ARTS 打卡 -05

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发布于: 2020 年 07 月 05 日

AlgorithmQuestion: Reverse String
class Solution {
public:
    void reverseString(vector<char>& s) {
        int len = s.size();        
        int i = 0;
        int j = len - 1;
        while(i < j){
            char temp = s[i];
            s[i] = s[j];
            s[j] = temp;
            i++;
            j--;
        }        
    }
};
Performance: runtime beats 66.27%, memory beats 62.28%.
ReviewSimplified Wrapper and Interface Generator (SWIG) is an interface compiler that connects programs written in C and C++ with scripting languages such as Perl, Python, Ruby, and Tcl. It works by taking the declarations found in C/C++ header files and using them to generate the wrapper code that scripting languages need to access the underlying C/C++ code. In addition, SWIG provides a variety of customization features that let you tailor the wrapping process to suit your application.
John Ousterhout (creator of Tcl) has written a paper that describes the benefits of scripting languages. SWIG makes it fairly easy to connect scripting languages with C/C++ code.
http://www.swig.org/exec.html
TipsSome useful commands when debugging the building errors:
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ShareHow to use SWIG to call OpenCV c++ code in pythonc++ code: postprocess.cpp, postprocess.hpp
SWIG file: postprocess.i
%module postprocess
%{
    #include "postprocess.hpp"
%}
﻿
void test();
setup.py
from distutils.core import setup, Extension
setup(name='postprocess_ext', version='1.0',
    ext_modules=[
        Extension('_postprocess', ['postprocess.cpp', 'postprocess.i'],
                  swig_opts=['-c++'],
                  depends=["postprocess.hpp"],
                  include_dirs=['/opt/intel/openvino_2020.2.120/opencv/include'],
                  library_dirs=['/opt/intel/openvino_2020.2.120/opencv/lib'],
                  libraries=['opencv_core', 'opencv_imgproc']
                  )
    ],
    py_modules=[
        "postprocess"
    ]
)
Command:
-python -c++ postprocess.i && python3 setup.py build_ext --inplace
So far, folder looks like this:
Now you can use postprocess like below:
from human_pose_estimation_demo.postprocess import postprocess
postprocess.test()
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