blob: 5efc39fd50aa5a64ad0ffb3d62ba4beeb28ae2d1 [file] [log] [blame]
//
// Copyright 2015 The ANGLE Project Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
//
// matrix_utils_unittests:
// Unit tests for the matrix utils.
//
#include "matrix_utils.h"
#include <gtest/gtest.h>
using namespace angle;
namespace
{
struct RotateArgs
{
float angle;
Vector3 axis;
};
struct TranslateArgs
{
float x;
float y;
float z;
};
struct ScaleArgs
{
float x;
float y;
float z;
};
struct FrustumArgs
{
float l;
float r;
float b;
float t;
float n;
float f;
};
void CheckMat4ExactlyEq(const Matrix<float> &a, const Matrix<float> &b)
{
for (unsigned int i = 0; i < 4; i++)
{
for (unsigned int j = 0; i < 4; i++)
{
EXPECT_EQ(a.at(i, j), b.at(i, j));
}
}
}
// TODO(lfy): Spec out requirements for matrix precision
void CheckMatrixCloseToGolden(float *golden, const Mat4 &m)
{
const float floatFaultTolarance = 0.000001f;
const auto &checkElts = m.elements();
for (size_t i = 0; i < checkElts.size(); i++)
{
EXPECT_NEAR(golden[i], checkElts[i], floatFaultTolarance);
}
}
void CheckMatrixCloseToGolden(const std::vector<float> &golden, const Mat4 &m)
{
const float floatFaultTolarance = 0.000001f;
const auto &checkElts = m.elements();
for (size_t i = 0; i < golden.size(); i++)
{
EXPECT_NEAR(golden[i], checkElts[i], floatFaultTolarance);
}
}
} // namespace
namespace
{
const unsigned int minDimensions = 2;
const unsigned int maxDimensions = 4;
TEST(MatrixUtilsTest, MatrixConstructorTest)
{
for (unsigned int i = minDimensions; i <= maxDimensions; i++)
{
for (unsigned int j = minDimensions; j <= maxDimensions; j++)
{
unsigned int numElements = i * j;
Matrix<float> m(std::vector<float>(numElements, 1.0f), i, j);
EXPECT_EQ(m.rows(), i);
EXPECT_EQ(m.columns(), j);
EXPECT_EQ(m.elements(), std::vector<float>(numElements, 1.0f));
}
}
for (unsigned int i = minDimensions; i <= maxDimensions; i++)
{
unsigned int numElements = i * i;
Matrix<float> m(std::vector<float>(numElements, 1.0f), i);
EXPECT_EQ(m.size(), i);
EXPECT_EQ(m.columns(), m.columns());
EXPECT_EQ(m.elements(), std::vector<float>(numElements, 1.0f));
}
}
TEST(MatrixUtilsTest, MatrixCompMultTest)
{
for (unsigned int i = minDimensions; i <= maxDimensions; i++)
{
unsigned int numElements = i * i;
Matrix<float> m1(std::vector<float>(numElements, 2.0f), i);
Matrix<float> actualResult = m1.compMult(m1);
std::vector<float> actualResultElements = actualResult.elements();
std::vector<float> expectedResultElements(numElements, 4.0f);
EXPECT_EQ(expectedResultElements, actualResultElements);
}
}
TEST(MatrixUtilsTest, MatrixOuterProductTest)
{
for (unsigned int i = minDimensions; i <= maxDimensions; i++)
{
for (unsigned int j = minDimensions; j <= maxDimensions; j++)
{
unsigned int numElements = i * j;
Matrix<float> m1(std::vector<float>(numElements, 2.0f), i, 1);
Matrix<float> m2(std::vector<float>(numElements, 2.0f), 1, j);
Matrix<float> actualResult = m1.outerProduct(m2);
EXPECT_EQ(actualResult.rows(), i);
EXPECT_EQ(actualResult.columns(), j);
std::vector<float> actualResultElements = actualResult.elements();
std::vector<float> expectedResultElements(numElements, 4.0f);
EXPECT_EQ(expectedResultElements, actualResultElements);
}
}
}
TEST(MatrixUtilsTest, MatrixTransposeTest)
{
for (unsigned int i = minDimensions; i <= maxDimensions; i++)
{
for (unsigned int j = minDimensions; j <= maxDimensions; j++)
{
unsigned int numElements = i * j;
Matrix<float> m1(std::vector<float>(numElements, 2.0f), i, j);
Matrix<float> expectedResult =
Matrix<float>(std::vector<float>(numElements, 2.0f), j, i);
Matrix<float> actualResult = m1.transpose();
EXPECT_EQ(expectedResult.elements(), actualResult.elements());
EXPECT_EQ(actualResult.rows(), expectedResult.rows());
EXPECT_EQ(actualResult.columns(), expectedResult.columns());
// transpose(transpose(A)) = A
Matrix<float> m2 = actualResult.transpose();
EXPECT_EQ(m1.elements(), m2.elements());
}
}
}
TEST(MatrixUtilsTest, MatrixDeterminantTest)
{
for (unsigned int i = minDimensions; i <= maxDimensions; i++)
{
unsigned int numElements = i * i;
Matrix<float> m(std::vector<float>(numElements, 2.0f), i);
EXPECT_EQ(m.determinant(), 0.0f);
}
}
TEST(MatrixUtilsTest, 2x2MatrixInverseTest)
{
float inputElements[] = {2.0f, 5.0f, 3.0f, 7.0f};
unsigned int numElements = 4;
std::vector<float> input(inputElements, inputElements + numElements);
Matrix<float> inputMatrix(input, 2);
float identityElements[] = {1.0f, 0.0f, 0.0f, 1.0f};
std::vector<float> identityMatrix(identityElements, identityElements + numElements);
// A * inverse(A) = I, where I is identity matrix.
