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Added ocl_medianFilter using Transparent API

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Elena Gvozdeva 11 years ago
parent 172a5d42a9
commit b2d8b491c0
3 changed files with 326 additions and 6 deletions
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  1. 167
      modules/imgproc/src/opencl/medianFilter.cl
  2. 54
      modules/imgproc/src/smooth.cpp
  3. 111
      modules/imgproc/test/ocl/test_medianfilter.cpp

167
modules/imgproc/src/opencl/medianFilter.cl
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@ -0,0 +1,167 @@
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, 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.
#define DATA_TYPE type
#define scnbytes ((int)sizeof(type))
#define op(a,b) { mid=a; a=min(a,b); b=max(mid,b);}
__kernel void medianFilter3(__global const uchar* srcptr, int srcStep, int srcOffset,
__global uchar* dstptr, int dstStep, int dstOffset,
int rows, int cols)
{
__local DATA_TYPE data[18][18];
int x = get_local_id(0);
int y = get_local_id(1);
int gx= get_global_id(0);
int gy= get_global_id(1);
int dx = gx - x - 1;
int dy = gy - y - 1;
const int id = min((int)(x*16+y), 9*18-1);
int dr = id / 18;
int dc = id % 18;
int c = clamp(dx+dc, 0, cols-1);
int r = clamp(dy+dr, 0, rows-1);
int index1 = mad24(r, srcStep, srcOffset + c*scnbytes);
r = clamp(dy+dr+9, 0, rows-1);
int index9 = mad24(r, srcStep, srcOffset + c*scnbytes);
__global DATA_TYPE * src = (__global DATA_TYPE *)(srcptr + index1);
data[dr][dc] = src[0];
src = (__global DATA_TYPE *)(srcptr + index9);
data[dr+9][dc] = src[0];
barrier(CLK_LOCAL_MEM_FENCE);
DATA_TYPE p0=data[y][x], p1=data[y][(x+1)], p2=data[y][(x+2)];
DATA_TYPE p3=data[y+1][x], p4=data[y+1][(x+1)], p5=data[y+1][(x+2)];
DATA_TYPE p6=data[y+2][x], p7=data[y+2][(x+1)], p8=data[y+2][(x+2)];
DATA_TYPE mid;
op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p1);
op(p3, p4); op(p6, p7); op(p1, p2); op(p4, p5);
op(p7, p8); op(p0, p3); op(p5, p8); op(p4, p7);
op(p3, p6); op(p1, p4); op(p2, p5); op(p4, p7);
op(p4, p2); op(p6, p4); op(p4, p2);
int dst_index = mad24( gy, dstStep, dstOffset + gx * scnbytes);
if( gy < rows && gx < cols)
{
__global DATA_TYPE* dst = (__global DATA_TYPE *)(dstptr + dst_index);
dst[0] = p4;
}
}
__kernel void medianFilter5(__global const uchar* srcptr, int srcStep, int srcOffset,
__global uchar* dstptr, int dstStep, int dstOffset,
int rows, int cols)
{
__local DATA_TYPE data[20][20];
int x =get_local_id(0);
int y =get_local_id(1);
int gx=get_global_id(0);
int gy=get_global_id(1);
int dx = gx - x - 2;
int dy = gy - y - 2;
const int id = min((int)(x*16+y), 10*20-1);
int dr=id/20;
int dc=id%20;
int c=clamp(dx+dc, 0, cols-1);
int r = clamp(dy+dr, 0, rows-1);
int index1 = mad24(r, srcStep, srcOffset + c*scnbytes);
r = clamp(dy+dr+10, 0, rows-1);
int index10 = mad24(r, srcStep, srcOffset + c*scnbytes);
__global DATA_TYPE * src = (__global DATA_TYPE *)(srcptr + index1);
data[dr][dc] = src[0];
src = (__global DATA_TYPE *)(srcptr + index10);
data[dr+10][dc] = src[0];
barrier(CLK_LOCAL_MEM_FENCE);
DATA_TYPE p0=data[y][x], p1=data[y][x+1], p2=data[y][x+2], p3=data[y][x+3], p4=data[y][x+4];
DATA_TYPE p5=data[y+1][x], p6=data[y+1][x+1], p7=data[y+1][x+2], p8=data[y+1][x+3], p9=data[y+1][x+4];
DATA_TYPE p10=data[y+2][x], p11=data[y+2][x+1], p12=data[y+2][x+2], p13=data[y+2][x+3], p14=data[y+2][x+4];
DATA_TYPE p15=data[y+3][x], p16=data[y+3][x+1], p17=data[y+3][x+2], p18=data[y+3][x+3], p19=data[y+3][x+4];
DATA_TYPE p20=data[y+4][x], p21=data[y+4][x+1], p22=data[y+4][x+2], p23=data[y+4][x+3], p24=data[y+4][x+4];
DATA_TYPE mid;
op(p1, p2); op(p0, p1); op(p1, p2); op(p4, p5); op(p3, p4);
op(p4, p5); op(p0, p3); op(p2, p5); op(p2, p3); op(p1, p4);
op(p1, p2); op(p3, p4); op(p7, p8); op(p6, p7); op(p7, p8);
op(p10, p11); op(p9, p10); op(p10, p11); op(p6, p9); op(p8, p11);
op(p8, p9); op(p7, p10); op(p7, p8); op(p9, p10); op(p0, p6);
op(p4, p10); op(p4, p6); op(p2, p8); op(p2, p4); op(p6, p8);
op(p1, p7); op(p5, p11); op(p5, p7); op(p3, p9); op(p3, p5);
op(p7, p9); op(p1, p2); op(p3, p4); op(p5, p6); op(p7, p8);
op(p9, p10); op(p13, p14); op(p12, p13); op(p13, p14); op(p16, p17);
op(p15, p16); op(p16, p17); op(p12, p15); op(p14, p17); op(p14, p15);
op(p13, p16); op(p13, p14); op(p15, p16); op(p19, p20); op(p18, p19);
op(p19, p20); op(p21, p22); op(p23, p24); op(p21, p23); op(p22, p24);
op(p22, p23); op(p18, p21); op(p20, p23); op(p20, p21); op(p19, p22);
op(p22, p24); op(p19, p20); op(p21, p22); op(p23, p24); op(p12, p18);
op(p16, p22); op(p16, p18); op(p14, p20); op(p20, p24); op(p14, p16);
op(p18, p20); op(p22, p24); op(p13, p19); op(p17, p23); op(p17, p19);
op(p15, p21); op(p15, p17); op(p19, p21); op(p13, p14); op(p15, p16);
op(p17, p18); op(p19, p20); op(p21, p22); op(p23, p24); op(p0, p12);
op(p8, p20); op(p8, p12); op(p4, p16); op(p16, p24); op(p12, p16);
op(p2, p14); op(p10, p22); op(p10, p14); op(p6, p18); op(p6, p10);
op(p10, p12); op(p1, p13); op(p9, p21); op(p9, p13); op(p5, p17);
op(p13, p17); op(p3, p15); op(p11, p23); op(p11, p15); op(p7, p19);
op(p7, p11); op(p11, p13); op(p11, p12);
int dst_index = mad24( gy, dstStep, dstOffset + gx * scnbytes);
if( gy < rows && gx < cols)
{
__global DATA_TYPE* dst = (__global DATA_TYPE *)(dstptr + dst_index);
dst[0] = p12;
}
}

