标签:数据类型 上进 之间 机器 保留 时间差 输出 txt 新版
C++ 是一种编译型(compiled)语言,设计重点是性能、效率和使用灵活性,偏向于系统编程、嵌入式、资源受限的软件和系统。
Python是一种解释型(interpreted)语言,同样也支持不同的编程范式。Python 内置了常用数据结构(str, tuple, list, dict),简洁的语法、丰富的内置库(os,sys,urllib,...)和三方库(numpy, tf, torch ...),功能强大。最为重要的是和能够和多种服务(flask…)和tensorflow、pytorch等无缝联合,从而方便将你的算法开放出去。
一方面,我们需要编译型语言(C++)性能;一方面,也需要解释型语言(Python)的灵活。这时,pybind11 可以用作 C++ 和 Python 之间沟通的桥梁。
Pybind11 是一个轻量级只包含头文件的库,用于 Python 和 C++ 之间接口转换,可以为现有的 C++ 代码创建 Python 接口绑定。Pybind11 通过 C++ 编译时的自省来推断类型信息,来最大程度地减少传统拓展 Python 模块时繁杂的样板代码, 已经实现了 STL 数据结构、智能指针、类、函数重载、实例方法等到Python的转换,其中函数可以接收和返回自定义数据类型的值、指针或引用。
由于在Windows上和在Linux上使用会有较大不同,所以我这里将分为两个部分来说明问题,本文为下篇,具体说明Linux+Cmake实现。
我认为在Linux上使用python调用c++函数更有现实价值,毕竟许多新的服务、深度运算等都是运行在linux上的。具体步骤可以参考如下。
下载pybind11git clone https://github.com/pybind/pybind11.git
安装pytestpip install pytest
编译安装。这个地方我建议你首先将下载下来的pybind11备份一份
cd pybind11mkdir build
cd build
cmake ..
cmake --build . --config Release --target check
$ c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` example.cpp -o example`python3-config --extension-suffix`
cmake_minimum_required(VERSION 2.8.12)
project(example)
add_subdirectory(pybind11)
pybind11_add_module(example example.cpp)
cmake .
make
import example
example.add(3, 4)
[out]: 7
project(example) cmake_minimum_required(VERSION 2.8.12)find_package(OpenCV REQUIRED) include_directories(${OpenCV_INCLUDE_DIRS})add_subdirectory(pybind11)
pybind11_add_module(example example.cpp)target_link_libraries(example PRIVATE ${OpenCV_LIBS}) project(example)
cmake_minimum_required(VERSION 2.8.12)
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
add_subdirectory(pybind11)
SET(SOURCES
${CMAKE_CURRENT_SOURCE_DIR}/example.cpp
)
pybind11_add_module(example ${SOURCES})
target_link_libraries(example PRIVATE ${OpenCV_LIBS})
简单分析一下这段Cmake,除了必须的项目名称等以外,就是简单地去寻找OpenCV等的地址,而后将lib输入进去。pybind11_add_module相当于建立项目,使用Set方法方便批量处理。其中注意两点:1、target_link_libraries(example PRIVATE ${OpenCV_LIBS}) 放最后2、xample PRIVATE 不可缺少,否则报这个错成功调用结果,注意绝对地址。
project(example)
cmake_minimum_required(VERSION 2.8.12)
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
add_subdirectory(pybind11)
SET(SOURCES
${CMAKE_CURRENT_SOURCE_DIR}/example.cpp
${CMAKE_CURRENT_SOURCE_DIR}/mat_warper.h
${CMAKE_CURRENT_SOURCE_DIR}/mat_warper.cpp
)
pybind11_add_module(example ${SOURCES})
target_link_libraries(example PRIVATE ${OpenCV_LIBS})
tar -xvf GOPyWarper0429.tar
mkdir buildcd buildcmake ..
make
cp GOPyWarper.cpython-36m-x86_64-linux-gnu.so ../democd ../demopython3 main.py
import cv2
import GOPyWarper
import numpy as np
#获取图片,彩色3通道。
#中文和空格不支持
src = cv2.imread(‘pip.jpg‘,1)
#GO_FindPips
#输入mat,输出为list(point1,point2,……),其中point代表一个找到的圆心。.
varCircles = GOPyWarper.GO_FindPips(src)
#print(varCircles)
#GO_Resize
#输入mat,输出为规则化后文件大小
varResize = GOPyWarper.GO_Resize(src)
#绘图
dst=cv2.resize(src,((int)(varResize[0]),(int)(varResize[1])),interpolation=cv2.INTER_CUBIC)
for i in varCircles[:]:
cv2.circle(dst,(i[0],i[1]),5,(0,255,0),-1)
cv2.imshow("dst",dst)
cv2.waitKey(0)
helu@helu-virtual-machine:~/sandbox$ tar -cvf GOPyWarper0430.tar GOPyWarper0430
......
helu@helu-virtual-machine:~/sandbox$ cd GOPyWarper0430
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430$ mkdir build
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430$ cd build
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430/build$ cmake ..
