これは名前空間の一覧です。
[表示階層
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| ► N cv | "black box" representation of the file storage associated with a file on disk |
| ► N aruco | |
| C Board | Board of markers |
| C 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 DetectorParameters | Parameters for the detectMarker process: |
| C Dictionary | Dictionary/Set of markers. It contains the inner codification |
| C 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 ( |
| ► N barcode | |
| C BarcodeDetector | |
| ► N bioinspired | |
| C Retina | Class which allows the Gipsa/Listic Labs model to be used with OpenCV |
| C RetinaFastToneMapping | Wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV |
| ► C RetinaParameters | Retina model parameters structure |
| C IplMagnoParameters | Inner Plexiform Layer Magnocellular channel (IplMagno) |
| C OPLandIplParvoParameters | Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters |
| C SegmentationParameters | Parameter structure that stores the transient events detector setup parameters |
| C TransientAreasSegmentationModule | Class which provides a transient/moving areas segmentation module |
| ► N ccalib | |
| C CustomPattern | |
| ► N ccm | |
| C ColorCorrectionModel | Core class of ccm model |
| ► N colored_kinfu | |
| C ColoredKinFu | KinectFusion implementation |
| C Params | |
| ► N cuda | |
| C BufferPool | BufferPool for use with CUDA streams |
| C DeviceInfo | Class providing functionality for querying the specified GPU properties |
| C Event | |
| C EventAccessor | Class that enables getting cudaEvent_t from cuda::Event |
| C GpuData | |
| ► C GpuMat | Base storage class for GPU memory with reference counting |
| C Allocator | |
| C GpuMatND | |
| C HostMem | Class with reference counting wrapping special memory type allocation functions from CUDA |
| C Stream | This class encapsulates a queue of asynchronous calls |
| C StreamAccessor | Class that enables getting cudaStream_t from cuda::Stream |
| C SURF_CUDA | Class used for extracting Speeded Up Robust Features (SURF) from an image. : |
| C TargetArchs | Class providing a set of static methods to check what NVIDIA* card architecture the CUDA module was built for |
| ► N detail | |
| ► N contrib_feature | |
| C CvFeatureEvaluator | |
| C CvFeatureParams | |
| ► C CvHaarEvaluator | |
| C FeatureHaar | |
| C CvHaarFeatureParams | |
| ► C CvHOGEvaluator | |
| C Feature | |
| C CvHOGFeatureParams | |
| ► C CvLBPEvaluator | |
| C Feature | |
| C CvLBPFeatureParams | |
| C CvParams | |
| ► N kalman_filters | |
| C AugmentedUnscentedKalmanFilterParams | Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter |
| C 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 UnscentedKalmanFilter | The interface for Unscented Kalman filter and Augmented Unscented Kalman filter |
| C UnscentedKalmanFilterParams | Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter |
| ► N online_boosting | |
| C BaseClassifier | |
| C ClassifierThreshold | |
| C Detector | |
| C EstimatedGaussDistribution | |
| C StrongClassifierDirectSelection | |
| C WeakClassifierHaarFeature | |
| ► N tbm | |
| C CosDistance | Allows computing cosine distance between two reidentification descriptors |
| C IDescriptorDistance | Declares an interface for distance computation between reidentification descriptors |
| C IImageDescriptor | Declares base class for image descriptor |
| C ITrackerByMatching | Tracker-by-Matching algorithm interface |
| C MatchTemplateDistance | Computes distance between images using MatchTemplate function from OpenCV library and its cross-correlation computation method in particular |
| C ResizedImageDescriptor | Uses resized image as descriptor |
| C Track | Describes tracks |
| C TrackedObject | The TrackedObject struct defines properties of detected object |
| C TrackerParams | The TrackerParams struct stores parameters of TrackerByMatching |
| ► N tracking | |
| ► N contrib_feature | |
| C CvFeatureEvaluator | |
| C CvFeatureParams | |
| ► C CvHaarEvaluator | |
| C FeatureHaar | |
| C CvHaarFeatureParams | |
| ► C CvHOGEvaluator | |
| C Feature | |
| C CvHOGFeatureParams | |
| ► C CvLBPEvaluator | |
| C Feature | |
| C CvLBPFeatureParams | |
| C CvParams | |
| ► N kalman_filters | |
| C AugmentedUnscentedKalmanFilterParams | Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter |
| C 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 UnscentedKalmanFilter | The interface for Unscented Kalman filter and Augmented Unscented Kalman filter |
| C UnscentedKalmanFilterParams | Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter |
| ► N online_boosting | |
| C BaseClassifier | |
| C ClassifierThreshold | |
| C Detector | |
| C EstimatedGaussDistribution | |
| C StrongClassifierDirectSelection | |
| C WeakClassifierHaarFeature | |
| ► N tbm | |
| C CosDistance | Allows computing cosine distance between two reidentification descriptors |
| C IDescriptorDistance | Declares an interface for distance computation between reidentification descriptors |
| C IImageDescriptor | Declares base class for image descriptor |
| C ITrackerByMatching | Tracker-by-Matching algorithm interface |
| C MatchTemplateDistance | Computes distance between images using MatchTemplate function from OpenCV library and its cross-correlation computation method in particular |
| C ResizedImageDescriptor | Uses resized image as descriptor |
| C Track | Describes tracks |
| C TrackedObject | The TrackedObject struct defines properties of detected object |
| C TrackerParams | The TrackerParams struct stores parameters of TrackerByMatching |
| C AugmentedUnscentedKalmanFilterParams | Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter |
| C BaseClassifier | |
| C ClassifierThreshold | |
| C CvFeatureEvaluator | |
| C CvFeatureParams | |
| ► C CvHaarEvaluator | |
| C FeatureHaar | |
| C CvHaarFeatureParams | |
| ► C CvHOGEvaluator | |
| C Feature | |
| C CvHOGFeatureParams | |
| ► C CvLBPEvaluator | |
| C Feature | |
| C CvLBPFeatureParams | |
