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

#include

#include

#include

#include

#include

#include"mat_warper.h"

namespace py = pybind11;

py::array_t test_rgb_to_gray(py::array_t& 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 test_gray_canny(py::array_t& 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& input) {

cv::Mat src = numpy_uint8_1c_to_cv_mat(input);

std::vector<:mat> dst;

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

py::list out;

for (int i = 0; i < dst.size(); i++)

{

out.append<:array_t 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

#include

#include

namespace py = pybind11;

cv::Mat numpy_uint8_1c_to_cv_mat(py::array_t& input);

cv::Mat numpy_uint8_3c_to_cv_mat(py::array_t& input);

py::array_t cv_mat_uint8_1c_to_numpy(cv::Mat & input);

py::array_t cv_mat_uint8_3c_to_numpy(cv::Mat & input);

#endif // !MAT_WARPER_H_

mat_warper.cpp

#include"mat_warper.h"

#include

/*

Python->C++ Mat

*/

cv::Mat numpy_uint8_1c_to_cv_mat(py::array_t& 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& 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 cv_mat_uint8_1c_to_numpy(cv::Mat& input) {

py::array_t dst = py::array_t({ input.rows,input.cols }, input.data);

return dst;

}

py::array_t cv_mat_uint8_3c_to_numpy(cv::Mat& input) {

py::array_t dst = py::array_t({ 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

rgb.jpg

GRAY灰度图像

lena_gray.jpg

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

#include

#include

#include

#include

#include

#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

#include

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

namespace pybind11 { namespace detail {

template <> struct type_caster<: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

#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 _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(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(i) = (double)PyInt_AsLong(oi);

else if( PyFloat_Check(oi) )

m.at(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

image.png

Sobel算子

image.png

image.png

直线检测

image.png