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@@ -251,3 +251,4 @@ tuning-competition-baseline/.venv/lib/python3.11/site-packages/torch/_inductor/_
 .venv/lib/python3.11/site-packages/pycparser/ply/__pycache__/yacc.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
 .venv/lib/python3.11/site-packages/torchvision.libs/libpng16.7f72a3c5.so.16 filter=lfs diff=lfs merge=lfs -text
 .venv/lib/python3.11/site-packages/msgspec/_core.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+.venv/lib/python3.11/site-packages/vllm/_moe_C.abi3.so filter=lfs diff=lfs merge=lfs -text

.venv/lib/python3.11/site-packages/cv2/Error/__init__.pyi

@@ -0,0 +1,118 @@
+__all__: list[str] = []
+
+# Enumerations
+StsOk: int
+STS_OK: int
+StsBackTrace: int
+STS_BACK_TRACE: int
+StsError: int
+STS_ERROR: int
+StsInternal: int
+STS_INTERNAL: int
+StsNoMem: int
+STS_NO_MEM: int
+StsBadArg: int
+STS_BAD_ARG: int
+StsBadFunc: int
+STS_BAD_FUNC: int
+StsNoConv: int
+STS_NO_CONV: int
+StsAutoTrace: int
+STS_AUTO_TRACE: int
+HeaderIsNull: int
+HEADER_IS_NULL: int
+BadImageSize: int
+BAD_IMAGE_SIZE: int
+BadOffset: int
+BAD_OFFSET: int
+BadDataPtr: int
+BAD_DATA_PTR: int
+BadStep: int
+BAD_STEP: int
+BadModelOrChSeq: int
+BAD_MODEL_OR_CH_SEQ: int
+BadNumChannels: int
+BAD_NUM_CHANNELS: int
+BadNumChannel1U: int
+BAD_NUM_CHANNEL1U: int
+BadDepth: int
+BAD_DEPTH: int
+BadAlphaChannel: int
+BAD_ALPHA_CHANNEL: int
+BadOrder: int
+BAD_ORDER: int
+BadOrigin: int
+BAD_ORIGIN: int
+BadAlign: int
+BAD_ALIGN: int
+BadCallBack: int
+BAD_CALL_BACK: int
+BadTileSize: int
+BAD_TILE_SIZE: int
+BadCOI: int
+BAD_COI: int
+BadROISize: int
+BAD_ROISIZE: int
+MaskIsTiled: int
+MASK_IS_TILED: int
+StsNullPtr: int
+STS_NULL_PTR: int
+StsVecLengthErr: int
+STS_VEC_LENGTH_ERR: int
+StsFilterStructContentErr: int
+STS_FILTER_STRUCT_CONTENT_ERR: int
+StsKernelStructContentErr: int
+STS_KERNEL_STRUCT_CONTENT_ERR: int
+StsFilterOffsetErr: int
+STS_FILTER_OFFSET_ERR: int
+StsBadSize: int
+STS_BAD_SIZE: int
+StsDivByZero: int
+STS_DIV_BY_ZERO: int
+StsInplaceNotSupported: int
+STS_INPLACE_NOT_SUPPORTED: int
+StsObjectNotFound: int
+STS_OBJECT_NOT_FOUND: int
+StsUnmatchedFormats: int
+STS_UNMATCHED_FORMATS: int
+StsBadFlag: int
+STS_BAD_FLAG: int
+StsBadPoint: int
+STS_BAD_POINT: int
+StsBadMask: int
+STS_BAD_MASK: int
+StsUnmatchedSizes: int
+STS_UNMATCHED_SIZES: int
+StsUnsupportedFormat: int
+STS_UNSUPPORTED_FORMAT: int
+StsOutOfRange: int
+STS_OUT_OF_RANGE: int
+StsParseError: int
+STS_PARSE_ERROR: int
+StsNotImplemented: int
+STS_NOT_IMPLEMENTED: int
+StsBadMemBlock: int
+STS_BAD_MEM_BLOCK: int
+StsAssert: int
+STS_ASSERT: int
+GpuNotSupported: int
+GPU_NOT_SUPPORTED: int
+GpuApiCallError: int
+GPU_API_CALL_ERROR: int
+OpenGlNotSupported: int
+OPEN_GL_NOT_SUPPORTED: int
+OpenGlApiCallError: int
+OPEN_GL_API_CALL_ERROR: int
+OpenCLApiCallError: int
+OPEN_CLAPI_CALL_ERROR: int
+OpenCLDoubleNotSupported: int
+OPEN_CLDOUBLE_NOT_SUPPORTED: int
+OpenCLInitError: int
+OPEN_CLINIT_ERROR: int
+OpenCLNoAMDBlasFft: int
+OPEN_CLNO_AMDBLAS_FFT: int
+Code = int
+"""One of [StsOk, STS_OK, StsBackTrace, STS_BACK_TRACE, StsError, STS_ERROR, StsInternal, STS_INTERNAL, StsNoMem, STS_NO_MEM, StsBadArg, STS_BAD_ARG, StsBadFunc, STS_BAD_FUNC, StsNoConv, STS_NO_CONV, StsAutoTrace, STS_AUTO_TRACE, HeaderIsNull, HEADER_IS_NULL, BadImageSize, BAD_IMAGE_SIZE, BadOffset, BAD_OFFSET, BadDataPtr, BAD_DATA_PTR, BadStep, BAD_STEP, BadModelOrChSeq, BAD_MODEL_OR_CH_SEQ, BadNumChannels, BAD_NUM_CHANNELS, BadNumChannel1U, BAD_NUM_CHANNEL1U, BadDepth, BAD_DEPTH, BadAlphaChannel, BAD_ALPHA_CHANNEL, BadOrder, BAD_ORDER, BadOrigin, BAD_ORIGIN, BadAlign, BAD_ALIGN, BadCallBack, BAD_CALL_BACK, BadTileSize, BAD_TILE_SIZE, BadCOI, BAD_COI, BadROISize, BAD_ROISIZE, MaskIsTiled, MASK_IS_TILED, StsNullPtr, STS_NULL_PTR, StsVecLengthErr, STS_VEC_LENGTH_ERR, StsFilterStructContentErr, STS_FILTER_STRUCT_CONTENT_ERR, StsKernelStructContentErr, STS_KERNEL_STRUCT_CONTENT_ERR, StsFilterOffsetErr, STS_FILTER_OFFSET_ERR, StsBadSize, STS_BAD_SIZE, StsDivByZero, STS_DIV_BY_ZERO, StsInplaceNotSupported, STS_INPLACE_NOT_SUPPORTED, StsObjectNotFound, STS_OBJECT_NOT_FOUND, StsUnmatchedFormats, STS_UNMATCHED_FORMATS, StsBadFlag, STS_BAD_FLAG, StsBadPoint, STS_BAD_POINT, StsBadMask, STS_BAD_MASK, StsUnmatchedSizes, STS_UNMATCHED_SIZES, StsUnsupportedFormat, STS_UNSUPPORTED_FORMAT, StsOutOfRange, STS_OUT_OF_RANGE, StsParseError, STS_PARSE_ERROR, StsNotImplemented, STS_NOT_IMPLEMENTED, StsBadMemBlock, STS_BAD_MEM_BLOCK, StsAssert, STS_ASSERT, GpuNotSupported, GPU_NOT_SUPPORTED, GpuApiCallError, GPU_API_CALL_ERROR, OpenGlNotSupported, OPEN_GL_NOT_SUPPORTED, OpenGlApiCallError, OPEN_GL_API_CALL_ERROR, OpenCLApiCallError, OPEN_CLAPI_CALL_ERROR, OpenCLDoubleNotSupported, OPEN_CLDOUBLE_NOT_SUPPORTED, OpenCLInitError, OPEN_CLINIT_ERROR, OpenCLNoAMDBlasFft, OPEN_CLNO_AMDBLAS_FFT]"""
+
+
+

