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Merge pull request #871 from bitwangyaoyao:2.4_acry

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This commit is contained in:
Vadim Pisarevsky 2013-05-13 23:04:11 +04:00 committed by OpenCV Buildbot
commit 6eb5a95a59
22 changed files with 334 additions and 1436 deletions
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120
modules/ocl/test/interpolation.hpp
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@ -1,120 +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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, 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 Intel Corporation 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_TEST_INTERPOLATION_HPP__
#define __OPENCV_TEST_INTERPOLATION_HPP__
template <typename T> T readVal(const cv::Mat &src, int y, int x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
if (border_type == cv::BORDER_CONSTANT)
return (y >= 0 && y < src.rows && x >= 0 && x < src.cols) ? src.at<T>(y, x * src.channels() + c) : cv::saturate_cast<T>(borderVal.val[c]);
return src.at<T>(cv::borderInterpolate(y, src.rows, border_type), cv::borderInterpolate(x, src.cols, border_type) * src.channels() + c);
}
template <typename T> struct NearestInterpolator
{
static T getValue(const cv::Mat &src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
return readVal<T>(src, cvFloor(y), cvFloor(x), c, border_type, borderVal);
}
};
template <typename T> struct LinearInterpolator
{
static T getValue(const cv::Mat &src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
x -= 0.5f;
y -= 0.5f;
int x1 = cvFloor(x);
int y1 = cvFloor(y);
int x2 = x1 + 1;
int y2 = y1 + 1;
float res = 0;
res += readVal<T>(src, y1, x1, c, border_type, borderVal) * ((x2 - x) * (y2 - y));
res += readVal<T>(src, y1, x2, c, border_type, borderVal) * ((x - x1) * (y2 - y));
res += readVal<T>(src, y2, x1, c, border_type, borderVal) * ((x2 - x) * (y - y1));
res += readVal<T>(src, y2, x2, c, border_type, borderVal) * ((x - x1) * (y - y1));
return cv::saturate_cast<T>(res);
}
};
template <typename T> struct CubicInterpolator
{
static float getValue(float p[4], float x)
{
return p[1] + 0.5 * x * (p[2] - p[0] + x * (2.0 * p[0] - 5.0 * p[1] + 4.0 * p[2] - p[3] + x * (3.0 * (p[1] - p[2]) + p[3] - p[0])));
}
static float getValue(float p[4][4], float x, float y)
{
float arr[4];
arr[0] = getValue(p[0], x);
arr[1] = getValue(p[1], x);
arr[2] = getValue(p[2], x);
arr[3] = getValue(p[3], x);
return getValue(arr, y);
}
static T getValue(const cv::Mat &src, float y, float x, int c, int border_type, cv::Scalar borderVal = cv::Scalar())
{
int ix = cvRound(x);
int iy = cvRound(y);
float vals[4][4] =
{
{readVal<T>(src, iy - 2, ix - 2, c, border_type, borderVal), readVal<T>(src, iy - 2, ix - 1, c, border_type, borderVal), readVal<T>(src, iy - 2, ix, c, border_type, borderVal), readVal<T>(src, iy - 2, ix + 1, c, border_type, borderVal)},
{readVal<T>(src, iy - 1, ix - 2, c, border_type, borderVal), readVal<T>(src, iy - 1, ix - 1, c, border_type, borderVal), readVal<T>(src, iy - 1, ix, c, border_type, borderVal), readVal<T>(src, iy - 1, ix + 1, c, border_type, borderVal)},
{readVal<T>(src, iy , ix - 2, c, border_type, borderVal), readVal<T>(src, iy , ix - 1, c, border_type, borderVal), readVal<T>(src, iy , ix, c, border_type, borderVal), readVal<T>(src, iy , ix + 1, c, border_type, borderVal)},
{readVal<T>(src, iy + 1, ix - 2, c, border_type, borderVal), readVal<T>(src, iy + 1, ix - 1, c, border_type, borderVal), readVal<T>(src, iy + 1, ix, c, border_type, borderVal), readVal<T>(src, iy + 1, ix + 1, c, border_type, borderVal)},
};
return cv::saturate_cast<T>(getValue(vals, (x - ix + 2.0) / 4.0, (y - iy + 2.0) / 4.0));
}
};
#endif // __OPENCV_TEST_INTERPOLATION_HPP__

1
modules/ocl/test/precomp.hpp
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@ -71,7 +71,6 @@
#include "opencv2/ocl/ocl.hpp"
#include "utility.hpp"
#include "interpolation.hpp"
//#include "add_test_info.h"
#endif

