About this book

OpenCV Computer Vision with Python shows you how to use the Python bindings for OpenCV. By following clear and concise examples, you will develop a computer vision application that tracks faces in live video and applies special effects to them. If you have always wanted to learn which version of these bindings to use, how to integrate with cross-platform Kinect drivers, and how to efficiently process image data with NumPy and SciPy, then this book is for you.

This book has practical, project-based tutorials for Python developers and hobbyists who want to get started with computer vision with OpenCV and Python. It is a hands-on guide that covers the fundamental tasks of computer vision, capturing, filtering, and analyzing images, with step-by-step instructions for writing both an application and reusable library classes.

Publication date:
April 2013
Publisher
Packt
Pages
122
ISBN
9781782163923

 

Chapter 1. Setting up OpenCV

This chapter is a quick guide to setting up Python 2.7, OpenCV, and related libraries. After setup, we also look at OpenCV's Python sample scripts and documentation.

The following related libraries are covered:

  • NumPy: This is a dependency of OpenCV's Python bindings. It provides numeric computing functionality, including efficient arrays.

  • SciPy: This is a scientific computing library that is closely related to NumPy. It is not required by OpenCV but it is useful for manipulating the data in OpenCV images.

  • OpenNI: This is an optional dependency of OpenCV. It adds support for certain depth cameras, such as Asus XtionPRO.

  • SensorKinect: This is an OpenNI plugin and optional dependency of OpenCV. It adds support for the Microsoft Kinect depth camera.

For this book's purposes, OpenNI and SensorKinect can be considered optional. They are used throughout Chapter 5, Separating Foreground/Background Regions Depth, but are not used in the other chapters or appendices.

At the time of writing, OpenCV 2.4.3 is the latest version. On some operating systems, it is easier to set up an earlier version (2.3.1). The differences between these versions should not affect the project that we are going to build in this book.

Some additional information, particularly about OpenCV's build options and their dependencies, is available in the OpenCV wiki at http://opencv.willowgarage.com/wiki/InstallGuide. However, at the time of writing, the wiki is not up-to-date with OpenCV 2.4.3.

 

Choosing and using the right setup tools


We are free to choose among various setup tools, depending on our operating system and how much configuration we want to do. Let's take an overview of the tools for Windows, Mac, Ubuntu, and other Unix-like systems.

Making the choice on Windows XP, Windows Vista, Windows 7, or Windows 8

Windows does not come with Python preinstalled. However, installation wizards are available for precompiled Python, NumPy, SciPy, and OpenCV. Alternatively, we can build from source. OpenCV's build system uses CMake for configuration and either Visual Studio or MinGW for compilation.

If we want support for depth cameras including Kinect, we should first install OpenNI and SensorKinect, which are available as precompiled binaries with installation wizards. Then, we must build OpenCV from source.

Note

The precompiled version of OpenCV does not offer support for depth cameras.

On Windows, OpenCV offers better support for 32-bit Python than 64-bit Python. Even if we are building from source, I recommend using 32-bit Python. Fortunately, 32-bit Python works fine on either 32-bit or 64-bit editions of Windows.

Note

Some of the following steps refer to editing the system's Path variable. This task can be done in the Environment Variables window of Control Panel.

On Windows Vista/Windows 7/Windows 8, open the Start menu and launch Control Panel. Now, go to System and Security | System | Advanced system settings. Click on the Environment Variables button.

On Windows XP, open the Start menu and go to Control Panel | System. Select the Advanced tab. Click on the Environment Variables button.

Now, under System variables, select Path and click on the Edit button. Make changes as directed. To apply the changes, click on all the OK buttons (until we are back in the main window of Control Panel). Then, log out and log back in (alternatively, reboot).

Using binary installers (no support for depth cameras)

Here are the steps to set up 32-bit Python 2.7, NumPy, and OpenCV:

  1. Download and install 32-bit Python 2.7.3 from http://www.python.org/ftp/python/2.7.3/python-2.7.3.msi.

  2. Download and install NumPy 1.6.2 from http://sourceforge.net/projects/numpy/files/NumPy/1.6.2/numpy-1.6.2-win32-superpack-python2.7.exe/download.

