//以Mat的allocator作为基类，Numpy的Allocator作为继承类//这样可以用派生对象指针对基类数据进行操作class NumpyAllocator : public MatAllocator{public: NumpyAllocator() { stdAllocator = Mat::getStdAllocator(); } ~NumpyAllocator() {}  UMatData* allocate(PyObject* o, int dims, const int* sizes, int type, size_t* step) const {  UMatData* u = new UMatData(this);  u->data = u->origdata = (uchar*)PyArray_DATA((PyArrayObject*) o);  npy_intp* _strides = PyArray_STRIDES((PyArrayObject*) o);  for( int i = 0; i < dims - 1; i++ )   step[i] = (size_t)_strides[i];  step[dims-1] = CV_ELEM_SIZE(type);  u->size = sizes[0]*step[0];  u->userdata = o;  return u; }  UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const {  if( data != 0 )  {   CV_Error(Error::StsAssert, "The data should normally be NULL!");   // probably this is safe to do in such extreme case   return stdAllocator->allocate(dims0, sizes, type, data, step, flags, usageFlags);  } //确保当前使用python的C API是线程安全的  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, dims = dims0;  cv::AutoBuffer<npy_intp> _sizes(dims + 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_(Error::StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));  return allocate(o, dims0, sizes, type, step); }  bool allocate(UMatData* u, int accessFlags, UMatUsageFlags usageFlags) const {  return stdAllocator->allocate(u, accessFlags, usageFlags); }  void deallocate(UMatData* u) const {  if(!u)   return;  PyEnsureGIL gil;  CV_Assert(u->urefcount >= 0);  CV_Assert(u->refcount >= 0);  if(u->refcount == 0)  {   PyObject* o = (PyObject*)u->userdata;   Py_XDECREF(o);   delete u;  } } //基类指针，调用allocate函数进行内存分配 const MatAllocator* stdAllocator;};

//将PyObject的特性幅值给size,ndims,type int typenum = PyArray_TYPE(oarr), 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;  //....  int ndims = PyArray_NDIM(oarr); //....  const npy_intp* _sizes = PyArray_DIMS(oarr);  const npy_intp* _strides = PyArray_STRIDES(oarr); for ( int i = ndims - 1; i >= 0; --i ) {  size[i] = (int)_sizes[i];  if ( size[i] > 1 )  {   step[i] = (size_t)_strides[i];   default_step = step[i] * size[i];  }  else  {   step[i] = default_step;   default_step *= size[i];  } } //.... //这一步直接用PyObject初始化Mat m m = Mat(ndims, size, type, PyArray_DATA(oarr), step); m.u = g_numpyAllocator.allocate(o, ndims, size, type, step); m.addref();

//将Mat转换为PyObject*template<>PyObject* pyopencv_from(const Mat& m){ if( !m.data )  Py_RETURN_NONE; Mat temp, *p = (Mat*)&m; //确保数据拷贝不会对原始数据m产生破坏 if(!p->u || p->allocator != &g_numpyAllocator) {  temp.allocator = &g_numpyAllocator;  ERRWRAP2(m.copyTo(temp));  p = &temp; } //将Mat封装好的userdata指针转给Pyobject* PyObject* o = (PyObject*)p->u->userdata; //引用计数器加一 Py_INCREF(o); return o;}