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Collection and a development kit of matlab mex functions for OpenCV library

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README.markdown

mexopencv

Collection and a development kit of matlab mex functions for OpenCV library

The package provides matlab mex functions that interface a hundred of OpenCV APIs. Also the package contains C++ class that converts between Matlab's native data type and OpenCV data types. The package is suitable for fast prototyping of OpenCV application in Matlab, use of OpenCV as an external toolbox in Matlab, and development of an original mex function.

Contents

The project tree is organized as follows.

+cv/            directory to put compiled mex files, wrappers, or help files
Doxyfile        config file for doxygen
Makefile        make script
README.markdown this file
doc/            directory for documentation
include/        header files
lib/            directory for compiled c++ library files
samples/        directory for sample application codes
src/            directory for c++ source files
src/+cv/        directory for mex source files
src/+cv/private directory for private mex source files
test/           directory for test scripts and resources
utils/          directory for utilities

Compile

Prerequisite:

  • Unix: matlab, opencv (>=2.3.1), g++, make, pkg-config
  • Windows: matlab, opencv (>=2.3.1), supported compiler

Note: opencv (>=2.1.0) is partially supported.

Unix

First make sure you have OpenCV installed in the system. If not, install the package available in your package manager (e.g., libopencv-dev in Debian/Ubuntu, opencv-devel in Fedora, opencv in Macports), or install the source package from http://opencv.willowgarage.com/wiki/ . Make sure pkg-config command can identify opencv path. If you have all the prerequisite, go to the mexopencv directory and type:

$ make

This will build and place all mex functions inside +cv/. Specify your matlab directory if you install matlab other than /usr/local/matlab

$ make MATLABDIR=/Applications/MATLAB_R2012a.app

Optionally you can test the library functionality

$ make test

If matlab says 'Library not loaded' or any other error in the test, it's likely the compatibility issue between a system library and matlab's internal library. You might be able to fix this issue by preloading the library file. On linux, set the correct library path in LD_PRELOAD environmental variable. For example, if you see GLIBCXX_3.4.15 error in mex, use the following to start matlab.

$ LD_PRELOAD=/usr/lib/libstdc++.so.6 matlab

Note that you need to find the correct path to the shared object. For example, /usr/lib64/ instead of /usr/lib/. You can use locate command to find the location of the shared object.

On Mac OS X, this variable is named DYLD_INSERT_LIBRARIES. You can check ldd command line tool to check the dependency of the mex file in linux. On mac, you can use otool -L command.

Developer documentation can be generated with doxygen if installed

$ make doc

This will create html and latex files under doc/.

Windows

Make sure you have OpenCV installed in the system and correctly set up Path system variable. e.g., c:\opencv\build\x86\vc10\bin. See http://opencv.willowgarage.com/wiki/ for the instruction. Also make sure you install a compiler supported by Matlab. See http://www.mathworks.com/support/sysreq/previous_releases.html for the list of supported compilers for different versions of Matlab. Windows 64-bit users need to install Windows SDK.

Once you satisfy the above requirement, in the matlab shell, type

>> cv.make

to build all mex functions. By default, mexopencv assumes the OpenCV library is installed in C:\opencv. If this is not the case, specify the path as an argument.

>> cv.make('opencv_path', 'c:\your\path\to\opencv')

Note that if you build OpenCV from source, this path specification does not work. You need to replace dll files in the OpenCV package with newly built binaries. Or, you need to modify +cv/make.m to correctly link your mex files with the library.

To remove existing mexopencv binaries, use the following command.

>> cv.make('clean')

Missing stdint.h in Visual Studio 2008

Visual Studio 2008 or earlier does not comply C99 standard and lacks stdint.h header file. Luckily, the header file is available on the Web. For example, http://msinttypes.googlecode.com/svn/trunk/stdint.h

Place this file under include directory in the mexopencv package.

Error: Invalid MEX file or Segmentation fault

The OpenCV windows package contains c++ binary files compiled with _SECURE_SCL=1 flag, but mex command in Matlab does not use this option by default, which results in Invalid MEX file or segmentation fault on execution. The current version of cv.make script adds _SECURE_SCL=1 flag in the build command and should have no problem with the distributed binary package.

If you see Invalid MEX file or segmentation fault with manually built OpenCV dll's, first make sure you compile OpenCV with the same _SECURE_SCL flag to the mex command. The default mex configuration, which is created with the mex -setup command in matlab, is located in the following path in recent versions of Windows.

C:\Users\(Username)\AppData\Roaming\MathWorks\MATLAB\(version)\mexopts.bat

Open this file and edit /D_SECURE_SCL option.

If you see Invalid MEX file error even when having the matched _SECURE_SCL flag, it probably indicates some other compatibility issues. Please file a bug report at http://github.com/kyamagu/mexopencv .

Usage

Once mex functions are compiled, you can add path to the project directory and call mex functions within matlab using package name cv.

addpath('/path/to/mexopencv');
result = cv.filter2D(img, kern);  % with package name 'cv'
import cv.*;
result = filter2D(img, kern);     % no need to specify 'cv' after imported

Note that some functions such as cv.imread overload Matlab's builtin function when imported. Use the scoped name when you need to avoid name collision. It is also possible to import individual functions. Check help import in matlab.

Check a list of functions available by help command in matlab.

