Doxygen tutorials: python basic
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Maksim Shabunin
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Introduction to OpenCV-Python Tutorials {#tutorial_py_intro}
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=======================================
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OpenCV
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------
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OpenCV was started at Intel in 1999 by **Gary Bradsky**, and the first release came out in 2000.
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**Vadim Pisarevsky** joined Gary Bradsky to manage Intel's Russian software OpenCV team. In 2005,
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OpenCV was used on Stanley, the vehicle that won the 2005 DARPA Grand Challenge. Later, its active
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development continued under the support of Willow Garage with Gary Bradsky and Vadim Pisarevsky
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leading the project. OpenCV now supports a multitude of algorithms related to Computer Vision and
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Machine Learning and is expanding day by day.
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OpenCV supports a wide variety of programming languages such as C++, Python, Java, etc., and is
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available on different platforms including Windows, Linux, OS X, Android, and iOS. Interfaces for
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high-speed GPU operations based on CUDA and OpenCL are also under active development.
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OpenCV-Python is the Python API for OpenCV, combining the best qualities of the OpenCV C++ API and
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the Python language.
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OpenCV-Python
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-------------
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OpenCV-Python is a library of Python bindings designed to solve computer vision problems.
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Python is a general purpose programming language started by **Guido van Rossum** that became very
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popular very quickly, mainly because of its simplicity and code readability. It enables the
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programmer to express ideas in fewer lines of code without reducing readability.
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Compared to languages like C/C++, Python is slower. That said, Python can be easily extended with
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C/C++, which allows us to write computationally intensive code in C/C++ and create Python wrappers
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that can be used as Python modules. This gives us two advantages: first, the code is as fast as the
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original C/C++ code (since it is the actual C++ code working in background) and second, it easier to
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code in Python than C/C++. OpenCV-Python is a Python wrapper for the original OpenCV C++
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implementation.
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OpenCV-Python makes use of **Numpy**, which is a highly optimized library for numerical operations
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with a MATLAB-style syntax. All the OpenCV array structures are converted to and from Numpy arrays.
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This also makes it easier to integrate with other libraries that use Numpy such as SciPy and
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Matplotlib.
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OpenCV-Python Tutorials
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-----------------------
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OpenCV introduces a new set of tutorials which will guide you through various functions available in
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OpenCV-Python. **This guide is mainly focused on OpenCV 3.x version** (although most of the
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tutorials will also work with OpenCV 2.x).
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Prior knowledge of Python and Numpy is recommended as they won't be covered in this guide.
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**Proficiency with Numpy is a must in order to write optimized code using OpenCV-Python.**
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This tutorial was originally started by *Abid Rahman K.* as part of the Google Summer of Code 2013
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program under the guidance of *Alexander Mordvintsev*.
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OpenCV Needs You !!!
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--------------------
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Since OpenCV is an open source initiative, all are welcome to make contributions to the library,
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documentation, and tutorials. If you find any mistake in this tutorial (from a small spelling
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mistake to an egregious error in code or concept), feel free to correct it by cloning OpenCV in
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[GitHub](https://github.com/Itseez/opencv) and submitting a pull request. OpenCV developers will
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check your pull request, give you important feedback and (once it passes the approval of the
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reviewer) it will be merged into OpenCV. You will then become an open source contributor :-)
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As new modules are added to OpenCV-Python, this tutorial will have to be expanded. If you are
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familiar with a particular algorithm and can write up a tutorial including basic theory of the
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algorithm and code showing example usage, please do so.
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Remember, we **together** can make this project a great success !!!
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Contributors
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------------
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Below is the list of contributors who submitted tutorials to OpenCV-Python.