Matrix<float> result = inputMatrix * inputMatrix.inverse();
EXPECT_EQ(identityMatrix, result.elements());
}
TEST(MatrixUtilsTest, 3x3MatrixInverseTest)
{
float inputElements[] = {11.0f, 23.0f, 37.0f, 13.0f, 29.0f, 41.0f, 19.0f, 31.0f, 43.0f};
unsigned int numElements = 9;
std::vector<float> input(inputElements, inputElements + numElements);
Matrix<float> inputMatrix(input, 3);
float identityElements[] = {1.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 1.0f};
std::vector<float> identityMatrix(identityElements, identityElements + numElements);
// A * inverse(A) = I, where I is identity matrix.
Matrix<float> result = inputMatrix * inputMatrix.inverse();
std::vector<float> resultElements = result.elements();
const float floatFaultTolarance = 0.000001f;
for (size_t i = 0; i < numElements; i++)
EXPECT_NEAR(resultElements[i], identityMatrix[i], floatFaultTolarance);
}
TEST(MatrixUtilsTest, 4x4MatrixInverseTest)
{
float inputElements[] = {29.0f, 43.0f, 61.0f, 79.0f, 31.0f, 47.0f, 67.0f, 83.0f,
37.0f, 53.0f, 71.0f, 89.0f, 41.0f, 59.0f, 73.0f, 97.0f};
unsigned int numElements = 16;
std::vector<float> input(inputElements, inputElements + numElements);
Matrix<float> inputMatrix(input, 4);
float identityElements[] = {
1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
};
std::vector<float> identityMatrix(identityElements, identityElements + numElements);
// A * inverse(A) = I, where I is identity matrix.
Matrix<float> result = inputMatrix * inputMatrix.inverse();
std::vector<float> resultElements = result.elements();
const float floatFaultTolarance = 0.00001f;
for (unsigned int i = 0; i < numElements; i++)
EXPECT_NEAR(resultElements[i], identityMatrix[i], floatFaultTolarance);
}
// Tests constructors for mat4; using raw float*, std::vector<float>,
// and Matrix<float>.
TEST(MatrixUtilsTest, Mat4Construction)
{
float elements[] = {
0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f,
8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f,
};
std::vector<float> elementsVector(16, 0);
for (int i = 0; i < 16; i++)
{
elementsVector[i] = elements[i];
}
Matrix<float> a(elements, 4);
Mat4 b(elements);
Mat4 bVec(elementsVector);
CheckMat4ExactlyEq(a, b);
CheckMat4ExactlyEq(b, bVec);
a.setToIdentity();
b = Mat4();
CheckMat4ExactlyEq(a, b);
Mat4 c(0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f,
14.0f, 15.0f);
Mat4 d(elements);
Mat4 e(Matrix<float>(elements, 4));
CheckMat4ExactlyEq(c, d);
CheckMat4ExactlyEq(e, d);
}
// Tests rotation matrices.
TEST(MatrixUtilsTest, Mat4Rotate)
{
// Sanity check.
float elementsExpected[] = {
1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
};
std::vector<float> elementsExpectedVector(16, 0);
for (int i = 0; i < 16; i++)
{
elementsExpectedVector[i] = elementsExpected[i];
}
Mat4 r = Mat4::Rotate(0.f, Vector3(0.f, 0.f, 1.f));
Mat4 golden(elementsExpected);
CheckMatrixCloseToGolden(elementsExpected, r);
CheckMatrixCloseToGolden(elementsExpectedVector, r);
CheckMat4ExactlyEq(r, golden);
// Randomly-generated inputs, outputs using GLM.