.venv/lib/python3.11/site-packages/cv2/aruco/__init__.pyi

@@ -0,0 +1,303 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+CORNER_REFINE_NONE: int
+CORNER_REFINE_SUBPIX: int
+CORNER_REFINE_CONTOUR: int
+CORNER_REFINE_APRILTAG: int
+CornerRefineMethod = int
+"""One of [CORNER_REFINE_NONE, CORNER_REFINE_SUBPIX, CORNER_REFINE_CONTOUR, CORNER_REFINE_APRILTAG]"""
+
+DICT_4X4_50: int
+DICT_4X4_100: int
+DICT_4X4_250: int
+DICT_4X4_1000: int
+DICT_5X5_50: int
+DICT_5X5_100: int
+DICT_5X5_250: int
+DICT_5X5_1000: int
+DICT_6X6_50: int
+DICT_6X6_100: int
+DICT_6X6_250: int
+DICT_6X6_1000: int
+DICT_7X7_50: int
+DICT_7X7_100: int
+DICT_7X7_250: int
+DICT_7X7_1000: int
+DICT_ARUCO_ORIGINAL: int
+DICT_APRILTAG_16h5: int
+DICT_APRILTAG_16H5: int
+DICT_APRILTAG_25h9: int
+DICT_APRILTAG_25H9: int
+DICT_APRILTAG_36h10: int
+DICT_APRILTAG_36H10: int
+DICT_APRILTAG_36h11: int
+DICT_APRILTAG_36H11: int
+DICT_ARUCO_MIP_36h12: int
+DICT_ARUCO_MIP_36H12: int
+PredefinedDictionaryType = int
+"""One of [DICT_4X4_50, DICT_4X4_100, DICT_4X4_250, DICT_4X4_1000, DICT_5X5_50, DICT_5X5_100, DICT_5X5_250, DICT_5X5_1000, DICT_6X6_50, DICT_6X6_100, DICT_6X6_250, DICT_6X6_1000, DICT_7X7_50, DICT_7X7_100, DICT_7X7_250, DICT_7X7_1000, DICT_ARUCO_ORIGINAL, DICT_APRILTAG_16h5, DICT_APRILTAG_16H5, DICT_APRILTAG_25h9, DICT_APRILTAG_25H9, DICT_APRILTAG_36h10, DICT_APRILTAG_36H10, DICT_APRILTAG_36h11, DICT_APRILTAG_36H11, DICT_ARUCO_MIP_36h12, DICT_ARUCO_MIP_36H12]"""
+
+
+
+# Classes
+class Board:
+    # Functions
+    @_typing.overload
+    def __init__(self, objPoints: _typing.Sequence[cv2.typing.MatLike], dictionary: Dictionary, ids: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def __init__(self, objPoints: _typing.Sequence[cv2.UMat], dictionary: Dictionary, ids: cv2.UMat) -> None: ...
+
+    def getDictionary(self) -> Dictionary: ...
+
+    def getObjPoints(self) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point3f]]: ...
+
+    def getIds(self) -> _typing.Sequence[int]: ...
+
+    def getRightBottomCorner(self) -> cv2.typing.Point3f: ...
+
+    @_typing.overload
+    def matchImagePoints(self, detectedCorners: _typing.Sequence[cv2.typing.MatLike], detectedIds: cv2.typing.MatLike, objPoints: cv2.typing.MatLike | None = ..., imgPoints: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def matchImagePoints(self, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, objPoints: cv2.UMat | None = ..., imgPoints: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def generateImage(self, outSize: cv2.typing.Size, img: cv2.typing.MatLike | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def generateImage(self, outSize: cv2.typing.Size, img: cv2.UMat | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.UMat: ...
+
+
+class GridBoard(Board):
+    # Functions
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, markerLength: float, markerSeparation: float, dictionary: Dictionary, ids: cv2.typing.MatLike | None = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, markerLength: float, markerSeparation: float, dictionary: Dictionary, ids: cv2.UMat | None = ...) -> None: ...
+
+    def getGridSize(self) -> cv2.typing.Size: ...
+
+    def getMarkerLength(self) -> float: ...
+
+    def getMarkerSeparation(self) -> float: ...
+
+
+class CharucoBoard(Board):
+    # Functions
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, squareLength: float, markerLength: float, dictionary: Dictionary, ids: cv2.typing.MatLike | None = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, squareLength: float, markerLength: float, dictionary: Dictionary, ids: cv2.UMat | None = ...) -> None: ...
+
+    def setLegacyPattern(self, legacyPattern: bool) -> None: ...
+
+    def getLegacyPattern(self) -> bool: ...
+
+    def getChessboardSize(self) -> cv2.typing.Size: ...
+
+    def getSquareLength(self) -> float: ...
+
+    def getMarkerLength(self) -> float: ...
+
+    def getChessboardCorners(self) -> _typing.Sequence[cv2.typing.Point3f]: ...
+
+    @_typing.overload
+    def checkCharucoCornersCollinear(self, charucoIds: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def checkCharucoCornersCollinear(self, charucoIds: cv2.UMat) -> bool: ...
+
+
+class DetectorParameters:
+    adaptiveThreshWinSizeMin: int
+    adaptiveThreshWinSizeMax: int
+    adaptiveThreshWinSizeStep: int
+    adaptiveThreshConstant: float
+    minMarkerPerimeterRate: float
+    maxMarkerPerimeterRate: float
+    polygonalApproxAccuracyRate: float
+    minCornerDistanceRate: float
+    minDistanceToBorder: int
+    minMarkerDistanceRate: float
+    minGroupDistance: float
+    cornerRefinementMethod: int
+    cornerRefinementWinSize: int
+    relativeCornerRefinmentWinSize: float
+    cornerRefinementMaxIterations: int
+    cornerRefinementMinAccuracy: float
+    markerBorderBits: int
+    perspectiveRemovePixelPerCell: int
+    perspectiveRemoveIgnoredMarginPerCell: float
+    maxErroneousBitsInBorderRate: float
+    minOtsuStdDev: float
+    errorCorrectionRate: float
+    aprilTagQuadDecimate: float
+    aprilTagQuadSigma: float
+    aprilTagMinClusterPixels: int
+    aprilTagMaxNmaxima: int
+    aprilTagCriticalRad: float
+    aprilTagMaxLineFitMse: float
+    aprilTagMinWhiteBlackDiff: int
+    aprilTagDeglitch: int
+    detectInvertedMarker: bool
+    useAruco3Detection: bool
+    minSideLengthCanonicalImg: int
+    minMarkerLengthRatioOriginalImg: float
+
+    # Functions
+    def __init__(self) -> None: ...
+
+    def readDetectorParameters(self, fn: cv2.FileNode) -> bool: ...
+
+    def writeDetectorParameters(self, fs: cv2.FileStorage, name: str = ...) -> bool: ...
+
+
+class RefineParameters:
+    minRepDistance: float
+    errorCorrectionRate: float
+    checkAllOrders: bool
+
+    # Functions
+    def __init__(self, minRepDistance: float = ..., errorCorrectionRate: float = ..., checkAllOrders: bool = ...) -> None: ...
+
+    def readRefineParameters(self, fn: cv2.FileNode) -> bool: ...
+
+    def writeRefineParameters(self, fs: cv2.FileStorage, name: str = ...) -> bool: ...
+
+
+class ArucoDetector(cv2.Algorithm):
+    # Functions
+    def __init__(self, dictionary: Dictionary = ..., detectorParams: DetectorParameters = ..., refineParams: RefineParameters = ...) -> None: ...
+
+    @_typing.overload
+    def detectMarkers(self, image: cv2.typing.MatLike, corners: _typing.Sequence[cv2.typing.MatLike] | None = ..., ids: cv2.typing.MatLike | None = ..., rejectedImgPoints: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike]]: ...