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-# Alexander Mordvintsev (GSoC-2013 mentor)
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2. Abid Rahman K. (GSoC-2013 intern)
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Additional Resources
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--------------------
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-# A Quick guide to Python - [A Byte of Python](http://swaroopch.com/notes/python/)
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2. [Basic Numpy Tutorials](http://wiki.scipy.org/Tentative_NumPy_Tutorial)
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3. [Numpy Examples List](http://wiki.scipy.org/Numpy_Example_List)
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4. [OpenCV Documentation](http://docs.opencv.org/)
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5. [OpenCV Forum](http://answers.opencv.org/questions/)
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Install OpenCV-Python in Fedora {#tutorial_py_setup_in_fedora}
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===============================
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Goals
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-----
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In this tutorial
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- We will learn to setup OpenCV-Python in your Fedora system. Below steps are tested for
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Fedora 18 (64-bit) and Fedora 19 (32-bit).
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Introduction
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------------
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OpenCV-Python can be installed in Fedora in two ways, 1) Install from pre-built binaries available
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in fedora repositories, 2) Compile from the source. In this section, we will see both.
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Another important thing is the additional libraries required. OpenCV-Python requires only **Numpy**
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(in addition to other dependencies, which we will see later). But in this tutorials, we also use
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**Matplotlib** for some easy and nice plotting purposes (which I feel much better compared to
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OpenCV). Matplotlib is optional, but highly recommended. Similarly we will also see **IPython**, an
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Interactive Python Terminal, which is also highly recommended.
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Installing OpenCV-Python from Pre-built Binaries
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------------------------------------------------
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Install all packages with following command in terminal as root.
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@code{.bash}
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\f$ yum install numpy opencv*
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@endcode
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Open Python IDLE (or IPython) and type following codes in Python terminal.
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@code{.python}
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import cv2
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print cv2.__version__
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@endcode
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If the results are printed out without any errors, congratulations !!! You have installed
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OpenCV-Python successfully.
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It is quite easy. But there is a problem with this. Yum repositories may not contain the latest
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version of OpenCV always. For example, at the time of writing this tutorial, yum repository contains
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2.4.5 while latest OpenCV version is 2.4.6. With respect to Python API, latest version will always
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contain much better support. Also, there may be chance of problems with camera support, video
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playback etc depending upon the drivers, ffmpeg, gstreamer packages present etc.
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So my personnel preference is next method, i.e. compiling from source. Also at some point of time,
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if you want to contribute to OpenCV, you will need this.
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Installing OpenCV from source
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-----------------------------
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Compiling from source may seem a little complicated at first, but once you succeeded in it, there is
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nothing complicated.
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First we will install some dependencies. Some are compulsory, some are optional. Optional
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dependencies, you can leave if you don't want.
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### Compulsory Dependencies
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We need **CMake** to configure the installation, **GCC** for compilation, **Python-devel** and
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**Numpy** for creating Python extensions etc.
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@code{.bash}
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yum install cmake
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yum install python-devel numpy
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yum install gcc gcc-c++
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@endcode
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Next we need **GTK** support for GUI features, Camera support (libdc1394, libv4l), Media Support
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(ffmpeg, gstreamer) etc.
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@code{.bash}
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yum install gtk2-devel
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yum install libdc1394-devel
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yum install libv4l-devel
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yum install ffmpeg-devel
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yum install gstreamer-plugins-base-devel
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@endcode
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### Optional Dependencies
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Above dependencies are sufficient to install OpenCV in your fedora machine. But depending upon your
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requirements, you may need some extra dependencies. A list of such optional dependencies are given
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below. You can either leave it or install it, your call :)
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OpenCV comes with supporting files for image formats like PNG, JPEG, JPEG2000, TIFF, WebP etc. But
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it may be a little old. If you want to get latest libraries, you can install development files for
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these formats.
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@code{.bash}
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yum install libpng-devel
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yum install libjpeg-turbo-devel
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yum install jasper-devel
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yum install openexr-devel
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yum install libtiff-devel
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yum install libwebp-devel
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@endcode
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Several OpenCV functions are parallelized with **Intel's Threading Building Blocks** (TBB). But if
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you want to enable it, you need to install TBB first. ( Also while configuring installation with
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CMake, don't forget to pass -D WITH_TBB=ON. More details below.)
