Python调用C++封装的进一步探索交流

//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 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 _sizes(dims + 1);
  for( i = 0; i  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;
}