如kyamagu所建议，您可以使用OpenCV的官方python包装器代码，尤其是
pyopencv_toand
pyopencv_from。
我一直在像您一样处理所有依赖项和生成的头文件。然而，可以通过
cv2.cpp像光炼金术士在这里那样“清洁”以仅保留必要的东西来降低其复杂性。您将需要使其适应您的需求和所使用的OpenCV版本，但其代码与我使用的基本相同。
#include <Python.h>#include "numpy/ndarrayobject.h"#include "opencv2/core/core.hpp"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*)"x78x56x34x12 ")*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 ) { _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){ if(!o || o == Py_None) { if( !m.data ) m.allocator = &g_numpyAllocator; return true; } if( PyInt_Check(o) ) { double v[] = {PyInt_AsLong((PyObject*)o), 0., 0., 0.}; m = Mat(4, 1, CV_64F, v).clone(); return true; } if( PyFloat_Check(o) ) { double v[] = {PyFloat_AsDouble((PyObject*)o), 0., 0., 0.}; m = Mat(4, 1, CV_64F, v).clone(); return true; } if( PyTuple_Check(o) ) { int i, sz = (int)PyTuple_Size((PyObject*)o); m = Mat(sz, 1, CV_64F); for( i = 0; i < sz; i++ ) { PyObject* oi = PyTuple_GET_ITEM(o, i); if( PyInt_Check(oi) ) m.at<double>(i) = (double)PyInt_AsLong(oi); else if( PyFloat_Check(oi) ) m.at<double>(i) = (double)PyFloat_AsDouble(oi); else { failmsg("%s is not a numerical tuple", name); m.release(); return false; } } return true; } if( !PyArray_Check(o) ) { failmsg("%s is not a numpy array, neither a scalar", name); return false; } bool needcopy = false, needcast = false; int typenum = PyArray_TYPE(o), new_typenum = typenum; 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 ? CV_32S : typenum == NPY_INT32 ? CV_32S : typenum == NPY_FLOAT ? CV_32F : typenum == NPY_DOUBLE ? CV_64F : -1; if( type < 0 ) { if( typenum == NPY_INT64 || typenum == NPY_UINT64 || type == NPY_LONG ) { needcopy = needcast = true; new_typenum = NPY_INT; type = CV_32S; } else { 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 ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX; for( int i = ndims-1; i >= 0 && !needcopy; i-- ) { // these checks handle cases of // a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases // b) transposed arrays, where _strides[] elements go in non-descending order // c) flipped arrays, where some of _strides[] elements are negative if( (i == ndims-1 && (size_t)_strides[i] != elemsize) || (i < ndims-1 && _strides[i] < _strides[i+1]) ) needcopy = true; } if( ismultichannel && _strides[1] != (npy_intp)elemsize*_sizes[2] ) needcopy = true; if (needcopy) { if( needcast ) o = (PyObject*)PyArray_Cast((PyArrayObject*)o, new_typenum); else o = (PyObject*)PyArray_GETConTIGUOUS((PyArrayObject*)o); _strides = PyArray_STRIDES(o); } for(int i = 0; i < ndims; i++) { size[i] = (int)_sizes[i]; step[i] = (size_t)_strides[i]; } // handle degenerate case if( ndims == 0) { size[ndims] = 1; step[ndims] = elemsize; ndims++; } if( ismultichannel ) { 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); if (!needcopy) { m.addref(); // protect the original numpy array from deallocation // (since Mat destructor will decrement the reference counter) } }; m.allocator = &g_numpyAllocator; 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; ERRWRAP2(m.copyTo(temp)); p = &temp; } p->addref(); return pyObjectFromRefcount(p->refcount);}清理
cv2.cpp文件后，以下是一些Cython代码，负责转换。请注意该
import_array()函数的定义和调用（这是NumPy函数，定义在其中的标头中
cv2.cpp），这对于定义使用的宏是必要的
pyopencv_to，如果不调用它，则会遇到lightalchemist指出的分段错误。
from cpython.ref cimport PyObject# Declares OpenCV's cv::Mat classcdef extern from "opencv2/core/core.hpp": cdef cppclass Mat: pass# Declares the official wrapper conversion functions + NumPy's import_array() functioncdef extern from "cv2.cpp": void import_array() PyObject* pyopencv_from(const _Mat&) int pyopencv_to(PyObject*, _Mat&)# Function to be called at initializationcdef void init(): import_array()# Python to C++ conversioncdef Mat nparrayToMat(object array): cdef Mat mat cdef PyObject* pyobject = <PyObject*> array pyopencv_to(pyobject, mat) return <Mat> mat# C++ to Python conversioncdef object matTonparray(Mat mat): return <object> pyopencv_from(mat)
注意：由于
import_array宏中有一个奇怪的return语句，在编译时由于某种原因我在Fedora 20上遇到了NumPy
1.8.0错误，我不得不手动将其删除以使其工作，但我在NumPy的1.8中找不到此return语句.0 GitHub源代码