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@code{.bash}
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yum install tbb-devel
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@endcode
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OpenCV uses another library **Eigen** for optimized mathematical operations. So if you have Eigen
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installed in your system, you can exploit it. ( Also while configuring installation with CMake,
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don't forget to pass -D WITH_EIGEN=ON. More details below.)
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@code{.bash}
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yum install eigen3-devel
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@endcode
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If you want to build **documentation** ( *Yes, you can create offline version of OpenCV's complete
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official documentation in your system in HTML with full search facility so that you need not access
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internet always if any question, and it is quite FAST!!!* ), you need to install **Sphinx** (a
|
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documentation generation tool) and **pdflatex** (if you want to create a PDF version of it). ( Also
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while configuring installation with CMake, don't forget to pass -D BUILD_DOCS=ON. More details
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below.)
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@code{.bash}
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yum install python-sphinx
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yum install texlive
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@endcode
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### Downloading OpenCV
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Next we have to download OpenCV. You can download the latest release of OpenCV from [sourceforge
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site](http://sourceforge.net/projects/opencvlibrary/). Then extract the folder.
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Or you can download latest source from OpenCV's github repo. (If you want to contribute to OpenCV,
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choose this. It always keeps your OpenCV up-to-date). For that, you need to install **Git** first.
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@code{.bash}
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yum install git
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git clone https://github.com/Itseez/opencv.git
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@endcode
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It will create a folder OpenCV in home directory (or the directory you specify). The cloning may
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take some time depending upon your internet connection.
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Now open a terminal window and navigate to the downloaded OpenCV folder. Create a new build folder
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and navigate to it.
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@code{.bash}
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mkdir build
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cd build
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@endcode
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### Configuring and Installing
|
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|
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Now we have installed all the required dependencies, let's install OpenCV. Installation has to be
|
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configured with CMake. It specifies which modules are to be installed, installation path, which
|
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additional libraries to be used, whether documentation and examples to be compiled etc. Below
|
||||
command is normally used for configuration (executed from build folder).
|
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@code{.bash}
|
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cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
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@endcode
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It specifies that build type is "Release Mode" and installation path is /usr/local. Observe the -D
|
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before each option and .. at the end. In short, this is the format:
|
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@code{.bash}
|
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cmake [-D <flag>] [-D <flag>] ..
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@endcode
|
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You can specify as many flags you want, but each flag should be preceded by -D.
|
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|
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So in this tutorial, we are installing OpenCV with TBB and Eigen support. We also build the
|
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documentation, but we exclude Performance tests and building samples. We also disable GPU related
|
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modules (since we use OpenCV-Python, we don't need GPU related modules. It saves us some time).
|
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*(All the below commands can be done in a single cmake statement, but it is split here for better
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understanding.)*
|
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|
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- Enable TBB and Eigen support:
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@code{.bash}
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cmake -D WITH_TBB=ON -D WITH_EIGEN=ON ..
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@endcode
|
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- Enable documentation and disable tests and samples
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@code{.bash}
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cmake -D BUILD_DOCS=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF ..
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@endcode
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- Disable all GPU related modules.
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@code{.bash}
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cmake -D WITH_OPENCL=OFF -D WITH_CUDA=OFF -D BUILD_opencv_gpu=OFF -D BUILD_opencv_gpuarithm=OFF -D BUILD_opencv_gpubgsegm=OFF -D BUILD_opencv_gpucodec=OFF -D BUILD_opencv_gpufeatures2d=OFF -D BUILD_opencv_gpufilters=OFF -D BUILD_opencv_gpuimgproc=OFF -D BUILD_opencv_gpulegacy=OFF -D BUILD_opencv_gpuoptflow=OFF -D BUILD_opencv_gpustereo=OFF -D BUILD_opencv_gpuwarping=OFF ..
