クラス階層

クラス階層一覧です。大雑把に文字符号順で並べられています。

[表示階層 1 2 3 4]

► C cv::_InputArray	This is the proxy class for passing read-only input arrays into OpenCV functions
► C cv::_OutputArray	This type is very similar to InputArray except that it is used for input/output and output function parameters
C cv::_InputOutputArray
C _IplConvKernel
C _IplConvKernelFP
C _IplImage
C _IplROI
C cv::dnn::details::_LayerStaticRegisterer
C cv::quality::QualityGMSD::_mat_data
C cv::quality::QualitySSIM::_mat_data
C cv::Accumulator< T >
C cv::Accumulator< char >
C cv::Accumulator< short >
C cv::Accumulator< unsigned char >
C cv::Accumulator< unsigned short >
► C cv::Algorithm	This is a base class for all more or less complex algorithms in OpenCV
► C cv::AlignExposures	The base class for algorithms that align images of the same scene with different exposures
C cv::AlignMTB	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
► C cv::BackgroundSubtractor	Base class for background/foreground segmentation. :
C cv::BackgroundSubtractorKNN	K-nearest neighbours - based Background/Foreground Segmentation Algorithm
C cv::BackgroundSubtractorMOG2	Gaussian Mixture-based Background/Foreground Segmentation Algorithm
C cv::BaseCascadeClassifier
C cv::CLAHE	Base class for Contrast Limited Adaptive Histogram Equalization
► C cv::CalibrateCRF	The base class for camera response calibration algorithms
C cv::CalibrateDebevec	Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. Objective function is constructed using pixel values on the same position in all images, extra term is added to make the result smoother
C cv::CalibrateRobertson	Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. This algorithm uses all image pixels
► C cv::DenseOpticalFlow
C cv::DISOpticalFlow	DIS optical flow algorithm
C cv::FarnebackOpticalFlow	Class computing a dense optical flow using the Gunnar Farneback's algorithm
C cv::VariationalRefinement	Variational optical flow refinement
C cv::optflow::DenseRLOFOpticalFlow	Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme
C cv::optflow::DualTVL1OpticalFlow	"Dual TV L1" Optical Flow Algorithm
C cv::optflow::OpticalFlowPCAFlow	PCAFlow algorithm
► C cv::DescriptorMatcher	Abstract base class for matching keypoint descriptors
C cv::BFMatcher	Brute-force descriptor matcher
► C cv::Feature2D	Abstract base class for 2D image feature detectors and descriptor extractors
C cv::AKAZE	Class implementing the AKAZE keypoint detector and descriptor extractor, described in [ANB13]
C cv::AffineFeature	Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in [YM11]
C cv::AgastFeatureDetector	Wrapping class for feature detection using the AGAST method. :
C cv::BRISK	Class implementing the BRISK keypoint detector and descriptor extractor, described in [LCS11]
C cv::FastFeatureDetector	Wrapping class for feature detection using the FAST method. :
C cv::GFTTDetector	Wrapping class for feature detection using the goodFeaturesToTrack function. :
C cv::KAZE	Class implementing the KAZE keypoint detector and descriptor extractor, described in [ABD12]
C cv::MSER	Maximally stable extremal region extractor
C cv::ORB	Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor
C cv::SIFT	Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04]
C cv::SimpleBlobDetector	Class for extracting blobs from an image. :
► C cv::xfeatures2d::AffineFeature2D	Class implementing affine adaptation for key points
C cv::xfeatures2d::TBMR	Class implementing the Tree Based Morse Regions (TBMR) as described in [Najman2014] extended with scaled extraction ability
C cv::xfeatures2d::BEBLID	Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [Suarez2020BEBLID]
C cv::xfeatures2d::BoostDesc	Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b]
C cv::xfeatures2d::BriefDescriptorExtractor	Class for computing BRIEF descriptors described in [calon2010]
C cv::xfeatures2d::DAISY	Class implementing DAISY descriptor, described in [Tola10]
C cv::xfeatures2d::FREAK	Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [AOV12]
C cv::xfeatures2d::HarrisLaplaceFeatureDetector	Class implementing the Harris-Laplace feature detector as described in [Mikolajczyk2004]
C cv::xfeatures2d::LATCH
C cv::xfeatures2d::LUCID	Class implementing the locally uniform comparison image descriptor, described in [LUCID]
C cv::xfeatures2d::MSDDetector	Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [Tombari14]
C cv::xfeatures2d::SURF	Class for extracting Speeded Up Robust Features from an image [Bay06]
C cv::xfeatures2d::StarDetector	The class implements the keypoint detector introduced by [Agrawal08], synonym of StarDetector. :
C cv::xfeatures2d::VGG	Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [Simonyan14]
► C cv::GeneralizedHough	Finds arbitrary template in the grayscale image using Generalized Hough Transform
C cv::GeneralizedHoughBallard	Finds arbitrary template in the grayscale image using Generalized Hough Transform
C cv::GeneralizedHoughGuil	Finds arbitrary template in the grayscale image using Generalized Hough Transform
► C cv::HistogramCostExtractor	Abstract base class for histogram cost algorithms
C cv::ChiHistogramCostExtractor	An Chi based cost extraction. :
C cv::EMDHistogramCostExtractor	An EMD based cost extraction. :
C cv::EMDL1HistogramCostExtractor	An EMD-L1 based cost extraction. :
C cv::NormHistogramCostExtractor	A norm based cost extraction. :
C cv::LMSolver
C cv::LineSegmentDetector	Line segment detector class
► C cv::MergeExposures	The base class algorithms that can merge exposure sequence to a single image
C cv::MergeDebevec	The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response
C cv::MergeMertens	Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids
C cv::MergeRobertson	The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response
► C cv::MinProblemSolver	Basic interface for all solvers
C cv::ConjGradSolver	This class is used to perform the non-linear non-constrained minimization of a function with known gradient,
C cv::DownhillSolver	This class is used to perform the non-linear non-constrained minimization of a function,
► C cv::ShapeDistanceExtractor	Abstract base class for shape distance algorithms
C cv::HausdorffDistanceExtractor	A simple Hausdorff distance measure between shapes defined by contours
C cv::ShapeContextDistanceExtractor	Implementation of the Shape Context descriptor and matching algorithm
► C cv::ShapeTransformer	Abstract base class for shape transformation algorithms
C cv::AffineTransformer	Wrapper class for the OpenCV Affine Transformation algorithm. :
C cv::ThinPlateSplineShapeTransformer	Definition of the transformation
► C cv::SparseOpticalFlow	Base interface for sparse optical flow algorithms
C cv::SparsePyrLKOpticalFlow	Class used for calculating a sparse optical flow
C cv::optflow::SparseRLOFOpticalFlow	Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms
► C cv::StereoMatcher	The base class for stereo correspondence algorithms
C cv::StereoBM	Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige
C cv::StereoSGBM	The class implements the modified H. Hirschmuller algorithm [HH08] that differs from the original one as follows:
► C cv::Tonemap	Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range
C cv::TonemapDrago	Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain
C cv::TonemapMantiuk	This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, transforms contrast values to HVS response and scales the response. After this the image is reconstructed from new contrast values
C cv::TonemapReinhard	This is a global tonemapping operator that models human visual system
C cv::xphoto::TonemapDurand	This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter and compresses contrast of the base layer thus preserving all the details
C cv::bioinspired::Retina	Class which allows the Gipsa/Listic Labs model to be used with OpenCV
