在cython中将向量向量转换为numpy数组

Question

我正在尝试从python访问opencvs cuda库。我正在使用cython进行扩展，我从Using Opencv Cuda functions from python跟随@DavidW的答案来实现我需要的一个功能。

例如，我需要在cython中转换以下三行。

Ptr< cuda::DescriptorMatcher > matcher = cuda::DescriptorMatcher::createBFMatcher();
vector< vector< DMatch> > matches;
matcher->knnMatch(descriptors_object_Gpu, descriptors_scene_Gpu, matches, 2);

基于来自Using Opencv Cuda functions from python的@DavidW的回答我实现了上面的行，现在我需要做的就是将变量“matches”（它是向量Dmatch的向量）发送回python。

现在这就是我被困住的地方。 因此，根据我的理解，当从python传递数据时，需要将其转换为opencv对象，在完成计算后，需要将相同的事情（反过来）发送回python。

用于转换对象我跟随@ostrumvulpes在pyopencv_converter.cpp（Accessing OpenCV CUDA Functions from Python (No PyCUDA)中所做的那样。）

下面给出了pyopencv_converter.cpp。

#include <Python.h>
#include "numpy/ndarrayobject.h"
#include "opencv2/core/core.hpp"

static PyObject* opencv_error = 0;

// === FAIL MESSAGE ====================================================================================================

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;
}

struct ArgInfo
{
    const char * name;
    bool outputarg;
    // more fields may be added if necessary

    ArgInfo(const char * name_, bool outputarg_)
        : name(name_)
        , outputarg(outputarg_) {}

    // to match with older pyopencv_to function signature
    operator const char *() const { return name; }
};

// === THREADING =======================================================================================================

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;
};

// === ERROR HANDLING ==================================================================================================

#define ERRWRAP2(expr) \
try \
{ \
    PyAllowThreads allowThreads; \
    expr; \
} \
catch (const cv::Exception &e) \
{ \
    PyErr_SetString(opencv_error, e.what()); \
    return 0; \
}

// === USING NAMESPACE CV ==============================================================================================

using namespace cv;

// === NUMPY ALLOCATOR =================================================================================================

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);
        }
        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;
        }
    }

    const MatAllocator* stdAllocator;
};

// === ALLOCATOR INITIALIZATION ========================================================================================

NumpyAllocator g_numpyAllocator;

// === CONVERTOR FUNCTIONS =============================================================================================

template<typename T> static
bool pyopencv_to(PyObject* obj, T& p, const char* name = "<unknown>");

template<typename T> static
PyObject* pyopencv_from(const T& src);

enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };

// special case, when the convertor needs full ArgInfo structure
static bool pyopencv_to(PyObject* o, Mat& m, const ArgInfo info)
{
    bool allowND = true;
    if(!o || o == Py_None)
    {
        if( !m.data )
            m.allocator = &g_numpyAllocator;
        return true;
    }

    if( PyInt_Check(o) )
    {
        double v[] = {static_cast<double>(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", info.name);
                m.release();
                return false;
            }
        }
        return true;
    }

    if( !PyArray_Check(o) )
    {
        failmsg("%s is not a numpy array, neither a scalar", info.name);
        return false;
    }

    PyArrayObject* oarr = (PyArrayObject*) o;

    bool needcopy = false, needcast = false;
    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;

    if( type < 0 )
    {
        if( typenum == NPY_INT64 || typenum == NPY_UINT64 || typenum == NPY_LONG )
        {
            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
        // the _sizes[i] > 1 is needed to avoid spurious copies when NPY_RELAXED_STRIDES is set
        if( (i == ndims-1 && _sizes[i] > 1 && (size_t)_strides[i] != elemsize) ||
            (i < ndims-1 && _sizes[i] > 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);
    }

    // Normalize strides in case NPY_RELAXED_STRIDES is set
    size_t default_step = elemsize;
    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];
        }
    }

    // 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;
}

template<>
bool pyopencv_to(PyObject* o, Mat& m, const char* name)
{
    return pyopencv_to(o, m, ArgInfo(name, 0));
}

template<>
PyObject* pyopencv_from(const Mat& m)
{
    if( !m.data )
        Py_RETURN_NONE;
    Mat temp, *p = (Mat*)&m;
    if(!p->u || p->allocator != &g_numpyAllocator)
    {
        temp.allocator = &g_numpyAllocator;
        ERRWRAP2(m.copyTo(temp));
        p = &temp;
    }
    PyObject* o = (PyObject*)p->u->userdata;
    Py_INCREF(o);
    return o;
}

现在在pyopencv_converter.cpp中，我看到只完成了Mat的转换。如果我想将矢量向量转换为numpy数组并将其发送回python，我需要做什么。

我从OpenCV的模块/ python / src2 / cv2.cpp查看了Opencv对象转换的源代码

我想我需要使用cv2.hpp中的以下部分。

template<typename _Tp> static inline PyObject* pyopencv_from_generic_vec(const std::vector<_Tp>& value)
{
    int i, n = (int)value.size();
    PyObject* seq = PyList_New(n);
    for( i = 0; i < n; i++ )
    {
        PyObject* item = pyopencv_from(value[i]);
        if(!item)
            break;
        PyList_SET_ITEM(seq, i, item);
    }
    if( i < n )
    {
        Py_DECREF(seq);
        return 0;
    }
    return seq;
}

template<> struct pyopencvVecConverter<DMatch>
{
    static PyObject* from(const std::vector<DMatch>& value)
    {
        return pyopencv_from_generic_vec(value);
    }
};

因为，我是cython的新手，我不知道如何在我的.pxd文件中实现上述代码。 它是将矢量向量转换为numpy的正确方法吗？如果没有那么什么是正确的方法？

如果有人可以帮我解决，我将非常感激。