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Differential D15348 Diff 53191 source/blender/blenlib/tests/BLI_length_parameterize_test.cc

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source/blender/blenlib/tests/BLI_length_parameterize_test.cc

/* SPDX-License-Identifier: Apache-2.0 */ /* SPDX-License-Identifier: Apache-2.0 */
#include "BLI_array.hh" #include "BLI_array.hh"
#include "BLI_length_parameterize.hh" #include "BLI_length_parameterize.hh"
#include "BLI_vector.hh" #include "BLI_vector.hh"
#include "testing/testing.h" #include "testing/testing.h"
namespace blender::length_parameterize::tests { namespace blender::length_parameterize::tests {
template<typename T> Array<float> calculate_lengths(const Span<T> values, const bool cyclic) template<typename T> Array<float> calculate_lengths(const Span<T> values, const bool cyclic)
{ {
Array<float> lengths(lengths_num(values.size(), cyclic)); Array<float> lengths(segments_num(values.size(), cyclic));
accumulate_lengths<T>(values, cyclic, lengths); accumulate_lengths<T>(values, cyclic, lengths);
return lengths; return lengths;
} }
template<typename T> void test_uniform_lengths(const Span<T> values) template<typename T> void test_uniform_lengths(const Span<T> values)
{ {
const float segment_length = math::distance(values.first(), values.last()) / (values.size() - 1); const float segment_length = math::distance(values.first(), values.last()) / (values.size() - 1);
for (const int i : values.index_range().drop_back(1)) { for (const int i : values.index_range().drop_back(1)) {
EXPECT_NEAR(math::distance(values[i], values[i + 1]), segment_length, 1e-5); EXPECT_NEAR(math::distance(values[i], values[i + 1]), segment_length, 1e-5);
} }
} }
TEST(length_parameterize, FloatSimple) TEST(length_parameterize, FloatSimple)
{ {
Array<float> values{{0, 1, 4}}; Array<float> values{{0, 1, 4}};
Array<float> lengths = calculate_lengths(values.as_span(), false); Array<float> lengths = calculate_lengths(values.as_span(), false);
Array<int> indices(4); Array<int> indices(4);
Array<float> factors(4); Array<float> factors(4);
create_uniform_samples(lengths, false, indices, factors); sample_uniform(lengths, true, indices, factors);
Array<float> results(4); Array<float> results(4);
linear_interpolation<float>(values, indices, factors, results); linear_interpolation<float>(values, indices, factors, results);
Array<float> expected({ Array<float> expected({
0.0f, 0.0f,
1.33333f, 1.33333f,
2.66667f, 2.66667f,
4.0f, 4.0f,
}); });
for (const int i : results.index_range()) { for (const int i : results.index_range()) {
EXPECT_NEAR(results[i], expected[i], 1e-5); EXPECT_NEAR(results[i], expected[i], 1e-5);
} }
test_uniform_lengths(results.as_span()); test_uniform_lengths(results.as_span());
} }
TEST(length_parameterize, Float) TEST(length_parameterize, Float)
{ {
Array<float> values{{1, 2, 3, 5, 10}}; Array<float> values{{1, 2, 3, 5, 10}};
Array<float> lengths = calculate_lengths(values.as_span(), false); Array<float> lengths = calculate_lengths(values.as_span(), false);
Array<int> indices(20); Array<int> indices(20);
Array<float> factors(20); Array<float> factors(20);
create_uniform_samples(lengths, false, indices, factors); sample_uniform(lengths, true, indices, factors);
Array<float> results(20); Array<float> results(20);
linear_interpolation<float>(values, indices, factors, results); linear_interpolation<float>(values, indices, factors, results);
Array<float> expected({ Array<float> expected({
1.0f, 1.47368f, 1.94737f, 2.42105f, 2.89474f, 3.36842f, 3.84211f, 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, 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, 7.63158f, 8.10526f, 8.57895f, 9.05263f, 9.52632f, 10.0f,
}); });
for (const int i : results.index_range()) { for (const int i : results.index_range()) {
EXPECT_NEAR(results[i], expected[i], 1e-5); EXPECT_NEAR(results[i], expected[i], 1e-5);
} }
test_uniform_lengths(results.as_span()); test_uniform_lengths(results.as_span());