C cv::bioinspired::RetinaFastToneMapping	Wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV
C cv::bioinspired::TransientAreasSegmentationModule	Class which provides a transient/moving areas segmentation module
C cv::ccalib::CustomPattern
► C cv::dnn::Layer	This interface class allows to build new Layers - are building blocks of networks
C cv::dnn::AccumLayer
► C cv::dnn::ActivationLayer
C cv::dnn::AbsLayer
C cv::dnn::BNLLLayer
C cv::dnn::BatchNormLayer
C cv::dnn::ChannelsPReLULayer
C cv::dnn::ELULayer
C cv::dnn::ExpLayer
C cv::dnn::MishLayer
C cv::dnn::PowerLayer
C cv::dnn::ReLU6Layer
C cv::dnn::ReLULayer
C cv::dnn::SigmoidLayer
C cv::dnn::SwishLayer
C cv::dnn::TanHLayer
► C cv::dnn::BaseConvolutionLayer
C cv::dnn::ConvolutionLayer
C cv::dnn::DeconvolutionLayer
C cv::dnn::BlankLayer
C cv::dnn::ConcatLayer
C cv::dnn::ConstLayer
C cv::dnn::CorrelationLayer
C cv::dnn::CropAndResizeLayer
C cv::dnn::CropLayer
C cv::dnn::DataAugmentationLayer
C cv::dnn::DetectionOutputLayer	Detection output layer
C cv::dnn::EltwiseLayer	Element wise operation on inputs
C cv::dnn::FlattenLayer
C cv::dnn::FlowWarpLayer
C cv::dnn::InnerProductLayer
C cv::dnn::InterpLayer	Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2
C cv::dnn::LRNLayer
C cv::dnn::LSTMLayer	LSTM recurrent layer
C cv::dnn::MVNLayer
C cv::dnn::MaxUnpoolLayer
C cv::dnn::NormalizeBBoxLayer	- normalization layer
C cv::dnn::PaddingLayer	Adds extra values for specific axes
C cv::dnn::PermuteLayer
C cv::dnn::PoolingLayer
C cv::dnn::PriorBoxLayer
C cv::dnn::ProposalLayer
C cv::dnn::RNNLayer	Classical recurrent layer
C cv::dnn::RegionLayer
C cv::dnn::ReorgLayer
C cv::dnn::ReshapeLayer
C cv::dnn::ResizeLayer	Resize input 4-dimensional blob by nearest neighbor or bilinear strategy
C cv::dnn::ScaleLayer
C cv::dnn::ShiftLayer
C cv::dnn::ShuffleChannelLayer
C cv::dnn::SliceLayer
C cv::dnn::SoftmaxLayer
C cv::dnn::SplitLayer
C cv::face::BIF
► C cv::face::FaceRecognizer	Abstract base class for all face recognition models
► C cv::face::BasicFaceRecognizer
C cv::face::EigenFaceRecognizer
C cv::face::FisherFaceRecognizer
C cv::face::LBPHFaceRecognizer
► C cv::face::Facemark	Abstract base class for all facemark models
C cv::face::FacemarkKazemi
► C cv::face::FacemarkTrain	Abstract base class for trainable facemark models
C cv::face::FacemarkAAM
C cv::face::FacemarkLBF
C cv::face::MACE	Minimum Average Correlation Energy Filter useful for authentication with (cancellable) biometrical features. (does not need many positives to train (10-50), and no negatives at all, also robust to noise/salting)
C cv::hfs::HfsSegment
► C cv::img_hash::ImgHashBase	The base class for image hash algorithms
C cv::img_hash::AverageHash	Computes average hash value of the input image
C cv::img_hash::BlockMeanHash	Image hash based on block mean
C cv::img_hash::ColorMomentHash	Image hash based on color moments
C cv::img_hash::MarrHildrethHash	Marr-Hildreth Operator Based Hash, slowest but more discriminative
C cv::img_hash::PHash	PHash
C cv::img_hash::RadialVarianceHash	Image hash based on Radon transform
C cv::legacy::tracking::MultiTracker	This class is used to track multiple objects using the specified tracker algorithm
C cv::legacy::tracking::Tracker	Base abstract class for the long-term tracker:
C cv::line_descriptor::BinaryDescriptor	Class implements both functionalities for detection of lines and computation of their binary descriptor
C cv::line_descriptor::BinaryDescriptorMatcher	Furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of Descriptor Matchers
C cv::line_descriptor::LSDDetector
C cv::mcc::CCheckerDetector	A class to find the positions of the ColorCharts in the image
C cv::optflow::GPCForest< T >
C cv::optflow::GPCTree	Class for individual tree
► C cv::phase_unwrapping::PhaseUnwrapping	Abstract base class for phase unwrapping
C cv::phase_unwrapping::HistogramPhaseUnwrapping	Class implementing two-dimensional phase unwrapping based on [histogramUnwrapping] This algorithm belongs to the quality-guided phase unwrapping methods. First, it computes a reliability map from second differences between a pixel and its eight neighbours. Reliability values lie between 0 and 16*pi*pi. Then, this reliability map is used to compute the reliabilities of "edges". An edge is an entity defined by two pixels that are connected horizontally or vertically. Its reliability is found by adding the the reliabilities of the two pixels connected through it. Edges are sorted in a histogram based on their reliability values. This histogram is then used to unwrap pixels, starting from the highest quality pixel
► C cv::quality::QualityBase
C cv::quality::QualityBRISQUE	BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) is a No Reference Image Quality Assessment (NR-IQA) algorithm
C cv::quality::QualityGMSD	Full reference GMSD algorithm http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm
C cv::quality::QualityMSE	Full reference mean square error algorithm https://en.wikipedia.org/wiki/Mean_squared_error
C cv::quality::QualityPSNR	Full reference peak signal to noise ratio (PSNR) algorithm https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
C cv::quality::QualitySSIM	Full reference structural similarity algorithm https://en.wikipedia.org/wiki/Structural_similarity
► C cv::rapid::Tracker	Abstract base class for stateful silhouette trackers
C cv::rapid::GOSTracker
C cv::rapid::OLSTracker
C cv::rapid::Rapid	Wrapper around silhouette based 3D object tracking function for uniform access
C cv::rgbd::DepthCleaner
► C cv::rgbd::Odometry
C cv::rgbd::FastICPOdometry
C cv::rgbd::ICPOdometry
C cv::rgbd::RgbdICPOdometry
C cv::rgbd::RgbdOdometry
C cv::rgbd::RgbdNormals
C cv::rgbd::RgbdPlane
► C cv::saliency::Saliency
► C cv::saliency::MotionSaliency
C cv::saliency::MotionSaliencyBinWangApr2014	Fast Self-tuning Background Subtraction Algorithm from [BinWangApr2014]
► C cv::saliency::Objectness
C cv::saliency::ObjectnessBING	Objectness algorithms based on [3] [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014
► C cv::saliency::StaticSaliency
C cv::saliency::StaticSaliencyFineGrained	Fine Grained Saliency approach from [FGS]
C cv::saliency::StaticSaliencySpectralResidual	Spectral Residual approach from [SR]
► C cv::stereo::StereoMatcher	Filters off small noise blobs (speckles) in the disparity map
C cv::stereo::StereoBinaryBM	Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige
C cv::stereo::StereoBinarySGBM	The class implements the modified H. Hirschmuller algorithm [HH08] that differs from the original one as follows:
► C cv::structured_light::StructuredLightPattern	Abstract base class for generating and decoding structured light patterns
C cv::structured_light::GrayCodePattern	Class implementing the Gray-code pattern, based on [UNDERWORLD]
C cv::structured_light::SinusoidalPattern	Class implementing Fourier transform profilometry (FTP) , phase-shifting profilometry (PSP) and Fourier-assisted phase-shifting profilometry (FAPS) based on [faps]
► C cv::superres::DenseOpticalFlowExt
C cv::superres::BroxOpticalFlow
C cv::superres::DualTVL1OpticalFlow
C cv::superres::FarnebackOpticalFlow
C cv::superres::PyrLKOpticalFlow
C cv::superres::SuperResolution	Base class for Super Resolution algorithms
C cv::text::ERFilter	Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [Neumann12]. :
C cv::xfeatures2d::PCTSignatures	Class implementing PCT (position-color-texture) signature extraction as described in [KrulisLS16]. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image
C cv::xfeatures2d::PCTSignaturesSQFD	Class implementing Signature Quadratic Form Distance (SQFD)
C cv::ximgproc::ContourFitting	Class for ContourFitting algorithms. ContourFitting match two contours and minimizing distance
