/*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*/

#include "precomp.hpp"

#ifdef HAVE_CUDA

using namespace cvtest;
using namespace testing;

int getValidMatchesCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches)
{
    int validCount = 0;

    for (size_t i = 0; i < matches.size(); ++i)
    {
        const cv::DMatch& m = matches[i];

        const cv::KeyPoint& p1 = keypoints1[m.queryIdx];
        const cv::KeyPoint& p2 = keypoints2[m.trainIdx];

        const float maxPtDif = 1.f;
        const float maxSizeDif = 1.f;
        const float maxAngleDif = 2.f;
        const float maxResponseDif = 0.1f;

        float dist = (float) cv::norm(p1.pt - p2.pt);

        if (dist < maxPtDif &&
            fabs(p1.size - p2.size) < maxSizeDif &&
            abs(p1.angle - p2.angle) < maxAngleDif &&
            abs(p1.response - p2.response) < maxResponseDif &&
            p1.octave == p2.octave &&
            p1.class_id == p2.class_id)
        {
            ++validCount;
        }
    }

    return validCount;
}

/////////////////////////////////////////////////////////////////////////////////////////////////
// SURF

struct SURF : TestWithParam<cv::gpu::DeviceInfo>
{
    cv::gpu::DeviceInfo devInfo;

    cv::Mat image;
    cv::Mat mask;

    std::vector<cv::KeyPoint> keypoints_gold;
    std::vector<float> descriptors_gold;
    
    virtual void SetUp()
    {
        devInfo = GetParam();

        cv::gpu::setDevice(devInfo.deviceID());

        image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
        ASSERT_FALSE(image.empty());

        mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1));
        mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));

        cv::SURF fdetector_gold;
        fdetector_gold.extended = false;
        fdetector_gold(image, mask, keypoints_gold, descriptors_gold);
    }
};

TEST_P(SURF, EmptyDataTest)
{
    cv::gpu::SURF_GPU fdetector;

    cv::gpu::GpuMat image;
    std::vector<cv::KeyPoint> keypoints;
    std::vector<float> descriptors;

    fdetector(image, cv::gpu::GpuMat(), keypoints, descriptors);

    EXPECT_TRUE(keypoints.empty());
    EXPECT_TRUE(descriptors.empty());
}

TEST_P(SURF, Accuracy)
{
    std::vector<cv::KeyPoint> keypoints;
    cv::Mat descriptors;

    cv::gpu::GpuMat dev_descriptors;
    cv::gpu::SURF_GPU fdetector; fdetector.extended = false;

    fdetector(loadMat(image), loadMat(mask), keypoints, dev_descriptors);

    dev_descriptors.download(descriptors);

    cv::BFMatcher matcher(cv::NORM_L2);
    std::vector<cv::DMatch> matches;

    matcher.match(cv::Mat(static_cast<int>(keypoints_gold.size()), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches);

    int validCount = getValidMatchesCount(keypoints_gold, keypoints, matches);

    double validRatio = (double) validCount / matches.size();

    EXPECT_GT(validRatio, 0.5);
}

INSTANTIATE_TEST_CASE_P(Features2D, SURF, DEVICES(cv::gpu::GLOBAL_ATOMICS));

/////////////////////////////////////////////////////////////////////////////////////////////////
// BruteForceMatcher

PARAM_TEST_CASE(BruteForceMatcher, cv::gpu::DeviceInfo, DistType, int)
{
    cv::gpu::DeviceInfo devInfo;
    cv::gpu::BruteForceMatcher_GPU_base::DistType distType;
    int dim;
        
    int queryDescCount;
    int countFactor;
    
    cv::Mat query, train;

    virtual void SetUp() 
    {
        devInfo = GET_PARAM(0);
        distType = (cv::gpu::BruteForceMatcher_GPU_base::DistType)(int)GET_PARAM(1);
        dim = GET_PARAM(2);

        cv::gpu::setDevice(devInfo.deviceID());
        
        queryDescCount = 300; // must be even number because we split train data in some cases in two
        countFactor = 4; // do not change it

        cv::RNG& rng = cvtest::TS::ptr()->get_rng();

        cv::Mat queryBuf, trainBuf;