} }
TEST(length_parameterize, Float2) TEST(length_parameterize, Float2)
{ {
Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}};
Array<float> lengths = calculate_lengths(values.as_span(), false); Array<float> lengths = calculate_lengths(values.as_span(), false);
Array<int> indices(12); Array<int> indices(12);
Array<float> factors(12); Array<float> factors(12);
create_uniform_samples(lengths, false, indices, factors); sample_uniform(lengths, true, indices, factors);
Array<float2> results(12); Array<float2> results(12);
linear_interpolation<float2>(values, indices, factors, results); linear_interpolation<float2>(values, indices, factors, results);
Array<float2> expected({ Array<float2> expected({
{0.0f, 0.0f}, {0.0f, 0.0f},
{0.272727f, 0.0f}, {0.272727f, 0.0f},
{0.545455f, 0.0f}, {0.545455f, 0.0f},
{0.818182f, 0.0f}, {0.818182f, 0.0f},
{1.0f, 0.0909091f}, {1.0f, 0.0909091f},
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TEST(length_parameterize, Float2Cyclic) TEST(length_parameterize, Float2Cyclic)
{ {
Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}};
Array<float> lengths = calculate_lengths(values.as_span(), true); Array<float> lengths = calculate_lengths(values.as_span(), true);
Array<int> indices(12); Array<int> indices(12);
Array<float> factors(12); Array<float> factors(12);
create_uniform_samples(lengths, true, indices, factors); sample_uniform(lengths, false, indices, factors);
Array<float2> results(12); Array<float2> results(12);
linear_interpolation<float2>(values, indices, factors, results); linear_interpolation<float2>(values, indices, factors, results);
Array<float2> expected({ Array<float2> expected({
{0.0f, 0.0f}, {0.0f, 0.0f},
{0.333333f, 0.0f}, {0.333333f, 0.0f},
{0.666667f, 0.0f}, {0.666667f, 0.0f},
{1.0f, 0.0f}, {1.0f, 0.0f},
{1.0f, 0.333333f}, {1.0f, 0.333333f},
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TEST(length_parameterize, LineMany) TEST(length_parameterize, LineMany)
{ {
Array<float> values{{1, 2}}; Array<float> values{{1, 2}};
Array<float> lengths = calculate_lengths(values.as_span(), false); Array<float> lengths = calculate_lengths(values.as_span(), false);
Array<int> indices(5007); Array<int> indices(5007);
Array<float> factors(5007); Array<float> factors(5007);
create_uniform_samples(lengths, false, indices, factors); sample_uniform(lengths, true, indices, factors);
Array<float> results(5007); Array<float> results(5007);
linear_interpolation<float>(values, indices, factors, results); linear_interpolation<float>(values, indices, factors, results);
Array<float> expected({ Array<float> expected({
1.9962f, 1.9964f, 1.9966f, 1.9968f, 1.997f, 1.9972f, 1.9974f, 1.9976f, 1.9978f, 1.998f, 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, 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()) { for (const int i : expected.index_range()) {
EXPECT_NEAR(results.as_span().take_back(20)[i], expected[i], 1e-5); EXPECT_NEAR(results.as_span().take_back(20)[i], expected[i], 1e-5);
} }
} }
TEST(length_parameterize, CyclicMany) TEST(length_parameterize, CyclicMany)
{ {
Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}};
Array<float> lengths = calculate_lengths(values.as_span(), true); Array<float> lengths = calculate_lengths(values.as_span(), true);
Array<int> indices(5007); Array<int> indices(5007);
Array<float> factors(5007); Array<float> factors(5007);
create_uniform_samples(lengths, true, indices, factors); sample_uniform(lengths, false, indices, factors);
Array<float2> results(5007); Array<float2> results(5007);
linear_interpolation<float2>(values, indices, factors, results); linear_interpolation<float2>(values, indices, factors, results);
Array<float2> expected({ Array<float2> expected({
{0, 0.0159776}, {0, 0.0151787}, {0, 0.0143797}, {0, 0.013581}, {0, 0.0127821}, {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, 0.0119832}, {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, 0.00798869}, {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}, {0, 0.00399446}, {0, 0.00319552}, {0, 0.00239658}, {0, 0.00159764}, {0, 0.000798941},
