__all__: list[str] = [] import cv2 import cv2.typing import typing as _typing BEBLID_SIZE_512_BITS: int BEBLID_SIZE_256_BITS: int BEBLID_BeblidSize = int """One of [BEBLID_SIZE_512_BITS, BEBLID_SIZE_256_BITS]""" TEBLID_SIZE_256_BITS: int TEBLID_SIZE_512_BITS: int TEBLID_TeblidSize = int """One of [TEBLID_SIZE_256_BITS, TEBLID_SIZE_512_BITS]""" DAISY_NRM_NONE: int DAISY_NRM_PARTIAL: int DAISY_NRM_FULL: int DAISY_NRM_SIFT: int DAISY_NormalizationType = int """One of [DAISY_NRM_NONE, DAISY_NRM_PARTIAL, DAISY_NRM_FULL, DAISY_NRM_SIFT]""" PCTSignatures_L0_25: int PCTSIGNATURES_L0_25: int PCTSignatures_L0_5: int PCTSIGNATURES_L0_5: int PCTSignatures_L1: int PCTSIGNATURES_L1: int PCTSignatures_L2: int PCTSIGNATURES_L2: int PCTSignatures_L2SQUARED: int PCTSIGNATURES_L2SQUARED: int PCTSignatures_L5: int PCTSIGNATURES_L5: int PCTSignatures_L_INFINITY: int PCTSIGNATURES_L_INFINITY: int PCTSignatures_DistanceFunction = int """One of [PCTSignatures_L0_25, PCTSIGNATURES_L0_25, PCTSignatures_L0_5, PCTSIGNATURES_L0_5, PCTSignatures_L1, PCTSIGNATURES_L1, PCTSignatures_L2, PCTSIGNATURES_L2, PCTSignatures_L2SQUARED, PCTSIGNATURES_L2SQUARED, PCTSignatures_L5, PCTSIGNATURES_L5, PCTSignatures_L_INFINITY, PCTSIGNATURES_L_INFINITY]""" PCTSignatures_UNIFORM: int PCTSIGNATURES_UNIFORM: int PCTSignatures_REGULAR: int PCTSIGNATURES_REGULAR: int PCTSignatures_NORMAL: int PCTSIGNATURES_NORMAL: int PCTSignatures_PointDistribution = int """One of [PCTSignatures_UNIFORM, PCTSIGNATURES_UNIFORM, PCTSignatures_REGULAR, PCTSIGNATURES_REGULAR, PCTSignatures_NORMAL, PCTSIGNATURES_NORMAL]""" PCTSignatures_MINUS: int PCTSIGNATURES_MINUS: int PCTSignatures_GAUSSIAN: int PCTSIGNATURES_GAUSSIAN: int PCTSignatures_HEURISTIC: int PCTSIGNATURES_HEURISTIC: int PCTSignatures_SimilarityFunction = int """One of [PCTSignatures_MINUS, PCTSIGNATURES_MINUS, PCTSignatures_GAUSSIAN, PCTSIGNATURES_GAUSSIAN, PCTSignatures_HEURISTIC, PCTSIGNATURES_HEURISTIC]""" # Classes class FREAK(cv2.Feature2D): # Functions @classmethod def create(cls, orientationNormalized: bool = ..., scaleNormalized: bool = ..., patternScale: float = ..., nOctaves: int = ..., selectedPairs: _typing.Sequence[int] = ...) -> FREAK: ... def setOrientationNormalized(self, orientationNormalized: bool) -> None: ... def getOrientationNormalized(self) -> bool: ... def setScaleNormalized(self, scaleNormalized: bool) -> None: ... def getScaleNormalized(self) -> bool: ... def setPatternScale(self, patternScale: float) -> None: ... def getPatternScale(self) -> float: ... def setNOctaves(self, nOctaves: int) -> None: ... def getNOctaves(self) -> int: ... def getDefaultName(self) -> str: ... class StarDetector(cv2.Feature2D): # Functions @classmethod def create(cls, maxSize: int = ..., responseThreshold: int = ..., lineThresholdProjected: int = ..., lineThresholdBinarized: int = ..., suppressNonmaxSize: int = ...) -> StarDetector: ... def setMaxSize(self, _maxSize: int) -> None: ... def getMaxSize(self) -> int: ... def setResponseThreshold(self, _responseThreshold: int) -> None: ... def getResponseThreshold(self) -> int: ... def setLineThresholdProjected(self, _lineThresholdProjected: int) -> None: ... def getLineThresholdProjected(self) -> int: ... def setLineThresholdBinarized(self, _lineThresholdBinarized: int) -> None: ... def getLineThresholdBinarized(self) -> int: ... def setSuppressNonmaxSize(self, _suppressNonmaxSize: int) -> None: ... def getSuppressNonmaxSize(self) -> int: ... def getDefaultName(self) -> str: ... class BriefDescriptorExtractor(cv2.Feature2D): # Functions @classmethod def create(cls, bytes: int = ..., use_orientation: bool = ...) -> BriefDescriptorExtractor: ... def setDescriptorSize(self, bytes: int) -> None: ... def getDescriptorSize(self) -> int: ... def setUseOrientation(self, use_orientation: bool) -> None: ... def getUseOrientation(self) -> bool: ... def getDefaultName(self) -> str: ... class LUCID(cv2.Feature2D): # Functions @classmethod def create(cls, lucid_kernel: