如何使用pyopencv_来运行？

Question

我有一个用python和OpenCV编写的程序。我想添加一个功能，这是大津阈值与掩模。所以，我从here得到一个用c++编写的代码。我试图将其转换为python，但是太慢了（因为python）。最后，我决定在python中使用c++。尝试找到函数。但是，我不能使用它，因为PyArray_Check（）。当程序进入此功能时，立即死亡。它不会给出任何错误消息。我想可能是分割错误。许多堆栈溢出的答案都是“使用import\u array（）”。但这对我不起作用。在

这是我的密码。在

在转换.cpp在

#include <Python.h>
#include "numpy/ndarrayobject.h"
#include "opencv2/core/core.hpp"
#include "convert.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*)"\x78\x56\x34\x12\0\0\0\0\0")*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 };

int init_numpy() {
    import_array();
    return 0;
}

const static int numpy_initialized = init_numpy();

int pyopencv_to(const PyObject* o, Mat& m, const char* name, bool allowND)
{
    if(!o || o == Py_None)
    {
        if( !m.data )
            m.allocator = &g_numpyAllocator;
        return true;
    }

    if( !PyArray_Check(o) )    // this line makes error without message
    {
        failmsg("%s is not a numpy array", name);
        return false;
    }

    // NPY_LONG (64 bit) is converted to CV_32S (32 bit)
    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 = cv::Mat(ndims, size, type, PyArray_DATA(o), step);

    if( m.data )
    {
        m.u->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 )
    {
        cv::Mat tmp;
        tmp.allocator = &g_numpyAllocator;
        transpose(m, tmp);
        m = tmp;
    }
    return true;
}

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

在阈值.cpp在

^{pr2}$

在转换.hpp在

#ifndef __CONVERT_HPP__
#define __CONVERT_HPP__

#include <Python.h>
#include "opencv2/opencv.hpp"

using namespace cv;

int pyopencv_to(const PyObject* o, Mat& m, const char* name = "<unknown>", bool allowND=true);
PyObject* pyopencv_from(const Mat& m);

#endif

Answer 1

为什么选择使用Python包C++来完成这个简单的任务？我认为只使用Python就可以很容易地获得相同的结果。。。？在

我假设您希望在OpenCV中使用自适应阈值方法。在

首先，可以计算输入灰度图像的自适应阈值。可通过以下函数计算该值：

def compute_otsu_value(im_gray):
    hist = cv2.calcHist([im_gray], [0], None, [256], [0, 256])
    hist_norm = hist.ravel() / hist.max()
    cum_sum_mat = hist_norm.cumsum()
    fn_min = np.inf
    thresh = -1
    for i in xrange(1, 256):
        p1, p2 = np.hsplit(hist_norm, [i])
        q1, q2 = cum_sum_mat[i], cum_sum_mat[255] - cum_sum_mat[i]
        if q1 == 0 or q2 == 0:
            continue
        b1, b2 = np.hsplit(np.arange(256), [i])
        m1, m2 = np.sum(p1 * b1) / q1, np.sum(p2 * b2) / q2
        v1, v2 = np.sum(((b1-m1)**2)*p1)/q1, np.sum(((b2-m2)**2)*p2)/q2
        fn = v1 * q1 + v2 * q2
        if fn < fn_min:
            fn_min = fn
            thresh = i
    return thresh

最后，在main（）函数中，可以将输入图像加载为灰色图像，并相应地获得阈值图像。在

^{pr2}$