>> help cv; % shows list of functions in package 'cv'

Contents of cv:

GaussianBlur                   - Smoothes an image using a Gaussian filter
Laplacian                      - Calculates the Laplacian of an image
VideoCapture                   - VideoCapture wrapper class
...

>> help cv.VideoCapture; % shows documentation of VideoCapture

VIDEOCAPTURE  VideoCapture wrapper class

 Class for video capturing from video files or cameras. The class
 provides Matlab API for capturing video from cameras or for reading
 video files. Here is how the class can be used:
...

Look at the samples/ directory for an example of an application.

Also mexopencv includes a simple documentation utility that generates HTML help files for matlab. The following command creates a user documentation under doc/matlab/ directory.

addpath('utils');
MDoc;

Online documentation is available at http://www.cs.stonybrook.edu/~kyamagu/mexopencv

You can test the functionality of compiled files by UnitTest class located inside test directory.

addpath('test');
UnitTest;

Developing a new mex function

All you need to do is to add your mex source file in src/+cv/. If you want to add a mex function called myfunc, create src/+cv/myfunc.cpp. The minimum contents of the myfunc.cpp would look like this:

#include "mexopencv.hpp"
void mexFunction( int nlhs, mxArray *plhs[],
                  int nrhs, const mxArray *prhs[] )
{
    // Check arguments
    if (nlhs!=1 || nrhs!=1)
        mexErrMsgIdAndTxt("myfunc:invalidArgs", "Wrong number of arguments");

    // Convert MxArray to cv::Mat
    cv::Mat mat = MxArray(prhs[0]).toMat();

    // Do whatever you want

    // Convert cv::Mat back to mxArray*
    plhs[0] = MxArray(mat);
}

This example simply copies an input to cv::Mat object and then copies again to the output. Notice how the MxArray class provided by mexopencv converts mxArray to cv::Mat object. Of course you would want to do something more with the object. Once you create a file, type make to build your new function. The compiled mex function will be located inside +cv/ and accessible through cv.myfunc within matlab.

The mexopencv.hpp header includes a class MxArray to manipulate mxArray object. Mostly this class is used to convert between opencv data types and mxArray.

int i            = MxArray(prhs[0]).toInt();
double d         = MxArray(prhs[0]).toDouble();
bool b           = MxArray(prhs[0]).toBool();
std::string s    = MxArray(prhs[0]).toString();
cv::Mat mat      = MxArray(prhs[0]).toMat();   // For pixels
cv::Mat ndmat    = MxArray(prhs[0]).toMatND(); // For N-D array
cv::Point pt     = MxArray(prhs[0]).toPoint();
cv::Size siz     = MxArray(prhs[0]).toSize();
cv::Rect rct     = MxArray(prhs[0]).toRect();
cv::Scalar sc    = MxArray(prhs[0]).toScalar();
cv::SparseMat sp = MxArray(prhs[0]).toSparseMat(); // Only double to float

mxArray* plhs[0] = MxArray(i);
mxArray* plhs[0] = MxArray(d);
mxArray* plhs[0] = MxArray(b);
mxArray* plhs[0] = MxArray(s);
mxArray* plhs[0] = MxArray(mat);
mxArray* plhs[0] = MxArray(ndmat);
mxArray* plhs[0] = MxArray(pt);
mxArray* plhs[0] = MxArray(siz);
mxArray* plhs[0] = MxArray(rct);
mxArray* plhs[0] = MxArray(sc);
mxArray* plhs[0] = MxArray(sp); // Only 2D float to double

Check MxArraay.hpp for a complete list of the conversion API.

If you rather want to develop a matlab function that internally calls a mex function, make use of the +cv/private directory. Any function placed under private directory is only accessible from +cv/ directory. So, for example, when you want to design a matlab class that wraps the various behavior of the mex function, define your class at +cv/MyClass.m and develop a mex function dedicated for that class in src/+cv/private/MyClass_.cpp. Inside of +cv/MyClass.m, you can call MyClass_() without cv namescope.

Testing

You can optionally add a testing script for your new function. The testing convention in mexopencv is that testing scripts are all written as a static function in a matlab class. For example, test/unit_tests/TestFilter2D.m is a class that describes test cases for filter2d function. Inside of the class, a couple of test cases are written as a static function whose name starts with 'test'.

If there is such a class inside test/unit_tests/, typing make test would invoke all test cases and show your result. Use test/ directory to place any resource file necessary for testing. An example of testing class is shown below:

classdef TestMyFunc
    methods (Static)
        function test_1
            src = imread('/path/to/myimg');
            ref = [1,2,3];                  % reference output
            dst = cv.myfunc(src);           % execute your function
            assert(all(dst(:) == ref(:)));  % check the output
        end

        function test_error_1
            try
                cv.myfunc('foo');           % myfunc should throw an error
                error('UnitTest:Fail','myfunc incorrectly returned');
            catch e
                assert(strcmp(e.identifier,'mexopencv:error'));
            end
        end
    end
end

In Windows, add path to the test directory and invoke UnitTest to run all the test routines.

Documenting

You can create a Matlab help documentation for mex function by having the same file with '.m' extension. For example, a help file for filter2D.mex* would be filter2D.m. Inside the help file should be only matlab comments. An example is shown below:

%MYFUNC  brief description about myfunc
%
% Detailed description of function continues
% ...

License

The code may be redistributed under BSD license.

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