491
modules/ocl/test/test_arithm.cpp
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@ -12,6 +12,7 @@
//
// 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.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
@ -21,6 +22,7 @@
// Jiang Liyuan,jlyuan001.good@163.com
// Rock Li, Rock.Li@amd.com
// Zailong Wu, bullet@yeah.net
// Yao Wang, bitwangyaoyao@gmail.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
@ -87,14 +89,13 @@ PARAM_TEST_CASE(ArithmTestBase, MatType, bool)
int maskx;
int masky;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat mat2_roi;
cv::Mat mask_roi;
cv::Mat dst_roi;
cv::Mat dst1_roi; //bak
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
cv::ocl::oclMat gdst1_whole; //bak
@ -125,10 +126,6 @@ PARAM_TEST_CASE(ArithmTestBase, MatType, bool)
val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0));
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -175,14 +172,22 @@ PARAM_TEST_CASE(ArithmTestBase, MatType, bool)
gmask = mask_roi; //end
}
void Near(double threshold = 0.)
{
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), threshold);
}
void Near1(double threshold = 0.)
{
EXPECT_MAT_NEAR(dst1, Mat(gdst1_whole), threshold);
}
};
////////////////////////////////lut/////////////////////////////////////////////////
struct Lut : ArithmTestBase {};
#define VARNAME(A) string(#A);
TEST_P(Lut, Mat)
{
@ -203,20 +208,12 @@ TEST_P(Lut, Mat)
cv::LUT(mat1_roi, mat2_roi, dst_roi);
cv::ocl::LUT(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download (cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0, s);
Near(0);
}
}
////////////////////////////////exp/////////////////////////////////////////////////
struct Exp : ArithmTestBase {};
TEST_P(Exp, Mat)
@ -227,20 +224,12 @@ TEST_P(Exp, Mat)
cv::exp(mat1_roi, dst_roi);
cv::ocl::exp(gmat1, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 2, s);
Near(2);
}
}
////////////////////////////////log/////////////////////////////////////////////////
struct Log : ArithmTestBase {};
TEST_P(Log, Mat)
@ -249,24 +238,14 @@ TEST_P(Log, Mat)
{
random_roi();
cv::log(mat1_roi, dst_roi);
cv::ocl::log(gmat1, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, s);
Near(1);
}
}
////////////////////////////////add/////////////////////////////////////////////////
struct Add : ArithmTestBase {};
TEST_P(Add, Mat)
@ -277,12 +256,7 @@ TEST_P(Add, Mat)
cv::add(mat1_roi, mat2_roi, dst_roi);
cv::ocl::add(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, s);
Near(0);
}
}
@ -294,14 +268,10 @@ TEST_P(Add, Mat_Mask)
cv::add(mat1_roi, mat2_roi, dst_roi, mask_roi);
cv::ocl::add(gmat1, gmat2, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, s);
Near(0);
}
}
TEST_P(Add, Scalar)
{
for(int j = 0; j < LOOP_TIMES; j++)
@ -310,12 +280,7 @@ TEST_P(Add, Scalar)
cv::add(mat1_roi, val, dst_roi);
cv::ocl::add(gmat1, val, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, s);
Near(1e-5);
}
}
@ -327,12 +292,7 @@ TEST_P(Add, Scalar_Mask)
cv::add(mat1_roi, val, dst_roi, mask_roi);
cv::ocl::add(gmat1, val, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, s);
Near(1e-5);
}
}
@ -349,12 +309,7 @@ TEST_P(Sub, Mat)
cv::subtract(mat1_roi, mat2_roi, dst_roi);
cv::ocl::subtract(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, s);
Near(0);
}
}
@ -366,14 +321,10 @@ TEST_P(Sub, Mat_Mask)
cv::subtract(mat1_roi, mat2_roi, dst_roi, mask_roi);
cv::ocl::subtract(gmat1, gmat2, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, s);
Near(0);
}
}
TEST_P(Sub, Scalar)
{
for(int j = 0; j < LOOP_TIMES; j++)
@ -382,12 +333,7 @@ TEST_P(Sub, Scalar)
cv::subtract(mat1_roi, val, dst_roi);
cv::ocl::subtract(gmat1, val, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, s);
Near(1e-5);
}
}
@ -399,12 +345,7 @@ TEST_P(Sub, Scalar_Mask)
cv::subtract(mat1_roi, val, dst_roi, mask_roi);
cv::ocl::subtract(gmat1, val, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, s);
Near(1e-5);
}
}
@ -421,12 +362,7 @@ TEST_P(Mul, Mat)
cv::multiply(mat1_roi, mat2_roi, dst_roi);
cv::ocl::multiply(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char s[1024];
sprintf(s, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, s);
Near(0);
}
}
@ -441,12 +377,7 @@ TEST_P(Mul, Mat_Scalar)
cv::multiply(mat1_roi, mat2_roi, dst_roi, s);
cv::ocl::multiply(gmat1, gmat2, gdst, s);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.001, sss);
Near(.001);
}
}
@ -462,13 +393,7 @@ TEST_P(Div, Mat)
cv::divide(mat1_roi, mat2_roi, dst_roi);
cv::ocl::divide(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
Near(1);
}
}
@ -483,13 +408,7 @@ TEST_P(Div, Mat_Scalar)
cv::divide(mat1_roi, mat2_roi, dst_roi, s);
cv::ocl::divide(gmat1, gmat2, gdst, s);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.001, sss);
Near(.001);
}
}
@ -504,13 +423,7 @@ TEST_P(Absdiff, Mat)
cv::absdiff(mat1_roi, mat2_roi, dst_roi);
cv::ocl::absdiff(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0, sss);
Near(0);
}
}
@ -522,13 +435,7 @@ TEST_P(Absdiff, Mat_Scalar)
cv::absdiff(mat1_roi, val, dst_roi);
cv::ocl::absdiff(gmat1, val, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -544,16 +451,8 @@ TEST_P(CartToPolar, angleInDegree)
cv::cartToPolar(mat1_roi, mat2_roi, dst_roi, dst1_roi, 1);
cv::ocl::cartToPolar(gmat1, gmat2, gdst, gdst1, 1);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
cv::Mat cpu_dst1;
gdst1_whole.download(cpu_dst1);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.5, sss);
EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.5, sss);
Near(.5);
Near1(.5);
}
}
@ -565,22 +464,12 @@ TEST_P(CartToPolar, angleInRadians)
cv::cartToPolar(mat1_roi, mat2_roi, dst_roi, dst1_roi, 0);
cv::ocl::cartToPolar(gmat1, gmat2, gdst, gdst1, 0);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
cv::Mat cpu_dst1;
gdst1_whole.download(cpu_dst1);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.5, sss);
EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.5, sss);
Near(.5);
Near1(.5);
}
}
struct PolarToCart : ArithmTestBase {};
TEST_P(PolarToCart, angleInDegree)
@ -591,17 +480,8 @@ TEST_P(PolarToCart, angleInDegree)
cv::polarToCart(mat1_roi, mat2_roi, dst_roi, dst1_roi, 1);
cv::ocl::polarToCart(gmat1, gmat2, gdst, gdst1, 1);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
cv::Mat cpu_dst1;
gdst1_whole.download(cpu_dst1);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.5, sss);
EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.5, sss);
Near(.5);
Near1(.5);
}
}
@ -613,17 +493,8 @@ TEST_P(PolarToCart, angleInRadians)
cv::polarToCart(mat1_roi, mat2_roi, dst_roi, dst1_roi, 0);
cv::ocl::polarToCart(gmat1, gmat2, gdst, gdst1, 0);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
cv::Mat cpu_dst1;
gdst1_whole.download(cpu_dst1);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.5, sss);
EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.5, sss);
Near(.5);
Near1(.5);
}
}
@ -640,19 +511,11 @@ TEST_P(Magnitude, Mat)
cv::magnitude(mat1_roi, mat2_roi, dst_roi);
cv::ocl::magnitude(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
struct Transpose : ArithmTestBase {};
TEST_P(Transpose, Mat)
@ -663,20 +526,11 @@ TEST_P(Transpose, Mat)
cv::transpose(mat1_roi, dst_roi);
cv::ocl::transpose(gmat1, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
struct Flip : ArithmTestBase {};
TEST_P(Flip, X)
@ -687,13 +541,7 @@ TEST_P(Flip, X)
cv::flip(mat1_roi, dst_roi, 0);
cv::ocl::flip(gmat1, gdst, 0);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -705,13 +553,7 @@ TEST_P(Flip, Y)
cv::flip(mat1_roi, dst_roi, 1);
cv::ocl::flip(gmat1, gdst, 1);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -723,18 +565,11 @@ TEST_P(Flip, BOTH)
cv::flip(mat1_roi, dst_roi, -1);
cv::ocl::flip(gmat1, gdst, -1);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
struct MinMax : ArithmTestBase {};
TEST_P(MinMax, MAT)
@ -765,12 +600,8 @@ TEST_P(MinMax, MAT)
double minVal_, maxVal_;
cv::ocl::minMax(gmat1, &minVal_, &maxVal_);
//check results
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_DOUBLE_EQ(minVal_, minVal) << sss;
EXPECT_DOUBLE_EQ(maxVal_, maxVal) << sss;
EXPECT_DOUBLE_EQ(minVal_, minVal);
EXPECT_DOUBLE_EQ(maxVal_, maxVal);
}
}
@ -803,12 +634,8 @@ TEST_P(MinMax, MASK)
double minVal_, maxVal_;
cv::ocl::minMax(gmat1, &minVal_, &maxVal_, gmask);
//check results
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_DOUBLE_EQ(minVal_, minVal) << sss;
EXPECT_DOUBLE_EQ(maxVal_, maxVal) << sss;
EXPECT_DOUBLE_EQ(minVal_, minVal);
EXPECT_DOUBLE_EQ(maxVal_, maxVal);
}
}
@ -919,17 +746,13 @@ TEST_P(MinMaxLoc, MAT)
error1 = ::abs(mat1_roi.at<double>(maxLoc_) - mat1_roi.at<double>(maxLoc));
}
//check results
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_DOUBLE_EQ(minVal_, minVal);
EXPECT_DOUBLE_EQ(maxVal_, maxVal);
EXPECT_DOUBLE_EQ(minlocVal_, minlocVal);
EXPECT_DOUBLE_EQ(maxlocVal_, maxlocVal);
EXPECT_DOUBLE_EQ(minVal_, minVal) << sss;
EXPECT_DOUBLE_EQ(maxVal_, maxVal) << sss;
EXPECT_DOUBLE_EQ(minlocVal_, minlocVal) << sss;
EXPECT_DOUBLE_EQ(maxlocVal_, maxlocVal) << sss;
EXPECT_DOUBLE_EQ(error0, 0.0) << sss;
EXPECT_DOUBLE_EQ(error1, 0.0) << sss;
EXPECT_DOUBLE_EQ(error0, 0.0);
EXPECT_DOUBLE_EQ(error1, 0.0);
}
}
@ -1040,17 +863,13 @@ TEST_P(MinMaxLoc, MASK)
error1 = ::abs(mat1_roi.at<double>(maxLoc_) - mat1_roi.at<double>(maxLoc));
}
//check results
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_DOUBLE_EQ(minVal_, minVal);
EXPECT_DOUBLE_EQ(maxVal_, maxVal);
EXPECT_DOUBLE_EQ(minlocVal_, minlocVal);
EXPECT_DOUBLE_EQ(maxlocVal_, maxlocVal);
EXPECT_DOUBLE_EQ(minVal_, minVal) << sss;
EXPECT_DOUBLE_EQ(maxVal_, maxVal) << sss;
EXPECT_DOUBLE_EQ(minlocVal_, minlocVal) << sss;
EXPECT_DOUBLE_EQ(maxlocVal_, maxlocVal) << sss;
EXPECT_DOUBLE_EQ(error0, 0.0) << sss;
EXPECT_DOUBLE_EQ(error1, 0.0) << sss;
EXPECT_DOUBLE_EQ(error0, 0.0);
EXPECT_DOUBLE_EQ(error1, 0.0);
}
}
@ -1064,14 +883,12 @@ TEST_P(Sum, MAT)
random_roi();
Scalar cpures = cv::sum(mat1_roi);
Scalar gpures = cv::ocl::sum(gmat1);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
//check results
EXPECT_NEAR(cpures[0], gpures[0], 0.1) << sss;
EXPECT_NEAR(cpures[1], gpures[1], 0.1) << sss;
EXPECT_NEAR(cpures[2], gpures[2], 0.1) << sss;
EXPECT_NEAR(cpures[3], gpures[3], 0.1) << sss;
EXPECT_NEAR(cpures[0], gpures[0], 0.1);
EXPECT_NEAR(cpures[1], gpures[1], 0.1);
EXPECT_NEAR(cpures[2], gpures[2], 0.1);
EXPECT_NEAR(cpures[3], gpures[3], 0.1);
}
}
@ -1086,11 +903,7 @@ TEST_P(CountNonZero, MAT)
int cpures = cv::countNonZero(mat1_roi);
int gpures = cv::ocl::countNonZero(gmat1);
//check results
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_DOUBLE_EQ((double)cpures, (double)gpures) << sss;
EXPECT_DOUBLE_EQ((double)cpures, (double)gpures);
}
}
@ -1112,13 +925,7 @@ TEST_P(Phase, Mat)
random_roi();
cv::phase(mat1_roi, mat2_roi, dst_roi, angelInDegrees ? true : false);
cv::ocl::phase(gmat1, gmat2, gdst, angelInDegrees ? true : false);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-2, sss);
Near(1e-2);
}
}
}
@ -1135,13 +942,7 @@ TEST_P(Bitwise_and, Mat)
cv::bitwise_and(mat1_roi, mat2_roi, dst_roi);
cv::ocl::bitwise_and(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
@ -1153,15 +954,10 @@ TEST_P(Bitwise_and, Mat_Mask)
cv::bitwise_and(mat1_roi, mat2_roi, dst_roi, mask_roi);
cv::ocl::bitwise_and(gmat1, gmat2, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
TEST_P(Bitwise_and, Scalar)
{
for(int j = 0; j < LOOP_TIMES; j++)
@ -1170,14 +966,7 @@ TEST_P(Bitwise_and, Scalar)
cv::bitwise_and(mat1_roi, val, dst_roi);
cv::ocl::bitwise_and(gmat1, val, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -1189,14 +978,7 @@ TEST_P(Bitwise_and, Scalar_Mask)
cv::bitwise_and(mat1_roi, val, dst_roi, mask_roi);
cv::ocl::bitwise_and(gmat1, val, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char *sss = new char[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
delete[] sss;
Near(1e-5);
}
}
@ -1214,13 +996,7 @@ TEST_P(Bitwise_or, Mat)
cv::bitwise_or(mat1_roi, mat2_roi, dst_roi);
cv::ocl::bitwise_or(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
@ -1232,15 +1008,10 @@ TEST_P(Bitwise_or, Mat_Mask)
cv::bitwise_or(mat1_roi, mat2_roi, dst_roi, mask_roi);
cv::ocl::bitwise_or(gmat1, gmat2, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
TEST_P(Bitwise_or, Scalar)
{
for(int j = 0; j < LOOP_TIMES; j++)
@ -1249,13 +1020,7 @@ TEST_P(Bitwise_or, Scalar)
cv::bitwise_or(mat1_roi, val, dst_roi);
cv::ocl::bitwise_or(gmat1, val, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -1267,13 +1032,7 @@ TEST_P(Bitwise_or, Scalar_Mask)
cv::bitwise_or(mat1_roi, val, dst_roi, mask_roi);
cv::ocl::bitwise_or(gmat1, val, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -1291,13 +1050,7 @@ TEST_P(Bitwise_xor, Mat)
cv::bitwise_xor(mat1_roi, mat2_roi, dst_roi);
cv::ocl::bitwise_xor(gmat1, gmat2, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
@ -1309,15 +1062,10 @@ TEST_P(Bitwise_xor, Mat_Mask)
cv::bitwise_xor(mat1_roi, mat2_roi, dst_roi, mask_roi);
cv::ocl::bitwise_xor(gmat1, gmat2, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
TEST_P(Bitwise_xor, Scalar)
{
for(int j = 0; j < LOOP_TIMES; j++)
@ -1326,13 +1074,7 @@ TEST_P(Bitwise_xor, Scalar)
cv::bitwise_xor(mat1_roi, val, dst_roi);
cv::ocl::bitwise_xor(gmat1, val, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -1344,13 +1086,7 @@ TEST_P(Bitwise_xor, Scalar_Mask)
cv::bitwise_xor(mat1_roi, val, dst_roi, mask_roi);
cv::ocl::bitwise_xor(gmat1, val, gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
@ -1367,13 +1103,7 @@ TEST_P(Bitwise_not, Mat)
cv::bitwise_not(mat1_roi, dst_roi);
cv::ocl::bitwise_not(gmat1, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
@ -1390,7 +1120,7 @@ TEST_P(Compare, Mat)
}
int cmp_codes[] = {CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE};
const char *cmp_str[] = {"CMP_EQ", "CMP_GT", "CMP_GE", "CMP_LT", "CMP_LE", "CMP_NE"};
//const char *cmp_str[] = {"CMP_EQ", "CMP_GT", "CMP_GE", "CMP_LT", "CMP_LE", "CMP_NE"};
int cmp_num = sizeof(cmp_codes) / sizeof(int);
for (int i = 0; i < cmp_num; ++i)
@ -1402,13 +1132,7 @@ TEST_P(Compare, Mat)
cv::compare(mat1_roi, mat2_roi, dst_roi, cmp_codes[i]);
cv::ocl::compare(gmat1, gmat2, gdst, cmp_codes[i]);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "cmptype=%s, roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", cmp_str[i], roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
Near(0);
}
}
@ -1430,14 +1154,7 @@ TEST_P(Pow, Mat)
double p = 4.5;
cv::pow(mat1_roi, p, dst_roi);
cv::ocl::pow(gmat1, p, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
Near(1);
}
}
@ -1448,36 +1165,18 @@ TEST_P(MagnitudeSqr, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
// random_roi();
// int64 start, end;
// start = cv::getTickCount();
random_roi();
for(int i = 0; i < mat1.rows; ++i)
for(int j = 0; j < mat1.cols; ++j)
{
float val1 = mat1.at<float>(i, j);
float val2 = mat2.at<float>(i, j);
((float *)(dst.data))[i * dst.step / 4 + j] = val1 * val1 + val2 * val2;
// float val1 =((float *)( mat1.data))[(i*mat1.step/8 +j)*2];
//
// float val2 =((float *)( mat1.data))[(i*mat1.step/8 +j)*2+ 1 ];
// ((float *)(dst.data))[i*dst.step/4 +j]= val1 * val1 +val2 * val2;
}
// end = cv::getTickCount();
cv::ocl::oclMat clmat1(mat1), clmat2(mat2), cldst;
cv::ocl::magnitudeSqr(clmat1, clmat2, cldst);
cv::Mat cpu_dst;
cldst.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
cv::ocl::oclMat clmat1(mat1), clmat2(mat2);
cv::ocl::magnitudeSqr(clmat1, clmat2, gdst);
Near(1);
}
}
@ -1498,21 +1197,13 @@ TEST_P(AddWeighted, Mat)
cv::ocl::addWeighted(gmat1, alpha, gmat2, beta, gama, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
}
//********test****************
INSTANTIATE_TEST_CASE_P(Arithm, Lut, Combine(

