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C++ & sip - 2013

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SIP Introduction

SIP provides C/C++ wrappers to the python code. Python is the driver but C/C++ is not. In other words, SIP just allows us to make C/C++ call from Python but does not make it work calling Python from C/C++. At least, I have not had any luck doing it.


In this tutorial, I'll make a library from C++ code, and use the class and its method from Python side. The example I'll use is the same one given in http://riverbankcomputing.co.uk/static/Docs/sip4/using.html. The C++ code is very simple, and it has a method called reverse() returning a reverse string of a member which was an argument for the constructor of the class.

All of the processes are included in this tutorial: downloading, installing sip, making bindings and using C++ library from Python code.

Among the features of Python that makes it so powerful is it's ability to take existing libraries, written in C/C++, and make them available as Python extension modules. Such extension modules are often called bindings for the library.
SIP is a tool that makes it very easy to create Python bindings for C and C++ libraries. SIP comprises a code generator and a Python module.

  1. The code generator
    processes a set of specification files(.sip) and generates C or C++ code which is then compiled to create the bindings extension module.
  2. The SIP Python module
    provides support functions to the automatically generated code.

The specification files contains a description of the interface of the C or C++ library, i.e. the classes, methods, functions and variables. The format of a specification file is almost identical to a C or C++ header file. Therefore, the easiest way of creating a specification file is to edit the corresponding header file.




What is SIP?

SIP is a tool for quickly writing Python modules that interface with C++ and C libraries.PythonInfo Wiki

SIP is Python extension module generator for C and C++ libraries. It is an extension module generator similar to SWIG but is specifically designed for creating Python modules http://pypi.python.org/pypi/SIP

SIP takes a set of specification (.sip) files describing the API and generates the required C++ code. This is then compiled to produce the Python extension modules. A .sip file is basically the class header file with some things removed (because SIP doesn't include a full C++ parser) and some things added (because C++ doesn't always provide enough information about how the API works). From http://en.wikipedia.org/wiki/SIP_(software)

SIP is a tool that lets you create C and C++ bindings for Python. It was originally created for the PyQt package, which provides Python bindings for Nokia's Qt toolkit. As such, Python-SIP has specific support for the Qt signal/slot mechanism. However, the tool can also be used to create bindings for any C++ API. SIP works in a very similar fashion to SWIG(Simplified Wrapper and Interface Generator), although it does not support the range of languages that SWIG does. SIP supports much of the C/C++ syntax for its interface specification files and uses a similar syntax for its commands as SWIG(i.e., tokens that start with a % symbol), although it supports a different set and style of commands to customize the binding. - From API design for C++ by Martin Reddy.




SIP Installation

We can get the latest release of the SIP source code from http://www.riverbankcomputing.com/software/sip/download. In this tutorial, I'm using Ubuntu 11.04.

$ tar xvzf sip-4.13.3.tar.gz

Let's check the files in the directory:

~/Downloads/sip-4.13.3$ ls
configure.py  LICENSE       NEWS             sipgen       specs
custom        LICENSE-GPL2  README           siplib       sphinx
doc           LICENSE-GPL3  sipdistutils.py  siputils.py

If we run the configure.py to configure SIP:

~/Downloads/sip-4.13.3$ python configure.py
This is SIP 4.13.3 for Python 2.7.1+ on linux2.
The SIP code generator will be installed in /usr/bin.
The sip module will be installed in /usr/lib/python2.7/dist-packages.
The sip.h header file will be installed in /usr/include/python2.7.
The default directory to install .sip files in is /usr/share/sip.
The platform/compiler configuration is linux-g++.
Creating siplib/sip.h...purp
Creating siplib/siplib.c...
Creating siplib/siplib.sbf...
Creating sipconfig.py...
Creating top level Makefile...
Creating sip code generator Makefile...
Creating sip module Makefile...

