Class List

Here are the classes, structs and unions with brief descriptions.

• cv
  • _InputArray — This is the proxy class for passing read-only input arrays into OpenCV functions.
  • _InputOutputArray
  • _OutputArray — This type is very similar to InputArray except that it is used for input/output and output function parameters.
  • Accumulator
  • Accumulator< char >
  • Accumulator< short >
  • Accumulator< unsigned char >
  • Accumulator< unsigned short >
  • Affine3 — Affine transform.
  • AffineFeature — Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in YM11 .
  • AffineTransformer — Wrapper class for the OpenCV Affine Transformation algorithm. :
  • AffineWarper — Affine warper factory class.
  • Algorithm — This is a base class for all more or less complex algorithms in OpenCV.
  • AlignExposures — The base class for algorithms that align images of the same scene with different exposures.
  • 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.
  • ALIKED — ALIKED feature detector and descriptor extractor.
  • Allocator
  • Animation — Represents an animation with multiple frames. The Animation struct is designed to store and manage data for animated sequences such as those from animated formats (e.g., GIF, AVIF, APNG, WebP). It provides support for looping, background color settings, frame timing, and frame storage.
  • ANNIndex
  • AsyncArray — Returns result of asynchronous operations.
  • AsyncPromise — Provides result of asynchronous operations.
  • AutoBuffer — Automatically Allocated Buffer Class.
  • AVIReadContainer
  • AVIWriteContainer
  • BackgroundSubtractor — Base class for background/foreground segmentation. :
  • BackgroundSubtractorKNN — K-nearest neighbours - based Background/Foreground Segmentation Algorithm.
  • BackgroundSubtractorMOG2 — Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
  • BaseCascadeClassifier
  • bfloat
  • BFMatcher — Brute-force descriptor matcher.
  • BufferPoolController
  • CalibrateCRF — The base class for camera response calibration algorithms.
  • 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.
  • 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.
  • CascadeClassifier — Cascade classifier class for object detection.
  • ChiHistogramCostExtractor — An Chi based cost extraction. :
  • CirclesGridFinderParameters
  • CLAHE — Base class for Contrast Limited Adaptive Histogram Equalization.
  • CommandLineParser — Designed for command line parsing.
  • Complex — A complex number class.
  • CompressedRectilinearPortraitWarper
  • CompressedRectilinearWarper
  • ConjGradSolver — This class is used to perform the non-linear non-constrained minimization of a function with known gradient,.
  • CylindricalWarper — Cylindrical warper factory class.
  • DataDepth — A helper class for cv::DataType.
  • DataType — Template "trait" class for OpenCV primitive data types.
  • DefaultDeleter< CvHaarClassifierCascade >
  • DenseOpticalFlow
  • DescriptorMatcher — Abstract base class for matching keypoint descriptors.
  • DetectionBasedTracker
  • DetectionROI — struct for detection region of interest (ROI)
  • DISK — DISK feature detector and descriptor, based on a DNN model.
  • DISOpticalFlow — DIS optical flow algorithm.
  • DMatch — Class for matching keypoint descriptors.
  • DownhillSolver — This class is used to perform the non-linear non-constrained minimization of a function,.
  • DualQuat
  • ECCParameters — struct ECCParameters is used by findTransformECCMultiScale
  • EMDHistogramCostExtractor — An EMD based cost extraction. :
  • EMDL1HistogramCostExtractor — An EMD-L1 based cost extraction. :
  • Exception — Class passed to an error.
  • FaceDetectorYN — DNN-based face detector.
  • FaceRecognizerSF — DNN-based face recognizer.
  • FarnebackOpticalFlow — Class computing a dense optical flow using the Gunnar Farneback's algorithm.
  • FastFeatureDetector — Wrapping class for feature detection using the FAST method.
  • Feature2D — Abstract base class for 2D image feature detectors and descriptor extractors.
  • FileNode — File Storage Node class.
  • FileNodeIterator — used to iterate through sequences and mappings.
  • FileStorage — XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or reading data to/from a file.
  • Filter2DParams
  • FisheyeWarper
  • FlannBasedMatcher — Flann-based descriptor matcher.
  • FontFace — Wrapper on top of a truetype/opentype/etc font, i.e. Freetype's FT_Face.
  • Formatted
  • Formatter
  • GArg
  • GArray — cv::GArray<T> template class represents a list of objects of class T in the graph.
  • GArrayDesc
  • GCall
  • GCompileArg — Represents an arbitrary compilation argument.
  • GCompiled — Represents a compiled computation (graph). Can only be used with image / data formats & resolutions it was compiled for, with some exceptions.
  • GComputation — GComputation class represents a captured computation graph. GComputation objects form boundaries for expression code user writes with G-API, allowing to compile and execute it.
  • GComputationT — This class is a typed wrapper over a regular GComputation.
  • GComputationT< R(Args...)>
  • GCPUContext
  • GCPUKernel
  • GCPUKernelImpl
  • GCPUStKernelImpl
  • GeneralizedHough — finds arbitrary template in the grayscale image using Generalized Hough Transform
  • GeneralizedHoughBallard — finds arbitrary template in the grayscale image using Generalized Hough Transform
  • GeneralizedHoughGuil — finds arbitrary template in the grayscale image using Generalized Hough Transform
  • GFluidKernel
  • GFluidKernelImpl
  • GFluidOutputRois — This structure allows to control the output image region which Fluid backend will produce in the graph.
  • GFluidParallelFor — This structure allows to customize the way how Fluid executes parallel regions.
  • GFluidParallelOutputRois — This structure forces Fluid backend to generate multiple parallel output regions in the graph. These regions execute in parallel.
  • GFrame — GFrame class represents an image or media frame in the graph.
  • GFrameDesc
  • GFTTDetector — Wrapping class for feature detection using the goodFeaturesToTrack function. :
  • GInfer
  • GInferBase
  • GInferList
  • GInferList2
  • GInferList2Base
  • GInferListBase
  • GInferROI
  • GInferROIBase
  • GIOProtoArgs
  • GKernel
  • GKernelImpl
  • GKernelPackage — A container class for heterogeneous kernel implementation collections and graph transformations.
  • GKernelType
  • GKernelTypeM
  • GMat — GMat class represents image or tensor data in the graph.
  • GMatDesc
  • GMatP
  • GNetworkType
  • GOCLContext
  • GOCLKernel
  • GOCLKernelImpl
  • GOpaque — cv::GOpaque<T> template class represents an object of class T in the graph.
  • GOpaqueDesc
  • GPlaidMLContext
  • GPlaidMLKernel
  • GPlaidMLKernelImpl
  • graph_dump_path — Ask G-API to dump compiled graph in Graphviz format under the given file name.
  • GraphicalCodeDetector
  • GRunArg
  • GScalar — GScalar class represents cv::Scalar data in the graph.
  • GScalarDesc
  • GStreamingCompiled — Represents a computation (graph) compiled for streaming.
  • GTransform
  • GTransformImpl
  • GTypeInfo
  • Hamming
  • HausdorffDistanceExtractor — A simple Hausdorff distance measure between shapes defined by contours.
  • hfloat
  • HistogramCostExtractor — Abstract base class for histogram cost algorithms.
  • HOGDescriptor — Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
  • ImageCollection — To read multi-page images on demand.
  • In_Tag
  • InferAPI
  • InferAPIList
  • InferAPIList2
  • InferAPIRoi
  • IStreamReader — Read data stream interface.
  • KalmanFilter — Kalman filter class.
  • KeyPoint — Data structure for salient point detectors.
  • KeyPointsFilter — A class filters a vector of keypoints.
  • L1
  • L2
  • LDA — Linear Discriminant Analysis.
  • LevMarq — Levenberg-Marquadt solver.
  • LightGlueMatcher — LightGlue feature matcher.
  • LineIterator — Class for iterating over all pixels on a raster line segment.
  • LineSegmentDetector — Line segment detector class.
  • Mat — Comma-separated Matrix Initializer.
  • Mat_ — Template matrix class derived from Mat.
  • MatAllocator — Custom array allocator.
  • MatConstIterator
  • MatConstIterator_ — Matrix read-only iterator.
  • 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 ( cv::Mat ), s for a cv::Scalar, alpha for a real-valued scalar ( double )):
  • MatIterator_ — Matrix read-write iterator.
  • MatOp
  • MatShape — Represents shape of a matrix/tensor. Previously, MatShape was defined as an alias of std::vector<int>, but now we use a special structure that provides a few extra benefits:
  • Matx — Template class for small matrices whose type and size are known at compilation time.
  • MediaFrame — cv::MediaFrame class represents an image/media frame obtained from an external source.
  • MercatorWarper
  • MergeDebevec — The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response.
  • MergeExposures — The base class algorithms that can merge exposure sequence to a single image.
  • MergeMertens — Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids.
  • MergeRobertson — The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response.
  • MinProblemSolver — Basic interface for all solvers.
  • Moments — struct returned by cv::moments
  • MSER — Maximally stable extremal region extractor.
  • MSTEdge — Represents an edge in a graph for Minimum Spanning Tree (MST) computation.
  • NAryMatIterator — n-ary multi-dimensional array iterator.
  • Node
  • NormHistogramCostExtractor — A norm based cost extraction. :
  • Octree — Octree for 3D vision.
  • Odometry
  • OdometryFrame — An object that keeps per-frame data for Odometry algorithms from user-provided images to algorithm-specific precalculated data. When not empty, it contains a depth image, a mask of valid pixels and a set of pyramids generated from that data. A BGR/Gray image and normals are optional. OdometryFrame is made to be used together with Odometry class to reuse precalculated data between Rt data calculations. A correct way to do that is to call Odometry::prepareFrames() on prev and next frames and then pass them to Odometry::compute() method.
  • OdometrySettings
  • ORB — Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor.
  • Out_Tag
  • PaniniPortraitWarper
  • PaniniWarper
  • ParallelLoopBody — Base class for parallel data processors.
  • ParallelLoopBodyLambdaWrapper
  • ParamType
  • ParamType< Algorithm >
  • ParamType< bool >
  • ParamType< double >
  • ParamType< float >
  • ParamType< Mat >
  • ParamType< Scalar >
  • ParamType< String >
  • ParamType< uchar >
  • ParamType< uint64 >
  • ParamType< unsigned >
  • PCA — Principal Component Analysis.
  • PlaneWarper — Plane warper factory class.
  • PluginStreamReader
  • Point3_ — Template class for 3D points specified by its coordinates x, y and z.
  • Point_ — Template class for 2D points specified by its coordinates x and y.