63
modules/ocl/test/test_blend.cpp
View File

@ -1,3 +1,47 @@
/*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.
//
// @Authors
// Nathan, liujun@multicorewareinc.com
//
// 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 oclMaterials 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 "precomp.hpp"
#include <iomanip>
@ -33,20 +77,14 @@ void blendLinearGold(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &we
PARAM_TEST_CASE(Blend, cv::Size, MatType/*, UseRoi*/)
{
//std::vector<cv::ocl::Info> oclinfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
//devInfo = GET_PARAM(0);
size = GET_PARAM(0);
type = GET_PARAM(1);
/*useRoi = GET_PARAM(3);*/
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
}
};
@ -59,12 +97,9 @@ TEST_P(Blend, Accuracy)
cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
cv::ocl::oclMat gimg1(size, type), gimg2(size, type), gweights1(size, CV_32F), gweights2(size, CV_32F);
cv::ocl::oclMat dst(size, type);
gimg1.upload(img1);
gimg2.upload(img2);
gweights1.upload(weights1);
gweights2.upload(weights2);
cv::ocl::oclMat gimg1(img1), gimg2(img2), gweights1(weights1), gweights2(weights2);
cv::ocl::oclMat dst;
cv::ocl::blendLinear(gimg1, gimg2, gweights1, gweights2, dst);
cv::Mat result;
cv::Mat result_gold;
@ -74,10 +109,10 @@ TEST_P(Blend, Accuracy)
else
blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.f : 1e-5f, 0);
EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.f : 1e-5f);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, Combine(
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Blend, Combine(
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC4))
));

14
modules/ocl/test/test_brute_force_matcher.cpp
View File

@ -7,12 +7,16 @@
// copy or use the software.
//
//
// Intel License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware inc., all rights reserved.
// 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.
//
// @Authors
// Nathan, liujun@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
@ -21,12 +25,12 @@
//
// * 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.
// and/or other oclMaterials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// * 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
// 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,

2
modules/ocl/test/test_calib3d.cpp
View File

@ -129,7 +129,7 @@ TEST_P(StereoMatchBP, Regression)
bp(d_left, d_right, d_disp);
d_disp.download(disp);
disp.convertTo(disp, disp_gold.depth());
EXPECT_MAT_NEAR(disp_gold, disp, 0.0, "");
EXPECT_MAT_NEAR(disp_gold, disp, 0.0);
}
INSTANTIATE_TEST_CASE_P(OCL_Calib3D, StereoMatchBP, testing::Combine(testing::Values(64),
testing::Values(8),testing::Values(2),testing::Values(25.0f),

