I'm trying to write a python wrapper for some C++ code that make use of OpenCV but I'm having difficulties returning the result, which is a OpenCV C++ Mat object, to the python interpreter.

I've looked at OpenCV's source and found the file cv2.cpp which has conversions functions to perform conversions to and fro between PyObject* and OpenCV's Mat. I made use of those conversions functions but got a segmentation fault when I tried to use them.

I basically need some suggestions/sample code/online references on how to interface python and C++ code that make use of OpenCV, specifically with the ability to return OpenCV's C++ Mat to the python interpreter or perhaps suggestions on how/where to start investigating the cause of the segmentation fault.

Currently I'm using Boost Python to wrap the code.

Thanks in advance to any replies.

The relevant code:

// This is the function that is giving the segmentation fault.
PyObject* ABC::doSomething(PyObject* image)
{
    Mat m;
    pyopencv_to(image, m);  // This line gives segmentation fault.

    // Some code to create cppObj from CPP library that uses OpenCV
    cv::Mat processedImage = cppObj->align(m);

    return pyopencv_from(processedImage);
}

The conversion functions taken from OpenCV's source follows. The conversion code gives segmentation fault at the commented line with "if (!PyArray_Check(o)) ...".

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) ) // Segmentation fault inside 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;
}

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;
        m.copyTo(temp);
        p = &temp;
    }
    p->addref();
    return pyObjectFromRefcount(p->refcount);
}

My python test program:

import pysomemodule # My python wrapped library.
import cv2

def main():
    myobj = pysomemodule.ABC("faces.train") # Create python object. This works.
    image = cv2.imread('61.jpg')
    processedImage = myobj.doSomething(image)
    cv2.imshow("test", processedImage)
    cv2.waitKey()

if __name__ == "__main__":
    main()