C cv::ximgproc::EdgeBoxes	Class implementing EdgeBoxes algorithm from [ZitnickECCV14edgeBoxes] :
C cv::ximgproc::EdgeDrawing	Class implementing the ED (EdgeDrawing) [topal2012edge], EDLines [akinlar2011edlines], EDPF [akinlar2012edpf] and EDCircles [akinlar2013edcircles] algorithms
C cv::ximgproc::FastLineDetector	Class implementing the FLD (Fast Line Detector) algorithm described in [Lee14]
C cv::ximgproc::RidgeDetectionFilter	Applies Ridge Detection Filter to an input image. Implements Ridge detection similar to the one in Mathematica using the eigen values from the Hessian Matrix of the input image using Sobel Derivatives. Additional refinement can be done using Skeletonization and Binarization. Adapted from [segleafvein] and [M_RF]
C cv::ximgproc::segmentation::GraphSegmentation	Graph Based Segmentation Algorithm. The class implements the algorithm described in [PFF2004]
C cv::ximgproc::segmentation::SelectiveSearchSegmentation	Selective search segmentation algorithm The class implements the algorithm described in [uijlings2013selective]
► C cv::ximgproc::segmentation::SelectiveSearchSegmentationStrategy	Strategie for the selective search segmentation algorithm The class implements a generic stragery for the algorithm described in [uijlings2013selective]
C cv::ximgproc::segmentation::SelectiveSearchSegmentationStrategyColor	Color-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective]
C cv::ximgproc::segmentation::SelectiveSearchSegmentationStrategyFill	Fill-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective]
C cv::ximgproc::segmentation::SelectiveSearchSegmentationStrategyMultiple	Regroup multiple strategies for the selective search segmentation algorithm
C cv::ximgproc::segmentation::SelectiveSearchSegmentationStrategySize	Size-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective]
C cv::ximgproc::segmentation::SelectiveSearchSegmentationStrategyTexture	Texture-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective]
► C cv::xphoto::WhiteBalancer	The base class for auto white balance algorithms
C cv::xphoto::GrayworldWB	Gray-world white balance algorithm
C cv::xphoto::LearningBasedWB	More sophisticated learning-based automatic white balance algorithm
C cv::xphoto::SimpleWB	A simple white balance algorithm that works by independently stretching each of the input image channels to the specified range. For increased robustness it ignores the top and bottom of pixel values
C cv::Allocator< _Tp >
C cv::cuda::GpuMat::Allocator
► C cv::utils::AllocatorStatisticsInterface
C cv::utils::AllocatorStatistics
C cv::ogl::Arrays	Wrapper for OpenGL Client-Side Vertex arrays
C cv::AsyncArray	Returns result of asynchronous operations
C cv::AsyncPromise	Provides result of asynchronous operations
C cv::AutoBuffer< _Tp, fixed_size >	Automatically Allocated Buffer Class
► C <AVCaptureVideoDataOutputSampleBufferDelegate>
C CvVideoCamera
C cv::dnn::BackendNode	Derivatives of this class encapsulates functions of certain backends
C cv::dnn::BackendWrapper	Derivatives of this class wraps cv::Mat for different backends and targets
C cv::barcode::BarcodeDetector
C cv::detail::BaseClassifier
C cv::detail::tracking::BaseClassifier
C cv::detail::tracking::online_boosting::BaseClassifier
► C cv::text::BaseOCR
C cv::text::OCRBeamSearchDecoder	OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm
C cv::text::OCRHMMDecoder	OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models
C cv::text::OCRHolisticWordRecognizer	OCRHolisticWordRecognizer class provides the functionallity of segmented wordspotting. Given a predefined vocabulary , a DictNet is employed to select the most probable word given an input image
C cv::text::OCRTesseract	OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++
C cv::face::FacemarkLBF::BBox
► C cv::detail::Blender	Base class for all blenders
C cv::detail::FeatherBlender	Simple blender which mixes images at its borders
C cv::detail::MultiBandBlender	Blender which uses multi-band blending algorithm (see [BA83])
► C cv::aruco::Board	Board of markers
C cv::aruco::CharucoBoard	ChArUco board Specific class for ChArUco boards. A ChArUco board is a planar board where the markers are placed inside the white squares of a chessboard. The benefits of ChArUco boards is that they provide both, ArUco markers versatility and chessboard corner precision, which is important for calibration and pose estimation. This class also allows the easy creation and drawing of ChArUco boards
C cv::aruco::GridBoard	Planar board with grid arrangement of markers More common type of board. All markers are placed in the same plane in a grid arrangement. The board can be drawn using drawPlanarBoard() function (
C cv::BOWImgDescriptorExtractor	Class to compute an image descriptor using the bag of visual words
► C cv::BOWTrainer	Abstract base class for training the bag of visual words vocabulary from a set of descriptors
C cv::BOWKMeansTrainer	Kmeans -based class to train visual vocabulary using the bag of visual words approach. :
C cv::ximgproc::Box
C cv::ogl::Buffer	Smart pointer for OpenGL buffer object with reference counting
C cv::cuda::BufferPool	BufferPool for use with CUDA streams
C cv::BufferPoolController
C cv::LMSolver::Callback
C cv::text::ERFilter::Callback	Callback with the classifier is made a class
C cv::parallel::tbb::ParallelForBackend::CallbackProxy
C cv::detail::CameraParams	Describes camera parameters
C cv::linemod::QuantizedPyramid::Candidate	Candidate feature with a score
C cv::CascadeClassifier	Cascade classifier class for object detection
C cv::mcc::CChecker	Checker object
C cv::mcc::CCheckerDraw	Checker draw
C cv::CirclesGridFinderParameters
C cv::text::OCRBeamSearchDecoder::ClassifierCallback	Callback with the character classifier is made a class
C cv::text::OCRHMMDecoder::ClassifierCallback	Callback with the character classifier is made a class
C cv::detail::ClassifierThreshold
C cv::detail::tracking::ClassifierThreshold
C cv::detail::tracking::online_boosting::ClassifierThreshold
C cv::ccm::ColorCorrectionModel	Core class of ccm model
C cv::colored_kinfu::ColoredKinFu	KinectFusion implementation
C cv::CommandLineParser	Designed for command line parsing
C cv::Complex< _Tp >	A complex number class
C cv::face::FacemarkAAM::Config	Optional parameter for fitting process
C cv::ocl::Context
C cv::face::CParams
C Cv16suf
C Cv32suf
C Cv64suf
C CvBox2D
C CvChain
C CvChainPtReader
C CvConnectedComp
C CvContour
C CvConvexityDefect
► C cv::detail::CvFeatureEvaluator
C cv::detail::tracking::contrib_feature::CvHOGEvaluator
C cv::detail::tracking::contrib_feature::CvHaarEvaluator
C cv::detail::tracking::contrib_feature::CvLBPEvaluator
► C cv::detail::tracking::contrib_feature::CvFeatureEvaluator
C cv::detail::tracking::contrib_feature::CvHOGEvaluator
C cv::detail::tracking::contrib_feature::CvHaarEvaluator
C cv::detail::tracking::contrib_feature::CvLBPEvaluator
► C cv::detail::tracking::CvFeatureEvaluator
C cv::detail::tracking::contrib_feature::CvHOGEvaluator
C cv::detail::tracking::contrib_feature::CvHaarEvaluator
C cv::detail::tracking::contrib_feature::CvLBPEvaluator
C CvFont
C CvGraph
C CvGraphEdge
C CvGraphScanner
C CvGraphVtx
C CvGraphVtx2D
C cvhalKeyPoint
C CvHistogram
C CvHuMoments
C CvLineIterator
C CvMat
C CvMatND
C CvMemBlock
C CvMemStorage
C CvMemStoragePos
C CvMoments
C CvNArrayIterator
► C cv::detail::CvParams
► C cv::detail::tracking::contrib_feature::CvFeatureParams
C cv::detail::tracking::contrib_feature::CvHOGFeatureParams
C cv::detail::tracking::contrib_feature::CvHaarFeatureParams
C cv::detail::tracking::contrib_feature::CvLBPFeatureParams
► C cv::detail::tracking::contrib_feature::CvParams
C cv::detail::tracking::contrib_feature::CvFeatureParams
► C cv::detail::tracking::CvParams
C cv::detail::tracking::contrib_feature::CvFeatureParams
C CvPoint
C CvPoint2D32f
C CvPoint2D64f
C CvPoint3D32f