+    @_typing.overload
+    def detectMarkers(self, image: cv2.UMat, corners: _typing.Sequence[cv2.UMat] | None = ..., ids: cv2.UMat | None = ..., rejectedImgPoints: _typing.Sequence[cv2.UMat] | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat]]: ...
+
+    @_typing.overload
+    def refineDetectedMarkers(self, image: cv2.typing.MatLike, board: Board, detectedCorners: _typing.Sequence[cv2.typing.MatLike], detectedIds: cv2.typing.MatLike, rejectedCorners: _typing.Sequence[cv2.typing.MatLike], cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., recoveredIdxs: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
+    @_typing.overload
+    def refineDetectedMarkers(self, image: cv2.UMat, board: Board, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, rejectedCorners: _typing.Sequence[cv2.UMat], cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., recoveredIdxs: cv2.UMat | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
+
+    def getDictionary(self) -> Dictionary: ...
+
+    def setDictionary(self, dictionary: Dictionary) -> None: ...
+
+    def getDetectorParameters(self) -> DetectorParameters: ...
+
+    def setDetectorParameters(self, detectorParameters: DetectorParameters) -> None: ...
+
+    def getRefineParameters(self) -> RefineParameters: ...
+
+    def setRefineParameters(self, refineParameters: RefineParameters) -> None: ...
+
+    def write(self, fs: cv2.FileStorage, name: str) -> None: ...
+
+    def read(self, fn: cv2.FileNode) -> None: ...
+
+
+class Dictionary:
+    bytesList: cv2.typing.MatLike
+    markerSize: int
+    maxCorrectionBits: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, bytesList: cv2.typing.MatLike, _markerSize: int, maxcorr: int = ...) -> None: ...
+
+    def readDictionary(self, fn: cv2.FileNode) -> bool: ...
+
+    def writeDictionary(self, fs: cv2.FileStorage, name: str = ...) -> None: ...
+
+    def identify(self, onlyBits: cv2.typing.MatLike, maxCorrectionRate: float) -> tuple[bool, int, int]: ...
+
+    @_typing.overload
+    def getDistanceToId(self, bits: cv2.typing.MatLike, id: int, allRotations: bool = ...) -> int: ...
+    @_typing.overload
+    def getDistanceToId(self, bits: cv2.UMat, id: int, allRotations: bool = ...) -> int: ...
+
+    @_typing.overload
+    def generateImageMarker(self, id: int, sidePixels: int, _img: cv2.typing.MatLike | None = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def generateImageMarker(self, id: int, sidePixels: int, _img: cv2.UMat | None = ..., borderBits: int = ...) -> cv2.UMat: ...
+
+    @staticmethod
+    def getByteListFromBits(bits: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+
+    @staticmethod
+    def getBitsFromByteList(byteList: cv2.typing.MatLike, markerSize: int) -> cv2.typing.MatLike: ...
+
+
+class CharucoParameters:
+    cameraMatrix: cv2.typing.MatLike
+    distCoeffs: cv2.typing.MatLike
+    minMarkers: int
+    tryRefineMarkers: bool
+
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class CharucoDetector(cv2.Algorithm):
+    # Functions
+    def __init__(self, board: CharucoBoard, charucoParams: CharucoParameters = ..., detectorParams: DetectorParameters = ..., refineParams: RefineParameters = ...) -> None: ...
+
+    def getBoard(self) -> CharucoBoard: ...
+
+    def setBoard(self, board: CharucoBoard) -> None: ...
+
+    def getCharucoParameters(self) -> CharucoParameters: ...
+
+    def setCharucoParameters(self, charucoParameters: CharucoParameters) -> None: ...
+
+    def getDetectorParameters(self) -> DetectorParameters: ...
+
+    def setDetectorParameters(self, detectorParameters: DetectorParameters) -> None: ...
+
+    def getRefineParameters(self) -> RefineParameters: ...
+
+    def setRefineParameters(self, refineParameters: RefineParameters) -> None: ...
+
+    @_typing.overload
+    def detectBoard(self, image: cv2.typing.MatLike, charucoCorners: cv2.typing.MatLike | None = ..., charucoIds: cv2.typing.MatLike | None = ..., markerCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., markerIds: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
+    @_typing.overload
+    def detectBoard(self, image: cv2.UMat, charucoCorners: cv2.UMat | None = ..., charucoIds: cv2.UMat | None = ..., markerCorners: _typing.Sequence[cv2.UMat] | None = ..., markerIds: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
+
+    @_typing.overload
+    def detectDiamonds(self, image: cv2.typing.MatLike, diamondCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., diamondIds: cv2.typing.MatLike | None = ..., markerCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., markerIds: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
+    @_typing.overload
+    def detectDiamonds(self, image: cv2.UMat, diamondCorners: _typing.Sequence[cv2.UMat] | None = ..., diamondIds: cv2.UMat | None = ..., markerCorners: _typing.Sequence[cv2.UMat] | None = ..., markerIds: cv2.UMat | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
+
+
+
+# Functions
+@_typing.overload
+def drawDetectedCornersCharuco(image: cv2.typing.MatLike, charucoCorners: cv2.typing.MatLike, charucoIds: cv2.typing.MatLike | None = ..., cornerColor: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawDetectedCornersCharuco(image: cv2.UMat, charucoCorners: cv2.UMat, charucoIds: cv2.UMat | None = ..., cornerColor: cv2.typing.Scalar = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def drawDetectedDiamonds(image: cv2.typing.MatLike, diamondCorners: _typing.Sequence[cv2.typing.MatLike], diamondIds: cv2.typing.MatLike | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawDetectedDiamonds(image: cv2.UMat, diamondCorners: _typing.Sequence[cv2.UMat], diamondIds: cv2.UMat | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def drawDetectedMarkers(image: cv2.typing.MatLike, corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawDetectedMarkers(image: cv2.UMat, corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.UMat: ...
+
+def extendDictionary(nMarkers: int, markerSize: int, baseDictionary: Dictionary = ..., randomSeed: int = ...) -> Dictionary: ...
+
+@_typing.overload
+def generateImageMarker(dictionary: Dictionary, id: int, sidePixels: int, img: cv2.typing.MatLike | None = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def generateImageMarker(dictionary: Dictionary, id: int, sidePixels: int, img: cv2.UMat | None = ..., borderBits: int = ...) -> cv2.UMat: ...
+
+def getPredefinedDictionary(dict: int) -> Dictionary: ...
+
+

