做网站排名大概要多少,免费做个人网站,行业网站开发方案,域名空间都有了怎么做网站前言C opencv中图像和矩阵的表示采用Mat类#xff0c;比如imread()读取的结果就是返回一个Mat对象。对于python而言#xff0c;numpy 通常用于矩阵运算#xff0c; 矩阵#xff0c;图像表示为numpy.ndarray类。因此#xff0c;想要将python numpy.ndarray的数据传递到C op…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MatCmain.cpp#include#include#include#include#include#include#includemat_warper.hnamespace 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#includenamespace 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#includemat_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 cv2import matplotlib.pyplot as pltimport demo11.cv_demo1 as cv_demo1import numpy as npimage_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, cmapplt.gray())var2 cv_demo1.test_gray_canny(image_gray)plt.figure(canny)plt.imshow(var2, cmapplt.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, cmapplt.gray())plt.show()测试图像:RGB图像rgb.jpgGRAY灰度图像lena_gray.jpg结果RGB转GRAYimage.png灰度图像Canny边缘检测image.png图像金字塔image.pngDemo2C#include #include #include #include #include #include #include ndarray_converter.hnamespace 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 dont 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# endifcpp// 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#endifstruct 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 staticimport_array1(false);return true;}/** The following conversion functions are taken/adapted from OpenCVs 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 casereturn 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_DIMconst int CV_MAX_DIM 32;#endifint 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_DIM1];size_t step[CV_MAX_DIM1];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 setif( (i ndims-1 _sizes[i] 1 (size_t)_strides[i] ! elemsize) ||(i ndims-1 _sizes[i] 1 _strides[i] _strides[i1]) )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 setsize_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 caseif( 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.pngSobel算子image.pngimage.png直线检测image.png
