python - What is the easiest way to convert ndarray into cv::Mat?

Question

I'm trying to create Python/Cython wrapper for C++ library that uses cv::Mat class from OpenCV. In official Python wrapper all functions take NumPy's ndarray instead of cv::Mat, which is quite convenient. But in my own wrapper, how do I do such conversion? That is, how do I create cv::Mat from np.ndarray?

Answer 1

As suggested by kyamagu, you can use OpenCV's official python wrapper code, especially the pyopencv_to and pyopencv_from.

I've been struggling as you did with all the dependencies and the generated header files. Nevertheless, it is possible to reduce the complexity of this by "cleaning" the cv2.cpp as lightalchemist did here in order to keep only what is necessary. You will need to adapt it to your need and to the version of OpenCV you're using but its basically the same code that I used.

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

static PyObject* opencv_error = 0;

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;
}

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;
};

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

using namespace cv;

static PyObject* failmsgp(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;
}

static size_t REFCOUNT_OFFSET = (size_t)&(((PyObject*)0)->ob_refcnt) +
    (0x12345678 != *(const size_t*)"x78x56x34x12")*sizeof(int);

static inline PyObject* pyObjectFromRefcount(const int* refcount)
{
    return (PyObject*)((size_t)refcount - REFCOUNT_OFFSET);
}

static inline int* refcountFromPyObject(const PyObject* obj)
{
    return (int*)((size_t)obj + REFCOUNT_OFFSET);
}

class NumpyAllocator : public MatAllocator
{
public:
    NumpyAllocator() {}
    ~NumpyAllocator() {}

    void allocate(int dims, const int* sizes, int type, int*& refcount,
                  uchar*& datastart, uchar*& data, size_t* step)
    {
        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;
        npy_intp _sizes[CV_MAX_DIM+1];
        for( i = 0; i < dims; i++ )
            _sizes[i] = sizes[i];
        if( cn > 1 )
        {
            /*if( _sizes[dims-1] == 1 )
                _sizes[dims-1] = cn;
            else*/
                _sizes[dims++] = cn;
        }
        PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);
        if(!o)
            CV_Error_(CV_StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));
        refcount = refcountFromPyObject(o);
        npy_intp* _strides = PyArray_STRIDES(o);
        for( i = 0; i < dims - (cn > 1); i++ )
            step[i] = (size_t)_strides[i];
        datastart = data = (uchar*)PyArray_DATA(o);
    }

    void deallocate(int* refcount, uchar*, uchar*)
    {
        PyEnsureGIL gil;
        if( !refcount )
            return;
        PyObject* o = pyObjectFromRefcount(refcount);
        Py_INCREF(o);
        Py_DECREF(o);
    }
};

NumpyAllocator g_numpyAllocator;

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

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

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

    bool needcopy = false, needcast = false;
    int typenum = PyArray_TYPE(o), 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 || type == NPY_LONG )
        {
            needcopy = needcast = true;
            new_typenum = NPY_INT;
            type = CV_32S;
        }
        else
        {
            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 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( 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", 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;
}

static PyObject* pyopencv_from(const Mat& m)
{
    if( !m.data )
        Py_RETURN_NONE;
    Mat temp, *p = (Mat*)&m;
    if(!p->refcount || p->allocator != &g_numpyAllocator)
    {
        temp.allocator = &g_numpyAllocator;
        ERRWRAP2(m.copyTo(temp));
        p = &temp;
    }
    p->addref();
    return pyObjectFromRefcount(p->refcount);
}

Once you have a cleaned up cv2.cpp file, here is some Cython code that takes care of the conversion. Notice the definition and the call to the import_array() function (it's a NumPy function defined in a header included somewhere in cv2.cpp), this is necessary to define some macros used by pyopencv_to, if you don't call it you will get segmentation faults as lightalchemist pointed out.

from cpython.ref cimport PyObject

# Declares OpenCV's cv::Mat class
cdef extern from "opencv2/core/core.hpp":
    cdef cppclass Mat:
        pass

# Declares the official wrapper conversion functions + NumPy's import_array() function
cdef extern from "cv2.cpp":
    void import_array()
    PyObject* pyopencv_from(const _Mat&)
    int pyopencv_to(PyObject*, _Mat&)


# Function to be called at initialization
cdef void init():
    import_array()

# Python to C++ conversion
cdef Mat nparrayToMat(object array):
    cdef Mat mat
    cdef PyObject* pyobject = <PyObject*> array
    pyopencv_to(pyobject, mat)
    return <Mat> mat

# C++ to Python conversion
cdef object matToNparray(Mat mat):
    return <object> pyopencv_from(mat)

Note: somehow I got an error with NumPy 1.8.0 on Fedora 20 while compiling due to a strange return statement in the import_array macro, I had to manually remove it to make it work but I can't find this return statement in the NumPy's 1.8.0 GitHub source code