  3. Download and install SciPy 11.0 from http://sourceforge.net/projects/scipy/files/scipy/0.11.0/scipy-0.11.0-win32-superpack-python2.7.exe/download.

  4. Download the self-extracting ZIP of OpenCV 2.4.3 from http://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.3/OpenCV-2.4.3.exe/download. Run the self-extracting ZIP and, when prompted, enter any destination folder, which we will refer to as <unzip_destination>. A subfolder, <unzip_destination>\opencv, is created.

  5. Copy <unzip_destination>\opencv\build\python\2.7\cv2.pyd to C:\Python2.7\Lib\site-packages (assuming we installed Python 2.7 to the default location). Now, the new Python installation can find OpenCV.

  6. A final step is necessary if we want Python scripts to run using the new Python installation by default. Edit the system's Path variable and append ;C:\Python2.7 (assuming we installed Python 2.7 to the default location). Remove any previous Python paths, such as ;C:\Python2.6. Log out and log back in (alternatively, reboot).

Using CMake and compilers

Windows does not come with any compilers or CMake. We need to install them. If we want support for depth cameras, including Kinect, we also need to install OpenNI and SensorKinect.

Let's assume that we have already installed 32-bit Python 2.7, NumPy, and SciPy either from binaries (as described previously) or from source. Now, we can proceed with installing compilers and CMake, optionally installing OpenNI and SensorKinect, and then building OpenCV from source:

  1. Download and install CMake 2.8.9 from http://www.cmake.org/files/v2.8/cmake-2.8.9-win32-x86.exe. When running the installer, select either Add CMake to the system PATH for all users or Add CMake to the system PATH for current user.

  2. Download and install Microsoft Visual Studio 2010, Microsoft Visual C++ Express 2010, or MinGW. Note that OpenCV 2.4.3 cannot be compiled with the more recent versions (Microsoft Visual Studio 2012 and Microsoft Visual Studio Express 2012).

    For Microsoft Visual Studio 2010, use any installation media you purchased. During installation, include any optional C++ components. Reboot after installation is complete.

    For Microsoft Visual C++ Express 2010, get the installer from http://www.microsoft.com/visualstudio/eng/downloads. Reboot after installation is complete.

    For MinGW get the installer from http://sourceforge.net/projects/mingw/files/Installer/mingw-get-inst/mingw-get-inst-20120426/mingw-get-inst-20120426.exe/download. When running the installer, make sure that the destination path does not contain spaces and that the optional C++ compiler is included. Edit the system's Path variable and append ;C:\MinGW\bin (assuming MinGW is installed to the default location.) Reboot the system.

  3. Optionally, download and install OpenNI 1.5.4.0 from http://www.openni.org/wp-content/uploads/2012/12/OpenNI-Win32-1.5.4.0-Dev1.zip (32 bit). Alternatively, for 64-bit Python, use http://www.openni.org/wp-content/uploads/2012/12/OpenNI-Win64-1.5.4.0-Dev.zip (64 bit).

  4. Optionally, download and install SensorKinect 0.93 from https://github.com/avin2/SensorKinect/blob/unstable/Bin/SensorKinect093-Bin-Win32-v5.1.2.1.msi?raw=true (32 bit). Alternatively, for 64-bit Python, use https://github.com/avin2/SensorKinect/blob/unstable/Bin/SensorKinect093-Bin-Win64-v5.1.2.1.msi?raw=true (64 bit).

  5. Download the self-extracting ZIP of OpenCV 2.4.3 from http://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.3/OpenCV-2.4.3.exe/download. Run the self-extracting ZIP and, when prompted, enter any destination folder, which we will refer to as <unzip_destination>. A subfolder, <unzip_destination>\opencv, is created.