6
modules/ocl/test/test_color.cpp
View File

@ -100,7 +100,7 @@ PARAM_TEST_CASE(CvtColor, cv::Size, MatDepth)
cv::cvtColor(src, dst_gold, CVTCODE(name));\
cv::Mat dst_mat;\
dst.download(dst_mat);\
EXPECT_MAT_NEAR(dst_gold, dst_mat, 1e-5, "");\
EXPECT_MAT_NEAR(dst_gold, dst_mat, 1e-5);\
}
//add new ones here using macro
@ -141,7 +141,7 @@ TEST_P(CvtColor_Gray2RGB, Accuracy)
cv::cvtColor(src, dst_gold, code);
cv::Mat dst_mat;
dst.download(dst_mat);
EXPECT_MAT_NEAR(dst_gold, dst_mat, 1e-5, "");
EXPECT_MAT_NEAR(dst_gold, dst_mat, 1e-5);
}
@ -171,7 +171,7 @@ TEST_P(CvtColor_YUV420, Accuracy)
cv::Mat dst_mat;
dst.download(dst_mat);
MAT_DIFF(dst_mat, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst_mat, 1e-5, "");
EXPECT_MAT_NEAR(dst_gold, dst_mat, 1e-5);
}
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, CvtColor, testing::Combine(

16
modules/ocl/test/test_columnsum.cpp
View File

@ -47,27 +47,16 @@
#include "precomp.hpp"
#include <iomanip>
///////////////////////////////////////////////////////////////////////////////
/// ColumnSum
#ifdef HAVE_OPENCL
////////////////////////////////////////////////////////////////////////
// ColumnSum
PARAM_TEST_CASE(ColumnSum, cv::Size, bool )
PARAM_TEST_CASE(ColumnSum, cv::Size)
{
cv::Size size;
cv::Mat src;
bool useRoi;
//std::vector<cv::ocl::Info> oclinfo;
virtual void SetUp()
{
size = GET_PARAM(0);
useRoi = GET_PARAM(1);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
}
};
@ -99,8 +88,7 @@ TEST_P(ColumnSum, Accuracy)
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine(
DIFFERENT_SIZES, testing::Values(Inverse(false), Inverse(true))));
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, ColumnSum, DIFFERENT_SIZES);
#endif

8
modules/ocl/test/test_fft.cpp
View File

@ -68,7 +68,7 @@ TEST_P(Dft, C2C)
cv::dft(a, b_gold, dft_flags);
cv::ocl::dft(cv::ocl::oclMat(a), d_b, a.size(), dft_flags);
EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), a.size().area() * 1e-4, "");
EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), a.size().area() * 1e-4);
}
TEST_P(Dft, R2C)
@ -81,11 +81,11 @@ TEST_P(Dft, R2C)
cv::dft(a, b_gold, cv::DFT_COMPLEX_OUTPUT | dft_flags);
b_gold_roi = b_gold(cv::Rect(0, 0, d_b.cols, d_b.rows));
EXPECT_MAT_NEAR(b_gold_roi, cv::Mat(d_b), a.size().area() * 1e-4, "");
EXPECT_MAT_NEAR(b_gold_roi, cv::Mat(d_b), a.size().area() * 1e-4);
cv::Mat c_gold;
cv::dft(b_gold, c_gold, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT | cv::DFT_SCALE);
EXPECT_MAT_NEAR(b_gold_roi, cv::Mat(d_b), a.size().area() * 1e-4, "");
EXPECT_MAT_NEAR(b_gold_roi, cv::Mat(d_b), a.size().area() * 1e-4);
}
TEST_P(Dft, R2CthenC2R)
@ -95,7 +95,7 @@ TEST_P(Dft, R2CthenC2R)
cv::ocl::oclMat d_b, d_c;
cv::ocl::dft(cv::ocl::oclMat(a), d_b, a.size(), 0);
cv::ocl::dft(d_b, d_c, a.size(), cv::DFT_SCALE | cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT);
EXPECT_MAT_NEAR(a, d_c, a.size().area() * 1e-4, "");
EXPECT_MAT_NEAR(a, d_c, a.size().area() * 1e-4);
}