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@endcode
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- Set installation path and build type
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@code{.bash}
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cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
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@endcode
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Each time you enter cmake statement, it prints out the resulting configuration setup. In the final
|
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setup you got, make sure that following fields are filled (below is the some important parts of
|
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configuration I got). These fields should be filled appropriately in your system also. Otherwise
|
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some problem has happened. So check if you have correctly performed above steps.
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@code{.bash}
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-- GUI:
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-- GTK+ 2.x: YES (ver 2.24.19)
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-- GThread : YES (ver 2.36.3)
|
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|
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-- Video I/O:
|
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-- DC1394 2.x: YES (ver 2.2.0)
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-- FFMPEG: YES
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-- codec: YES (ver 54.92.100)
|
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-- format: YES (ver 54.63.104)
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-- util: YES (ver 52.18.100)
|
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-- swscale: YES (ver 2.2.100)
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-- gentoo-style: YES
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-- GStreamer:
|
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-- base: YES (ver 0.10.36)
|
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-- video: YES (ver 0.10.36)
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-- app: YES (ver 0.10.36)
|
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-- riff: YES (ver 0.10.36)
|
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-- pbutils: YES (ver 0.10.36)
|
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|
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-- V4L/V4L2: Using libv4l (ver 1.0.0)
|
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|
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-- Other third-party libraries:
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-- Use Eigen: YES (ver 3.1.4)
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-- Use TBB: YES (ver 4.0 interface 6004)
|
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-- Python:
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-- Interpreter: /usr/bin/python2 (ver 2.7.5)
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-- Libraries: /lib/libpython2.7.so (ver 2.7.5)
|
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-- numpy: /usr/lib/python2.7/site-packages/numpy/core/include (ver 1.7.1)
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-- packages path: lib/python2.7/site-packages
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|
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-- Documentation:
|
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-- Build Documentation: YES
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-- Sphinx: /usr/bin/sphinx-build (ver 1.1.3)
|
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-- PdfLaTeX compiler: /usr/bin/pdflatex
|
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--
|
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-- Tests and samples:
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-- Tests: NO
|
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-- Performance tests: NO
|
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-- C/C++ Examples: NO
|
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@endcode
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Many other flags and settings are there. It is left for you for further exploration.
|
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|
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Now you build the files using make command and install it using make install command. make install
|
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should be executed as root.
|
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@code{.bash}
|
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make
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su
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make install
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@endcode
|
||||
Installation is over. All files are installed in /usr/local/ folder. But to use it, your Python
|
||||
should be able to find OpenCV module. You have two options for that.
|
||||
|
||||
-# **Move the module to any folder in Python Path** : Python path can be found out by entering
|
||||
import sys;print sys.path in Python terminal. It will print out many locations. Move
|
||||
/usr/local/lib/python2.7/site-packages/cv2.so to any of this folder. For example,
|
||||
@code{.bash}
|
||||
su mv /usr/local/lib/python2.7/site-packages/cv2.so /usr/lib/python2.7/site-packages
|
||||
@endcode
|
||||
But you will have to do this every time you install OpenCV.
|
||||
|
||||
-# **Add /usr/local/lib/python2.7/site-packages to the PYTHON_PATH**: It is to be done only once.
|
||||
Just open \~/.bashrc and add following line to it, then log out and come back.
|
||||
@code{.bash}
|
||||
export PYTHONPATH=\f$PYTHONPATH:/usr/local/lib/python2.7/site-packages
|
||||
@endcode
|
||||
Thus OpenCV installation is finished. Open a terminal and try import cv2.
|
||||
|
||||
To build the documentation, just enter following commands:
|
||||
@code{.bash}
|
||||
make docs
|
||||
make html_docs
|
||||
@endcode
|
||||
Then open opencv/build/doc/_html/index.html and bookmark it in the browser.
|
||||
|
||||
Additional Resources
|
||||
--------------------
|
||||
|
||||
Exercises
|
||||
---------
|
||||
|
||||
-# Compile OpenCV from source in your Fedora machine.
