I'm a newbie in boost.python and i'm getting this error that I would like to get some help with. As a part of a larger project I'm writing a wrapper for a vector class that I have. As you will notice from the code bellow, this class can be 2D or 3D, but not a big difference for the current problem I'm having. I'm trying to wrap some operators defined in the vector class I have and this works fine as long as the function (or overloaded operator) do not return a "value" of type Vector, so I guess that python doesn't know what to do with the objects I'm returning by value. this is the code:
struct D2
{
//...
typedef Eigen::Matrix< long double, 2, 1 > Vector;
//...
};
struct D3
{
//...
typedef Eigen::Matrix< long double, 3, 1 > Vector;
//...
};
Now the following macro is defined to do the same job for the 2 possible dimensions
BOOST_PYTHON_MODULE(types)
{
using namespace boost::python;
#define GPS_PY_EXPOSE_VECTOR(DIM, NAME) \
class_<DIM::Vector>(NAME) \
.def(init<DIM::Vector>()) \
.def("norm", &DIM::Vector::norm) \
开发者_JS百科 .def("__setitem__", &PyVectorHelper<DIM>::setVectorElem) \
.def("__getitem__", &PyVectorHelper<DIM>::getVectorElem) \
.def(self += self) \
.def(self -= self) \
.def(self + self) /*Not working!!!*/ \
.def(self - self) /*Not working!!!*/ \
.def("toStr", &PyVectorHelper<DIM>::toPyString) \
.def("__str__", &PyVectorHelper<DIM>::toStdString) \
;
GPS_PY_EXPOSE_VECTOR(D2, "Vector2D")
GPS_PY_EXPOSE_VECTOR(D3, "Vector3D")
}
the above code works right, except when I try to use the "+" or "-" operator in python as follows:
>>>import types
>>>v1 = types.Vector2D()
>>>v2 = types.Vector2D()
>>>v3 = v1 + v2 //ERROR!!!
The error is the following:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: No to_python (by-value) converter found for C++ type: Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<long double>, Eigen::Matrix<long double, 2, 1, 0, 2, 1> const, Eigen::Matrix<long double, 2, 1, 0, 2, 1> const>
I followed the tutorial here, and I found no info related with the error on internet.
any help is very welcome! thanks in advance.
EDIT after Luc Danton's answer
To Luc Danton: thanks for your answer, I tried what you said but it doesn't work. this is the error I got:
error: no matching function for call to ‘boost::python::class_<Eigen::Matrix<long double, 2, 1, 0, 2, 1>, boost::python::detail::not_specified, boost::python::detail::not_specified, boost::python::detail::not_specified>::def(boost::python::detail::operator_<(boost::python::detail::operator_id)0u, boost::python::self_ns::self_t, boost::python::self_ns::self_t>, boost::python::return_value_policy<boost::python::to_python_value<Eigen::Matrix<long double, 2, 1, 0, 2, 1> >, boost::python::default_call_policies>)’
However, thanks to your comment, i got this idea: to write a helper function that performs the addition and convert the Eigen "expression object" (which is the private type representing the operator). This is:
template <typename TDim>
struct PyVectorHelper
{
//...
static typename TDim::Vector sum(typename TDim::Vector const &v1, typename TDim::Vector const &v2)
{
return v1 + v2;
}
//...
};
and then
.def("__add__", &PyVectorHelper<DIM>::sum)
this works fine and does as I want since PyVectorHelper<DIM>::sum
returns a DIM::Vector
. The problem is that almost every operator overloaded by Eigen returns an expression object, meaning that I would have to implement similar functions for almost all of them! that wouldn't be nice nor practical :P.
Maybe is still possible to use the return_value_policy
and to_python_value
so I can avoid this tedious operators rewriting?
It seems the matrix/vector library is using expression templates. In this case, and as you have remarked, operator+
is not returning a vector but a private type that represents the operation. The returned value is left trapped on the C++ side of things and can't be passed to Python as the private type is not registered.
Now I'm not too familiar with Boost.Python, but I think you can fix your problem with the return value policy. Passing a return_value_policy<to_python_value<DIM::Vector> >
as policy would force converting the returned type to DIM::Vector
, which you have registered. This would look like:
.def(self + self, return_value_policy<to_python_value<DIM::Vector> >())
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