OpenCV for Unity
2.5.9
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.9.0/index.html ) for the details of the argument of the method.

▼NOpenCVForUnity  
▶NArucoModule  
CAruco  
CEstimateParameters  Pose estimation parameters 
▶NBgsegmModule  
CBackgroundSubtractorCNT  Background subtraction based on counting 
CBackgroundSubtractorGMG  Background Subtractor module based on the algorithm given in [Gold2012] 
CBackgroundSubtractorGSOC  Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper 
CBackgroundSubtractorLSBP  Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at [LGuo2016] 
CBackgroundSubtractorLSBPDesc  This is for calculation of the LSBP descriptors 
CBackgroundSubtractorMOG  Gaussian Mixturebased Background/Foreground Segmentation Algorithm 
CBgsegm  
CSyntheticSequenceGenerator  Synthetic frame sequence generator for testing background subtraction algorithms 
▶NBioinspiredModule  
CBioinspired  
CRetina  Class which allows the Gipsa/Listic Labs model to be used with OpenCV 
CRetinaFastToneMapping  Wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV 
CTransientAreasSegmentationModule  Class which provides a transient/moving areas segmentation module 
▶NCalib3dModule  
CCalib3d  
CStereoBM  Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige 
CStereoMatcher  The base class for stereo correspondence algorithms 
CStereoSGBM  The class implements the modified H. Hirschmuller algorithm [HH08] that differs from the original one as follows: 
CUsacParams  
▶NCoreModule  
CAlgorithm  This is a base class for all more or less complex algorithms in OpenCV 
▶CCore  
CMinMaxLocResult  
CCvException  The exception that is thrown by OpenCVForUntiy. 
CCvType  
CDMatch  
CKeyPoint  
CMat  
CMatOfByte  
CMatOfDMatch  
CMatOfDouble  
CMatOfFloat  
CMatOfFloat4  
CMatOfFloat6  
CMatOfInt  
CMatOfInt4  
CMatOfKeyPoint  
CMatOfPoint  
CMatOfPoint2f  
CMatOfPoint3  
CMatOfPoint3f  
CMatOfRect  
CMatOfRect2d  
CMatOfRotatedRect  
CPoint  
CPoint3  
CRange  
CRect  
CRect2d  
CRotatedRect  
CScalar  
CSize  
CTermCriteria  
CTickMeter  Class to measure passing time 
▶NDnn_superresModule  
CDnn_superres  
CDnnSuperResImpl  A class to upscale images via convolutional neural networks. The following four models are implemented: 
▶NDnnModule  
CClassificationModel  This class represents highlevel API for classification models 
CDetectionModel  This class represents highlevel API for object detection networks 
CDictValue  This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64 
CDnn  
CImage2BlobParams  Processing params of image to blob 
CKeypointsModel  This class represents highlevel API for keypoints models 
CLayer  This interface class allows to build new Layers  are building blocks of networks 
CModel  This class is presented highlevel API for neural networks 
CNet  This class allows to create and manipulate comprehensive artificial neural networks 
CSegmentationModel  This class represents highlevel API for segmentation models 
CTextDetectionModel  Base class for text detection networks 
CTextDetectionModel_DB  This class represents highlevel API for text detection DL networks compatible with DB model 
CTextDetectionModel_EAST  This class represents highlevel API for text detection DL networks compatible with EAST model 
CTextRecognitionModel  This class represents highlevel API for text recognition networks 
▶NFaceModule  
CBasicFaceRecognizer  
CBIF  
CEigenFaceRecognizer  
CFace  
CFacemark  Abstract base class for all facemark models 
CFacemarkAAM  
CFacemarkKazemi  
CFacemarkLBF  
CFacemarkTrain  Abstract base class for trainable facemark models 
CFaceRecognizer  Abstract base class for all face recognition models 
CFisherFaceRecognizer  
CLBPHFaceRecognizer  
CMACE  Minimum Average Correlation Energy Filter useful for authentication with (cancellable) biometrical features. (does not need many positives to train (1050), and no negatives at all, also robust to noise/salting) 
CPredictCollector  Abstract base class for all strategies of prediction result handling 
CStandardCollector  Default predict collector 
▶NFeatures2dModule  
CAffineFeature  Class for implementing the wrapper which makes detectors and extractors to be affine invariant, described as ASIFT in [YM11] 
CAgastFeatureDetector  Wrapping class for feature detection using the AGAST method. : 
CAKAZE  Class implementing the AKAZE keypoint detector and descriptor extractor, described in [ANB13] 
