OpenCV  4.2.0
Open Source Computer Vision
Modules
Here is a list of all modules:
[detail level 1234]
 CUDA-accelerated Computer Vision
 Core functionality
 Image ProcessingThis module includes image-processing functions
 Image file reading and writing
 Video I/ORead and write video or images sequence with OpenCV
 High-level GUIWhile OpenCV was designed for use in full-scale applications and can be used within functionally rich UI frameworks (such as Qt*, WinForms*, or Cocoa*) or without any UI at all, sometimes there it is required to try functionality quickly and visualize the results. This is what the HighGUI module has been designed for
 Video Analysis
 Camera Calibration and 3D ReconstructionThe functions in this section use a so-called pinhole camera model. In this model, a scene view is formed by projecting 3D points into the image plane using a perspective transformation
 2D Features Framework
 Object Detection
 Deep Neural Network moduleThis module contains:
 Machine LearningThe Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data
 Clustering and Search in Multi-Dimensional SpacesThis section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional features. More information about FLANN can be found in [Muja2009]
 Computational PhotographyThis module includes photo processing algorithms
 Images stitchingThis figure illustrates the stitching module pipeline implemented in the Stitcher class. Using that class it's possible to configure/remove some steps, i.e. adjust the stitching pipeline according to the particular needs. All building blocks from the pipeline are available in the detail namespace, one can combine and use them separately
 G-API Core functionality
 G-API Image processing functionality
 G-API Drawing and composition functionalityFunctions for in-graph drawing
 G-API framework
 ArUco Marker DetectionThis module is dedicated to square fiducial markers (also known as Augmented Reality Markers) These markers are useful for easy, fast and robust camera pose estimation.รง
 Improved Background-Foreground Segmentation Methods
 Biologically inspired vision models and derivated toolsThe module provides biological visual systems models (human visual system and others). It also provides derivated objects that take advantage of those bio-inspired models
 Custom Calibration Pattern for 3D reconstruction
 3D object recognition and pose estimation APIAs CNN based learning algorithm shows better performance on the classification issues, the rich labeled data could be more useful in the training stage. 3D object classification and pose estimation is a jointed mission aiming at separate different posed apart in the descriptor form
 GUI for Interactive Visual Debugging of Computer Vision ProgramsNamespace for all functions is cvv, i.e. cvv::showImage()
 Framework for working with different datasetsThe datasets module includes classes for working with different datasets: load data, evaluate different algorithms on them, contains benchmarks, etc
 DNN used for object detection
 DNN used for super resolutionThis module contains functionality for upscaling an image via convolutional neural networks. The following four models are implemented:
 Deformable Part-based Models
 Face Analysis
 Drawing UTF-8 strings with freetype/harfbuzzThis modules is to draw UTF-8 strings with freetype/harfbuzz
 Image processing based on fuzzy mathematicsNamespace for all functions is ft. The module brings implementation of the last image processing algorithms based on fuzzy mathematics. Method are named based on the pattern FT_degree_dimension_method
 Hierarchical Data Format I/O routinesThis module provides storage routines for Hierarchical Data Format objects
 Hierarchical Feature Selection for Efficient Image SegmentationThe opencv hfs module contains an efficient algorithm to segment an image. This module is implemented based on the paper Hierarchical Feature Selection for Efficient Image Segmentation, ECCV 2016. The original project was developed by Yun Liu(https://github.com/yun-liu/hfs)
 The module brings implementations of different image hashing algorithms.Provide algorithms to extract the hash of images and fast way to figure out most similar images in huge data set
 Binary descriptors for lines extracted from an image
 Optical Flow AlgorithmsDense optical flow algorithms compute motion for each point:
 OGRE 3D Visualiser
 Phase Unwrapping APITwo-dimensional phase unwrapping is found in different applications like terrain elevation estimation in synthetic aperture radar (SAR), field mapping in magnetic resonance imaging or as a way of finding corresponding pixels in structured light reconstruction with sinusoidal patterns
 Plot function for Mat data
 Image Quality Analysis (IQA) API
 Image RegistrationThe Registration module implements parametric image registration. The implemented method is direct alignment, that is, it uses directly the pixel values for calculating the registration between a pair of images, as opposed to feature-based registration. The implementation follows essentially the corresponding part of [Szeliski06]
 RGB-Depth Processing
 Saliency APIMany computer vision applications may benefit from understanding where humans focus given a scene. Other than cognitively understanding the way human perceive images and scenes, finding salient regions and objects in the images helps various tasks such as speeding up object detection, object recognition, object tracking and content-aware image editing
 Structure From MotionThe opencv_sfm module contains algorithms to perform 3d reconstruction from 2d images.
The core of the module is based on a light version of Libmv originally developed by Sameer Agarwal and Keir Mierle
 Shape Distance and Matching
 Stereo Correspondance Algorithms
 Structured Light APIStructured light is considered one of the most effective techniques to acquire 3D models. This technique is based on projecting a light pattern and capturing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be quickly found and 3D information easily retrieved
 Super ResolutionThe Super Resolution module contains a set of functions and classes that can be used to solve the problem of resolution enhancement. There are a few methods implemented, most of them are described in the papers [Farsiu03] and [Mitzel09]
 Surface Matching
 Scene Text Detection and RecognitionThe opencv_text module provides different algorithms for text detection and recognition in natural scene images
 Tracking API
 Video StabilizationThe video stabilization module contains a set of functions and classes that can be used to solve the problem of video stabilization. There are a few methods implemented, most of them are described in the papers [OF06] and [G11] . However, there are some extensions and deviations from the original paper methods
 3D VisualizerThis section describes 3D visualization window as well as classes and methods that are used to interact with it
 Extended Image Processing
 Extended object detection
 Additional photo processing algorithms
 Imgproc_hal_functions
 Imgproc_hal_interface
 Highgui_winrt
 F1_math
 F_image
 Kinect_fusion
 Simple_pipeline
 Text_recognize
 Videostab_marching
 Ximgproc_filters
 Ximgproc_edgeboxes
 Ximgproc_fast_line_detector
 Ximgproc_fourier
 Ximgproc_superpixel
 Ximgproc_run_length_morphology
 Ximgproc_segmentation