| C CvParams | |
| C Detector | |
| C EstimatedGaussDistribution | |
| C StrongClassifierDirectSelection | |
| C TrackerContribFeature | Abstract base class for TrackerContribFeature that represents the feature |
| ► C TrackerContribFeatureHAAR | TrackerContribFeature based on HAAR features, used by TrackerMIL and many others algorithms |
| C Params | |
| C TrackerContribFeatureSet | Class that manages the extraction and selection of features |
| C 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 TrackerContribSamplerAlgorithm | Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler |
| ► C TrackerContribSamplerCSC | TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL |
| C Params | |
| C TrackerFeature | Abstract base class for TrackerFeature that represents the feature |
| C TrackerFeatureFeature2d | TrackerContribFeature based on Feature2D |
| C TrackerFeatureHOG | TrackerContribFeature based on HOG |
| C TrackerFeatureLBP | TrackerContribFeature based on LBP |
| C TrackerFeatureSet | Class that manages the extraction and selection of features |
| C TrackerModel | Abstract class that represents the model of the target |
| C 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 TrackerSamplerAlgorithm | Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler |
| ► C TrackerSamplerCS | TrackerContribSampler based on CS (current state), used by algorithm TrackerBoosting |
| C Params | |
| ► C TrackerSamplerCSC | TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL |
| C Params | |
| ► C TrackerSamplerPF | This sampler is based on particle filtering |
| C 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 TrackerStateEstimator | Abstract base class for TrackerStateEstimator that estimates the most likely target state |
| ► C TrackerStateEstimatorAdaBoosting | TrackerStateEstimatorAdaBoosting based on ADA-Boosting |
| C TrackerAdaBoostingTargetState | Implementation of the target state for TrackerAdaBoostingTargetState |
| C TrackerStateEstimatorSVM | TrackerStateEstimator based on SVM |
| C TrackerTargetState | Abstract base class for TrackerTargetState that represents a possible state of the target |
| C 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 UnscentedKalmanFilter | The interface for Unscented Kalman filter and Augmented Unscented Kalman filter |
| C UnscentedKalmanFilterParams | Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter |
| C WeakClassifierHaarFeature | |
| C AffineBasedEstimator | Affine transformation based estimator |
| C 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 AffineWarper | Affine warper that uses rotations and translations |
| C AugmentedUnscentedKalmanFilterParams | Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter |
| C BaseClassifier | |
| C 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 BestOf2NearestRangeMatcher | |
| C Blender | Base class for all blenders |
| C BlocksChannelsCompensator | Exposure compensator which tries to remove exposure related artifacts by adjusting image block on each channel |
| C BlocksCompensator | Exposure compensator which tries to remove exposure related artifacts by adjusting image blocks |
| C BlocksGainCompensator | Exposure compensator which tries to remove exposure related artifacts by adjusting image block intensities, see [UES01] for details |
| C 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 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 BundleAdjusterBase | Base class for all camera parameters refinement methods |
| C 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 BundleAdjusterReproj | Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection error squares |
| C CameraParams | Describes camera parameters |
| C ChannelsCompensator | Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities on each channel independently |
| C ClassifierThreshold | |
| C CompressedRectilinearPortraitProjector | |
| C CompressedRectilinearPortraitWarper | |
| C CompressedRectilinearProjector | |
| C CompressedRectilinearWarper | |
| C CvFeatureEvaluator | |
| C CvFeatureParams | |
| ► C CvHaarEvaluator | |
| C FeatureHaar | |
| C CvHaarFeatureParams | |
| ► C CvHOGEvaluator | |
| C Feature | |
| C CvHOGFeatureParams | |
| ► C CvLBPEvaluator | |
| C Feature | |
| C CvLBPFeatureParams | |
| C CvParams | |
| C CylindricalPortraitProjector | |
| C CylindricalPortraitWarper | |
| C CylindricalProjector | |
| C CylindricalWarper | Warper that maps an image onto the x*x + z*z = 1 cylinder |
| C CylindricalWarperGpu | |
| C Detector | |
| C DisjointSets | |
| C DpSeamFinder | |
| C EstimatedGaussDistribution | |
| C Estimator | Rotation estimator base class |
| C ExposureCompensator | Base class for all exposure compensators |
| C FeatherBlender | Simple blender which mixes images at its borders |
| C FeaturesMatcher | Feature matchers base class |
| C FisheyeProjector | |
| C FisheyeWarper | |
| C GainCompensator | Exposure compensator which tries to remove exposure related artifacts by adjusting image intensities, see [BL07] and [WJ10] for details |
| C Graph | |
| C GraphCutSeamFinder | Minimum graph cut-based seam estimator. See details in [V03] |
| C GraphCutSeamFinderBase | Base class for all minimum graph-cut-based seam estimators |
| C GraphEdge | |
| C HomographyBasedEstimator | Homography based rotation estimator |
| C ImageFeatures | Structure containing image keypoints and descriptors |
| C MatchesInfo | Structure containing information about matches between two images |
| C MercatorProjector | |
| C MercatorWarper | |
| C MultiBandBlender | Blender which uses multi-band blending algorithm (see [BA83]) |
| C NoBundleAdjuster | Stub bundle adjuster that does nothing |
| C NoExposureCompensator | Stub exposure compensator which does nothing |
| C NoSeamFinder | Stub seam estimator which does nothing |
| C PairwiseSeamFinder | Base class for all pairwise seam estimators |
| C PaniniPortraitProjector | |
| C PaniniPortraitWarper | |
| C PaniniProjector | |
| C PaniniWarper | |
