opencv-MIRROR/modules/photo/test/test_inpaint.cpp

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#include "test_precomp.hpp"

namespace opencv_test { namespace {

class CV_InpaintTest : public cvtest::BaseTest
{
public:
    CV_InpaintTest();
    ~CV_InpaintTest();
protected:
    void run(int);
};

CV_InpaintTest::CV_InpaintTest()
{
}
CV_InpaintTest::~CV_InpaintTest() {}

void CV_InpaintTest::run( int )
{
    string folder = string(ts->get_data_path()) + "inpaint/";
    Mat orig = imread(folder + "orig.png");
    Mat exp1 = imread(folder + "exp1.png");
    Mat exp2 = imread(folder + "exp2.png");
    Mat mask = imread(folder + "mask.png");

    if (orig.empty() || exp1.empty() || exp2.empty() || mask.empty())
    {
        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
        return;
    }

    Mat inv_mask;
    mask.convertTo(inv_mask, CV_8UC3, -1.0, 255.0);

    Mat mask1ch;
    cv::cvtColor(mask, mask1ch, COLOR_BGR2GRAY);

    Mat test = orig.clone();
    test.setTo(Scalar::all(255), mask1ch);

    Mat res1, res2;
    inpaint( test, mask1ch, res1, 5, INPAINT_NS );
    inpaint( test, mask1ch, res2, 5, INPAINT_TELEA );

    Mat diff1, diff2;
    absdiff( orig, res1, diff1 );
    absdiff( orig, res2, diff2 );

    double n1 = cvtest::norm(diff1.reshape(1), NORM_INF, inv_mask.reshape(1));
    double n2 = cvtest::norm(diff2.reshape(1), NORM_INF, inv_mask.reshape(1));

    if (n1 != 0 || n2 != 0)
    {
        ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
        return;
    }

    absdiff( exp1, res1, diff1 );
    absdiff( exp2, res2, diff2 );

    n1 = cvtest::norm(diff1.reshape(1), NORM_INF, mask.reshape(1));
    n2 = cvtest::norm(diff2.reshape(1), NORM_INF, mask.reshape(1));

    const int jpeg_thres = 3;
    if (n1 > jpeg_thres || n2 > jpeg_thres)
    {
        ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
        return;
    }

    ts->set_failed_test_info(cvtest::TS::OK);
}

TEST(Photo_Inpaint, regression) { CV_InpaintTest test; test.safe_run(); }

typedef testing::TestWithParam<tuple<perf::MatType> > formats;

TEST_P(formats, basic)
{
    const int type = get<0>(GetParam());
    Mat src(100, 100, type);
    src.setTo(Scalar::all(128));
    Mat ref = src.clone();
    Mat dst, mask = Mat::zeros(src.size(), CV_8U);

    circle(src, Point(50, 50), 5, Scalar::all(200), 6);
    circle(mask, Point(50, 50), 5, Scalar::all(200), 6);
    inpaint(src, mask, dst, 10, INPAINT_NS);

    Mat dst2;
    inpaint(src, mask, dst2, 10, INPAINT_TELEA);

    ASSERT_EQ(cv::norm(dst, ref, NORM_INF), 0.);
    ASSERT_EQ(cv::norm(dst2, ref, NORM_INF), 0.);
}

INSTANTIATE_TEST_CASE_P(Photo_Inpaint, formats, testing::Values(CV_32FC1, CV_16UC1, CV_8UC1, CV_8UC3));

TEST(Photo_InpaintBorders, regression)
{
    Mat img(64, 64, CV_8U);
    img = 128;
    img(Rect(0, 0, 16, 64)) = 0;

    Mat mask(64, 64, CV_8U);
    mask = 0;
    mask(Rect(0, 0, 16, 64)) = 255;

    Mat inpainted;
    inpaint(img, mask, inpainted, 1, INPAINT_TELEA);

    Mat diff;
    cv::absdiff(inpainted, 128*Mat::ones(inpainted.size(), inpainted.type()), diff);
    ASSERT_TRUE(countNonZero(diff) == 0);
}

typedef testing::TestWithParam<tuple<perf::MatType>> Photo_InpaintSmallBorders;

TEST_P(Photo_InpaintSmallBorders, regression)
{
    int type = get<0>(GetParam());
    Mat img(5, 5, type, Scalar::all(128));
    Mat expected = img.clone();

    Mat mask = Mat::zeros(5, 5, CV_8U);
    mask(Rect(1, 1, 3, 3)) = 255;

    img.setTo(Scalar::all(0), mask);

    Mat inpainted, diff;

    inpaint(img, mask, inpainted, 1, INPAINT_TELEA);
    cv::absdiff(inpainted, expected, diff);
    ASSERT_EQ(countNonZero(diff.reshape(1)), 0);

    inpaint(img, mask, inpainted, 1, INPAINT_NS);
    cv::absdiff(inpainted, expected, diff);
    ASSERT_EQ(countNonZero(diff.reshape(1)), 0);
}

INSTANTIATE_TEST_CASE_P(/*nothing*/, Photo_InpaintSmallBorders,  Values(CV_8UC1, CV_8UC3));

typedef testing::TestWithParam<tuple<perf::MatType>> Photo_InpaintNonuniform;

TEST_P(Photo_InpaintNonuniform, regression)
{
    const int type = get<0>(GetParam());
    const int size = 16;

