pybind11-opencv 图像处理（numpy 数据交换）

前言

C++ opencv中图像和矩阵的表示采用Mat类，比如imread()读取的结果就是返回一个Mat对象。对于python而言，numpy 通常用于矩阵运算， 矩阵，图像表示为numpy.ndarray类。

因此，想要将python numpy.ndarray的数据传递到C++ opencv Mat, 或者C++ Mat将数据返回到python numpy.ndarray, 核心问题——如何绑定Mat

---

C++

main.cpp

#include<iostream>
#include<vector>
#include<opencv2/opencv.hpp>
#include<pybind11/pybind11.h>
#include<pybind11/numpy.h>
#include<pybind11/stl.h>
#include"mat_warper.h"

namespace py = pybind11;

py::array_t<unsigned char> test_rgb_to_gray(py::array_t<unsigned char>& input) {

    cv::Mat img_rgb = numpy_uint8_3c_to_cv_mat(input);
    cv::Mat dst;
    cv::cvtColor(img_rgb, dst, cv::COLOR_RGB2GRAY);
    return cv_mat_uint8_1c_to_numpy(dst);

}

py::array_t<unsigned char> test_gray_canny(py::array_t<unsigned char>& input) {
    cv::Mat src = numpy_uint8_1c_to_cv_mat(input);
    cv::Mat dst;
    cv::Canny(src, dst, 30, 60);
    return cv_mat_uint8_1c_to_numpy(dst);
}


/*
@return Python list
*/
py::list test_pyramid_image(py::array_t<unsigned char>& input) {
    cv::Mat src = numpy_uint8_1c_to_cv_mat(input);
    std::vector<cv::Mat> dst;

    cv::buildPyramid(src, dst, 4);

    py::list out;
    for (int i = 0; i < dst.size(); i++)
    {
        out.append<py::array_t<unsigned char>>(cv_mat_uint8_1c_to_numpy(dst.at(i)));
    }
    
    return out;
}

PYBIND11_MODULE(cv_demo1, m) {
    
    m.doc() = "Simple opencv demo";

    m.def("test_rgb_to_gray", &test_rgb_to_gray);
    m.def("test_gray_canny", &test_gray_canny);
    m.def("test_pyramid_image", &test_pyramid_image);

}

mat_warper.h

#ifndef MAT_WARPER_H_

#include<opencv2/opencv.hpp>
#include<pybind11/pybind11.h>
#include<pybind11/numpy.h>

namespace py = pybind11;

cv::Mat numpy_uint8_1c_to_cv_mat(py::array_t<unsigned char>& input);

cv::Mat numpy_uint8_3c_to_cv_mat(py::array_t<unsigned char>& input);

py::array_t<unsigned char> cv_mat_uint8_1c_to_numpy(cv::Mat & input);

py::array_t<unsigned char> cv_mat_uint8_3c_to_numpy(cv::Mat & input);

#endif // !MAT_WARPER_H_

mat_warper.cpp

#include"mat_warper.h"
#include <pybind11/numpy.h>

/*
Python->C++ Mat
*/


cv::Mat numpy_uint8_1c_to_cv_mat(py::array_t<unsigned char>& input) {

    if (input.ndim() != 2)
        throw std::runtime_error("1-channel image must be 2 dims ");

    py::buffer_info buf = input.request();

    cv::Mat mat(buf.shape[0], buf.shape[1], CV_8UC1, (unsigned char*)buf.ptr);
    
    return mat;
}


cv::Mat numpy_uint8_3c_to_cv_mat(py::array_t<unsigned char>& input) {

    if (input.ndim() != 3)
        throw std::runtime_error("3-channel image must be 3 dims ");

    py::buffer_info buf = input.request();

    cv::Mat mat(buf.shape[0], buf.shape[1], CV_8UC3, (unsigned char*)buf.ptr);

    return mat;
}


/*
C++ Mat ->numpy
*/
py::array_t<unsigned char> cv_mat_uint8_1c_to_numpy(cv::Mat& input) {

    py::array_t<unsigned char> dst = py::array_t<unsigned char>({ input.rows,input.cols }, input.data);
    return dst;
}

py::array_t<unsigned char> cv_mat_uint8_3c_to_numpy(cv::Mat& input) {

    py::array_t<unsigned char> dst = py::array_t<unsigned char>({ input.rows,input.cols,3}, input.data);
    return dst;
}



//PYBIND11_MODULE(cv_mat_warper, m) {
//
//  m.doc() = "OpenCV Mat -> Numpy.ndarray warper";
//
//  m.def("numpy_uint8_1c_to_cv_mat", &numpy_uint8_1c_to_cv_mat);
//  m.def("numpy_uint8_1c_to_cv_mat", &numpy_uint8_1c_to_cv_mat);
//
//
//}