| C PlanePortraitProjector | |
| C PlanePortraitWarper | |
| C PlaneProjector | |
| C PlaneWarper | Warper that maps an image onto the z = 1 plane |
| C PlaneWarperGpu | |
| C ProjectorBase | Base class for warping logic implementation |
| C RotationWarper | Rotation-only model image warper interface |
| C RotationWarperBase | Base class for rotation-based warper using a detail::ProjectorBase_ derived class |
| C SeamFinder | Base class for a seam estimator |
| C SphericalPortraitProjector | |
| C SphericalPortraitWarper | |
| C SphericalProjector | |
| C SphericalWarper | Warper that maps an image onto the unit sphere located at the origin |
| C SphericalWarperGpu | |
| C StereographicProjector | |
| C StereographicWarper | |
| C StrongClassifierDirectSelection | |
| C Timelapser | |
| C TimelapserCrop | |
| C TrackerContribFeature | Abstract base class for TrackerContribFeature that represents the feature |
| ► C TrackerContribFeatureHAAR | TrackerContribFeature based on HAAR features, used by TrackerMIL and many others algorithms |
| C Params | |
| C TrackerContribFeatureSet | Class that manages the extraction and selection of features |
| C 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 TrackerContribSamplerAlgorithm | Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler |
| ► C TrackerContribSamplerCSC | TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL |
| C Params | |
| C TrackerFeature | Abstract base class for TrackerFeature that represents the feature |
| C TrackerFeatureFeature2d | TrackerContribFeature based on Feature2D |
| C TrackerFeatureHOG | TrackerContribFeature based on HOG |
| C TrackerFeatureLBP | TrackerContribFeature based on LBP |
| C TrackerFeatureSet | Class that manages the extraction and selection of features |
| C TrackerModel | Abstract class that represents the model of the target |
| C 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 TrackerSamplerAlgorithm | Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler |
| ► C TrackerSamplerCS | TrackerContribSampler based on CS (current state), used by algorithm TrackerBoosting |
| C Params | |
| ► C TrackerSamplerCSC | TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL |
| C Params | |
| ► C TrackerSamplerPF | This sampler is based on particle filtering |
| C 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 TrackerStateEstimator | Abstract base class for TrackerStateEstimator that estimates the most likely target state |
| ► C TrackerStateEstimatorAdaBoosting | TrackerStateEstimatorAdaBoosting based on ADA-Boosting |
| C TrackerAdaBoostingTargetState | Implementation of the target state for TrackerAdaBoostingTargetState |
| C TrackerStateEstimatorSVM | TrackerStateEstimator based on SVM |
| C TrackerTargetState | Abstract base class for TrackerTargetState that represents a possible state of the target |
| C TransverseMercatorProjector | |
| C TransverseMercatorWarper | |
| C 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 UnscentedKalmanFilter | The interface for Unscented Kalman filter and Augmented Unscented Kalman filter |
| C UnscentedKalmanFilterParams | Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter |
| C VoronoiSeamFinder | Voronoi diagram-based seam estimator |
| C WeakClassifierHaarFeature | |
| N directx | |
| ► N dnn | |
| ► N details | |
| C _LayerStaticRegisterer | |
| C _Range | |
| C AbsLayer | |
| C AccumLayer | |
| C ActivationLayer | |
| C BackendNode | Derivatives of this class encapsulates functions of certain backends |
| C BackendWrapper | Derivatives of this class wraps cv::Mat for different backends and targets |
| C BaseConvolutionLayer | |
| C BatchNormLayer | |
| C BlankLayer | |
| C BNLLLayer | |
| C ChannelsPReLULayer | |
| C ClassificationModel | This class represents high-level API for classification models |
| C ConcatLayer | |
| C ConstLayer | |
| C ConvolutionLayer | |
| C CorrelationLayer | |
| C CropAndResizeLayer | |
| C CropLayer | |
| C DataAugmentationLayer | |
| C DeconvolutionLayer | |
| C DetectionModel | This class represents high-level API for object detection networks |
| C DetectionOutputLayer | Detection output layer |
| C Dict | This class implements name-value dictionary, values are instances of DictValue |
| C DictValue | This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64 |
| C EltwiseLayer | Element wise operation on inputs |
| C ELULayer | |
| C ExpLayer | |
| C FlattenLayer | |
| C FlowWarpLayer | |
| C InnerProductLayer | |
| C InterpLayer | Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2 |
| C KeypointsModel | This class represents high-level API for keypoints models |
| C Layer | This interface class allows to build new Layers - are building blocks of networks |
| C LayerFactory | Layer factory allows to create instances of registered layers |
| C LayerParams | This class provides all data needed to initialize layer |
| C LRNLayer | |
| C LSTMLayer | LSTM recurrent layer |
| C MaxUnpoolLayer | |
| C MishLayer | |
| C Model | This class is presented high-level API for neural networks |
| C MVNLayer | |
| C Net | This class allows to create and manipulate comprehensive artificial neural networks |
| C NormalizeBBoxLayer |
- normalization layer
|
| C PaddingLayer | Adds extra values for specific axes |
| C PermuteLayer | |
| C PoolingLayer | |
| C PowerLayer | |
| C PriorBoxLayer | |
| C ProposalLayer | |
| C RegionLayer | |
| C ReLU6Layer | |
| C ReLULayer | |
| C ReorgLayer | |
| C ReshapeLayer | |
| C ResizeLayer | Resize input 4-dimensional blob by nearest neighbor or bilinear strategy |
| C RNNLayer | Classical recurrent layer |
| C ScaleLayer | |
| C SegmentationModel | This class represents high-level API for segmentation models |
| C ShiftLayer | |
| C ShuffleChannelLayer | |
| C SigmoidLayer | |
| C SliceLayer | |
| C SoftmaxLayer | |
| C SplitLayer | |
| C SwishLayer | |
| C TanHLayer | |
| C TextDetectionModel | Base class for text detection networks |