.venv/lib/python3.11/site-packages/cv2/barcode/__init__.pyi

@@ -0,0 +1,39 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class BarcodeDetector(cv2.GraphicalCodeDetector):
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, prototxt_path: str, model_path: str) -> None: ...
+
+    @_typing.overload
+    def decodeWithType(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str]]: ...
+    @_typing.overload
+    def decodeWithType(self, img: cv2.UMat, points: cv2.UMat) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str]]: ...
+
+    @_typing.overload
+    def detectAndDecodeWithType(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ...) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str], cv2.typing.MatLike]: ...
+    @_typing.overload
+    def detectAndDecodeWithType(self, img: cv2.UMat, points: cv2.UMat | None = ...) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str], cv2.UMat]: ...
+
+    def getDownsamplingThreshold(self) -> float: ...
+
+    def setDownsamplingThreshold(self, thresh: float) -> BarcodeDetector: ...
+
+    def getDetectorScales(self) -> _typing.Sequence[float]: ...
+
+    def setDetectorScales(self, sizes: _typing.Sequence[float]) -> BarcodeDetector: ...
+
+    def getGradientThreshold(self) -> float: ...
+
+    def setGradientThreshold(self, thresh: float) -> BarcodeDetector: ...
+
+
+

.venv/lib/python3.11/site-packages/cv2/cuda/__init__.pyi

@@ -0,0 +1,511 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+FEATURE_SET_COMPUTE_10: int
+FEATURE_SET_COMPUTE_11: int
+FEATURE_SET_COMPUTE_12: int
+FEATURE_SET_COMPUTE_13: int
+FEATURE_SET_COMPUTE_20: int
+FEATURE_SET_COMPUTE_21: int
+FEATURE_SET_COMPUTE_30: int
+FEATURE_SET_COMPUTE_32: int
+FEATURE_SET_COMPUTE_35: int
+FEATURE_SET_COMPUTE_50: int
+GLOBAL_ATOMICS: int
+SHARED_ATOMICS: int
+NATIVE_DOUBLE: int
+WARP_SHUFFLE_FUNCTIONS: int
+DYNAMIC_PARALLELISM: int
+FeatureSet = int
+"""One of [FEATURE_SET_COMPUTE_10, FEATURE_SET_COMPUTE_11, FEATURE_SET_COMPUTE_12, FEATURE_SET_COMPUTE_13, FEATURE_SET_COMPUTE_20, FEATURE_SET_COMPUTE_21, FEATURE_SET_COMPUTE_30, FEATURE_SET_COMPUTE_32, FEATURE_SET_COMPUTE_35, FEATURE_SET_COMPUTE_50, GLOBAL_ATOMICS, SHARED_ATOMICS, NATIVE_DOUBLE, WARP_SHUFFLE_FUNCTIONS, DYNAMIC_PARALLELISM]"""
+
+
+HostMem_PAGE_LOCKED: int
+HOST_MEM_PAGE_LOCKED: int
+HostMem_SHARED: int
+HOST_MEM_SHARED: int
+HostMem_WRITE_COMBINED: int
+HOST_MEM_WRITE_COMBINED: int
+HostMem_AllocType = int
+"""One of [HostMem_PAGE_LOCKED, HOST_MEM_PAGE_LOCKED, HostMem_SHARED, HOST_MEM_SHARED, HostMem_WRITE_COMBINED, HOST_MEM_WRITE_COMBINED]"""
+
+Event_DEFAULT: int
+EVENT_DEFAULT: int
+Event_BLOCKING_SYNC: int
+EVENT_BLOCKING_SYNC: int
+Event_DISABLE_TIMING: int
+EVENT_DISABLE_TIMING: int
+Event_INTERPROCESS: int
+EVENT_INTERPROCESS: int
+Event_CreateFlags = int
+"""One of [Event_DEFAULT, EVENT_DEFAULT, Event_BLOCKING_SYNC, EVENT_BLOCKING_SYNC, Event_DISABLE_TIMING, EVENT_DISABLE_TIMING, Event_INTERPROCESS, EVENT_INTERPROCESS]"""
+
+DeviceInfo_ComputeModeDefault: int
+DEVICE_INFO_COMPUTE_MODE_DEFAULT: int
+DeviceInfo_ComputeModeExclusive: int
+DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE: int
+DeviceInfo_ComputeModeProhibited: int
+DEVICE_INFO_COMPUTE_MODE_PROHIBITED: int
+DeviceInfo_ComputeModeExclusiveProcess: int
+DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS: int
+DeviceInfo_ComputeMode = int
+"""One of [DeviceInfo_ComputeModeDefault, DEVICE_INFO_COMPUTE_MODE_DEFAULT, DeviceInfo_ComputeModeExclusive, DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE, DeviceInfo_ComputeModeProhibited, DEVICE_INFO_COMPUTE_MODE_PROHIBITED, DeviceInfo_ComputeModeExclusiveProcess, DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS]"""
+
+
+# Classes
+class GpuMat:
+    @property
+    def step(self) -> int: ...
+
+    # Classes
+    class Allocator:
+        ...
+
+
+    # Functions
+    @_typing.overload
+    def __init__(self, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, rows: int, cols: int, type: int, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, type: int, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, rows: int, cols: int, type: int, s: cv2.typing.Scalar, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, type: int, s: cv2.typing.Scalar, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, m: GpuMat) -> None: ...
+    @_typing.overload
+    def __init__(self, m: GpuMat, rowRange: cv2.typing.Range, colRange: cv2.typing.Range) -> None: ...
+    @_typing.overload
+    def __init__(self, m: GpuMat, roi: cv2.typing.Rect) -> None: ...
+    @_typing.overload
+    def __init__(self, arr: cv2.typing.MatLike, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, arr: GpuMat, allocator: GpuMat.Allocator = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, arr: cv2.UMat, allocator: GpuMat.Allocator = ...) -> None: ...
+
+    @staticmethod
+    def defaultAllocator() -> GpuMat.Allocator: ...
+
+    @staticmethod
+    def setDefaultAllocator(allocator: GpuMat.Allocator) -> None: ...
+
+    @staticmethod
+    def getStdAllocator() -> GpuMat.Allocator: ...
+
+    @_typing.overload
+    def create(self, rows: int, cols: int, type: int) -> None: ...
+    @_typing.overload
+    def create(self, size: cv2.typing.Size, type: int) -> None: ...
+
+    def release(self) -> None: ...
+
+    def swap(self, mat: GpuMat) -> None: ...
+
+    @_typing.overload
+    def upload(self, arr: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def upload(self, arr: GpuMat) -> None: ...
+    @_typing.overload
+    def upload(self, arr: cv2.UMat) -> None: ...
+    @_typing.overload
+    def upload(self, arr: cv2.typing.MatLike, stream: Stream) -> None: ...
+    @_typing.overload
+    def upload(self, arr: GpuMat, stream: Stream) -> None: ...
+    @_typing.overload
+    def upload(self, arr: cv2.UMat, stream: Stream) -> None: ...
+
+    @_typing.overload
+    def download(self, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def download(self, dst: GpuMat | None = ...) -> GpuMat: ...
+    @_typing.overload
+    def download(self, dst: cv2.UMat | None = ...) -> cv2.UMat: ...
+    @_typing.overload
+    def download(self, stream: Stream, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def download(self, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
+    @_typing.overload
+    def download(self, stream: Stream, dst: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def clone(self) -> GpuMat: ...
+
+    @_typing.overload
+    def copyTo(self, dst: GpuMat | None = ...) -> GpuMat: ...
+    @_typing.overload
+    def copyTo(self, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
+    @_typing.overload
+    def copyTo(self, mask: GpuMat, dst: GpuMat | None = ...) -> GpuMat: ...
+    @_typing.overload
+    def copyTo(self, mask: GpuMat, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
+
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, stream: Stream) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, mask: cv2.typing.MatLike) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, mask: GpuMat) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, mask: cv2.UMat) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, mask: cv2.typing.MatLike, stream: Stream) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, mask: GpuMat, stream: Stream) -> GpuMat: ...
+    @_typing.overload
+    def setTo(self, s: cv2.typing.Scalar, mask: cv2.UMat, stream: Stream) -> GpuMat: ...
+
+    @_typing.overload
+    def convertTo(self, rtype: int, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
+    @_typing.overload
+    def convertTo(self, rtype: int, dst: GpuMat | None = ..., alpha: float = ..., beta: float = ...) -> GpuMat: ...
+    @_typing.overload
+    def convertTo(self, rtype: int, alpha: float, beta: float, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
+
+    def assignTo(self, m: GpuMat, type: int = ...) -> None: ...
+
+    def row(self, y: int) -> GpuMat: ...
+
+    def col(self, x: int) -> GpuMat: ...
+
+    @_typing.overload
+    def rowRange(self, startrow: int, endrow: int) -> GpuMat: ...
+    @_typing.overload
+    def rowRange(self, r: cv2.typing.Range) -> GpuMat: ...
+
+    @_typing.overload
+    def colRange(self, startcol: int, endcol: int) -> GpuMat: ...
+    @_typing.overload
+    def colRange(self, r: cv2.typing.Range) -> GpuMat: ...
+
+    def reshape(self, cn: int, rows: int = ...) -> GpuMat: ...
+
+    def locateROI(self, wholeSize: cv2.typing.Size, ofs: cv2.typing.Point) -> None: ...
+