Then, we have the following files in the same directory:

~/Downloads/sip-4.13.3$ ls
configure.py  LICENSE-GPL2  README           sipgen        specs
custom        LICENSE-GPL3  sipconfig.py     siplib        sphinx
doc           Makefile      sipconfig.pyc    siputils.py
LICENSE       NEWS          sipdistutils.py  siputils.pyc

The configure.py generated a Makefile for us:

# Makefile
all:
        @(cd sipgen; $(MAKE))
        @(cd siplib; $(MAKE))

install:
        @(cd sipgen; $(MAKE) install)
        @(cd siplib; $(MAKE) install)
        @test -d $(DESTDIR)/usr/lib/python2.7/dist-packages || mkdir -p $(DESTDIR)/usr/lib/python2.7/dist-packages
        cp -f sipconfig.py $(DESTDIR)/usr/lib/python2.7/dist-packages/sipconfig.py
        cp -f /home/kihyuck/Downloads/sip-4.13.3/sipdistutils.py $(DESTDIR)/usr/lib/python2.7/dist-packages/sipdistutils.py

clean:
        @(cd sipgen; $(MAKE) clean)
        @(cd siplib; $(MAKE) clean)

So, if we do make:

~/Downloads/sip-4.13.3$ make
gcc -c -pipe -O2 -w -DNDEBUG -I. -o main.o main.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o transform.o transform.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o gencode.o gencode.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o extracts.o extracts.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o export.o export.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o heap.o heap.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o parser.o parser.c
gcc -c -pipe -O2 -w -DNDEBUG -I. -o lexer.o lexer.c
g++  -o sip main.o transform.o gencode.o extracts.o export.o heap.o parser.o lexer.o 
...
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o siplib.o siplib.c
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o apiversions.o apiversions.c
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o descriptors.o descriptors.c
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o qtlib.o qtlib.c
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o threads.o threads.c
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o objmap.o objmap.c
gcc -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o voidptr.o voidptr.c
g++ -c -pipe -fPIC -O2 -w -DNDEBUG -I. -I/usr/include/python2.7 -o bool.o bool.cpp
g++ -shared -Wl,--version-script=sip.exp -o sip.so siplib.o apiversions.o descriptors.o qtlib.o threads.o objmap.o voidptr.o bool.o 
...

The final step is to install SIP by running the make install command:

~/Downloads/sip-4.13.3$ sudo make install 
make[1]: Entering directory `/home/kihyuck/Downloads/sip-4.13.3/sipgen'
cp -f sip /usr/bin/sip
make[1]: Leaving directory `/home/kihyuck/Downloads/sip-4.13.3/sipgen'
make[1]: Entering directory `/home/kihyuck/Downloads/sip-4.13.3/siplib'
cp -f sip.so /usr/lib/python2.7/dist-packages/sip.so
strip /usr/lib/python2.7/dist-packages/sip.so
cp -f /home/kihyuck/Downloads/sip-4.13.3/siplib/sip.h /usr/include/python2.7/sip.h
make[1]: Leaving directory `/home/kihyuck/Downloads/sip-4.13.3/siplib'
cp -f sipconfig.py /usr/lib/python2.7/dist-packages/sipconfig.py
cp -f /home/kihyuck/Downloads/sip-4.13.3/sipdistutils.py /usr/lib/python2.7/dist-packages/sipdistutils.py
YoeSooC.png



Using SIP



Defining SIP wrapper

Let's make a new directory, MySip, and with a specification file, word.sip.

~/MySip$ ls
word.sip

and the word.sip should look like below and it is very similar to word.h:

// word.sip
// Define the SIP wrapper to the word library.

%Module word

class Word {

%TypeHeaderCode
#include <word.h>
%End

public:
    Word(const char *w);
purp
    char *reverse() const;
};

Let's compare it with word.h below:

// Define the interface to the word library.

class Word {
    const char *the_word;

public:
    Word(const char *w);

    char *reverse() const;
};

If we want, we can now generate the C++ code in the current directory:

~/MySip$ sip -c . word.sip
~/MySip$ ls
sipAPIword.h  sipwordcmodule.cpp  sipwordWord.cpp  word.sip

It created the following:

  1. sipAPI$(module).h, sip$(module)$(class).cpp
    A pair of corresponding header and C++ files for each wrapped class.
  2. $(module)cmodule.cpp
    Which contains the module global and initialization code.