CBFMatcher  Bruteforce descriptor matcher 
CBOWImgDescriptorExtractor  Class to compute an image descriptor using the bag of visual words 
CBOWKMeansTrainer  Kmeans based class to train visual vocabulary using the bag of visual words approach. : 
CBOWTrainer  Abstract base class for training the bag of visual words vocabulary from a set of descriptors 
CBRISK  Class implementing the BRISK keypoint detector and descriptor extractor, described in [LCS11] 
CDescriptorMatcher  Abstract base class for matching keypoint descriptors 
CFastFeatureDetector  Wrapping class for feature detection using the FAST method. : 
CFeature2D  Abstract base class for 2D image feature detectors and descriptor extractors 
CFeatures2d  
CFlannBasedMatcher  Flannbased descriptor matcher 
CGFTTDetector  Wrapping class for feature detection using the goodFeaturesToTrack function. : 
CKAZE  Class implementing the KAZE keypoint detector and descriptor extractor, described in [ABD12] 
CMSER  Maximally stable extremal region extractor 
CORB  Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor 
CSIFT  Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04] 
CSimpleBlobDetector  Class for extracting blobs from an image. : 
CSimpleBlobDetector_Params  
▶NImg_hashModule  
CAverageHash  Computes average hash value of the input image 
CBlockMeanHash  Image hash based on block mean 
CColorMomentHash  Image hash based on color moments 
CImg_hash  
CImgHashBase  The base class for image hash algorithms 
CMarrHildrethHash  MarrHildreth Operator Based Hash, slowest but more discriminative 
CPHash  PHash 
CRadialVarianceHash  Image hash based on Radon transform 
▶NImgcodecsModule  
CImgcodecs  
▶NImgprocModule  
CCLAHE  Base class for Contrast Limited Adaptive Histogram Equalization 
CGeneralizedHough  Finds arbitrary template in the grayscale image using Generalized Hough Transform 
CGeneralizedHoughBallard  Finds arbitrary template in the grayscale image using Generalized Hough Transform 
CGeneralizedHoughGuil  Finds arbitrary template in the grayscale image using Generalized Hough Transform 
CImgproc  
CIntelligentScissorsMB  Intelligent Scissors image segmentation 
CLineSegmentDetector  Line segment detector class 
CMoments  
CSubdiv2D  
▶NMlModule  
CANN_MLP  Artificial Neural Networks  MultiLayer Perceptrons 
CBoost  Boosted tree classifier derived from DTrees 
CDTrees  The class represents a single decision tree or a collection of decision trees 
CEM  The class implements the Expectation Maximization algorithm 
CKNearest  The class implements KNearest Neighbors model 
CLogisticRegression  Implements Logistic Regression classifier 
CMl  
CNormalBayesClassifier  Bayes classifier for normally distributed data 
CParamGrid  The structure represents the logarithmic grid range of statmodel parameters 
CRTrees  The class implements the random forest predictor 
CStatModel  Base class for statistical models in OpenCV ML 
CSVM  Support Vector Machines 
CSVMSGD  
CTrainData  Class encapsulating training data 
▶NObjdetectModule  
CArucoDetector  The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method 
CBarcodeDetector  
CBaseCascadeClassifier  
CBoard  Board of ArUco markers 
CCascadeClassifier  Cascade classifier class for object detection 
CCharucoBoard  ChArUco board is a planar chessboard where the markers are placed inside the white squares of a chessboard 
CCharucoDetector  
CCharucoParameters  
CDetectorParameters  Struct DetectorParameters is used by ArucoDetector 
CDictionary  Dictionary is a set of unique ArUco markers of the same size 
CFaceDetectorYN  DNNbased face detector 
CFaceRecognizerSF  DNNbased face recognizer 
CGraphicalCodeDetector  
CGridBoard  Planar board with grid arrangement of markers 
CHOGDescriptor  Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector 
CObjdetect  
CQRCodeDetector  
CQRCodeDetectorAruco  
CQRCodeDetectorAruco_Params  
CQRCodeEncoder  Groups the object candidate rectangles. rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.) groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. eps Relative difference between sides of the rectangles to merge them into a group 
CQRCodeEncoder_Params  QR code encoder parameters 
CRefineParameters  Struct RefineParameters is used by ArucoDetector 
▶NPhase_unwrappingModule  
