/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 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 "test_precomp.hpp"

namespace opencv_test { namespace {

#define MESSAGE_ERROR_COUNT "Count non zero elements returned by OpenCV function is incorrect."

#define sign(a) a > 0 ? 1 : a == 0 ? 0 : -1

#define MAX_WIDTH 100
#define MAX_HEIGHT 100

class CV_CountNonZeroTest: public cvtest::BaseTest
{
public:
    CV_CountNonZeroTest();
    ~CV_CountNonZeroTest();

protected:
    void run (int);

private:
    float eps_32;
    double eps_64;
    Mat src;
    int current_type;

    void generate_src_data(cv::Size size, int type);
    void generate_src_data(cv::Size size, int type, int count_non_zero);
    void generate_src_stat_data(cv::Size size, int type, int distribution);

    int get_count_non_zero();

    void print_information(int right, int result);
};

CV_CountNonZeroTest::CV_CountNonZeroTest(): eps_32(std::numeric_limits<float>::min()), eps_64(std::numeric_limits<double>::min()), src(Mat()), current_type(-1) {}
CV_CountNonZeroTest::~CV_CountNonZeroTest() {}

void CV_CountNonZeroTest::generate_src_data(cv::Size size, int type)
{
    src.create(size, CV_MAKETYPE(type, 1));

    for (int j = 0; j < size.width; ++j)
        for (int i = 0; i < size.height; ++i)
            switch (type)
            {
            case CV_8U: { src.at<uchar>(i, j) = cv::randu<uchar>(); break; }
            case CV_8S: { src.at<char>(i, j) = cv::randu<uchar>() - 128; break; }
            case CV_16U: { src.at<ushort>(i, j) = cv::randu<ushort>(); break; }
            case CV_16S: { src.at<short>(i, j) = cv::randu<short>(); break; }
            case CV_32S: { src.at<int>(i, j) = cv::randu<int>(); break; }
            case CV_32F: { src.at<float>(i, j) = cv::randu<float>(); break; }
            case CV_64F: { src.at<double>(i, j) = cv::randu<double>(); break; }
            default: break;
            }
}

void CV_CountNonZeroTest::generate_src_data(cv::Size size, int type, int count_non_zero)
{
    src = Mat::zeros(size, CV_MAKETYPE(type, 1));

    int n = 0; RNG& rng = ts->get_rng();

    while (n < count_non_zero)
    {
        int i = rng.next()%size.height, j = rng.next()%size.width;

        switch (type)
        {
        case CV_8U: { if (!src.at<uchar>(i, j)) {src.at<uchar>(i, j) = cv::randu<uchar>(); n += (src.at<uchar>(i, j) > 0);} break; }
        case CV_8S: { if (!src.at<char>(i, j)) {src.at<char>(i, j) = cv::randu<uchar>() - 128; n += abs(sign(src.at<char>(i, j)));} break; }
        case CV_16U: { if (!src.at<ushort>(i, j)) {src.at<ushort>(i, j) = cv::randu<ushort>(); n += (src.at<ushort>(i, j) > 0);} break; }
        case CV_16S: { if (!src.at<short>(i, j)) {src.at<short>(i, j) = cv::randu<short>(); n += abs(sign(src.at<short>(i, j)));} break; }
        case CV_32S: { if (!src.at<int>(i, j)) {src.at<int>(i, j) = cv::randu<int>(); n += abs(sign(src.at<int>(i, j)));} break; }
        case CV_32F: { if (fabs(src.at<float>(i, j)) <= eps_32) {src.at<float>(i, j) = cv::randu<float>(); n += (fabs(src.at<float>(i, j)) > eps_32);} break; }
        case CV_64F: { if (fabs(src.at<double>(i, j)) <= eps_64) {src.at<double>(i, j) = cv::randu<double>(); n += (fabs(src.at<double>(i, j)) > eps_64);} break; }

        default: break;
        }
    }

}

void CV_CountNonZeroTest::generate_src_stat_data(cv::Size size, int type, int distribution)
{
    src.create(size, CV_MAKETYPE(type, 1));

    double mean = 0.0, sigma = 1.0;
    double left = -1.0, right = 1.0;