| C TextDetectionModel_DB | This class represents high-level API for text detection DL networks compatible with DB model |
| C TextDetectionModel_EAST | This class represents high-level API for text detection DL networks compatible with EAST model |
| C TextRecognitionModel | This class represents high-level API for text recognition networks |
| ► N dnn_objdetect | |
| C InferBbox | A class to post process model predictions |
| C object | Structure to hold the details pertaining to a single bounding box |
| ► N dnn_superres | |
| C DnnSuperResImpl | A class to upscale images via convolutional neural networks. The following four models are implemented: |
| ► N dpm | |
| ► C DPMDetector | This is a C++ abstract class, it provides external user API to work with DPM |
| C ObjectDetection | |
| ► N dynafu | |
| C DynaFu | |
| ► N face | |
| C BasicFaceRecognizer | |
| C BIF | |
| C CParams | |
| C EigenFaceRecognizer | |
| C Facemark | Abstract base class for all facemark models |
| ► C FacemarkAAM | |
| C Config | Optional parameter for fitting process |
| C Data | Data container for the facemark::getData function |
| ► C Model | The model of AAM Algorithm |
| C Texture | |
| C Params | |
| ► C FacemarkKazemi | |
| C Params | |
| ► C FacemarkLBF | |
| C BBox | |
| C Params | |
| C FacemarkTrain | Abstract base class for trainable facemark models |
| C FaceRecognizer | Abstract base class for all face recognition models |
| C FisherFaceRecognizer | |
| C LBPHFaceRecognizer | |
| C 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 PredictCollector | Abstract base class for all strategies of prediction result handling |
| ► C StandardCollector | Default predict collector |
| C PredictResult | |
| N fisheye | The methods in this namespace use a so-called fisheye camera model |
| ► N flann | |
| C CvType | |
| C CvType< char > | |
| C CvType< double > | |
| C CvType< float > | |
| C CvType< int > | |
| C CvType< short > | |
| C CvType< unsigned char > | |
| C CvType< unsigned short > | |
| C GenericIndex | The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built |
| N ft | |
| ► N hal | |
| C DCT2D | |
| C DFT1D | |
| C DFT2D | |
| ► N hfs | |
| C HfsSegment | |
| ► N img_hash | |
| C AverageHash | Computes average hash value of the input image |
| C BlockMeanHash | Image hash based on block mean |
| C ColorMomentHash | Image hash based on color moments |
| C ImgHashBase | The base class for image hash algorithms |
| C MarrHildrethHash | Marr-Hildreth Operator Based Hash, slowest but more discriminative |
| C PHash | PHash |
| C RadialVarianceHash | Image hash based on Radon transform |
| ► N instr | |
| C NodeData | |
| C NodeDataTls | |
| N intensity_transform | |
| ► N kinfu | |
| ► N detail | |
| C PoseGraph | |
| ► C Intr | |
| C Projector | |
| C Reprojector | Camera intrinsics |
| C KinFu | KinectFusion implementation |
| C Params | |
| C Volume | |
| C VolumeParams | |
| ► N large_kinfu | |
| C LargeKinfu | Large Scale Dense Depth Fusion implementation |
| C Params | |
| ► N legacy | |
| ► N tracking | |
| C MultiTracker | This class is used to track multiple objects using the specified tracker algorithm |
| C MultiTracker_Alt | Base abstract class for the long-term Multi Object Trackers: |
| C MultiTrackerTLD | Multi Object Tracker for TLD |
| C Tracker | Base abstract class for the long-term tracker: |
| ► C TrackerBoosting | Boosting tracker |
| C Params | |
| ► C TrackerCSRT | CSRT tracker |
| C Params | |
| ► C TrackerKCF | KCF (Kernelized Correlation Filter) tracker |
| C Params | |
| ► C TrackerMedianFlow | Median Flow tracker |
| C Params | |
| ► C TrackerMIL | The MIL algorithm trains a classifier in an online manner to separate the object from the background |
| C Params | |
| C TrackerMOSSE | MOSSE (Minimum Output Sum of Squared Error) tracker |
| ► C TrackerTLD | TLD (Tracking, learning and detection) tracker |
| C Params | |
| C MultiTracker | This class is used to track multiple objects using the specified tracker algorithm |
| C MultiTracker_Alt | Base abstract class for the long-term Multi Object Trackers: |
| C MultiTrackerTLD | Multi Object Tracker for TLD |
| C Tracker | Base abstract class for the long-term tracker: |
| ► C TrackerBoosting | Boosting tracker |
| C Params | |
| ► C TrackerCSRT | CSRT tracker |
| C Params | |
| ► C TrackerKCF | KCF (Kernelized Correlation Filter) tracker |
| C Params | |
| ► C TrackerMedianFlow | Median Flow tracker |
| C Params | |
| ► C TrackerMIL | The MIL algorithm trains a classifier in an online manner to separate the object from the background |
| C Params | |
| C TrackerMOSSE | MOSSE (Minimum Output Sum of Squared Error) tracker |
| ► C TrackerTLD | TLD (Tracking, learning and detection) tracker |
| C Params | |
| ► N line_descriptor | |
| ► C BinaryDescriptor | Class implements both functionalities for detection of lines and computation of their binary descriptor |
| C Params | List of BinaryDescriptor parameters: |
| C 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 DrawLinesMatchesFlags | |
| C KeyLine | A class to represent a line |
| C LSDDetector | |
| C LSDParam | |
| ► N linemod | |
| C ColorGradient | Modality that computes quantized gradient orientations from a color image |
| C DepthNormal | Modality that computes quantized surface normals from a dense depth map |
| C Detector | Object detector using the LINE template matching algorithm with any set of modalities |
| C Feature | Discriminant feature described by its location and label |
| C Match | Represents a successful template match |
| C Modality | Interface for modalities that plug into the LINE template matching representation |
| ► C QuantizedPyramid | Represents a modality operating over an image pyramid |
| C Candidate | Candidate feature with a score |
| C Template | |
| ► N mcc | |
| C CChecker | Checker object |
| C CCheckerDetector | A class to find the positions of the ColorCharts in the image |
| C CCheckerDraw | Checker draw |
| C DetectorParameters | Parameters for the detectMarker process: |
| N motempl | |
