我解决了这个问题，所以我想我会在这里与可能有同样问题的其他人分享。
基本上，为了摆脱分段错误，我需要调用 numpy 的 import_array() 函数。
从 python 运行 C++ 代码的“高级”视图是这样的：
假设您foo(arg)在 python 中有一个函数，它是某个 C++ 函数的绑定。当您调用foo(myObj)时，必须有一些代码将 python 对象“myObj”转换为您的 C++ 代码可以执行的形式。此代码通常是使用 SWIG 或 Boost::Python 等工具半自动创建的。（我在下面的示例中使用 Boost::Python。）
现在，foo(arg)是一些 C++ 函数的 python 绑定。这个 C++ 函数将接收一个通用PyObject指针作为参数。您将需要 C++ 代码将此PyObject指针转换为“等效”C++ 对象。在我的例子中，我的 python 代码将 OpenCV 图像的 OpenCV numpy 数组作为参数传递给函数。C++ 中的“等价”形式是 OpenCV C++ Mat 对象。OpenCV 在 cv2.cpp（转载如下）中提供了一些代码，用于将PyObject指针（表示 numpy 数组）转换为 C++ Mat。简单的数据类型如 int 和 string 不需要用户编写这些转换函数，因为它们是由 Boost::Python 自动转换的。
PyObject指针转换为合适的 C++ 形式后，C++ 代码就可以对其进行操作。当数据必须从 C++ 返回到 python 时，会出现类似的情况，需要 C++ 代码将数据的 C++ 表示形式转换为某种形式的PyObject. Boost::Python 将负责将其余的转换PyObject为相应的 python 形式。当foo(arg)在python中返回结果时，它是python可以使用的形式。而已。
下面的代码显示了如何包装一个 C++ 类“ABC”并公开其方法“doSomething”，该方法从 python 中获取一个 numpy 数组（用于图像），将其转换为 OpenCV 的 C++ Mat，进行一些处理，将结果转换为 PyObject *，并将其返回给 python 解释器。您可以公开任意数量的函数/方法（请参阅下面代码中的注释）。
abc.hpp：
#ifndef ABC_HPP
#define ABC_HPP
#include <Python.h>
#include <string>
class ABC
{
// Other declarations
ABC();
ABC(const std::string& someConfigFile);
virtual ~ABC();
PyObject* doSomething(PyObject* image); // We want our python code to be able to call this function to do some processing using OpenCV and return the result.
// Other declarations
};
#endif
abc.cpp：
#include "abc.hpp"
#include "my_cpp_library.h" // This is what we want to make available in python. It uses OpenCV to perform some processing.
#include "numpy/ndarrayobject.h"
#include "opencv2/core/core.hpp"
// The following conversion functions are taken from OpenCV's cv2.cpp file inside modules/python/src2 folder.
static PyObject* opencv_error = 0;
static int failmsg(const char *fmt, ...)
{
char str[1000];
va_list ap;
va_start(ap, fmt);
vsnprintf(str, sizeof(str), fmt, ap);
va_end(ap);
PyErr_SetString(PyExc_TypeError, str);
return 0;
}
class PyAllowThreads
{
public:
PyAllowThreads() : _state(PyEval_SaveThread()) {}
~PyAllowThreads()
{
PyEval_RestoreThread(_state);
}
private:
PyThreadState* _state;
};
class PyEnsureGIL
{
public:
PyEnsureGIL() : _state(PyGILState_Ensure()) {}
~PyEnsureGIL()
{
PyGILState_Release(_state);
}
private:
PyGILState_STATE _state;
};
#define ERRWRAP2(expr) \
try \
{ \
PyAllowThreads allowThreads; \
expr; \
} \
catch (const cv::Exception &e) \
{ \
PyErr_SetString(opencv_error, e.what()); \
return 0; \
}
using namespace cv;
static PyObject* failmsgp(const char *fmt, ...)