682
modules/ocl/test/test_filters.cpp
View File

@ -12,6 +12,7 @@
//
// 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.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
@ -19,6 +20,7 @@
// Jia Haipeng, jiahaipeng95@gmail.com
// Zero Lin, Zero.Lin@amd.com
// Zhang Ying, zhangying913@gmail.com
// Yao Wang, bitwangyaoyao@gmail.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
@ -55,121 +57,13 @@ using namespace testing;
using namespace std;
PARAM_TEST_CASE(FilterTestBase, MatType, bool)
PARAM_TEST_CASE(FilterTestBase,
MatType,
cv::Size, // kernel size
cv::Size, // dx,dy
int // border type, or iteration
)
{
int type;
cv::Scalar val;
//src mat
cv::Mat mat1;
cv::Mat mat2;
cv::Mat mask;
cv::Mat dst;
cv::Mat dst1; //bak, for two outputs
// set up roi
int roicols;
int roirows;
int src1x;
int src1y;
int src2x;
int src2y;
int dstx;
int dsty;
int maskx;
int masky;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat mat2_roi;
cv::Mat mask_roi;
cv::Mat dst_roi;
cv::Mat dst1_roi; //bak
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
cv::ocl::oclMat gdst1_whole; //bak
//ocl mat with roi
cv::ocl::oclMat gmat1;
cv::ocl::oclMat gmat2;
cv::ocl::oclMat gdst;
cv::ocl::oclMat gdst1; //bak
cv::ocl::oclMat gmask;
virtual void SetUp()
{
type = GET_PARAM(0);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
mat2 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
dst1 = randomMat(rng, size, type, 5, 16, false);
mask = randomMat(rng, size, CV_8UC1, 0, 2, false);
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0));
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(1, mat1.cols);
roirows = rng.uniform(1, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
src1y = rng.uniform(0, mat1.rows - roirows);
src2x = rng.uniform(0, mat2.cols - roicols);
src2y = rng.uniform(0, mat2.rows - roirows);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
maskx = rng.uniform(0, mask.cols - roicols);
masky = rng.uniform(0, mask.rows - roirows);
#else
roicols = mat1.cols;
roirows = mat1.rows;
src1x = 0;
src1y = 0;
src2x = 0;
src2y = 0;
dstx = 0;
dsty = 0;
maskx = 0;
masky = 0;
#endif
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
mat2_roi = mat2(Rect(src2x, src2y, roicols, roirows));
mask_roi = mask(Rect(maskx, masky, roicols, roirows));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
dst1_roi = dst1(Rect(dstx, dsty, roicols, roirows));
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gdst1_whole = dst1;
gdst1 = gdst1_whole(Rect(dstx, dsty, roicols, roirows));
gmat1 = mat1_roi;
gmat2 = mat2_roi;
gmask = mask_roi;
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
// blur
PARAM_TEST_CASE(Blur, MatType, cv::Size, int)
{
int type;
cv::Size ksize;
int bordertype;
//src mat
cv::Mat mat1;
cv::Mat dst;
@ -185,7 +79,7 @@ PARAM_TEST_CASE(Blur, MatType, cv::Size, int)
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -193,23 +87,6 @@ PARAM_TEST_CASE(Blur, MatType, cv::Size, int)
cv::ocl::oclMat gmat1;
cv::ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
bordertype = GET_PARAM(2);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
{
#ifdef RANDOMROI
@ -236,10 +113,37 @@ PARAM_TEST_CASE(Blur, MatType, cv::Size, int)
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gmat1 = mat1_roi;
}
void Init(int mat_type)
{
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(size, mat_type, 5, 16);
dst = randomMat(size, mat_type, 5, 16);
}
void Near(double threshold)
{
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), threshold);
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
// blur
struct Blur : FilterTestBase
{
int type;
cv::Size ksize;
int bordertype;
virtual void SetUp()
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
bordertype = GET_PARAM(3);
Init(type);
}
};
TEST_P(Blur, Mat)
@ -247,116 +151,36 @@ TEST_P(Blur, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::blur(mat1_roi, dst_roi, ksize, Point(-1, -1), bordertype);
cv::ocl::blur(gmat1, gdst, ksize, Point(-1, -1), bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
Near(1.0);
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
//Laplacian
PARAM_TEST_CASE(LaplacianTestBase, MatType, int)
struct Laplacian : FilterTestBase
{
int type;
int ksize;
//src mat
cv::Mat mat;
cv::Mat dst;
// set up roi
int roicols;
int roirows;
int srcx;
int srcy;
int dstx;
int dsty;
//src mat with roi
cv::Mat mat_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
//ocl mat with roi
cv::ocl::oclMat gmat;
cv::ocl::oclMat gdst;
cv::Size ksize;
virtual void SetUp()
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(2, mat.cols);
roirows = rng.uniform(2, mat.rows);
srcx = rng.uniform(0, mat.cols - roicols);
srcy = rng.uniform(0, mat.rows - roirows);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
#else
roicols = mat.cols;
roirows = mat.rows;
srcx = 0;
srcy = 0;
dstx = 0;
dsty = 0;
#endif
mat_roi = mat(Rect(srcx, srcy, roicols, roirows));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gmat = mat_roi;
Init(type);
}
};
struct Laplacian : LaplacianTestBase {};
TEST_P(Laplacian, Accuracy)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::Laplacian(mat_roi, dst_roi, -1, ksize, 1);
cv::ocl::Laplacian(gmat, gdst, -1, ksize, 1);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, srcx, srcy, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
cv::Laplacian(mat1_roi, dst_roi, -1, ksize.width, 1);
cv::ocl::Laplacian(gmat1, gdst, -1, ksize.width, 1);
Near(1e-5);
}
}
@ -364,8 +188,7 @@ TEST_P(Laplacian, Accuracy)
/////////////////////////////////////////////////////////////////////////////////////////////////
// erode & dilate
PARAM_TEST_CASE(ErodeDilateBase, MatType, int)
struct ErodeDilate : FilterTestBase
{
int type;
int iterations;
@ -373,210 +196,54 @@ PARAM_TEST_CASE(ErodeDilateBase, MatType, int)
//erode or dilate kernel
cv::Mat kernel;
//src mat
cv::Mat mat1;
cv::Mat dst;
// set up roi
int roicols;
int roirows;
int src1x;
int src1y;
int dstx;
int dsty;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
//ocl mat with roi
cv::ocl::oclMat gmat1;
cv::ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
iterations = GET_PARAM(1);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
iterations = GET_PARAM(3);
Init(type);
// rng.fill(kernel, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
kernel = randomMat(rng, Size(3, 3), CV_8UC1, 0, 3, false);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(2, mat1.cols);
roirows = rng.uniform(2, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
src1y = rng.uniform(0, mat1.rows - roirows);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
#else
roicols = mat1.cols;
roirows = mat1.rows;
src1x = 0;
src1y = 0;
dstx = 0;
dsty = 0;
#endif
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gmat1 = mat1_roi;
kernel = randomMat(Size(3, 3), CV_8UC1, 0, 3);
}
};
// erode
struct Erode : ErodeDilateBase {};
TEST_P(Erode, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::erode(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
cv::ocl::erode(gmat1, gdst, kernel, Point(-1, -1), iterations);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
}
}
// dilate
struct Dilate : ErodeDilateBase {};
TEST_P(Dilate, Mat)
TEST_P(ErodeDilate, Mat)
{
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::erode(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
cv::ocl::erode(gmat1, gdst, kernel, Point(-1, -1), iterations);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5, sss);
Near(1e-5);
}
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::dilate(mat1_roi, dst_roi, kernel, Point(-1, -1), iterations);
cv::ocl::dilate(gmat1, gdst, kernel, Point(-1, -1), iterations);
Near(1e-5);
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
// Sobel
PARAM_TEST_CASE(Sobel, MatType, int, int, int, int)
struct Sobel : FilterTestBase
{
int type;
int dx, dy, ksize, bordertype;
//src mat
cv::Mat mat1;
cv::Mat dst;
// set up roi
int roicols;
int roirows;
int src1x;
int src1y;
int dstx;
int dsty;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
//ocl mat with roi
cv::ocl::oclMat gmat1;
cv::ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
dx = GET_PARAM(1);
dy = GET_PARAM(2);
ksize = GET_PARAM(3);
bordertype = GET_PARAM(4);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
Size s = GET_PARAM(1);
ksize = s.width;
s = GET_PARAM(2);
dx = s.width;
dy = s.height;
bordertype = GET_PARAM(3);
Init(type);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(2, mat1.cols);
roirows = rng.uniform(2, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
src1y = rng.uniform(0, mat1.rows - roirows);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
#else
roicols = mat1.cols;
roirows = mat1.rows;
src1x = 0;
src1y = 0;
dstx = 0;
dsty = 0;
#endif
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gmat1 = mat1_roi;
}
};
TEST_P(Sobel, Mat)
@ -584,103 +251,29 @@ TEST_P(Sobel, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::Sobel(mat1_roi, dst_roi, -1, dx, dy, ksize, /*scale*/0.00001,/*delta*/0, bordertype);
cv::ocl::Sobel(gmat1, gdst, -1, dx, dy, ksize,/*scale*/0.00001,/*delta*/0, bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
Near(1);
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
// Scharr
PARAM_TEST_CASE(Scharr, MatType, int, int, int)
struct Scharr : FilterTestBase
{
int type;
int dx, dy, bordertype;
//src mat
cv::Mat mat1;
cv::Mat dst;
// set up roi
int roicols;
int roirows;
int src1x;
int src1y;
int dstx;
int dsty;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
//ocl mat with roi
cv::ocl::oclMat gmat1;
cv::ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
dx = GET_PARAM(1);
dy = GET_PARAM(2);
Size s = GET_PARAM(2);
dx = s.width;
dy = s.height;
bordertype = GET_PARAM(3);
dx = 1;
dy = 0;
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
Init(type);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(2, mat1.cols);
roirows = rng.uniform(2, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
src1y = rng.uniform(0, mat1.rows - roirows);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
#else
roicols = mat1.cols;
roirows = mat1.rows;
src1x = 0;
src1y = 0;
dstx = 0;
dsty = 0;
#endif
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gmat1 = mat1_roi;
}
};
TEST_P(Scharr, Mat)
@ -688,16 +281,9 @@ TEST_P(Scharr, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::Scharr(mat1_roi, dst_roi, -1, dx, dy, /*scale*/1,/*delta*/0, bordertype);
cv::ocl::Scharr(gmat1, gdst, -1, dx, dy,/*scale*/1,/*delta*/0, bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
Near(1);
}
}
@ -705,89 +291,23 @@ TEST_P(Scharr, Mat)
/////////////////////////////////////////////////////////////////////////////////////////////////
// GaussianBlur
PARAM_TEST_CASE(GaussianBlur, MatType, cv::Size, int)
struct GaussianBlur : FilterTestBase
{
int type;
cv::Size ksize;
int bordertype;
double sigma1, sigma2;
//src mat
cv::Mat mat1;
cv::Mat dst;
// set up roi
int roicols;
int roirows;
int src1x;
int src1y;
int dstx;
int dsty;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
//ocl mat with roi
cv::ocl::oclMat gmat1;
cv::ocl::oclMat gdst;
virtual void SetUp()
{
type = GET_PARAM(0);
ksize = GET_PARAM(1);
bordertype = GET_PARAM(2);
bordertype = GET_PARAM(3);
Init(type);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
sigma1 = rng.uniform(0.1, 1.0);
sigma2 = rng.uniform(0.1, 1.0);
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
{
#ifdef RANDOMROI
//randomize ROI
cv::RNG &rng = TS::ptr()->get_rng();
roicols = rng.uniform(2, mat1.cols);
roirows = rng.uniform(2, mat1.rows);
src1x = rng.uniform(0, mat1.cols - roicols);
src1y = rng.uniform(0, mat1.rows - roirows);
dstx = rng.uniform(0, dst.cols - roicols);
dsty = rng.uniform(0, dst.rows - roirows);
#else
roicols = mat1.cols;
roirows = mat1.rows;
src1x = 0;
src1y = 0;
dstx = 0;
dsty = 0;
#endif
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
gdst_whole = dst;
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
gmat1 = mat1_roi;
}
};
TEST_P(GaussianBlur, Mat)
@ -795,53 +315,53 @@ TEST_P(GaussianBlur, Mat)
for(int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
cv::GaussianBlur(mat1_roi, dst_roi, ksize, sigma1, sigma2, bordertype);
cv::ocl::GaussianBlur(gmat1, gdst, ksize, sigma1, sigma2, bordertype);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
Near(1);
}
}
INSTANTIATE_TEST_CASE_P(Filter, Blur, Combine(Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
INSTANTIATE_TEST_CASE_P(Filter, Blur, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
Values(cv::Size(3, 3), cv::Size(5, 5), cv::Size(7, 7)),
Values(Size(0, 0)), //not use
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE, (MatType)cv::BORDER_REFLECT, (MatType)cv::BORDER_REFLECT_101)));
INSTANTIATE_TEST_CASE_P(Filters, Laplacian, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
Values(1, 3)));
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
Values(Size(3, 3)),
Values(Size(0, 0)), //not use
Values(0))); //not use
INSTANTIATE_TEST_CASE_P(Filter, Erode, Combine(Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(1)));
//INSTANTIATE_TEST_CASE_P(Filter, Erode, Combine(Values(CV_8UC1, CV_8UC1), Values(false)));
INSTANTIATE_TEST_CASE_P(Filter, Dilate, Combine(Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(1)));
//INSTANTIATE_TEST_CASE_P(Filter, Dilate, Combine(Values(CV_8UC1, CV_8UC1), Values(false)));
INSTANTIATE_TEST_CASE_P(Filter, ErodeDilate, Combine(
Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4),
Values(Size(0, 0)), //not use
Values(Size(0, 0)), //not use
Values(1)));
INSTANTIATE_TEST_CASE_P(Filter, Sobel, Combine(Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
Values(1, 2), Values(0, 1), Values(3, 5), Values((MatType)cv::BORDER_CONSTANT,
(MatType)cv::BORDER_REPLICATE)));
INSTANTIATE_TEST_CASE_P(Filter, Sobel, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
Values(Size(3, 3), Size(5, 5)),
Values(Size(1, 0), Size(1, 1), Size(2, 0), Size(2, 1)),
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
INSTANTIATE_TEST_CASE_P(Filter, Scharr, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4), Values(0, 1), Values(0, 1),
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
Values(Size(0, 0)), //not use
Values(Size(0, 1), Size(1, 0)),
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
INSTANTIATE_TEST_CASE_P(Filter, GaussianBlur, Combine(
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
Values(cv::Size(3, 3), cv::Size(5, 5)),
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));
Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC4),
Values(Size(3, 3), Size(5, 5)),
Values(Size(0, 0)), //not use
Values((MatType)cv::BORDER_CONSTANT, (MatType)cv::BORDER_REPLICATE)));