    Mat mask = Mat::zeros(size, size, CV_8U);
    circle(mask, Point(size / 2, size / 2), size / 5, Scalar::all(255), -1);
    rectangle(mask, Rect(2, size - 6, 4, 4), Scalar::all(255), -1);

    Mat img(size, size, type);
    if (type == CV_8UC3)
    {
        for (int y = 0; y < size; y++)
            for (int x = 0; x < size; x++)
                img.at<Vec3b>(y, x) = Vec3b(
                    (uchar)((y * 37 + x * 53) % 256),
                    (uchar)((y * 17 + x * 91 + 40) % 256),
                    (uchar)((y * 73 + x * 11 + 90) % 256));
    }
    else
    {
        const double scale = type == CV_8UC1 ? 255.0 : type == CV_16UC1 ? 65535.0 : 1000.0;
        for (int y = 0; y < size; y++)
            for (int x = 0; x < size; x++)
            {
                double v = ((y * 37 + x * 53 + (x * y) % 29) % 211) * scale / 210.0;
                if (type == CV_8UC1) img.at<uchar>(y, x) = (uchar)v;
                else if (type == CV_16UC1) img.at<ushort>(y, x) = (ushort)v;
                else img.at<float>(y, x) = (float)v;
            }
    }

    const int rows[] = {8, 8, 10, 11, 6};
    const int cols[] = {8, 7, 3, 4, 8};

    Mat dst;
    inpaint(img, mask, dst, 3, INPAINT_TELEA);
    switch (type)
    {
    case CV_8UC1:
    {
        const int expected[] = {123, 134, 113, 133, 149};
        for (int k = 0; k < 5; k++) ASSERT_NEAR((int)dst.at<uchar>(rows[k], cols[k]), expected[k], 1);
        break;
    }
    case CV_16UC1:
    {
        const int expected[] = {31891, 34654, 28621, 34077, 38605};
        for (int k = 0; k < 5; k++) ASSERT_NEAR((int)dst.at<ushort>(rows[k], cols[k]), expected[k], 1);
        break;
    }
    case CV_32FC1:
    {
        const float expected[] = {485.2213f, 527.2879f, 438.2543f, 519.2518f, 588.0099f};
        for (int k = 0; k < 5; k++) ASSERT_NEAR(dst.at<float>(rows[k], cols[k]), expected[k], 1e-2);
        break;
    }
    default: // CV_8UC3
    {
        const Vec3b expected[] = {
            Vec3b(148, 135, 127), Vec3b(138, 111, 122), Vec3b(181, 129, 76), Vec3b(150, 95, 104), Vec3b(97, 141, 148)
        };
        for (int k = 0; k < 5; k++) ASSERT_LE(cvtest::norm(dst.at<Vec3b>(rows[k], cols[k]), expected[k], NORM_INF), 1.);
        break;
    }
    }

    inpaint(img, mask, dst, 3, INPAINT_NS);
    switch (type)
    {
    case CV_8UC1:
    {
        const int expected[] = {141, 159, 115, 146, 129};
        for (int k = 0; k < 5; k++) ASSERT_NEAR((int)dst.at<uchar>(rows[k], cols[k]), expected[k], 1);
        break;
    }
    case CV_16UC1:
    {
        const int expected[] = {36271, 41082, 29646, 37694, 33247};
        for (int k = 0; k < 5; k++) ASSERT_NEAR((int)dst.at<ushort>(rows[k], cols[k]), expected[k], 1);
        break;
    }
    case CV_32FC1:
    {
        const float expected[] = {553.4644f, 626.8738f, 452.3825f, 575.1885f, 507.3150f};
        for (int k = 0; k < 5; k++) ASSERT_NEAR(dst.at<float>(rows[k], cols[k]), expected[k], 1e-2);
        break;
    }
    default: // CV_8UC3
    {
        const Vec3b expected[] = {
            Vec3b(118, 123, 112), Vec3b(117, 122, 91), Vec3b(177, 149, 71), Vec3b(151, 102, 101), Vec3b(117, 153, 84)
        };
        for (int k = 0; k < 5; k++) ASSERT_LE(cvtest::norm(dst.at<Vec3b>(rows[k], cols[k]), expected[k], NORM_INF), 1.);
        break;
    }
    }
}

INSTANTIATE_TEST_CASE_P(/*nothing*/, Photo_InpaintNonuniform, Values(CV_8UC1, CV_16UC1, CV_32FC1, CV_8UC3));
TEST(Photo_InpaintTelea, tiny_2x2_rounding)
{
    Mat src = (Mat_<uchar>(2, 2) << 178, 171, 108, 62);
    Mat mask = (Mat_<uchar>(2, 2) << 0, 0, 255, 0);
    Mat dst;
    inpaint(src, mask, dst, 1, INPAINT_TELEA);
    EXPECT_NEAR((int)dst.at<uchar>(1, 0), 120.5, 0.5);
    EXPECT_EQ(178, (int)dst.at<uchar>(0, 0));
    EXPECT_EQ(171, (int)dst.at<uchar>(0, 1));
    EXPECT_EQ(62, (int)dst.at<uchar>(1, 1));

    inpaint(src, mask, dst, 1, INPAINT_NS);
    EXPECT_EQ(120, (int)dst.at<uchar>(1, 0));
    EXPECT_EQ(178, (int)dst.at<uchar>(0, 0));
    EXPECT_EQ(171, (int)dst.at<uchar>(0, 1));
    EXPECT_EQ(62, (int)dst.at<uchar>(1, 1));
}

}} // namespace