| ► N multicalib | |
| ► C 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 edge | |
| C vertex | |
| ► N ocl | |
| ► C Context | |
| C UserContext | |
| C Device | |
| C Image2D | |
| C Kernel | |
| C KernelArg | |
| C OpenCLExecutionContext | |
| C OpenCLExecutionContextScope | |
| C Platform | |
| C PlatformInfo | |
| C Program | |
| C ProgramSource | |
| C Queue | |
| C Timer | |
| ► N ogl | |
| C Arrays | Wrapper for OpenGL Client-Side Vertex arrays |
| C Buffer | Smart pointer for OpenGL buffer object with reference counting |
| C Texture2D | Smart pointer for OpenGL 2D texture memory with reference counting |
| N omnidir | |
| ► N optflow | |
| C DenseRLOFOpticalFlow | Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme |
| C DualTVL1OpticalFlow | "Dual TV L1" Optical Flow Algorithm |
| C GPCDetails | |
| C GPCForest | |
| C GPCMatchingParams | Class encapsulating matching parameters |
| C GPCPatchDescriptor | |
| C GPCPatchSample | |
| C GPCTrainingParams | Class encapsulating training parameters |
| C GPCTrainingSamples | Class encapsulating training samples |
| ► C GPCTree | Class for individual tree |
| C Node | |
| C OpticalFlowPCAFlow | PCAFlow algorithm |
| C 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 RLOFOpticalFlowParameter | This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm |
| C SparseRLOFOpticalFlow | Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms |
| ► N parallel | |
| ► N openmp | |
| C ParallelForBackend | |
| ► N tbb | |
| ► C ParallelForBackend | |
| C CallbackProxy | |
| C ParallelForAPI | |
| ► N phase_unwrapping | |
| ► C 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 Params | Parameters of phaseUnwrapping constructor |
| C PhaseUnwrapping | Abstract base class for phase unwrapping |
| ► N ppf_match_3d | |
| C hashnode_i | |
| C HSHTBL_i | |
| C 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 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 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 PPF3DDetector | Class, allowing the load and matching 3D models. Typical Use: |
| C THash | Struct, holding a node in the hashtable |
| ► N quality | |
| C QualityBase | |
| C QualityBRISQUE | BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) is a No Reference Image Quality Assessment (NR-IQA) algorithm |
| ► C QualityGMSD | Full reference GMSD algorithm http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm |
| C _mat_data | |
| C QualityMSE | Full reference mean square error algorithm https://en.wikipedia.org/wiki/Mean_squared_error |
| C QualityPSNR | Full reference peak signal to noise ratio (PSNR) algorithm https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio |
| ► C QualitySSIM | Full reference structural similarity algorithm https://en.wikipedia.org/wiki/Structural_similarity |
| C _mat_data | |
| ► N randpattern | |
| C 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 RandomPatternGenerator | |
| ► N rapid | |
| C GOSTracker | |
| C OLSTracker | |
| C Rapid | Wrapper around silhouette based 3D object tracking function for uniform access |
| C Tracker | Abstract base class for stateful silhouette trackers |
| ► N reg | |
| C Map | Base class for modelling a Map between two images |
| C MapAffine | |
| C Mapper | Base class for modelling an algorithm for calculating a map |
| C MapperGradAffine | |
| C MapperGradEuclid | |
| C MapperGradProj | |
| C MapperGradShift | |
| C MapperGradSimilar | |
| C MapperPyramid | |
| C MapProjec | |
| C MapShift | |
| C MapTypeCaster | |
| ► N rgbd | |
| C DepthCleaner | |
| C FastICPOdometry | |
| C ICPOdometry | |
| C Odometry | |
| C OdometryFrame | |
| C RgbdFrame | |
| C RgbdICPOdometry | |
| C RgbdNormals | |
| C RgbdOdometry | |
| C RgbdPlane | |
| ► N saliency | |
| C MotionSaliency | |
| C MotionSaliencyBinWangApr2014 | Fast Self-tuning Background Subtraction Algorithm from [BinWangApr2014] |
| C Objectness | |
| C 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 Saliency | |
| C StaticSaliency | |
| C StaticSaliencyFineGrained | Fine Grained Saliency approach from [FGS] |
| C StaticSaliencySpectralResidual | Spectral Residual approach from [SR] |
| N samples | |
| ► N segmentation | |
| C IntelligentScissorsMB | Intelligent Scissors image segmentation |
| ► N sfinae | |
| C has_parenthesis_operator | |
| ► N stereo | |
| C MatchQuasiDense | |
| C PropagationParameters | |
| C QuasiDenseStereo | Class containing the methods needed for Quasi Dense Stereo computation |
| C StereoBinaryBM | Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige |
| C StereoBinarySGBM | The class implements the modified H. Hirschmuller algorithm [HH08] that differs from the original one as follows: |
| C StereoMatcher | Filters off small noise blobs (speckles) in the disparity map |
| ► N structured_light | |
| ► C GrayCodePattern | Class implementing the Gray-code pattern, based on [UNDERWORLD] |
| C Params | Parameters of StructuredLightPattern constructor |
| ► C SinusoidalPattern | Class implementing Fourier transform profilometry (FTP) , phase-shifting profilometry (PSP) and Fourier-assisted phase-shifting profilometry (FAPS) based on [faps] |
| C Params | Parameters of SinusoidalPattern constructor |
| C StructuredLightPattern | Abstract base class for generating and decoding structured light patterns |
| ► N superres | |
| C BroxOpticalFlow | |
| C DenseOpticalFlowExt | |
| C DualTVL1OpticalFlow | |
| C FarnebackOpticalFlow | |
| C FrameSource | |
| C PyrLKOpticalFlow | |
| C SuperResolution | Base class for Super Resolution algorithms |
| ► N text | |
| C BaseOCR | |
| ► C ERFilter | Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [Neumann12]. : |
| C Callback | Callback with the classifier is made a class |
| C ERStat | The ERStat structure represents a class-specific Extremal Region (ER) |
| ► C OCRBeamSearchDecoder | OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm |
| C ClassifierCallback | Callback with the character classifier is made a class |