{
char str[1000];
va_list ap;
va_start(ap, fmt);
vsnprintf(str, sizeof(str), fmt, ap);
va_end(ap);
PyErr_SetString(PyExc_TypeError, str);
return 0;
}
static size_t REFCOUNT_OFFSET = (size_t)&(((PyObject*)0)->ob_refcnt) +
(0x12345678 != *(const size_t*)"\x78\x56\x34\x12\0\0\0\0\0")*sizeof(int);
static inline PyObject* pyObjectFromRefcount(const int* refcount)
{
return (PyObject*)((size_t)refcount - REFCOUNT_OFFSET);
}
static inline int* refcountFromPyObject(const PyObject* obj)
{
return (int*)((size_t)obj + REFCOUNT_OFFSET);
}
class NumpyAllocator : public MatAllocator
{
public:
NumpyAllocator() {}
~NumpyAllocator() {}
void allocate(int dims, const int* sizes, int type, int*& refcount,
uchar*& datastart, uchar*& data, size_t* step)
{
PyEnsureGIL gil;
int depth = CV_MAT_DEPTH(type);
int cn = CV_MAT_CN(type);
const int f = (int)(sizeof(size_t)/8);
int typenum = depth == CV_8U ? NPY_UBYTE : depth == CV_8S ? NPY_BYTE :
depth == CV_16U ? NPY_USHORT : depth == CV_16S ? NPY_SHORT :
depth == CV_32S ? NPY_INT : depth == CV_32F ? NPY_FLOAT :
depth == CV_64F ? NPY_DOUBLE : f*NPY_ULONGLONG + (f^1)*NPY_UINT;
int i;
npy_intp _sizes[CV_MAX_DIM+1];
for( i = 0; i < dims; i++ )
{
_sizes[i] = sizes[i];
}
if( cn > 1 )
{
/*if( _sizes[dims-1] == 1 )
_sizes[dims-1] = cn;
else*/
_sizes[dims++] = cn;
}
PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);
if(!o)
{
CV_Error_(CV_StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));
}
refcount = refcountFromPyObject(o);
npy_intp* _strides = PyArray_STRIDES(o);
for( i = 0; i < dims - (cn > 1); i++ )
step[i] = (size_t)_strides[i];
datastart = data = (uchar*)PyArray_DATA(o);
}
void deallocate(int* refcount, uchar*, uchar*)
{
PyEnsureGIL gil;
if( !refcount )
return;
PyObject* o = pyObjectFromRefcount(refcount);
Py_INCREF(o);
Py_DECREF(o);
}
};
NumpyAllocator g_numpyAllocator;
enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };
static int pyopencv_to(const PyObject* o, Mat& m, const char* name = "<unknown>", bool allowND=true)
{
//NumpyAllocator g_numpyAllocator;
if(!o || o == Py_None)
{
if( !m.data )
m.allocator = &g_numpyAllocator;
return true;
}
if( !PyArray_Check(o) )
{
failmsg("%s is not a numpy array", name);
return false;
}
int typenum = PyArray_TYPE(o);
int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :
typenum == NPY_USHORT ? CV_16U : typenum == NPY_SHORT ? CV_16S :
typenum == NPY_INT || typenum == NPY_LONG ? CV_32S :
typenum == NPY_FLOAT ? CV_32F :
typenum == NPY_DOUBLE ? CV_64F : -1;
if( type < 0 )
{
failmsg("%s data type = %d is not supported", name, typenum);
return false;
}
int ndims = PyArray_NDIM(o);
if(ndims >= CV_MAX_DIM)
{
failmsg("%s dimensionality (=%d) is too high", name, ndims);
return false;
}
int size[CV_MAX_DIM+1];
size_t step[CV_MAX_DIM+1], elemsize = CV_ELEM_SIZE1(type);
const npy_intp* _sizes = PyArray_DIMS(o);
const npy_intp* _strides = PyArray_STRIDES(o);
bool transposed = false;
for(int i = 0; i < ndims; i++)
{
size[i] = (int)_sizes[i];
step[i] = (size_t)_strides[i];
}
if( ndims == 0 || step[ndims-1] > elemsize ) {
size[ndims] = 1;
step[ndims] = elemsize;
ndims++;
}
if( ndims >= 2 && step[0] < step[1] )
{
std::swap(size[0], size[1]);
std::swap(step[0], step[1]);
transposed = true;
}
if( ndims == 3 && size[2] <= CV_CN_MAX && step[1] == elemsize*size[2] )
{
ndims--;
type |= CV_MAKETYPE(0, size[2]);
}
if( ndims > 2 && !allowND )
{
failmsg("%s has more than 2 dimensions", name);
return false;
}
m = Mat(ndims, size, type, PyArray_DATA(o), step);
if( m.data )
{
m.refcount = refcountFromPyObject(o);
m.addref(); // protect the original numpy array from deallocation
// (since Mat destructor will decrement the reference counter)
};
m.allocator = &g_numpyAllocator;
if( transposed )
{
Mat tmp;
tmp.allocator = &g_numpyAllocator;
transpose(m, tmp);
m = tmp;
}
return true;
}
static PyObject* pyopencv_from(const Mat& m)
{
if( !m.data )
Py_RETURN_NONE;
Mat temp, *p = (Mat*)&m;
if(!p->refcount || p->allocator != &g_numpyAllocator)
{
temp.allocator = &g_numpyAllocator;
m.copyTo(temp);
p = &temp;
}
p->addref();
return pyObjectFromRefcount(p->refcount);
}
ABC::ABC() {}
ABC::~ABC() {}
// Note the import_array() from NumPy must be called else you will experience segmentation faults.