}); });
for (const int i : expected.index_range()) { 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].x, expected[i].x, 1e-5);
EXPECT_NEAR(results.as_span().take_back(20)[i].y, expected[i].y, 1e-5); EXPECT_NEAR(results.as_span().take_back(20)[i].y, expected[i].y, 1e-5);
} }
} }
TEST(length_parameterize, InterpolateColor) TEST(length_parameterize, InterpolateColor)
{ {
Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}};
Array<float> lengths = calculate_lengths(values.as_span(), true); 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<ColorGeometry4f> colors{{{0, 0, 0, 1}, {1, 0, 0, 1}, {1, 1, 0, 1}, {0, 1, 0, 1}}};
Array<int> indices(10); Array<int> indices(10);
Array<float> factors(10); Array<float> factors(10);
create_uniform_samples(lengths, true, indices, factors); sample_uniform(lengths, false, indices, factors);
Array<ColorGeometry4f> results(10); Array<ColorGeometry4f> results(10);
linear_interpolation<ColorGeometry4f>(colors, indices, factors, results); linear_interpolation<ColorGeometry4f>(colors, indices, factors, results);
Array<ColorGeometry4f> expected({ Array<ColorGeometry4f> expected({
{0, 0, 0, 1}, {0, 0, 0, 1},
{0.4, 0, 0, 1}, {0.4, 0, 0, 1},
{0.8, 0, 0, 1}, {0.8, 0, 0, 1},
{1, 0.2, 0, 1}, {1, 0.2, 0, 1},
{1, 0.6, 0, 1}, {1, 0.6, 0, 1},
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TEST(length_parameterize, ArbitraryFloatSimple) TEST(length_parameterize, ArbitraryFloatSimple)
{ {
Array<float> values{{0, 1, 4}}; Array<float> values{{0, 1, 4}};
Array<float> lengths = calculate_lengths(values.as_span(), false); Array<float> lengths = calculate_lengths(values.as_span(), false);
Array<float> sample_lengths{{0.5f, 1.5f, 2.0f, 4.0f}}; Array<float> sample_lengths{{0.5f, 1.5f, 2.0f, 4.0f}};
Array<int> indices(4); Array<int> indices(4);
Array<float> factors(4); Array<float> factors(4);
create_samples_from_sorted_lengths(lengths, sample_lengths, false, indices, factors); sample_at_lengths(lengths, sample_lengths, indices, factors);
Array<float> results(4); Array<float> results(4);
linear_interpolation<float>(values, indices, factors, results); linear_interpolation<float>(values, indices, factors, results);
results.as_span().print_as_lines("results"); results.as_span().print_as_lines("results");
Array<float> expected({ Array<float> expected({
0.5f, 0.5f,
1.5f, 1.5f,
2.0f, 2.0f,
4.0f, 4.0f,
}); });
for (const int i : results.index_range()) { for (const int i : results.index_range()) {
EXPECT_NEAR(results[i], expected[i], 1e-5); EXPECT_NEAR(results[i], expected[i], 1e-5);
} }
} }
TEST(length_parameterize, ArbitraryFloat2) TEST(length_parameterize, ArbitraryFloat2)
{ {
Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}}; Array<float2> values{{{0, 0}, {1, 0}, {1, 1}, {0, 1}}};
Array<float> lengths = calculate_lengths(values.as_span(), true); Array<float> lengths = calculate_lengths(values.as_span(), true);
Array<float> sample_lengths{ Array<float> sample_lengths{
{0.5f, 1.5f, 2.0f, 2.0f, 2.1f, 2.5f, 3.5f, 3.6f, 3.8f, 3.85f, 3.90f, 4.0f}}; {0.5f, 1.5f, 2.0f, 2.0f, 2.1f, 2.5f, 3.5f, 3.6f, 3.8f, 3.85f, 3.90f, 4.0f}};
Array<int> indices(12); Array<int> indices(12);
Array<float> factors(12); Array<float> factors(12);
create_samples_from_sorted_lengths(lengths, sample_lengths, true, indices, factors); sample_at_lengths(lengths, sample_lengths, indices, factors);
Array<float2> results(12); Array<float2> results(12);
linear_interpolation<float2>(values, indices, factors, results); linear_interpolation<float2>(values, indices, factors, results);
results.as_span().print_as_lines("results"); results.as_span().print_as_lines("results");
Array<float2> expected({ Array<float2> expected({
{0.5f, 0.0f}, {0.5f, 0.0f},
{1.0f, 0.5f}, {1.0f, 0.5f},
{1.0f, 1.0f}, {1.0f, 1.0f},
{1.0f, 1.0f}, {1.0f, 1.0f},
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