std::vector<RotateArgs> rotationGoldenInputs;
std::vector<std::vector<float>> rotationGoldenOutputs;
rotationGoldenInputs.push_back(
{-123.2026214599609375f,
Vector3(1.4951171875000000f, 8.4370708465576172f, 1.8489227294921875f)});
rotationGoldenInputs.push_back(
{-185.3049316406250000f,
Vector3(8.3313980102539062f, 5.8317651748657227f, -5.5201716423034668f)});
rotationGoldenInputs.push_back(
{89.1301574707031250f,
Vector3(-8.5962724685668945f, 2.4547367095947266f, -3.2461600303649902f)});
rotationGoldenInputs.push_back(
{64.2547302246093750f,
Vector3(-1.9640445709228516f, -9.6942234039306641f, 9.1921043395996094f)});
rotationGoldenInputs.push_back(
{-298.5585021972656250f,
Vector3(-5.3600544929504395f, -6.4333534240722656f, -5.3734750747680664f)});
rotationGoldenInputs.push_back(
{288.2606201171875000f,
Vector3(-2.3043875694274902f, -9.8447618484497070f, -0.9124794006347656f)});
rotationGoldenInputs.push_back(
{142.3956298828125000f,
Vector3(-1.0044975280761719f, -2.5834980010986328f, -0.8451175689697266f)});
rotationGoldenInputs.push_back(
{-140.0445861816406250f,
Vector3(-1.3710060119628906f, -2.5042991638183594f, -9.7572088241577148f)});
rotationGoldenInputs.push_back(
{-338.5443420410156250f,
Vector3(6.8056621551513672f, 2.7508878707885742f, -5.8343429565429688f)});
rotationGoldenInputs.push_back(
{79.0578613281250000f,
Vector3(9.0518493652343750f, -5.5615901947021484f, 6.3559799194335938f)});
rotationGoldenInputs.push_back(
{4464.6367187500000000f,
Vector3(-53.9424285888671875f, -10.3614959716796875f, 54.3564453125000000f)});
rotationGoldenInputs.push_back(
{-2820.6347656250000000f,
Vector3(62.1694793701171875f, 82.4977569580078125f, -60.0084800720214844f)});
rotationGoldenInputs.push_back(
{3371.0527343750000000f,
Vector3(-74.5660324096679688f, -31.3026275634765625f, 96.7252349853515625f)});
rotationGoldenInputs.push_back(
{5501.7167968750000000f,
Vector3(15.0308380126953125f, 23.2323913574218750f, 66.8295593261718750f)});
rotationGoldenInputs.push_back(
{392.1757812500000000f,
Vector3(36.5722198486328125f, 69.2820892333984375f, 24.1789474487304688f)});
rotationGoldenInputs.push_back(
{-2206.7138671875000000f,
Vector3(-91.5292282104492188f, 68.2716674804687500f, 42.0627288818359375f)});
rotationGoldenInputs.push_back(
{-4648.8623046875000000f,
Vector3(50.7790374755859375f, 43.3964080810546875f, -36.3877525329589844f)});
rotationGoldenInputs.push_back(
{2794.6015625000000000f,
Vector3(76.2934265136718750f, 63.4901885986328125f, 79.5993041992187500f)});
rotationGoldenInputs.push_back(
{2294.6787109375000000f,
Vector3(-81.3662338256835938f, 77.6944580078125000f, 10.8423461914062500f)});
rotationGoldenInputs.push_back(
{2451.0468750000000000f,
Vector3(-80.6299896240234375f, 51.8244628906250000f, 13.6877517700195312f)});
rotationGoldenOutputs.push_back({
-0.5025786161422729f,
0.0775775760412216f,
0.8610438108444214f,
0.0000000000000000f,
0.4305581450462341f,
0.8861245512962341f,
0.1714731603860855f,
0.0000000000000000f,
-0.7496895790100098f,
0.4569081664085388f,
-0.4787489175796509f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.0388860106468201f,
0.6800884604454041f,
-0.7320981621742249f,
0.0000000000000000f,
0.7683026194572449f,
-0.4887983798980713f,
-0.4132642149925232f,
0.0000000000000000f,
-0.6389046907424927f,
-0.5464027523994446f,
-0.5415209531784058f,
-0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.8196773529052734f,
-0.5709969997406006f,
0.0457325875759125f,
0.0000000000000000f,
0.1115358471870422f,
0.0807825028896332f,
-0.9904716014862061f,
0.0000000000000000f,
0.5618618726730347f,
0.8169679641723633f,
0.1299021989107132f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.4463376104831696f,
0.6722379326820374f,
0.5906597971916199f,
0.0000000000000000f,
-0.5541059374809265f,
0.7259115576744080f,
-0.4074544310569763f,
0.0000000000000000f,
-0.7026730179786682f,
-0.1454258561134338f,
0.6964926123619080f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.6295375227928162f,
-0.2925493717193604f,
0.7197898030281067f,
0.0000000000000000f,
0.6561784744262695f,
0.6962769031524658f,
-0.2909094095230103f,
0.0000000000000000f,
-0.4160676598548889f,
0.6554489731788635f,
0.6302970647811890f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.3487194776535034f,
0.2365041077136993f,
-0.9068960547447205f,
0.0000000000000000f,
0.0657925531268120f,
0.9590729475021362f,
0.2754095196723938f,
0.0000000000000000f,
0.9349150061607361f,
-0.1557076722383499f,
