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()