CHistogramPhaseUnwrapping  Class implementing twodimensional phase unwrapping based on [histogramUnwrapping] This algorithm belongs to the qualityguided 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 
CHistogramPhaseUnwrapping_Params  Parameters of phaseUnwrapping constructor 
CPhase_unwrapping  
CPhaseUnwrapping  Abstract base class for phase unwrapping 
▶NPhotoModule  
CAlignExposures  The base class for algorithms that align images of the same scene with different exposures 
CAlignMTB  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 
CCalibrateCRF  The base class for camera response calibration algorithms 
CCalibrateDebevec  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 
CCalibrateRobertson  Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. This algorithm uses all image pixels 
CMergeDebevec  The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response 
CMergeExposures  The base class algorithms that can merge exposure sequence to a single image 
CMergeMertens  Pixels are weighted using contrast, saturation and wellexposedness measures, than images are combined using laplacian pyramids 
CMergeRobertson  The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response 
CPhoto  
CTonemap  Base class for tonemapping algorithms  tools that are used to map HDR image to 8bit range 
CTonemapDrago  Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain 
CTonemapMantiuk  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 
CTonemapReinhard  This is a global tonemapping operator that models human visual system 
▶NPlotModule  
CPlot  
CPlot2d  
▶NStructured_lightModule  
CGrayCodePattern  Class implementing the Graycode pattern, based on [UNDERWORLD] 
CSinusoidalPattern  Class implementing Fourier transform profilometry (FTP) , phaseshifting profilometry (PSP) and Fourierassisted phaseshifting profilometry (FAPS) based on [faps] 
CSinusoidalPattern_Params  Parameters of SinusoidalPattern constructor width Projector's width. height Projector's height. nbrOfPeriods Number of period along the patterns direction. shiftValue Phase shift between two consecutive patterns. methodId Allow to choose between FTP, PSP and FAPS. nbrOfPixelsBetweenMarkers Number of pixels between two consecutive markers on the same row. setMarkers Allow to set markers on the patterns. markersLocation vector used to store markers location on the patterns 
CStructured_light  
CStructuredLightPattern  Abstract base class for generating and decoding structured light patterns 
▶NTextModule  
CBaseOCR  
CERFilter  Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [Neumann12]. : 
CERFilter_Callback  Callback with the classifier is made a class 
COCRBeamSearchDecoder  OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm 
COCRBeamSearchDecoder_ClassifierCallback  Callback with the character classifier is made a class 
COCRHMMDecoder  OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models 
COCRHMMDecoder_ClassifierCallback  Callback with the character classifier is made a class 
CText  
CTextDetector  An abstract class providing interface for text detection algorithms 
CTextDetectorCNN  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 pretrained 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 
▶NTrackingModule  
Clegacy_MultiTracker  This class is used to track multiple objects using the specified tracker algorithm 
Clegacy_Tracker  Base abstract class for the longterm tracker: 
Clegacy_TrackerBoosting  Boosting tracker 
Clegacy_TrackerCSRT  CSRT tracker 
Clegacy_TrackerKCF  KCF (Kernelized Correlation Filter) tracker 
Clegacy_TrackerMedianFlow  Median Flow tracker 
Clegacy_TrackerMIL  The MIL algorithm trains a classifier in an online manner to separate the object from the background 
Clegacy_TrackerMOSSE  MOSSE (Minimum Output Sum of Squared Error) tracker 
Clegacy_TrackerTLD  TLD (Tracking, learning and detection) tracker 
CTrackerCSRT  CSRT tracker 
CTrackerCSRT_Params  
CTrackerKCF  KCF (Kernelized Correlation Filter) tracker 
CTrackerKCF_Params  
CTracking  
▶NUnityUtils  
▶NHelper  
CImageOptimizationHelper  Image optimization helper. v 1.1.0 
CVideoCaptureCameraInputToMatHelper  VideoCaptureCameraInput to mat helper. v 1.0.1 Depends on OpenCVForUnity version 2.4.4 (WebCamTextureToMatHelper v 1.1.3) or later. (Use the WebCamDevice.isFrontFacing and WebCamTexture.videoRotationAngle properties to flip the camera input image in VideoCaptue to the correct orientation.) 
