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Diffstat (limited to 'source/blender/blenlib/tests/BLI_length_parameterize_test.cc')
| -rw-r--r-- | source/blender/blenlib/tests/BLI_length_parameterize_test.cc | 202 |
1 files changed, 202 insertions, 0 deletions
diff --git a/source/blender/blenlib/tests/BLI_length_parameterize_test.cc b/source/blender/blenlib/tests/BLI_length_parameterize_test.cc new file mode 100644 index 00000000000..4a8b7095888 --- /dev/null +++ b/source/blender/blenlib/tests/BLI_length_parameterize_test.cc @@ -0,0 +1,202 @@ +/* SPDX-License-Identifier: Apache-2.0 */ + +#include "BLI_array.hh" +#include "BLI_length_parameterize.hh" +#include "BLI_vector.hh" + +#include "testing/testing.h" + +namespace blender::length_parameterize::tests { + +template<typename T> Array<float> calculate_lengths(const Span<T> values, const bool cyclic) +{ + Array<float> lengths(lengths_num(values.size(), cyclic)); + accumulate_lengths<T>(values, cyclic, lengths); + return lengths; +} + +template<typename T> void test_uniform_lengths(const Span<T> values) +{ + const float segment_length = math::distance(values.first(), values.last()) / (values.size() - 1); + for (const int i : values.index_range().drop_back(1)) { + EXPECT_NEAR(math::distance(values[i], values[i + 1]), segment_length, 1e-5); + } +} + +TEST(length_parameterize, FloatSimple) +{ + Array<float> values{{0, 1, 4}}; + Array<float> lengths = calculate_lengths(values.as_span(), false); + + Array<int> indices(4); + Array<float> factors(4); + create_uniform_samples(lengths, false, indices, factors); + Array<float> results(4); + linear_interpolation<float>(values, indices, factors, results); + Array<float> expected({ + 0.0f, + 1.33333f, + 2.66667f, + 4.0f, + }); + for (const int i : results.index_range()) { + EXPECT_NEAR(results[i], expected[i], 1e-5); + } + test_uniform_lengths(results.as_span()); +} + +TEST(length_parameterize, Float) +{ + Array<float> values{{1, 2, 3, 5, 10}}; + Array<float> lengths = calculate_lengths(values.as_span(), false); + + Array<int> indices(20); + Array<float> factors(20); + create_uniform_samples(lengths, false, indices, factors); + Array<float> results(20); + linear_interpolation<float>(values, indices, factors, results); + Array<float> expected({ + 1.0f, 1.47368f, 1.94737f, 2.42105f, 2.89474f, 3.36842f, 3.84211f, + 4.31579f, 4.78947f, 5.26316f, 5.73684f, 6.21053f, 6.68421f, 7.1579f, + 7.63158f, 8.10526f, 8.57895f, 9.05263f, 9.52632f, 10.0f, + }); + for (const int i : results.index_range()) { + EXPECT_NEAR(results[i], expected[i], 1e-5); + } + test_uniform_lengths(results.as_span()); +} + +TEST(length_parameterize, Float2) +{ + Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; + Array<float> lengths = calculate_lengths(values.as_span(), false); + + Array<int> indices(12); + Array<float> factors(12); + create_uniform_samples(lengths, false, indices, factors); + Array<float2> results(12); + linear_interpolation<float2>(values, indices, factors, results); + Array<float2> expected({ + {0.0f, 0.0f}, + {0.272727f, 0.0f}, + {0.545455f, 0.0f}, + {0.818182f, 0.0f}, + {1.0f, 0.0909091f}, + {1.0f, 0.363636f}, + {1.0f, 0.636364f}, + {1.0f, 0.909091f}, + {0.818182f, 1.0f}, + {0.545455f, 1.0f}, + {0.272727f, 1.0f}, + {0.0f, 1.0f}, + }); + for (const int i : results.index_range()) { + EXPECT_NEAR(results[i].x, expected[i].x, 1e-5); + EXPECT_NEAR(results[i].y, expected[i].y, 1e-5); + } +} + +TEST(length_parameterize, Float2Cyclic) +{ + Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; + Array<float> lengths = calculate_lengths(values.as_span(), true); + + Array<int> indices(12); + Array<float> factors(12); + create_uniform_samples(lengths, true, indices, factors); + Array<float2> results(12); + linear_interpolation<float2>(values, indices, factors, results); + Array<float2> expected({ + {0.0f, 0.0f}, + {0.333333f, 0.0f}, + {0.666667f, 0.0f}, + {1.0f, 0.0f}, + {1.0f, 0.333333f}, + {1.0f, 0.666667f}, + {1.0f, 1.0f}, + {0.666667f, 1.0f}, + {0.333333f, 1.0f}, + {0.0f, 1.0f}, + {0.0f, 0.666667f}, + {0.0f, 0.333333f}, + }); + for (const int i : results.index_range()) { + EXPECT_NEAR(results[i].x, expected[i].x, 1e-5); + EXPECT_NEAR(results[i].y, expected[i].y, 1e-5); + } +} + +TEST(length_parameterize, LineMany) +{ + Array<float> values{{1, 2}}; + Array<float> lengths = calculate_lengths(values.as_span(), false); + + Array<int> indices(5007); + Array<float> factors(5007); + create_uniform_samples(lengths, false, indices, factors); + Array<float> results(5007); + linear_interpolation<float>(values, indices, factors, results); + Array<float> expected({ + 1.9962f, 1.9964f, 1.9966f, 1.9968f, 1.997f, 1.9972f, 1.9974f, 1.9976f, 1.9978f, 1.998f, + 1.9982f, 1.9984f, 1.9986f, 1.9988f, 1.999f, 1.9992f, 1.9994f, 1.9996f, 1.9998f, 2.0f, + }); + for (const int i : expected.index_range()) { + EXPECT_NEAR(results.as_span().take_back(20)[i], expected[i], 1e-5); + } +} + +TEST(length_parameterize, CyclicMany) +{ + Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; + Array<float> lengths = calculate_lengths(values.as_span(), true); + + Array<int> indices(5007); + Array<float> factors(5007); + create_uniform_samples(lengths, true, indices, factors); + Array<float2> results(5007); + linear_interpolation<float2>(values, indices, factors, results); + Array<float2> expected({ + {0, 0.0159776}, {0, 0.0151787}, {0, 0.0143797}, {0, 0.013581}, {0, 0.0127821}, + {0, 0.0119832}, {0, 0.0111842}, {0, 0.0103855}, {0, 0.00958657}, {0, 0.00878763}, + {0, 0.00798869}, {0, 0.00718999}, {0, 0.00639105}, {0, 0.00559211}, {0, 0.00479317}, + {0, 0.00399446}, {0, 0.00319552}, {0, 0.00239658}, {0, 0.00159764}, {0, 0.000798941}, + }); + for (const int i : expected.index_range()) { + EXPECT_NEAR(results.as_span().take_back(20)[i].x, expected[i].x, 1e-5); + EXPECT_NEAR(results.as_span().take_back(20)[i].y, expected[i].y, 1e-5); + } +} + +TEST(length_parameterize, InterpolateColor) +{ + Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; + Array<float> lengths = calculate_lengths(values.as_span(), true); + + Array<ColorGeometry4f> colors{{{0, 0, 0, 1}, {1, 0, 0, 1}, {1, 1, 0, 1}, {0, 1, 0, 1}}}; + + Array<int> indices(10); + Array<float> factors(10); + create_uniform_samples(lengths, true, indices, factors); + Array<ColorGeometry4f> results(10); + linear_interpolation<ColorGeometry4f>(colors, indices, factors, results); + Array<ColorGeometry4f> expected({ + {0, 0, 0, 1}, + {0.4, 0, 0, 1}, + {0.8, 0, 0, 1}, + {1, 0.2, 0, 1}, + {1, 0.6, 0, 1}, + {1, 1, 0, 1}, + {0.6, 1, 0, 1}, + {0.2, 1, 0, 1}, + {0, 0.8, 0, 1}, + {0, 0.4, 0, 1}, + }); + for (const int i : results.index_range()) { + EXPECT_NEAR(results[i].r, expected[i].r, 1e-6); + EXPECT_NEAR(results[i].g, expected[i].g, 1e-6); + EXPECT_NEAR(results[i].b, expected[i].b, 1e-6); + EXPECT_NEAR(results[i].a, expected[i].a, 1e-6); + } +} + +} // namespace blender::length_parameterize::tests |