+    def adjustROI(self, dtop: int, dbottom: int, dleft: int, dright: int) -> GpuMat: ...
+
+    def isContinuous(self) -> bool: ...
+
+    def elemSize(self) -> int: ...
+
+    def elemSize1(self) -> int: ...
+
+    def type(self) -> int: ...
+
+    def depth(self) -> int: ...
+
+    def channels(self) -> int: ...
+
+    def step1(self) -> int: ...
+
+    def size(self) -> cv2.typing.Size: ...
+
+    def empty(self) -> bool: ...
+
+    def cudaPtr(self) -> cv2.typing.IntPointer: ...
+
+    def updateContinuityFlag(self) -> None: ...
+
+
+class GpuData:
+    ...
+
+class GpuMatND:
+    ...
+
+class BufferPool:
+    # Functions
+    def __init__(self, stream: Stream) -> None: ...
+
+    @_typing.overload
+    def getBuffer(self, rows: int, cols: int, type: int) -> GpuMat: ...
+    @_typing.overload
+    def getBuffer(self, size: cv2.typing.Size, type: int) -> GpuMat: ...
+
+    def getAllocator(self) -> GpuMat.Allocator: ...
+
+
+class HostMem:
+    @property
+    def step(self) -> int: ...
+
+    # Functions
+    @_typing.overload
+    def __init__(self, alloc_type: HostMem_AllocType = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, rows: int, cols: int, type: int, alloc_type: HostMem_AllocType = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, size: cv2.typing.Size, type: int, alloc_type: HostMem_AllocType = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, arr: cv2.typing.MatLike, alloc_type: HostMem_AllocType = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, arr: GpuMat, alloc_type: HostMem_AllocType = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, arr: cv2.UMat, alloc_type: HostMem_AllocType = ...) -> None: ...
+
+    def swap(self, b: HostMem) -> None: ...
+
+    def clone(self) -> HostMem: ...
+
+    def create(self, rows: int, cols: int, type: int) -> None: ...
+
+    def reshape(self, cn: int, rows: int = ...) -> HostMem: ...
+
+    def createMatHeader(self) -> cv2.typing.MatLike: ...
+
+    def isContinuous(self) -> bool: ...
+
+    def elemSize(self) -> int: ...
+
+    def elemSize1(self) -> int: ...
+
+    def type(self) -> int: ...
+
+    def depth(self) -> int: ...
+
+    def channels(self) -> int: ...
+
+    def step1(self) -> int: ...
+
+    def size(self) -> cv2.typing.Size: ...
+
+    def empty(self) -> bool: ...
+
+
+class Stream:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, allocator: GpuMat.Allocator) -> None: ...
+    @_typing.overload
+    def __init__(self, cudaFlags: int) -> None: ...
+
+    def queryIfComplete(self) -> bool: ...
+
+    def waitForCompletion(self) -> None: ...
+
+    def waitEvent(self, event: Event) -> None: ...
+
+    @classmethod
+    def Null(cls) -> Stream: ...
+
+    def cudaPtr(self) -> cv2.typing.IntPointer: ...
+
+
+class Event:
+    # Functions
+    def __init__(self, flags: Event_CreateFlags = ...) -> None: ...
+
+    def record(self, stream: Stream = ...) -> None: ...
+
+    def queryIfComplete(self) -> bool: ...
+
+    def waitForCompletion(self) -> None: ...
+
+    @staticmethod
+    def elapsedTime(start: Event, end: Event) -> float: ...
+
+
+class TargetArchs:
+    # Functions
+    @staticmethod
+    def has(major: int, minor: int) -> bool: ...
+
+    @staticmethod
+    def hasPtx(major: int, minor: int) -> bool: ...
+
+    @staticmethod
+    def hasBin(major: int, minor: int) -> bool: ...
+
+    @staticmethod
+    def hasEqualOrLessPtx(major: int, minor: int) -> bool: ...
+
+    @staticmethod
+    def hasEqualOrGreater(major: int, minor: int) -> bool: ...
+
+    @staticmethod
+    def hasEqualOrGreaterPtx(major: int, minor: int) -> bool: ...
+
+    @staticmethod
+    def hasEqualOrGreaterBin(major: int, minor: int) -> bool: ...
+
+
+class DeviceInfo:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, device_id: int) -> None: ...
+
+    def deviceID(self) -> int: ...
+
+    def totalGlobalMem(self) -> int: ...
+
+    def sharedMemPerBlock(self) -> int: ...
+
+    def regsPerBlock(self) -> int: ...
+
+    def warpSize(self) -> int: ...
+
+    def memPitch(self) -> int: ...
+
+    def maxThreadsPerBlock(self) -> int: ...
+
+    def maxThreadsDim(self) -> cv2.typing.Vec3i: ...
+
+    def maxGridSize(self) -> cv2.typing.Vec3i: ...
+
+    def clockRate(self) -> int: ...
+
+    def totalConstMem(self) -> int: ...
+
+    def majorVersion(self) -> int: ...
+
+    def minorVersion(self) -> int: ...
+
+    def textureAlignment(self) -> int: ...
+
+    def texturePitchAlignment(self) -> int: ...
+
+    def multiProcessorCount(self) -> int: ...
+
+    def kernelExecTimeoutEnabled(self) -> bool: ...
+
+    def integrated(self) -> bool: ...
+
+    def canMapHostMemory(self) -> bool: ...
+
+    def computeMode(self) -> DeviceInfo_ComputeMode: ...
+
+    def maxTexture1D(self) -> int: ...
+
+    def maxTexture1DMipmap(self) -> int: ...
+
+    def maxTexture1DLinear(self) -> int: ...
+
+    def maxTexture2D(self) -> cv2.typing.Vec2i: ...
+
+    def maxTexture2DMipmap(self) -> cv2.typing.Vec2i: ...
+
+    def maxTexture2DLinear(self) -> cv2.typing.Vec3i: ...
+
+    def maxTexture2DGather(self) -> cv2.typing.Vec2i: ...
+
+    def maxTexture3D(self) -> cv2.typing.Vec3i: ...
+
+    def maxTextureCubemap(self) -> int: ...
+
+    def maxTexture1DLayered(self) -> cv2.typing.Vec2i: ...
+
+    def maxTexture2DLayered(self) -> cv2.typing.Vec3i: ...
+
+    def maxTextureCubemapLayered(self) -> cv2.typing.Vec2i: ...
+
+    def maxSurface1D(self) -> int: ...
+
+    def maxSurface2D(self) -> cv2.typing.Vec2i: ...
+
+    def maxSurface3D(self) -> cv2.typing.Vec3i: ...
+
+    def maxSurface1DLayered(self) -> cv2.typing.Vec2i: ...
+
+    def maxSurface2DLayered(self) -> cv2.typing.Vec3i: ...
+
+    def maxSurfaceCubemap(self) -> int: ...
+
+    def maxSurfaceCubemapLayered(self) -> cv2.typing.Vec2i: ...
+
+    def surfaceAlignment(self) -> int: ...
+
+    def concurrentKernels(self) -> bool: ...
+
+    def ECCEnabled(self) -> bool: ...
+
+    def pciBusID(self) -> int: ...
+
+    def pciDeviceID(self) -> int: ...
+
+    def pciDomainID(self) -> int: ...
+
+    def tccDriver(self) -> bool: ...
+
+    def asyncEngineCount(self) -> int: ...
+
+    def unifiedAddressing(self) -> bool: ...
+
+    def memoryClockRate(self) -> int: ...
+
+    def memoryBusWidth(self) -> int: ...
+
+    def l2CacheSize(self) -> int: ...
+
+    def maxThreadsPerMultiProcessor(self) -> int: ...
+
+    def queryMemory(self, totalMemory: int, freeMemory: int) -> None: ...
+
+    def freeMemory(self) -> int: ...
+
+    def totalMemory(self) -> int: ...
+
+    def isCompatible(self) -> bool: ...
+
+
+
+# Functions
+@_typing.overload
+def createContinuous(rows: int, cols: int, type: int, arr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def createContinuous(rows: int, cols: int, type: int, arr: GpuMat | None = ...) -> GpuMat: ...
+@_typing.overload
+def createContinuous(rows: int, cols: int, type: int, arr: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def createGpuMatFromCudaMemory(rows: int, cols: int, type: int, cudaMemoryAddress: int, step: int = ...) -> GpuMat: ...
+@_typing.overload
+def createGpuMatFromCudaMemory(size: cv2.typing.Size, type: int, cudaMemoryAddress: int, step: int = ...) -> GpuMat: ...
+
+@_typing.overload
+def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: GpuMat | None = ...) -> GpuMat: ...
+@_typing.overload
+def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+def fastNlMeansDenoising(src: GpuMat, h: float, dst: GpuMat | None = ..., search_window: int = ..., block_size: int = ..., stream: Stream = ...) -> GpuMat: ...
+
+def fastNlMeansDenoisingColored(src: GpuMat, h_luminance: float, photo_render: float, dst: GpuMat | None = ..., search_window: int = ..., block_size: int = ..., stream: Stream = ...) -> GpuMat: ...
+
+def getCudaEnabledDeviceCount() -> int: ...
+
+def getDevice() -> int: ...
+
+def nonLocalMeans(src: GpuMat, h: float, dst: GpuMat | None = ..., search_window: int = ..., block_size: int = ..., borderMode: int = ..., stream: Stream = ...) -> GpuMat: ...
+
+def printCudaDeviceInfo(device: int) -> None: ...
+
+def printShortCudaDeviceInfo(device: int) -> None: ...
+
+def registerPageLocked(m: cv2.typing.MatLike) -> None: ...
+
+def resetDevice() -> None: ...
+
+def setBufferPoolConfig(deviceId: int, stackSize: int, stackCount: int) -> None: ...
+
+def setBufferPoolUsage(on: bool) -> None: ...
+
+def setDevice(device: int) -> None: ...
+
+def unregisterPageLocked(m: cv2.typing.MatLike) -> None: ...
+
+def wrapStream(cudaStreamMemoryAddress: int) -> Stream: ...
+
+