| ► C OCRHMMDecoder | OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models |
| C ClassifierCallback | Callback with the character classifier is made a class |
| C 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 OCRTesseract | OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++ |
| C TextDetector | An abstract class providing interface for text detection algorithms |
| C 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
|
| ► N tracking | |
| ► C TrackerCSRT | CSRT tracker |
| C Params | |
| ► C TrackerKCF | KCF (Kernelized Correlation Filter) tracker |
| C Params | |
| ► N traits | |
| C Depth | |
| C Depth< Complex< _Tp > > | |
| C Depth< Matx< _Tp, m, n > > | |
| C Depth< Moments > | |
| C Depth< Point3_< _Tp > > | |
| C Depth< Point_< _Tp > > | |
| C Depth< Range > | |
| C Depth< Rect_< _Tp > > | |
| C Depth< RotatedRect > | |
| C Depth< Scalar_< _Tp > > | |
| C Depth< Size_< _Tp > > | |
| C Depth< Vec< _Tp, cn > > | |
| C SafeFmt | |
| C SafeFmt< T, false > | |
| C SafeFmt< T, true > | |
| C SafeType | |
| C SafeType< T, false > | |
| C SafeType< T, true > | |
| C Type | |
| C Type< Complex< _Tp > > | |
| C Type< Matx< _Tp, m, n > > | |
| C Type< Moments > | |
| C Type< Point3_< _Tp > > | |
| C Type< Point_< _Tp > > | |
| C Type< Range > | |
| C Type< Rect_< _Tp > > | |
| C Type< RotatedRect > | |
| C Type< Scalar_< _Tp > > | |
| C Type< Size_< _Tp > > | |
| C Type< Vec< _Tp, cn > > | |
| ► N utils | |
| ► N logging | |
| C LogTag | |
| C LogTagAuto | |
| C AllocatorStatistics | |
| C AllocatorStatisticsInterface | |
| N va_intel | |
| N videoio_registry | |
| ► N videostab | |
| C ColorAverageInpainter | |
| C ColorInpainter | |
| C ConsistentMosaicInpainter | |
| C DeblurerBase | |
| C FastMarchingMethod | Describes the Fast Marching Method implementation |
| C FromFileMotionReader | |
| C GaussianMotionFilter | |
| C IDenseOptFlowEstimator | |
| C IFrameSource | |
| C ILog | |
| C ImageMotionEstimatorBase | Base class for global 2D motion estimation methods which take frames as input |
| C IMotionStabilizer | |
| C InpainterBase | |
| C InpaintingPipeline | |
| C IOutlierRejector | |
| C ISparseOptFlowEstimator | |
| C KeypointBasedMotionEstimator | Describes a global 2D motion estimation method which uses keypoints detection and optical flow for matching |
| C LogToStdout | |
| C LpMotionStabilizer | |
| C MaskFrameSource | |
| C MoreAccurateMotionWobbleSuppressor | |
| C MoreAccurateMotionWobbleSuppressorBase | |
| C MotionEstimatorBase | Base class for all global motion estimation methods |
| C MotionEstimatorL1 | Describes a global 2D motion estimation method which minimizes L1 error |
| C MotionEstimatorRansacL2 | Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error |
| C MotionFilterBase | |
| C MotionInpainter | |
| C MotionStabilizationPipeline | |
| C NullDeblurer | |
| C NullFrameSource | |
| C NullInpainter | |
| C NullLog | |
| C NullOutlierRejector | |
| C NullWobbleSuppressor | |
| C OnePassStabilizer | |
| C PyrLkOptFlowEstimatorBase | |
| C RansacParams | Describes RANSAC method parameters |
| C SparsePyrLkOptFlowEstimator | |
| C StabilizerBase | |
| C ToFileMotionWriter | |
| C TranslationBasedLocalOutlierRejector | |
| C TwoPassStabilizer | |
| C VideoFileSource | |
| C WeightingDeblurer | |
| C WobbleSuppressorBase | |
| ► N wechat_qrcode | |
| C 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 |
| ► N xfeatures2d | |
| C AffineFeature2D | Class implementing affine adaptation for key points |
| C BEBLID | Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [Suarez2020BEBLID] |
| C BoostDesc | Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b] |
| C BriefDescriptorExtractor | Class for computing BRIEF descriptors described in [calon2010] |
| C DAISY | Class implementing DAISY descriptor, described in [Tola10] |
| C Elliptic_KeyPoint | Elliptic region around an interest point |
| C FREAK | Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [AOV12] |
| C HarrisLaplaceFeatureDetector | Class implementing the Harris-Laplace feature detector as described in [Mikolajczyk2004] |
| C LATCH | |
| C LUCID | Class implementing the locally uniform comparison image descriptor, described in [LUCID] |
| C MSDDetector | Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in [Tombari14] |
| C 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 PCTSignaturesSQFD | Class implementing Signature Quadratic Form Distance (SQFD) |
| C StarDetector | The class implements the keypoint detector introduced by [Agrawal08], synonym of StarDetector. : |
| C SURF | Class for extracting Speeded Up Robust Features from an image [Bay06] |
| C TBMR | Class implementing the Tree Based Morse Regions (TBMR) as described in [Najman2014] extended with scaled extraction ability |
| C VGG | Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [Simonyan14] |
| ► N ximgproc | |
| N rl | |
| ► N segmentation | |
| C GraphSegmentation | Graph Based Segmentation Algorithm. The class implements the algorithm described in [PFF2004] |
| C SelectiveSearchSegmentation | Selective search segmentation algorithm The class implements the algorithm described in [uijlings2013selective] |
| C SelectiveSearchSegmentationStrategy | Strategie for the selective search segmentation algorithm The class implements a generic stragery for the algorithm described in [uijlings2013selective] |
| C SelectiveSearchSegmentationStrategyColor | Color-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] |
| C SelectiveSearchSegmentationStrategyFill | Fill-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] |
| C SelectiveSearchSegmentationStrategyMultiple | Regroup multiple strategies for the selective search segmentation algorithm |
| C SelectiveSearchSegmentationStrategySize | Size-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] |
| C SelectiveSearchSegmentationStrategyTexture | Texture-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] |
| C Box | |
| C ContourFitting | Class for
ContourFitting
algorithms.