  • PyRotationWarper
  • QRCodeDetector — QR code detector.
  • QRCodeDetectorAruco — QR code detector based on Aruco markers detection code.
  • QRCodeEncoder — QR code encoder.
  • QtFont — QtFont available only for Qt. See cv::fontQt.
  • Quat
  • QuatEnum
  • Range — Template class specifying a continuous subsequence (slice) of a sequence.
  • Rect_ — Template class for 2D rectangles.
  • RegionGrowing3D — Region Growing algorithm in 3D point cloud.
  • RgbdNormals
  • RMat
  • RNG — Random Number Generator.
  • RNG_MT19937 — Mersenne Twister random number generator.
  • RotatedRect — The class represents rotated (i.e. not up-right) rectangles on a plane.
  • SACSegmentation — Sample Consensus algorithm segmentation of 3D point cloud model.
  • Scalar_ — Template class for a 4-element vector derived from Vec.
  • ShapeContextDistanceExtractor — Implementation of the Shape Context descriptor and matching algorithm.
  • ShapeDistanceExtractor — Abstract base class for shape distance algorithms.
  • ShapeTransformer — Abstract base class for shape transformation algorithms.
  • SIFT — Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe Lowe04 .
  • SimilarRects — This class is used for grouping object candidates detected by Cascade Classifier, HOG etc.
  • SimpleBlobDetector — Class for extracting blobs from an image. :
  • Size_ — Template class for specifying the size of an image or rectangle.
  • SL2
  • softdouble
  • softfloat
  • SparseMat — The class SparseMat represents multi-dimensional sparse numerical arrays.
  • SparseMat_ — Template sparse n-dimensional array class derived from SparseMat.
  • SparseMatConstIterator — Read-Only Sparse Matrix Iterator.
  • SparseMatConstIterator_ — Template Read-Only Sparse Matrix Iterator Class.
  • SparseMatIterator — Read-write Sparse Matrix Iterator.
  • SparseMatIterator_ — Template Read-Write Sparse Matrix Iterator Class.
  • SparseOpticalFlow — Base interface for sparse optical flow algorithms.
  • SparsePyrLKOpticalFlow — Class used for calculating a sparse optical flow.
  • SphericalWarper — Spherical warper factory class.
  • StereoBM — Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige.
  • StereographicWarper
  • StereoMatcher — The base class for stereo correspondence algorithms.
  • StereoSGBM — The class implements the modified H. Hirschmuller algorithm HH08 that differs from the original one as follows:
  • Stitcher — High level image stitcher.
  • Subdiv2D
  • SVD — Singular Value Decomposition.
  • TermCriteria — The class defining termination criteria for iterative algorithms.
  • ThinPlateSplineShapeTransformer — Definition of the transformation.
  • TickMeter — a Class to measure passing time.
  • TLSData — Simple TLS data class.
  • TLSDataAccumulator — TLS data accumulator with gathering methods.
  • TLSDataContainer
  • Tonemap — Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
  • TonemapDrago — Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain.
  • 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.
  • TonemapReinhard — This is a global tonemapping operator that models human visual system.
  • Tracker — Base abstract class for the long-term tracker.
  • TrackerCSRT — the CSRT tracker
  • TrackerDaSiamRPN
  • TrackerKCF — the KCF (Kernelized Correlation Filter) tracker
  • TrackerMIL — The MIL algorithm trains a classifier in an online manner to separate the object from the background.
  • TrackerNano — the Nano tracker is a super lightweight dnn-based general object tracking.
  • TrackerVit — the VIT tracker is a super lightweight dnn-based general object tracking.
  • TransverseMercatorWarper
  • TriangleRasterizeSettings — Structure to keep settings for rasterization.
  • UMat
  • UsacParams
  • use_threaded_executor — Ask G-API to use threaded executor when cv::GComputation is compiled via cv::GComputation::compile method.
  • v_reg
  • V_TypeTraits
  • VariationalRefinement — Variational optical flow refinement.
  • Vec — Template class for short numerical vectors, a partial case of Matx.
  • VideoCapture — Class for video capturing from video files, image sequences or cameras.
  • VideoWriter — Video writer class.
  • Volume
  • VolumeSettings
  • WarperCreator — Image warper factories base class.
• cv::ALIKED
  • Params
• cv::Allocator
  • rebind
• cv::aruco
  • ArucoDetector — The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method.
  • Board — Board of ArUco markers.
  • CharucoBoard — ChArUco board is a planar chessboard where the markers are placed inside the white squares of a chessboard.
  • CharucoDetector
  • CharucoParameters
  • DetectorParameters — struct DetectorParameters is used by ArucoDetector
  • Dictionary — Dictionary is a set of unique ArUco markers of the same size.
  • GridBoard — Planar board with grid arrangement of markers.
  • RefineParameters — struct RefineParameters is used by ArucoDetector
• cv::barcode
  • BarcodeDetector
• cv::BaseCascadeClassifier
  • MaskGenerator
• cv::bgsegm
  • BackgroundSubtractorCNT — Background subtraction based on counting.
  • BackgroundSubtractorGMG — Background Subtractor module based on the algorithm given in Gold2012 .
  • BackgroundSubtractorGSOC — Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
  • BackgroundSubtractorLSBP — Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at LGuo2016.
  • BackgroundSubtractorLSBPDesc — This is for calculation of the LSBP descriptors.
  • BackgroundSubtractorMOG — Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
  • SyntheticSequenceGenerator — Synthetic frame sequence generator for testing background subtraction algorithms.
• cv::bioinspired
  • Retina — class which allows the Gipsa/Listic Labs model to be used with OpenCV.
  • RetinaFastToneMapping — a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV.
  • RetinaParameters — retina model parameters structure
  • SegmentationParameters — parameter structure that stores the transient events detector setup parameters
  • TransientAreasSegmentationModule — class which provides a transient/moving areas segmentation module
• cv::bioinspired::RetinaParameters
  • IplMagnoParameters
  • OPLandIplParvoParameters
• cv::cann
  • AscendEvent — AscendEvent to synchronize between different streams.
  • AscendEventAccessor — Class that enables getting aclrtAscendEvent from cann::AscendEvent.
  • AscendMat
  • AscendStream — In AscendCL Stream(AscendStream) is a task queue. Stream is used to manage the parallelism of tasks. The tasks inside a Stream are executed sequentially, that is, the Stream executes sequentially according to the sent tasks; the tasks in different Streams are executed in parallel.
  • AscendStreamAccessor — Class that enables getting aclrtAscendStream from cann::AscendStream.
• cv::cann::AscendEvent
  • Impl
• cv::cann::AscendMat
  • Allocator
• cv::cann::AscendStream
  • Impl
• cv::ccalib
  • CustomPattern
• cv::ccm
  • ColorCorrectionModel — Core class of ccm model.
• cv::cnn_3dobj
  • descriptorExtractor — Caffe based 3D images descriptor. A class to extract features from an image. The so obtained descriptors can be used for classification and pose estimation goals wohlhart15.
  • icoSphere — Icosohedron based camera view data generator. The class create some sphere views of camera towards a 3D object meshed from .ply files hinterstoisser2008panter .
• cv::colored_kinfu
  • ColoredKinFu — KinectFusion implementation.
  • Params
• cv::cuda
  • BackgroundSubtractorFGD — The class discriminates between foreground and background pixels by building and maintaining a model of the background.
  • BackgroundSubtractorGMG — Background/Foreground Segmentation Algorithm.
  • BackgroundSubtractorMOG — Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
  • BackgroundSubtractorMOG2 — Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
  • BroxOpticalFlow — Class computing the optical flow for two images using Brox et al Optical Flow algorithm (Brox2004).
  • BufferPool — BufferPool for use with CUDA streams.
  • CannyEdgeDetector — Base class for Canny Edge Detector. :
  • CascadeClassifier — Cascade classifier class used for object detection. Supports HAAR and LBP cascades. :
  • CLAHE — Base class for Contrast Limited Adaptive Histogram Equalization. :
  • Convolution — Base class for convolution (or cross-correlation) operator. :
  • CornernessCriteria — Base class for Cornerness Criteria computation. :
  • CornersDetector — Base class for Corners Detector. :
  • DenseOpticalFlow — Base interface for dense optical flow algorithms.
  • DensePyrLKOpticalFlow — Class used for calculating a dense optical flow.
  • DescriptorMatcher — Abstract base class for matching keypoint descriptors.
  • DeviceInfo — Class providing functionality for querying the specified GPU properties.
  • DFT — Base class for DFT operator as a cv::Algorithm. :
  • DisparityBilateralFilter — Class refining a disparity map using joint bilateral filtering. :
  • Event
  • EventAccessor — Class that enables getting cudaEvent_t from cuda::Event.
  • FarnebackOpticalFlow — Class computing a dense optical flow using the Gunnar Farneback's algorithm.
  • FastFeatureDetector — Wrapping class for feature detection using the FAST method.
  • FastOpticalFlowBM
  • Feature2DAsync — Abstract base class for CUDA asynchronous 2D image feature detectors and descriptor extractors.
  • FGDParams
  • Filter — Common interface for all CUDA filters :
  • GpuData
  • GpuMat — Base storage class for GPU memory with reference counting.
  • GpuMatND
  • HOG — The class implements Histogram of Oriented Gradients (Dalal2005) object detector.
  • HostMem — Class with reference counting wrapping special memory type allocation functions from CUDA.
  • HoughCirclesDetector — Base class for circles detector algorithm. :
  • HoughLinesDetector — Base class for lines detector algorithm. :
  • HoughSegmentDetector — Base class for line segments detector algorithm. :
  • ImagePyramid
  • LookUpTable — Base class for transform using lookup table.
  • NvidiaHWOpticalFlow — Base Interface for optical flow algorithms using NVIDIA Optical Flow SDK.
  • NvidiaOpticalFlow_1_0 — Class for computing the optical flow vectors between two images using NVIDIA Optical Flow hardware and Optical Flow SDK 1.0.
  • NvidiaOpticalFlow_2_0 — Class for computing the optical flow vectors between two images using NVIDIA Optical Flow hardware and Optical Flow SDK 2.0.
  • OpticalFlowDual_TVL1 — Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method.
  • ORB — Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor.
  • SparseOpticalFlow — Base interface for sparse optical flow algorithms.
  • SparsePyrLKOpticalFlow — Class used for calculating a sparse optical flow.