        // Generate query descriptors randomly.
        // Descriptor vector elements are integer values.
        queryBuf.create(queryDescCount, dim, CV_32SC1);
        rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
        queryBuf.convertTo(queryBuf, CV_32FC1);

        // Generate train decriptors as follows:
        // copy each query descriptor to train set countFactor times
        // and perturb some one element of the copied descriptors in
        // in ascending order. General boundaries of the perturbation
        // are (0.f, 1.f).
        trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
        float step = 1.f / countFactor;
        for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
        {
            cv::Mat queryDescriptor = queryBuf.row(qIdx);
            for (int c = 0; c < countFactor; c++)
            {
                int tIdx = qIdx * countFactor + c;
                cv::Mat trainDescriptor = trainBuf.row(tIdx);
                queryDescriptor.copyTo(trainDescriptor);
                int elem = rng(dim);
                float diff = rng.uniform(step * c, step * (c + 1));
                trainDescriptor.at<float>(0, elem) += diff;
            }
        }

        queryBuf.convertTo(query, CV_32F);
        trainBuf.convertTo(train, CV_32F);
    }
};

TEST_P(BruteForceMatcher, Match)
{
    std::vector<cv::DMatch> matches;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);

    matcher.match(loadMat(query), loadMat(train), matches);

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        cv::DMatch match = matches[i];
        if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
            badCount++;
    }

    ASSERT_EQ(0, badCount);
}


TEST_P(BruteForceMatcher, MatchAdd)
{
    std::vector<cv::DMatch> matches;

    bool isMaskSupported;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);

    cv::gpu::GpuMat d_train(train);

    // make add() twice to test such case
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows/2)));
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows/2, train.rows)));

    // prepare masks (make first nearest match illegal)
    std::vector<cv::gpu::GpuMat> masks(2);
    for (int mi = 0; mi < 2; mi++)
    {
        masks[mi] = cv::gpu::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1));
        for (int di = 0; di < queryDescCount/2; di++)
            masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
    }

    matcher.match(cv::gpu::GpuMat(query), matches, masks);

    isMaskSupported = matcher.isMaskSupported();

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        cv::DMatch match = matches[i];
        int shift = isMaskSupported ? 1 : 0;
        {
            if ((int)i < queryDescCount / 2)
            {
                if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0))
                    badCount++;
            }
            else
            {
                if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1))
                    badCount++;
            }
        }
    }

    ASSERT_EQ(0, badCount);
}

TEST_P(BruteForceMatcher, KnnMatch2)
{
    const int knn = 2;

    std::vector< std::vector<cv::DMatch> > matches;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
    matcher.knnMatch(loadMat(query), loadMat(train), matches, knn);

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        if ((int)matches[i].size() != knn)
            badCount++;
        else
        {
            int localBadCount = 0;
            for (int k = 0; k < knn; k++)
            {
                cv::DMatch match = matches[i][k];
                if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
                    localBadCount++;
            }
            badCount += localBadCount > 0 ? 1 : 0;
        }
    }

    ASSERT_EQ(0, badCount);
}

TEST_P(BruteForceMatcher, KnnMatch3)
{
    const int knn = 3;

    std::vector< std::vector<cv::DMatch> > matches;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
    matcher.knnMatch(loadMat(query), loadMat(train), matches, knn);

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        if ((int)matches[i].size() != knn)
            badCount++;
        else
        {
            int localBadCount = 0;
            for (int k = 0; k < knn; k++)
            {
                cv::DMatch match = matches[i][k];
                if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
                    localBadCount++;
            }
            badCount += localBadCount > 0 ? 1 : 0;
        }
    }

    ASSERT_EQ(0, badCount);
}

TEST_P(BruteForceMatcher, KnnMatchAdd2)
{
    const int knn = 2;
    std::vector< std::vector<cv::DMatch> > matches;

    bool isMaskSupported;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);

    cv::gpu::GpuMat d_train(train);

    // make add() twice to test such case
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));