int = ..., blur_kernel: int = ...) -> LUCID: ... def setLucidKernel(self, lucid_kernel: int) -> None: ... def getLucidKernel(self) -> int: ... def setBlurKernel(self, blur_kernel: int) -> None: ... def getBlurKernel(self) -> int: ... def getDefaultName(self) -> str: ... class LATCH(cv2.Feature2D): # Functions @classmethod def create(cls, bytes: int = ..., rotationInvariance: bool = ..., half_ssd_size: int = ..., sigma: float = ...) -> LATCH: ... def setBytes(self, bytes: int) -> None: ... def getBytes(self) -> int: ... def setRotationInvariance(self, rotationInvariance: bool) -> None: ... def getRotationInvariance(self) -> bool: ... def setHalfSSDsize(self, half_ssd_size: int) -> None: ... def getHalfSSDsize(self) -> int: ... def setSigma(self, sigma: float) -> None: ... def getSigma(self) -> float: ... def getDefaultName(self) -> str: ... class BEBLID(cv2.Feature2D): # Functions @classmethod def create(cls, scale_factor: float, n_bits: int = ...) -> BEBLID: ... def setScaleFactor(self, scale_factor: float) -> None: ... def getScaleFactor(self) -> float: ... def getDefaultName(self) -> str: ... class TEBLID(cv2.Feature2D): # Functions @classmethod def create(cls, scale_factor: float, n_bits: int = ...) -> TEBLID: ... def getDefaultName(self) -> str: ... class DAISY(cv2.Feature2D): # Functions @classmethod @_typing.overload def create(cls, radius: float = ..., q_radius: int = ..., q_theta: int = ..., q_hist: int = ..., norm: DAISY_NormalizationType = ..., H: cv2.typing.MatLike | None = ..., interpolation: bool = ..., use_orientation: bool = ...) -> DAISY: ... @classmethod @_typing.overload def create(cls, radius: float = ..., q_radius: int = ..., q_theta: int = ..., q_hist: int = ..., norm: DAISY_NormalizationType = ..., H: cv2.UMat | None = ..., interpolation: bool = ..., use_orientation: bool = ...) -> DAISY: ... def setRadius(self, radius: float) -> None: ... def getRadius(self) -> float: ... def setQRadius(self, q_radius: int) -> None: ... def getQRadius(self) -> int: ... def setQTheta(self, q_theta: int) -> None: ... def getQTheta(self) -> int: ... def setQHist(self, q_hist: int) -> None: ... def getQHist(self) -> int: ... def setNorm(self, norm: int) -> None: ... def getNorm(self) -> int: ... @_typing.overload def setH(self, H: cv2.typing.MatLike) -> None: ... @_typing.overload def setH(self, H: cv2.UMat) -> None: ... def getH(self) -> cv2.typing.MatLike: ... def setInterpolation(self, interpolation: bool) -> None: ... def getInterpolation(self) -> bool: ... def setUseOrientation(self, use_orientation: bool) -> None: ... def getUseOrientation(self) -> bool: ... def getDefaultName(self) -> str: ... class MSDDetector(cv2.Feature2D): # Functions @classmethod def create(cls, m_patch_radius: int = ..., m_search_area_radius: int = ..., m_nms_radius: int = ..., m_nms_scale_radius: int = ..., m_th_saliency: float = ..., m_kNN: int = ..., m_scale_factor: float = ..., m_n_scales: int = ..., m_compute_orientation: bool = ...) -> MSDDetector: ... def setPatchRadius(self, patch_radius: int) -> None: ... def getPatchRadius(self) -> int: ... def setSearchAreaRadius(self, use_orientation: int) -> None: ... def getSearchAreaRadius(self) -> int: ... def setNmsRadius(self, nms_radius: int) -> None: ... def getNmsRadius(self) -> int: ... def setNmsScaleRadius(self, nms_scale_radius: int) -> None: ... def getNmsScaleRadius(self) -> int: ... def setThSaliency(self, th_saliency: float) -> None: ... def getThSaliency(self) -> float: ... def setKNN(self, kNN: int) -> None: ... def getKNN(self) -> int: ... def setScaleFactor(self, scale_factor: float) -> None: ... def getScaleFactor(self) -> float: ... def setNScales(self, use_orientation: int) -> None: ... def getNScales(self) -> int: ... def setComputeOrientation(self, compute_orientation: bool) -> None: ... def getComputeOrientation(self) -> bool: ... def getDefaultName(self) -> str: ... class VGG(cv2.Feature2D): # Functions @classmethod def create(cls, desc: int = ..., isigma: float = ..., img_normalize: bool = ..., use_scale_orientation: bool = ..., scale_factor: float = ..., dsc_normalize: bool = ...) -> VGG: ... def getDefaultName(self) -> str: ... def setSigma(self, isigma: float) -> None: ... def getSigma(self) -> float: ... def setUseNormalizeImage(self, img_normalize: bool) -> None: ... def getUseNormalizeImage(self) -> bool: ... def setUseScaleOrientation(self, use_scale_orientation: bool) -> None: ... def getUseScaleOrientation(self) -> bool: ... def setScaleFactor(self, scale_factor: float) -> None: ... def getScaleFactor(self) -> float: ... def setUseNormalizeDescriptor(self, dsc_normalize: bool) -> None: ... def getUseNormalizeDescriptor(self) -> bool: ... class BoostDesc(cv2.Feature2D): # Functions @classmethod def create(cls, desc: int = ..., use_scale_orientation: bool = ..., scale_factor: float = ...) -> BoostDesc: ... def getDefaultName(self) -> str: ... def setUseScaleOrientation(self, use_scale_orientation: bool) -> None: ... def getUseScaleOrientation(self) -> bool: ... def setScaleFactor(self, scale_factor: float) -> None: ... def getScaleFactor(self) -> float: ... class PCTSignatures(cv2.Algorithm): # Functions @classmethod @_typing.overload def create(cls, initSampleCount: int = ..., initSeedCount: int = ..., pointDistribution: int = ...) -> PCTSignatures: ... @classmethod @_typing.overload def create(cls, initSamplingPoints: _typing.Sequence[cv2.typing.Point2f], initSeedCount: int) -> PCTSignatures: ... @classmethod @_typing.overload def create(cls, initSamplingPoints: _typing.Sequence[cv2.typing.Point2f], initClusterSeedIndexes: _typing.Sequence[int]) -> PCTSignatures: ... @_typing.overload def computeSignature(self, image: cv2.typing.MatLike, signature: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ... @_typing.overload def computeSignature(self, image: cv2.UMat, signature: cv2.UMat | None = ...) -> cv2.UMat: ... def computeSignatures(self, images: _typing.Sequence[cv2.typing.MatLike], signatures: _typing.Sequence[cv2.typing.MatLike]) -> None: ... @staticmethod @_typing.overload def drawSignature(source: cv2.typing.MatLike, signature: cv2.typing.MatLike, result: cv2.typing.MatLike | None = ..., radiusToShorterSideRatio: float = ..., borderThickness: int = ...) -> cv2.typing.MatLike: ... @staticmethod @_typing.overload def drawSignature(source: cv2.UMat, signature: cv2.UMat, result: cv2.UMat | None = ..., radiusToShorterSideRatio: float = ..., borderThickness: int = ...) -> cv2.UMat: ... @staticmethod def generateInitPoints(initPoints: _typing.Sequence[cv2.typing.Point2f], count: int, pointDistribution: int) -> None: ... def getSampleCount(self) -> int: ... def getGrayscaleBits(self) -> int: ... def setGrayscaleBits(self, grayscaleBits: int) -> None: ... def getWindowRadius(self) -> int: ... def setWindowRadius(self, radius: int) -> None: ... def getWeightX(self) -> float: ... def setWeightX(self, weight: float) -> None: ... def getWeightY(self) -> float: ... def setWeightY(self, weight: float) -> None: ... def getWeightL(self) -> float: ... def setWeightL(self, weight: float) -> None: ... def getWeightA(self) -> float: ... def setWeightA(self, weight: float) -> None: ... def getWeightB(self) -> float: ... def setWeightB(self, weight: float) -> None: ... def getWeightContrast(self) -> float: ... def setWeightContrast(self, weight: float) -> None: ... def getWeightEntropy(self) -> float: ... def setWeightEntropy(self, weight: float) -> None: ... def getSamplingPoints(self) -> _typing.Sequence[cv2.typing.Point2f]: ... def setWeight(self, idx: int, value: float) -> None: ... def setWeights(self, weights: _typing.Sequence[float]) -> None: ... def setTranslation(self, idx: int, value: float) -> None: ... def setTranslations(self, translations: _typing.Sequence[float]) -> None: ... def setSamplingPoints(self, samplingPoints: _typing.Sequence[cv2.typing.Point2f]) -> None: ... def getInitSeedIndexes(self) -> _typing.Sequence[int]: ... def