  • StereoBeliefPropagation — Class computing stereo correspondence using the belief propagation algorithm. :
  • StereoBM — Class computing stereo correspondence (disparity map) using the block matching algorithm. :
  • StereoConstantSpaceBP — Class computing stereo correspondence using the constant space belief propagation algorithm. :
  • StereoSGM — The class implements the modified H. Hirschmuller algorithm HH08. Limitation and difference are as follows:
  • Stream — This class encapsulates a queue of asynchronous calls.
  • StreamAccessor — Class that enables getting cudaStream_t from cuda::Stream.
  • SURF_CUDA — Class used for extracting Speeded Up Robust Features (SURF) from an image. :
  • TargetArchs — Class providing a set of static methods to check what NVIDIA* card architecture the CUDA module was built for.
  • TemplateMatching — Base class for Template Matching. :
• cv::cuda::GpuMat
  • Allocator
• cv::cudacodec
  • EncodeQp
  • EncoderCallback — Interface for encoder callbacks.
  • EncoderParams — Different parameters for CUDA video encoder.
  • FormatInfo — Struct providing information about video file format. :
  • NVSurfaceToColorConverter — Class for converting the raw YUV Surface output from VideoReader if output color format is set to ColorFormat::NV_YUV_SURFACE_FORMAT (VideoReader::set(ColorFormat::NV_YUV_SURFACE_FORMAT)) to the requested ColorFormat.
  • RawVideoSource — Interface for video demultiplexing. :
  • VideoReader — Video reader interface, see createVideoReader().
  • VideoReaderInitParams — VideoReader initialization parameters.
  • VideoWriter — Video writer interface, see createVideoWriter().
• cv::cudev
  • abs_func
  • abs_func< double >
  • abs_func< float >
  • abs_func< schar >
  • abs_func< short >
  • abs_func< uchar >
  • abs_func< uint >
  • abs_func< ushort >
  • absdiff_func
  • AffineMapPtr
  • AffineMapPtrSz
  • ArrayWrapper
  • Avg
  • binary_function
  • BinaryNegate
  • BinaryTransformPtr
  • BinaryTransformPtrSz
  • BinaryTupleAdapter
  • Binder1st
  • Binder2nd
  • bit_and
  • bit_lshift
  • bit_not
  • bit_or
  • bit_rshift
  • bit_xor
  • Block
  • BrdBase
  • BrdConstant
  • BrdReflect
  • BrdReflect101
  • BrdReplicate
  • BrdWrap
  • CommonAreaInterPtr
  • CommonAreaInterPtrSz
  • ConstantPtr
  • ConstantPtrSz
  • ConvertTuple
  • CountNonZeroExprBody
  • CubicInterPtr
  • CubicInterPtrSz
  • DefaultCopyPolicy
  • DefaultGlobReducePolicy
  • DefaultHistogramPolicy
  • DefaultReduceToVecPolicy
  • DefaultSplitMergePolicy
  • DefaultTransformPolicy
  • DefaultTransposePolicy
  • DerivXPtr
  • DerivXPtrSz
  • DerivYPtr
  • DerivYPtrSz
  • direction_func
  • direction_interleaved_func
  • DisableIf
  • divides
  • DynamicSharedMem
  • DynamicSharedMem< double >
  • EnableIf
  • equal_to
  • Expr
  • FindMaxValExprBody
  • FindMinMaxValExprBody
  • FindMinValExprBody
  • GlobPtr — Structure similar to cv::cudev::GlobPtrSz but containing only a pointer and row step.
  • GlobPtrSz — Lightweight class encapsulating pitched memory on a GPU and passed to nvcc-compiled code (CUDA kernels).
  • GpuMat_
  • greater
  • greater_equal
  • HistogramBody
  • identity
  • InRangeComparator — Functor that checks if a CUDA vector v is in the range between lowerb and upperb.
  • InRangeCopier — Functor that copies a cv::Scalar into a CUDA vector, e.g. a uchar3.
  • InRangeFunc — unary_function implementation of inRange
  • Int2Type
  • IntegerAreaInterPtr
  • IntegerAreaInterPtrSz
  • IntegralBody
  • IsBinaryFunction
  • IsPowerOf2
  • IsUnaryFunction
  • LaplacianPtr
  • LaplacianPtr< 1, SrcPtr >
  • LaplacianPtr< 3, SrcPtr >
  • LaplacianPtrSz
  • LargerType
  • less
  • less_equal
  • LinearInterPtr
  • LinearInterPtrSz
  • Log2
  • Log2< N, 0, COUNT >
  • logical_and
  • logical_not
  • logical_or
  • LutPtr
  • LutPtrSz
  • magnitude_direction_interleaved_func
  • magnitude_func
  • magnitude_interleaved_func
  • magnitude_sqr_func
  • magnitude_sqr_interleaved_func
  • MakeVec
  • MakeVec< bool, 1 >
  • MakeVec< bool, 2 >
  • MakeVec< bool, 3 >
  • MakeVec< bool, 4 >
  • MakeVec< schar, 1 >
  • MakeVec< schar, 2 >
  • MakeVec< schar, 3 >
  • MakeVec< schar, 4 >
  • Max
  • maximum
  • Min
  • minimum
  • minus
  • modulus
  • multiplies
  • NearestInterPtr
  • NearestInterPtrSz
  • negate
  • NormHamming
  • NormL1
  • NormL1< float >
  • NormL2
  • not_equal_to
  • NullType
  • numeric_limits
  • numeric_limits< bool >
  • numeric_limits< double >
  • numeric_limits< float >
  • numeric_limits< schar >
  • numeric_limits< short >
  • numeric_limits< uchar >
  • numeric_limits< uint >
  • numeric_limits< ushort >
  • PerspectiveMapPtr
  • PerspectiveMapPtrSz
  • plus
  • pow_func
  • pow_func< double >
  • project1st
  • project2nd
  • PtrTraits
  • PtrTraits< AffineMapPtrSz >
  • PtrTraits< BinaryTransformPtrSz< Src1Ptr, Src2Ptr, Op > >
  • PtrTraits< CommonAreaInterPtrSz< SrcPtr > >
  • PtrTraits< ConstantPtrSz< T > >
  • PtrTraits< CubicInterPtrSz< SrcPtr > >
  • PtrTraits< DerivXPtrSz< SrcPtr > >
  • PtrTraits< DerivYPtrSz< SrcPtr > >
  • PtrTraits< Expr< Body > >
  • PtrTraits< GlobPtrSz< T > >
  • PtrTraits< GpuMat_< T > >
  • PtrTraits< IntegerAreaInterPtrSz< SrcPtr > >
  • PtrTraits< LaplacianPtrSz< ksize, SrcPtr > >
  • PtrTraits< LinearInterPtrSz< SrcPtr > >
  • PtrTraits< LutPtrSz< SrcPtr, TablePtr > >
  • PtrTraits< NearestInterPtrSz< SrcPtr > >
  • PtrTraits< PerspectiveMapPtrSz >
  • PtrTraits< RemapPtr1Sz< SrcPtr, MapPtr > >
  • PtrTraits< RemapPtr2Sz< SrcPtr, MapXPtr, MapYPtr > >
  • PtrTraits< ResizePtrSz< SrcPtr > >
  • PtrTraits< ScharrXPtrSz< SrcPtr > >
  • PtrTraits< ScharrYPtrSz< SrcPtr > >
  • PtrTraits< SingleMaskChannelsSz< MaskPtr > >
  • PtrTraits< SobelXPtrSz< SrcPtr > >
  • PtrTraits< SobelYPtrSz< SrcPtr > >
  • PtrTraits< Texture< T, R > >
  • PtrTraits< UnaryTransformPtrSz< SrcPtr, Op > >
  • PtrTraits< ZipPtrSz< PtrTuple > >
  • PtrTraitsBase
  • PyrDownBody
  • PyrUpBody
  • ReduceToColumnBody
  • ReduceToRowBody
  • RemapPtr1
  • RemapPtr1Sz
  • RemapPtr2
  • RemapPtr2Sz
  • ResizePtr
  • ResizePtrSz
  • saturate_cast_fp16_func
  • saturate_cast_fp16_func< float, short >
  • saturate_cast_fp16_func< short, float >
  • saturate_cast_func
  • ScharrXPtr
  • ScharrXPtrSz
  • ScharrYPtr
  • ScharrYPtrSz
  • SelectIf
  • SelectIf< false, ThenType, ElseType >
  • SingleMaskChannels
  • SingleMaskChannelsSz
  • SobelXPtr
  • SobelXPtrSz
  • SobelYPtr
  • SobelYPtrSz
  • sqr_func
  • Sum
  • SumExprBody
  • Texture — sharable smart CUDA texture object
  • TextureOff — sharable smart CUDA texture object with offset TextureOff is a smart sharable wrapper for a cudaTextureObject_t handle which ensures that the handle is destroyed after use.
  • TextureOffPtr
  • TexturePtr — Simple lightweight structures that encapsulate information about an image texture on the device. They are intended to be passed to nvcc-compiled code.
  • TexturePtr< uint64, R >
  • ThreshBinaryFunc
  • ThreshBinaryInvFunc
  • ThreshToZeroFunc
  • ThreshToZeroInvFunc
  • ThreshTruncFunc
  • TransposeBody
  • TupleTraits
  • TupleTraits< tuple< P0, P1, P2, P3, P4, P5, P6, P7, P8, P9 > >
  • TypesEquals
  • TypesEquals< A, A >
  • TypeTraits
  • unary_function
  • UnaryNegate
  • UnaryTransformPtr
  • UnaryTransformPtrSz
  • UnaryTupleAdapter
  • UniqueTexture — non-copyable smart CUDA texture object
  • VecTraits
  • VecTraits< char1 >
  • VecTraits< char2 >
  • VecTraits< char3 >
  • VecTraits< char4 >
  • VecTraits< schar >
  • Warp
  • WithOutMask
  • ZipPtr
  • ZipPtr< tuple< Ptr0, Ptr1 > >
  • ZipPtr< tuple< Ptr0, Ptr1, Ptr2 > >
  • ZipPtr< tuple< Ptr0, Ptr1, Ptr2, Ptr3 > >
  • ZipPtrSz
• cv::cudev::Avg
  • rebind
• cv::cudev::IsBinaryFunction
  • No
• cv::cudev::IsUnaryFunction
  • No
• cv::cudev::Max
  • rebind
• cv::cudev::Min
  • rebind
• cv::cudev::Sum
  • rebind
• cv::datasets
  • AR_hmdb
  • AR_hmdbObj
  • AR_sports
  • AR_sportsObj
  • cameraParam
  • cameraPos
  • Dataset
  • FR_adience
  • FR_adienceObj
  • FR_lfw
  • FR_lfwObj
  • GR_chalearn
  • GR_chalearnObj
  • GR_skig
  • GR_skigObj
  • groundTruth
  • HPE_humaneva
  • HPE_humanevaObj
  • HPE_parse
  • HPE_parseObj
  • IR_affine
  • IR_affineObj
  • IR_robot
  • IR_robotObj
  • IS_bsds
  • IS_bsdsObj
  • IS_weizmann
  • IS_weizmannObj
  • join
  • MSM_epfl
  • MSM_epflObj
  • MSM_middlebury
  • MSM_middleburyObj
  • Object
  • OR_imagenet
  • OR_imagenetObj
  • OR_mnist
  • OR_mnistObj
  • OR_pascal
  • OR_pascalObj
  • OR_sun
  • OR_sunObj
  • PascalObj
  • PascalPart
  • PD_caltech
  • PD_caltechObj
  • PD_inria
  • PD_inriaObj
  • pose
  • skeleton
  • SLAM_kitti
  • SLAM_kittiObj
  • SLAM_tumindoor
  • SLAM_tumindoorObj
  • SR_bsds
  • SR_bsdsObj
  • SR_div2k
  • SR_div2kObj
  • SR_general100
  • SR_general100Obj
  • tag
  • TR_chars
  • TR_charsObj
  • TR_icdar
  • TR_icdarObj
  • TR_svt
  • TR_svtObj
  • TRACK_alov
  • TRACK_alovObj
  • TRACK_vot
  • TRACK_votObj
  • word
• cv::DescriptorMatcher
  • DescriptorCollection
• cv::detail
  • accepted_infer_types
  • AffineBasedEstimator — Affine transformation based estimator.