|
||||
|
||||
@@ -0,0 +1,145 @@
|
||||
Install OpenCV-Python in Windows {#tutorial_py_setup_in_windows}
|
||||
================================
|
||||
|
||||
Goals
|
||||
-----
|
||||
|
||||
In this tutorial
|
||||
- We will learn to setup OpenCV-Python in your Windows system.
|
||||
|
||||
Below steps are tested in a Windows 7-64 bit machine with Visual Studio 2010 and Visual Studio
|
||||
2012. The screenshots shows VS2012.
|
||||
|
||||
Installing OpenCV from prebuilt binaries
|
||||
----------------------------------------
|
||||
|
||||
-# Below Python packages are to be downloaded and installed to their default locations.
|
||||
|
||||
1.1. [Python-2.7.x](http://python.org/ftp/python/2.7.5/python-2.7.5.msi).
|
||||
|
||||
1.2.
|
||||
[Numpy](http://sourceforge.net/projects/numpy/files/NumPy/1.7.1/numpy-1.7.1-win32-superpack-python2.7.exe/download).
|
||||
|
||||
1.3.
|
||||
[Matplotlib](https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.3.0/matplotlib-1.3.0.win32-py2.7.exe)
|
||||
(*Matplotlib is optional, but recommended since we use it a lot in our tutorials*).
|
||||
|
||||
-# Install all packages into their default locations. Python will be installed to **C:/Python27/**.
|
||||
3. After installation, open Python IDLE. Enter import numpy and make sure Numpy is working fine.
|
||||
4. Download latest OpenCV release from [sourceforge
|
||||
site](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.6/OpenCV-2.4.6.0.exe/download)
|
||||
and double-click to extract it.
|
||||
5. Goto **opencv/build/python/2.7** folder.
|
||||
6. Copy **cv2.pyd** to **C:/Python27/lib/site-packages**.
|
||||
7. Open Python IDLE and type following codes in Python terminal.
|
||||
|
||||
\>\>\> import cv2 \>\>\> print cv2.__version__
|
||||
|
||||
If the results are printed out without any errors, congratulations !!! You have installed
|
||||
OpenCV-Python successfully.
|
||||
|
||||
Building OpenCV from source
|
||||
---------------------------
|
||||
|
||||
-# Download and install Visual Studio and CMake.
|
||||
|
||||
1.1. [Visual Studio 2012](http://go.microsoft.com/?linkid=9816768)
|
||||
|
||||
1.2. [CMake](http://www.cmake.org/files/v2.8/cmake-2.8.11.2-win32-x86.exe)
|
||||
|
||||
-# Download and install necessary Python packages to their default locations
|
||||
|
||||
2.1. [Python 2.7.x](http://python.org/ftp/python/2.7.5/python-2.7.5.msi)
|
||||
|
||||
2.2.
|
||||
[Numpy](http://sourceforge.net/projects/numpy/files/NumPy/1.7.1/numpy-1.7.1-win32-superpack-python2.7.exe/download)
|
||||
|
||||
2.3.
|
||||
[Matplotlib](https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.3.0/matplotlib-1.3.0.win32-py2.7.exe)
|
||||
(*Matplotlib is optional, but recommended since we use it a lot in our tutorials.*)
|
||||
|
||||
@note In this case, we are using 32-bit binaries of Python packages. But if you want to use OpenCV
|
||||
for x64, 64-bit binaries of Python packages are to be installed. Problem is that, there is no
|
||||
official 64-bit binaries of Numpy. You have to build it on your own. For that, you have to use the
|
||||
same compiler used to build Python. When you start Python IDLE, it shows the compiler details. You
|
||||
can get more [information here](http://stackoverflow.com/q/2676763/1134940). So your system must
|
||||
have the same Visual Studio version and build Numpy from source.
|
||||
|
||||
@note Another method to have 64-bit Python packages is to use ready-made Python distributions from
|
||||
third-parties like [Anaconda](http://www.continuum.io/downloads),
|
||||
[Enthought](https://www.enthought.com/downloads/) etc. It will be bigger in size, but will have
|
||||
everything you need. Everything in a single shell. You can also download 32-bit versions also. 3.
