Open Source Computer Vision Library https://opencv.org/
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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.
//
// @Authors
// Fangfang Bai, fangfang@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.
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// * The name of the copyright holders may not be used to endorse or promote products
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//M*/
#include "precomp.hpp"
#include <iomanip>
#ifdef HAVE_OPENCL
using namespace cv;
using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
using namespace std;
PARAM_TEST_CASE(Blend, MatType, int)
{
int type;
int channels;
std::vector<cv::ocl::Info> oclinfo;
virtual void SetUp()
{
type = GET_PARAM(0);
channels = GET_PARAM(1);
//int devnums = getDevice(oclinfo);
//CV_Assert(devnums > 0);
//cv::ocl::setBinpath(CLBINPATH);
}
};
TEST_P(Blend, Performance)
{
cv::Size size(MWIDTH, MHEIGHT);
cv::Mat img1_host = randomMat(size, CV_MAKETYPE(type, channels), 0, type == CV_8U ? 255.0 : 1.0);
cv::Mat img2_host = randomMat(size, CV_MAKETYPE(type, channels), 0, type == CV_8U ? 255.0 : 1.0);
cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
cv::ocl::oclMat gimg1(size, CV_MAKETYPE(type, channels)), gimg2(size, CV_MAKETYPE(type, channels)), gweights1(size, CV_32F), gweights2(size, CV_32F);
cv::ocl::oclMat gdst(size, CV_MAKETYPE(type, channels));
double totalgputick_all = 0;
double totalgputick_kernel = 0;
double t1 = 0;
double t2 = 0;
for (int j = 0; j < LOOP_TIMES + 1; j ++) //LOOP_TIMES=100
{
t1 = (double)cvGetTickCount();
cv::ocl::oclMat gimg1 = cv::ocl::oclMat(img1_host);
cv::ocl::oclMat gimg2 = cv::ocl::oclMat(img2_host);
cv::ocl::oclMat gweights1 = cv::ocl::oclMat(weights1);
cv::ocl::oclMat gweights2 = cv::ocl::oclMat(weights1);
t2 = (double)cvGetTickCount();
cv::ocl::blendLinear(gimg1, gimg2, gweights1, gweights2, gdst);
t2 = (double)cvGetTickCount() - t2;
cv::Mat m;
gdst.download(m);
t1 = (double)cvGetTickCount() - t1;
if (j == 0)
{
continue;
}
totalgputick_all = t1 + totalgputick_all;
totalgputick_kernel = t2 + totalgputick_kernel;
};
cout << "average gpu total runtime is " << totalgputick_all / ((double)cvGetTickFrequency()* LOOP_TIMES * 1000.) << "ms" << endl;
cout << "average gpu runtime without data transfering is " << totalgputick_kernel / ((double)cvGetTickFrequency()* LOOP_TIMES * 1000.) << "ms" << endl;
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, Combine(
Values(CV_8U, CV_32F), Values(1, 4)));
#endif