---

python中测试

python代码

import cv2
import matplotlib.pyplot as plt
import demo11.cv_demo1 as cv_demo1
import numpy as np


image_rgb = cv2.imread('F:\\lena\\lena_rgb.jpg', cv2.IMREAD_UNCHANGED)
image_gray = cv2.imread('F:\\lena\\lena_gray.jpg', cv2.IMREAD_UNCHANGED)

var1 = cv_demo1.test_rgb_to_gray(image_rgb)
print(var1.shape)
plt.figure('rgb-gray')
plt.imshow(var1, cmap=plt.gray())

var2 = cv_demo1.test_gray_canny(image_gray)
plt.figure('canny')
plt.imshow(var2, cmap=plt.gray())

var3 = cv_demo1.test_pyramid_image(image_gray)
var3 = var3[1:]
plt.figure('pyramid_demo')
for i, image in enumerate(var3, 1):
    plt.subplot(2, 2, i)
    plt.axis('off')
    plt.imshow(image, cmap=plt.gray())

plt.show()

测试图像:
RGB图像

GRAY灰度图像

结果

• RGB转GRAY

  
  
  

  image.png

• 灰度图像Canny边缘检测

  
  
  

  image.png

• 图像金字塔

  
  
  

  image.png

---

Demo2

C++

#include <pybind11/pybind11.h>

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc.hpp>

#include <string>
#include <iostream>

#include "ndarray_converter.h"

namespace py = pybind11;

void show_image(cv::Mat image)
{
    cv::imshow("image_from_Cpp", image);
    cv::waitKey(0);
}

cv::Mat read_image(std::string image_name)
{
    cv::Mat image = cv::imread(image_name, CV_LOAD_IMAGE_COLOR);
    return image;
}

cv::Mat passthru(cv::Mat image)
{
    return image;
}

cv::Mat cloneimg(cv::Mat image)
{
    return image.clone();
}

cv::Mat gaussian_blur_demo(cv::Mat& image) {
    
    cv::Mat dst;
    cv::GaussianBlur(image, dst, cv::Size(7, 7),1.5,1.5);

    return dst;
}

cv::Mat image_filter(cv::Mat& image, cv::Mat& kernel){
    cv::Mat dst;
    cv::filter2D(image, dst, -1, kernel);
    return dst;
}

PYBIND11_MODULE(example,m) 
{
    NDArrayConverter::init_numpy();
    
   
    m.def("read_image", &read_image, "A function that read an image", 
        py::arg("image"));

    m.def("show_image", &show_image, "A function that show an image", 
        py::arg("image"));
        
    m.def("passthru", &passthru, "Passthru function", py::arg("image"));
    m.def("clone", &cloneimg, "Clone function", py::arg("image"));

    m.def("gaussian_blur_demo", &gaussian_blur_demo);
    m.def("image_filter", &image_filter);

}

convert_.h

# ifndef __NDARRAY_CONVERTER_H__
# define __NDARRAY_CONVERTER_H__

#include <Python.h>
#include <opencv2/core/core.hpp>


class NDArrayConverter {
public:
    // must call this first, or the other routines don't work!
    static bool init_numpy();
    
    static bool toMat(PyObject* o, cv::Mat &m);
    static PyObject* toNDArray(const cv::Mat& mat);
};

//
// Define the type converter
//

#include <pybind11/pybind11.h>

namespace pybind11 { namespace detail {
    
template <> struct type_caster<cv::Mat> {
public:
    
    PYBIND11_TYPE_CASTER(cv::Mat, _("numpy.ndarray"));
    
    bool load(handle src, bool) {
        return NDArrayConverter::toMat(src.ptr(), value);
    }
    
    static handle cast(const cv::Mat &m, return_value_policy, handle defval) {
        return handle(NDArrayConverter::toNDArray(m));
    }
};
    
    
}} // namespace pybind11::detail

# endif

cpp

// borrowed in spirit from https://github.com/yati-sagade/opencv-ndarray-conversion
// MIT License

#include "ndarray_converter.h"

#define NPY_NO_DEPRECATED_API NPY_1_15_API_VERSION
#include <numpy/ndarrayobject.h>

#if PY_VERSION_HEX >= 0x03000000
    #define PyInt_Check PyLong_Check
    #define PyInt_AsLong PyLong_AsLong
#endif

struct Tmp {
    const char * name;

    Tmp(const char * name ) : name(name) {}
};

Tmp info("return value");

bool NDArrayConverter::init_numpy() {
    // this has to be in this file, since PyArray_API is defined as static
    import_array1(false);
    return true;
}

/*
 * The following conversion functions are taken/adapted from OpenCV's cv2.cpp file
 * inside modules/python/src2 folder (OpenCV 3.1.0)
 */

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;

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

    UMatData* allocate(PyObject* o, int dims, const int* sizes, int type, size_t* step) const
    {
        UMatData* u = new UMatData(this);
        u->data = u->origdata = (uchar*)PyArray_DATA((PyArrayObject*) o);
        npy_intp* _strides = PyArray_STRIDES((PyArrayObject*) o);
        for( int i = 0; i < dims - 1; i++ )
            step[i] = (size_t)_strides[i];
        step[dims-1] = CV_ELEM_SIZE(type);
        u->size = sizes[0]*step[0];
        u->userdata = o;
        return u;
    }