3
modules/ocl/test/test_gemm.cpp
View File

@ -53,13 +53,12 @@ PARAM_TEST_CASE(Gemm, int, cv::Size, int)
int type;
cv::Size mat_size;
int flags;
//vector<cv::ocl::Info> info;
virtual void SetUp()
{
type = GET_PARAM(0);
mat_size = GET_PARAM(1);
flags = GET_PARAM(2);
//cv::ocl::getDevice(info);
}
};

1
modules/ocl/test/test_haar.cpp
View File

@ -12,6 +12,7 @@
//
// 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.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors

5
modules/ocl/test/test_hog.cpp
View File

@ -240,12 +240,11 @@ TEST_P(HOG, Detect)
}
}
char s[100] = {0};
EXPECT_MAT_NEAR(cv::Mat(d_comp), cv::Mat(comp), 3, s);
EXPECT_MAT_NEAR(cv::Mat(d_comp), cv::Mat(comp), 3);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HOG, testing::Combine(
INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, HOG, testing::Combine(
testing::Values(cv::Size(64, 128), cv::Size(48, 96)),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4))));

176
modules/ocl/test/test_imgproc.cpp
View File

@ -12,6 +12,7 @@
//
// 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.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
@ -327,7 +328,7 @@ PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bo
cv::Mat mask_roi;
cv::Mat dst_roi;
cv::Mat dst1_roi; //bak
//std::vector<cv::ocl::Info> oclinfo;
//ocl mat
cv::ocl::oclMat clmat1;
cv::ocl::oclMat clmat2;
@ -352,10 +353,6 @@ PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bo
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(MWIDTH, MHEIGHT);
double min = 1, max = 20;
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
if(type1 != nulltype)
{
@ -445,6 +442,13 @@ PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bo
clmask_roi = clmask(Rect(maskx, masky, roicols, roirows));
}
}
void Near(double threshold)
{
cv::Mat cpu_cldst;
cldst.download(cpu_cldst);
EXPECT_MAT_NEAR(dst, cpu_cldst, threshold);
}
};
////////////////////////////////equalizeHist//////////////////////////////////////////
@ -464,11 +468,7 @@ TEST_P(equalizeHist, Mat)
random_roi();
cv::equalizeHist(mat1_roi, dst_roi);
cv::ocl::equalizeHist(clmat1_roi, cldst_roi);
cv::Mat cpu_cldst;
cldst.download(cpu_cldst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_cldst, 1.1, sss);
Near(1.1);
}
}
}
@ -488,7 +488,7 @@ TEST_P(bilateralFilter, Mat)
int d = 2 * radius + 1;
double sigmaspace = 20.0;
int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE, cv::BORDER_REFLECT, cv::BORDER_WRAP, cv::BORDER_REFLECT_101};
const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101"};
//const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101"};
if (mat1.depth() != CV_8U || mat1.type() != dst.type())
{
@ -517,25 +517,7 @@ TEST_P(bilateralFilter, Mat)
cv::bilateralFilter(mat1_roi, dst_roi, d, sigmacolor, sigmaspace, bordertype[i] | cv::BORDER_ISOLATED);
cv::ocl::bilateralFilter(clmat1_roi, cldst_roi, d, sigmacolor, sigmaspace, bordertype[i] | cv::BORDER_ISOLATED);
cv::Mat cpu_cldst;
cldst.download(cpu_cldst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,radius=%d,boredertype=%s", roicols, roirows, src1x, src1y, dstx, dsty, radius, borderstr[i]);
//for(int i=0;i<dst.rows;i++)
//{
// for(int j=0;j<dst.cols*dst.channels();j++)
// {
// if(dst.at<uchar>(i,j)!=cpu_cldst.at<uchar>(i,j))
// cout<< i <<" "<< j <<" "<< (int)dst.at<uchar>(i,j)<<" "<< (int)cpu_cldst.at<uchar>(i,j)<<" ";
// }
// cout<<endl;
//}
EXPECT_MAT_NEAR(dst, cpu_cldst, 1.0, sss);
Near(1.);
}
}
}
@ -549,7 +531,7 @@ struct CopyMakeBorder : ImgprocTestBase {};
TEST_P(CopyMakeBorder, Mat)
{
int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE, cv::BORDER_REFLECT, cv::BORDER_WRAP, cv::BORDER_REFLECT_101};
const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101"};
//const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101"};
cv::RNG &rng = TS::ptr()->get_rng();
int top = rng.uniform(0, 10);
int bottom = rng.uniform(0, 10);
@ -587,24 +569,12 @@ TEST_P(CopyMakeBorder, Mat)
cv::Mat cpu_cldst;
#ifndef RANDOMROI
cldst_roi.download(cpu_cldst);
EXPECT_MAT_NEAR(dst_roi, cpu_cldst, 0.0);
#else
cldst.download(cpu_cldst);
EXPECT_MAT_NEAR(dst, cpu_cldst, 0.0);
#endif
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,top=%d,bottom=%d,left=%d,right=%d, bordertype=%s", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, top, bottom, left, right, borderstr[i]);
#ifndef RANDOMROI
EXPECT_MAT_NEAR(dst_roi, cpu_cldst, 0.0, sss);
#else
//for(int i=0;i<dst.rows;i++)
//{
//for(int j=0;j<dst.cols;j++)
//{
// cout<< (int)dst.at<uchar>(i,j)<<" ";
//}
//cout<<endl;
//}
EXPECT_MAT_NEAR(dst, cpu_cldst, 0.0, sss);
#endif
}
}
}
@ -627,14 +597,7 @@ TEST_P(cornerMinEigenVal, Mat)
int borderType = cv::BORDER_REFLECT;
cv::cornerMinEigenVal(mat1_roi, dst_roi, blockSize, apertureSize, borderType);
cv::ocl::cornerMinEigenVal(clmat1_roi, cldst_roi, blockSize, apertureSize, borderType);
cv::Mat cpu_cldst;
cldst.download(cpu_cldst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_cldst, 1, sss);
Near(1.);
}
}
@ -657,13 +620,7 @@ TEST_P(cornerHarris, Mat)
int borderType = cv::BORDER_REFLECT;
cv::cornerHarris(mat1_roi, dst_roi, blockSize, apertureSize, k, borderType);
cv::ocl::cornerHarris(clmat1_roi, cldst_roi, blockSize, apertureSize, k, borderType);
cv::Mat cpu_cldst;
cldst.download(cpu_cldst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_cldst, 1, sss);
Near(1.);
}
}
@ -680,15 +637,11 @@ TEST_P(integral, Mat)
cv::ocl::integral(clmat1_roi, cldst_roi, cldst1_roi);
cv::integral(mat1_roi, dst_roi, dst1_roi);
Near(0);
cv::Mat cpu_cldst, cpu_cldst1;
cldst.download(cpu_cldst);
cv::Mat cpu_cldst1;
cldst1.download(cpu_cldst1);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_cldst, 0.0, sss);
EXPECT_MAT_NEAR(dst1, cpu_cldst1, 0.0, sss);
EXPECT_MAT_NEAR(dst1, cpu_cldst1, 0.0);
}
}
@ -720,7 +673,7 @@ PARAM_TEST_CASE(WarpTestBase, MatType, int)
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -740,10 +693,6 @@ PARAM_TEST_CASE(WarpTestBase, MatType, int)
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -805,10 +754,7 @@ TEST_P(WarpAffine, Mat)
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
}
}
@ -837,10 +783,7 @@ TEST_P(WarpPerspective, Mat)
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
}
}
@ -905,9 +848,6 @@ PARAM_TEST_CASE(Remap, MatType, MatType, MatType, int, int)
interpolation = GET_PARAM(3);
bordertype = GET_PARAM(4);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size srcSize = cv::Size(MWIDTH, MHEIGHT);
cv::Size map1Size = cv::Size(MWIDTH, MHEIGHT);
@ -1004,7 +944,7 @@ TEST_P(Remap, Mat)
return;
}
int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE/*,BORDER_REFLECT,BORDER_WRAP,BORDER_REFLECT_101*/};
const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE"/*, "BORDER_REFLECT","BORDER_WRAP","BORDER_REFLECT_101"*/};
//const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE"/*, "BORDER_REFLECT","BORDER_WRAP","BORDER_REFLECT_101"*/};
// for(int i = 0; i < sizeof(bordertype)/sizeof(int); i++)
for(int j = 0; j < LOOP_TIMES; j++)
{
@ -1014,13 +954,9 @@ TEST_P(Remap, Mat)
cv::Mat cpu_dst;
gdst.download(cpu_dst);
char sss[1024];
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d bordertype=%s", src_roicols, src_roirows, dst_roicols, dst_roirows, srcx, srcy, dstx, dsty, borderstr[0]);
if(interpolation == 0)
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, 2.0, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
EXPECT_MAT_NEAR(dst, cpu_dst, 2.0);
}
}
@ -1051,7 +987,6 @@ PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
int dstx;
int dsty;
//std::vector<cv::ocl::Info> oclinfo;
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
@ -1090,10 +1025,6 @@ PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, dsize, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -1149,10 +1080,7 @@ TEST_P(Resize, Mat)
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, src1x, src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0);
}
}
@ -1181,7 +1109,7 @@ PARAM_TEST_CASE(Threshold, MatType, ThreshOp)
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -1199,11 +1127,6 @@ PARAM_TEST_CASE(Threshold, MatType, ThreshOp)
mat1 = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -1251,12 +1174,7 @@ TEST_P(Threshold, Mat)
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
//EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5)
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x , src1y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, 1);
}
}
@ -1288,7 +1206,6 @@ PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, cv::TermCriteria)
cv::ocl::oclMat gdst;
cv::ocl::oclMat gdstCoor;
//std::vector<cv::ocl::Info> oclinfo;
//ocl mat with roi
cv::ocl::oclMat gsrc_roi;
cv::ocl::oclMat gdst_roi;
@ -1311,10 +1228,6 @@ PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, cv::TermCriteria)
dst = randomMat(rng, size, type, 5, 16, false);
dstCoor = randomMat(rng, size, typeCoor, 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -1367,11 +1280,7 @@ TEST_P(meanShiftFiltering, Mat)
cv::ocl::meanShiftFiltering(gsrc_roi, gdst_roi, sp, sr, crit);
gdst.download(cpu_gdst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx=%d,srcy=%d,dstx=%d,dsty=%d\n", roicols, roirows, srcx, srcy, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0, sss);
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0);
}
}
@ -1393,11 +1302,8 @@ TEST_P(meanShiftProc, Mat)
gdst.download(cpu_gdst);
gdstCoor.download(cpu_gdstCoor);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx=%d,srcy=%d,dstx=%d,dsty=%d\n", roicols, roirows, srcx, srcy, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0, sss);
EXPECT_MAT_NEAR(dstCoor, cpu_gdstCoor, 0.0, sss);
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0);
EXPECT_MAT_NEAR(dstCoor, cpu_gdstCoor, 0.0);
}
}
@ -1436,7 +1342,6 @@ PARAM_TEST_CASE(histTestBase, MatType, MatType)
cv::ocl::oclMat gdst_hist;
//ocl mat with roi
cv::ocl::oclMat gsrc_roi;
// std::vector<cv::ocl::Info> oclinfo;
virtual void SetUp()
{
@ -1447,10 +1352,6 @@ PARAM_TEST_CASE(histTestBase, MatType, MatType)
src = randomMat(rng, size, type_src, 0, 256, false);
// int devnums = getDevice(oclinfo);
// CV_Assert(devnums > 0);
//if you want to use undefault device, set it here
//setDevice(oclinfo[0]);
}
void random_roi()
@ -1489,10 +1390,7 @@ TEST_P(calcHist, Mat)
cv::ocl::calcHist(gsrc_roi, gdst_hist);
gdst_hist.download(cpu_hist);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx=%d,srcy=%d\n", roicols, roirows, srcx, srcy);
EXPECT_MAT_NEAR(dst_hist, cpu_hist, 0.0, sss);
EXPECT_MAT_NEAR(dst_hist, cpu_hist, 0.0);
}
}
@ -1629,11 +1527,7 @@ TEST_P(Convolve, Mat)
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, src2x, src2y);
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-1, sss);
EXPECT_MAT_NEAR(dst, cpu_dst, .1);
}
}