Using configure.py for SIP wrapper

However, we want to use script, configure.py, for the tasks such as compiling the generated code and linking it against all the necessary libraries

~/MySip$ ls
configure.py  word.sip

The configure.py looks like this:

# configure.py
######################
import os
import sipconfig

# The name of the SIP build file generated by SIP and used by the build
# system.
build_file = "word.sbf"

# Get the SIP configuration information.
config = sipconfig.Configuration()

# Run SIP to generate the code.
os.system(" ".join([config.sip_bin, "-c", ".", "-b", build_file, "word.sip"]))

# Create the Makefile.
makefile = sipconfig.SIPModuleMakefile(config, build_file)

# Add the library we are wrapping.  The name doesn't include any platform
# specific prefixes or extensions (e.g. the "lib" prefix on UNIX, or the
# ".dll" extension on Windows).
makefile.extra_libs = ["word"]

# Generate the Makefile itself.
makefile.generate()
#####################

Then, we can run the script configure.py, and see what's been changed:

~/MySip$ python configure.py

~/MySip$ ls
configure.py  Makefile  sipAPIword.h  sipwordcmodule.cpp  sipwordWord.cpp  word.sbf  word.sip






Creating corresponding sip header and modules

If we run make, we get the following error:

~/MySip$ make
g++ -c -pipe -fPIC -O2 -Wall -W -DNDEBUG -I. -I/usr/include/python2.7 -o sipwordWord.o sipwordWord.cpp
word.sip:8:18: fatal error: word.h: No such file or directory
compilation terminated.
make: *** [sipwordWord.o] Error 1

So, we need to put word.h at standard location for compiler such as /usr/include/. After doing it, if we run make, we get the following:

~/MySip$ make
g++ -c -pipe -fPIC -O2 -Wall -W -DNDEBUG -I. -I/usr/include/python2.7 -o sipwordWord.o sipwordWord.cpp
g++ -shared -Wl,--version-script=word.exp -o word.so sipwordcmodule.o sipwordWord.o -lword
/usr/bin/ld: cannot find -lword
collect2: ld returned 1 exit status
make: *** [word.so] Error 1

That's because we do not have a wordlib.a in the standard search location for compiler. The sip documents from http://www.riverbankcomputing.co.uk/static/Docs/sip4/using.html assumed that the word library we are wrapping and it's header file are installed in standard system locations and will be found by the compiler and linker without having to specify any additional flags. But I'll put the word.h into /usr/include/ and libword.a into /usr/lib.

So, we need to make the wordlib.a, and here is the code:

// word.cpp

#include <string.h>
#include <iostream>
#include <word.h>

using namespace std;

Word::Word(const char *w)
{
        the_word = w;
}

char* Word::reverse() const
{
        int len = strlen(the_word);
        char *str = new char[len+1];
        for(int i = len-1;i >= 0 ;i--) {
                str[len-1-i] = the_word[i];
        }
        str[len+1]='\0';
        return str;
}

and then, made the lib:

~/TEST/$ g++ -c word.cpp
~/TEST/$ ar -crs libword.a word.o
~/TEST/$ sudo cp libword.a /usr/lib

Then, we need to make and make install:
run the make again:

~/MySip$ make
g++ -c -pipe -fPIC -O2 -Wall -W -DNDEBUG -I. -I/usr/include/python2.7 -o sipwordcmodule.o sipwordcmodule.cpp
g++ -c -pipe -fPIC -O2 -Wall -W -DNDEBUG -I. -I/usr/include/python2.7 -o sipwordWord.o sipwordWord.cpp
g++ -shared -Wl,--version-script=word.exp -o word.so sipwordcmodule.o sipwordWord.o -lword

~/MySip$ ls
configure.py  sipAPIword.h        sipwordcmodule.o  sipwordWord.o  word.sbf  word.so
Makefile      sipwordcmodule.cpp  sipwordWord.cpp   word.exp       word.sip

and then install:

~/MySip$ sudo make install
cp -f word.so /usr/lib/python2.7/dist-packages/word.so
strip /usr/lib/python2.7/dist-packages/word.so

Now we can access to the C++ library from Python.




Python run

Here is a very simple Python file to run:

from word import Word

w = Word("reverse me")
print  w.reverse()

If we run it:

em esrever





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Python 2 vs Python 3

virtualenv and virtualenvwrapper

Uploading a big file to AWS S3 using boto module

Scheduled stopping and starting an AWS instance

Cloudera CDH5 - Scheduled stopping and starting services

Removing Cloud Files - Rackspace API with curl and subprocess

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Apache Spark 1.3 with PySpark (Spark Python API) Shell

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bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

Fabric - streamlining the use of SSH for application deployment

Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

Neural Networks with backpropagation for XOR using one hidden layer

NLP - NLTK (Natural Language Toolkit) ...

