630e7d651a
Unused except in unit tests. Change-Id: I07bb70515a383898ad59c0fa918461052b582e35 Reviewed-on: https://skia-review.googlesource.com/c/168860 Reviewed-by: Herb Derby <herb@google.com> Commit-Queue: Mike Klein <mtklein@google.com>
83 lines
2.3 KiB
C++
83 lines
2.3 KiB
C++
/*
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* Copyright 2017 Google Inc.
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*
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* Use of this source code is governed by a BSD-style license that can be
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* found in the LICENSE file.
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*/
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#include "SkGaussFilter.h"
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#include <cmath>
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#include <tuple>
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#include <vector>
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#include "Test.h"
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// one part in a million
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static constexpr double kEpsilon = 0.000001;
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static double careful_add(int n, double* gauss) {
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// Sum smallest to largest to retain precision.
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double sum = 0;
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for (int i = n - 1; i >= 1; i--) {
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sum += 2.0 * gauss[i];
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}
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sum += gauss[0];
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return sum;
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}
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DEF_TEST(SkGaussFilterCommon, r) {
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using Test = std::tuple<double, std::vector<double>>;
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auto golden_check = [&](const Test& test) {
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double sigma; std::vector<double> golden;
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std::tie(sigma, golden) = test;
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SkGaussFilter filter{sigma};
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double result[SkGaussFilter::kGaussArrayMax];
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int n = 0;
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for (auto d : filter) {
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result[n++] = d;
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}
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REPORTER_ASSERT(r, static_cast<size_t>(n) == golden.size());
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double sum = careful_add(n, result);
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REPORTER_ASSERT(r, sum == 1.0);
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for (size_t i = 0; i < golden.size(); i++) {
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REPORTER_ASSERT(r, std::abs(golden[i] - result[i]) < kEpsilon);
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}
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};
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// The following two sigmas account for about 85% of all sigmas used for masks.
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// Golden values generated using Mathematica.
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auto tests = {
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// GaussianMatrix[{{Automatic}, {.788675}}]
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Test{0.788675, {0.593605, 0.176225, 0.0269721}},
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// GaussianMatrix[{{4}, {1.07735}}, Method -> "Bessel"]
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Test{1.07735, {0.429537, 0.214955, 0.059143, 0.0111337}},
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};
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for (auto& test : tests) {
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golden_check(test);
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}
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}
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DEF_TEST(SkGaussFilterSweep, r) {
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// The double just before 2.0.
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const double maxSigma = nextafter(2.0, 0.0);
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auto check = [&](double sigma) {
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SkGaussFilter filter{sigma};
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double result[SkGaussFilter::kGaussArrayMax];
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int n = 0;
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for (auto d : filter) {
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result[n++] = d;
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}
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REPORTER_ASSERT(r, n <= SkGaussFilter::kGaussArrayMax);
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double sum = careful_add(n, result);
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REPORTER_ASSERT(r, sum == 1.0);
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};
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for (double sigma = 0.0; sigma < 2.0; sigma += 0.1) {
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check(sigma);
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}
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check(maxSigma);
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}
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