C CvPoint3D64f
C CvRect
C CvScalar
C CvSeq
C CvSeqBlock
C CvSeqReader
C CvSeqWriter
C CvSet
C CvSetElem
C CvSize
C CvSize2D32f
C CvSlice
C CvSparseMat
C CvSparseMatIterator
C CvSparseNode
C CvTermCriteria
C CvTreeNodeIterator
C cv::flann::CvType< T >
C cv::flann::CvType< char >
C cv::flann::CvType< double >
C cv::flann::CvType< float >
C cv::flann::CvType< int >
C cv::flann::CvType< short >
C cv::flann::CvType< unsigned char >
C cv::flann::CvType< unsigned short >
C cv::face::FacemarkAAM::Data	Data container for the facemark::getData function
C cv::DataDepth< _Tp >	A helper class for cv::DataType
C cv::DataType< _Tp >	Template "trait" class for OpenCV primitive data types
C cv::DataType< bool >
C cv::DataType< char >
C cv::DataType< Complex< _Tp > >
C cv::DataType< double >
C cv::DataType< float >
C cv::DataType< float16_t >
C cv::DataType< int >
C cv::DataType< Matx< _Tp, m, n > >
C cv::DataType< Moments >
C cv::DataType< Point3_< _Tp > >
C cv::DataType< Point_< _Tp > >
C cv::DataType< Range >
C cv::DataType< Rect_< _Tp > >
C cv::DataType< RotatedRect >
C cv::DataType< Scalar_< _Tp > >
C cv::DataType< schar >
C cv::DataType< short >
C cv::DataType< Size_< _Tp > >
C cv::DataType< uchar >
C cv::DataType< ushort >
C cv::DataType< Vec< _Tp, cn > >
C cv::hal::DCT2D
► C cv::videostab::DeblurerBase
C cv::videostab::NullDeblurer
C cv::videostab::WeightingDeblurer
C cv::DefaultDeleter< Y >
C cv::DefaultDeleter< CvHaarClassifierCascade >
C cv::traits::Depth< T >
C cv::traits::Depth< Complex< _Tp > >
C cv::traits::Depth< Matx< _Tp, m, n > >
C cv::traits::Depth< Moments >
C cv::traits::Depth< Point3_< _Tp > >
C cv::traits::Depth< Point_< _Tp > >
C cv::traits::Depth< Range >
C cv::traits::Depth< Rect_< _Tp > >
C cv::traits::Depth< RotatedRect >
C cv::traits::Depth< Scalar_< _Tp > >
C cv::traits::Depth< Size_< _Tp > >
C cv::traits::Depth< Vec< _Tp, cn > >
C cv::DescriptorMatcher::DescriptorCollection
C cv::DetectionBasedTracker
C cv::DetectionROI	Struct for detection region of interest (ROI)
C cv::detail::Detector
C cv::detail::tracking::Detector
C cv::detail::tracking::online_boosting::Detector
C cv::linemod::Detector	Object detector using the LINE template matching algorithm with any set of modalities
C cv::aruco::DetectorParameters	Parameters for the detectMarker process:
C cv::mcc::DetectorParameters	Parameters for the detectMarker process:
C cv::ocl::Device
C cv::cuda::DeviceInfo	Class providing functionality for querying the specified GPU properties
C cv::hal::DFT1D
C cv::hal::DFT2D
► C cv::dnn::Dict	This class implements name-value dictionary, values are instances of DictValue
C cv::dnn::LayerParams	This class provides all data needed to initialize layer
C cv::aruco::Dictionary	Dictionary/Set of markers. It contains the inner codification
C cv::dnn::DictValue	This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64
C cv::detail::DisjointSets
C cv::DMatch	Class for matching keypoint descriptors
C cv::dnn_superres::DnnSuperResImpl	A class to upscale images via convolutional neural networks. The following four models are implemented:
C cv::dpm::DPMDetector	This is a C++ abstract class, it provides external user API to work with DPM
C cv::line_descriptor::DrawLinesMatchesFlags
C cv::DualQuat< _Tp >
C cv::dynafu::DynaFu
C cv::multicalib::MultiCameraCalibration::edge
C cv::text::ERStat	The ERStat structure represents a class-specific Extremal Region (ER)
C cv::detail::EstimatedGaussDistribution
C cv::detail::tracking::EstimatedGaussDistribution
C cv::detail::tracking::online_boosting::EstimatedGaussDistribution
► C cv::detail::Estimator	Rotation estimator base class
C cv::detail::AffineBasedEstimator	Affine transformation based estimator
► C cv::detail::BundleAdjusterBase	Base class for all camera parameters refinement methods
C cv::detail::BundleAdjusterAffine	Bundle adjuster that expects affine transformation represented in homogeneous coordinates in R for each camera param. Implements camera parameters refinement algorithm which minimizes sum of the reprojection error squares
C cv::detail::BundleAdjusterAffinePartial	Bundle adjuster that expects affine transformation with 4 DOF represented in homogeneous coordinates in R for each camera param. Implements camera parameters refinement algorithm which minimizes sum of the reprojection error squares
C cv::detail::BundleAdjusterRay	Implementation of the camera parameters refinement algorithm which minimizes sum of the distances between the rays passing through the camera center and a feature. :
C cv::detail::BundleAdjusterReproj	Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection error squares
C cv::detail::NoBundleAdjuster	Stub bundle adjuster that does nothing
C cv::detail::HomographyBasedEstimator	Homography based rotation estimator
C cv::cuda::Event
C cv::cuda::EventAccessor	Class that enables getting cudaEvent_t from cuda::Event
► C std::exception
C cv::Exception	Class passed to an error
► C cv::detail::ExposureCompensator	Base class for all exposure compensators
► C cv::detail::BlocksCompensator	Exposure compensator which tries to remove exposure related artifacts by adjusting image blocks
C cv::detail::BlocksChannelsCompensator	Exposure compensator which tries to remove exposure related artifacts by adjusting image block on each channel
C cv::detail::BlocksGainCompensator	Exposure compensator which tries to remove exposure related artifacts by adjusting image block intensities, see [UES01] for details
C cv::detail::ChannelsCompensator	Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities on each channel independently
C cv::detail::GainCompensator	Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities, see [BL07] and [WJ10] for details
C cv::detail::NoExposureCompensator	Stub exposure compensator which does nothing
C cv::DetectionBasedTracker::ExtObject
► C std::false_type
C cv::has_custom_delete< T, typename >
C cv::videostab::FastMarchingMethod	Describes the Fast Marching Method implementation
C cv::detail::tracking::contrib_feature::CvHOGEvaluator::Feature
C cv::detail::tracking::contrib_feature::CvLBPEvaluator::Feature
C cv::linemod::Feature	Discriminant feature described by its location and label
C cv::detail::tracking::contrib_feature::CvHaarEvaluator::FeatureHaar
► C cv::detail::FeaturesMatcher	Feature matchers base class
► C cv::detail::BestOf2NearestMatcher	Features matcher which finds two best matches for each feature and leaves the best one only if the ratio between descriptor distances is greater than the threshold match_conf
C cv::detail::AffineBestOf2NearestMatcher	Features matcher similar to cv::detail::BestOf2NearestMatcher which finds two best matches for each feature and leaves the best one only if the ratio between descriptor distances is greater than the threshold match_conf
C cv::detail::BestOf2NearestRangeMatcher
C cv::FileNode	File Storage Node class
C cv::FileNodeIterator	Used to iterate through sequences and mappings
C cv::FileStorage	XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or reading data to/from a file
C cv::Formatted
C cv::Formatter
► C cv::superres::FrameSource
C cv::superres::SuperResolution	Base class for Super Resolution algorithms
C cv::MinProblemSolver::Function	Represents function being optimized
C cv::flann::GenericIndex< Distance >	The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built
C cv::optflow::GPCDetails
C cv::optflow::GPCMatchingParams	Class encapsulating matching parameters
C cv::optflow::GPCPatchDescriptor
C cv::optflow::GPCPatchSample
C cv::optflow::GPCTrainingParams	Class encapsulating training parameters
C cv::optflow::GPCTrainingSamples	Class encapsulating training samples
C cv::cuda::GpuData
C cv::cuda::GpuMat	Base storage class for GPU memory with reference counting
C cv::cuda::GpuMatND
C cv::detail::Graph