RabbitMQ(Message broker server) and Celery(Task queue) ...

OpenCV3 and Matplotlib ...

Simple tool - Concatenating slides using FFmpeg ...

iPython - Signal Processing with NumPy

iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

iPython and Jupyter Notebook with Embedded D3.js

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Python tutorial



Python Home

Introduction

Running Python Programs (os, sys, import)

Modules and IDLE (Import, Reload, exec)

Object Types - Numbers, Strings, and None

Strings - Escape Sequence, Raw String, and Slicing

Strings - Methods

Formatting Strings - expressions and method calls

Files and os.path

Traversing directories recursively

Subprocess Module

Regular Expressions with Python

Regular Expressions Cheat Sheet

Object Types - Lists

Object Types - Dictionaries and Tuples

Functions def, *args, **kargs

Functions lambda

Built-in Functions

map, filter, and reduce

Decorators

List Comprehension

Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism

Hashing (Hash tables and hashlib)

Dictionary Comprehension with zip

The yield keyword

Generator Functions and Expressions

generator.send() method

Iterators

Classes and Instances (__init__, __call__, etc.)

if__name__ == '__main__'

argparse

Exceptions

@static method vs class method

Private attributes and private methods

bits, bytes, bitstring, and constBitStream

json.dump(s) and json.load(s)

Python Object Serialization - pickle and json

Python Object Serialization - yaml and json

Priority queue and heap queue data structure

Graph data structure

Dijkstra's shortest path algorithm

Prim's spanning tree algorithm

Closure

Functional programming in Python

Remote running a local file using ssh

SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table

SQLite 3 - B. Selecting, updating and deleting data

MongoDB with PyMongo I - Installing MongoDB ...

Python HTTP Web Services - urllib, httplib2

Web scraping with Selenium for checking domain availability

REST API : Http Requests for Humans with Flask

Blog app with Tornado

Multithreading ...

Python Network Programming I - Basic Server / Client : A Basics

Python Network Programming I - Basic Server / Client : B File Transfer

Python Network Programming II - Chat Server / Client

Python Network Programming III - Echo Server using socketserver network framework

Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn

Python Coding Questions I

Python Coding Questions II

Python Coding Questions III

Python Coding Questions IV

Python Coding Questions V

Python Coding Questions VI

Python Coding Questions VII

Python Coding Questions VIII

Python Coding Questions IX

Python Coding Questions X

Image processing with Python image library Pillow

Python and C++ with SIP

PyDev with Eclipse

Matplotlib

Redis with Python

NumPy array basics A

NumPy Matrix and Linear Algebra

Pandas with NumPy and Matplotlib

Celluar Automata

Batch gradient descent algorithm

Longest Common Substring Algorithm

Python Unit Test - TDD using unittest.TestCase class

Simple tool - Google page ranking by keywords

Google App Hello World

Google App webapp2 and WSGI

Uploading Google App Hello World

Python 2 vs Python 3

virtualenv and virtualenvwrapper

Uploading a big file to AWS S3 using boto module

Scheduled stopping and starting an AWS instance

Cloudera CDH5 - Scheduled stopping and starting services

Removing Cloud Files - Rackspace API with curl and subprocess

Checking if a process is running/hanging and stop/run a scheduled task on Windows

Apache Spark 1.3 with PySpark (Spark Python API) Shell

Apache Spark 1.2 Streaming

bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...

Flask app with Apache WSGI on Ubuntu14/CentOS7 ...

Selenium WebDriver

Fabric - streamlining the use of SSH for application deployment

Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App

Neural Networks with backpropagation for XOR using one hidden layer

NLP - NLTK (Natural Language Toolkit) ...

RabbitMQ(Message broker server) and Celery(Task queue) ...

OpenCV3 and Matplotlib ...

Simple tool - Concatenating slides using FFmpeg ...

iPython - Signal Processing with NumPy

iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github

iPython and Jupyter Notebook with Embedded D3.js

Downloading YouTube videos using youtube-dl embedded with Python

Machine Learning : scikit-learn ...

Django 1.6/1.8 Web Framework ...


Sponsor Open Source development activities and free contents for everyone.

Thank you.