0.3188872337341309f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
-0.5768983364105225f,
0.3758956193923950f,
0.7251832485198975f,
0.0000000000000000f,
0.7318079471588135f,
0.6322251558303833f,
0.2544573545455933f,
0.0000000000000000f,
-0.3628296852111816f,
0.6774909496307373f,
-0.6398130059242249f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
-0.7344170808792114f,
0.6750319004058838f,
0.0704519301652908f,
0.0000000000000000f,
-0.5576634407043457f,
-0.6593509316444397f,
0.5042498111724854f,
0.0000000000000000f,
0.3868372440338135f,
0.3310411870479584f,
0.8606789708137512f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.9672066569328308f,
-0.2128375321626663f,
-0.1386056393384933f,
0.0000000000000000f,
0.2423491925001144f,
0.9366652965545654f,
0.2528339624404907f,
0.0000000000000000f,
0.0760145336389542f,
-0.2781336307525635f,
0.9575299024581909f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.6229416728019714f,
0.2379529774188995f,
0.7451993227005005f,
0.0000000000000000f,
-0.7701886892318726f,
0.3533243536949158f,
0.5310096740722656f,
0.0000000000000000f,
-0.1369417309761047f,
-0.9047321081161499f,
0.4033691287040710f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.0691163539886475f,
0.5770183801651001f,
-0.8138013482093811f,
0.0000000000000000f,
-0.2371776401996613f,
-0.7828579545021057f,
-0.5752218365669250f,
-0.0000000000000000f,
-0.9690044522285461f,
0.2327727228403091f,
0.0827475786209106f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.6423799991607666f,
-0.2559574246406555f,
-0.7223805189132690f,
0.0000000000000000f,
0.6084498763084412f,
0.7434381246566772f,
0.2776480317115784f,
0.0000000000000000f,
0.4659791886806488f,
-0.6178879141807556f,
0.6333070397377014f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
-0.0772442221641541f,
0.8218145370483398f,
-0.5644945502281189f,
0.0000000000000000f,
-0.3352625370025635f,
-0.5546256303787231f,
-0.7615703344345093f,
-0.0000000000000000f,
-0.9389528036117554f,
0.1304270327091217f,
0.3183652758598328f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
-0.1511210501194000f,
0.9849811196327209f,
-0.0835132896900177f,
0.0000000000000000f,
-0.8243820667266846f,
-0.0789582207798958f,
0.5604994893074036f,
0.0000000000000000f,
0.5454874038696289f,
0.1535501033067703f,
0.8239331245422363f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.8769769668579102f,
0.2149325907230377f,
-0.4297852218151093f,
0.0000000000000000f,
-0.0991527736186981f,
0.9560844898223877f,
0.2758098840713501f,
0.0000000000000000f,
0.4701915681362152f,
-0.1992645263671875f,
0.8597752451896667f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.8634905815124512f,
-0.3842780590057373f,
0.3266716897487640f,
0.0000000000000000f,
0.1189628839492798f,
0.7845909595489502f,
0.6084939241409302f,
0.0000000000000000f,
-0.4901345074176788f,
-0.4865669906139374f,
0.7232017517089844f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.9201348423957825f,
-0.1924955844879150f,
-0.3410238325595856f,
0.0000000000000000f,
0.3022402822971344f,
0.9028221964836121f,
0.3058805167675018f,
0.0000000000000000f,
0.2490032464265823f,
-0.3845224678516388f,
0.8888981342315674f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.4109522104263306f,
-0.3483857214450836f,
0.8424642086029053f,
0.0000000000000000f,
0.8988372087478638f,
0.3092637956142426f,
-0.3105604350566864f,
0.0000000000000000f,
-0.1523487865924835f,
0.8848635554313660f,
0.4402346611022949f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.1795312762260437f,
-0.7746179103851318f,
-0.6064119935035706f,
0.0000000000000000f,
-0.9110413789749146f,
0.1016657948493958f,
-0.3995841145515442f,
0.0000000000000000f,
0.3711764216423035f,
0.6242043375968933f,
-0.6874569058418274f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
rotationGoldenOutputs.push_back({
0.8035809993743896f,
-0.4176068902015686f,
0.4241013824939728f,
0.0000000000000000f,
-0.1537265181541443f,
0.5427432060241699f,
0.8257105946540833f,
0.0000000000000000f,
-0.5750005841255188f,
-0.7287209630012512f,
0.3719408810138702f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.0000000000000000f,
});
EXPECT_EQ(rotationGoldenInputs.size(), rotationGoldenOutputs.size());
for (size_t i = 0; i < rotationGoldenInputs.size(); i++)
{
const auto &input = rotationGoldenInputs[i];
const auto &output = rotationGoldenOutputs[i];
Mat4 rot = Mat4::Rotate(input.angle, input.axis);
CheckMatrixCloseToGolden(output, rot);
}
}
// Tests mat4 translation.