  6. Open Command Prompt and make another folder where our build will go:

    > mkdir<build_folder>
    

    Change directory to the build folder:

    > cd <build_folder>
    
  7. Now, we are ready to configure our build. To understand all the options, we could read the code in <unzip_destination>\opencv\CMakeLists.txt. However, for this book's purposes, we only need to use the options that will give us a release build with Python bindings and, optionally, depth camera support via OpenNI and SensorKinect.

    For Visual Studio 2010 or Visual C++ Express 2010, run:

    > cmake -D:CMAKE_BUILD_TYPE=RELEASE -D:WITH_OPENNI=ON -G "Visual Studio 10" <unzip_destination>\opencv
    

    Alternatively, for MinGW, run:

    > cmake -D:CMAKE_BUILD_TYPE=RELEASE -D:WITH_OPENNI=ON -G "MinGWMakefiles" <unzip_destination>\opencv
    

    If OpenNI is not installed, omit -D:WITH_OPENNI=ON. (In this case, depth cameras will not be supported.) If OpenNI and SensorKinect are installed to non-default locations, modify the command to include -D:OPENNI_LIB_DIR=<openni_install_destination>\Lib -D:OPENNI_INCLUDE_DIR=<openni_install_destination>\Include -D:OPENNI_PRIME_SENSOR_MODULE_BIN_DIR=<sensorkinect_install_destination>\Sensor\Bin.

    CMake might report that it has failed to find some dependencies. Many of OpenCV's dependencies are optional; so, do not be too concerned yet. If the build fails to complete or you run into problems later, try installing missing dependencies (often available as prebuilt binaries) and then rebuild OpenCV from this step.

  8. Having configured our build system, we are ready to compile.

    For Visual Studio or Visual C++ Express, open <build_folder>/OpenCV.sln. Select Release configuration and build. If you get build errors, double-check that Release configuration is selected.

    Alternatively, for MinGW, run:

    > mingw32-make.
    
  9. Copy <build_folder>\lib\Release\cv2.pyd (from a Visual Studio build) or <build_folder>\lib\cv2.pyd (from a MinGW build) to C:\Python2.7\Lib\site-packages (assuming Python 2.7 is installed to the default location). Now, the Python installation can find part of OpenCV.

  10. Finally, we need to make sure that Python and other processes can find the rest of OpenCV. Edit the system's Path variable and append ;<build_folder>/bin/Release (for a Visual Studio build) or ;<build_folder>/bin (for a MinGW build). Reboot your system.

Making the choice on Mac OS X Snow Leopard, Mac OS X Lion, or Mac OS X Mountain Lion

Some versions of Mac come with Python 2.7 preinstalled. However, the preinstalled Python is customized by Apple for the system's internal needs. Normally, we should not install any libraries atop Apple's Python. If we do, our libraries might break during system updates or, worse, might conflict with preinstalled libraries that the system requires. Instead, we should install standard Python 2.7 and then install our libraries atop it.

For Mac, there are several possible approaches to obtaining standard Python 2.7, NumPy, SciPy, and OpenCV. All approaches ultimately require OpenCV to be compiled from source using Xcode Developer Tools. However, depending on the approach, this task is automated for us by third-party tools in various ways. We will look at approaches using MacPorts or Homebrew. These tools can potentially do everything that CMake can do, plus they help us resolve dependencies and separate our development libraries from the system libraries.

Tip

I recommend MacPorts, especially if you want to compile OpenCV with depth camera support via OpenNI and SensorKinect. Relevant patches and build scripts, including some that I maintain, are ready-made for MacPorts. By contrast, Homebrew does not currently provide a ready-made solution for compiling OpenCV with depth camera support.

Before proceeding, let's make sure that the Xcode Developer Tools are properly set up:

  1. Download and install Xcode from the Mac App Store or http://connect.apple.com/. During installation, if there is an option to install Command Line Tools, select it.

  2. Open Xcode and accept the license agreement.

  3. A final step is necessary if the installer did not give us the option to install Command Line Tools. Go to Xcode | Preferences | Downloads and click on the Install button next to Command Line Tools. Wait for the installation to finish and quit Xcode.