.venv/lib/python3.11/site-packages/cv2/data/__init__.py

@@ -0,0 +1,3 @@
+import os
+
+haarcascades = os.path.join(os.path.dirname(__file__), "")

.venv/lib/python3.11/site-packages/cv2/data/haarcascade_license_plate_rus_16stages.xml

@@ -0,0 +1,1404 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<!-- Automatically converted from haarcascade2, window size = 64x16 -->
+<haarcascade_pltzzz64x16_16STG type_id="opencv-haar-classifier">
+  <size>
+    64 16</size>
+  <stages>
+    <_>
+      <!-- stage 0 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  32 2 8 6 -1.</_>
+                <_>
+                  32 4 8 2 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>1.6915600746870041e-002</threshold>
+            <left_val>-9.5547717809677124e-001</left_val>
+            <right_val>8.9129137992858887e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 4 6 10 -1.</_>
+                <_>
+                  3 4 3 10 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.4228349328041077e-002</threshold>
+            <left_val>-9.2089319229125977e-001</left_val>
+            <right_val>8.8723921775817871e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  55 0 8 6 -1.</_>
+                <_>
+                  55 0 4 3 2.</_>
+                <_>
+                  59 3 4 3 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-1.0168660432100296e-002</threshold>
+            <left_val>8.8940089941024780e-001</left_val>
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+            <!-- root node -->
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+                <_>
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+                <_>
+                  42 0 2 3 2.</_>
+                <_>
+                  44 3 2 3 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-2.7560539820115082e-005</threshold>
+            <left_val>1.</left_val>
+            <right_val>-9.9543339014053345e-001</right_val></_></_></trees>
+      <stage_threshold>-9.1314977407455444e-001</stage_threshold>
+      <parent>14</parent>
+      <next>-1</next></_></stages></haarcascade_pltzzz64x16_16STG>
+</opencv_storage>