TEST(MatrixUtilsTest, Mat4Translate)
{
float elementsExpected[] = {
1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
};
std::vector<float> elementsExpectedVector(16, 0);
for (int i = 0; i < 16; i++)
{
elementsExpectedVector[i] = elementsExpected[i];
}
Mat4 r = Mat4::Translate(Vector3(0.f, 0.f, 0.f));
Mat4 golden(elementsExpected);
CheckMatrixCloseToGolden(elementsExpected, r);
CheckMatrixCloseToGolden(elementsExpectedVector, r);
CheckMat4ExactlyEq(r, golden);
float elementsExpected1[] = {
1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 1.0f, 1.0f, 1.0f, 1.0f,
};
float elementsExpected2[] = {
1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 2.0f, 2.0f, 2.0f, 1.0f,
};
Mat4 golden1(elementsExpected1);
Mat4 golden2(elementsExpected2);
Mat4 t1 = Mat4::Translate(Vector3(1.f, 1.f, 1.f));
Mat4 t2 = Mat4::Translate(Vector3(2.f, 2.f, 2.f));
Mat4 t1t1 = t1.product(t1);
CheckMat4ExactlyEq(t1, golden1);
CheckMat4ExactlyEq(t2, golden2);
CheckMat4ExactlyEq(t1t1, golden2);
}
// Tests scale for mat4.
TEST(MatrixUtilsTest, Mat4Scale)
{
// Sanity check.
float elementsExpected[] = {
1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
};
std::vector<float> elementsExpectedVector(16, 0);
for (int i = 0; i < 16; i++)
{
elementsExpectedVector[i] = elementsExpected[i];
}
Mat4 r = Mat4::Scale(Vector3(1.f, 1.f, 1.f));
Mat4 golden(elementsExpected);
CheckMatrixCloseToGolden(elementsExpected, r);
CheckMatrixCloseToGolden(elementsExpectedVector, r);
CheckMat4ExactlyEq(r, golden);
float elementsExpected2[] = {
2.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 0.0f, 0.0f,
0.0f, 0.0f, 2.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
};
float elementsExpected4[] = {
4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 4.0f, 0.0f, 0.0f,
0.0f, 0.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
};
Mat4 golden2(elementsExpected2);
Mat4 golden4(elementsExpected4);
Mat4 t2 = Mat4::Scale(Vector3(2.f, 2.f, 2.f));
Mat4 t2t2 = t2.product(t2);
CheckMat4ExactlyEq(t2, golden2);
CheckMat4ExactlyEq(t2t2, golden4);
}
// Tests frustum matrices.
TEST(MatrixUtilsTest, Mat4Frustum)
{
// Randomly-generated inputs, outputs using GLM.
std::vector<FrustumArgs> frustumGoldenInputs;
std::vector<std::vector<float>> frustumGoldenOutputs;
EXPECT_EQ(frustumGoldenInputs.size(), frustumGoldenOutputs.size());
frustumGoldenInputs.push_back({6.5640869140625000f, 2.6196002960205078f, 7.6299438476562500f,
-3.5341463088989258f, 1.5971517562866211f, 3.3254327774047852f});
frustumGoldenInputs.push_back({-9.4570913314819336f, -5.3334407806396484f, 0.8660326004028320f,
-4.5830192565917969f, -6.7980914115905762f,
4.4744148254394531f});
frustumGoldenInputs.push_back({1.4876422882080078f, 2.8094434738159180f, -1.6803054809570312f,
-0.8823823928833008f, 9.7977848052978516f,
-8.6204166412353516f});
frustumGoldenInputs.push_back({-3.3456401824951172f, 9.8235015869140625f, 3.5869121551513672f,
5.2356719970703125f, -4.0609388351440430f, 7.7973194122314453f});
frustumGoldenInputs.push_back({9.5381393432617188f, 7.5244369506835938f, 3.2054557800292969f,
-5.9051651954650879f, -8.1114397048950195f,
-8.9626598358154297f});
frustumGoldenInputs.push_back({4.5731735229492188f, 1.3278274536132812f, -3.2062773704528809f,
-7.9090023040771484f, -6.6076564788818359f,
5.1205596923828125f});
frustumGoldenInputs.push_back({1.2519702911376953f, 1.8767604827880859f, 2.7256736755371094f,
-9.6021852493286133f, -3.9267930984497070f,
2.3794260025024414f});
frustumGoldenInputs.push_back({-8.9887380599975586f, 6.2763042449951172f, 5.8243169784545898f,
9.2890701293945312f, 1.3859443664550781f, -6.4422101974487305f});
frustumGoldenInputs.push_back({5.7714862823486328f, 1.3688507080078125f, 6.2611656188964844f,
-8.5859031677246094f, -3.2825427055358887f,
-9.7015857696533203f});
frustumGoldenInputs.push_back({5.4493808746337891f, 7.7383766174316406f, -1.1092796325683594f,
-3.6691951751708984f, -8.1641988754272461f,
4.3122081756591797f});
frustumGoldenInputs.push_back({-47.3135452270507812f, 1.1683349609375000f, 36.1483764648437500f,
-54.2228546142578125f, 76.4831085205078125f,
51.5773468017578125f});
frustumGoldenInputs.push_back({60.6750030517578125f, -35.2967681884765625f,
-32.8269577026367188f, 77.3887939453125000f,
73.3114624023437500f, -54.2619438171386719f});
frustumGoldenInputs.push_back({19.5157546997070312f, 1.3348083496093750f, 34.2350463867187500f,
-11.5907974243164062f, -6.4835968017578125f,
30.2026824951171875f});
frustumGoldenInputs.push_back({16.5014953613281250f, -59.4170761108398438f,
-22.8201065063476562f, 62.5094299316406250f,
-3.9552688598632812f, -76.2280197143554688f});
frustumGoldenInputs.push_back({35.6607666015625000f, -49.5569000244140625f,
97.1632690429687500f, 23.1938247680664062f, 18.6621780395507812f,
55.2039489746093750f});
frustumGoldenInputs.push_back({12.7565383911132812f, -0.8035964965820312f, 94.0040435791015625f,
-73.9960327148437500f, -51.3727264404296875f,
-21.3958053588867188f});
frustumGoldenInputs.push_back({0.6055984497070312f, -21.7872161865234375f, 22.3246612548828125f,