ContourFitting
match two contours
and
minimizing distance
|
| C EdgeBoxes | Class implementing EdgeBoxes algorithm from [ZitnickECCV14edgeBoxes] : |
| ► C EdgeDrawing | Class implementing the ED (EdgeDrawing) [topal2012edge], EDLines [akinlar2011edlines], EDPF [akinlar2012edpf] and EDCircles [akinlar2013edcircles] algorithms |
| C Params | |
| C FastLineDetector | Class implementing the FLD (Fast Line Detector) algorithm described in [Lee14] |
| C 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] |
| ► N xobjdetect | |
| C WBDetector | WaldBoost detector |
| ► N xphoto | |
| C GrayworldWB | Gray-world white balance algorithm |
| C LearningBasedWB | More sophisticated learning-based automatic white balance algorithm |
| C 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 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 WhiteBalancer | The base class for auto white balance algorithms |
| C _InputArray | This is the proxy class for passing read-only input arrays into OpenCV functions |
| C _InputOutputArray | |
| C _OutputArray | This type is very similar to InputArray except that it is used for input/output and output function parameters |
| C Accumulator | |
| C Accumulator< char > | |
| C Accumulator< short > | |
| C Accumulator< unsigned char > | |
| C Accumulator< unsigned short > | |
| C AffineFeature | Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in [YM11] |
| C AffineTransformer | Wrapper class for the OpenCV Affine Transformation algorithm. : |
| C AffineWarper | Affine warper factory class |
| C AgastFeatureDetector | Wrapping class for feature detection using the AGAST method. : |
| C AKAZE | Class implementing the AKAZE keypoint detector and descriptor extractor, described in [ANB13] |
| C Algorithm | This is a base class for all more or less complex algorithms in OpenCV |
| C AlignExposures | The base class for algorithms that align images of the same scene with different exposures |
| C 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 Allocator | |
| C rebind | |
| C AsyncArray | Returns result of asynchronous operations |
| C AsyncPromise | Provides result of asynchronous operations |
| C AutoBuffer | Automatically Allocated Buffer Class |
| C BackgroundSubtractor | Base class for background/foreground segmentation. : |
| C BackgroundSubtractorKNN | K-nearest neighbours - based Background/Foreground Segmentation Algorithm |
| C BackgroundSubtractorMOG2 | Gaussian Mixture-based Background/Foreground Segmentation Algorithm |
| ► C BaseCascadeClassifier | |
| C MaskGenerator | |
| C BFMatcher | Brute-force descriptor matcher |
| C BOWImgDescriptorExtractor | Class to compute an image descriptor using the bag of visual words |
| C BOWKMeansTrainer | Kmeans -based class to train visual vocabulary using the bag of visual words approach. : |
| C BOWTrainer | Abstract base class for training the bag of visual words vocabulary from a set of descriptors |
| C BRISK | Class implementing the BRISK keypoint detector and descriptor extractor, described in [LCS11] |
| C BufferPoolController | |
| C CalibrateCRF | The base class for camera response calibration algorithms |
| C 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 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 CascadeClassifier | Cascade classifier class for object detection |
| C ChiHistogramCostExtractor | An Chi based cost extraction. : |
| C CirclesGridFinderParameters | |
| C CLAHE | Base class for Contrast Limited Adaptive Histogram Equalization |
| C CommandLineParser | Designed for command line parsing |
| C Complex | A complex number class |
| C CompressedRectilinearPortraitWarper | |
| C CompressedRectilinearWarper | |
| C ConjGradSolver | This class is used to perform the non-linear non-constrained minimization of a function with known gradient, |
| C CylindricalWarper | Cylindrical warper factory class |
| C DataDepth | A helper class for cv::DataType |
| C DataType | Template "trait" class for OpenCV primitive data types |
| C DataType< bool > | |
| C DataType< char > | |
| C DataType< Complex< _Tp > > | |
| C DataType< double > | |
| C DataType< float > | |
| C DataType< float16_t > | |
| C DataType< int > | |
| C DataType< Matx< _Tp, m, n > > | |
| C DataType< Moments > | |
| C DataType< Point3_< _Tp > > | |
| C DataType< Point_< _Tp > > | |
| C DataType< Range > | |
| C DataType< Rect_< _Tp > > | |
| C DataType< RotatedRect > | |
| C DataType< Scalar_< _Tp > > | |
| C DataType< schar > | |
| C DataType< short > | |
| C DataType< Size_< _Tp > > | |
| C DataType< uchar > | |
| C DataType< ushort > | |
| C DataType< Vec< _Tp, cn > > | |
| C DefaultDeleter | |
| C DefaultDeleter< CvHaarClassifierCascade > | |
| C DenseOpticalFlow | |
| ► C DescriptorMatcher | Abstract base class for matching keypoint descriptors |
| C DescriptorCollection | |
| ► C DetectionBasedTracker | |
| C ExtObject | |
| C IDetector | |
| C InnerParameters | |
| C Parameters | |
| C TrackedObject | |
| C DetectionROI | Struct for detection region of interest (ROI) |
| C DISOpticalFlow | DIS optical flow algorithm |
| C DMatch | Class for matching keypoint descriptors |
| C DownhillSolver | This class is used to perform the non-linear non-constrained minimization of a function, |
| C DualQuat | |
| C EMDHistogramCostExtractor | An EMD based cost extraction. : |
| C EMDL1HistogramCostExtractor | An EMD-L1 based cost extraction. : |
| C Exception | Class passed to an error |
| C FarnebackOpticalFlow | Class computing a dense optical flow using the Gunnar Farneback's algorithm |
| C FastFeatureDetector | Wrapping class for feature detection using the FAST method. : |
| C Feature2D | Abstract base class for 2D image feature detectors and descriptor extractors |
| C FileNode | File Storage Node class |
| C FileNodeIterator | Used to iterate through sequences and mappings |
| C FileStorage | XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or reading data to/from a file |
| C FisheyeWarper | |
| C Formatted | |
| C Formatter | |
| C GeneralizedHough | Finds arbitrary template in the grayscale image using Generalized Hough Transform |
| C GeneralizedHoughBallard | Finds arbitrary template in the grayscale image using Generalized Hough Transform |
| C GeneralizedHoughGuil | Finds arbitrary template in the grayscale image using Generalized Hough Transform |
| C GFTTDetector | Wrapping class for feature detection using the goodFeaturesToTrack function. : |
| C Hamming | |
| C has_custom_delete | |
| C has_custom_delete< T, typename std::enable_if< sfinae::has_parenthesis_operator< DefaultDeleter< T >, void, T * >::value >::type > | |
| C HausdorffDistanceExtractor | A simple Hausdorff distance measure between shapes defined by contours |
| C HistogramCostExtractor | Abstract base class for histogram cost algorithms |
| C HOGDescriptor | Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector |
| C KalmanFilter | Kalman filter class |
| C KAZE | Class implementing the KAZE keypoint detector and descriptor extractor, described in [ABD12] |