  • 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.
  • AffineWarper — Affine warper that uses rotations and translations.
  • BasicOpaqueRef
  • BasicVectorRef
  • 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.
  • BestOf2NearestRangeMatcher
  • Blender — Base class for all blenders.
  • BlocksChannelsCompensator — Exposure compensator which tries to remove exposure related artifacts by adjusting image block on each channel.
  • BlocksCompensator — Exposure compensator which tries to remove exposure related artifacts by adjusting image blocks.
  • BlocksGainCompensator — Exposure compensator which tries to remove exposure related artifacts by adjusting image block intensities, see UES01 for details.
  • 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.
  • 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.
  • BundleAdjusterBase — Base class for all camera parameters refinement methods.
  • 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. :
  • BundleAdjusterReproj — Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection error squares.
  • callCustomGetBorder
  • callCustomGetBorder< false, Impl >
  • callCustomGetBorder< true, Impl >
  • CameraParams — Describes camera parameters.
  • ChannelsCompensator — Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities on each channel independently.
  • CompileArgTag
  • CompileArgTag< GFluidOutputRois >
  • CompileArgTag< GFluidParallelFor >
  • CompileArgTag< GFluidParallelOutputRois >
  • CompressedRectilinearPortraitProjector
  • CompressedRectilinearPortraitWarper
  • CompressedRectilinearProjector
  • CompressedRectilinearWarper
  • contains_shape_field
  • CylindricalPortraitProjector
  • CylindricalPortraitWarper
  • CylindricalProjector
  • CylindricalWarper — Warper that maps an image onto the x*x + z*z = 1 cylinder.
  • CylindricalWarperGpu
  • DisjointSets
  • DpSeamFinder
  • Estimator — Rotation estimator base class.
  • ExposureCompensator — Base class for all exposure compensators.
  • ExtractArgsCallback
  • ExtractMetaCallback
  • FeatherBlender — Simple blender which mixes images at its borders.
  • FeaturesMatcher — Feature matchers base class.
  • FisheyeProjector
  • FisheyeWarper
  • flatten_g
  • fluid_get_in
  • FluidCallHelper
  • GainCompensator — Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities, see BL07 and WJ10 for details.
  • GArrayU
  • GCompoundCallHelper
  • GCompoundContext
  • GCompoundKernel
  • GCompoundKernelImpl
  • get_border_helper
  • get_border_helper< false, Impl, Ins... >
  • get_border_helper< true, Impl, Ins... >
  • get_compound_in
  • get_in
  • get_out
  • get_window_helper
  • get_window_helper< false, Impl, Ins... >
  • get_window_helper< true, Impl, Ins... >
  • GInferInputsTyped
  • GInferOutputsTyped
  • GObtainCtor
  • GOpaqueTraits
  • GOpaqueTraits< bool >
  • GOpaqueTraits< double >
  • GOpaqueTraits< float >
  • GOpaqueTraits< int64_t >
  • GOpaqueTraits< uint64_t >
  • GOpaqueU
  • Graph
  • GraphCutSeamFinder — Minimum graph cut-based seam estimator. See details in Kwatra03 .
  • GraphCutSeamFinderBase — Base class for all minimum graph-cut-based seam estimators.
  • GraphEdge
  • GTypeOf
  • GTypeTraits
  • has_custom_wrap
  • has_gshape
  • has_Window
  • HomographyBasedEstimator — Homography based rotation estimator.
  • ImageFeatures — Structure containing image keypoints and descriptors.
  • in_variant
  • InferROITraits
  • InferROITraits< GInferListBase >
  • InferROITraits< GInferROIBase >
  • InOutInfo
  • is_gmat_type
  • is_meta_descr
  • is_meta_descr< GArrayDesc >
  • is_meta_descr< GMatDesc >
  • is_meta_descr< GOpaqueDesc >
  • is_meta_descr< GScalarDesc >
  • KernelTag
  • KernelTypeMedium
  • last_type
  • LevMarqBackend
  • LevMarqBase — Base class for Levenberg-Marquadt solvers.
  • LightGlueFeaturesMatcher — Features matcher that adapts LightGlueMatcher (DescriptorMatcher) to the stitching pipeline's FeaturesMatcher interface.
  • MatchesInfo — Structure containing information about matches between two images.
  • MercatorProjector
  • MercatorWarper
  • MetaHelper
  • MetaType
  • MultiBandBlender — Blender which uses multi-band blending algorithm (see BA83).
  • NoBundleAdjuster — Stub bundle adjuster that does nothing.
  • NoExposureCompensator — Stub exposure compensator which does nothing.
  • NoSeamFinder — Stub seam estimator which does nothing.
  • NoTag
  • ocl_get_in
  • ocl_get_out
  • OCLCallHelper
  • OCVCallHelper
  • OCVSetupHelper
  • OCVStCallHelper
  • OpaqueRef
  • OpaqueRefT
  • OptRef
  • PairwiseSeamFinder — Base class for all pairwise seam estimators.
  • PaniniPortraitProjector
  • PaniniPortraitWarper
  • PaniniProjector
  • PaniniWarper
  • plaidml_get_in
  • plaidml_get_out
  • PlaidMLCallHelper
  • PlanePortraitProjector
  • PlanePortraitWarper
  • PlaneProjector
  • PlaneWarper — Warper that maps an image onto the z = 1 plane.
  • PlaneWarperGpu
  • PoseGraph
  • ProjectorBase — Base class for warping logic implementation.
  • ProtoToMeta
  • ProtoToParam
  • RotationWarper — Rotation-only model image warper interface.
  • RotationWarperBase — Base class for rotation-based warper using a detail::ProjectorBase_ derived class.
  • scratch_helper
  • scratch_helper< false, Impl, Ins... >
  • scratch_helper< true, Impl, Ins... >
  • SeamFinder — Base class for a seam estimator.
  • SphericalPortraitProjector
  • SphericalPortraitWarper
  • SphericalProjector
  • SphericalWarper — Warper that maps an image onto the unit sphere located at the origin.
  • SphericalWarperGpu
  • StereographicProjector
  • StereographicWarper
  • Submap
  • SubmapManager — : Manages all the created submaps for a particular scene
  • Timelapser
  • TimelapserCrop
  • tracked_cv_mat
  • tracked_cv_umat
  • TransformTag
  • TransHelper
  • TransverseMercatorProjector
  • TransverseMercatorWarper
  • TypeHint
  • TypeHintBase
  • 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.
  • UnscentedKalmanFilter — The interface for Unscented Kalman filter and Augmented Unscented Kalman filter.
  • UnscentedKalmanFilterParams — Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter.
  • valid_infer2_types
  • VectorRef
  • VectorRefT
  • VoronoiSeamFinder — Voronoi diagram-based seam estimator.