► C cv::detail::GraphCutSeamFinderBase	Base class for all minimum graph-cut-based seam estimators
C cv::detail::GraphCutSeamFinder	Minimum graph cut-based seam estimator. See details in [V03]
C cv::detail::GraphEdge
C cv::Hamming
C cv::sfinae::has_parenthesis_operator< C, Ret, Args >
C cv::ppf_match_3d::hashnode_i
C cv::SparseMat::Hdr	Sparse matrix header
C cv::HOGDescriptor	Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector
C cv::cuda::HostMem	Class with reference counting wrapping special memory type allocation functions from CUDA
C cv::ppf_match_3d::HSHTBL_i
C cv::ppf_match_3d::ICP	This class implements a very efficient and robust variant of the iterative closest point (ICP) algorithm. The task is to register a 3D model (or point cloud) against a set of noisy target data. The variants are put together by myself after certain tests. The task is to be able to match partial, noisy point clouds in cluttered scenes, quickly. You will find that my emphasis is on the performance, while retaining the accuracy. This implementation is based on Tolga Birdal's MATLAB implementation in here: http://www.mathworks.com/matlabcentral/fileexchange/47152-icp-registration-using-efficient-variants-and-multi-resolution-scheme The main contributions come from:
C cv::videostab::IDenseOptFlowEstimator
► C cv::detail::tracking::tbm::IDescriptorDistance	Declares an interface for distance computation between reidentification descriptors
C cv::detail::tracking::tbm::CosDistance	Allows computing cosine distance between two reidentification descriptors
C cv::detail::tracking::tbm::MatchTemplateDistance	Computes distance between images using MatchTemplate function from OpenCV library and its cross-correlation computation method in particular
C cv::DetectionBasedTracker::IDetector
► C cv::videostab::IFrameSource
C cv::videostab::MaskFrameSource
C cv::videostab::NullFrameSource
C cv::videostab::OnePassStabilizer
C cv::videostab::TwoPassStabilizer
C cv::videostab::VideoFileSource
► C cv::detail::tracking::tbm::IImageDescriptor	Declares base class for image descriptor
C cv::detail::tracking::tbm::ResizedImageDescriptor	Uses resized image as descriptor
► C cv::videostab::ILog
C cv::videostab::LogToStdout
C cv::videostab::NullLog
C cv::ocl::Image2D
C cv::detail::ImageFeatures	Structure containing image keypoints and descriptors
► C cv::videostab::ImageMotionEstimatorBase	Base class for global 2D motion estimation methods which take frames as input
C cv::videostab::FromFileMotionReader
C cv::videostab::KeypointBasedMotionEstimator	Describes a global 2D motion estimation method which uses keypoints detection and optical flow for matching
C cv::videostab::ToFileMotionWriter
► C cv::videostab::IMotionStabilizer
C cv::videostab::LpMotionStabilizer
► C cv::videostab::MotionFilterBase
C cv::videostab::GaussianMotionFilter
C cv::videostab::MotionStabilizationPipeline
C cv::dnn_objdetect::InferBbox	A class to post process model predictions
C cv::DetectionBasedTracker::InnerParameters
► C cv::videostab::InpainterBase
C cv::videostab::ColorAverageInpainter
C cv::videostab::ColorInpainter
C cv::videostab::ConsistentMosaicInpainter
C cv::videostab::InpaintingPipeline
C cv::videostab::MotionInpainter
C cv::videostab::NullInpainter
C cv::segmentation::IntelligentScissorsMB	Intelligent Scissors image segmentation
C cv::kinfu::Intr
► C cv::videostab::IOutlierRejector
C cv::videostab::NullOutlierRejector
C cv::videostab::TranslationBasedLocalOutlierRejector
C cv::bioinspired::RetinaParameters::IplMagnoParameters	Inner Plexiform Layer Magnocellular channel (IplMagno)
► C cv::videostab::ISparseOptFlowEstimator
C cv::videostab::SparsePyrLkOptFlowEstimator
C cv::detail::tracking::tbm::ITrackerByMatching	Tracker-by-Matching algorithm interface
C cv::KalmanFilter	Kalman filter class
C cv::ocl::Kernel
C cv::ocl::KernelArg
C cv::line_descriptor::KeyLine	A class to represent a line
► C cv::KeyPoint	Data structure for salient point detectors
C cv::xfeatures2d::Elliptic_KeyPoint	Elliptic region around an interest point
C cv::KeyPointsFilter	A class filters a vector of keypoints
C cv::kinfu::KinFu	KinectFusion implementation
C cv::L1< T >
C cv::L2< T >
C cv::large_kinfu::LargeKinfu	Large Scale Dense Depth Fusion implementation
C cv::dnn::LayerFactory	Layer factory allows to create instances of registered layers
C cv::LDA	Linear Discriminant Analysis
C cv::LineIterator	Line iterator
► C cv::utils::logging::LogTag
C cv::utils::logging::LogTagAuto
C cv::line_descriptor::LSDParam
► C cv::reg::Map	Base class for modelling a Map between two images
C cv::reg::MapAffine
C cv::reg::MapProjec
C cv::reg::MapShift
► C cv::reg::Mapper	Base class for modelling an algorithm for calculating a map
C cv::reg::MapperGradAffine
C cv::reg::MapperGradEuclid
C cv::reg::MapperGradProj
C cv::reg::MapperGradShift
C cv::reg::MapperGradSimilar
C cv::reg::MapperPyramid
C cv::reg::MapTypeCaster
C cv::BaseCascadeClassifier::MaskGenerator
► C cv::Mat	N-dimensional dense array class
C cv::Mat_< _Tp >	Template matrix class derived from Mat
C cv::MatAllocator	Custom array allocator
C cv::linemod::Match	Represents a successful template match
C cv::detail::MatchesInfo	Structure containing information about matches between two images
C cv::stereo::MatchQuasiDense
C cv::MatCommaInitializer_< _Tp >	Comma-separated Matrix Initializer
► C cv::MatConstIterator
► C cv::MatConstIterator_< _Tp >	Matrix read-only iterator
C cv::MatIterator_< _Tp >	Matrix read-write iterator
C cv::MatExpr	Matrix expression representation This is a list of implemented matrix operations that can be combined in arbitrary complex expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a real-valued scalar ( double )):
C cv::MatOp
C cv::MatSize
C cv::MatStep
► C cv::Matx< _Tp, m, n >	Template class for small matrices whose type and size are known at compilation time
C cv::Vec< float, 2 >
► C cv::Matx< _Tp, cn, 1 >
► C cv::Vec< _Tp, 4 >
C cv::Scalar_< _Tp >	Template class for a 4-element vector derived from Vec
► C cv::Vec< _Tp, cn >	Template class for short numerical vectors, a partial case of Matx
C cv::Scalar_< double >
C cv::Matx< float, 2, 3 >
C cv::MatxCommaInitializer< _Tp, m, n >	Comma-separated Matrix Initializer
► C cv::MatxCommaInitializer< _Tp, m, 1 >
C cv::VecCommaInitializer< _Tp, m >	Comma-separated Vec Initializer
► C cv::linemod::Modality	Interface for modalities that plug into the LINE template matching representation
C cv::linemod::ColorGradient	Modality that computes quantized gradient orientations from a color image
C cv::linemod::DepthNormal	Modality that computes quantized surface normals from a dense depth map
► C cv::dnn::Model	This class is presented high-level API for neural networks
C cv::dnn::ClassificationModel	This class represents high-level API for classification models
C cv::dnn::DetectionModel	This class represents high-level API for object detection networks
C cv::dnn::KeypointsModel	This class represents high-level API for keypoints models
C cv::dnn::SegmentationModel	This class represents high-level API for segmentation models
► C cv::dnn::TextDetectionModel	Base class for text detection networks
C cv::dnn::TextDetectionModel_DB	This class represents high-level API for text detection DL networks compatible with DB model
C cv::dnn::TextDetectionModel_EAST	This class represents high-level API for text detection DL networks compatible with EAST model
C cv::dnn::TextRecognitionModel	This class represents high-level API for text recognition networks
C cv::face::FacemarkAAM::Model	The model of AAM Algorithm
C cv::Moments	Struct returned by cv::moments
► C cv::videostab::MotionEstimatorBase	Base class for all global motion estimation methods
C cv::videostab::MotionEstimatorL1	Describes a global 2D motion estimation method which minimizes L1 error