Now we have the required compilers for any approach.

Using MacPorts with ready-made packages

We can use the MacPorts package manager to help us set up Python 2.7, NumPy, and OpenCV. MacPorts provides Terminal commands that automate the process of downloading, compiling, and installing various pieces of open source software (OSS). MacPorts also installs dependencies as needed. For each piece of software, the dependencies and build recipe are defined in a configuration file called a Portfile . A MacPorts repository is a collection of Portfiles.

Starting from a system where Xcode and its Command Line Tools are already set up, the following steps will give us an OpenCV installation via MacPorts:

  1. Download and install MacPorts from http://www.macports.org/install.php.

  2. If we want support for the Kinect depth camera, we need to tell MacPorts where to download some custom Portfiles that I have written. To do so, edit /opt/local/etc/macports/sources.conf (assuming MacPorts is installed to the default location). Just above the line rsync://rsync.macports.org/release/ports/ [default], add the following line:

    http://nummist.com/opencv/ports.tar.gz

    Save the file. Now, MacPorts knows to search for Portfiles in my online repository first and, then, the default online repository.

  3. Open Terminal and run the following command to update MacPorts:

    $ sudo port selfupdate
    

    When prompted, enter your password.

  4. Now (if we are using my repository), run the following command to install OpenCV with Python 2.7 bindings and support for depth cameras including Kinect:

    $ sudo port install opencv +python27 +openni_sensorkinect
    

    Alternatively (with or without my repository), run the following command to install OpenCV with Python 2.7 bindings and support for depth cameras excluding Kinect:

    $ sudo port install opencv +python27 +openni
    

    Dependencies, including Python 2.7, NumPy, OpenNI, and (in the first example) SensorKinect, are automatically installed as well.

    By adding +python27 to the command, we are specifying that we want the opencv variant (build configuration) with Python 2.7 bindings. Similarly, +openni_sensorkinect specifies the variant with the broadest possible support for depth cameras via OpenNI and SensorKinect. You may omit +openni_sensorkinect if you do not intend to use depth cameras or you may replace it with +openni if you do intend to use OpenNI-compatible depth cameras but just not Kinect. To see the full list of available variants before installing, we can enter:

    $ port variants opencv
    

    Depending on our customization needs, we can add other variants to the install command. For even more flexibility, we can write our own variants (as described in the next section).

  5. Also, run the following command to install SciPy:

    $ sudo port install py27-scipy
    
  6. The Python installation's executable is named python2.7. If we want to link the default python executable to python2.7, let's also run:

    $ sudo port install python_select
    $ sudo port select python python27
    

Using MacPorts with your own custom packages

With a few extra steps, we can change the way that MacPorts compiles OpenCV or any other piece of software. As previously mentioned, MacPorts' build recipes are defined in configuration files called Portfiles. By creating or editing Portfiles, we can access highly configurable build tools, such as CMake, while also benefitting from MacPorts' features, such as dependency resolution.

Let's assume that we already have MacPorts installed. Now, we can configure MacPorts to use custom Portfiles that we write:

  1. Create a folder somewhere to hold our custom Portfiles. We will refer to this folder as <local_repository>.

  2. Edit the file /opt/local/etc/macports/sources.conf (assuming MacPorts is installed to the default location). Just above the line rsync://rsync.macports.org/release/ports/ [default], add this line:

    file://<local_repository>

    For example, if <local_repository> is /Users/Joe/Portfiles, add:

    file:///Users/Joe/Portfiles

    Note the triple slashes.

    Save the file. Now, MacPorts knows to search for Portfiles in <local_repository> first and, then, its default online repository.

  3. Open Terminal and update MacPorts to ensure that we have the latest Portfiles from the default repository:

    $ sudo port selfupdate
    
  4. Let's copy the default repository's opencv Portfile as an example. We should also copy the directory structure, which determines how the package is categorized by MacPorts.