▶CVideoCaptureToMatHelper  VideoCapture to mat helper. v 1.0.4 
CErrorUnityEvent  
▶CWebCamTextureToMatHelper  WebCamTexture to mat helper. v 1.1.6 
CErrorUnityEvent  
CARUtils  AR utilities. 
CDebugMatUtils  
CMatIndexer  
CMatUtils  
CPoseData  
CUtils  
▶NUtilsModule  
CConverters  
▶NVideoioModule  
CVideoCapture  Class for video capturing from video files, image sequences or cameras 
CVideoio  
CVideoWriter  Video writer class 
▶NVideoModule  
CBackgroundSubtractor  Base class for background/foreground segmentation. : 
CBackgroundSubtractorKNN  Knearest neighbours  based Background/Foreground Segmentation Algorithm 
CBackgroundSubtractorMOG2  Gaussian Mixturebased Background/Foreground Segmentation Algorithm 
CDenseOpticalFlow  
CDISOpticalFlow  DIS optical flow algorithm 
CFarnebackOpticalFlow  Class computing a dense optical flow using the Gunnar Farneback's algorithm 
CKalmanFilter  Kalman filter class 
CSparseOpticalFlow  Base interface for sparse optical flow algorithms 
CSparsePyrLKOpticalFlow  Class used for calculating a sparse optical flow 
CTracker  Base abstract class for the longterm tracker 
CTrackerDaSiamRPN  
CTrackerDaSiamRPN_Params  
CTrackerGOTURN  GOTURN (Generic Object Tracking Using Regression Networks) tracker 
CTrackerGOTURN_Params  
CTrackerMIL  The MIL algorithm trains a classifier in an online manner to separate the object from the background 
CTrackerMIL_Params  
CTrackerNano  Nano tracker is a super lightweight dnnbased general object tracking 
CTrackerNano_Params  
CTrackerVit  VIT tracker is a super lightweight dnnbased general object tracking 
CTrackerVit_Params  
CVariationalRefinement  Variational optical flow refinement 
CVideo  
▶NWechat_qrcodeModule  
CWechat_qrcode  
CWeChatQRCode  WeChat QRCode includes two CNNbased 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 
▶NXfeatures2dModule  
CAffineFeature2D  Class implementing affine adaptation for key points 
CBEBLID  Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [Suarez2020BEBLID] 
CBoostDesc  Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [Trzcinski13a] and [Trzcinski13b] 
CBriefDescriptorExtractor  Class for computing BRIEF descriptors described in [calon2010] 
CDAISY  Class implementing DAISY descriptor, described in [Tola10] 
CFREAK  Class implementing the FREAK (Fast Retina Keypoint) keypoint descriptor, described in [AOV12] 
CHarrisLaplaceFeatureDetector  Class implementing the HarrisLaplace feature detector as described in [Mikolajczyk2004] 
CLATCH  
CLUCID  Class implementing the locally uniform comparison image descriptor, described in [LUCID] 
CMSDDetector  Class implementing the MSD (Maximal SelfDissimilarity) keypoint detector, described in [Tombari14] 
CPCTSignatures  Class implementing PCT (positioncolortexture) 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 kmeans algorithm. Resulting set of clusters is the signature of the input image 
CPCTSignaturesSQFD  Class implementing Signature Quadratic Form Distance (SQFD) 
CStarDetector  The class implements the keypoint detector introduced by [Agrawal08], synonym of StarDetector. : 
CTBMR  Class implementing the Tree Based Morse Regions (TBMR) as described in [Najman2014] extended with scaled extraction ability 
CTEBLID  Class implementing TEBLID (Tripletbased Efficient Binary Local Image Descriptor), described in [Suarez2021TEBLID] 
CVGG  Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [Simonyan14] 
CXfeatures2d  
▶NXimgprocModule  
CAdaptiveManifoldFilter  Interface for Adaptive Manifold Filter realizations 
CContourFitting  Class for ContourFitting algorithms. ContourFitting match two contours \($ z_a \)$ and \($ z_b \)$ minimizing distance \[ d(z_a,z_b)=\sum (a_n  s b_n e^{j(n \alpha +\phi )})^2 \] where \($ a_n \)$ and \($ b_n \)$ are Fourier descriptors of \($ z_a \)$ and \($ z_b \)$ and s is a scaling factor and \($ \phi \)$ is angle rotation and \($ \alpha \)$ is starting point factor adjustement 