    // prepare masks (make first nearest match illegal)
    std::vector<cv::gpu::GpuMat> masks(2);
    for (int mi = 0; mi < 2; mi++ )
    {
        masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
        for (int di = 0; di < queryDescCount / 2; di++)
            masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
    }

    matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);

    isMaskSupported = matcher.isMaskSupported();

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    int shift = isMaskSupported ? 1 : 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        if ((int)matches[i].size() != knn)
            badCount++;
        else
        {
            int localBadCount = 0;
            for (int k = 0; k < knn; k++)
            {
                cv::DMatch match = matches[i][k];
                {
                    if ((int)i < queryDescCount / 2)
                    {
                        if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
                            localBadCount++;
                    }
                    else
                    {
                        if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
                            localBadCount++;
                    }
                }
            }
            badCount += localBadCount > 0 ? 1 : 0;
        }
    }

    ASSERT_EQ(0, badCount);
}

TEST_P(BruteForceMatcher, KnnMatchAdd3)
{
    const int knn = 3;
    std::vector< std::vector<cv::DMatch> > matches;

    bool isMaskSupported;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);

    cv::gpu::GpuMat d_train(train);

    // make add() twice to test such case
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));

    // prepare masks (make first nearest match illegal)
    std::vector<cv::gpu::GpuMat> masks(2);
    for (int mi = 0; mi < 2; mi++ )
    {
        masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
        for (int di = 0; di < queryDescCount / 2; di++)
            masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
    }

    matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);

    isMaskSupported = matcher.isMaskSupported();

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    int shift = isMaskSupported ? 1 : 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        if ((int)matches[i].size() != knn)
            badCount++;
        else
        {
            int localBadCount = 0;
            for (int k = 0; k < knn; k++)
            {
                cv::DMatch match = matches[i][k];
                {
                    if ((int)i < queryDescCount / 2)
                    {
                        if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
                            localBadCount++;
                    }
                    else
                    {
                        if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
                            localBadCount++;
                    }
                }
            }
            badCount += localBadCount > 0 ? 1 : 0;
        }
    }

    ASSERT_EQ(0, badCount);
}

TEST_P(BruteForceMatcher, RadiusMatch)
{
    if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS))
        return;

    const float radius = 1.f / countFactor;


    std::vector< std::vector<cv::DMatch> > matches;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);

    matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius);

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    for (size_t i = 0; i < matches.size(); i++)
    {
        if ((int)matches[i].size() != 1)
            badCount++;
        else
        {
            cv::DMatch match = matches[i][0];
            if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
                badCount++;
        }
    }

    ASSERT_EQ(0, badCount);
}

TEST_P(BruteForceMatcher, RadiusMatchAdd)
{
    if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS))
        return;

    int n = 3;
    const float radius = 1.f / countFactor * n;

    std::vector< std::vector<cv::DMatch> > matches;

    bool isMaskSupported;

    cv::gpu::BruteForceMatcher_GPU_base matcher(distType);

    cv::gpu::GpuMat d_train(train);

    // make add() twice to test such case
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
    matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));

    // prepare masks (make first nearest match illegal)
    std::vector<cv::gpu::GpuMat> masks(2);
    for (int mi = 0; mi < 2; mi++)
    {
        masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
        for (int di = 0; di < queryDescCount / 2; di++)
            masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
    }

    matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius, masks);

    isMaskSupported = matcher.isMaskSupported();

    ASSERT_EQ(queryDescCount, matches.size());

    int badCount = 0;
    int shift = isMaskSupported ? 1 : 0;
    int needMatchCount = isMaskSupported ? n-1 : n;
    for (size_t i = 0; i < matches.size(); i++)
    {
        if ((int)matches[i].size() != needMatchCount)
            badCount++;
        else
        {
            int localBadCount = 0;
            for (int k = 0; k < needMatchCount; k++)
            {
                cv::DMatch match = matches[i][k];
                {
                    if ((int)i < queryDescCount / 2)
                    {
                        if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
                            localBadCount++;
                    }
                    else
                    {
                        if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
                            localBadCount++;
                    }
                }
            }
            badCount += localBadCount > 0 ? 1 : 0;
        }
    }