.venv/lib/python3.11/site-packages/cv2/data/haarcascade_russian_plate_number.xml

@@ -0,0 +1,2656 @@
+<?xml version="1.0"?>
+<opencv_storage>
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+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
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+  <width>60</width>
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+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>20</stageNum>
+  <stages>
+    <!-- stage 0 -->
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+      <stageThreshold>-1.3110191822052002e+000</stageThreshold>
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+    <!-- stage 1 -->
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+    <!-- stage 2 -->
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+    <!-- stage 3 -->
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+    <!-- stage 6 -->
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+    <!-- stage 10 -->
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+    <!-- stage 11 -->
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+      <stageThreshold>-1.1183819770812988e+000</stageThreshold>
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+    <!-- stage 12 -->
+    <_>
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+      <stageThreshold>-1.5434337854385376e+000</stageThreshold>
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+    <!-- stage 13 -->
+    <_>
+      <maxWeakCount>12</maxWeakCount>
+      <stageThreshold>-1.4440233707427979e+000</stageThreshold>
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+        <_>
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+    <!-- stage 14 -->
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+        <_>
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+        <_>
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+    <!-- stage 15 -->
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+    <!-- stage 16 -->
+    <_>
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+    <!-- stage 17 -->
+    <_>
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+    <_>
+      <rects>
+        <_>
+          52 8 8 4 -1.</_>
+        <_>
+          52 8 8 2 2.</_></rects>
+      <tilted>1</tilted></_>
+    <_>
+      <rects>
+        <_>
+          54 3 6 15 -1.</_>
+        <_>
+          57 3 3 15 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          54 8 6 7 -1.</_>
+        <_>
+          57 8 3 7 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          57 11 3 6 -1.</_>
+        <_>
+          57 13 3 2 3.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          59 8 1 3 -1.</_>
+        <_>
+          59 9 1 1 3.</_></rects>
+      <tilted>0</tilted></_></features></cascade>
+</opencv_storage>

