#include "perf_precomp.hpp"

#define GPU_PERF_TEST_P(fixture, name, params)  \
    class fixture##_##name : public fixture {\
     public:\
      fixture##_##name() {}\
     protected:\
        virtual void __cpu();\
        virtual void __gpu();\
      virtual void PerfTestBody();\
    };\
    TEST_P(fixture##_##name, name /*perf*/){ RunPerfTestBody(); if (PERF_RUN_GPU()) __gpu(); else __cpu();}\
    INSTANTIATE_TEST_CASE_P(/*none*/, fixture##_##name, params);\
    void fixture##_##name::PerfTestBody()

#define RUN_CPU(fixture, name)\
    void fixture##_##name::__cpu()

#define RUN_GPU(fixture, name)\
    void fixture##_##name::__gpu()

#define NO_CPU(fixture, name)\
void fixture##_##name::__cpu() { FAIL() << "No such CPU implementation analogy";}

namespace {
    struct DetectionLess
    {
        bool operator()(const cv::gpu::SoftCascade::Detection& a,
            const cv::gpu::SoftCascade::Detection& b) const
        {
            if (a.x != b.x) return a.x < b.x;
            else if (a.y != b.y) return a.y < b.y;
            else if (a.w != b.w) return a.w < b.w;
            else return a.h < b.h;
        }

        bool operator()(const cv::SoftCascade::Detection& a,
            const cv::SoftCascade::Detection& b) const
        {
            const cv::Rect& ra = a.rect;
            const cv::Rect& rb = b.rect;

            if (ra.x != rb.x) return ra.x < rb.x;
            else if (ra.y != rb.y) return ra.y < rb.y;
            else if (ra.width != rb.width) return ra.width < rb.width;
            else return ra.height < rb.height;
        }
    };

    cv::Mat sortDetections(cv::gpu::GpuMat& objects)
    {
        cv::Mat detections(objects);

        typedef cv::gpu::SoftCascade::Detection Detection;
        Detection* begin = (Detection*)(detections.ptr<char>(0));
        Detection* end = (Detection*)(detections.ptr<char>(0) + detections.cols);
        std::sort(begin, end, DetectionLess());

        return detections;
    }
}


typedef std::tr1::tuple<std::string, std::string> fixture_t;
typedef perf::TestBaseWithParam<fixture_t> SoftCascadeTest;

GPU_PERF_TEST_P(SoftCascadeTest, detect,
    testing::Combine(
        testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
        testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png"))))
{ }

RUN_GPU(SoftCascadeTest, detect)
{
    cv::Mat cpu = readImage (GET_PARAM(1));
    ASSERT_FALSE(cpu.empty());
    cv::gpu::GpuMat colored(cpu);

    cv::gpu::SoftCascade cascade;
    ASSERT_TRUE(cascade.load(perf::TestBase::getDataPath(GET_PARAM(0))));

    cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::gpu::SoftCascade::Detection), CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1), trois;
    rois.setTo(1);
    cv::gpu::transpose(rois, trois);

    cv::gpu::GpuMat curr = objectBoxes;
    cascade.detectMultiScale(colored, trois, curr);

    TEST_CYCLE()
    {
        curr = objectBoxes;
        cascade.detectMultiScale(colored, trois, curr);
    }

    SANITY_CHECK(sortDetections(curr));
}

RUN_CPU(SoftCascadeTest, detect)
{
    cv::Mat colored = readImage(GET_PARAM(1));
    ASSERT_FALSE(colored.empty());

    cv::SoftCascade cascade;
    ASSERT_TRUE(cascade.load(getDataPath(GET_PARAM(0))));

    std::vector<cv::Rect> rois;

    typedef cv::SoftCascade::Detection Detection;
    std::vector<Detection>objects;
    cascade.detectMultiScale(colored, rois, objects);

    TEST_CYCLE()
    {
        cascade.detectMultiScale(colored, rois, objects);
    }

    std::sort(objects.begin(), objects.end(), DetectionLess());
    SANITY_CHECK(objects);
}

static cv::Rect getFromTable(int idx)
{
    static const cv::Rect rois[] =
    {
        cv::Rect( 65,  20,  35, 80),
        cv::Rect( 95,  35,  45, 40),
        cv::Rect( 45,  35,  45, 40),
        cv::Rect( 25,  27,  50, 45),
        cv::Rect(100,  50,  45, 40),

        cv::Rect( 60,  30,  45, 40),
        cv::Rect( 40,  55,  50, 40),
        cv::Rect( 48,  37,  72, 80),
        cv::Rect( 48,  32,  85, 58),
        cv::Rect( 48,   0,  32, 27)
    };

    return rois[idx];
}

typedef std::tr1::tuple<std::string, std::string, int> roi_fixture_t;
typedef perf::TestBaseWithParam<roi_fixture_t> SoftCascadeTestRoi;