setInitSeedIndexes(self, initSeedIndexes: _typing.Sequence[int]) -> None: ... def getInitSeedCount(self) -> int: ... def getIterationCount(self) -> int: ... def setIterationCount(self, iterationCount: int) -> None: ... def getMaxClustersCount(self) -> int: ... def setMaxClustersCount(self, maxClustersCount: int) -> None: ... def getClusterMinSize(self) -> int: ... def setClusterMinSize(self, clusterMinSize: int) -> None: ... def getJoiningDistance(self) -> float: ... def setJoiningDistance(self, joiningDistance: float) -> None: ... def getDropThreshold(self) -> float: ... def setDropThreshold(self, dropThreshold: float) -> None: ... def getDistanceFunction(self) -> int: ... def setDistanceFunction(self, distanceFunction: int) -> None: ... class PCTSignaturesSQFD(cv2.Algorithm): # Functions @classmethod def create(cls, distanceFunction: int = ..., similarityFunction: int = ..., similarityParameter: float = ...) -> PCTSignaturesSQFD: ... @_typing.overload def computeQuadraticFormDistance(self, _signature0: cv2.typing.MatLike, _signature1: cv2.typing.MatLike) -> float: ... @_typing.overload def computeQuadraticFormDistance(self, _signature0: cv2.UMat, _signature1: cv2.UMat) -> float: ... def computeQuadraticFormDistances(self, sourceSignature: cv2.typing.MatLike, imageSignatures: _typing.Sequence[cv2.typing.MatLike], distances: _typing.Sequence[float]) -> None: ... class HarrisLaplaceFeatureDetector(cv2.Feature2D): # Functions @classmethod def create(cls, numOctaves: int = ..., corn_thresh: float = ..., DOG_thresh: float = ..., maxCorners: int = ..., num_layers: int = ...) -> HarrisLaplaceFeatureDetector: ... def setNumOctaves(self, numOctaves_: int) -> None: ... def getNumOctaves(self) -> int: ... def setCornThresh(self, corn_thresh_: float) -> None: ... def getCornThresh(self) -> float: ... def setDOGThresh(self, DOG_thresh_: float) -> None: ... def getDOGThresh(self) -> float: ... def setMaxCorners(self, maxCorners_: int) -> None: ... def getMaxCorners(self) -> int: ... def setNumLayers(self, num_layers_: int) -> None: ... def getNumLayers(self) -> int: ... def getDefaultName(self) -> str: ... class AffineFeature2D(cv2.Feature2D): ... class TBMR(AffineFeature2D): # Functions @classmethod def create(cls, min_area: int = ..., max_area_relative: float = ..., scale_factor: float = ..., n_scales: int = ...) -> TBMR: ... def setMinArea(self, minArea: int) -> None: ... def getMinArea(self) -> int: ... def setMaxAreaRelative(self, maxArea: float) -> None: ... def getMaxAreaRelative(self) -> float: ... def setScaleFactor(self, scale_factor: float) -> None: ... def getScaleFactor(self) -> float: ... def setNScales(self, n_scales: int) -> None: ... def getNScales(self) -> int: ... def getDefaultName(self) -> str: ... class SURF(cv2.Feature2D): # Functions @classmethod def create(cls, hessianThreshold: float = ..., nOctaves: int = ..., nOctaveLayers: int = ..., extended: bool = ..., upright: bool = ...) -> SURF: ... def setHessianThreshold(self, hessianThreshold: float) -> None: ... def getHessianThreshold(self) -> float: ... def setNOctaves(self, nOctaves: int) -> None: ... def getNOctaves(self) -> int: ... def setNOctaveLayers(self, nOctaveLayers: int) -> None: ... def getNOctaveLayers(self) -> int: ... def setExtended(self, extended: bool) -> None: ... def getExtended(self) -> bool: ... def setUpright(self, upright: bool) -> None: ... def getUpright(self) -> bool: ... def getDefaultName(self) -> str: ... # Functions def matchGMS(size1: cv2.typing.Size, size2: cv2.typing.Size, keypoints1: _typing.Sequence[cv2.KeyPoint], keypoints2: _typing.Sequence[cv2.KeyPoint], matches1to2: _typing.Sequence[cv2.DMatch], withRotation: bool = ..., withScale: bool = ..., thresholdFactor: float = ...) -> _typing.Sequence[cv2.DMatch]: ... def matchLOGOS(keypoints1: _typing.Sequence[cv2.KeyPoint], keypoints2: _typing.Sequence[cv2.KeyPoint], nn1: _typing.Sequence[int], nn2: _typing.Sequence[int]) -> _typing.Sequence[cv2.DMatch]: ...