// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. #include "test_precomp.hpp" namespace opencv_test { namespace { static const int fixedShiftU8 = 8; static const int64_t fixedOne = (1L << fixedShiftU8); int64_t v[][9] = { { fixedOne }, // size 1, sigma 0 { fixedOne >> 2, fixedOne >> 1, fixedOne >> 2 }, // size 3, sigma 0 { fixedOne >> 4, fixedOne >> 2, 6 * (fixedOne >> 4), fixedOne >> 2, fixedOne >> 4 }, // size 5, sigma 0 { fixedOne >> 5, 7 * (fixedOne >> 6), 7 * (fixedOne >> 5), 9 * (fixedOne >> 5), 7 * (fixedOne >> 5), 7 * (fixedOne >> 6), fixedOne >> 5 }, // size 7, sigma 0 { 4, 13, 30, 51, 60, 51, 30, 13, 4 }, // size 9, sigma 0 #if 1 #define CV_TEST_INACCURATE_GAUSSIAN_BLUR { 81, 94, 81 }, // size 3, sigma 1.75 { 65, 126, 65 }, // size 3, sigma 0.875 { 0, 7, 242, 7, 0 }, // size 5, sigma 0.375 { 4, 56, 136, 56, 4 } // size 5, sigma 0.75 #endif }; template T eval(Mat src, vector kernelx, vector kernely) { static const int64_t fixedRound = ((1LL << (fixedShift * 2)) >> 1); int64_t val = 0; for (size_t j = 0; j < kernely.size(); j++) { int64_t lineval = 0; for (size_t i = 0; i < kernelx.size(); i++) lineval += src.at((int)j, (int)i) * kernelx[i]; val += lineval * kernely[j]; } return saturate_cast((val + fixedRound) >> (fixedShift * 2)); } TEST(GaussianBlur_Bitexact, Linear8U) { struct testmode { int type; Size sz; Size kernel; double sigma_x; double sigma_y; vector kernel_x; vector kernel_y; } modes[] = { { CV_8UC1, Size( 1, 1), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 2, 2), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 3, 1), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 1, 3), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 3, 3), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 3, 3), Size(5, 5), 0, 0, vector(v[2], v[2]+5), vector(v[2], v[2]+5) }, { CV_8UC1, Size( 3, 3), Size(7, 7), 0, 0, vector(v[3], v[3]+7), vector(v[3], v[3]+7) }, { CV_8UC1, Size( 5, 5), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 5, 5), Size(5, 5), 0, 0, vector(v[2], v[2]+5), vector(v[2], v[2]+5) }, { CV_8UC1, Size( 3, 5), Size(5, 5), 0, 0, vector(v[2], v[2]+5), vector(v[2], v[2]+5) }, { CV_8UC1, Size( 5, 5), Size(5, 5), 0, 0, vector(v[2], v[2]+5), vector(v[2], v[2]+5) }, { CV_8UC1, Size( 5, 5), Size(7, 7), 0, 0, vector(v[3], v[3]+7), vector(v[3], v[3]+7) }, { CV_8UC1, Size( 7, 7), Size(7, 7), 0, 0, vector(v[3], v[3]+7), vector(v[3], v[3]+7) }, { CV_8UC1, Size( 256, 128), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC2, Size( 256, 128), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC3, Size( 256, 128), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC4, Size( 256, 128), Size(3, 3), 0, 0, vector(v[1], v[1]+3), vector(v[1], v[1]+3) }, { CV_8UC1, Size( 256, 128), Size(5, 5), 0, 0, vector(v[2], v[2]+5), vector(v[2], v[2]+5) }, { CV_8UC1, Size( 256, 128), Size(7, 7), 0, 0, vector(v[3], v[3]+7), vector(v[3], v[3]+7) }, { CV_8UC1, Size( 256, 128), Size(9, 9), 0, 0, vector(v[4], v[4]+9), vector(v[4], v[4]+9) }, #ifdef CV_TEST_INACCURATE_GAUSSIAN_BLUR { CV_8UC1, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector(v[5], v[5]+3), vector(v[6], v[6]+3) }, { CV_8UC2, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector(v[5], v[5]+3), vector(v[6], v[6]+3) }, { CV_8UC3, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector(v[5], v[5]+3), vector(v[6], v[6]+3) }, { CV_8UC4, Size( 256, 128), Size(3, 3), 1.75, 0.875, vector(v[5], v[5]+3), vector(v[6], v[6]+3) }, { CV_8UC1, Size( 256, 128), Size(5, 5), 0.375, 0.75, vector(v[7], v[7]+5), vector(v[8], v[8]+5) } #endif }; int bordermodes[] = { BORDER_CONSTANT | BORDER_ISOLATED, BORDER_REPLICATE | BORDER_ISOLATED, BORDER_REFLECT | BORDER_ISOLATED, BORDER_WRAP | BORDER_ISOLATED, BORDER_REFLECT_101 | BORDER_ISOLATED // BORDER_CONSTANT, // BORDER_REPLICATE, // BORDER_REFLECT, // BORDER_WRAP, // BORDER_REFLECT_101 }; for (int modeind = 0, _modecnt = sizeof(modes) / sizeof(modes[0]); modeind < _modecnt; ++modeind) { int type = modes[modeind].type, depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); int dcols = modes[modeind].sz.width, drows = modes[modeind].sz.height; Size kernel = modes[modeind].kernel; int rows = drows + 20, cols = dcols + 20; Mat src(rows, cols, type), refdst(drows, dcols, type), dst; for (int j = 0; j < rows; j++) { uint8_t* line = src.ptr(j); for (int i = 0; i < cols; i++) for (int c = 0; c < cn; c++) { RNG rnd(0x123456789abcdefULL); double val = j < rows / 2 ? (i < cols / 2 ? ((sin((i + 1)*CV_PI / 256.)*sin((j + 1)*CV_PI / 256.)*sin((cn + 4)*CV_PI / 8.) + 1.)*128.) : (((i / 128 + j / 128) % 2) * 250 + (j / 128) % 2)) : (i < cols / 2 ? ((i / 128) * (85 - j / 256 * 40) * ((j / 128) % 2) + (7 - i / 128) * (85 - j / 256 * 40) * ((j / 128 + 1) % 2)) : ((uchar)rnd)); if (depth == CV_8U) line[i*cn + c] = (uint8_t)val; else if (depth == CV_16U) ((uint16_t*)line)[i*cn + c] = (uint16_t)val; else if (depth == CV_16S) ((int16_t*)line)[i*cn + c] = (int16_t)val; else if (depth == CV_32S) ((int32_t*)line)[i*cn + c] = (int32_t)val; else CV_Assert(0); } } Mat src_roi = src(Rect(10, 10, dcols, drows)); for (int borderind = 0, _bordercnt = sizeof(bordermodes) / sizeof(bordermodes[0]); borderind < _bordercnt; ++borderind) { Mat src_border; cv::copyMakeBorder(src_roi, src_border, kernel.height / 2, kernel.height / 2, kernel.width / 2, kernel.width / 2, bordermodes[borderind]); for (int c = 0; c < src_border.channels(); c++) { int fromTo[2] = { c, 0 }; int toFrom[2] = { 0, c }; Mat src_chan(src_border.size(), CV_MAKETYPE(src_border.depth(),1)); Mat dst_chan(refdst.size(), CV_MAKETYPE(refdst.depth(), 1)); mixChannels(src_border, src_chan, fromTo, 1); for (int j = 0; j < drows; j++) for (int i = 0; i < dcols; i++) { if (depth == CV_8U) dst_chan.at(j, i) = eval(src_chan(Rect(i,j,kernel.width,kernel.height)), modes[modeind].kernel_x, modes[modeind].kernel_y); else if (depth == CV_16U) dst_chan.at(j, i) = eval(src_chan(Rect(i, j, kernel.width, kernel.height)), modes[modeind].kernel_x, modes[modeind].kernel_y); else if (depth == CV_16S) dst_chan.at(j, i) = eval(src_chan(Rect(i, j, kernel.width, kernel.height)), modes[modeind].kernel_x, modes[modeind].kernel_y); else if (depth == CV_32S) dst_chan.at(j, i) = eval(src_chan(Rect(i, j, kernel.width, kernel.height)), modes[modeind].kernel_x, modes[modeind].kernel_y); else CV_Assert(0); } mixChannels(dst_chan, refdst, toFrom, 1); } cv::GaussianBlur(src_roi, dst, kernel, modes[modeind].sigma_x, modes[modeind].sigma_y, bordermodes[borderind]); EXPECT_GE(0, cvtest::norm(refdst, dst, cv::NORM_L1)) << "GaussianBlur " << cn << "-chan mat " << drows << "x" << dcols << " by kernel " << kernel << " sigma(" << modes[modeind].sigma_x << ";" << modes[modeind].sigma_y << ") failed with max diff " << cvtest::norm(refdst, dst, cv::NORM_INF); } } } TEST(GaussianBlur_Bitexact, regression_15015) { Mat src(100,100,CV_8UC3,Scalar(255,255,255)); Mat dst; GaussianBlur(src, dst, Size(5, 5), 0); ASSERT_EQ(0.0, cvtest::norm(dst, src, NORM_INF)); } static void checkGaussianBlur_8Uvs32F(const Mat& src8u, const Mat& src32f, int N, double sigma) { Mat dst8u; GaussianBlur(src8u, dst8u, Size(N, N), sigma); // through bit-exact path Mat dst8u_32f; dst8u.convertTo(dst8u_32f, CV_32F); Mat dst32f; GaussianBlur(src32f, dst32f, Size(N, N), sigma); // without bit-exact computations double normINF_32f = cv::norm(dst8u_32f, dst32f, NORM_INF); EXPECT_LE(normINF_32f, 1.0); } TEST(GaussianBlur_Bitexact, regression_9863) { Mat src8u = imread(cvtest::findDataFile("shared/lena.png")); Mat src32f; src8u.convertTo(src32f, CV_32F); checkGaussianBlur_8Uvs32F(src8u, src32f, 151, 30); } }} // namespace