我解決了這個問題，所以我想我會跟其他人誰可能有同樣的問題在這裏分享。
基本上，爲了擺脫分段錯誤，我需要調用numpy的import_array（）函數。
「高級別」的觀點從蟒蛇運行C++代碼是這樣的：
假設你有Python中的函數foo(arg)是一些C++函數的綁定。當您撥打foo(myObj)時，必須有一些代碼將python對象「myObj」轉換爲您的C++代碼可以執行的形式。此代碼通常是使用諸如SWIG或Boost :: Python之類的工具半自動創建的。 （我在下面的例子中使用了Boost :: Python。）
現在，foo(arg)是一些用於某些C++函數的python綁定。這個C++函數將收到一個通用的PyObject指針作爲參數。您需要有C++代碼才能將此PyObject指針轉換爲「等效」C++對象。在我的情況下，我的python代碼爲OpenCV圖像傳遞了一個OpenCV numpy數組作爲函數的參數。 C++中的「等價」形式是一個OpenCV C++ Mat對象。 OpenCV在cv2.cpp中提供了一些代碼（轉載如下），將PyObject指針（代表numpy數組）轉換爲C++ Mat。簡單的數據類型，如int和string，不需要用戶編寫這些轉換函數，因爲它們是由Boost :: Python自動轉換的。
將PyObject指針轉換爲合適的C++表單後，C++代碼可以對其執行操作。當數據必須從C++返回到python時，需要使用C++代碼將數據的C++表示形式轉換爲某種形式的PyObject，這時會出現類似的情況。 Boost :: Python將負責將PyObject轉換爲相應的Python表單。當foo(arg)在python中返回結果時，它的格式是python可用的。而已。
下面的代碼展示瞭如何包裝一個C++類「ABC」並暴露它的方法「doSomething」，它接受來自python的numpy數組（對於圖像），將其轉換爲OpenCV的C++ Mat，做一些處理，轉換PyObject *的結果，並將其返回給python解釋器。您可以公開許多您希望的功能/方法（請參閱下面的代碼中的註釋）。
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
}
的代碼來使用升壓Python創建蟒模塊。我把這個和下面的Makefile從http://jayrambhia.wordpress.com/tag/boost/：
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.
;
}
最後，在Makefile（編譯成功在Ubuntu，但應以最小的調整可能在其他地方工作）。
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
成功編譯庫後，應該在目錄中有一個文件「pysomemodule.so」。把這個lib文件放在python解釋器可以訪問的地方。然後，您可以導入該模塊並創建類「ABC」的一個實例，上面如下：
import pysomemodule
foo = pysomemodule.ABC("config.txt") # This will create an instance of ABC
現在，給定的OpenCV numpy的陣列圖像時，我們可以使用調用C++函數：
processedImage = foo.doSomething(image) # Where the argument "image" is a OpenCV numpy image.
請注意，您將需要Boost Python，Numpy dev以及Python開發庫來創建綁定。
以下兩個鏈接中的NumPy文檔在幫助理解轉換代碼中使用的方法以及爲什麼必須調用import_array（）方面特別有用。特別是官方的numpy文檔有助於理解OpenCV的Python綁定代碼。
http://dsnra.jpl.nasa.gov/software/Python/numpydoc/numpy-13.html http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html
希望這有助於。
嗨，lightalchemist，感謝您張貼您的解決方案。我已經使用OpenCV 2.4.3（從cv2.cpp獲取pyopencv_to和pyopencv_from函數）以及暴露一個函數的類似解決方案。該模塊在ipython中可以加載，該函數在那裏可見，它可以解析參數，但一旦達到PyEnsureGIL就會崩潰。我已經嘗試過你的解決方案，並且舊的pyopencv_to函數可以工作（它會執行），但是在嘗試輸出時會崩潰。我將發佈一個單獨的問題，並在短時間內爲您提供一個鏈接，以防您發現問題出在哪裏。 –
這裏是我的問題的鏈接：http://stackoverflow.com/questions/13745265/exposing-opencv-based-c-function-with-mat-numpy-conversion-to-python –