10.5279464721679688f, -56.8082237243652344f,
24.1726150512695312f});
frustumGoldenInputs.push_back({69.2176513671875000f, -59.0015220642089844f,
-38.5509605407714844f, 74.0315246582031250f,
47.9032897949218750f, -89.4692459106445312f});
frustumGoldenInputs.push_back({90.4153137207031250f, 10.0325012207031250f, 16.1712417602539062f,
-9.9705123901367188f, 25.6828689575195312f,
51.8659057617187500f});
frustumGoldenInputs.push_back({-89.5869369506835938f, -87.6541290283203125f,
-3.0015182495117188f, -46.5026855468750000f,
29.3566131591796875f, -3.5230865478515625f});
frustumGoldenOutputs.push_back({
-0.8098147511482239f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-0.2861230373382568f,
0.0000000000000000f,
0.0000000000000000f,
-2.3282337188720703f,
-0.3668724894523621f,
-2.8482546806335449f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-6.1462464332580566f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-3.2971229553222656f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
2.4951465129852295f,
0.0000000000000000f,
0.0000000000000000f,
-3.5867569446563721f,
0.6821345686912537f,
0.2061366289854050f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
5.3967552185058594f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
14.8248996734619141f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
24.5582180023193359f,
0.0000000000000000f,
0.0000000000000000f,
3.2509319782257080f,
-3.2116978168487549f,
0.0639241635799408f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-9.1714696884155273f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-0.6167355179786682f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-4.9260525703430176f,
0.0000000000000000f,
0.0000000000000000f,
0.4918970167636871f,
5.3510427474975586f,
-0.3150867819786072f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
5.3404870033264160f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
8.0562448501586914f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.7806558609008789f,
0.0000000000000000f,
0.0000000000000000f,
-8.4732360839843750f,
0.2963255345821381f,
-20.0583839416503906f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
170.8137969970703125f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
4.0720810890197754f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
2.8101394176483154f,
0.0000000000000000f,
0.0000000000000000f,
-1.8182963132858276f,
2.3635828495025635f,
0.1267964988946915f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
5.7698287963867188f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-12.5699577331542969f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.6370600461959839f,
0.0000000000000000f,
0.0000000000000000f,
5.0076503753662109f,
0.5578025579452515f,
0.2453716099262238f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
2.9632694721221924f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
0.1815840899944305f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.8000248670578003f,
0.0000000000000000f,
0.0000000000000000f,
-0.1776892393827438f,
4.3620386123657227f,
-0.6459077596664429f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-2.2811365127563477f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
1.4911717176437378f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.4421805739402771f,
0.0000000000000000f,
0.0000000000000000f,
-1.6218323707580566f,
0.1565788835287094f,
-2.0227515697479248f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
9.9223108291625977f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-7.1334328651428223f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
6.3784909248352051f,
0.0000000000000000f,
0.0000000000000000f,
5.7613725662231445f,
1.8666533231735229f,
0.3087419867515564f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
5.6435680389404297f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
3.1551213264465332f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-1.6926428079605103f,
0.0000000000000000f,
0.0000000000000000f,
-0.9518032073974609f,
0.2000025659799576f,
5.1418004035949707f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
316.7777709960937500f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-1.5277713537216187f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
1.3303264379501343f,
0.0000000000000000f,
0.0000000000000000f,
-0.2644343674182892f,
0.4043145775794983f,
0.1493220180273056f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-62.3644447326660156f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
0.7132298350334167f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.2829668223857880f,
0.0000000000000000f,
0.0000000000000000f,
-1.1468359231948853f,
-0.4941370785236359f,
-0.6465383172035217f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
10.6754913330078125f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
0.1041976660490036f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-0.0927057415246964f,
0.0000000000000000f,
0.0000000000000000f,
0.5652843713760376f,
0.4651299417018890f,
