opencv_contrib/modules

An overview of the opencv_contrib modules

This list gives an overview of all modules available inside the contrib repository. To turn off building one of these module repositories, set the names in bold below to

$ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<reponame>=OFF <opencv_source_directory>

• aruco: ArUco and ChArUco Markers -- Augmented reality ArUco marker and "ChARUco" markers where ArUco markers embedded inside the white areas of the checker board.

• bgsegm: Background segmentation algorithm combining statistical background image estimation and per-pixel Bayesian segmentation.

• bioinspired: Biological Vision -- Biologically inspired vision model: minimize noise and luminance variance, transient event segmentation, high dynamic range tone mapping methods.

• ccalib: Custom Calibration -- Patterns for 3D reconstruction, omnidirectional camera calibration, random pattern calibration and multi-camera calibration.

• cnn_3dobj: Deep Object Recognition and Pose -- Uses Caffe Deep Neural Net library to build, train and test a CNN model of visual object recognition and pose.

• contrib_world: opencv_contrib holder -- contrib_world is the module that when built, contains all other opencv_contrib modules. It may be used for the more convenient redistribution of opencv binaries.

• cvv: Computer Vision Debugger -- Simple code that you can add to your program that pops up a GUI allowing you to interactively and visually debug computer vision programs.

• datasets: Datasets Reader -- Code for reading existing computer vision databases and samples of using the readers to train, test and run using that dataset's data.

• dnn: Deep Neural Networks (DNNs) -- This module can read in image recogniton networks trained in the Caffe neural netowrk library and run them efficiently on CPU.

• dnns_easily_fooled: Subvert DNNs -- This code can use the activations in a network to fool the networks into recognizing something else.

• dpm: Deformable Part Model -- Felzenszwalb's Cascade with deformable parts object recognition code.

• face: Face Recognition -- Face recognition techniques: Eigen, Fisher and Local Binary Pattern Histograms LBPH methods.

• fuzzy: Fuzzy Logic in Vision -- Fuzzy logic image transform and inverse; Fuzzy image processing.

• freetype: Drawing text using freetype and harfbuzz.

• hdf: Hierarchical Data Storage -- This module contains I/O routines for Hierarchical Data Format: https://en.m.wikipedia.org/wiki/Hierarchical_Data_Format meant to store large amounts of data.

• line_descriptor: Line Segment Extract and Match -- Methods of extracting, describing and latching line segments using binary descriptors.

• matlab: Matlab Interface -- OpenCV Matlab Mex wrapper code generator for certain opencv core modules.

• optflow: Optical Flow -- Algorithms for running and evaluating deepflow, simpleflow, sparsetodenseflow and motion templates (silhouette flow).

• plot: Plotting -- The plot module allows you to easily plot data in 1D or 2D.

• reg: Image Registration -- Pixels based image registration for precise alignment. Follows the paper "Image Alignment and Stitching: A Tutorial", by Richard Szeliski.

• rgbd: RGB-Depth Processing module -- Linemod 3D object recognition; Fast surface normals and 3D plane finding. 3D visual odometry

• saliency: Saliency API -- Where humans would look in a scene. Has routines for static, motion and "objectness" saliency.

• sfm: Structure from Motion -- This module contains algorithms to perform 3d reconstruction from 2d images. The core of the module is a light version of Libmv.

• stereo: Stereo Correspondence -- Stereo matching done with different descriptors: Census / CS-Census / MCT / BRIEF / MV.

• structured_light: Structured Light Use -- How to generate and project gray code patterns and use them to find dense depth in a scene.

• surface_matching: Point Pair Features -- Implements 3d object detection and localization using multimodal point pair features.

• text: Visual Text Matching -- In a visual scene, detect text, segment words and recognise the text.

• tracking: Vision Based Object Tracking -- Use and/or evaluate one of 5 different visual object tracking techniques.

• xfeatures2d: Features2D extra -- Extra 2D Features Framework containing experimental and non-free 2D feature detector/descriptor algorithms. SURF, SIFT, BRIEF, Censure, Freak, LUCID, Daisy, Self-similar.

• ximgproc: Extended Image Processing -- Structured Forests / Domain Transform Filter / Guided Filter / Adaptive Manifold Filter / Joint Bilateral Filter / Superpixels.

• xobjdetect: Boosted 2D Object Detection -- Uses a Waldboost cascade and local binary patterns computed as integral features for 2D object detection.

• xphoto: Extra Computational Photography -- Additional photo processing algorithms: Color balance / Denoising / Inpainting.