C cv::videostab::MotionEstimatorRansacL2	Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error
C cv::multicalib::MultiCameraCalibration	Class for multiple camera calibration that supports pinhole camera and omnidirection camera. For omnidirectional camera model, please refer to omnidir.hpp in ccalib module. It first calibrate each camera individually, then a bundle adjustment like optimization is applied to refine extrinsic parameters. So far, it only support "random" pattern for calibration, see randomPattern.hpp in ccalib module for details. Images that are used should be named by "cameraIdx-timestamp.*", several images with the same timestamp means that they are the same pattern that are photographed. cameraIdx should start from 0
► C cv::legacy::MultiTracker_Alt	Base abstract class for the long-term Multi Object Trackers:
C cv::legacy::tracking::MultiTrackerTLD	Multi Object Tracker for TLD
► C cv::legacy::tracking::MultiTracker_Alt	Base abstract class for the long-term Multi Object Trackers:
C cv::legacy::tracking::MultiTrackerTLD	Multi Object Tracker for TLD
C cv::NAryMatIterator	N-ary multi-dimensional array iterator
C cv::dnn::Net	This class allows to create and manipulate comprehensive artificial neural networks
C cv::Node< OBJECT >
C cv::optflow::GPCTree::Node
C cv::SparseMat::Node	Sparse matrix node - element of a hash table
C cv::instr::NodeData
C cv::instr::NodeDataTls
► C NSObject
► C CvAbstractCamera
C CvPhotoCamera
C CvVideoCamera
► C <NSObject>
C <CvPhotoCameraDelegate>
► C <NSObjectNSObject>
C <CvVideoCameraDelegate>
C cv::dnn_objdetect::object	Structure to hold the details pertaining to a single bounding box
C cv::dpm::DPMDetector::ObjectDetection
C cv::ocl::OpenCLExecutionContext
C cv::ocl::OpenCLExecutionContextScope
C cv::bioinspired::RetinaParameters::OPLandIplParvoParameters	Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters
► C cv::parallel::ParallelForAPI
C cv::parallel::openmp::ParallelForBackend
C cv::parallel::tbb::ParallelForBackend
► C cv::ParallelLoopBody	Base class for parallel data processors
C cv::ParallelLoopBodyLambdaWrapper
C cv::DetectionBasedTracker::Parameters
C cv::colored_kinfu::Params
C cv::detail::tracking::TrackerContribFeatureHAAR::Params
C cv::detail::tracking::TrackerContribSamplerCSC::Params
C cv::detail::tracking::TrackerSamplerCS::Params
C cv::detail::tracking::TrackerSamplerCSC::Params
C cv::detail::tracking::TrackerSamplerPF::Params	This structure contains all the parameters that can be varied during the course of sampling algorithm. Below is the structure exposed, together with its members briefly explained with reference to the above discussion on algorithm's working
C cv::face::FacemarkAAM::Params
C cv::face::FacemarkKazemi::Params
C cv::face::FacemarkLBF::Params
C cv::kinfu::Params
C cv::large_kinfu::Params
C cv::legacy::tracking::TrackerBoosting::Params
C cv::legacy::tracking::TrackerMedianFlow::Params
C cv::legacy::tracking::TrackerTLD::Params
C cv::line_descriptor::BinaryDescriptor::Params	List of BinaryDescriptor parameters:
C cv::phase_unwrapping::HistogramPhaseUnwrapping::Params	Parameters of phaseUnwrapping constructor
C cv::SimpleBlobDetector::Params
C cv::structured_light::GrayCodePattern::Params	Parameters of StructuredLightPattern constructor
C cv::structured_light::SinusoidalPattern::Params	Parameters of SinusoidalPattern constructor
C cv::TrackerDaSiamRPN::Params
C cv::TrackerGOTURN::Params
► C cv::TrackerMIL::Params
C cv::legacy::tracking::TrackerMIL::Params
► C cv::tracking::TrackerCSRT::Params
C cv::legacy::tracking::TrackerCSRT::Params
► C cv::tracking::TrackerKCF::Params
C cv::legacy::tracking::TrackerKCF::Params
C cv::ximgproc::EdgeDrawing::Params
C cv::ParamType< _Tp, _EnumTp >
C cv::ParamType< _Tp, typename std::enable_if< std::is_enum< _Tp >::value >::type >
C cv::ParamType< Algorithm >
C cv::ParamType< bool >
C cv::ParamType< double >
C cv::ParamType< float >
C cv::ParamType< int >
C cv::ParamType< Mat >
C cv::ParamType< Scalar >
C cv::ParamType< std::vector< Mat > >
C cv::ParamType< String >
C cv::ParamType< uchar >
C cv::ParamType< uint64 >
C cv::ParamType< unsigned >
C cv::PCA	Principal Component Analysis
C cv::optflow::PCAPrior	This class can be used for imposing a learned prior on the resulting optical flow. Solution will be regularized according to this prior. You need to generate appropriate prior file with "learn_prior.py" script beforehand
C cv::ocl::Platform
C cv::ocl::PlatformInfo
C cv::Point3_< _Tp >	Template class for 3D points specified by its coordinates x, y and z
C cv::Point_< _Tp >	Template class for 2D points specified by its coordinates x and y
C cv::Point_< float >
C cv::Point_< int >
C cv::ppf_match_3d::Pose3D	Class, allowing the storage of a pose. The data structure stores both the quaternions and the matrix forms. It supports IO functionality together with various helper methods to work with poses
C cv::ppf_match_3d::PoseCluster3D	When multiple poses (see Pose3D) are grouped together (contribute to the same transformation) pose clusters occur. This class is a general container for such groups of poses. It is possible to store, load and perform IO on these poses
C cv::kinfu::detail::PoseGraph
C cv::ppf_match_3d::PPF3DDetector	Class, allowing the load and matching 3D models. Typical Use:
► C cv::face::PredictCollector	Abstract base class for all strategies of prediction result handling
C cv::face::StandardCollector	Default predict collector
C cv::face::StandardCollector::PredictResult
C cv::ocl::Program
C cv::ocl::ProgramSource
C cv::kinfu::Intr::Projector
► C cv::detail::ProjectorBase	Base class for warping logic implementation
C cv::detail::CompressedRectilinearPortraitProjector
C cv::detail::CompressedRectilinearProjector
C cv::detail::CylindricalPortraitProjector
C cv::detail::CylindricalProjector
C cv::detail::FisheyeProjector
C cv::detail::MercatorProjector
C cv::detail::PaniniPortraitProjector
C cv::detail::PaniniProjector
C cv::detail::PlanePortraitProjector
C cv::detail::PlaneProjector
C cv::detail::SphericalPortraitProjector
C cv::detail::SphericalProjector
C cv::detail::StereographicProjector
C cv::detail::TransverseMercatorProjector
C cv::stereo::PropagationParameters
► C cv::videostab::PyrLkOptFlowEstimatorBase
C cv::videostab::SparsePyrLkOptFlowEstimator
C cv::PyRotationWarper
C cv::QRCodeDetector
C cv::QtFont	QtFont available only for Qt. See cv::fontQt
C cv::Subdiv2D::QuadEdge
C cv::linemod::QuantizedPyramid	Represents a modality operating over an image pyramid
C cv::stereo::QuasiDenseStereo	Class containing the methods needed for Quasi Dense Stereo computation
C cv::Quat< _Tp >
C cv::QuatEnum
C cv::ocl::Queue
C cv::randpattern::RandomPatternCornerFinder	Class for finding features points and corresponding 3D in world coordinate of a "random" pattern, which can be to be used in calibration. It is useful when pattern is partly occluded or only a part of pattern can be observed in multiple cameras calibration. The pattern can be generated by RandomPatternGenerator class described in this file
C cv::randpattern::RandomPatternGenerator
► C cv::Range	Template class specifying a continuous subsequence (slice) of a sequence
C cv::dnn::_Range
C cv::videostab::RansacParams	Describes RANSAC method parameters
C cv::Allocator< _Tp >::rebind< U >
C cv::Rect_< _Tp >	Template class for 2D rectangles
C cv::kinfu::Intr::Reprojector	Camera intrinsics
C cv::bioinspired::RetinaParameters	Retina model parameters structure
► C cv::rgbd::RgbdFrame
C cv::rgbd::OdometryFrame
C cv::optflow::RLOFOpticalFlowParameter	This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm
C cv::RNG	Random Number Generator
C cv::RNG_MT19937	Mersenne Twister random number generator
C cv::RotatedRect	The class represents rotated (i.e. not up-right) rectangles on a plane
► C cv::detail::RotationWarper	Rotation-only model image warper interface
