You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

292 lines
8.6 KiB

// Copyright (c) 2013 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "ui/gfx/geometry/matrix3_f.h"
#include <algorithm>
#include <cmath>
#include <limits>
#include "base/numerics/math_constants.h"
#include "base/strings/stringprintf.h"
namespace {
// This is only to make accessing indices self-explanatory.
enum MatrixCoordinates {
M00,
M01,
M02,
M10,
M11,
M12,
M20,
M21,
M22,
M_END
};
template<typename T>
double Determinant3x3(T data[M_END]) {
// This routine is separated from the Matrix3F::Determinant because in
// computing inverse we do want higher precision afforded by the explicit
// use of 'double'.
return
static_cast<double>(data[M00]) * (
static_cast<double>(data[M11]) * data[M22] -
static_cast<double>(data[M12]) * data[M21]) +
static_cast<double>(data[M01]) * (
static_cast<double>(data[M12]) * data[M20] -
static_cast<double>(data[M10]) * data[M22]) +
static_cast<double>(data[M02]) * (
static_cast<double>(data[M10]) * data[M21] -
static_cast<double>(data[M11]) * data[M20]);
}
} // namespace
namespace gfx {
Matrix3F::Matrix3F() {
}
Matrix3F::~Matrix3F() {
}
// static
Matrix3F Matrix3F::Zeros() {
Matrix3F matrix;
matrix.set(0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f);
return matrix;
}
// static
Matrix3F Matrix3F::Ones() {
Matrix3F matrix;
matrix.set(1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f);
return matrix;
}
// static
Matrix3F Matrix3F::Identity() {
Matrix3F matrix;
matrix.set(1.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 1.0f);
return matrix;
}
// static
Matrix3F Matrix3F::FromOuterProduct(const Vector3dF& a, const Vector3dF& bt) {
Matrix3F matrix;
matrix.set(a.x() * bt.x(), a.x() * bt.y(), a.x() * bt.z(),
a.y() * bt.x(), a.y() * bt.y(), a.y() * bt.z(),
a.z() * bt.x(), a.z() * bt.y(), a.z() * bt.z());
return matrix;
}
bool Matrix3F::IsEqual(const Matrix3F& rhs) const {
return 0 == memcmp(data_, rhs.data_, sizeof(data_));
}
bool Matrix3F::IsNear(const Matrix3F& rhs, float precision) const {
DCHECK(precision >= 0);
for (int i = 0; i < M_END; ++i) {
if (std::abs(data_[i] - rhs.data_[i]) > precision)
return false;
}
return true;
}
Matrix3F Matrix3F::Add(const Matrix3F& rhs) const {
Matrix3F result;
for (int i = 0; i < M_END; ++i)
result.data_[i] = data_[i] + rhs.data_[i];
return result;
}
Matrix3F Matrix3F::Subtract(const Matrix3F& rhs) const {
Matrix3F result;
for (int i = 0; i < M_END; ++i)
result.data_[i] = data_[i] - rhs.data_[i];
return result;
}
Matrix3F Matrix3F::Inverse() const {
Matrix3F inverse = Matrix3F::Zeros();
double determinant = Determinant3x3(data_);
if (std::numeric_limits<float>::epsilon() > std::abs(determinant))
return inverse; // Singular matrix. Return Zeros().
inverse.set(
static_cast<float>((data_[M11] * data_[M22] - data_[M12] * data_[M21]) /
determinant),
static_cast<float>((data_[M02] * data_[M21] - data_[M01] * data_[M22]) /
determinant),
static_cast<float>((data_[M01] * data_[M12] - data_[M02] * data_[M11]) /
determinant),
static_cast<float>((data_[M12] * data_[M20] - data_[M10] * data_[M22]) /
determinant),
static_cast<float>((data_[M00] * data_[M22] - data_[M02] * data_[M20]) /
determinant),
static_cast<float>((data_[M02] * data_[M10] - data_[M00] * data_[M12]) /
determinant),
static_cast<float>((data_[M10] * data_[M21] - data_[M11] * data_[M20]) /
determinant),
static_cast<float>((data_[M01] * data_[M20] - data_[M00] * data_[M21]) /
determinant),
static_cast<float>((data_[M00] * data_[M11] - data_[M01] * data_[M10]) /
determinant));
return inverse;
}
Matrix3F Matrix3F::Transpose() const {
Matrix3F transpose;
transpose.set(data_[M00], data_[M10], data_[M20], data_[M01], data_[M11],
data_[M21], data_[M02], data_[M12], data_[M22]);
return transpose;
}
float Matrix3F::Determinant() const {
return static_cast<float>(Determinant3x3(data_));
}
Vector3dF Matrix3F::SolveEigenproblem(Matrix3F* eigenvectors) const {
// The matrix must be symmetric.