......
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430/build$ make
Scanning dependencies of target GOPyWarper
[ 20%] Building CXX object CMakeFiles/GOPyWarper.dir/src/GOPyWarper.cpp.o
[ 40%] Building CXX object CMakeFiles/GOPyWarper.dir/src/mat_warper.cpp.o
[ 60%] Building CXX object CMakeFiles/GOPyWarper.dir/src/GOCVHelper_2019_11_29.cpp.o
[ 80%] Building CXX object CMakeFiles/GOPyWarper.dir/src/GOFindPips.cpp.o
[100%] Linking CXX shared module GOPyWarper.cpython-36m-x86_64-linux-gnu.so
[100%] Built target GOPyWarper
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430/build$ cp GOPyWarper.cpython-36m-x86_64-linux-gnu.so ../demo/
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430/build$ cd ../demo/
helu@helu-virtual-machine:~/sandbox/GOPyWarper0430/demo$ python3 main.py
helu@helu-virtual-machine:~/workstation/GOPyWarper0430$ cd build/
helu@helu-virtual-machine:~/workstation/GOPyWarper0430/build$ cmake ..
-- Found OpenCV: /usr/local (found version "4.3.0")
-- Found PythonInterp: /usr/bin/python3.8 (found version "3.8.2")
-- Found PythonLibs: /usr/lib/x86_64-linux-gnu/libpython3.8.so
-- pybind11 v2.5.dev1
-- Performing Test HAS_FLTO
-- Performing Test HAS_FLTO - Success
-- LTO enabled
-- Configuring done
-- Generating done
-- Build files have been written to: /home/helu/workstation/GOPyWarper0430/build
helu@helu-virtual-machine:~/workstation/GOPyWarper0430/build$ make
Scanning dependencies of target GOPyWarper
[ 20%] Building CXX object CMakeFiles/GOPyWarper.dir/src/GOPyWarper.cpp.o
[ 40%] Building CXX object CMakeFiles/GOPyWarper.dir/src/mat_warper.cpp.o
[ 60%] Building CXX object CMakeFiles/GOPyWarper.dir/src/GOCVHelper_2019_11_29.cpp.o
[ 80%] Building CXX object CMakeFiles/GOPyWarper.dir/src/GOFindPips.cpp.o
[100%] Linking CXX shared module GOPyWarper.cpython-38-x86_64-linux-gnu.so
[100%] Built target GOPyWarper
helu@helu-virtual-machine:~/workstation/GOPyWarper0430/build$
GaussBlur | windows实体机 c++原生 | ubuntu虚拟机 opencv_python | ubuntu虚拟机 pbind11 |
1次 | 32 | 34 | 40 |
重复100次 | 2819 | 3740 | 3891 |
int main() {
string path = "e:/template/lena.jpg";
cv::Mat src = cv::imread(path);
Mat dst;
//开始计时
double dstart = (double)cv::getTickCount();
for (int i=0;i<=100;i++)
{
cv::GaussianBlur(src, dst, cv::Size(101, 101), 1.0, 1.0);
printf("%d times %f ms\n", i,1000 * (getTickCount() - dstart) / getTickFrequency());
}
cv::waitKey(0);
return 0;
}
import cv2
import GOPyWarper
import numpy as np
src = cv2.imread(‘/home/helu/images/lena.jpg‘,1)
dstart = cv2.getTickCount()
for i in range(100):
blur = cv2.GaussianBlur(src,(101,101),1.0,None,1.0,borderType=4)
print(1000 * ( cv2.getTickCount() - dstart) /cv2.getTickFrequency())
import cv2
import GOPyWarper
import numpy as np
#获取图片,彩色3通道。
#中文和空格不支持
src = cv2.imread(‘/home/helu/images/lena.jpg‘,1)
dstart = cv2.getTickCount()
for i in range(100):
blur = GOPyWarper.test_gaussblur(src)
print(1000 * ( cv2.getTickCount() - dstart) /cv2.getTickFrequency())
基于pybind11实现Python调用c++编写的CV算法--下 (Linux+Cmake)
标签:数据类型 上进 之间 机器 保留 时间差 输出 txt 新版
原文地址:https://www.cnblogs.com/jsxyhelu/p/12827052.html