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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.
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| ▼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 Mixture-based 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 high-level API for classification models |
| CDetectionModel | This class represents high-level 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 high-level API for keypoints models |
| CLayer | This interface class allows to build new Layers - are building blocks of networks |
| CModel | This class is presented high-level API for neural networks |
| CNet | This class allows to create and manipulate comprehensive artificial neural networks |
| CSegmentationModel | This class represents high-level API for segmentation models |
| CTextDetectionModel | Base class for text detection networks |
| CTextDetectionModel_DB | This class represents high-level API for text detection DL networks compatible with DB model |
| CTextDetectionModel_EAST | This class represents high-level API for text detection DL networks compatible with EAST model |
| CTextRecognitionModel | This class represents high-level 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 (10-50), 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 | Brute-force 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 | Flann-based 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 | Marr-Hildreth 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 - Multi-Layer 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 K-Nearest 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 | DNN-based face detector |
| CFaceRecognizerSF | DNN-based 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 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 |
| 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 well-exposedness 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 8-bit 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 Gray-code pattern, based on [UNDERWORLD] |
| CSinusoidalPattern | Class implementing Fourier transform profilometry (FTP) , phase-shifting profilometry (PSP) and Fourier-assisted phase-shifting 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 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 |
| ▶NTrackingModule | |
| Clegacy_MultiTracker | This class is used to track multiple objects using the specified tracker algorithm |
| Clegacy_Tracker | Base abstract class for the long-term 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 | K-nearest neighbours - based Background/Foreground Segmentation Algorithm |
| CBackgroundSubtractorMOG2 | Gaussian Mixture-based 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 long-term 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 dnn-based general object tracking |
| CTrackerNano_Params | |
| CTrackerVit | VIT tracker is a super lightweight dnn-based general object tracking |
| CTrackerVit_Params | |
| CVariationalRefinement | Variational optical flow refinement |
| CVideo | |
| ▶NWechat_qrcodeModule | |
| CWechat_qrcode | |
| CWeChatQRCode | 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 |
| ▶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 Harris-Laplace 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 Self-Dissimilarity) keypoint detector, described in [Tombari14] |
| CPCTSignatures | 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 |
| 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 (Triplet-based 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 left-right-consistency-based confidence to refine the results in half-occlusions and uniform areas |
| CDTFilter | Interface for realizations of Domain Transform filter |
| CEdgeAwareInterpolator | Sparse match interpolation algorithm based on modified locally-weighted affine estimator from [Revaud2015] and Fast Global Smoother as post-processing 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 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 |
| 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 F-DBSCAN (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 | Color-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] |
| CSelectiveSearchSegmentationStrategyFill | Fill-based 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 | Size-based strategy for the selective search segmentation algorithm The class is implemented from the algorithm described in [uijlings2013selective] |
| CSelectiveSearchSegmentationStrategyTexture | Texture-based 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 per-pixel 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 Energy-Driven Sampling) superpixels algorithm described in [VBRV14] |
| CSuperpixelSLIC | Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in [Achanta2012] |
| CXimgproc | |
| ▶NXphotoModule | |
| CGrayworldWB | Gray-world white balance algorithm |
| CLearningBasedWB | More sophisticated learning-based 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 |
1.8.14