54
modules/imgproc/src/smooth.cpp
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@ -1661,19 +1661,61 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
}
namespace cv
{
static bool ocl_medianFilter ( InputArray _src, OutputArray _dst, int m)
{
int type = _src.type();
int depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if (!((depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F) && (cn == 1 || cn == 4)))
return false;
const char * kernelName;
if (m==3)
kernelName = "medianFilter3";
else if (m==5)
kernelName = "medianFilter5";
else
return false;
ocl::Kernel k(kernelName,ocl::imgproc::medianFilter_oclsrc,format("-D type=%s",ocl::typeToStr(type)));
if (k.empty())
return false;
_dst.create(_src.size(),type);
UMat src = _src.getUMat(), dst = _dst.getUMat();
size_t globalsize[2] = {(src.cols + 18) / 16 * 16, (src.rows + 15) / 16 * 16};
size_t localsize[2] = {16, 16};
return k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst)).run(2,globalsize,localsize,false);
}
}
void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize )
{
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
CV_Assert( ksize % 2 == 1 );
if( ksize <= 1 )
{
{
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
src0.copyTo(dst);
return;
}
CV_Assert( ksize % 2 == 1 );
bool use_opencl = ocl::useOpenCL() && _dst.isUMat();
// if ( use_opencl && ocl_medianFilter(_src0,_dst, ksize))
if (use_opencl)
{ CV_Assert (ocl_medianFilter(_src0,_dst,ksize));
return;}
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::medianBlur(src0, dst, ksize))

111
modules/imgproc/test/ocl/test_medianfilter.cpp
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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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, 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 "test_precomp.hpp"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace cvtest {
namespace ocl {
/////////////////////////////////////////////medianFilter//////////////////////////////////////////////////////////
PARAM_TEST_CASE(MedianFilter, MatDepth, Channels, int, bool)
{
int type;
int ksize;
bool use_roi;
TEST_DECLARE_INPUT_PARAMETER(src)
TEST_DECLARE_OUTPUT_PARAMETER(dst)
virtual void SetUp()
{
type = CV_MAKE_TYPE(GET_PARAM(0), GET_PARAM(1));
ksize = GET_PARAM(2);
use_roi = GET_PARAM(3);
}
virtual void generateTestData()
{
Size roiSize = randomSize(1, MAX_VALUE);
Border srcBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(src, src_roi, roiSize, srcBorder, type, -MAX_VALUE, MAX_VALUE);
Border dstBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(dst, dst_roi, roiSize, dstBorder, type, -MAX_VALUE, MAX_VALUE);
UMAT_UPLOAD_INPUT_PARAMETER(src)
UMAT_UPLOAD_OUTPUT_PARAMETER(dst)
}
void Near(double threshold = 0.0)
{
EXPECT_MAT_NEAR(dst, udst, threshold);
EXPECT_MAT_NEAR(dst_roi, udst_roi, threshold);
}
};
OCL_TEST_P(MedianFilter, Mat)
{
for (int j = 0; j < test_loop_times; j++)
{
generateTestData();
OCL_OFF(cv::medianBlur(src_roi, dst_roi, ksize));
OCL_ON(cv::medianBlur(usrc_roi, udst_roi, ksize));
Near(0);
}
}
OCL_INSTANTIATE_TEST_CASE_P(ImageProc, MedianFilter, Combine(
Values(CV_8U, CV_16U, CV_16S, CV_32F),
Values(1, 4),
Values(3, 5),
Bool())
);
} } // namespace cvtest::ocl
#endif
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