► C cv::detail::RotationWarperBase< CompressedRectilinearPortraitProjector >
C cv::detail::CompressedRectilinearPortraitWarper
► C cv::detail::RotationWarperBase< CompressedRectilinearProjector >
C cv::detail::CompressedRectilinearWarper
► C cv::detail::RotationWarperBase< CylindricalPortraitProjector >
C cv::detail::CylindricalPortraitWarper
► C cv::detail::RotationWarperBase< CylindricalProjector >
► C cv::detail::CylindricalWarper	Warper that maps an image onto the x*x + z*z = 1 cylinder
C cv::detail::CylindricalWarperGpu
► C cv::detail::RotationWarperBase< FisheyeProjector >
C cv::detail::FisheyeWarper
► C cv::detail::RotationWarperBase< MercatorProjector >
C cv::detail::MercatorWarper
► C cv::detail::RotationWarperBase< PaniniPortraitProjector >
C cv::detail::PaniniPortraitWarper
► C cv::detail::RotationWarperBase< PaniniProjector >
C cv::detail::PaniniWarper
► C cv::detail::RotationWarperBase< PlanePortraitProjector >
C cv::detail::PlanePortraitWarper
► C cv::detail::RotationWarperBase< PlaneProjector >
► C cv::detail::PlaneWarper	Warper that maps an image onto the z = 1 plane
C cv::detail::AffineWarper	Affine warper that uses rotations and translations
C cv::detail::PlaneWarperGpu
► C cv::detail::RotationWarperBase< SphericalPortraitProjector >
C cv::detail::SphericalPortraitWarper
► C cv::detail::RotationWarperBase< SphericalProjector >
► C cv::detail::SphericalWarper	Warper that maps an image onto the unit sphere located at the origin
C cv::detail::SphericalWarperGpu
► C cv::detail::RotationWarperBase< StereographicProjector >
C cv::detail::StereographicWarper
► C cv::detail::RotationWarperBase< TransverseMercatorProjector >
C cv::detail::TransverseMercatorWarper
C cv::detail::RotationWarperBase< P >	Base class for rotation-based warper using a detail::ProjectorBase_ derived class
C cv::traits::SafeFmt< T, available >
C cv::traits::SafeFmt< T, false >
C cv::traits::SafeFmt< T, true >
C cv::traits::SafeType< T, available >
C cv::traits::SafeType< T, false >
C cv::traits::SafeType< T, true >
► C cv::detail::SeamFinder	Base class for a seam estimator
C cv::detail::DpSeamFinder
C cv::detail::GraphCutSeamFinder	Minimum graph cut-based seam estimator. See details in [V03]
C cv::detail::NoSeamFinder	Stub seam estimator which does nothing
► C cv::detail::PairwiseSeamFinder	Base class for all pairwise seam estimators
C cv::detail::VoronoiSeamFinder	Voronoi diagram-based seam estimator
C cv::bioinspired::SegmentationParameters	Parameter structure that stores the transient events detector setup parameters
► C std::shared_ptr
C cv::Ptr< T >
C cv::SimilarRects
C cv::Size_< _Tp >	Template class for specifying the size of an image or rectangle
C cv::Size_< float >
C cv::SL2< T >
C cv::softdouble
C cv::softfloat
► C cv::SparseMat	The class SparseMat represents multi-dimensional sparse numerical arrays
C cv::SparseMat_< _Tp >	Template sparse n-dimensional array class derived from SparseMat
► C cv::SparseMatConstIterator	Read-Only Sparse Matrix Iterator
► C cv::SparseMatConstIterator_< _Tp >	Template Read-Only Sparse Matrix Iterator Class
C cv::SparseMatIterator_< _Tp >	Template Read-Write Sparse Matrix Iterator Class
C cv::SparseMatIterator	Read-write Sparse Matrix Iterator
► C cv::videostab::StabilizerBase
C cv::videostab::OnePassStabilizer
C cv::videostab::TwoPassStabilizer
C cv::Stitcher	High level image stitcher
C cv::cuda::Stream	This class encapsulates a queue of asynchronous calls
C cv::cuda::StreamAccessor	Class that enables getting cudaStream_t from cuda::Stream
C cv::detail::StrongClassifierDirectSelection
C cv::detail::tracking::online_boosting::StrongClassifierDirectSelection
C cv::detail::tracking::StrongClassifierDirectSelection
C cv::Subdiv2D
C cv::cuda::SURF_CUDA	Class used for extracting Speeded Up Robust Features (SURF) from an image. :
C cv::SVD	Singular Value Decomposition
C cv::cuda::TargetArchs	Class providing a set of static methods to check what NVIDIA* card architecture the CUDA module was built for
C cv::linemod::Template
C cv::TermCriteria	The class defining termination criteria for iterative algorithms
► C cv::text::TextDetector	An abstract class providing interface for text detection algorithms
C cv::text::TextDetectorCNN	TextDetectorCNN class provides the functionallity of text bounding box detection. This class is representing to find bounding boxes of text words given an input image. This class uses OpenCV dnn module to load pre-trained model described in [LiaoSBWL17]. The original repository with the modified SSD Caffe version: https://github.com/MhLiao/TextBoxes. Model can be downloaded from DropBox. Modified .prototxt file with the model description can be found in opencv_contrib/modules/text/samples/textbox.prototxt
C cv::face::FacemarkAAM::Model::Texture
C cv::ogl::Texture2D	Smart pointer for OpenGL 2D texture memory with reference counting
C cv::ppf_match_3d::THash	Struct, holding a node in the hashtable
C cv::TickMeter	Class to measure passing time
► C cv::detail::Timelapser
C cv::detail::TimelapserCrop
C cv::ocl::Timer
► C cv::TLSDataContainer
► C cv::TLSData< T >	Simple TLS data class
C cv::TLSDataAccumulator< T >	TLS data accumulator with gathering methods
C cv::detail::tracking::tbm::Track	Describes tracks
C cv::detail::tracking::tbm::TrackedObject	The TrackedObject struct defines properties of detected object
C cv::DetectionBasedTracker::TrackedObject
► C cv::Tracker	Base abstract class for the long-term tracker
C cv::TrackerDaSiamRPN
C cv::TrackerGOTURN	GOTURN (Generic Object Tracking Using Regression Networks) tracker
C cv::TrackerMIL	The MIL algorithm trains a classifier in an online manner to separate the object from the background
C cv::tracking::TrackerCSRT	CSRT tracker
C cv::tracking::TrackerKCF	KCF (Kernelized Correlation Filter) tracker
C cv::legacy::TrackerBoosting	Boosting tracker
C cv::legacy::tracking::TrackerBoosting	Boosting tracker
C cv::detail::TrackerContribFeatureSet	Class that manages the extraction and selection of features
C cv::detail::tracking::TrackerContribFeatureSet	Class that manages the extraction and selection of features
C cv::detail::TrackerContribSampler	Class that manages the sampler in order to select regions for the update the model of the tracker [AAM] Sampling e Labeling. See table I and section III B
C cv::detail::tracking::TrackerContribSampler	Class that manages the sampler in order to select regions for the update the model of the tracker [AAM] Sampling e Labeling. See table I and section III B
C cv::legacy::TrackerCSRT	CSRT tracker
C cv::legacy::tracking::TrackerCSRT	CSRT tracker
► C cv::detail::TrackerFeature	Abstract base class for TrackerFeature that represents the feature
► C cv::detail::tracking::TrackerContribFeature	Abstract base class for TrackerContribFeature that represents the feature
C cv::detail::tracking::TrackerContribFeatureHAAR	TrackerContribFeature based on HAAR features, used by TrackerMIL and many others algorithms
C cv::detail::tracking::TrackerFeatureFeature2d	TrackerContribFeature based on Feature2D
C cv::detail::tracking::TrackerFeatureHOG	TrackerContribFeature based on HOG
C cv::detail::tracking::TrackerFeatureLBP	TrackerContribFeature based on LBP
► C cv::detail::tracking::TrackerFeature	Abstract base class for TrackerFeature that represents the feature
C cv::detail::tracking::TrackerContribFeature	Abstract base class for TrackerContribFeature that represents the feature
C cv::detail::TrackerFeatureSet	Class that manages the extraction and selection of features
C cv::detail::tracking::TrackerFeatureSet	Class that manages the extraction and selection of features
C cv::legacy::TrackerKCF	KCF (Kernelized Correlation Filter) tracker
C cv::legacy::tracking::TrackerKCF	KCF (Kernelized Correlation Filter) tracker
C cv::legacy::TrackerMedianFlow	Median Flow tracker
