixel position in source image taking image offset into account
+ int srcX = x + srcOffsetX - RADIUSX;
+ int srcY = y + srcOffsetY - RADIUSY;
+ int xb = srcX;
+ int yb = srcY;
+
+ // extrapolate coordinates, if needed
+ // and read my own source pixel into local memory
+ // with account for extra border pixels, which will be read by starting workitems
+ int clocY = liy;
+ int cSrcY = srcY;
+ do
+ {
+ int yb = cSrcY;
+ EXTRAPOLATE(yb, (height));
+
+ int clocX = lix;
+ int cSrcX = srcX;
+ do
+ {
+ int xb = cSrcX;
+ EXTRAPOLATE(xb,(width));
+ lsmem[clocY][clocX] = ELEM(xb, yb, (width), (height), 0 );
+
+ clocX += BLK_X;
+ cSrcX += BLK_X;
+ }
+ while(clocX < BLK_X+(RADIUSX*2));
+
+ clocY += BLK_Y;
+ cSrcY += BLK_Y;
+ }
+ while (clocY < BLK_Y+(RADIUSY*2));
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // do vertical filter pass
+ // and store intermediate results to second local memory array
+ int i, clocX = lix;
+ WT sum = 0;
+ do
+ {
+ sum = 0;
+ for (i=0; i<=2*RADIUSY; i++)
+ sum = mad(lsmem[liy+i][clocX], mat_kernelY[i], sum);
+ lsmemDy[liy][clocX] = sum;
+ clocX += BLK_X;
+ }
+ while(clocX < BLK_X+(RADIUSX*2));
+ barrier(CLK_LOCAL_MEM_FENCE);
+
+ // if this pixel happened to be out of image borders because of global size rounding,
+ // then just return
+ if( x >= dst_cols || y >=dst_rows )
+ return;
+
+ // do second horizontal filter pass
+ // and calculate final result
+ sum = 0;
+ for (i=0; i<=2*RADIUSX; i++)
+ sum = mad(lsmemDy[liy][lix+i], mat_kernelX[i], sum);
+
+ sum = sum >> (GAUSSIAN_COEF_BITS * 2);
+
+ //store result into destination image
+ storepix(convertToDstT(sum), Dst + mad24(y, dst_step, mad24(x, DSTSIZE, dst_offset)));
+}
#include "precomp.hpp"
#include "opencl_kernels.hpp"
+#include <iostream>
/*
* This file includes the code, contributed by Simon Perreault
ky = getGaussianKernel( ksize.height, sigma2, std::max(depth, CV_32F) );
}
+#define GAUSSIAN_COEF_BITS 11
+
+static bool GaussianBlur_8u(InputArray _src, OutputArray _dst, Size ksize,
+ double sigma1, double sigma2,
+ int borderType)
+{
+ int type = _src.type();
+ Mat kx, ky;
+ createGaussianKernels(kx, ky, CV_64F, ksize, sigma1, sigma2);
+ Mat kx_8u, ky_8u;
+
+ int scale_coef = 1 << GAUSSIAN_COEF_BITS;
+ kx.convertTo(kx_8u, CV_32S, scale_coef);
+ ky.convertTo(ky_8u, CV_32S, scale_coef);
+
+ kx_8u.reshape(1, 1);
+ ky_8u.reshape(1, 1);
+
+ Size size = _src.size(), wholeSize;
+ Point origin;
+ int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype),
+ esz = CV_ELEM_SIZE(stype), wdepth = CV_32S,
+ ddepth = sdepth;
+ size_t src_step = _src.step(), src_offset = _src.offset();
+
+ if ((src_offset % src_step) % esz != 0 || !(borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE ||
+ borderType == BORDER_REFLECT || borderType == BORDER_WRAP ||
+ borderType == BORDER_REFLECT_101))
+ return false;
+
+ size_t lt2[2] = { 16, 16 };
+ size_t gt2[2] = { lt2[0] * (1 + (size.width - 1) / lt2[0]), lt2[1] * (1 + (size.height - 1) / lt2[1]) };
+
+ char cvt[2][40];
+ const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP",
+ "BORDER_REFLECT_101" };
+
+ String opts = cv::format("-D BLK_X=%d -D BLK_Y=%d -D RADIUSX=%d -D RADIUSY=%d%s%s"
+ " -D srcT=%s -D convertToWT=%s -D WT=%s -D dstT=%s -D convertToDstT=%s"
+ " -D %s -D srcT1=%s -D dstT1=%s -D CN=%d -D GAUSSIAN_COEF_BITS=%d", (int)lt2[0], (int)lt2[1],
+ kx.rows / 2, kx.rows / 2,
+ ocl::kernelToStr(kx_8u, CV_32S, "KERNEL_MATRIX_X").c_str(),
+ ocl::kernelToStr(ky_8u, CV_32S, "KERNEL_MATRIX_Y").c_str(),
+ ocl::typeToStr(stype), ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0]),
+ ocl::typeToStr(CV_MAKE_TYPE(wdepth, cn)), ocl::typeToStr(stype),
+ ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1]), borderMap[borderType],
+ ocl::typeToStr(sdepth), ocl::typeToStr(ddepth), cn, GAUSSIAN_COEF_BITS);
+
+ ocl::Kernel k("gaussian_blur_8u", ocl::imgproc::gaussian_blur_8u_oclsrc, opts);
+ if (k.empty())
+ return false;
+
+ UMat src = _src.getUMat();
+ _dst.create(size, stype);
+ UMat dst = _dst.getUMat();
+
+ int src_offset_x = static_cast<int>((src_offset % src_step) / esz);
+ int src_offset_y = static_cast<int>(src_offset / src_step);
+
+ src.locateROI(wholeSize, origin);
+
+ k.args(ocl::KernelArg::PtrReadOnly(src), (int)src_step, src_offset_x, src_offset_y,
+ wholeSize.height, wholeSize.width, ocl::KernelArg::WriteOnly(dst));
+
+ return k.run(2, gt2, lt2, false);
+}
+
}
cv::Ptr<cv::FilterEngine> cv::createGaussianFilter( int type, Size ksize,
}
+
+
void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2,
int borderType )
}
#endif
+ if (type == CV_8U)
+ {
+ CV_OCL_RUN_(_dst.isUMat() && _src.dims() <= 2 &&
+ (!(borderType & BORDER_ISOLATED) || _src.offset() == 0),
+ GaussianBlur_8u(_src, _dst, ksize, sigma1, sigma2, borderType))
+ }
+
Mat kx, ky;
createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2);
sepFilter2D(_src, _dst, CV_MAT_DEPTH(type), kx, ky, Point(-1,-1), 0, borderType );