-1.1094539165496826f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
8.3434572219848633f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-0.4379884898662567f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-0.5045915246009827f,
0.0000000000000000f,
0.0000000000000000f,
0.1630663424730301f,
-1.6271190643310547f,
-2.0214161872863770f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-56.3862075805664062f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
7.5770230293273926f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.6115797758102417f,
0.0000000000000000f,
0.0000000000000000f,
-0.8814766407012939f,
-0.1190952509641647f,
2.4274852275848389f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-73.3338088989257812f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
5.0737905502319336f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
9.6311941146850586f,
0.0000000000000000f,
0.0000000000000000f,
0.9459113478660583f,
-2.7848947048187256f,
0.4030041098594666f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
33.9142799377441406f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-0.7472094297409058f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.8509900569915771f,
0.0000000000000000f,
0.0000000000000000f,
-0.0796770751476288f,
0.3151517212390900f,
-0.3025783598423004f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-62.3977890014648438f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
-0.6390139460563660f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-1.9648925065994263f,
0.0000000000000000f,
0.0000000000000000f,
-1.2496180534362793f,
-0.2371963709592819f,
-2.9617946147918701f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-101.7502517700195312f,
0.0000000000000000f,
});
frustumGoldenOutputs.push_back({
30.3771648406982422f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-1.3496931791305542f,
0.0000000000000000f,
0.0000000000000000f,
-91.7013320922851562f,
1.1379971504211426f,
0.7856983542442322f,
-1.0000000000000000f,
0.0000000000000000f,
0.0000000000000000f,
-6.2911696434020996f,
0.0000000000000000f,
});
for (size_t i = 0; i < frustumGoldenInputs.size(); i++)
{
const auto &input = frustumGoldenInputs[i];
const auto &output = frustumGoldenOutputs[i];
Mat4 r = Mat4::Frustum(input.l, input.r, input.b, input.t, input.n, input.f);
CheckMatrixCloseToGolden(output, r);
}
}
// Tests orthographic projection matrices.
TEST(MatrixUtilsTest, Mat4Ortho)
{
// Randomly-generated inputs, outputs using GLM.
std::vector<FrustumArgs> orthoGoldenInputs;
std::vector<std::vector<float>> orthoGoldenOutputs;
orthoGoldenInputs.push_back({-1.2515230178833008f, 5.6523618698120117f, -0.7427234649658203f,
-2.9657564163208008f, -5.4732933044433594f, -9.6416902542114258f});
orthoGoldenInputs.push_back({-7.8948307037353516f, -8.4146118164062500f, -4.3893952369689941f,
7.4392738342285156f, -8.1261911392211914f, 3.1107978820800781f});
orthoGoldenInputs.push_back({3.1667709350585938f, 3.9134168624877930f, -7.1993961334228516f,
-0.2519502639770508f, 5.4625358581542969f, 8.8320560455322266f});
orthoGoldenInputs.push_back({0.3597183227539062f, 5.7859621047973633f, 4.6786174774169922f,
-6.4736566543579102f, -2.7510557174682617f, 2.9977531433105469f});
orthoGoldenInputs.push_back({3.2480134963989258f, 9.3551750183105469f, -7.5922241210937500f,
-2.5135083198547363f, -4.5282001495361328f, -5.4564580917358398f});
orthoGoldenInputs.push_back({-6.6918325424194336f, -9.6249752044677734f, -6.9591665267944336f,
-2.7099137306213379f, -5.5196690559387207f, -9.0791969299316406f});
orthoGoldenInputs.push_back({5.9386453628540039f, -9.1784019470214844f, -1.4078102111816406f,
-1.0552892684936523f, 3.7563705444335938f, -6.6715431213378906f});
orthoGoldenInputs.push_back({-8.6238059997558594f, -0.2995386123657227f, 5.6623821258544922f,
7.6483421325683594f, 5.6686410903930664f, -7.1456899642944336f});
orthoGoldenInputs.push_back({2.3857555389404297f, -2.6020836830139160f, 6.7841358184814453f,
0.9868297576904297f, 5.6463518142700195f, -1.7597074508666992f});
orthoGoldenInputs.push_back({4.6028537750244141f, 0.1757516860961914f, -6.1434607505798340f,
6.8524093627929688f, 8.4380626678466797f, -1.4824752807617188f});
orthoGoldenInputs.push_back({40.3382720947265625f, -34.5338973999023438f, -11.1726379394531250f,
21.4206924438476562f, 17.5087890625000000f, 70.2734069824218750f});
orthoGoldenInputs.push_back({85.2872314453125000f, 22.3899002075195312f, -92.8537139892578125f,
7.6059341430664062f, 32.9500732421875000f, -8.1374511718750000f});
orthoGoldenInputs.push_back({33.8771057128906250f, -27.6973648071289062f, 90.3841094970703125f,
85.8473358154296875f, 36.0423278808593750f,
-36.5140991210937500f});
orthoGoldenInputs.push_back({-92.4729461669921875f, 7.1592102050781250f, -75.2177963256835938f,
14.4945983886718750f, 10.6297378540039062f, 53.9828796386718750f});
orthoGoldenInputs.push_back({90.2037658691406250f, 54.5332946777343750f, -58.9839515686035156f,
56.7301330566406250f, 63.2403869628906250f, 81.1043853759765625f});