  • WeakClassifierHaarFeature
  • WrapValue
  • wref_spec
  • Yield
  • Yield< GFrame >
• cv::detail::CompileArgTag< cv
  • GKernelPackage >
  • graph_dump_path >
  • use_threaded_executor >
• cv::detail::CompileArgTag< cv::gapi
  • GNetPackage >
  • use_only >
• cv::detail::CompileArgTag< cv::gapi::calib3d::cpu
  • StereoInitParam >
• cv::detail::CompileArgTag< cv::gapi::onnx
  • WorkloadTypeONNXPtr >
• cv::detail::CompileArgTag< cv::gapi::ot
  • ObjectTrackerParams >
• cv::detail::CompileArgTag< cv::gapi::plaidml
  • config >
• cv::detail::CompileArgTag< cv::gapi::streaming
  • queue_capacity >
• cv::detail::CompileArgTag< cv::gapi::video
  • BackgroundSubtractorParams >
• cv::detail::CompileArgTag< cv::gapi::wip::draw
  • freetype_font >
• cv::detail::CompileArgTag< cv::gapi::wip::ov
  • benchmark_mode >
  • WorkloadTypeOVPtr >
• cv::detail::CompileArgTag< gapi::oak
  • ColorCameraParams >
  • EncoderConfig >
• cv::detail::CompileArgTag< gapi::streaming
  • sync_policy >
• cv::detail::contains_shape_field< TaggedTypeCandidate, void_t< decltype(TaggedTypeCandidate
  • shape)> >
• cv::detail::DpSeamFinder
  • ClosePoints
  • ImagePairLess
• cv::detail::flatten_g< cv
  • GMat >
  • GScalar >
• cv::detail::fluid_get_in< cv
  • GArray< U > >
  • GMat >
  • GOpaque< U > >
  • GScalar >
• cv::detail::FluidCallHelper< Impl, std::tuple< Ins... >, std
  • tuple< Outs... >, UseScratch >
• cv::detail::GCompoundCallHelper< Impl, std::tuple< Ins... >, std
  • tuple< Outs... > >
• cv::detail::get_compound_in< cv
  • GArray< U > >
  • GMatP >
  • GOpaque< U > >
• cv::detail::get_in< cv
  • GArray< U > >
  • GFrame >
  • GMat >
  • GMatP >
  • GOpaque< U > >
  • GScalar >
• cv::detail::get_in< cv::GArray< cv
  • GArray< U > > >
  • GMat > >
  • GScalar > >
• cv::detail::get_in< cv::GOpaque< cv
  • GMat > >
  • GScalar > >
• cv::detail::get_out< cv
  • GArray< U > >
  • GFrame >
  • GMat >
  • GMatP >
  • GOpaque< U > >
  • GScalar >
• cv::detail::get_out< cv::GArray< cv
  • GArray< U > > >
  • GMat > >
• cv::detail::GInferInputsTyped
  • Priv
• cv::detail::GInferOutputsTyped
  • Priv
• cv::detail::GOpaqueTraits< cv
  • GMat >
  • Mat >
  • Point >
  • Point2f >
  • Point3f >
  • Rect >
  • Scalar >
  • Size >
• cv::detail::GOpaqueTraits< cv::gapi::wip::draw
  • Prim >
• cv::detail::GOpaqueTraits< std
  • string >
• cv::detail::GTypeOf< cv
  • Mat >
  • MediaFrame >
  • RMat >
  • Scalar >
  • UMat >
• cv::detail::GTypeOf< cv::gapi::wip::IStreamSource
  • Ptr >
• cv::detail::GTypeOf< std
  • vector< U > >
• cv::detail::GTypeTraits< cv
  • GArray< T > >
  • GFrame >
  • GMat >
  • GMatP >
  • GOpaque< T > >
  • GScalar >
• cv::detail::has_custom_wrap
  • check
• cv::detail::KernelTypeMedium< K, std
  • function< R(Args...)> >
• cv::detail::KernelTypeMedium< K, std::function< std
  • tuple< R... >(Args...)> >
• cv::detail::MetaHelper< K, std
  • tuple< Ins... >, Out >
• cv::detail::MetaHelper< K, std::tuple< Ins... >, std
  • tuple< Outs... > >
• cv::detail::MetaType< cv
  • GArray< U > >
  • GFrame >
  • GMat >
  • GMatP >
  • GOpaque< U > >
  • GScalar >
• cv::detail::ocl_get_in< cv
  • GArray< U > >
  • GFrame >
  • GMat >
  • GOpaque< U > >
  • GScalar >
• cv::detail::ocl_get_out< cv
  • GArray< U > >
  • GMat >
  • GOpaque< U > >
  • GScalar >
• cv::detail::OCLCallHelper< Impl, std::tuple< Ins... >, std
  • tuple< Outs... > >
• cv::detail::OCLCallHelper< Impl, std::tuple< Ins... >, std::tuple< Outs... > >
  • call_and_postprocess
• cv::detail::OCVCallHelper< Impl, std::tuple< Ins... >, std
  • tuple< Outs... > >
• cv::detail::OCVCallHelper< Impl, std::tuple< Ins... >, std::tuple< Outs... > >
  • call_and_postprocess
• cv::detail::OCVSetupHelper< Impl, std
  • tuple< Ins... > >
• cv::detail::OCVStCallHelper< Impl, std::tuple< Ins... >, std
  • tuple< Outs... > >
• cv::detail::OCVStCallHelper< Impl, std::tuple< Ins... >, std::tuple< Outs... > >
  • call_and_postprocess
• cv::detail::OptRef
  • Holder
  • OptHolder
• cv::detail::plaidml_get_in< cv
  • GMat >
• cv::detail::plaidml_get_out< cv
  • GMat >
• cv::detail::PlaidMLCallHelper< Impl, std::tuple< Ins... >, std
  • tuple< Outs... > >
• cv::detail::ProtoToMeta< cv
  • GArray< U > >
  • GMat >
  • GOpaque< U > >
  • GScalar >
• cv::detail::ProtoToParam< cv
  • GArray< U > >
  • GMat >
  • GOpaque< U > >
  • GScalar >
• cv::detail::ProtoToParam< cv::GArray< cv
  • GMat > >
• cv::detail::Submap
  • PoseConstraint
• cv::detail::SubmapManager
  • ActiveSubmapData
• cv::detail::tracking
  • TrackerContribFeature — Abstract base class for TrackerContribFeature that represents the feature.
  • TrackerContribFeatureHAAR — TrackerContribFeature based on HAAR features, used by TrackerMIL and many others algorithms.
  • TrackerContribFeatureSet — Class that manages the extraction and selection of features.
  • 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.
  • TrackerContribSamplerAlgorithm — Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler.
  • TrackerContribSamplerCSC — TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL.
  • TrackerFeature — Abstract base class for TrackerFeature that represents the feature.
  • TrackerFeatureFeature2d — TrackerContribFeature based on Feature2D.
  • TrackerFeatureHOG — TrackerContribFeature based on HOG.
  • TrackerFeatureLBP — TrackerContribFeature based on LBP.
  • TrackerFeatureSet — Class that manages the extraction and selection of features.
  • TrackerModel — Abstract class that represents the model of the target.
  • 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.
  • TrackerSamplerAlgorithm — Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler.
  • TrackerSamplerCS — TrackerContribSampler based on CS (current state), used by algorithm TrackerBoosting.
  • TrackerSamplerCSC — TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL.
  • TrackerSamplerPF — This sampler is based on particle filtering.
  • TrackerStateEstimator — Abstract base class for TrackerStateEstimator that estimates the most likely target state.
  • TrackerStateEstimatorAdaBoosting — TrackerStateEstimatorAdaBoosting based on ADA-Boosting.
  • TrackerStateEstimatorSVM — TrackerStateEstimator based on SVM.
  • TrackerTargetState — Abstract base class for TrackerTargetState that represents a possible state of the target.
• cv::detail::tracking::tbm
  • CosDistance — The CosDistance class allows computing cosine distance between two reidentification descriptors.
  • IDescriptorDistance — The IDescriptorDistance class declares an interface for distance computation between reidentification descriptors.
  • IImageDescriptor — The IImageDescriptor class declares base class for image descriptor.
  • ITrackerByMatching — Tracker-by-Matching algorithm interface.
  • MatchTemplateDistance — Computes distance between images using MatchTemplate function from OpenCV library and its cross-correlation computation method in particular.
  • ResizedImageDescriptor — Uses resized image as descriptor.
  • Track — The Track class describes tracks.
  • TrackedObject — The TrackedObject struct defines properties of detected object.
  • TrackerParams — The TrackerParams struct stores parameters of TrackerByMatching.
• cv::detail::tracking::TrackerContribFeatureHAAR
  • Params
• cv::detail::tracking::TrackerContribSamplerCSC
  • Params
• cv::detail::tracking::TrackerSamplerCS
  • Params
• cv::detail::tracking::TrackerSamplerCSC
  • Params
• cv::detail::tracking::TrackerSamplerPF
  • Params
• cv::detail::tracking::TrackerStateEstimatorAdaBoosting
  • TrackerAdaBoostingTargetState
• cv::detail::TransHelper< K, std
  • tuple< Ins... >, Out >
• cv::detail::valid_infer2_types< std::tuple< Ns... >, std
  • tuple<> >
• cv::detail::valid_infer2_types< std::tuple<>, std
  • tuple< Ts... > >
• cv::detail::Yield< cv
  • GArray< U > >
  • GMat >
  • GMatP >
  • GOpaque< U > >
  • GScalar >
• cv::details
  • FPDenormalsIgnoreHintScope
  • FPDenormalsIgnoreHintScopeNOOP
  • FPDenormalsModeState
• cv::DetectionBasedTracker
  • ExtObject
  • IDetector
  • InnerParameters
  • Parameters
  • TrackedObject
• cv::dnn
  • AbsLayer
  • AccumLayer
  • AcoshLayer
  • AcosLayer
  • ActivationInt8Params
  • ActivationLayer
  • ActivationLayerInt8
  • AffineGridLayer
  • Arg
  • ArgData
  • ArgLayer — ArgMax/ArgMin layer.
  • AsinhLayer
  • AsinLayer
  • AtanhLayer
  • AtanLayer
  • AttentionLayer
  • AttentionOnnxAiLayer
  • AveragePoolLayer
  • BackendNode — Derivatives of this class encapsulates functions of certain backends.
  • BackendWrapper — Derivatives of this class wraps cv::Mat for different backends and targets.
  • BaseConvolutionLayer
  • BatchNorm2Layer
  • BatchNormLayer
  • BatchNormLayerInt8
  • BitShiftLayer
  • BlackmanWindowLayer
  • BlankLayer
  • BNLLLayer
  • Cast2Layer
  • CastLayer
  • CeilLayer
  • CeluLayer
  • CenterCropPadLayer
  • ChannelsPReLULayer
  • ClassificationModel — This class represents high-level API for classification models.
  • ClipLayer
  • CompareLayer
  • Concat2Layer
  • ConcatLayer
  • ConstantOfShapeLayer
  • ConstLayer
  • Conv2Int8Layer
  • Conv2Int8Params
  • Conv2Layer
  • ConvolutionLayer
  • ConvolutionLayerInt8
  • ConvTranspose2Layer
  • CorrelationLayer
  • CoshLayer
  • CosLayer
  • CropAndResizeLayer
  • CropLayer
  • CumSumLayer
  • DataAugmentationLayer
  • DeconvolutionLayer
  • DepthToSpaceLayer
  • DequantizeLayer
  • DequantizeLinearLayer
  • DetectionModel — This class represents high-level API for object detection networks.
  • DetectionOutputLayer — Detection output layer.
  • DetLayer
  • DFTLayer
  • Dict — This class implements name-value dictionary, values are instances of DictValue.
  • DictValue — This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64.
  • EinsumLayer — This function performs array summation based on the Einstein summation convention. The function allows for concise expressions of various mathematical operations using subscripts.
  • Eltwise2Int8Layer
  • Eltwise2Int8Params
  • EltwiseLayer — Element wise operation on inputs.
  • EltwiseLayerInt8
  • ELULayer
  • ErfLayer
  • Expand2Layer
  • ExpandLayer
  • ExpLayer
  • EyeLikeLayer
  • FlattenLayer
  • FloorLayer
  • FlowWarpLayer
  • Gather2Layer
  • GatherElementsLayer — GatherElements layer GatherElements takes two inputs data and indices of the same rank r >= 1 and an optional attribute axis and works such that: output[i][j][k] = data[index[i][j][k]][j][k] if axis = 0 and r = 3 output[i][j][k] = data[i][index[i][j][k]][k] if axis = 1 and r = 3 output[i][j][k] = data[i][j][index[i][j][k]] if axis = 2 and r = 3.