    $ mkdir <local_repository>/graphics/
    $ cp /opt/local/var/macports/sources/rsync.macports.org/release/ports/graphics/opencv <local_repository>/graphics

    Alternatively, for an example that includes Kinect support, we could download my online repository from http://nummist.com/opencv/ports.tar.gz, unzip it and copy its entire graphics folder into <local_repository>:

    $ cp <unzip_destination>/graphics <local_repository>
  5. Edit <local_repository>/graphics/opencv/Portfile. Note that this file specifies CMake configuration flags, dependencies, and variants. For details on Portfile editing, go to http://guide.macports.org/#development.

    To see which CMake configuration flags are relevant to OpenCV, we need to look at its source code. Download the source code archive from http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.3/OpenCV-2.4.3.tar.bz2/download, unzip it to any location, and read <unzip_destination>/OpenCV-2.4.3/CMakeLists.txt.

    After making any edits to the Portfile, save it.

  6. Now, we need to generate an index file in our local repository so that MacPorts can find the new Portfile:

    $ cd <local_repository>
    $ portindex
    
  7. From now on, we can treat our custom opencv just like any other MacPorts package. For example, we can install it as follows:

    $ sudo port install opencv +python27 +openni_sensorkinect
    

    Note that our local repository's Portfile takes precedence over the default repository's Portfile because of the order in which they are listed in /opt/local/etc/macports/sources.conf.

Using Homebrew with ready-made packages (no support for depth cameras)

Homebrew is another package manager that can help us. Normally, MacPorts and Homebrew should not be installed on the same machine.

Starting from a system where Xcode and its Command Line Tools are already set up, the following steps will give us an OpenCV installation via Homebrew:

  1. Open Terminal and run the following command to install Homebrew:

    $ ruby -e "$(curl -fsSkLraw.github.com/mxcl/homebrew/go)"
    
  2. Unlike MacPorts, Homebrew does not automatically put its executables in PATH. To do so, create or edit the file ~/.profile and add this line at the top:

    export PATH=/usr/local/bin:/usr/local/sbin:$PATH
    

    Save the file and run this command to refresh PATH:

    $ source ~/.profile
    

    Note that executables installed by Homebrew now take precedence over executables installed by the system.

  3. For Homebrew's self-diagnostic report, run:

    $ brew doctor
    

    Follow any troubleshooting advice it gives.

  4. Now, update Homebrew:

    $ brew update
    
  5. Run the following command to install Python 2.7:

    $ brew install python
    
  6. Now, we can install NumPy. Homebrew's selection of Python library packages is limited so we use a separate package management tool called pip , which comes with Homebrew's Python:

    $ pip install numpy
    
  7. SciPy contains some Fortran code, so we need an appropriate compiler. We can use Homebrew to install the gfortran compiler:

    $ brew install gfortran
    

    Now, we can install SciPy:

    $ pip install scipy
    
  8. To install OpenCV on a 64-bit system (all new Mac hardware since late 2006), run:

    $ brew install opencv
    

    Alternatively, to install OpenCV on a 32-bit system, run:

    $ brew install opencv --build32
    

Tip

Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

Using Homebrew with your own custom packages

Homebrew makes it easy to edit existing package definitions:

$ brew edit opencv

The package definitions are actually scripts in the Ruby programming language. Tips on editing them can be found in the Homebrew wiki at https://github.com/mxcl/homebrew/wiki/Formula-Cookbook. A script may specify Make or CMake configuration flags, among other things.

To see which CMake configuration flags are relevant to OpenCV, we need to look at its source code. Download the source code archive from http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.3/OpenCV-2.4.3.tar.bz2/download, unzip it to any location, and read <unzip_destination>/OpenCV-2.4.3/CMakeLists.txt.

After making any edits to the Ruby script, save it.

The customized package can be treated as normal. For example, it can be installed as follows:

$ brew install opencv

Making the choice on Ubuntu 12.04 LTS or Ubuntu 12.10

Ubuntu comes with Python 2.7 preinstalled. The standard Ubuntu repository contains OpenCV 2.3.1 packages without support for depth cameras. Alternatively, OpenCV 2.4.3 can be built from source using CMake and GCC. When built from source, OpenCV can support depth cameras via OpenNI and SensorKinect, which are available as precompiled binaries with installation scripts.