const float epsilon = std::numeric_limits<float>::epsilon();
if (std::abs(data_[M01] - data_[M10]) > epsilon ||
std::abs(data_[M02] - data_[M20]) > epsilon ||
std::abs(data_[M12] - data_[M21]) > epsilon) {
NOTREACHED();
return Vector3dF();
}
float eigenvalues[3];
float p =
data_[M01] * data_[M01] +
data_[M02] * data_[M02] +
data_[M12] * data_[M12];
bool diagonal = std::abs(p) < epsilon;
if (diagonal) {
eigenvalues[0] = data_[M00];
eigenvalues[1] = data_[M11];
eigenvalues[2] = data_[M22];
} else {
float q = Trace() / 3.0f;
p = (data_[M00] - q) * (data_[M00] - q) +
(data_[M11] - q) * (data_[M11] - q) +
(data_[M22] - q) * (data_[M22] - q) +
2 * p;
p = std::sqrt(p / 6);
// The computation below puts B as (A - qI) / p, where A is *this.
Matrix3F matrix_b(*this);
matrix_b.data_[M00] -= q;
matrix_b.data_[M11] -= q;
matrix_b.data_[M22] -= q;
for (int i = 0; i < M_END; ++i)
matrix_b.data_[i] /= p;
double half_det_b = Determinant3x3(matrix_b.data_) / 2.0;
// half_det_b should be in <-1, 1>, but beware of rounding error.
double phi = 0.0f;
if (half_det_b <= -1.0)
phi = base::kPiDouble / 3;
else if (half_det_b < 1.0)
phi = acos(half_det_b) / 3;
eigenvalues[0] = q + 2 * p * static_cast<float>(cos(phi));
eigenvalues[2] =
q + 2 * p * static_cast<float>(cos(phi + 2.0 * base::kPiDouble / 3.0));
eigenvalues[1] = 3 * q - eigenvalues[0] - eigenvalues[2];
}
// Put eigenvalues in the descending order.
int indices[3] = {0, 1, 2};
if (eigenvalues[2] > eigenvalues[1]) {
std::swap(eigenvalues[2], eigenvalues[1]);
std::swap(indices[2], indices[1]);
}
if (eigenvalues[1] > eigenvalues[0]) {
std::swap(eigenvalues[1], eigenvalues[0]);
std::swap(indices[1], indices[0]);
}
if (eigenvalues[2] > eigenvalues[1]) {
std::swap(eigenvalues[2], eigenvalues[1]);
std::swap(indices[2], indices[1]);
}
if (eigenvectors != NULL && diagonal) {
// Eigenvectors are e-vectors, just need to be sorted accordingly.
*eigenvectors = Zeros();
for (int i = 0; i < 3; ++i)
eigenvectors->set(indices[i], i, 1.0f);
} else if (eigenvectors != NULL) {
// Consult the following for a detailed discussion:
// Joachim Kopp
// Numerical diagonalization of hermitian 3x3 matrices
// arXiv.org preprint: physics/0610206
// Int. J. Mod. Phys. C19 (2008) 523-548
// TODO(motek): expand to handle correctly negative and multiple
// eigenvalues.
for (int i = 0; i < 3; ++i) {
float l = eigenvalues[i];
// B = A - l * I
Matrix3F matrix_b(*this);
matrix_b.data_[M00] -= l;
matrix_b.data_[M11] -= l;
matrix_b.data_[M22] -= l;
Vector3dF e1 = CrossProduct(matrix_b.get_column(0),
matrix_b.get_column(1));
Vector3dF e2 = CrossProduct(matrix_b.get_column(1),
matrix_b.get_column(2));
Vector3dF e3 = CrossProduct(matrix_b.get_column(2),
matrix_b.get_column(0));
// e1, e2 and e3 should point in the same direction.
if (DotProduct(e1, e2) < 0)
e2 = -e2;
if (DotProduct(e1, e3) < 0)
e3 = -e3;
Vector3dF eigvec = e1 + e2 + e3;
// Normalize.
eigvec.Scale(1.0f / eigvec.Length());
eigenvectors->set_column(i, eigvec);
}
}
return Vector3dF(eigenvalues[0], eigenvalues[1], eigenvalues[2]);
}
Matrix3F MatrixProduct(const Matrix3F& lhs, const Matrix3F& rhs) {
Matrix3F result = Matrix3F::Zeros();
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
result.set(i, j, DotProduct(lhs.get_row(i), rhs.get_column(j)));
}
}
return result;
}
Vector3dF MatrixProduct(const Matrix3F& lhs, const Vector3dF& rhs) {
return Vector3dF(DotProduct(lhs.get_row(0), rhs),
DotProduct(lhs.get_row(1), rhs),
DotProduct(lhs.get_row(2), rhs));
}
std::string Matrix3F::ToString() const {
return base::StringPrintf(
"[[%+0.4f, %+0.4f, %+0.4f],"
" [%+0.4f, %+0.4f, %+0.4f],"
" [%+0.4f, %+0.4f, %+0.4f]]",
data_[M00], data_[M01], data_[M02], data_[M10], data_[M11], data_[M12],
data_[M20], data_[M21], data_[M22]);
}
} // namespace gfx