  • GatherLayer — Gather layer.
  • GatherNDLayer — GatherND layer.
  • GeluApproximationLayer
  • GeluLayer
  • GemmLayer
  • GlobalAveragePoolLayer
  • Graph — Represents graph or subgraph of a model. The graph (in mathematical terms it's rather a multigraph) is represented as a topologically-sorted linear sequence of operations. Each operation is a smart pointer to a Layer (some of its derivative class instance), which includes a list of inputs and outputs, as well as an optional list of subgraphs (e.g. 'If' contains 2 subgraphs).
  • GridSampleLayer
  • GroupNormLayer
  • GRULayer — GRU recurrent one-layer.
  • HammingWindowLayer
  • HannWindowLayer
  • HardmaxLayer
  • HardSigmoidLayer
  • HardSwishLayer
  • IfLayer
  • Image2BlobParams — Processing params of image to blob.
  • InnerProductInt8Params
  • InnerProductLayer
  • InnerProductLayerInt8
  • InstanceNormLayer
  • InterpLayer — Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2.
  • IsInfLayer
  • IsNaNLayer
  • KeypointsModel — This class represents high-level API for keypoints models.
  • Layer — This interface class allows to build new Layers - are building blocks of networks.
  • LayerFactory — Layer factory allows to create instances of registered layers.
  • LayerNorm2Layer
  • LayerNormLayer
  • LayerParams — This class provides all data needed to initialize layer.
  • LogLayer
  • LoopLayer
  • LRNLayer
  • LSTM2Layer
  • LSTMLayer — LSTM recurrent layer.
  • MatMulInt8Layer
  • MatMulInt8Params
  • MatMulLayer
  • MaxPoolLayer
  • MaxUnpoolLayer
  • MishLayer
  • Model — This class is presented high-level API for neural networks.
  • MVNLayer
  • NaryEltwiseLayer
  • NegativeLogLikelihoodLossLayer
  • Net — This class allows to create and manipulate comprehensive artificial neural networks.
  • NonMaxSuppressionLayer
  • NonZeroLayer
  • NormalizeBBoxLayer — $ L_p $ - normalization layer.
  • NotLayer
  • OneHotLayer
  • Pad2Layer
  • PaddingLayer — Adds extra values for specific axes.
  • PermuteLayer
  • Pool2Int8Layer
  • Pool2Int8Params
  • PoolingLayer
  • PoolingLayerInt8
  • PowerLayer
  • PriorBoxLayer
  • ProposalLayer
  • QuantizeLayer
  • QuantizeLinearLayer
  • RandomNormalLikeLayer
  • RangeLayer
  • ReciprocalLayer
  • Reduce2Layer
  • ReduceLayer
  • RegionLayer
  • ReLU6Layer
  • ReLULayer
  • ReorgLayer
  • RequantizeLayer
  • Reshape2Layer
  • ReshapeLayer
  • Resize2Layer
  • ResizeLayer — Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
  • RMSNormLayer
  • RNNLayer — Classical recurrent layer.
  • RoiAlignLayer
  • RotaryEmbeddingLayer
  • RoundLayer
  • ScaleLayer
  • ScaleLayerInt8
  • ScatterLayer
  • ScatterNDLayer
  • SDPALayer
  • SegmentationModel — This class represents high-level API for segmentation models.
  • SeluLayer
  • ShapeLayer
  • ShiftLayer
  • ShiftLayerInt8
  • ShrinkLayer
  • ShuffleChannelLayer
  • SigmoidLayer
  • SignLayer
  • SinhLayer
  • SinLayer
  • SizeLayer
  • Slice2Layer
  • SliceLayer
  • SoftmaxCrossEntropyLossLayer
  • SoftmaxLayer
  • SoftmaxLayerInt8
  • SoftplusLayer
  • SoftsignLayer
  • SpaceToDepthLayer
  • Split2Layer
  • SplitLayer
  • SqrtLayer
  • SqueezeLayer
  • SwishLayer
  • TanHLayer
  • TanLayer
  • TextDetectionModel — Base class for text detection networks.
  • TextDetectionModel_DB — This class represents high-level API for text detection DL networks compatible with DB model.
  • TextDetectionModel_EAST — This class represents high-level API for text detection DL networks compatible with EAST model.
  • TextRecognitionModel — This class represents high-level API for text recognition networks.
  • ThresholdedReluLayer
  • Tile2Layer
  • TileLayer
  • Tokenizer — High-level tokenizer wrapper for DNN usage.
  • TopK2Layer
  • TopKLayer
  • TransformLayoutLayer
  • TransposeLayer
  • TriluLayer
  • UniqueLayer
  • UnsqueezeLayer
• cv::dnn_objdetect
  • InferBbox — A class to post process model predictions.
  • object — Structure to hold the details pertaining to a single bounding box.
• cv::dnn_superres
  • DnnSuperResImpl — A class to upscale images via convolutional neural networks. The following four models are implemented:
• cv::dpm
  • DPMDetector — This is a C++ abstract class, it provides external user API to work with DPM.
• cv::dpm::DPMDetector
  • ObjectDetection
• cv::face
  • BasicFaceRecognizer
  • CParams
  • EigenFaceRecognizer
  • Facemark — Abstract base class for all facemark models.
  • FacemarkAAM
  • FacemarkLBF
  • FacemarkTrain — Abstract base class for trainable facemark models.
  • FaceRecognizer — Abstract base class for all face recognition models.
  • FisherFaceRecognizer
  • LBPHFaceRecognizer
  • 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)
  • PredictCollector — Abstract base class for all strategies of prediction result handling.
  • StandardCollector — Default predict collector.
• cv::face::FacemarkAAM
  • Config
  • Data
  • Model
  • Params
• cv::face::FacemarkAAM::Model
  • Texture
• cv::face::FacemarkLBF
  • BBox
  • Params
• cv::face::StandardCollector
  • PredictResult
• cv::fastcv
  • FCVMSER — MSER blob detector for grayscale images.
  • QcAllocator — Qualcomm's custom allocator. This allocator uses Qualcomm's memory management functions.
  • QcResourceManager — Resource manager for FastCV allocations. This class manages active allocations.
• cv::fastcv::FCVMSER
  • ContourData
• cv::flann
  • CvType
  • CvType< char >
  • CvType< double >
  • CvType< float >
  • CvType< short >
  • CvType< unsigned char >
  • CvType< unsigned short >
  • GenericIndex — The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built.
• cv::freetype
  • FreeType2
• cv::gapi
  • GBackend
  • Generic — Generic network type: input and output layers are configured dynamically at runtime.
  • GFunctor
  • GNetPackage — A container class for network configurations. Similar to GKernelPackage. Use cv::gapi::networks() to construct this object.
  • GNetParam
  • KalmanParams — Structure for the Kalman filter's initialization parameters.
  • use_only — cv::gapi::use_only() is a special combinator which hints G-API to use only kernels specified in cv::GComputation::compile() (and not to extend kernels available by default with that package).
• cv::gapi::plaidml
  • config — This structure represents the basic parameters for the experimental PlaidML backend.
• cv::gapi::s11n
  • IIStream — This structure is an interface for deserialization routines.
  • IOStream — This structure is an interface for serialization routines.
• cv::gapi::s11n::detail
  • NotImplemented
  • S11N — This structure allows to implement serialization routines for custom types.
• cv::gapi::wip::draw
  • Circle — This structure represents a circle to draw.
  • FText — This structure represents a text string to draw using FreeType renderer.
  • Image — This structure represents an image to draw.
  • Line — This structure represents a line to draw.
  • Mosaic — This structure represents a mosaicing operation.
  • Poly — This structure represents a polygon to draw.
  • Rect — This structure represents a rectangle to draw.
  • Text — This structure represents a text string to draw.
• cv::GComputationT< R(Args...)>
  • GCompiledT
• cv::GComputationT< std
  • tuple< R... >(Args...)>
• cv::GComputationT< std::tuple< R... >(Args...)>
  • GCompiledT
• cv::GKernelType< K, std
  • function< R(Args...)> >
• cv::GKernelTypeM< K, std::function< std
  • tuple< R... >(Args...)> >
• cv::GNetworkType< K, std
  • function< R(Args...)> >
• cv::GNetworkType< K, std::function< std
  • tuple< R... >(Args...)> >
• cv::GTransformImpl< K, std
  • function< R(Args...)> >
• cv::hal
  • DCT2D
  • DFT1D
  • DFT2D
• cv::hdf
  • HDF5 — Hierarchical Data Format version 5 interface.
• cv::hfs
  • HfsSegment
• cv::ImageCollection
  • iterator
• cv::img_hash
  • AverageHash — Computes average hash value of the input image.
  • BlockMeanHash — Image hash based on block mean.
  • ColorMomentHash — Image hash based on color moments.
  • ImgHashBase — The base class for image hash algorithms.
  • MarrHildrethHash — Marr-Hildreth Operator Based Hash, slowest but more discriminative.
  • PHash — pHash
  • RadialVarianceHash — Image hash based on Radon transform.
• cv::instr
  • NodeData
  • NodeDataTls
• cv::kinfu
  • KinFu — KinectFusion implementation.
  • Params
  • VolumeParams
• cv::legacy
  • MultiTracker — This class is used to track multiple objects using the specified tracker algorithm.
  • MultiTracker_Alt — Base abstract class for the long-term Multi Object Trackers:
  • MultiTrackerTLD — Multi Object Tracker for TLD.
  • Tracker — Base abstract class for the long-term tracker:
  • TrackerBoosting — the Boosting tracker
  • TrackerCSRT — the CSRT tracker
  • TrackerKCF — the KCF (Kernelized Correlation Filter) tracker
  • TrackerMedianFlow — the Median Flow tracker
  • TrackerMIL — The MIL algorithm trains a classifier in an online manner to separate the object from the background.
  • TrackerMOSSE — the MOSSE (Minimum Output Sum of Squared Error) tracker
  • TrackerTLD — the TLD (Tracking, learning and detection) tracker
• cv::legacy::TrackerBoosting
  • Params
• cv::legacy::TrackerCSRT
  • Params
• cv::legacy::TrackerKCF
  • Params
• cv::legacy::TrackerMedianFlow
  • Params
• cv::legacy::TrackerMIL
  • Params
• cv::legacy::TrackerTLD
  • Params
• cv::LevMarq
  • Report
  • Settings
• cv::line_descriptor
  • BinaryDescriptor — Class implements both functionalities for detection of lines and computation of their binary 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
  • DrawLinesMatchesFlags
  • KeyLine — A class to represent a line.