Using the Ubuntu repository (no support for depth cameras)

We can install OpenCV 2.3.1 and its dependencies using the Apt package manager:

  1. Open Terminal and run this command to update Apt:

    $ sudo apt-get update
    
  2. Now, run these commands to install NumPy, SciPy, and OpenCV with Python bindings:

    $ sudo apt-get install python-numpy
    $ sudo apt-get install python-scipy
    $ sudo apt-get install libopencv-*
    $ sudo apt-get install python-opencv
    

    Enter Y whenever prompted about package installation.

Equivalently, we could have used Ubuntu Software Center, which is Apt's graphical frontend.

Using CMake via a ready-made script that you may customize

Ubuntu comes with the GCC compilers preinstalled. However, we need to install the CMake build system. We also need to install or reinstall various other libraries, some of which need to be specially configured for compatibility with OpenCV. Because the dependencies are complex, I have written a script that downloads, configures, and builds OpenCV and related libraries so that the resulting OpenCV installation has support for depth cameras including Kinect:

  1. Download my installation script from http://nummist.com/opencv/install_opencv_ubuntu.sh and put it in any destination, say <script_folder>.

  2. Optionally, edit the script to customize OpenCV's build configuration. To see which CMake configuration flags are relevant to OpenCV, we need to look at its source code. Download the source code archive from http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.3/OpenCV-2.4.3.tar.bz2/download, unzip it to any location, and read <unzip_destination>/OpenCV-2.4.3/CMakeLists.txt.

    After making any edits to the script, save it.

  3. Open Terminal and run this command to update Apt:

    $ sudo apt-get update
    
  4. Change directory to <script_folder>:

    $ cd <script_folder>
    

    Set the script's permissions so that it is executable:

    $ chmod +x install_opencv_ubuntu.sh
    

    Execute the script:

    $ ./install_opencv_ubuntu.sh
    

    When prompted, enter your password. Enter Y whenever prompted about package installation.

  5. The installation script creates a folder, <script_folder>/opencv, which contains downloads and built files that are temporarily used by the script. After the installation script terminates, <script_folder>/opencv may safely be deleted; although, first, you might want to look at OpenCV's Python samples in <script_folder>/opencv/samples/python and <script_folder>/opencv/samples/python2.

Making the choice on other Unix-like systems

The approaches for Ubuntu (as described previously) are likely to work on any Linux distribution derived from Ubuntu 12.04 LTS or Ubuntu 12.10, such as:

  • Kubuntu 12.04 LTS or Kubuntu 12.10

  • Xubuntu 12.04 LTS or Xubuntu 12.10

  • Linux Mint 13 or Linux Mint 14

On Debian Linux and its derivatives, the Apt package manager works the same as on Ubuntu, though the available packages may differ.

On Gentoo Linux and its derivatives, the Portage package manager is similar to MacPorts (as described previously), though the available packages may differ.

On other Unix-like systems, the package manager and available packages may differ. Consult your package manager's documentation and search for any packages with opencv in their names. Remember that OpenCV and its Python bindings might be split into multiple packages.

Also, look for any installation notes published by the system provider, the repository maintainer, or the community. Because OpenCV uses camera drivers and media codecs, getting all of its functionality to work can be tricky on systems with poor multimedia support. Under some circumstances, system packages might need to be reconfigured or reinstalled for compatibility.

If packages are available for OpenCV, check their version number. OpenCV 2.3.1 or greater is recommended for this book's purposes. Also check whether the packages offer Python bindings and whether they offer depth camera support via OpenNI and SensorKinect. Finally, check whether anyone in the developer community has reported success or failure in using the packages.

If instead we want to do a custom build of OpenCV from source, it might be helpful to refer to the installation script for Ubuntu (discussed previously) and adapt it to the package manager and packages that are present on another system.