    RNG& rng = ts->get_rng();

    if (distribution == RNG::NORMAL)
        rng.fill(src, RNG::NORMAL, Scalar::all(mean), Scalar::all(sigma));
    else if (distribution == RNG::UNIFORM)
        rng.fill(src, RNG::UNIFORM, Scalar::all(left), Scalar::all(right));
}

int CV_CountNonZeroTest::get_count_non_zero()
{
    int result = 0;

    for (int i = 0; i < src.rows; ++i)
        for (int j = 0; j < src.cols; ++j)
        {
            if (current_type == CV_8U) result += (src.at<uchar>(i, j) > 0);
            else if (current_type == CV_8S) result += abs(sign(src.at<char>(i, j)));
            else if (current_type == CV_16U) result += (src.at<ushort>(i, j) > 0);
            else if (current_type == CV_16S) result += abs(sign(src.at<short>(i, j)));
            else if (current_type == CV_32S) result += abs(sign(src.at<int>(i, j)));
            else if (current_type == CV_32F) result += (fabs(src.at<float>(i, j)) > eps_32);
            else result += (fabs(src.at<double>(i, j)) > eps_64);
        }

    return result;
}

void CV_CountNonZeroTest::print_information(int right, int result)
{
    cout << endl; cout << "Checking for the work of countNonZero function..." << endl; cout << endl;
    cout << "Type of Mat: ";
    switch (current_type)
    {
    case 0: {cout << "CV_8U"; break;}
    case 1: {cout << "CV_8S"; break;}
    case 2: {cout << "CV_16U"; break;}
    case 3: {cout << "CV_16S"; break;}
    case 4: {cout << "CV_32S"; break;}
    case 5: {cout << "CV_32F"; break;}
    case 6: {cout << "CV_64F"; break;}
    default: break;
    }
    cout << endl;
    cout << "Number of rows: " << src.rows << "   Number of cols: " << src.cols << endl;
    cout << "True count non zero elements: " << right << "   Result: " << result << endl;
    cout << endl;
}

void CV_CountNonZeroTest::run(int)
{
    const size_t N = 1500;

    for (int k = 1; k <= 3; ++k)
        for (size_t i = 0; i < N; ++i)
        {
        RNG& rng = ts->get_rng();

        int w = rng.next()%MAX_WIDTH + 1, h = rng.next()%MAX_HEIGHT + 1;

        current_type = rng.next()%7;

        switch (k)
        {
        case 1: {
                generate_src_data(Size(w, h), current_type);
                int right = get_count_non_zero(), result = countNonZero(src);
                if (result != right)
                {
                    cout << "Number of experiment: " << i << endl;
                    cout << "Method of data generation: RANDOM" << endl;
                    print_information(right, result);
                    CV_Error(cv::Error::StsError, MESSAGE_ERROR_COUNT);
                    return;
                }

                break;
            }

        case 2: {
                int count_non_zero = rng.next()%(w*h);
                generate_src_data(Size(w, h), current_type, count_non_zero);
                int result = countNonZero(src);
                if (result != count_non_zero)
                {
                    cout << "Number of experiment: " << i << endl;
                    cout << "Method of data generation: HALF-RANDOM" << endl;
                    print_information(count_non_zero, result);
                    CV_Error(cv::Error::StsError, MESSAGE_ERROR_COUNT);
                    return;
                }

                break;
            }

        case 3: {
                int distribution = cv::randu<uchar>()%2;
                generate_src_stat_data(Size(w, h), current_type, distribution);
                int right = get_count_non_zero(), result = countNonZero(src);
                if (right != result)
                {
                    cout << "Number of experiment: " << i << endl;
                    cout << "Method of data generation: STATISTIC" << endl;
                    print_information(right, result);
                    CV_Error(cv::Error::StsError, MESSAGE_ERROR_COUNT);
                    return;
                }

                break;
            }

        default: break;
        }
    }
}

TEST (Core_CountNonZero, accuracy) { CV_CountNonZeroTest test; test.safe_run(); }


typedef testing::TestWithParam<tuple<int, int> > CountNonZeroND;