13
modules/ocl/test/test_match_template.cpp
View File

@ -62,7 +62,6 @@ PARAM_TEST_CASE(MatchTemplate8U, cv::Size, TemplateSize, Channels, TemplateMetho
cv::Size templ_size;
int cn;
int method;
//std::vector<cv::ocl::Info> oclinfo;
virtual void SetUp()
{
@ -70,8 +69,6 @@ PARAM_TEST_CASE(MatchTemplate8U, cv::Size, TemplateSize, Channels, TemplateMetho
templ_size = GET_PARAM(1);
cn = GET_PARAM(2);
method = GET_PARAM(3);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
}
};
@ -92,12 +89,10 @@ TEST_P(MatchTemplate8U, Accuracy)
cv::Mat dst_gold;
cv::matchTemplate(image, templ, dst_gold, method);
char sss [100] = "";
cv::Mat mat_dst;
dst.download(mat_dst);
EXPECT_MAT_NEAR(dst_gold, mat_dst, templ_size.area() * 1e-1, sss);
EXPECT_MAT_NEAR(dst_gold, mat_dst, templ_size.area() * 1e-1);
}
PARAM_TEST_CASE(MatchTemplate32F, cv::Size, TemplateSize, Channels, TemplateMethod)
@ -114,8 +109,6 @@ PARAM_TEST_CASE(MatchTemplate32F, cv::Size, TemplateSize, Channels, TemplateMeth
templ_size = GET_PARAM(1);
cn = GET_PARAM(2);
method = GET_PARAM(3);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
}
};
@ -130,12 +123,10 @@ TEST_P(MatchTemplate32F, Accuracy)
cv::Mat dst_gold;
cv::matchTemplate(image, templ, dst_gold, method);
char sss [100] = "";
cv::Mat mat_dst;
dst.download(mat_dst);
EXPECT_MAT_NEAR(dst_gold, mat_dst, templ_size.area() * 1e-1, sss);
EXPECT_MAT_NEAR(dst_gold, mat_dst, templ_size.area() * 1e-1);
}
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MatchTemplate8U,

71
modules/ocl/test/test_matrix_operation.cpp
View File

@ -12,6 +12,7 @@
//
// 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.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
@ -72,7 +73,7 @@ PARAM_TEST_CASE(ConvertToTestBase, MatType, MatType)
//src mat with roi
cv::Mat mat_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -90,11 +91,6 @@ PARAM_TEST_CASE(ConvertToTestBase, MatType, MatType)
mat = randomMat(rng, size, type, 5, 16, false);
dst = randomMat(rng, size, type, 5, 16, false);
//std::vector<cv::ocl::Info> oclinfo;
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -139,12 +135,7 @@ TEST_P(ConvertTo, Accuracy)
mat_roi.convertTo(dst_roi, dst_type);
gmat.convertTo(gdst, dst_type);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx =%d,srcy=%d,dstx=%d,dsty=%d", roicols, roirows, srcx , srcy, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), 0.0);
}
}
@ -175,7 +166,7 @@ PARAM_TEST_CASE(CopyToTestBase, MatType, bool)
cv::Mat mat_roi;
cv::Mat mask_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -197,10 +188,6 @@ PARAM_TEST_CASE(CopyToTestBase, MatType, bool)
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -250,12 +237,7 @@ TEST_P(CopyTo, Without_mask)
mat_roi.copyTo(dst_roi);
gmat.copyTo(gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx =%d,srcy=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d", roicols, roirows, srcx , srcy, dstx, dsty, maskx, masky);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), 0.0);
}
}
@ -268,12 +250,7 @@ TEST_P(CopyTo, With_mask)
mat_roi.copyTo(dst_roi, mask_roi);
gmat.copyTo(gdst, gmask);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx =%d,srcy=%d,dstx=%d,dsty=%d,maskx=%d,masky=%d", roicols, roirows, srcx , srcy, dstx, dsty, maskx, masky);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), 0.0);
}
}
@ -301,7 +278,7 @@ PARAM_TEST_CASE(SetToTestBase, MatType, bool)
//src mat with roi
cv::Mat mat_roi;
cv::Mat mask_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gmat_whole;
@ -322,10 +299,6 @@ PARAM_TEST_CASE(SetToTestBase, MatType, bool)
cv::threshold(mask, mask, 0.5, 255., CV_8UC1);
val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0));
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -369,12 +342,7 @@ TEST_P(SetTo, Without_mask)
mat_roi.setTo(val);
gmat.setTo(val);
cv::Mat cpu_dst;
gmat_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx =%d,srcy=%d,maskx=%d,masky=%d", roicols, roirows, srcx , srcy, maskx, masky);
EXPECT_MAT_NEAR(mat, cpu_dst, 1., sss);
EXPECT_MAT_NEAR(mat, Mat(gmat_whole), 1.);
}
}
@ -387,12 +355,7 @@ TEST_P(SetTo, With_mask)
mat_roi.setTo(val, mask_roi);
gmat.setTo(val, gmask);
cv::Mat cpu_dst;
gmat_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,srcx =%d,srcy=%d,maskx=%d,masky=%d", roicols, roirows, srcx , srcy, maskx, masky);
EXPECT_MAT_NEAR(mat, cpu_dst, 1., sss);
EXPECT_MAT_NEAR(mat, Mat(gmat_whole), 1.);
}
}
@ -417,7 +380,7 @@ PARAM_TEST_CASE(convertC3C4, MatType, cv::Size)
//src mat with roi
cv::Mat mat1_roi;
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -430,13 +393,6 @@ PARAM_TEST_CASE(convertC3C4, MatType, cv::Size)
type = GET_PARAM(0);
ksize = GET_PARAM(1);
//dst = randomMat(rng, size, type, 5, 16, false);
//int devnums = getDevice(oclinfo);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[1]);
}
void random_roi()
@ -483,11 +439,8 @@ TEST_P(convertC3C4, Accuracy)
mat1 = randomMat(rng, size, type, 0, 40, false);
gmat1 = mat1;
cv::Mat cpu_dst;
gmat1.download(cpu_dst);
char sss[1024];
sprintf(sss, "cols=%d,rows=%d", mat1.cols, mat1.rows);
EXPECT_MAT_NEAR(mat1, cpu_dst, 0.0, sss);
EXPECT_MAT_NEAR(mat1, Mat(gmat1), 0.0);
}
}

