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core: eliminate 'if' logic from Matx::inv()/solve()

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- 'if' logic is moved into templates.
- removed unnecessary cv::Mat objects creation.
- fixed inv() test (invA * A == eye)
- added more Matx tests to cover all defined template specializations
pull/11969/head
Alexander Alekhin 7 years ago
parent 23fc96e98f
commit 3c74fde349
2 changed files with 96 additions and 49 deletions
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  1. 87
      modules/core/include/opencv2/core/operations.hpp
  2. 58
      modules/core/test/test_math.cpp

87
modules/core/include/opencv2/core/operations.hpp
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@ -63,9 +63,9 @@ namespace internal
template<typename _Tp, int m, int n> struct Matx_FastInvOp
{
bool operator()(const Matx<_Tp, m, n>&, Matx<_Tp, n, m>&, int) const
bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const
{
return false;
return invert(a, b, method) != 0;
}
};
@ -73,25 +73,32 @@ template<typename _Tp, int m> struct Matx_FastInvOp<_Tp, m, m>
{
bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const
{
Matx<_Tp, m, m> temp = a;
if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
{
Matx<_Tp, m, m> temp = a;
// assume that b is all 0's on input => make it a unity matrix
for( int i = 0; i < m; i++ )
b(i, i) = (_Tp)1;
// assume that b is all 0's on input => make it a unity matrix
for (int i = 0; i < m; i++)
b(i, i) = (_Tp)1;
if( method == DECOMP_CHOLESKY )
return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m);
if (method == DECOMP_CHOLESKY)
return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m);
return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0;
return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0;
}
else
{
return invert(a, b, method) != 0;
}
}
};
template<typename _Tp> struct Matx_FastInvOp<_Tp, 2, 2>
{
bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int) const
bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const
{
_Tp d = (_Tp)determinant(a);
if( d == 0 )
if (d == 0)
return false;
d = 1/d;
b(1,1) = a(0,0)*d;
@ -104,10 +111,10 @@ template<typename _Tp> struct Matx_FastInvOp<_Tp, 2, 2>
template<typename _Tp> struct Matx_FastInvOp<_Tp, 3, 3>
{
bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int) const
bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const
{
_Tp d = (_Tp)determinant(a);
if( d == 0 )
if (d == 0)
return false;
d = 1/d;
b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d;
@ -128,10 +135,10 @@ template<typename _Tp> struct Matx_FastInvOp<_Tp, 3, 3>
template<typename _Tp, int m, int l, int n> struct Matx_FastSolveOp
{
bool operator()(const Matx<_Tp, m, l>&, const Matx<_Tp, m, n>&,
Matx<_Tp, l, n>&, int) const
bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b,
Matx<_Tp, l, n>& x, int method) const
{
return false;
return cv::solve(a, b, x, method);
}
};
@ -140,12 +147,19 @@ template<typename _Tp, int m, int n> struct Matx_FastSolveOp<_Tp, m, m, n>
bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b,
Matx<_Tp, m, n>& x, int method) const
{
Matx<_Tp, m, m> temp = a;
x = b;
if( method == DECOMP_CHOLESKY )
return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n);
if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
{
Matx<_Tp, m, m> temp = a;
x = b;
if( method == DECOMP_CHOLESKY )
return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n);
return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0;
return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0;
}
else
{
return cv::solve(a, b, x, method);
}
}
};
@ -155,7 +169,7 @@ template<typename _Tp> struct Matx_FastSolveOp<_Tp, 2, 2, 1>
Matx<_Tp, 2, 1>& x, int) const
{
_Tp d = (_Tp)determinant(a);
if( d == 0 )
if (d == 0)
return false;
d = 1/d;
x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d;
@ -170,7 +184,7 @@ template<typename _Tp> struct Matx_FastSolveOp<_Tp, 3, 3, 1>
Matx<_Tp, 3, 1>& x, int) const
{
_Tp d = (_Tp)determinant(a);
if( d == 0 )
if (d == 0)
return false;
d = 1/d;
x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) -
@ -210,18 +224,8 @@ template<typename _Tp, int m, int n> inline
Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const
{
Matx<_Tp, n, m> b;
bool ok;
if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
{
CV_Assert(m == n);
ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method);
}
else
{
Mat A(*this, false), B(b, false);
ok = (invert(A, B, method) != 0);
}
if( NULL != p_is_ok ) { *p_is_ok = ok; }
bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method);
if (p_is_ok) *p_is_ok = ok;
return ok ? b : Matx<_Tp, n, m>::zeros();
}
@ -229,18 +233,7 @@ template<typename _Tp, int m, int n> template<int l> inline
Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const
{
Matx<_Tp, n, l> x;
bool ok;
if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
{
CV_Assert(m == n);
ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method);
}
else
{
Mat A(*this, false), B(rhs, false), X(x, false);
ok = cv::solve(A, B, X, method);
}
bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method);
return ok ? x : Matx<_Tp, n, l>::zeros();
}

58
modules/core/test/test_math.cpp
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@ -3139,9 +3139,63 @@ TEST(Core_Solve, regression_11888)
cv::Vec<float, 3> b(4, 5, 7);
cv::Matx<float, 2, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 2, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, CV_RELATIVE_L2), 0.001);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 0.001);
cv::Matx<float, 2, 3> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(A*iA, Matx<float, 3, 3>::eye(), CV_RELATIVE_L2), 0.6);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 2, 2>::eye(), NORM_L2), 1e-3);
EXPECT_ANY_THROW({
/*cv::Matx<float, 2, 1> xLU =*/ A.solve(b, DECOMP_LU);
std::cout << "FATAL ERROR" << std::endl;
});
}
TEST(Core_Solve, Matx_2_2)
{
cv::Matx<float, 2, 2> A(
2, 1,
1, 1
);
cv::Vec<float, 2> b(4, 5);
cv::Matx<float, 2, 1> xLU = A.solve(b, DECOMP_LU);
cv::Matx<float, 2, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 2, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 1e-3);
EXPECT_LE(cvtest::norm(xQR, xLU, NORM_L2 | NORM_RELATIVE), 1e-3);
cv::Matx<float, 2, 2> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 2, 2>::eye(), NORM_L2), 1e-3);
}
TEST(Core_Solve, Matx_3_3)
{
cv::Matx<float, 3, 3> A(
2, 1, 0,
0, 1, 1,
1, 0, 1
);
cv::Vec<float, 3> b(4, 5, 6);
cv::Matx<float, 3, 1> xLU = A.solve(b, DECOMP_LU);
cv::Matx<float, 3, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 3, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 1e-3);
EXPECT_LE(cvtest::norm(xQR, xLU, NORM_L2 | NORM_RELATIVE), 1e-3);
cv::Matx<float, 3, 3> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 3, 3>::eye(), NORM_L2), 1e-3);
}
TEST(Core_Solve, Matx_4_4)
{
cv::Matx<float, 4, 4> A(
2, 1, 0, 4,
0, 1, 1, 3,
1, 0, 1, 2,
2, 2, 0, 1
);
cv::Vec<float, 4> b(4, 5, 6, 7);
cv::Matx<float, 4, 1> xLU = A.solve(b, DECOMP_LU);
cv::Matx<float, 4, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 4, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 1e-3);
EXPECT_LE(cvtest::norm(xQR, xLU, NORM_L2 | NORM_RELATIVE), 1e-3);
cv::Matx<float, 4, 4> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 4, 4>::eye(), NORM_L2), 1e-3);
}
softdouble naiveExp(softdouble x)

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