ABC::ABC(const std::string &someConfigFile)
{
// Initialization code. Possibly store someConfigFile etc.
import_array(); // This is a function from NumPy that MUST be called.
// Do other stuff
}
// The conversions functions above are taken from OpenCV. The following function is
// what we define to access the C++ code we are interested in.
PyObject* ABC::doSomething(PyObject* image)
{
cv::Mat cvImage;
pyopencv_to(image, cvImage); // From OpenCV's source
MyCPPClass obj; // Some object from the C++ library.
cv::Mat processedImage = obj.process(cvImage);
return pyopencv_from(processedImage); // From OpenCV's source
}
使用 Boost Python 创建 python 模块的代码。我从http://jayrambhia.wordpress.com/tag/boost/获取了这个和以下 Makefile ：
pysomemodule.cpp：
#include <string>
#include<boost/python.hpp>
#include "abc.hpp"
using namespace boost::python;
BOOST_PYTHON_MODULE(pysomemodule)
{
class_<ABC>("ABC", init<const std::string &>())
.def(init<const std::string &>())
.def("doSomething", &ABC::doSomething) // doSomething is the method in class ABC you wish to expose. One line for each method (or function depending on how you structure your code). Note: You don't have to expose everything in the library, just the ones you wish to make available to python.
;
}
最后，Makefile（在 Ubuntu 上成功编译，但应该可以在其他地方工作，可能需要进行最小的调整）。
PYTHON_VERSION = 2.7
PYTHON_INCLUDE = /usr/include/python$(PYTHON_VERSION)
# location of the Boost Python include files and library
BOOST_INC = /usr/local/include/boost
BOOST_LIB = /usr/local/lib
OPENCV_LIB = `pkg-config --libs opencv`
OPENCV_CFLAGS = `pkg-config --cflags opencv`
MY_CPP_LIB = lib_my_cpp_library.so
TARGET = pysomemodule
SRC = pysomemodule.cpp abc.cpp
OBJ = pysomemodule.o abc.o
$(TARGET).so: $(OBJ)
g++ -shared $(OBJ) -L$(BOOST_LIB) -lboost_python -L/usr/lib/python$(PYTHON_VERSION)/config -lpython$(PYTHON_VERSION) -o $(TARGET).so $(OPENCV_LIB) $(MY_CPP_LIB)
$(OBJ): $(SRC)
g++ -I$(PYTHON_INCLUDE) -I$(BOOST_INC) $(OPENCV_CFLAGS) -fPIC -c $(SRC)
clean:
rm -f $(OBJ)
rm -f $(TARGET).so
成功编译库后，目录中应该有一个文件“pysomemodule.so”。将此 lib 文件放在 python 解释器可以访问的位置。然后，您可以导入此模块并创建上述“ABC”类的实例，如下所示：
import pysomemodule
foo = pysomemodule.ABC("config.txt") # This will create an instance of ABC
现在，给定一个 OpenCV numpy 数组图像，我们可以使用以下命令调用 C++ 函数：
processedImage = foo.doSomething(image) # Where the argument "image" is a OpenCV numpy image.
请注意，您将需要 Boost Python、Numpy 开发以及 Python 开发库来创建绑定。
以下两个链接中的 NumPy 文档在帮助理解转换代码中使用的方法以及为什么必须调用 import_array() 方面特别有用。特别是，官方 numpy 文档有助于理解 OpenCV 的 python 绑定代码。
http://dsnra.jpl.nasa.gov/software/Python/numpydoc/numpy-13.html
http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html
希望这可以帮助。