我解决了这个问题，所以我想在这里与可能有相同问题的其他人分享。

基本上，要摆脱分段错误，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，该函数是某些C 函数的绑定。当您调用时foo(myObj)，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。通常使用SWIG或Boost ::
Python之类的工具半自动创建此代码。（在下面的示例中，我使用Boost :: Python。）

现在，foo(arg)是一些C 函数的python绑定。此C 函数将接收通用PyObject指针作为参数。您将需要C
代码才能将此PyObject指针转换为“等效” C 对象。就我而言，我的python代码将OpenCV图像的OpenCV
numpy数组作为函数的参数传递。C 中的“等效”形式是OpenCV C
Mat对象。OpenCV在cv2.cpp中提供了一些代码（如下所示），以将PyObject指针（代表numpy数组）转换为C ++
Mat。诸如int和string之类的简单数据类型不需要用户编写这些转换函数，因为它们由Boost :: Python自动转换。

将PyObject指针转换为合适的C 形式后，C 代码可以对其执行操作。当必须将数据从C 返回到python时，会出现类似的情况，即需要C
代码将数据的C ++表示形式转换为的某种形式PyObject。Boost ::
Python将把剩下的工作转换PyObject为相应的python形式。当foo(arg)以python返回结果时，其格式为python可用。而已。

下面的代码显示了如何包装C 类“ ABC”并公开其方法“
doSomething”，该方法从python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，然后将结果转换为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（请参见下面的代码中的注释）。

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/中获取了以下Makefile文件：

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（可以在Ubuntu上成功编译，但是应该可以在其他地方进行少量调整）。

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解释器可访问的位置。然后，您可以导入该模块并创建上述类“ ABC”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 dev和Python dev库来创建绑定。

以下两个链接中的NumPy文档在帮助人们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，官方的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。