 

Running samples


Running a few sample scripts is a good way to test that OpenCV is correctly set up. The samples are included in OpenCV's source code archive.

On Windows, we should have already downloaded and unzipped OpenCV's self-extracting ZIP. Find the samples in <unzip_destination>/opencv/samples.

On Unix-like systems, including Mac, download the source code archive from http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.3/OpenCV-2.4.3.tar.bz2/download and unzip it to any location (if we have not already done so). Find the samples in <unzip_destination>/OpenCV-2.4.3/samples.

Some of the sample scripts require command-line arguments. However, the following scripts (among others) should work without any arguments:

  • python/camera.py: This displays a webcam feed (assuming a webcam is plugged in).

  • python/drawing.py: This draws a series of shapes, like a screensaver.

  • python2/hist.py: This displays a photo. Press A, B, C, D, or E to see variations of the photo, along with a corresponding histogram of color or grayscale values.

  • python2/opt_flow.py (missing from the Ubuntu package): This displays a webcam feed with a superimposed visualization of optical flow (direction of motion). For example, slowly wave your hand at the webcam to see the effect. Press 1 or 2 for alternative visualizations.

To exit a script, press Esc (not the window's close button).

If we encounter the message, ImportError: No module named cv2.cv, then we are running the script from a Python installation that does not know anything about OpenCV. There are two possible explanations:

  • Some steps in the OpenCV installation might have failed or been missed. Go back and review the steps.

  • If we have multiple Python installations on the machine, we might be using the wrong Python to launch the script. For example, on Mac, it might be the case that OpenCV is installed for MacPorts Python but we are running the script with the system's Python. Go back and review the installation steps about editing the system path. Also, try launching the script manually from the command line using commands such as:

    $ python python/camera.py
    

    You can also use the following command:

    $ python2.7 python/camera.py
    

    As another possible means of selecting a different Python installation, try editing the sample script to remove #! lines. These lines might explicitly associate the script with the wrong Python installation (for our particular setup).

 

Finding documentation, help, and updates


OpenCV's documentation is online at http://docs.opencv.org/. The documentation includes a combined API reference for OpenCV's new C++ API, its new Python API (which is based on the C++ API), its old C API, and its old Python API (which is based on the C API). When looking up a class or function, be sure to read the section about the new Python API (cv2 module), not the old Python API (cv module).

Note

The documentation entitled OpenCV 2.1 Python Reference (http://opencv.willowgarage.com/documentation/python/) might show up in Google searches for OpenCV Python API. Avoid this documentation, since it is out-of-date and covers only the old (C-like) Python API.

The documentation is also available as several downloadable PDF files:

If you write code on airplanes or other places without Internet access, you will definitely want to keep offline copies of the documentation.

If the documentation does not seem to answer your question, try talking to the OpenCV community. Here are some sites where you will find helpful people:

Last, if you are an advanced user who wants to try new features, bug-fixes, and sample scripts from the latest (unstable) OpenCV source code, have a look at the project's repository at https://github.com/Itseez/opencv/.

 

Summary


By now, we should have an OpenCV installation that can do everything we need for the project described in this book. Depending on which approach we took, we might also have a set of tools and scripts that are usable to reconfigure and rebuild OpenCV for our future needs.

We know where to find OpenCV's Python samples. These samples cover a different range of functionality than this book's project, but they are useful as additional learning aids.

About the Author

  • Joseph Howse

    Joseph Howse lives in a Canadian fishing village with four cats; the cats like fish, but they prefer chicken.

    Joseph provides computer vision expertise through his company, Nummist Media. His books include OpenCV 4 for Secret Agents, OpenCV 3 Blueprints, Android Application Programming with OpenCV 3, iOS Application Development with OpenCV 3, Learning OpenCV 3 Computer Vision with Python, and Python Game Programming by Example, published by Packt.

    Browse publications by this author

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it is perfect... the ebook is perfect for my students reference...
The book is well structured and easy to understand. The overall format also allows for easy reading of figures and code segments.
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