  • LSDDetector
  • LSDParam
• cv::line_descriptor::BinaryDescriptor
  • EdgeChains
  • EDLineDetector
  • EDLineParam
  • LineChains
  • OctaveLine
  • OctaveSingleLine
  • Params
  • Pixel
  • SingleLine
• cv::line_descriptor::BinaryDescriptorMatcher
  • bitarray
  • BucketGroup
  • Mihasher
  • SparseHashtable
• cv::linemod
  • ColorGradient — Modality that computes quantized gradient orientations from a color image.
  • DepthNormal — Modality that computes quantized surface normals from a dense depth map.
  • Detector — Object detector using the LINE template matching algorithm with any set of modalities.
  • Feature — Discriminant feature described by its location and label.
  • Match — Represents a successful template match.
  • Modality — Interface for modalities that plug into the LINE template matching representation.
  • QuantizedPyramid — Represents a modality operating over an image pyramid.
  • Template
• cv::linemod::QuantizedPyramid
  • Candidate
• cv::mcc
  • CChecker — checker object
  • CCheckerDetector — A class to find the positions of the ColorCharts in the image.
  • DetectorParametersMCC — struct DetectorParametersMCC is used by CCheckerDetector
• cv::MediaFrame
  • IAdapter — An interface class for MediaFrame data adapters.
  • View — Provides access to the MediaFrame's underlying data.
• cv::MinProblemSolver
  • Function
• cv::ml
  • ANN_MLP — Artificial Neural Networks - Multi-Layer Perceptrons.
  • Boost — Boosted tree classifier derived from DTrees.
  • DTrees — The class represents a single decision tree or a collection of decision trees.
  • EM — The class implements the Expectation Maximization algorithm.
  • KNearest — The class implements K-Nearest Neighbors model.
  • LogisticRegression — Implements Logistic Regression classifier.
  • NormalBayesClassifier — Bayes classifier for normally distributed data.
  • ParamGrid — The structure represents the logarithmic grid range of statmodel parameters.
  • RTrees — The class implements the random forest predictor.
  • SimulatedAnnealingSolverSystem — This class declares example interface for system state used in simulated annealing optimization algorithm.
  • StatModel — Base class for statistical models in OpenCV ML.
  • SVM — Support Vector Machines.
  • SVMSGD — Stochastic Gradient Descent SVM classifier.
  • TrainData — Class encapsulating training data.
• cv::ml::DTrees
  • Node
  • Split
• cv::ml::SVM
  • Kernel
• 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.
• cv::multicalib::MultiCameraCalibration
  • edge
  • vertex
• cv::ocl
  • Context
  • Device
  • Image2D
  • Kernel
  • KernelArg
  • OpenCLExecutionContext
  • OpenCLExecutionContextScope
  • Platform
  • PlatformInfo
  • Program
  • ProgramSource
  • Queue
  • Timer
• cv::ocl::Context
  • UserContext
• cv::ogl
  • Arrays — Wrapper for OpenGL Client-Side Vertex arrays.
  • Buffer — Smart pointer for OpenGL buffer object with reference counting.
  • Texture2D — Smart pointer for OpenGL 2D texture memory with reference counting.
• cv::optflow
  • DenseRLOFOpticalFlow — Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme.
  • DualTVL1OpticalFlow — "Dual TV L1" Optical Flow Algorithm.
  • GPCDetails
  • GPCForest
  • GPCMatchingParams — Class encapsulating matching parameters.
  • GPCPatchDescriptor
  • GPCPatchSample
  • GPCTrainingParams — Class encapsulating training parameters.
  • GPCTrainingSamples — Class encapsulating training samples.
  • GPCTree — Class for individual tree.
  • OpticalFlowPCAFlow — PCAFlow algorithm.
  • 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.
  • RLOFOpticalFlowParameter — This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm.
  • SparseRLOFOpticalFlow — Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms.
• cv::optflow::GPCForest
  • ParallelTrailsFilling
  • Trail
• cv::optflow::GPCTree
  • Node
• cv::ovis
  • WindowScene
• cv::parallel
  • ParallelForAPI
• cv::parallel::openmp
  • ParallelForBackend
• cv::parallel::tbb
  • ParallelForBackend
• cv::parallel::tbb::ParallelForBackend
  • CallbackProxy
• cv::ParamType< _Tp, typename std::enable_if< std::is_enum< _Tp >::value >
  • type >
• cv::ParamType< std
  • vector< Mat > >
• 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.
  • PhaseUnwrapping — Abstract base class for phase unwrapping.
• cv::phase_unwrapping::HistogramPhaseUnwrapping
  • Params
• cv::plot
  • Plot2d
• 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:
  • 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.
  • 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.
  • PPF3DDetector — Class, allowing the load and matching 3D models. Typical Use:
• cv::QRCodeDetectorAruco
  • Params
• cv::QRCodeEncoder
  • Params
• cv::quality
  • QualityBase
• 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.
  • RandomPatternGenerator
• cv::rapid
  • GOSTracker
  • OLSTracker
  • Rapid — wrapper around silhouette based 3D object tracking function for uniform access
  • Tracker — Abstract base class for stateful silhouette trackers.
• cv::reg
  • Map — Base class for modelling a Map between two images.
  • MapAffine
  • Mapper — Base class for modelling an algorithm for calculating a map.
  • MapperGradAffine
  • MapperGradEuclid
  • MapperGradProj
  • MapperGradShift
  • MapperGradSimilar
  • MapperPyramid
  • MapProjec
  • MapShift
  • MapTypeCaster
• cv::RMat
  • IAdapter
  • View
• cv::saliency
  • MotionSaliency
  • MotionSaliencyBinWangApr2014 — the Fast Self-tuning Background Subtraction Algorithm from BinWangApr2014
  • Objectness
  • ObjectnessBING — Objectness algorithms based on [3] [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.
  • Saliency
  • StaticSaliency
  • StaticSaliencyFineGrained — the Fine Grained Saliency approach from FGS
  • StaticSaliencySpectralResidual — the Spectral Residual approach from SR
• cv::saliency::ObjectnessBING
  • FilterTIG
  • ValStructVec
• cv::segmentation
  • IntelligentScissorsMB — Intelligent Scissors image segmentation.
• cv::sfm
  • BaseSFM — base class BaseSFM declares a common API that would be used in a typical scene reconstruction scenario
  • libmv_CameraIntrinsicsOptions — Data structure describing the camera model and its parameters.
  • libmv_ReconstructionOptions — Data structure describing the reconstruction options.
  • SFMLibmvEuclideanReconstruction — SFMLibmvEuclideanReconstruction class provides an interface with the Libmv Structure From Motion pipeline.
• cv::SimpleBlobDetector
  • Params
• cv::SparseMat
  • Hdr
  • Node
• cv::stereo
  • MatchQuasiDense
  • PropagationParameters
  • QuasiDenseStereo — Class containing the methods needed for Quasi Dense Stereo computation.
• cv::structured_light
  • GrayCodePattern — Class implementing the Gray-code pattern, based on UNDERWORLD.
  • SinusoidalPattern — Class implementing Fourier transform profilometry (FTP) , phase-shifting profilometry (PSP) and Fourier-assisted phase-shifting profilometry (FAPS) based on faps.
  • StructuredLightPattern — Abstract base class for generating and decoding structured light patterns.
• cv::structured_light::GrayCodePattern
  • Params
• cv::structured_light::SinusoidalPattern
  • Params
• cv::Subdiv2D
  • QuadEdge
  • Vertex
• cv::superres
  • BroxOpticalFlow
  • DenseOpticalFlowExt
  • DualTVL1OpticalFlow
  • FarnebackOpticalFlow
  • FrameSource
  • PyrLKOpticalFlow
  • SuperResolution — Base class for Super Resolution algorithms.
• cv::text
  • BaseOCR
  • ERFilter — Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm Neumann12. :
  • ERStat — The ERStat structure represents a class-specific Extremal Region (ER).
  • OCRBeamSearchDecoder — OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm.
  • OCRHMMDecoder — OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models.
  • 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.
  • OCRTesseract — OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++.
  • TextDetector — An abstract class providing interface for text detection algorithms.
  • 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.
• cv::text::ERFilter
  • Callback
• cv::text::OCRBeamSearchDecoder
  • ClassifierCallback
• cv::text::OCRHMMDecoder
  • ClassifierCallback
• cv::TrackerCSRT
  • Params
• cv::TrackerDaSiamRPN
  • Params
• cv::TrackerKCF
  • Params
• cv::TrackerMIL
  • Params
• cv::TrackerNano
  • Params
• cv::TrackerVit
  • Params
• cv::traits
  • Depth
  • Depth< Affine3< _Tp > >
  • Depth< Complex< _Tp > >
  • Depth< Moments >
  • Depth< Point3_< _Tp > >
  • Depth< Point_< _Tp > >
  • Depth< Range >
  • Depth< Rect_< _Tp > >
  • Depth< RotatedRect >
  • Depth< Scalar_< _Tp > >
  • Depth< Size_< _Tp > >
  • SafeFmt
  • SafeType
  • SafeType< T, false >
  • SafeType< T, true >
  • Type
  • Type< Affine3< _Tp > >
  • Type< Complex< _Tp > >
  • Type< Moments >
  • Type< Point3_< _Tp > >
  • Type< Point_< _Tp > >
  • Type< Range >
  • Type< Rect_< _Tp > >
  • Type< RotatedRect >
  • Type< Scalar_< _Tp > >
  • Type< Size_< _Tp > >
• cv::utils
  • BufferArea — Manages memory block shared by muliple buffers.
• cv::utils::logging
  • LogTagAuto
• cv::videostab
  • ColorAverageInpainter
  • ColorInpainter
  • ConsistentMosaicInpainter
  • DeblurerBase
  • FastMarchingMethod — Describes the Fast Marching Method implementation.
  • FromFileMotionReader
  • GaussianMotionFilter
  • IDenseOptFlowEstimator
  • IFrameSource
  • ILog
  • ImageMotionEstimatorBase — Base class for global 2D motion estimation methods which take frames as input.
  • IMotionStabilizer
  • InpainterBase
  • InpaintingPipeline
  • IOutlierRejector
  • ISparseOptFlowEstimator
  • KeypointBasedMotionEstimator — Describes a global 2D motion estimation method which uses keypoints detection and optical flow for matching.
  • LogToStdout
  • LpMotionStabilizer
  • MaskFrameSource
  • MoreAccurateMotionWobbleSuppressor
  • MoreAccurateMotionWobbleSuppressorBase
  • MotionEstimatorBase — Base class for all global motion estimation methods.