| C KeyPoint | Data structure for salient point detectors |
| C KeyPointsFilter | A class filters a vector of keypoints |
| C L1 | |
| C L2 | |
| C LDA | Linear Discriminant Analysis |
| C LineIterator | Line iterator |
| C LineSegmentDetector | Line segment detector class |
| ► C LMSolver | |
| C Callback | |
| C Mat | N-dimensional dense array class |
| C Mat_ | Template matrix class derived from Mat |
| C MatAllocator | Custom array allocator |
| C MatCommaInitializer_ | Comma-separated Matrix Initializer |
| C MatConstIterator | |
| C MatConstIterator_ | Matrix read-only iterator |
| C 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 MatIterator_ | Matrix read-write iterator |
| C MatOp | |
| C MatSize | |
| C MatStep | |
| C Matx | Template class for small matrices whose type and size are known at compilation time |
| C MatxCommaInitializer | Comma-separated Matrix Initializer |
| C MercatorWarper | |
| C MergeDebevec | The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response |
| C MergeExposures | The base class algorithms that can merge exposure sequence to a single image |
| C MergeMertens | Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids |
| C MergeRobertson | The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response |
| ► C MinProblemSolver | Basic interface for all solvers |
| C Function | Represents function being optimized |
| C Moments | Struct returned by cv::moments |
| C MSER | Maximally stable extremal region extractor |
| C NAryMatIterator | N-ary multi-dimensional array iterator |
| C Node | |
| C NormHistogramCostExtractor | A norm based cost extraction. : |
| C ORB | Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor |
| C PaniniPortraitWarper | |
| C PaniniWarper | |
| C ParallelLoopBody | Base class for parallel data processors |
| C ParallelLoopBodyLambdaWrapper | |
| C ParamType | |
| C ParamType< _Tp, typename std::enable_if< std::is_enum< _Tp >::value >::type > | |
| C ParamType< Algorithm > | |
| C ParamType< bool > | |
| C ParamType< double > | |
| C ParamType< float > | |
| C ParamType< int > | |
| C ParamType< Mat > | |
| C ParamType< Scalar > | |
| C ParamType< std::vector< Mat > > | |
| C ParamType< String > | |
| C ParamType< uchar > | |
| C ParamType< uint64 > | |
| C ParamType< unsigned > | |
| C PCA | Principal Component Analysis |
| C PlaneWarper | Plane warper factory class |
| C Point3_ | Template class for 3D points specified by its coordinates
x,
y
and
z
|
| C Point_ | Template class for 2D points specified by its coordinates
x
and
y
|
| C Ptr | |
| C PyRotationWarper | |
| C QRCodeDetector | |
| C QtFont | QtFont available only for Qt. See cv::fontQt |
| C Quat | |
| C QuatEnum | |
| C Range | Template class specifying a continuous subsequence (slice) of a sequence |
| C Rect_ | Template class for 2D rectangles |
| C RNG | Random Number Generator |
| C RNG_MT19937 | Mersenne Twister random number generator |
| C RotatedRect | The class represents rotated (i.e. not up-right) rectangles on a plane |
| C Scalar_ | Template class for a 4-element vector derived from Vec |
| C ShapeContextDistanceExtractor | Implementation of the Shape Context descriptor and matching algorithm |
| C ShapeDistanceExtractor | Abstract base class for shape distance algorithms |
| C ShapeTransformer | Abstract base class for shape transformation algorithms |
| C SIFT | Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04] |
| C SimilarRects | |
| ► C SimpleBlobDetector | Class for extracting blobs from an image. : |
| C Params | |
| C Size_ | Template class for specifying the size of an image or rectangle |
| C SL2 | |
| C softdouble | |
| C softfloat | |
| ► C SparseMat | The class SparseMat represents multi-dimensional sparse numerical arrays |
| C Hdr | Sparse matrix header |
| C Node | Sparse matrix node - element of a hash table |
| C SparseMat_ | Template sparse n-dimensional array class derived from SparseMat |
| C SparseMatConstIterator | Read-Only Sparse Matrix Iterator |
| C SparseMatConstIterator_ | Template Read-Only Sparse Matrix Iterator Class |
| C SparseMatIterator | Read-write Sparse Matrix Iterator |
| C SparseMatIterator_ | Template Read-Write Sparse Matrix Iterator Class |
| C SparseOpticalFlow | Base interface for sparse optical flow algorithms |
| C SparsePyrLKOpticalFlow | Class used for calculating a sparse optical flow |
| C SphericalWarper | Spherical warper factory class |
| C StereoBM | Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige |
| C StereographicWarper | |
| C StereoMatcher | The base class for stereo correspondence algorithms |
| C StereoSGBM | The class implements the modified H. Hirschmuller algorithm [HH08] that differs from the original one as follows: |
| C Stitcher | High level image stitcher |
| ► C Subdiv2D | |
| C QuadEdge | |
| C Vertex | |
| C SVD | Singular Value Decomposition |
| C TermCriteria | The class defining termination criteria for iterative algorithms |
| C ThinPlateSplineShapeTransformer | Definition of the transformation |
| C TickMeter | Class to measure passing time |
| C TLSData | Simple TLS data class |
| C TLSDataAccumulator | TLS data accumulator with gathering methods |
| C TLSDataContainer | |
| C Tonemap | Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range |
| C TonemapDrago | Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain |
| C 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 TonemapReinhard | This is a global tonemapping operator that models human visual system |
| C Tracker | Base abstract class for the long-term tracker |
| ► C TrackerCSRT | CSRT tracker |
| C Params | |
| ► C TrackerDaSiamRPN | |
| C Params | |
| ► C TrackerGOTURN | GOTURN (Generic Object Tracking Using Regression Networks) tracker |
| C Params | |
| ► C TrackerKCF | KCF (Kernelized Correlation Filter) tracker |
| C Params | |
| ► C TrackerMIL | The MIL algorithm trains a classifier in an online manner to separate the object from the background |
| C Params | |
| C TransverseMercatorWarper | |
| C UMat | |
| C UMatData | |
| C UsacParams | |
| C v_float32x4 | |
| C v_float64x2 | |
| C v_int16x8 | |
| C v_int32x4 | |
| C v_int64x2 | |
| C v_int8x16 | |
| C v_reg | |
| C V_TypeTraits | |
| C v_uint16x8 | |
| C v_uint32x4 | |
| C v_uint64x2 | |
| C v_uint8x16 | |
| C VariationalRefinement | Variational optical flow refinement |
| C Vec | Template class for short numerical vectors, a partial case of Matx |
| C VecCommaInitializer | Comma-separated Vec Initializer |
| C vfloat32mf2_t | |
| C vfloat64mf2_t | |
| C VideoCapture | Class for video capturing from video files, image sequences or cameras |
| C VideoWriter | Video writer class |
| C vint16mf2_t | |
| C vint32mf2_t | |
| C vint64mf2_t | |
| C vint8mf2_t | |
| C vint8mf4_t | |
| C vuint16mf2_t | |
| C vuint32mf2_t | |
| C vuint64mf2_t | |
| C vuint8mf2_t | |
| C vuint8mf4_t | |
| C WarperCreator | Image warper factories base class |
| N CV__SIMD_NAMESPACE |
