OpenCV  3.4.20-dev
Open Source Computer Vision
Transition guide

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Changes overview

This document is intended to software developers who want to migrate their code to OpenCV 3.0.

OpenCV 3.0 introduced many new algorithms and features comparing to version 2.4. Some modules have been rewritten, some have been reorganized. Although most of the algorithms from 2.4 are still present, the interfaces can differ.

This section describes most notable changes in general, all details and examples of transition actions are in the next part of the document.

Contrib repository

This is a place for all new, experimental and non-free algorithms. It does not receive so much attention from the support team comparing to main repository, but the community makes an effort to keep it in a good shape.

To build OpenCV with contrib repository, add the following option to your cmake command:

Headers layout

In 2.4 all headers are located in corresponding module subfolder (opencv2/<module>/<module>.hpp), in 3.0 there are top-level module headers containing the most of the module functionality: opencv2/<module>.hpp and all C-style API definitions have been moved to separate headers (for example opencv2/core/core_c.h).

Algorithm interfaces

General algorithm usage pattern has changed: now it must be created on heap wrapped in smart pointer cv::Ptr. Version 2.4 allowed both stack and heap allocations, directly or via smart pointer.

get and set methods have been removed from the cv::Algorithm class along with CV_INIT_ALGORITHM macro. In 3.0 all properties have been converted to the pairs of getProperty/setProperty pure virtual methods. As a result it is not possible to create and use cv::Algorithm instance by name (using generic Algorithm::create(String) method), one should call corresponding factory method explicitly.

Changed modules

Transition hints

This section describes concrete actions with examples.

Prepare 2.4

Some changes made in the latest 2.4.11 OpenCV version allow you to prepare current codebase to migration:

New headers layout

Note: Changes intended to ease the migration have been made in OpenCV 3.0, thus the following instructions are not necessary, but recommended.

  1. Replace inclusions of old module headers:
    // old header
    #include "opencv2/<module>/<module>.hpp"
    // new header
    #include "opencv2/<module>.hpp"
  2. If your code is using C API (cv* functions, Cv* structures or CV_* enumerations), include corresponding *_c.h headers. Although it is recommended to use C++ API, most of C-functions are still accessible in separate header files (opencv2/core/core_c.h, opencv2/core/types_c.h, opencv2/imgproc/imgproc_c.h, etc.).

Modern way to use algorithm

  1. Algorithm instances must be created with cv::makePtr function or corresponding static factory method if available:
    // good ways
    Ptr<SomeAlgo> algo = makePtr<SomeAlgo>(...);
    Ptr<SomeAlgo> algo = SomeAlgo::create(...);
    Other ways are deprecated:
    // bad ways
    Ptr<SomeAlgo> algo = new SomeAlgo(...);
    SomeAlgo * algo = new SomeAlgo(...);
    SomeAlgo algo(...);
    Ptr<SomeAlgo> algo = Algorithm::create<SomeAlgo>("name");
  2. Algorithm properties should be accessed via corresponding virtual methods, getSomeProperty/setSomeProperty, generic get/set methods have been removed:
    // good way
    double clipLimit = clahe->getClipLimit();
    // bad way
    double clipLimit = clahe->getDouble("clipLimit");
    clahe->set("clipLimit", clipLimit);
    clahe->setDouble("clipLimit", clipLimit);
  3. Remove initModule_<moduleName>() calls

Machine learning module

Since this module has been rewritten, it will take some effort to adapt your software to it. All algorithms are located in separate ml namespace along with their base class StatModel. Separate SomeAlgoParams classes have been replaced with a sets of corresponding getProperty/setProperty methods.

The following table illustrates correspondence between 2.4 and 3.0 machine learning classes.

2.4 3.0
CvStatModel cv::ml::StatModel
CvNormalBayesClassifier cv::ml::NormalBayesClassifier
CvKNearest cv::ml::KNearest
CvSVM cv::ml::SVM
CvDTree cv::ml::DTrees
CvBoost cv::ml::Boost
CvGBTrees Not implemented
CvRTrees cv::ml::RTrees
CvERTrees Not implemented
EM cv::ml::EM
Not implemented cv::ml::LogisticRegression
CvMLData cv::ml::TrainData

Although rewritten ml algorithms in 3.0 allow you to load old trained models from xml/yml file, deviations in prediction process are possible.

The following code snippets from the points_classifier.cpp example illustrate differences in model training process:

using namespace cv;
// ======== version 2.4 ========
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
CvBoost boost;
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
CvBoostParams params( CvBoost::DISCRETE, // boost_type
100, // weak_count
0.95, // weight_trim_rate
2, // max_depth
false, //use_surrogates
0 // priors
boost.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
// ======== version 3.0 ========
Ptr<Boost> boost = Boost::create();
boost->train(prepare_train_data()); // 'prepare_train_data' returns an instance of ml::TrainData class

Features detect

Some algorithms (FREAK, BRIEF, SIFT, SURF) has been moved to opencv_contrib repository, to xfeatures2d module, xfeatures2d namespace. Their interface has been also changed (inherit from cv::Feature2D base class).

List of xfeatures2d module classes:

Following steps are needed:

  1. Add opencv_contrib to compilation process
  2. Include opencv2/xfeatures2d.h header
  3. Use namespace xfeatures2d
  4. Replace operator() calls with detect, compute or detectAndCompute if needed

Some classes now use general methods detect, compute or detectAndCompute provided by Feature2D base class instead of custom operator()

Following code snippets illustrate the difference (from video_homography.cpp example):

using namespace cv;
// ====== 2.4 =======
BriefDescriptorExtractor brief(32);
GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
// ...
detector.detect(gray, query_kpts); //Find interest points
brief.compute(gray, query_kpts, query_desc); //Compute brief descriptors at each keypoint location
// ====== 3.0 =======
using namespace cv::xfeatures2d;
// ...
detector->detect(gray, query_kpts); //Find interest points
brief->compute(gray, query_kpts, query_desc); //Compute brief descriptors at each keypoint location


All specialized ocl implementations has been hidden behind general C++ algorithm interface. Now the function execution path can be selected dynamically at runtime: CPU or OpenCL; this mechanism is also called "Transparent API".

New class cv::UMat is intended to hide data exchange with OpenCL device in a convenient way.

Following example illustrate API modifications (from OpenCV site):


cuda module has been split into several smaller pieces:

gpu namespace has been removed, use cv::cuda namespace instead. Many classes has also been renamed, for example:

Documentation format

Documentation has been converted to Doxygen format. You can find updated documentation writing guide in Tutorials section of OpenCV reference documentation (Writing documentation for OpenCV).

Support both versions

In some cases it is possible to support both versions of OpenCV.

Source code

To check library major version in your application source code, the following method should be used:

// do opencv 2 code
// do opencv 3 code
Do not use CV_VERSION_MAJOR, it has different meaning for 2.4 and 3.x branches!

Build system

It is possible to link different modules or enable/disable some of the features in your application by checking library version in the build system. Standard cmake or pkg-config variables can be used for this:


# use 2.4 modules
# use 3.x modules