CDisparityFilter  Main interface for all disparity map filters 
CDisparityWLSFilter  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 leftrightconsistencybased confidence to refine the results in halfocclusions and uniform areas 
CDTFilter  Interface for realizations of Domain Transform filter 
CEdgeAwareInterpolator  Sparse match interpolation algorithm based on modified locallyweighted affine estimator from [Revaud2015] and Fast Global Smoother as postprocessing filter 
CEdgeBoxes  Class implementing EdgeBoxes algorithm from [ZitnickECCV14edgeBoxes] : 
CEdgeDrawing  Class implementing the ED (EdgeDrawing) [topal2012edge], EDLines [akinlar2011edlines], EDPF [akinlar2012edpf] and EDCircles [akinlar2013edcircles] algorithms 
CEdgeDrawing_Params  
CFastBilateralSolverFilter  Interface for implementations of Fast Bilateral Solver 
CFastGlobalSmootherFilter  Interface for implementations of Fast Global Smoother filter 
CFastLineDetector  Class implementing the FLD (Fast Line Detector) algorithm described in [Lee14] 
CGraphSegmentation  Graph Based Segmentation Algorithm. The class implements the algorithm described in [PFF2004] 
CGuidedFilter  Interface for realizations of Guided Filter 
CRFFeatureGetter  
CRICInterpolator  Sparse match interpolation algorithm based on modified piecewise locallyweighted affine estimator called Robust Interpolation method of Correspondences or RIC from [Hu2017] and Variational and Fast Global Smoother as postprocessing filter. The RICInterpolator is a extension of the EdgeAwareInterpolator. Main concept of this extension is an piecewise affine model based on oversegmentation via SLIC superpixel estimation. The method contains an efficient propagation mechanism to estimate among the pieceswise models 
CRidgeDetectionFilter  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] 
CScanSegment  Class implementing the FDBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels algorithm by Loke SC, et al. [loke2021accelerated] for original paper 
CSelectiveSearchSegmentation  Selective search segmentation algorithm The class implements the algorithm described in [uijlings2013selective] 
CSelectiveSearchSegmentationStrategy  Strategie for the selective search segmentation algorithm The class implements a generic stragery for the algorithm described in [uijlings2013selective] 
CSelectiveSearchSegmentationStrategyColor  Colorbased strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] 
CSelectiveSearchSegmentationStrategyFill  Fillbased strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] 
CSelectiveSearchSegmentationStrategyMultiple  Regroup multiple strategies for the selective search segmentation algorithm 
CSelectiveSearchSegmentationStrategySize  Sizebased strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] 
CSelectiveSearchSegmentationStrategyTexture  Texturebased strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] 
CSparseMatchInterpolator  Main interface for all filters, that take sparse matches as an input and produce a dense perpixel matching (optical flow) as an output 
CStructuredEdgeDetection  Class implementing edge detection algorithm from [Dollar2013] : 
CSuperpixelLSC  Class implementing the LSC (Linear Spectral Clustering) superpixels algorithm described in [LiCVPR2015LSC] 
CSuperpixelSEEDS  Class implementing the SEEDS (Superpixels Extracted via EnergyDriven Sampling) superpixels algorithm described in [VBRV14] 
CSuperpixelSLIC  Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in [Achanta2012] 
CXimgproc  
▶NXphotoModule  
CGrayworldWB  Grayworld white balance algorithm 
CLearningBasedWB  More sophisticated learningbased automatic white balance algorithm 
CSimpleWB  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 
CTonemapDurand  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 
CWhiteBalancer  The base class for auto white balance algorithms 
CXphoto  
CDisposableObject  
CDisposableOpenCVObject 