  • MotionEstimatorL1 — Describes a global 2D motion estimation method which minimizes L1 error.
  • MotionEstimatorRansacL2 — Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error.
  • MotionFilterBase
  • MotionInpainter
  • MotionStabilizationPipeline
  • NullDeblurer
  • NullFrameSource
  • NullInpainter
  • NullLog
  • NullOutlierRejector
  • NullWobbleSuppressor
  • OnePassStabilizer
  • PyrLkOptFlowEstimatorBase
  • RansacParams — Describes RANSAC method parameters.
  • SparsePyrLkOptFlowEstimator
  • StabilizerBase
  • ToFileMotionWriter
  • TranslationBasedLocalOutlierRejector
  • TwoPassStabilizer
  • VideoFileSource
  • WeightingDeblurer
  • WobbleSuppressorBase
• cv::videostab::FastMarchingMethod
  • DXY
• cv::viz
  • Camera — This class wraps intrinsic parameters of a camera.
  • Color — This class represents color in BGR order.
  • KeyboardEvent — This class represents a keyboard event.
  • Mesh — This class wraps mesh attributes, and it can load a mesh from a ply file. :
  • MouseEvent — This class represents a mouse event.
  • Viz3d — The Viz3d class represents a 3D visualizer window. This class is implicitly shared.
  • WArrow — This 3D Widget defines an arrow.
  • WCameraPosition — This 3D Widget represents camera position in a scene by its axes or viewing frustum. :
  • WCircle — This 3D Widget defines a circle.
  • WCloud — Clouds.
  • WCloudCollection — This 3D Widget defines a collection of clouds. :
  • WCloudNormals — This 3D Widget represents normals of a point cloud. :
  • WCone — This 3D Widget defines a cone. :
  • WCoordinateSystem — Compound widgets.
  • WCube — This 3D Widget defines a cube.
  • WCylinder — This 3D Widget defines a cylinder. :
  • WGrid — This 3D Widget defines a grid. :
  • Widget — Base class of all widgets. Widget is implicitly shared.
  • Widget2D — Base class of all 2D widgets.
  • Widget3D — Base class of all 3D widgets.
  • WidgetAccessor — This class is for users who want to develop their own widgets using VTK library API. :
  • WImage3D — This 3D Widget represents an image in 3D space. :
  • WImageOverlay — This 2D Widget represents an image overlay. :
  • WLine — Simple widgets.
  • WMesh — Constructs a WMesh.
  • WPaintedCloud
  • WPlane — This 3D Widget defines a finite plane.
  • WPolyLine — This 3D Widget defines a poly line. :
  • WSphere — This 3D Widget defines a sphere. :
  • WText — Text and image widgets.
  • WText3D — This 3D Widget represents 3D text. The text always faces the camera.
  • WTrajectory — Trajectories.
  • WTrajectoryFrustums — This 3D Widget represents a trajectory. :
  • WTrajectorySpheres — This 3D Widget represents a trajectory using spheres and lines.
  • WWidgetMerger — This class allows to merge several widgets to single one.
• 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.
• cv::xfeatures2d
  • AffineFeature2D — Class implementing affine adaptation for key points.
  • AgastFeatureDetector — Wrapping class for feature detection using the AGAST method. :
  • AKAZE — Class implementing the AKAZE keypoint detector and descriptor extractor, described in ANB13.
  • BEBLID — Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in Suarez2020BEBLID .
  • BoostDesc — Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in Trzcinski13a and Trzcinski13b.
  • BOWImgDescriptorExtractor — Class to compute an image descriptor using the bag of visual words.
  • BOWKMeansTrainer — kmeans -based class to train visual vocabulary using the bag of visual words approach. :
  • BOWTrainer — Abstract base class for training the bag of visual words vocabulary from a set of descriptors.
  • BriefDescriptorExtractor — Class for computing BRIEF descriptors described in calon2010 .
  • BRISK — Class implementing the BRISK keypoint detector and descriptor extractor, described in LCS11 .
  • DAISY — Class implementing DAISY descriptor, described in Tola10.
  • Elliptic_KeyPoint — Elliptic region around an interest point.
  • FREAK — Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in AOV12 .
  • HarrisLaplaceFeatureDetector — Class implementing the Harris-Laplace feature detector as described in Mikolajczyk2004.
  • KAZE — Class implementing the KAZE keypoint detector and descriptor extractor, described in ABD12 .
  • LATCH
  • LUCID — Class implementing the locally uniform comparison image descriptor, described in LUCID.
  • MSDDetector — Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in Tombari14.
  • 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.
  • PCTSignaturesSQFD — Class implementing Signature Quadratic Form Distance (SQFD).
  • StarDetector — The class implements the keypoint detector introduced by Agrawal08, synonym of StarDetector. :
  • SURF — Class for extracting Speeded Up Robust Features from an image Bay06 .
  • TBMR — Class implementing the Tree Based Morse Regions (TBMR) as described in Najman2014 extended with scaled extraction ability.
  • TEBLID — Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), described in Suarez2021TEBLID.
  • VGG — Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in Simonyan14.
• cv::ximgproc
  • AdaptiveManifoldFilter — Interface for Adaptive Manifold Filter realizations.
  • Box
  • ContourFitting — Class for ContourFitting algorithms. ContourFitting match two contours $ z_a $ and $ z_b $ minimizing distance.
  • DisparityFilter — Main interface for all disparity map filters.
  • DisparityWLSFilter — Disparity map filter based on Weighted Least Squares filter (in form of Fast Global Smoother that is a lot faster than traditional Weighted Least Squares filter implementations) and optional use of left-right-consistency-based confidence to refine the results in half-occlusions and uniform areas.
  • DTFilter — Interface for realizations of Domain Transform filter.
  • EdgeAwareInterpolator — Sparse match interpolation algorithm based on modified locally-weighted affine estimator from Revaud2015 and Fast Global Smoother as post-processing filter.
  • EdgeBoxes — Class implementing EdgeBoxes algorithm from ZitnickECCV14edgeBoxes :
  • EdgeDrawing — Class implementing the ED (EdgeDrawing) topal2012edge, EDLines akinlar2011edlines, EDPF akinlar2012edpf, EDCircles akinlar2013edcircles and ColorED akinlar201782 algorithms.
  • FastBilateralSolverFilter — Interface for implementations of Fast Bilateral Solver.
  • FastGlobalSmootherFilter — Interface for implementations of Fast Global Smoother filter.
  • FastLineDetector — Class implementing the FLD (Fast Line Detector) algorithm described in Lee14 .
  • GuidedFilter — Interface for realizations of (Fast) Guided Filter.
  • RFFeatureGetter
  • RICInterpolator — Sparse match interpolation algorithm based on modified piecewise locally-weighted affine estimator called Robust Interpolation method of Correspondences or RIC from Hu2017 and Variational and Fast Global Smoother as post-processing filter. The RICInterpolator is a extension of the EdgeAwareInterpolator. Main concept of this extension is an piece-wise affine model based on over-segmentation via SLIC superpixel estimation. The method contains an efficient propagation mechanism to estimate among the pieces-wise models.
  • 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.
  • SparseMatchInterpolator — Main interface for all filters, that take sparse matches as an input and produce a dense per-pixel matching (optical flow) as an output.
  • StructuredEdgeDetection — Class implementing edge detection algorithm from Dollar2013 :
  • SuperpixelLSC — Class implementing the LSC (Linear Spectral Clustering) superpixels algorithm described in LiCVPR2015LSC.
  • SuperpixelSEEDS — Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm described in VBRV14 .
  • SuperpixelSLIC — Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in Achanta2012.
• cv::ximgproc::EdgeDrawing
  • Params
• cv::ximgproc::segmentation
  • GraphSegmentation — Graph Based Segmentation Algorithm. The class implements the algorithm described in PFF2004 .
  • SelectiveSearchSegmentation — Selective search segmentation algorithm The class implements the algorithm described in uijlings2013selective.
  • SelectiveSearchSegmentationStrategy — Strategie for the selective search segmentation algorithm The class implements a generic stragery for the algorithm described in uijlings2013selective.
  • SelectiveSearchSegmentationStrategyColor — Color-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in uijlings2013selective.
  • SelectiveSearchSegmentationStrategyFill — Fill-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in uijlings2013selective.
  • SelectiveSearchSegmentationStrategyMultiple — Regroup multiple strategies for the selective search segmentation algorithm.
  • SelectiveSearchSegmentationStrategySize — Size-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in uijlings2013selective.
  • SelectiveSearchSegmentationStrategyTexture — Texture-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in uijlings2013selective.
• cv::xobjdetect
  • WBDetector — WaldBoost detector.
• cv::xphoto
  • GrayworldWB — Gray-world white balance algorithm.
  • LearningBasedWB — More sophisticated learning-based automatic white balance algorithm.
  • 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 $p\%$ of pixel values.
  • 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.
  • WhiteBalancer — The base class for auto white balance algorithms.
• cvv
  • FinalShowCaller — RAII-class to call finalShow() in it's dtor.
• cvv::impl
  • CallMetaData — Optional information about a location in Code.
• NcvCTprep
  • assertTest
  • CT_ASSERT_FAILURE
  • CT_ASSERT_FAILURE< true >
• (global namespace)
  • Cv16suf
  • Cv32suf
  • Cv64suf
  • CvAbstractCamera
  • cvhalKeyPoint
  • CvPhotoCamera
  • CvPhotoCameraDelegate-p
  • CvVideoCamera
  • CvVideoCameraDelegate-p
  • HaarClassifierCascadeDescriptor
  • HaarClassifierNode128
  • HaarClassifierNodeDescriptor32
  • HaarFeature64
  • HaarFeatureDescriptor32
  • HaarStage64
  • hashnode_i
  • hashtable_int
  • INCVMemAllocator
  • NCVBroxOpticalFlowDescriptor — Model and solver parameters.
  • NCVMatrix
  • NCVMatrixAlloc
  • NCVMatrixReuse
  • NCVMemNativeAllocator
  • NCVMemPtr
  • NCVMemSegment
  • NCVMemStackAllocator
  • NcvPoint2D32s
  • NcvPoint2D32u
  • NcvRect32s
  • NcvRect32u
  • NcvRect8u
  • NcvSize32s
  • NcvSize32u
  • NCVVector
  • NCVVectorAlloc
  • NCVVectorReuse
  • NppStInterpolationState
  • OpenCV_API_Header
  • THash — Struct, holding a node in the hashtable.