Использование документации по OpenCV

>>> help(cv.xfeatures2d)
 
Help on module cv2.xfeatures2d in cv2:
 
NAME
    cv2.xfeatures2d
 
FUNCTIONS
    BoostDesc_create(...)
        BoostDesc_create([, desc[, use_scale_orientation[, scale_factor]]]) -> retval
        .
    
    BriefDescriptorExtractor_create(...)
        BriefDescriptorExtractor_create([, bytes[, use_orientation]]) -> retval
        .
    
    DAISY_create(...)
        DAISY_create([, radius[, q_radius[, q_theta[, q_hist[, norm[, H[, interpolation[, use_orientation]]]]]]]]) -> retval
        .
    
    FREAK_create(...)
        FREAK_create([, orientationNormalized[, scaleNormalized[, patternScale[, nOctaves[, selectedPairs]]]]]) -> retval
        .   @param orientationNormalized Enable orientation normalization.
        .       @param scaleNormalized Enable scale normalization.
        .       @param patternScale Scaling of the description pattern.
        .       @param nOctaves Number of octaves covered by the detected keypoints.
        .       @param selectedPairs (Optional) user defined selected pairs indexes,
    
    HarrisLaplaceFeatureDetector_create(...)
        HarrisLaplaceFeatureDetector_create([, numOctaves[, corn_thresh[, DOG_thresh[, maxCorners[, num_layers]]]]]) -> retval
        .   * @brief Creates a new implementation instance.
        .        *
        .        * @param numOctaves the number of octaves in the scale-space pyramid
        .        * @param corn_thresh the threshold for the Harris cornerness measure
        .        * @param DOG_thresh the threshold for the Difference-of-Gaussians scale selection
        .        * @param maxCorners the maximum number of corners to consider
        .        * @param num_layers the number of intermediate scales per octave
    
    LATCH_create(...)
        LATCH_create([, bytes[, rotationInvariance[, half_ssd_size[, sigma]]]]) -> retval
        .
    
    LUCID_create(...)
        LUCID_create([, lucid_kernel[, blur_kernel]]) -> retval
        .   * @param lucid_kernel kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth
        .        * @param blur_kernel kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth
    
    PCTSignaturesSQFD_create(...)
        PCTSignaturesSQFD_create([, distanceFunction[, similarityFunction[, similarityParameter]]]) -> retval
        .   * @brief Creates the algorithm instance using selected distance function,
        .       *       similarity function and similarity function parameter.
        .       * @param distanceFunction Distance function selector. Default: L2
        .       *       Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY
        .       * @param similarityFunction Similarity function selector. Default: HEURISTIC
        .       *       Available: MINUS, GAUSSIAN, HEURISTIC
        .       * @param similarityParameter Parameter of the similarity function.
    
    PCTSignatures_create(...)
        PCTSignatures_create([, initSampleCount[, initSeedCount[, pointDistribution]]]) -> retval
        .   * @brief Creates PCTSignatures algorithm using sample and seed count.
        .       *       It generates its own sets of sampling points and clusterization seed indexes.
        .       * @param initSampleCount Number of points used for image sampling.
        .       * @param initSeedCount Number of initial clusterization seeds.
        .       *       Must be lower or equal to initSampleCount
        .       * @param pointDistribution Distribution of generated points. Default: UNIFORM.
        .       *       Available: UNIFORM, REGULAR, NORMAL.
        .       * @return Created algorithm.
        
        
        
        PCTSignatures_create(initSamplingPoints, initSeedCount) -> retval
        .   * @brief Creates PCTSignatures algorithm using pre-generated sampling points
        .       *       and number of clusterization seeds. It uses the provided
        .       *       sampling points and generates its own clusterization seed indexes.
        .       * @param initSamplingPoints Sampling points used in image sampling.
        .       * @param initSeedCount Number of initial clusterization seeds.
        .       *       Must be lower or equal to initSamplingPoints.size().
        .       * @return Created algorithm.
        
        
        
        PCTSignatures_create(initSamplingPoints, initClusterSeedIndexes) -> retval
        .   * @brief Creates PCTSignatures algorithm using pre-generated sampling points
        .       *       and clusterization seeds indexes.
        .       * @param initSamplingPoints Sampling points used in image sampling.
        .       * @param initClusterSeedIndexes Indexes of initial clusterization seeds.
        .       *       Its size must be lower or equal to initSamplingPoints.size().
        .       * @return Created algorithm.
    
    PCTSignatures_drawSignature(...)
        PCTSignatures_drawSignature(source, signature[, result[, radiusToShorterSideRatio[, borderThickness]]]) -> result
        .   * @brief Draws signature in the source image and outputs the result.
        .       *       Signatures are visualized as a circle
        .       *       with radius based on signature weight
        .       *       and color based on signature color.
        .       *       Contrast and entropy are not visualized.
        .       * @param source Source image.
        .       * @param signature Image signature.
        .       * @param result Output result.
        .       * @param radiusToShorterSideRatio Determines maximal radius of signature in the output image.
        .       * @param borderThickness Border thickness of the visualized signature.
    