    UMatData* allocate(int dims0, const int* sizes, int type, void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const
    {
        if( data != 0 )
        {
            CV_Error(Error::StsAssert, "The data should normally be NULL!");
            // probably this is safe to do in such extreme case
            return stdAllocator->allocate(dims0, sizes, type, data, step, flags, usageFlags);
        }
        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, dims = dims0;
        cv::AutoBuffer<npy_intp> _sizes(dims + 1);
        for( i = 0; i < dims; i++ )
            _sizes[i] = sizes[i];
        if( cn > 1 )
            _sizes[dims++] = cn;
        PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);
        if(!o)
            CV_Error_(Error::StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));
        return allocate(o, dims0, sizes, type, step);
    }

    bool allocate(UMatData* u, int accessFlags, UMatUsageFlags usageFlags) const
    {
        return stdAllocator->allocate(u, accessFlags, usageFlags);
    }

    void deallocate(UMatData* u) const
    {
        if(!u)
            return;
        PyEnsureGIL gil;
        CV_Assert(u->urefcount >= 0);
        CV_Assert(u->refcount >= 0);
        if(u->refcount == 0)
        {
            PyObject* o = (PyObject*)u->userdata;
            Py_XDECREF(o);
            delete u;
        }
    }

    const MatAllocator* stdAllocator;
};

NumpyAllocator g_numpyAllocator;

bool NDArrayConverter::toMat(PyObject *o, Mat &m)
{
    bool allowND = true;
    if(!o || o == Py_None)
    {
        if( !m.data )
            m.allocator = &g_numpyAllocator;
        return true;
    }

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

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

    PyArrayObject* oarr = (PyArrayObject*) o;

    bool needcopy = false, needcast = false;
    int typenum = PyArray_TYPE(oarr), 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 || typenum == NPY_LONG )
        {
            needcopy = needcast = true;
            new_typenum = NPY_INT;
            type = CV_32S;
        }
        else
        {
            failmsg("%s data type = %d is not supported", info.name, typenum);
            return false;
        }
    }

#ifndef CV_MAX_DIM
    const int CV_MAX_DIM = 32;
#endif

    int ndims = PyArray_NDIM(oarr);
    if(ndims >= CV_MAX_DIM)
    {
        failmsg("%s dimensionality (=%d) is too high", info.name, ndims);
        return false;
    }

    int size[CV_MAX_DIM+1];
    size_t step[CV_MAX_DIM+1];
    size_t elemsize = CV_ELEM_SIZE1(type);
    const npy_intp* _sizes = PyArray_DIMS(oarr);
    const npy_intp* _strides = PyArray_STRIDES(oarr);
    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
        // the _sizes[i] > 1 is needed to avoid spurious copies when NPY_RELAXED_STRIDES is set
        if( (i == ndims-1 && _sizes[i] > 1 && (size_t)_strides[i] != elemsize) ||
            (i < ndims-1 && _sizes[i] > 1 && _strides[i] < _strides[i+1]) )
            needcopy = true;
    }

    if( ismultichannel && _strides[1] != (npy_intp)elemsize*_sizes[2] )
        needcopy = true;

    if (needcopy)
    {
        //if (info.outputarg)
        //{
        //    failmsg("Layout of the output array %s is incompatible with cv::Mat (step[ndims-1] != elemsize or step[1] != elemsize*nchannels)", info.name);
        //    return false;
        //}

        if( needcast ) {
            o = PyArray_Cast(oarr, new_typenum);
            oarr = (PyArrayObject*) o;
        }
        else {
            oarr = PyArray_GETCONTIGUOUS(oarr);
            o = (PyObject*) oarr;
        }

        _strides = PyArray_STRIDES(oarr);
    }

    // Normalize strides in case NPY_RELAXED_STRIDES is set
    size_t default_step = elemsize;
    for ( int i = ndims - 1; i >= 0; --i )
    {
        size[i] = (int)_sizes[i];
        if ( size[i] > 1 )
        {
            step[i] = (size_t)_strides[i];
            default_step = step[i] * size[i];
        }
        else
        {
            step[i] = default_step;
            default_step *= size[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", info.name);
        return false;
    }

    m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
    m.u = g_numpyAllocator.allocate(o, ndims, size, type, step);
    m.addref();

    if( !needcopy )
    {
        Py_INCREF(o);
    }
    m.allocator = &g_numpyAllocator;

    return true;
}

PyObject* NDArrayConverter::toNDArray(const cv::Mat& m)
{
    if( !m.data )
        Py_RETURN_NONE;
    Mat temp, *p = (Mat*)&m;
    if(!p->u || p->allocator != &g_numpyAllocator)
    {
        temp.allocator = &g_numpyAllocator;
        ERRWRAP2(m.copyTo(temp));
        p = &temp;
    }
    PyObject* o = (PyObject*)p->u->userdata;
    Py_INCREF(o);
    return o;
}

Gaussian模糊

Sobel算子

直线检测