TEST_P (CountNonZeroND, ndim)
{
    const int dims = get<0>(GetParam());
    const int type = get<1>(GetParam());
    const int ONE_SIZE = 5;

    vector<int> sizes(dims);
    std::fill(sizes.begin(), sizes.end(), ONE_SIZE);

    Mat data(sizes, CV_MAKETYPE(type, 1));
    data = 0;
    EXPECT_EQ(0, cv::countNonZero(data));
    data = Scalar::all(1);
    int expected = static_cast<int>(pow(static_cast<float>(ONE_SIZE), dims));
    EXPECT_EQ(expected, cv::countNonZero(data));
}

INSTANTIATE_TEST_CASE_P(Core, CountNonZeroND,
    testing::Combine(
        testing::Range(2, 9),
        testing::Values(CV_8U, CV_8S, CV_32F)
    )
);


typedef testing::TestWithParam<tuple<int, cv::Size> > CountNonZeroBig;

TEST_P(CountNonZeroBig, /**/)
{
    const int type = get<0>(GetParam());
    const Size sz = get<1>(GetParam());

    EXPECT_EQ(0, cv::countNonZero(cv::Mat::zeros(sz, type)));
    EXPECT_EQ(sz.area(), cv::countNonZero(cv::Mat::ones(sz, type)));
}

INSTANTIATE_TEST_CASE_P(Core, CountNonZeroBig,
    testing::Combine(
        testing::Values(CV_8UC1, CV_32FC1),
        testing::Values(Size(1, 524190), Size(524190, 1), Size(3840, 2160))
    )
);

typedef testing::TestWithParam<int> CountNonZero1D;

TEST_P(CountNonZero1D, /**/)
{
    const int depth = GetParam();
    int i, M = 112 + depth, N = 3 + depth;
    std::vector<uint8_t> v(M);
    int nz_ref = 0;
    for (i = 0; i < M; i++) {
        v[i] = (uint8_t)(rand() % 7 == 0);
        nz_ref += v[i] != 0;
    }
    Mat mv;
    Mat(v).convertTo(mv, depth);
    EXPECT_EQ(mv.dims, 1);
    size_t esz = mv.elemSize();
    // check countNonZero on a vector transformed to Mat inplace, e.g. on 1xM matrix
    int nz0 = countNonZero(mv);
    EXPECT_EQ(nz0, nz_ref);
    // another method to get 1xM matrix, this time 2D matrix
    int nz0_ = countNonZero(Mat(Size(M, 1), depth, mv.data));
    EXPECT_EQ(nz0_, nz_ref);
    // let's now transpose it and get Mx1
    Mat m1 = mv.t();
    int nz1 = countNonZero(m1);
    EXPECT_EQ(nz1, nz_ref);
    Mat mwide(M, N, mv.type());
    randu(mwide, 0, 3);
    int colidx = rand()%N;
    Mat mcol = mwide.col(colidx);
    EXPECT_EQ(mcol.data, mwide.data + colidx*esz);
    // let's now embed this column into a wider matrix
    // make sure it's copied inside, not reallocated.
    m1.copyTo(mcol);
    EXPECT_EQ(mcol.data, mwide.data + colidx*esz);
    // now it's not continuous
    EXPECT_EQ(mcol.isContinuous(), false);
    int nz2 = countNonZero(mcol);
    EXPECT_EQ(nz2, nz_ref);
}

INSTANTIATE_TEST_CASE_P(Core, CountNonZero1D,
    testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32U, CV_32S, CV_64U, CV_64S, CV_32F, CV_64F, CV_16F, CV_16BF, CV_Bool)
);

}} // namespace