| /* |
| * Copyright 2017 Google Inc. |
| * |
| * Use of this source code is governed by a BSD-style license that can be |
| * found in the LICENSE file. |
| */ |
| |
| #include "SkGaussFilter.h" |
| |
| #include <cmath> |
| #include <tuple> |
| #include <vector> |
| #include "Test.h" |
| |
| // one part in a million |
| static constexpr double kEpsilon = 0.000001; |
| |
| static double careful_add(int n, double* gauss) { |
| // Sum smallest to largest to retain precision. |
| double sum = 0; |
| for (int i = n - 1; i >= 1; i--) { |
| sum += 2.0 * gauss[i]; |
| } |
| sum += gauss[0]; |
| return sum; |
| } |
| |
| DEF_TEST(SkGaussFilterCommon, r) { |
| using Test = std::tuple<double, SkGaussFilter::Type, std::vector<double>>; |
| |
| auto golden_check = [&](const Test& test) { |
| double sigma; SkGaussFilter::Type type; std::vector<double> golden; |
| std::tie(sigma, type, golden) = test; |
| SkGaussFilter filter{sigma, type}; |
| double result[SkGaussFilter::kGaussArrayMax]; |
| int n = 0; |
| for (auto d : filter) { |
| result[n++] = d; |
| } |
| REPORTER_ASSERT(r, static_cast<size_t>(n) == golden.size()); |
| double sum = careful_add(n, result); |
| REPORTER_ASSERT(r, sum == 1.0); |
| for (size_t i = 0; i < golden.size(); i++) { |
| REPORTER_ASSERT(r, std::abs(golden[i] - result[i]) < kEpsilon); |
| } |
| }; |
| |
| // The following two sigmas account for about 85% of all sigmas used for masks. |
| // Golden values generated using Mathematica. |
| auto tests = { |
| // 0.788675 - most common mask sigma. |
| // GaussianMatrix[{{Automatic}, {.788675}}, Method -> "Gaussian"] |
| Test{0.788675, SkGaussFilter::Type::Gaussian, {0.506205, 0.226579, 0.0203189}}, |
| |
| // GaussianMatrix[{{Automatic}, {.788675}}] |
| Test{0.788675, SkGaussFilter::Type::Bessel, {0.593605, 0.176225, 0.0269721}}, |
| |
| // 1.07735 - second most common mask sigma. |
| // GaussianMatrix[{{Automatic}, {1.07735}}, Method -> "Gaussian"] |
| Test{1.07735, SkGaussFilter::Type::Gaussian, {0.376362, 0.244636, 0.0671835}}, |
| |
| // GaussianMatrix[{{4}, {1.07735}}, Method -> "Bessel"] |
| Test{1.07735, SkGaussFilter::Type::Bessel, {0.429537, 0.214955, 0.059143, 0.0111337}}, |
| }; |
| |
| for (auto& test : tests) { |
| golden_check(test); |
| } |
| } |
| |
| DEF_TEST(SkGaussFilterSweep, r) { |
| // The double just before 2.0. |
| const double maxSigma = nextafter(2.0, 0.0); |
| auto check = [&](double sigma, SkGaussFilter::Type type) { |
| SkGaussFilter filter{sigma, type}; |
| double result[SkGaussFilter::kGaussArrayMax]; |
| int n = 0; |
| for (auto d : filter) { |
| result[n++] = d; |
| } |
| REPORTER_ASSERT(r, n <= SkGaussFilter::kGaussArrayMax); |
| double sum = careful_add(n, result); |
| REPORTER_ASSERT(r, sum == 1.0); |
| }; |
| |
| { |
| |
| for (double sigma = 0.0; sigma < 2.0; sigma += 0.1) { |
| check(sigma, SkGaussFilter::Type::Gaussian); |
| } |
| |
| check(maxSigma, SkGaussFilter::Type::Gaussian); |
| } |
| |
| { |
| |
| for (double sigma = 0.0; sigma < 2.0; sigma += 0.1) { |
| check(sigma, SkGaussFilter::Type::Bessel); |
| } |
| |
| check(maxSigma, SkGaussFilter::Type::Bessel); |
| } |
| } |