10
modules/ocl/test/test_moments.cpp
View File

@ -10,7 +10,7 @@ using namespace cvtest;
using namespace testing;
using namespace std;
extern string workdir;
PARAM_TEST_CASE(MomentsTestBase, MatType, bool)
PARAM_TEST_CASE(MomentsTest, MatType, bool)
{
int type;
cv::Mat mat1;
@ -30,13 +30,13 @@ PARAM_TEST_CASE(MomentsTestBase, MatType, bool)
Mat gpu_dst, cpu_dst;
HuMoments(cpu, cpu_dst);
HuMoments(gpu, gpu_dst);
EXPECT_MAT_NEAR(gpu_dst,cpu_dst, .5, "");
EXPECT_MAT_NEAR(gpu_dst,cpu_dst, .5);
}
};
struct ocl_Moments : MomentsTestBase {};
TEST_P(ocl_Moments, Mat)
TEST_P(MomentsTest, Mat)
{
bool binaryImage = 0;
SetUp();
@ -67,6 +67,6 @@ TEST_P(ocl_Moments, Mat)
}
}
INSTANTIATE_TEST_CASE_P(Moments, ocl_Moments, Combine(
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MomentsTest, Combine(
Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_64FC1), Values(true,false)));
#endif // HAVE_OPENCL

19
modules/ocl/test/test_pyrdown.cpp
View File

@ -65,15 +65,6 @@ PARAM_TEST_CASE(PyrDown, MatType, int)
{
type = GET_PARAM(0);
channels = GET_PARAM(1);
//int devnums = getDevice(oclinfo);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void Cleanup()
{
}
};
@ -92,17 +83,11 @@ TEST_P(PyrDown, Mat)
cv::pyrDown(src, dst_cpu);
cv::ocl::pyrDown(gsrc, gdst);
cv::Mat dst;
gdst.download(dst);
char s[1024] = {0};
EXPECT_MAT_NEAR(dst, dst_cpu, dst.depth() == CV_32F ? 1e-4f : 1.0f, s);
Cleanup();
EXPECT_MAT_NEAR(dst_cpu, Mat(gdst), type == CV_32F ? 1e-4f : 1.0f);
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, PyrDown, Combine(
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, PyrDown, Combine(
Values(CV_8U, CV_32F), Values(1, 3, 4)));

12
modules/ocl/test/test_pyrlk.cpp
View File

@ -50,19 +50,7 @@ using namespace cvtest;
using namespace testing;
using namespace std;
//#define DUMP
/////////////////////////////////////////////////////////////////////////////////////////////////
// BroxOpticalFlow
extern string workdir;
#define BROX_OPTICAL_FLOW_DUMP_FILE "opticalflow/brox_optical_flow.bin"
#define BROX_OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/brox_optical_flow_cc20.bin"
/////////////////////////////////////////////////////////////////////////////////////////////////
// PyrLKOpticalFlow
//IMPLEMENT_PARAM_CLASS(UseGray, bool)
PARAM_TEST_CASE(Sparse, bool, bool)
{

10
modules/ocl/test/test_pyrup.cpp
View File

@ -58,12 +58,9 @@ PARAM_TEST_CASE(PyrUp, MatType, int)
{
int type;
int channels;
//std::vector<cv::ocl::Info> oclinfo;
virtual void SetUp()
{
//int devnums = cv::ocl::getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
type = GET_PARAM(0);
channels = GET_PARAM(1);
}
@ -80,17 +77,14 @@ TEST_P(PyrUp, Accuracy)
ocl::oclMat dst;
ocl::oclMat srcMat(src);
ocl::pyrUp(srcMat, dst);
Mat cpu_dst;
dst.download(cpu_dst);
char s[100] = {0};
EXPECT_MAT_NEAR(dst_gold, cpu_dst, (src.depth() == CV_32F ? 1e-4f : 1.0), s);
EXPECT_MAT_NEAR(dst_gold, Mat(dst), (type == CV_32F ? 1e-4f : 1.0));
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, PyrUp, testing::Combine(
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, PyrUp, testing::Combine(
Values(CV_8U, CV_32F), Values(1, 3, 4)));

39
modules/ocl/test/test_split_merge.cpp
View File

@ -12,6 +12,7 @@
//
// 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.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
@ -87,7 +88,7 @@ PARAM_TEST_CASE(MergeTestBase, MatType, int)
//dst mat with roi
cv::Mat dst_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst_whole;
@ -112,10 +113,6 @@ PARAM_TEST_CASE(MergeTestBase, MatType, int)
mat4 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
dst = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -205,12 +202,7 @@ TEST_P(Merge, Accuracy)
cv::merge(dev_src, dst_roi);
cv::ocl::merge(dev_gsrc, gdst);
cv::Mat cpu_dst;
gdst_whole.download(cpu_dst);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,src1x =%d,src1y=%d,src2x =%d,src2y=%d,src3x =%d,src3y=%d,src4x =%d,src4y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, src2x , src2y, src3x , src3y, src4x , src4y, dstx, dsty);
EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
EXPECT_MAT_NEAR(dst, Mat(gdst_whole), 0.0);
}
}
@ -252,7 +244,7 @@ PARAM_TEST_CASE(SplitTestBase, MatType, int)
cv::Mat dst2_roi;
cv::Mat dst3_roi;
cv::Mat dst4_roi;
//std::vector<cv::ocl::Info> oclinfo;
//ocl dst mat for testing
cv::ocl::oclMat gdst1_whole;
cv::ocl::oclMat gdst2_whole;
@ -280,10 +272,6 @@ PARAM_TEST_CASE(SplitTestBase, MatType, int)
dst3 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
dst4 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
}
void random_roi()
@ -356,28 +344,17 @@ TEST_P(Split, Accuracy)
cv::split(mat_roi, dev_dst);
cv::ocl::split(gmat, dev_gdst);
cv::Mat cpu_dst1;
cv::Mat cpu_dst2;
cv::Mat cpu_dst3;
cv::Mat cpu_dst4;
gdst1_whole.download(cpu_dst1);
gdst2_whole.download(cpu_dst2);
gdst3_whole.download(cpu_dst3);
gdst4_whole.download(cpu_dst4);
char sss[1024];
sprintf(sss, "roicols=%d,roirows=%d,dst1x =%d,dsty=%d,dst2x =%d,dst2y=%d,dst3x =%d,dst3y=%d,dst4x =%d,dst4y=%d,srcx=%d,srcy=%d", roicols, roirows, dst1x , dst1y, dst2x , dst2y, dst3x , dst3y, dst4x , dst4y, srcx, srcy);
if(channels >= 1)
EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.0, sss);
EXPECT_MAT_NEAR(dst1, Mat(gdst1_whole), 0.0);
if(channels >= 2)
EXPECT_MAT_NEAR(dst2, cpu_dst2, 0.0, sss);
EXPECT_MAT_NEAR(dst2, Mat(gdst2_whole), 0.0);
if(channels >= 3)
EXPECT_MAT_NEAR(dst3, cpu_dst3, 0.0, sss);
EXPECT_MAT_NEAR(dst3, Mat(gdst3_whole), 0.0);
if(channels >= 4)
EXPECT_MAT_NEAR(dst4, cpu_dst4, 0.0, sss);
EXPECT_MAT_NEAR(dst4, Mat(gdst4_whole), 0.0);
}
}

8
modules/ocl/test/utility.hpp
View File

@ -76,20 +76,20 @@ double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2);
EXPECT_LE(checkNorm(cv::Mat(mat)), eps) \
}
/*#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps); \
}*/
}
/*
#define EXPECT_MAT_NEAR(mat1, mat2, eps,s) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps)<<s; \
}
*/
#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \