ctypes实现numpy和OpenCV Mat之间的数据交互

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1、目的

将c/c++编译成动态链接库，通过python调用，实现常见变量比如int，字符串之间的交互，以及np.ndarray和cv::Mat间的交互. 更简单的方式参考：pybind11实现numpy和OpenCV Mat的数据交互-CSDN博客

2 步骤

新建CMakeLists.txt，如下，需要安装opencv，安装教程参考Ubuntu 18.04 安装opencv4.2.0，如果遇到IPPICV问题参考解决编译opencv时，卡在IPPICV

cmake_minimum_required(VERSION 3.10)
project(test)
add_definitions(-std=c++11)
add_definitions(-DAPI_EXPORTS)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
find_package(OpenCV)
include_directories(${OpenCV_INCLUDE_DIRS})
add_library(myplugins SHARED ${PROJECT_SOURCE_DIR}/test.cpp)
target_link_libraries(myplugins ${OpenCV_LIBS})

新建test.cpp

#include <opencv2/opencv.hpp>
#include <string>
using namespace std;#if defined(_MSC_VER)
#define API __declspec(dllimport)
#else
#define API
#endif  // API_EXPORTStypedef struct test
{/* data */int ages;std::string name;cv::Mat pic;
} Person;extern "C" API void * init(int age_,  char * name_ptr, unsigned char *src_data, int rows, int cols)
{Person *handle = new Person();std::string name_ = name_ptr;handle->ages = age_;handle->name = name_;handle->pic = cv::Mat(rows, cols, CV_8UC3, src_data);return (void*) handle;
}extern "C" API int get_ages(void* handle_)
{Person * handle = (Person*)handle_;return handle->ages;
}extern "C" API char* get_name(void* handle_)
{Person * handle = (Person*)handle_;return (char*)handle->name.c_str();
}extern "C" API void get_pic(void* handle_, int rows, int cols, unsigned char* out_data)
{Person * handle = (Person*)handle_;cv::imshow("raw", handle->pic);cv::waitKey();//数据处理//....//返回结果memcpy(out_data, handle->pic.data, rows*cols*3);
}

新建test.py，具体转换见代码注释

from ctypes import *
import cv2
import numpy as np
import numpy.ctypeslib as npct#加载链接库
lib = CDLL('./build/libmyplugins.so')#定义函数的输入类型和输出类型
lib.get_ages.restype = c_int
lib.get_ages.argtypes = [c_void_p] #输入句柄(指针)
lib.get_name.restype = c_void_p #返回char*
lib.get_name.argtypes = [c_void_p] #输入句柄(指针)img = cv2.imread("oil-bin-1.png") #修改成自己的图片
ages = 23
name = bytes("kitty", "utf-8") #要对应c函数中的char*，需要转为二进制(rows, cols) = (img.shape[0], img.shape[1])
image_type = npct.ndpointer(dtype = np.uint8, ndim = 3, shape = img.shape, flags="C_CONTIGUOUS") #指定numpy图片的格式lib.init.restype = c_void_p #返回句柄(指针)
lib.init.argtypes = [c_int, c_void_p, image_type, c_int, c_int]#将np格式的图片传递到c
hd = lib.init(c_int(ages), c_char_p(name), img, c_int(rows), c_int(cols))print(lib.get_ages(hd)) #返回整数，直接打印
name = c_char_p(lib.get_name(hd)).value #返回char*, 需要转换
print(name.decode('utf-8')) #解码成字符串##第一种读取图片的方式，使用ndpointer指定图片类型
out_image = np.zeros_like(img).astype(np.uint8) #需要先开辟内存
lib.get_pic.argtypes = [c_void_p, c_int, c_int, image_type]
lib.get_pic(hd, c_int(rows), c_int(cols), out_image)
cv2.imshow("out1", out_image)##第二种读取图片的方式，不使用ndpointer指定图片类型
out_image = np.zeros_like(img).astype(np.uint8) #需要先开辟内存
lib.get_pic.argtypes = [c_void_p, c_int, c_int, c_void_p] #句柄(指针)，rows，cols, 图片指针
lib.get_pic(hd, c_int(rows), c_int(cols), out_image.ctypes.data_as(POINTER(c_ubyte)))cv2.imshow("out2", out_image)
cv2.waitKey()

3、测试

将上述3个文件放到同一个目录，然后在命令行中进入到该目录，依次运行，会弹出图片，单击图片，按任意键即可

mkdir build
cd build
cmake ..
make
cd ..
python test.py

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