GPU_PERF_TEST_P(SoftCascadeTestRoi, detectInRoi,
    testing::Combine(
        testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
        testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png")),
        testing::Range(0, 5)))
{}

RUN_GPU(SoftCascadeTestRoi, detectInRoi)
{
    cv::Mat cpu = readImage (GET_PARAM(1));
    ASSERT_FALSE(cpu.empty());
    cv::gpu::GpuMat colored(cpu);

    cv::gpu::SoftCascade cascade;
    ASSERT_TRUE(cascade.load(perf::TestBase::getDataPath(GET_PARAM(0))));

    cv::gpu::GpuMat objectBoxes(1, 16384 * 20, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
    rois.setTo(0);

    int nroi = GET_PARAM(2);
    cv::RNG rng;
    for (int i = 0; i < nroi; ++i)
    {
        cv::Rect r = getFromTable(rng(10));
        cv::gpu::GpuMat sub(rois, r);
        sub.setTo(1);
    }

    cv::gpu::GpuMat trois;
    cv::gpu::transpose(rois, trois);

    cv::gpu::GpuMat curr = objectBoxes;
    cascade.detectMultiScale(colored, trois, curr);

    TEST_CYCLE()
    {
        curr = objectBoxes;
        cascade.detectMultiScale(colored, trois, curr);
    }

    SANITY_CHECK(sortDetections(curr));
}

NO_CPU(SoftCascadeTestRoi, detectInRoi)


GPU_PERF_TEST_P(SoftCascadeTestRoi, detectEachRoi,
    testing::Combine(
        testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
        testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png")),
        testing::Range(0, 10)))
{}

RUN_GPU(SoftCascadeTestRoi, detectEachRoi)
{
    cv::Mat cpu = readImage (GET_PARAM(1));
    ASSERT_FALSE(cpu.empty());
    cv::gpu::GpuMat colored(cpu);

    cv::gpu::SoftCascade cascade;
    ASSERT_TRUE(cascade.load(perf::TestBase::getDataPath(GET_PARAM(0))));

    cv::gpu::GpuMat objectBoxes(1, 16384 * 20, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
    rois.setTo(0);

    int idx = GET_PARAM(2);
    cv::Rect r = getFromTable(idx);
    cv::gpu::GpuMat sub(rois, r);
    sub.setTo(1);

    cv::gpu::GpuMat curr = objectBoxes;
    cv::gpu::GpuMat trois;
    cv::gpu::transpose(rois, trois);

    cascade.detectMultiScale(colored, trois, curr);

    TEST_CYCLE()
    {
        curr = objectBoxes;
        cascade.detectMultiScale(colored, trois, curr);
    }

    SANITY_CHECK(sortDetections(curr));
}

NO_CPU(SoftCascadeTestRoi, detectEachRoi)

GPU_PERF_TEST_P(SoftCascadeTest, detectOnIntegral,
    testing::Combine(
        testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
        testing::Values(std::string("cv/cascadeandhog/integrals.xml"))))
{ }

    static std::string itoa(long i)
    {
        static char s[65];
        sprintf(s, "%ld", i);
        return std::string(s);
    }

RUN_GPU(SoftCascadeTest, detectOnIntegral)
{
    cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
    ASSERT_TRUE(fs.isOpened());

    cv::gpu::GpuMat hogluv(121 * 10, 161, CV_32SC1);
    for (int i = 0; i < 10; ++i)
    {
        cv::Mat channel;
        fs[std::string("channel") + itoa(i)] >> channel;
        cv::gpu::GpuMat gchannel(hogluv, cv::Rect(0, 121 * i, 161, 121));
        gchannel.upload(channel);
    }

    cv::gpu::SoftCascade cascade;
    ASSERT_TRUE(cascade.load(perf::TestBase::getDataPath(GET_PARAM(0))));

    cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::gpu::SoftCascade::Detection), CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1), trois;
    rois.setTo(1);
    cv::gpu::transpose(rois, trois);

    cv::gpu::GpuMat curr = objectBoxes;
    cascade.detectMultiScale(hogluv, trois, curr);

    TEST_CYCLE()
    {
        curr = objectBoxes;
        cascade.detectMultiScale(hogluv, trois, curr);
    }

    SANITY_CHECK(sortDetections(curr));
}

NO_CPU(SoftCascadeTest, detectOnIntegral)