opencv/modules/python/src2/cv2_numpy.hpp

#ifndef CV2_NUMPY_HPP
#define CV2_NUMPY_HPP

#include "cv2.hpp"
#include "opencv2/core.hpp"

class NumpyAllocator : public cv::MatAllocator
{
public:
    NumpyAllocator() { stdAllocator = cv::Mat::getStdAllocator(); }
    ~NumpyAllocator() {}

    cv::UMatData* allocate(PyObject* o, int dims, const int* sizes, int type, size_t* step) const;
    cv::UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, cv::AccessFlag flags, cv::UMatUsageFlags usageFlags) const CV_OVERRIDE;
    bool allocate(cv::UMatData* u, cv::AccessFlag accessFlags, cv::UMatUsageFlags usageFlags) const CV_OVERRIDE;
    void deallocate(cv::UMatData* u) const CV_OVERRIDE;

    const cv::MatAllocator* stdAllocator;
};

extern NumpyAllocator g_numpyAllocator;

//======================================================================================================================

// HACK(?): function from cv2_util.hpp
extern int failmsg(const char *fmt, ...);

namespace {

template<class T>
NPY_TYPES asNumpyType()
{
    return NPY_OBJECT;
}

template<>
NPY_TYPES asNumpyType<bool>()
{
    return NPY_BOOL;
}

#define CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(src, dst) \
    template<>                                             \
    NPY_TYPES asNumpyType<src>()                           \
    {                                                      \
        return NPY_##dst;                                  \
    }                                                      \
    template<>                                             \
    NPY_TYPES asNumpyType<u##src>()                        \
    {                                                      \
        return NPY_U##dst;                                 \
    }

CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int8_t, INT8)

CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int16_t, INT16)

CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int32_t, INT32)

CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION(int64_t, INT64)

#undef CV_GENERATE_INTEGRAL_TYPE_NPY_CONVERSION

template<>
NPY_TYPES asNumpyType<float>()
{
    return NPY_FLOAT;
}

template<>
NPY_TYPES asNumpyType<double>()
{
    return NPY_DOUBLE;
}

template <class T>
PyArray_Descr* getNumpyTypeDescriptor()
{
    return PyArray_DescrFromType(asNumpyType<T>());
}

template <>
PyArray_Descr* getNumpyTypeDescriptor<size_t>()
{
#if SIZE_MAX == ULONG_MAX
    return PyArray_DescrFromType(NPY_ULONG);
#elif SIZE_MAX == ULLONG_MAX
    return PyArray_DescrFromType(NPY_ULONGLONG);
#else
    return PyArray_DescrFromType(NPY_UINT);
#endif
}

template <class T, class U>
bool isRepresentable(U value) {
    return (std::numeric_limits<T>::min() <= value) && (value <= std::numeric_limits<T>::max());
}

template<class T>
bool canBeSafelyCasted(PyObject* obj, PyArray_Descr* to)
{
    return PyArray_CanCastTo(PyArray_DescrFromScalar(obj), to) != 0;
}


template<>
bool canBeSafelyCasted<size_t>(PyObject* obj, PyArray_Descr* to)
{
    PyArray_Descr* from = PyArray_DescrFromScalar(obj);
    if (PyArray_CanCastTo(from, to))
    {
        return true;
    }
    else
    {
        // False negative scenarios:
        // - Signed input is positive so it can be safely cast to unsigned output
        // - Input has wider limits but value is representable within output limits
        // - All the above
        if (PyDataType_ISSIGNED(from))
        {
            int64_t input = 0;
            PyArray_CastScalarToCtype(obj, &input, getNumpyTypeDescriptor<int64_t>());
            return (input >= 0) && isRepresentable<size_t>(static_cast<uint64_t>(input));
        }
        else
        {
            uint64_t input = 0;
            PyArray_CastScalarToCtype(obj, &input, getNumpyTypeDescriptor<uint64_t>());
            return isRepresentable<size_t>(input);
        }
        return false;
    }
}


template<class T>
bool parseNumpyScalar(PyObject* obj, T& value)
{
    if (PyArray_CheckScalar(obj))
    {
        // According to the numpy documentation:
        // There are 21 statically-defined PyArray_Descr objects for the built-in data-types
        // So descriptor pointer is not owning.
        PyArray_Descr* to = getNumpyTypeDescriptor<T>();
        if (canBeSafelyCasted<T>(obj, to))
        {
            PyArray_CastScalarToCtype(obj, &value, to);
            return true;
        }
    }
    return false;
}


struct SafeSeqItem
{
    PyObject * item;
    SafeSeqItem(PyObject *obj, size_t idx) { item = PySequence_GetItem(obj, idx); }
    ~SafeSeqItem() { Py_XDECREF(item); }

private:
    SafeSeqItem(const SafeSeqItem&); // = delete
    SafeSeqItem& operator=(const SafeSeqItem&); // = delete
};

template <class T>
class RefWrapper
{
public:
    RefWrapper(T& item) : item_(item) {}

    T& get() CV_NOEXCEPT { return item_; }

private:
    T& item_;
};

// In order to support this conversion on 3.x branch - use custom reference_wrapper
// and C-style array instead of std::array<T, N>
template <class T, std::size_t N>
bool parseSequence(PyObject* obj, RefWrapper<T> (&value)[N], const ArgInfo& info)
{
    if (!obj || obj == Py_None)
    {
        return true;
    }
    if (!PySequence_Check(obj))
    {
        failmsg("Can't parse '%s'. Input argument doesn't provide sequence "
                "protocol", info.name);
        return false;
    }
    const std::size_t sequenceSize = PySequence_Size(obj);
    if (sequenceSize != N)
    {
        failmsg("Can't parse '%s'. Expected sequence length %lu, got %lu",
                info.name, N, sequenceSize);
        return false;
    }
    for (std::size_t i = 0; i < N; ++i)
    {
        SafeSeqItem seqItem(obj, i);
        if (!pyopencv_to(seqItem.item, value[i].get(), info))
        {
            failmsg("Can't parse '%s'. Sequence item with index %lu has a "
                    "wrong type", info.name, i);
            return false;
        }
    }
    return true;
}

} // namespace


#endif // CV2_NUMPY_HPP