/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
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//  copy or use the software.
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//
//                           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
//    Jin Ma,       jin@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
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//   * Redistribution's of source code must retain the above copyright notice,
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//     and/or other oclMaterials provided with the distribution.
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//     derived from this software without specific prior written permission.
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//M*/
#include "perf_precomp.hpp"
using namespace perf;
using namespace std;
using namespace cv::ocl;
using namespace cv;
using std::tr1::tuple;
using std::tr1::get;
///////////// Kalman Filter ////////////////////////

typedef tuple<int> KalmanFilterType;
typedef TestBaseWithParam<KalmanFilterType> KalmanFilterFixture;

PERF_TEST_P(KalmanFilterFixture, KalmanFilter,
            ::testing::Values(1000, 1500))
{
    KalmanFilterType params = GetParam();
    const int dim = get<0>(params);

    cv::Mat sample(dim, 1, CV_32FC1), dresult;
    randu(sample, -1, 1);
    cv::ocl::oclMat dsample(sample);

    cv::Mat statePre_;

    if(RUN_PLAIN_IMPL)
    {
        TEST_CYCLE()
        {
            cv::KalmanFilter kalman;
            kalman.init(dim, dim);
            kalman.correct(sample);
            kalman.predict();
            statePre_ = kalman.statePre;
        }
        SANITY_CHECK(statePre_);
    }else if(RUN_OCL_IMPL)
    {
        OCL_TEST_CYCLE()
        {
            cv::ocl::KalmanFilter kalman_ocl;
            kalman_ocl.init(dim, dim); 
            kalman_ocl.correct(dsample);
            kalman_ocl.predict();
            kalman_ocl.statePre.download(statePre_);
        }
        SANITY_CHECK(statePre_);
    }else
        OCL_PERF_ELSE
}