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author | Herb Derby <herb@google.com> | 2017-11-03 13:36:55 -0400 |
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committer | Skia Commit-Bot <skia-commit-bot@chromium.org> | 2017-11-09 20:18:17 +0000 |
commit | 53ec7dc7cb523f220a9f5cd713b241c706779c81 (patch) | |
tree | a953ee541f352cc3c909b665f3386bedb4d9856b /tests/SkGaussFilterTest.cpp | |
parent | db042788df1a3487e2cefbe0eb70d6d09983793f (diff) |
Gauss filter calculation
Change-Id: I921ef815d4f788c312aa729f353b6ea154140555
Reviewed-on: https://skia-review.googlesource.com/67723
Commit-Queue: Herb Derby <herb@google.com>
Reviewed-by: Robert Phillips <robertphillips@google.com>
Diffstat (limited to 'tests/SkGaussFilterTest.cpp')
-rw-r--r-- | tests/SkGaussFilterTest.cpp | 88 |
1 files changed, 88 insertions, 0 deletions
diff --git a/tests/SkGaussFilterTest.cpp b/tests/SkGaussFilterTest.cpp new file mode 100644 index 0000000000..958e811ce5 --- /dev/null +++ b/tests/SkGaussFilterTest.cpp @@ -0,0 +1,88 @@ +/* + * 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[5]; + size_t n = filter.filterDouble(result); + REPORTER_ASSERT(r, 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); + for (auto type : {SkGaussFilter::Type::Gaussian, SkGaussFilter::Type::Bessel}) { + + auto check = [&](double sigma) { + SkGaussFilter filter{sigma, type}; + double result[5]; + int n = filter.filterDouble(result); + REPORTER_ASSERT(r, n <= 5); + 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); + } + + check(maxSigma); + } +} |