C++ Mat::addref方法代码示例

  Mat* THTensor_(toMat) (THTensor* tensor)
  {
    int i;
    Mat* mat;

#if defined(TH_REAL_IS_BYTE)
      int type = CV_8U;
#elif defined(TH_REAL_IS_CHAR)
    int type = CV_8S;
#elif defined(TH_REAL_IS_SHORT)
    int type = CV_16S;
#elif defined(TH_REAL_IS_INT)
    int type = CV_32S;
#elif defined(TH_REAL_IS_LONG)
    int type = CV_32S;
    THError("No analog for long in opencv please convert");
#elif defined(TH_REAL_IS_FLOAT)
    int type = CV_32F;
#elif defined(TH_REAL_IS_DOUBLE)
    int type = CV_64F;
#else
#error "Unknown type"
#endif

    if (!THTensor_(isContiguous)(tensor))
      THError("must pass contiguous tensor to opencv");

    real* data = THTensor_(data)(tensor);
    int ndims  = tensor->nDimension;
    if (ndims == 2) {
      int rows = tensor->size[0];
      int cols = tensor->size[1];
      mat = new Mat(rows,cols,type,data);
    } else if ((ndims == 3) && (tensor->size[2] <= 4 )) {
      int rows = tensor->size[0];
      int cols = tensor->size[1];
      int ctype = CV_MAKETYPE(type, tensor->size[2]);
      mat = new Mat(rows,cols,ctype,data);
    } else {
      int sizes[ndims];
      for(i=0;i<ndims;i++){
        sizes[i] = tensor->size[i];
      }
      mat = new Mat (ndims, sizes, type , data);
    }

    mat->addref(); // make sure the matrix sticks around

    return mat;
  }

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( !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;
}

//.........这里部分代码省略.........
            needcopy = needcast = true;
            new_typenum = NPY_INT;
            type = CV_32S;
        }
        else
        {
            failmsg("%s data type = %d is not supported", info.name, typenum);
            return false;
        }
    }

#ifndef CV_MAX_DIM
    const int CV_MAX_DIM = 32;
#endif

    int ndims = PyArray_NDIM(oarr);
    if(ndims >= CV_MAX_DIM)
    {
        failmsg("%s dimensionality (=%d) is too high", info.name, ndims);
        return false;
    }

    int size[CV_MAX_DIM+1];
    size_t step[CV_MAX_DIM+1];
    size_t elemsize = CV_ELEM_SIZE1(type);
    const npy_intp* _sizes = PyArray_DIMS(oarr);
    const npy_intp* _strides = PyArray_STRIDES(oarr);
    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 (info.outputarg)
        {
            failmsg("Layout of the output array %s is incompatible with cv::Mat (step[ndims-1] != elemsize or step[1] != elemsize*nchannels)", info.name);
            return false;
        }

        if( needcast ) {
            o = PyArray_Cast(oarr, new_typenum);
            oarr = (PyArrayObject*) o;
        }
        else {
            oarr = PyArray_GETCONTIGUOUS(oarr);
            o = (PyObject*) oarr;
        }

        _strides = PyArray_STRIDES(oarr);
    }

    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", info.name);
        return false;
    }

    m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
    m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
    m.addref();

    if( !needcopy )
    {
        Py_INCREF(o);
    }
    m.allocator = &g_numpyAllocator;

    return true;
}

//.........这里部分代码省略.........
               typenum == NPY_BYTE ? CV_8S :
               typenum == NPY_USHORT ? CV_16U :
               typenum == NPY_SHORT ? CV_16S :
               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_INT32;
            type = CV_32S;
        }
        else
        {
            failmsg("%s data type = %d is not supported", info.name, typenum);
            return false;
        }
    }

    int ndims = PyArray_NDIM(o);
    if(ndims >= CV_MAX_DIM)
    {
        failmsg("%s dimensionality (=%d) is too high", info.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 (needcopy)
    {
        if (info.outputarg)
        {
            failmsg("output array %s is not row-contiguous (step[ndims-1] != elemsize)", info.name);
            return false;
        }
        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", info.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;
}