All Classes and Interfaces
Class
Description
Class for implementing the wrapper which makes detectors and extractors to be affine invariant,
described as ASIFT in CITE: YM11 .
Wrapping class for feature detection using the AGAST method.
Class implementing the AKAZE keypoint detector and descriptor extractor, described in CITE: ANB13.
This is a base class for all more or less complex algorithms in OpenCV
especially for classes of algorithms, for which there can be multiple implementations.
The base class for algorithms that align images of the same scene with different exposures
This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
Represents an animation with multiple frames.
Artificial Neural Networks - Multi-Layer Perceptrons.
The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method.
Base class for background/foreground segmentation.
K-nearest neighbours - based Background/Foreground Segmentation Algorithm.
Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
Brute-force descriptor matcher.
Board of ArUco markers
A board is a set of markers in the 3D space with a common coordinate system.
Boosted tree classifier derived from DTrees
SEE: REF: ml_intro_boost
Class to compute an image descriptor using the *bag of visual words*.
kmeans -based class to train visual vocabulary using the *bag of visual words* approach.
Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors.
Class implementing the BRISK keypoint detector and descriptor extractor, described in CITE: LCS11 .
The base class for camera response calibration algorithms.
Inverse camera response function is extracted for each brightness value by minimizing an objective
function as linear system.
Inverse camera response function is extracted for each brightness value by minimizing an objective
function as linear system.
This is a basic class, implementing the interaction with Camera and OpenCV library.
This class interface is abstract representation of single frame from camera for onCameraFrame callback
Attention: Do not use objects, that represents this interface out of onCameraFrame callback!
Cascade classifier class for object detection.
ChArUco board is a planar chessboard where the markers are placed inside the white squares of a chessboard.
Base class for Contrast Limited Adaptive Histogram Equalization.
This class represents high-level API for classification models.
Base class for dense optical flow algorithms
Abstract base class for matching keypoint descriptors.
This class represents high-level API for object detection networks.
struct DetectorParameters is used by ArucoDetector
Dictionary is a set of unique ArUco markers of the same size
bytesList storing as 2-dimensions Mat with 4-th channels (CV_8UC4 type was used) and contains the marker codewords where:
- bytesList.rows is the dictionary size
- each marker is encoded using nbytes = ceil(markerSize*markerSize/8.) bytes
- each row contains all 4 rotations of the marker, so its length is 4*nbytes
- the byte order in the bytesList[i] row:
//bytes without rotation/bytes with rotation 1/bytes with rotation 2/bytes with rotation 3//
So bytesList.ptr(i)[k*nbytes + j] is the j-th byte of i-th marker, in its k-th rotation.This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64.
DIS optical flow algorithm.
Structure for matching: query descriptor index, train descriptor index, train
image index and distance between descriptors.
The class represents a single decision tree or a collection of decision trees.
The class implements the Expectation Maximization algorithm.
DNN-based face detector
model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet
DNN-based face recognizer
model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface
Class computing a dense optical flow using the Gunnar Farneback's algorithm.
Wrapping class for feature detection using the FAST method.
Abstract base class for 2D image feature detectors and descriptor extractors
Flann-based descriptor matcher.
finds arbitrary template in the grayscale image using Generalized Hough Transform
finds arbitrary template in the grayscale image using Generalized Hough Transform
Detects position only without translation and rotation CITE: Ballard1981 .
finds arbitrary template in the grayscale image using Generalized Hough Transform
Detects position, translation and rotation CITE: Guil1999 .
Wrapping class for feature detection using the goodFeaturesToTrack function.
Planar board with grid arrangement of markers
More common type of board.
Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
Processing params of image to blob.
Intelligent Scissors image segmentation
This class is used to find the path (contour) between two points
which can be used for image segmentation.
Read data stream interface
This class is an implementation of the Bridge View between OpenCV and Java Camera.
This class is an implementation of the Bridge View between OpenCV and Java Camera.
Kalman filter class.
Class implementing the KAZE keypoint detector and descriptor extractor, described in CITE: ABD12 .
This class represents high-level API for keypoints models
KeypointsModel allows to set params for preprocessing input image.
The class implements K-Nearest Neighbors model
SEE: REF: ml_intro_knn
This interface class allows to build new Layers - are building blocks of networks.
Line segment detector class
following the algorithm described at CITE: Rafael12 .
Implements Logistic Regression classifier.
The resulting HDR image is calculated as weighted average of the exposures considering exposure
values and camera response.
The base class algorithms that can merge exposure sequence to a single image.
Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
combined using laplacian pyramids.
The resulting HDR image is calculated as weighted average of the exposures considering exposure
values and camera response.
This class is presented high-level API for neural networks.
Maximally stable extremal region extractor
The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki
article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
This class is an implementation of a bridge between SurfaceView and OpenCV VideoCapture.
This class allows to create and manipulate comprehensive artificial neural networks.
Bayes classifier for normally distributed data.
Dummy interface to allow some integration testing within OSGi implementation.
Helper class provides common initialization methods for OpenCV library.
This class is intended to provide a convenient way to load OpenCV's native
library from the Java bundle.
Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
described in CITE: RRKB11 .
The structure represents the logarithmic grid range of statmodel parameters.
Groups the object candidate rectangles.
QR code encoder parameters.
struct RefineParameters is used by ArucoDetector
The class implements the random forest predictor.
This class represents high-level API for segmentation models
SegmentationModel allows to set params for preprocessing input image.
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform
(SIFT) algorithm by D.
Class for extracting blobs from an image.
Base interface for sparse optical flow algorithms.
Class used for calculating a sparse optical flow.
Base class for statistical models in OpenCV ML.
Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K.
The base class for stereo correspondence algorithms.
The class implements the modified H.
Support Vector Machines.
*************************************************************************************\
Stochastic Gradient Descent SVM Classifier *
\***************************************************************************************
Base class for text detection networks
This class represents high-level API for text detection DL networks compatible with DB model.
This class represents high-level API for text detection DL networks compatible with EAST model.
This class represents high-level API for text recognition networks.
a Class to measure passing time.
Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
logarithmic domain.
This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
transforms contrast values to HVS response and scales the response.
This is a global tonemapping operator that models human visual system.
Base abstract class for the long-term tracker
the GOTURN (Generic Object Tracking Using Regression Networks) tracker
GOTURN (CITE: GOTURN) is kind of trackers based on Convolutional Neural Networks (CNN).
The MIL algorithm trains a classifier in an online manner to separate the object from the
background.
the Nano tracker is a super lightweight dnn-based general object tracking.
the VIT tracker is a super lightweight dnn-based general object tracking.
Class encapsulating training data.
Variational optical flow refinement
This class implements variational refinement of the input flow field, i.e.
Class for video capturing from video files, image sequences or cameras.
Video writer class.