orthoGoldenInputs.push_back({-78.6419067382812500f, 65.5156250000000000f, -78.8265304565429688f,
-37.5036048889160156f, 76.9322204589843750f,
-0.2213287353515625f});
orthoGoldenInputs.push_back({80.1368560791015625f, 60.2946777343750000f, -27.3523788452148438f,
88.5419616699218750f, -75.3445968627929688f,
83.3852081298828125f});
orthoGoldenInputs.push_back({55.0712585449218750f, -17.4033813476562500f, -98.6088104248046875f,
81.6600952148437500f, 61.1217803955078125f, 73.5973815917968750f});
orthoGoldenInputs.push_back({-48.7522583007812500f, 20.8100433349609375f, -45.6365356445312500f,
-13.2819519042968750f, -29.7577133178710938f,
62.1014862060546875f});
orthoGoldenInputs.push_back({-60.3634567260742188f, 71.4023284912109375f, 59.0719757080078125f,
22.6195831298828125f, -32.6802139282226562f,
-56.3766899108886719f});
orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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orthoGoldenOutputs.push_back({
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});
orthoGoldenOutputs.push_back({
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});
EXPECT_EQ(orthoGoldenInputs.size(), orthoGoldenOutputs.size());
for (size_t i = 0; i < orthoGoldenInputs.size(); i++)
{
const auto &input = orthoGoldenInputs[i];
const auto &output = orthoGoldenOutputs[i];
Mat4 r = Mat4::Ortho(input.l, input.r, input.b, input.t, input.n, input.f);
CheckMatrixCloseToGolden(output, r);
}
}
// Tests for inverse transpose of mat4, which is a frequent operation done to
// the modelview matrix, for things such as transformation of normals.
TEST(MatrixUtilsTest, Mat4InvTr)
{
std::vector<std::vector<float>> invTrInputs;
std::vector<std::vector<float>> invTrOutputs;
invTrInputs.push_back({
0.3247877955436707f,
-0.8279561400413513f,
0.4571666717529297f,
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-0.4070515632629395f,
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0.5455390810966492f,
0.7907638549804688f,
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1.0000000000000000f,
});
invTrInputs.push_back({
-0.8843839168548584f,
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0.0000000000000000f,
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-0.6068999171257019f,
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-0.7010400891304016f,
-0.7118276357650757f,
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1.0000000000000000f,
});
invTrInputs.push_back({
-0.4487786889076233f,
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0.0000000000000000f,
0.0000000000000000f,
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0.0000000000000000f,
1.0000000000000000f,
});
invTrInputs.push_back({
0.9865614175796509f,
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1.0000000000000000f,
});
invTrInputs.push_back({
-0.5566663742065430f,
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1.0000000000000000f,
});
invTrInputs.push_back({
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1.0000000000000000f,
});
invTrInputs.push_back({
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1.0000000000000000f,
});
invTrInputs.push_back({
0.3662135303020477f,
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1.0000000000000000f,
});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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1.0000000000000000f,
});
invTrInputs.push_back({
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1.0000000000000000f,
});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrInputs.push_back({
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});
invTrOutputs.push_back({
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});
invTrOutputs.push_back({
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});
invTrOutputs.push_back({
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});
invTrOutputs.push_back({
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});
invTrOutputs.push_back({
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EXPECT_EQ(invTrInputs.size(), invTrInputs.size());
for (size_t i = 0; i < invTrInputs.size(); i++)
{
Mat4 a(invTrInputs[i]);
CheckMatrixCloseToGolden(invTrOutputs[i], a.transpose().inverse());
}
}
// Tests mat4 matrix/matrix multiplication by multiplying two translation matrices.
TEST(MatrixUtilsTest, Mat4Mult)
{
Mat4 a, b;
CheckMatrixCloseToGolden(a.data(), a.product(b));
Mat4 r = Mat4::Translate(Vector3(1.f, 1.f, 1.f));
CheckMatrixCloseToGolden(r.data(), a.product(r));
Mat4 s = Mat4::Translate(Vector3(2.f, 2.f, 2.f));
Mat4 t = r.product(r);
CheckMatrixCloseToGolden(s.data(), t);
}
// Tests exact equality.
TEST(MatrixUtilsTest, ExactEquality)
{
Mat4 a, b;
EXPECT_EQ(a, b);
Mat4 c;
c(0, 0) += 0.000001f;
EXPECT_NE(a, c);
}
// Tests near equality.
TEST(MatrixUtilsTest, NearEquality)
{
Mat4 a, b;
float *bData = b.data();
for (int i = 0; i < 16; i++)
{
bData[i] += 0.09f;
}
EXPECT_TRUE(a.nearlyEqual(0.1f, b));
for (int i = 0; i < 16; i++)
{
bData[i] -= 2 * 0.09f;
}
EXPECT_TRUE(a.nearlyEqual(0.1f, b));
}
} // namespace