- K Hong






OpenCV 3 image and video processing with Python



OpenCV 3 with Python

Image - OpenCV BGR : Matplotlib RGB

Basic image operations - pixel access

iPython - Signal Processing with NumPy

Signal Processing with NumPy I - FFT and DFT for sine, square waves, unitpulse, and random signal

Signal Processing with NumPy II - Image Fourier Transform : FFT & DFT

Inverse Fourier Transform of an Image with low pass filter: cv2.idft()

Image Histogram

Video Capture and Switching colorspaces - RGB / HSV

Adaptive Thresholding - Otsu's clustering-based image thresholding

Edge Detection - Sobel and Laplacian Kernels

Canny Edge Detection

Hough Transform - Circles

Watershed Algorithm : Marker-based Segmentation I

Watershed Algorithm : Marker-based Segmentation II

Image noise reduction : Non-local Means denoising algorithm

Image object detection : Face detection using Haar Cascade Classifiers

Image segmentation - Foreground extraction Grabcut algorithm based on graph cuts

Image Reconstruction - Inpainting (Interpolation) - Fast Marching Methods

Video : Mean shift object tracking

Machine Learning : Clustering - K-Means clustering I

Machine Learning : Clustering - K-Means clustering II

Machine Learning : Classification - k-nearest neighbors (k-NN) algorithm




Machine Learning with scikit-learn



scikit-learn installation

scikit-learn : Features and feature extraction - iris dataset

scikit-learn : Machine Learning Quick Preview

scikit-learn : Data Preprocessing I - Missing / Categorical data

scikit-learn : Data Preprocessing II - Partitioning a dataset / Feature scaling / Feature Selection / Regularization

scikit-learn : Data Preprocessing III - Dimensionality reduction vis Sequential feature selection / Assessing feature importance via random forests

Data Compression via Dimensionality Reduction I - Principal component analysis (PCA)

scikit-learn : Data Compression via Dimensionality Reduction II - Linear Discriminant Analysis (LDA)

scikit-learn : Data Compression via Dimensionality Reduction III - Nonlinear mappings via kernel principal component (KPCA) analysis

scikit-learn : Logistic Regression, Overfitting & regularization

scikit-learn : Supervised Learning & Unsupervised Learning - e.g. Unsupervised PCA dimensionality reduction with iris dataset

scikit-learn : Unsupervised_Learning - KMeans clustering with iris dataset

scikit-learn : Linearly Separable Data - Linear Model & (Gaussian) radial basis function kernel (RBF kernel)

scikit-learn : Decision Tree Learning I - Entropy, Gini, and Information Gain

scikit-learn : Decision Tree Learning II - Constructing the Decision Tree

scikit-learn : Random Decision Forests Classification

scikit-learn : Support Vector Machines (SVM)

scikit-learn : Support Vector Machines (SVM) II

Flask with Embedded Machine Learning I : Serializing with pickle and DB setup

Flask with Embedded Machine Learning II : Basic Flask App

Flask with Embedded Machine Learning III : Embedding Classifier

Flask with Embedded Machine Learning IV : Deploy

Flask with Embedded Machine Learning V : Updating the classifier

scikit-learn : Sample of a spam comment filter using SVM - classifying a good one or a bad one




Machine learning algorithms and concepts

Batch gradient descent algorithm

Single Layer Neural Network - Perceptron model on the Iris dataset using Heaviside step activation function

Batch gradient descent versus stochastic gradient descent

Single Layer Neural Network - Adaptive Linear Neuron using linear (identity) activation function with batch gradient descent method

Single Layer Neural Network : Adaptive Linear Neuron using linear (identity) activation function with stochastic gradient descent (SGD)

Logistic Regression

VC (Vapnik-Chervonenkis) Dimension and Shatter

Bias-variance tradeoff

Maximum Likelihood Estimation (MLE)

Neural Networks with backpropagation for XOR using one hidden layer

minHash

tf-idf weight

Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)

Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)

Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)

Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)

Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)




Artificial Neural Networks (ANN)

[Note] Sources are available at Github - Jupyter notebook files

1. Introduction

2. Forward Propagation

3. Gradient Descent

4. Backpropagation of Errors

5. Checking gradient

6. Training via BFGS

7. Overfitting & Regularization

8. Deep Learning I : Image Recognition (Image uploading)

9. Deep Learning II : Image Recognition (Image classification)

10 - Deep Learning III : Deep Learning III : Theano, TensorFlow, and Keras









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