C cv::legacy::tracking::TrackerMedianFlow	Median Flow tracker
C cv::legacy::TrackerMIL	The MIL algorithm trains a classifier in an online manner to separate the object from the background
C cv::legacy::tracking::TrackerMIL	The MIL algorithm trains a classifier in an online manner to separate the object from the background
C cv::detail::TrackerModel	Abstract class that represents the model of the target
C cv::detail::tracking::TrackerModel	Abstract class that represents the model of the target
C cv::legacy::TrackerMOSSE	MOSSE (Minimum Output Sum of Squared Error) tracker
C cv::legacy::tracking::TrackerMOSSE	MOSSE (Minimum Output Sum of Squared Error) tracker
C cv::detail::tracking::tbm::TrackerParams	The TrackerParams struct stores parameters of TrackerByMatching
C cv::detail::TrackerSampler	Class that manages the sampler in order to select regions for the update the model of the tracker [AAM] Sampling e Labeling. See table I and section III B
C cv::detail::tracking::TrackerSampler	Class that manages the sampler in order to select regions for the update the model of the tracker [AAM] Sampling e Labeling. See table I and section III B
► C cv::detail::TrackerSamplerAlgorithm	Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler
► C cv::detail::tracking::TrackerContribSamplerAlgorithm	Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler
C cv::detail::tracking::TrackerContribSamplerCSC	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL
C cv::detail::tracking::TrackerSamplerCS	TrackerContribSampler based on CS (current state), used by algorithm TrackerBoosting
C cv::detail::tracking::TrackerSamplerPF	This sampler is based on particle filtering
C cv::detail::tracking::TrackerSamplerCSC	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL
► C cv::detail::tracking::TrackerSamplerAlgorithm	Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler
C cv::detail::tracking::TrackerContribSamplerAlgorithm	Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler
C cv::detail::tracking::TrackerSamplerCSC	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL
► C cv::detail::TrackerStateEstimator	Abstract base class for TrackerStateEstimator that estimates the most likely target state
C cv::detail::tracking::TrackerStateEstimatorAdaBoosting	TrackerStateEstimatorAdaBoosting based on ADA-Boosting
C cv::detail::tracking::TrackerStateEstimatorSVM	TrackerStateEstimator based on SVM
► C cv::detail::tracking::TrackerStateEstimator	Abstract base class for TrackerStateEstimator that estimates the most likely target state
C cv::detail::tracking::TrackerStateEstimatorAdaBoosting	TrackerStateEstimatorAdaBoosting based on ADA-Boosting
C cv::detail::tracking::TrackerStateEstimatorSVM	TrackerStateEstimator based on SVM
► C cv::detail::TrackerTargetState	Abstract base class for TrackerTargetState that represents a possible state of the target
C cv::detail::tracking::TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState	Implementation of the target state for TrackerAdaBoostingTargetState
► C cv::detail::tracking::TrackerTargetState	Abstract base class for TrackerTargetState that represents a possible state of the target
C cv::detail::tracking::TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState	Implementation of the target state for TrackerAdaBoostingTargetState
C cv::legacy::TrackerTLD	TLD (Tracking, learning and detection) tracker
C cv::legacy::tracking::TrackerTLD	TLD (Tracking, learning and detection) tracker
► C std::true_type
C cv::has_custom_delete< T, typename std::enable_if< sfinae::has_parenthesis_operator< DefaultDeleter< T >, void, T * >::value >::type >
C cv::traits::Type< T >
C cv::traits::Type< Complex< _Tp > >
C cv::traits::Type< Matx< _Tp, m, n > >
C cv::traits::Type< Moments >
C cv::traits::Type< Point3_< _Tp > >
C cv::traits::Type< Point_< _Tp > >
C cv::traits::Type< Range >
C cv::traits::Type< Rect_< _Tp > >
C cv::traits::Type< RotatedRect >
C cv::traits::Type< Scalar_< _Tp > >
C cv::traits::Type< Size_< _Tp > >
C cv::traits::Type< Vec< _Tp, cn > >
C cv::detail::tracking::kalman_filters::UkfSystemModel	Model of dynamical system for Unscented Kalman filter. The interface for dynamical system model. It contains functions for computing the next state and the measurement. It must be inherited for using UKF
C cv::detail::tracking::UkfSystemModel	Model of dynamical system for Unscented Kalman filter. The interface for dynamical system model. It contains functions for computing the next state and the measurement. It must be inherited for using UKF
C cv::detail::UkfSystemModel	Model of dynamical system for Unscented Kalman filter. The interface for dynamical system model. It contains functions for computing the next state and the measurement. It must be inherited for using UKF
C cv::UMat
C cv::UMatData
C cv::detail::tracking::kalman_filters::UnscentedKalmanFilter	The interface for Unscented Kalman filter and Augmented Unscented Kalman filter
C cv::detail::tracking::UnscentedKalmanFilter	The interface for Unscented Kalman filter and Augmented Unscented Kalman filter
C cv::detail::UnscentedKalmanFilter	The interface for Unscented Kalman filter and Augmented Unscented Kalman filter
► C cv::detail::tracking::kalman_filters::UnscentedKalmanFilterParams	Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter
C cv::detail::tracking::kalman_filters::AugmentedUnscentedKalmanFilterParams	Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter
► C cv::detail::tracking::UnscentedKalmanFilterParams	Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter
C cv::detail::tracking::kalman_filters::AugmentedUnscentedKalmanFilterParams	Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter
► C cv::detail::UnscentedKalmanFilterParams	Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter
C cv::detail::tracking::kalman_filters::AugmentedUnscentedKalmanFilterParams	Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter
C cv::UsacParams
C cv::ocl::Context::UserContext
C cv::v_float32x4
C cv::v_float64x2
C cv::v_int16x8
C cv::v_int32x4
C cv::v_int64x2
C cv::v_int8x16
C cv::v_reg< _Tp, n >
C cv::V_TypeTraits< _Tp >
C cv::v_uint16x8
C cv::v_uint32x4
C cv::v_uint64x2
C cv::v_uint8x16
C cv::multicalib::MultiCameraCalibration::vertex
C cv::Subdiv2D::Vertex
C cv::vfloat32mf2_t
C cv::vfloat64mf2_t
C cv::VideoCapture	Class for video capturing from video files, image sequences or cameras
C cv::VideoWriter	Video writer class
C cv::vint16mf2_t
C cv::vint32mf2_t
C cv::vint64mf2_t
C cv::vint8mf2_t
C cv::vint8mf4_t
C cv::kinfu::Volume
C cv::kinfu::VolumeParams
C cv::vuint16mf2_t
C cv::vuint32mf2_t
C cv::vuint64mf2_t
C cv::vuint8mf2_t
C cv::vuint8mf4_t
► C cv::WarperCreator	Image warper factories base class
C cv::AffineWarper	Affine warper factory class
C cv::CompressedRectilinearPortraitWarper
C cv::CompressedRectilinearWarper
C cv::CylindricalWarper	Cylindrical warper factory class
C cv::FisheyeWarper
C cv::MercatorWarper
C cv::PaniniPortraitWarper
C cv::PaniniWarper
C cv::PlaneWarper	Plane warper factory class
C cv::SphericalWarper	Spherical warper factory class
C cv::StereographicWarper
C cv::TransverseMercatorWarper
C cv::xobjdetect::WBDetector	WaldBoost detector
C cv::detail::tracking::online_boosting::WeakClassifierHaarFeature
C cv::detail::tracking::WeakClassifierHaarFeature
C cv::detail::WeakClassifierHaarFeature
C cv::wechat_qrcode::WeChatQRCode	WeChat QRCode includes two CNN-based models: A object detection model and a super resolution model. Object detection model is applied to detect QRCode with the bounding box. super resolution model is applied to zoom in QRCode when it is small
► C cv::videostab::WobbleSuppressorBase
► C cv::videostab::MoreAccurateMotionWobbleSuppressorBase
C cv::videostab::MoreAccurateMotionWobbleSuppressor
C cv::videostab::NullWobbleSuppressor