    ASSERT_EQ(0, badCount);
}

INSTANTIATE_TEST_CASE_P(Features2D, BruteForceMatcher, Combine(
                        ALL_DEVICES,
                        Values(cv::gpu::BruteForceMatcher_GPU_base::L1Dist, cv::gpu::BruteForceMatcher_GPU_base::L2Dist),
                        Values(57, 64, 83, 128, 179, 256, 304)));

/////////////////////////////////////////////////////////////////////////////////////////////////
// FAST

struct FAST : TestWithParam<cv::gpu::DeviceInfo>
{
    cv::gpu::DeviceInfo devInfo;

    cv::Mat image;

    int threshold;

    std::vector<cv::KeyPoint> keypoints_gold;
    
    virtual void SetUp()
    {
        devInfo = GetParam();

        cv::gpu::setDevice(devInfo.deviceID());
        
        image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
        ASSERT_FALSE(image.empty());

        cv::RNG& rng = cvtest::TS::ptr()->get_rng();
        threshold = 30;

        cv::FAST(image, keypoints_gold, threshold);
    }
};

struct HashEq
{
    size_t hash;
    inline HashEq(size_t hash_) : hash(hash_) {}
    inline bool operator ()(const cv::KeyPoint& kp) const
    {
        return kp.hash() == hash;
    }
};

struct KeyPointCompare
{
    inline bool operator ()(const cv::KeyPoint& kp1, const cv::KeyPoint& kp2) const
    {
        return kp1.pt.y < kp2.pt.y || (kp1.pt.y == kp2.pt.y && kp1.pt.x < kp2.pt.x);
    }
};

TEST_P(FAST, Accuracy)
{
    std::vector<cv::KeyPoint> keypoints;

    cv::gpu::FAST_GPU fastGPU(threshold);

    fastGPU(cv::gpu::GpuMat(image), cv::gpu::GpuMat(), keypoints);
    
    ASSERT_EQ(keypoints.size(), keypoints_gold.size());

    std::sort(keypoints.begin(), keypoints.end(), KeyPointCompare());

    for (size_t i = 0; i < keypoints_gold.size(); ++i)
    {
        const cv::KeyPoint& kp1 = keypoints[i];
        const cv::KeyPoint& kp2 = keypoints_gold[i];

        size_t h1 = kp1.hash();
        size_t h2 = kp2.hash();

        ASSERT_EQ(h1, h2);
    }
}

INSTANTIATE_TEST_CASE_P(Features2D, FAST, DEVICES(cv::gpu::GLOBAL_ATOMICS));

/////////////////////////////////////////////////////////////////////////////////////////////////
// ORB

struct ORB : TestWithParam<cv::gpu::DeviceInfo>
{
    cv::gpu::DeviceInfo devInfo;

    cv::Mat image;
    cv::Mat mask;

    int npoints;

    std::vector<cv::KeyPoint> keypoints_gold;
    cv::Mat descriptors_gold;
    
    virtual void SetUp()
    {
        devInfo = GetParam();

        cv::gpu::setDevice(devInfo.deviceID());
        
        image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
        ASSERT_FALSE(image.empty());        
        
        mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1));
        mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));

        npoints = 1000;

        cv::ORB orbCPU(npoints);

        orbCPU(image, mask, keypoints_gold, descriptors_gold);
    }
};

TEST_P(ORB, Accuracy)
{
    std::vector<cv::KeyPoint> keypoints;
    cv::Mat descriptors;

    cv::gpu::ORB_GPU orbGPU(npoints);
    cv::gpu::GpuMat d_descriptors;

    orbGPU(cv::gpu::GpuMat(image), cv::gpu::GpuMat(mask), keypoints, d_descriptors);

    d_descriptors.download(descriptors);

    cv::BFMatcher matcher(cv::NORM_HAMMING);
    std::vector<cv::DMatch> matches;

    matcher.match(descriptors_gold, descriptors, matches);

    int count = getValidMatchesCount(keypoints_gold, keypoints, matches);
    double ratio = (double) count / matches.size();

    ASSERT_GE(ratio, 0.65);
}

INSTANTIATE_TEST_CASE_P(Features2D, ORB, DEVICES(cv::gpu::GLOBAL_ATOMICS));

#endif // HAVE_CUDA