    PCTSignatures_generateInitPoints(...)
        PCTSignatures_generateInitPoints(initPoints, count, pointDistribution) -> None
        .   * @brief Generates initial sampling points according to selected point distribution.
        .       * @param initPoints Output vector where the generated points will be saved.
        .       * @param count Number of points to generate.
        .       * @param pointDistribution Point distribution selector.
        .       *       Available: UNIFORM, REGULAR, NORMAL.
        .       * @note Generated coordinates are in range [0..1)
    
    SIFT_create(...)
        SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> retval
        .   Use cv.SIFT_create() instead
    
    SURF_create(...)
        SURF_create([, hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) -> retval
        .   @param hessianThreshold Threshold for hessian keypoint detector used in SURF.
        .       @param nOctaves Number of pyramid octaves the keypoint detector will use.
        .       @param nOctaveLayers Number of octave layers within each octave.
        .       @param extended Extended descriptor flag (true - use extended 128-element descriptors; false - use
        .       64-element descriptors).
        .       @param upright Up-right or rotated features flag (true - do not compute orientation of features;
        .       false - compute orientation).
    
    StarDetector_create(...)
        StarDetector_create([, maxSize[, responseThreshold[, lineThresholdProjected[, lineThresholdBinarized[, suppressNonmaxSize]]]]]) -> retval
        .
    
    VGG_create(...)
        VGG_create([, desc[, isigma[, img_normalize[, use_scale_orientation[, scale_factor[, dsc_normalize]]]]]]) -> retval
        .
    
    matchGMS(...)
        matchGMS(size1, size2, keypoints1, keypoints2, matches1to2[, withRotation[, withScale[, thresholdFactor]]]) -> matchesGMS
        .   @brief GMS (Grid-based Motion Statistics) feature matching strategy described in @cite Bian2017gms .
        .       @param size1 Input size of image1.
        .       @param size2 Input size of image2.
        .       @param keypoints1 Input keypoints of image1.
        .       @param keypoints2 Input keypoints of image2.
        .       @param matches1to2 Input 1-nearest neighbor matches.
        .       @param matchesGMS Matches returned by the GMS matching strategy.
        .       @param withRotation Take rotation transformation into account.
        .       @param withScale Take scale transformation into account.
        .       @param thresholdFactor The higher, the less matches.
        .       @note
        .           Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly.
        .           If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480).
        .           If your images have big rotation and scale changes, please set withRotation or withScale to true.
    
    matchLOGOS(...)
        matchLOGOS(keypoints1, keypoints2, nn1, nn2, matches1to2) -> None
        .   @brief LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in @cite Lowry2018LOGOSLG .
        .       @param keypoints1 Input keypoints of image1.
        .       @param keypoints2 Input keypoints of image2.
        .       @param nn1 Index to the closest BoW centroid for each descriptors of image1.
        .       @param nn2 Index to the closest BoW centroid for each descriptors of image2.
        .       @param matches1to2 Matches returned by the LOGOS matching strategy.
        .       @note
        .           This matching strategy is suitable for features matching against large scale database.
        .           First step consists in constructing the bag-of-words (BoW) from a representative image database.
        .           Image descriptors are then represented by their closest codevector (nearest BoW centroid).
 
DATA
    DAISY_NRM_FULL = 102
    DAISY_NRM_NONE = 100
    DAISY_NRM_PARTIAL = 101
    DAISY_NRM_SIFT = 103
    PCTSIGNATURES_GAUSSIAN = 1
    PCTSIGNATURES_HEURISTIC = 2
    PCTSIGNATURES_L0_25 = 0
    PCTSIGNATURES_L0_5 = 1
    PCTSIGNATURES_L1 = 2
    PCTSIGNATURES_L2 = 3
    PCTSIGNATURES_L2SQUARED = 4
    PCTSIGNATURES_L5 = 5
    PCTSIGNATURES_L_INFINITY = 6
    PCTSIGNATURES_MINUS = 0
    PCTSIGNATURES_NORMAL = 2
    PCTSIGNATURES_REGULAR = 1
    PCTSIGNATURES_UNIFORM = 0
    PCTSignatures_GAUSSIAN = 1
    PCTSignatures_HEURISTIC = 2
    PCTSignatures_L0_25 = 0
    PCTSignatures_L0_5 = 1
    PCTSignatures_L1 = 2
    PCTSignatures_L2 = 3
    PCTSignatures_L2SQUARED = 4
    PCTSignatures_L5 = 5
    PCTSignatures_L_INFINITY = 6
    PCTSignatures_MINUS = 0
    PCTSignatures_NORMAL = 2
    PCTSignatures_REGULAR = 1
    PCTSignatures_UNIFORM = 0
 
FILE
    (built-in)