|
||||
Make sure Python and Numpy are working fine.
|
||||
|
||||
-# Download OpenCV source. It can be from
|
||||
[Sourceforge](http://sourceforge.net/projects/opencvlibrary/) (for official release version) or
|
||||
from [Github](https://github.com/Itseez/opencv) (for latest source).
|
||||
5. Extract it to a folder, opencv and create a new folder build in it.
|
||||
6. Open CMake-gui (*Start \> All Programs \> CMake-gui*)
|
||||
7. Fill the fields as follows (see the image below):
|
||||
|
||||
7.1. Click on **Browse Source...** and locate the opencv folder.
|
||||
|
||||
7.2. Click on **Browse Build...** and locate the build folder we created.
|
||||
|
||||
7.3. Click on **Configure**.
|
||||
|
||||
|
||||
|
||||
7.4. It will open a new window to select the compiler. Choose appropriate compiler (here,
|
||||
Visual Studio 11) and click **Finish**.
|
||||
|
||||
|
||||
|
||||
7.5. Wait until analysis is finished.
|
||||
|
||||
-# You will see all the fields are marked in red. Click on the **WITH** field to expand it. It
|
||||
decides what extra features you need. So mark appropriate fields. See the below image:
|
||||
|
||||
|
||||
|
||||
-# Now click on **BUILD** field to expand it. First few fields configure the build method. See the
|
||||
below image:
|
||||
|
||||
|
||||
|
||||
-# Remaining fields specify what modules are to be built. Since GPU modules are not yet supported
|
||||
by OpenCV-Python, you can completely avoid it to save time (But if you work with them, keep it
|
||||
there). See the image below:
|
||||
|
||||
|
||||
|
||||
-# Now click on **ENABLE** field to expand it. Make sure **ENABLE_SOLUTION_FOLDERS** is unchecked
|
||||
(Solution folders are not supported by Visual Studio Express edition). See the image below:
|
||||
|
||||
|
||||
|
||||
-# Also make sure that in the **PYTHON** field, everything is filled. (Ignore
|
||||
PYTHON_DEBUG_LIBRARY). See image below:
|
||||
|
||||
|
||||
|
||||
-# Finally click the **Generate** button.
|
||||
14. Now go to our **opencv/build** folder. There you will find **OpenCV.sln** file. Open it with
|
||||
Visual Studio.
|
||||
15. Check build mode as **Release** instead of **Debug**.
|
||||
16. In the solution explorer, right-click on the **Solution** (or **ALL_BUILD**) and build it. It
|
||||
will take some time to finish.
|
||||
17. Again, right-click on **INSTALL** and build it. Now OpenCV-Python will be installed.
|
||||
|
||||
|
||||
|
||||
-# Open Python IDLE and enter import cv2. If no error, it is installed correctly.
|
||||
|
||||
@note We have installed with no other support like TBB, Eigen, Qt, Documentation etc. It would be
|
||||
difficult to explain it here. A more detailed video will be added soon or you can just hack around.
|
||||
|
||||
Additional Resources
|
||||
--------------------
|
||||
|
||||
Exercises
|
||||
---------
|
||||
|
||||
-# If you have a windows machine, compile the OpenCV from source. Do all kinds of hacks. If you
|
||||
meet any problem, visit OpenCV forum and explain your problem.
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
Introduction to OpenCV {#tutorial_py_table_of_contents_setup}
|
||||
======================
|
||||
|
||||
- @subpage tutorial_py_intro
|
||||
|
||||
Getting Started with
|
||||
OpenCV-Python
|
||||
|
||||
- @subpage tutorial_py_setup_in_windows
|
||||
|
||||
Set Up
|
||||
OpenCV-Python in Windows
|
||||
|
||||
- @subpage tutorial_py_setup_in_fedora
|
||||
|
||||
Set Up
|
||||
OpenCV-Python in Fedora
|
||||
Reference in New Issue
Block a user