.venv/lib/python3.11/site-packages/cv2/detail/__init__.pyi

@@ -0,0 +1,600 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.gapi
+import cv2.gapi.ie
+import cv2.gapi.onnx
+import cv2.gapi.ov
+import cv2.typing
+import numpy
+import typing as _typing
+
+
+# Enumerations
+TEST_CUSTOM: int
+TEST_EQ: int
+TEST_NE: int
+TEST_LE: int
+TEST_LT: int
+TEST_GE: int
+TEST_GT: int
+TestOp = int
+"""One of [TEST_CUSTOM, TEST_EQ, TEST_NE, TEST_LE, TEST_LT, TEST_GE, TEST_GT]"""
+
+WAVE_CORRECT_HORIZ: int
+WAVE_CORRECT_VERT: int
+WAVE_CORRECT_AUTO: int
+WaveCorrectKind = int
+"""One of [WAVE_CORRECT_HORIZ, WAVE_CORRECT_VERT, WAVE_CORRECT_AUTO]"""
+
+OpaqueKind_CV_UNKNOWN: int
+OPAQUE_KIND_CV_UNKNOWN: int
+OpaqueKind_CV_BOOL: int
+OPAQUE_KIND_CV_BOOL: int
+OpaqueKind_CV_INT: int
+OPAQUE_KIND_CV_INT: int
+OpaqueKind_CV_INT64: int
+OPAQUE_KIND_CV_INT64: int
+OpaqueKind_CV_DOUBLE: int
+OPAQUE_KIND_CV_DOUBLE: int
+OpaqueKind_CV_FLOAT: int
+OPAQUE_KIND_CV_FLOAT: int
+OpaqueKind_CV_UINT64: int
+OPAQUE_KIND_CV_UINT64: int
+OpaqueKind_CV_STRING: int
+OPAQUE_KIND_CV_STRING: int
+OpaqueKind_CV_POINT: int
+OPAQUE_KIND_CV_POINT: int
+OpaqueKind_CV_POINT2F: int
+OPAQUE_KIND_CV_POINT2F: int
+OpaqueKind_CV_POINT3F: int
+OPAQUE_KIND_CV_POINT3F: int
+OpaqueKind_CV_SIZE: int
+OPAQUE_KIND_CV_SIZE: int
+OpaqueKind_CV_RECT: int
+OPAQUE_KIND_CV_RECT: int
+OpaqueKind_CV_SCALAR: int
+OPAQUE_KIND_CV_SCALAR: int
+OpaqueKind_CV_MAT: int
+OPAQUE_KIND_CV_MAT: int
+OpaqueKind_CV_DRAW_PRIM: int
+OPAQUE_KIND_CV_DRAW_PRIM: int
+OpaqueKind = int
+"""One of [OpaqueKind_CV_UNKNOWN, OPAQUE_KIND_CV_UNKNOWN, OpaqueKind_CV_BOOL, OPAQUE_KIND_CV_BOOL, OpaqueKind_CV_INT, OPAQUE_KIND_CV_INT, OpaqueKind_CV_INT64, OPAQUE_KIND_CV_INT64, OpaqueKind_CV_DOUBLE, OPAQUE_KIND_CV_DOUBLE, OpaqueKind_CV_FLOAT, OPAQUE_KIND_CV_FLOAT, OpaqueKind_CV_UINT64, OPAQUE_KIND_CV_UINT64, OpaqueKind_CV_STRING, OPAQUE_KIND_CV_STRING, OpaqueKind_CV_POINT, OPAQUE_KIND_CV_POINT, OpaqueKind_CV_POINT2F, OPAQUE_KIND_CV_POINT2F, OpaqueKind_CV_POINT3F, OPAQUE_KIND_CV_POINT3F, OpaqueKind_CV_SIZE, OPAQUE_KIND_CV_SIZE, OpaqueKind_CV_RECT, OPAQUE_KIND_CV_RECT, OpaqueKind_CV_SCALAR, OPAQUE_KIND_CV_SCALAR, OpaqueKind_CV_MAT, OPAQUE_KIND_CV_MAT, OpaqueKind_CV_DRAW_PRIM, OPAQUE_KIND_CV_DRAW_PRIM]"""
+
+ArgKind_OPAQUE_VAL: int
+ARG_KIND_OPAQUE_VAL: int
+ArgKind_OPAQUE: int
+ARG_KIND_OPAQUE: int
+ArgKind_GOBJREF: int
+ARG_KIND_GOBJREF: int
+ArgKind_GMAT: int
+ARG_KIND_GMAT: int
+ArgKind_GMATP: int
+ARG_KIND_GMATP: int
+ArgKind_GFRAME: int
+ARG_KIND_GFRAME: int
+ArgKind_GSCALAR: int
+ARG_KIND_GSCALAR: int
+ArgKind_GARRAY: int
+ARG_KIND_GARRAY: int
+ArgKind_GOPAQUE: int
+ARG_KIND_GOPAQUE: int
+ArgKind = int
+"""One of [ArgKind_OPAQUE_VAL, ARG_KIND_OPAQUE_VAL, ArgKind_OPAQUE, ARG_KIND_OPAQUE, ArgKind_GOBJREF, ARG_KIND_GOBJREF, ArgKind_GMAT, ARG_KIND_GMAT, ArgKind_GMATP, ARG_KIND_GMATP, ArgKind_GFRAME, ARG_KIND_GFRAME, ArgKind_GSCALAR, ARG_KIND_GSCALAR, ArgKind_GARRAY, ARG_KIND_GARRAY, ArgKind_GOPAQUE, ARG_KIND_GOPAQUE]"""
+
+
+Blender_NO: int
+BLENDER_NO: int
+Blender_FEATHER: int
+BLENDER_FEATHER: int
+Blender_MULTI_BAND: int
+BLENDER_MULTI_BAND: int
+
+ExposureCompensator_NO: int
+EXPOSURE_COMPENSATOR_NO: int
+ExposureCompensator_GAIN: int
+EXPOSURE_COMPENSATOR_GAIN: int
+ExposureCompensator_GAIN_BLOCKS: int
+EXPOSURE_COMPENSATOR_GAIN_BLOCKS: int
+ExposureCompensator_CHANNELS: int
+EXPOSURE_COMPENSATOR_CHANNELS: int
+ExposureCompensator_CHANNELS_BLOCKS: int
+EXPOSURE_COMPENSATOR_CHANNELS_BLOCKS: int
+
+SeamFinder_NO: int
+SEAM_FINDER_NO: int
+SeamFinder_VORONOI_SEAM: int
+SEAM_FINDER_VORONOI_SEAM: int
+SeamFinder_DP_SEAM: int
+SEAM_FINDER_DP_SEAM: int
+
+DpSeamFinder_COLOR: int
+DP_SEAM_FINDER_COLOR: int
+DpSeamFinder_COLOR_GRAD: int
+DP_SEAM_FINDER_COLOR_GRAD: int
+DpSeamFinder_CostFunction = int
+"""One of [DpSeamFinder_COLOR, DP_SEAM_FINDER_COLOR, DpSeamFinder_COLOR_GRAD, DP_SEAM_FINDER_COLOR_GRAD]"""
+
+Timelapser_AS_IS: int
+TIMELAPSER_AS_IS: int
+Timelapser_CROP: int
+TIMELAPSER_CROP: int
+
+GraphCutSeamFinderBase_COST_COLOR: int
+GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR: int
+GraphCutSeamFinderBase_COST_COLOR_GRAD: int
+GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR_GRAD: int
+GraphCutSeamFinderBase_CostType = int
+"""One of [GraphCutSeamFinderBase_COST_COLOR, GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR, GraphCutSeamFinderBase_COST_COLOR_GRAD, GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR_GRAD]"""
+
+TrackerSamplerCSC_MODE_INIT_POS: int
+TRACKER_SAMPLER_CSC_MODE_INIT_POS: int
+TrackerSamplerCSC_MODE_INIT_NEG: int
+TRACKER_SAMPLER_CSC_MODE_INIT_NEG: int
+TrackerSamplerCSC_MODE_TRACK_POS: int
+TRACKER_SAMPLER_CSC_MODE_TRACK_POS: int
+TrackerSamplerCSC_MODE_TRACK_NEG: int
+TRACKER_SAMPLER_CSC_MODE_TRACK_NEG: int
+TrackerSamplerCSC_MODE_DETECT: int
+TRACKER_SAMPLER_CSC_MODE_DETECT: int
+TrackerSamplerCSC_MODE = int
+"""One of [TrackerSamplerCSC_MODE_INIT_POS, TRACKER_SAMPLER_CSC_MODE_INIT_POS, TrackerSamplerCSC_MODE_INIT_NEG, TRACKER_SAMPLER_CSC_MODE_INIT_NEG, TrackerSamplerCSC_MODE_TRACK_POS, TRACKER_SAMPLER_CSC_MODE_TRACK_POS, TrackerSamplerCSC_MODE_TRACK_NEG, TRACKER_SAMPLER_CSC_MODE_TRACK_NEG, TrackerSamplerCSC_MODE_DETECT, TRACKER_SAMPLER_CSC_MODE_DETECT]"""
+
+
+# Classes
+class Blender:
+    # Functions
+    @classmethod
+    def createDefault(cls, type: int, try_gpu: bool = ...) -> Blender: ...
+
+    @_typing.overload
+    def prepare(self, corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> None: ...
+    @_typing.overload
+    def prepare(self, dst_roi: cv2.typing.Rect) -> None: ...
+
+    @_typing.overload
+    def feed(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
+    @_typing.overload
+    def feed(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
+
+    @_typing.overload
+    def blend(self, dst: cv2.typing.MatLike, dst_mask: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def blend(self, dst: cv2.UMat, dst_mask: cv2.UMat) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+
+class FeatherBlender(Blender):
+    # Functions
+    def __init__(self, sharpness: float = ...) -> None: ...
+
+    def sharpness(self) -> float: ...
+
+    def setSharpness(self, val: float) -> None: ...
+
+    def prepare(self, dst_roi: cv2.typing.Rect) -> None: ...
+
+    @_typing.overload
+    def feed(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
+    @_typing.overload
+    def feed(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
+
+    @_typing.overload
+    def blend(self, dst: cv2.typing.MatLike, dst_mask: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def blend(self, dst: cv2.UMat, dst_mask: cv2.UMat) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+    def createWeightMaps(self, masks: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], weight_maps: _typing.Sequence[cv2.UMat]) -> tuple[cv2.typing.Rect, _typing.Sequence[cv2.UMat]]: ...
+
+
+class MultiBandBlender(Blender):
+    # Functions
+    def __init__(self, try_gpu: int = ..., num_bands: int = ..., weight_type: int = ...) -> None: ...
+
+    def numBands(self) -> int: ...
+
+    def setNumBands(self, val: int) -> None: ...
+
+    def prepare(self, dst_roi: cv2.typing.Rect) -> None: ...
+
+    @_typing.overload
+    def feed(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
+    @_typing.overload
+    def feed(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
+
+    @_typing.overload
+    def blend(self, dst: cv2.typing.MatLike, dst_mask: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def blend(self, dst: cv2.UMat, dst_mask: cv2.UMat) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+
+class CameraParams:
+    focal: float
+    aspect: float
+    ppx: float
+    ppy: float
+    R: cv2.typing.MatLike
+    t: cv2.typing.MatLike
+
+    # Functions
+    def K(self) -> cv2.typing.MatLike: ...
+
+
+class ExposureCompensator:
+    # Functions
+    @classmethod
+    def createDefault(cls, type: int) -> ExposureCompensator: ...
+
+    def feed(self, corners: _typing.Sequence[cv2.typing.Point], images: _typing.Sequence[cv2.UMat], masks: _typing.Sequence[cv2.UMat]) -> None: ...
+
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
+
+    def getMatGains(self, arg1: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def setMatGains(self, arg1: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+    def setUpdateGain(self, b: bool) -> None: ...
+
+    def getUpdateGain(self) -> bool: ...
+
+
+class NoExposureCompensator(ExposureCompensator):
+    # Functions
+    @_typing.overload
+    def apply(self, arg1: int, arg2: cv2.typing.Point, arg3: cv2.typing.MatLike, arg4: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, arg1: int, arg2: cv2.typing.Point, arg3: cv2.UMat, arg4: cv2.UMat) -> cv2.UMat: ...
+
+    def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+
+class GainCompensator(ExposureCompensator):
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, nr_feeds: int) -> None: ...
+
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
+
+    def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+    def setNrFeeds(self, nr_feeds: int) -> None: ...
+
+    def getNrFeeds(self) -> int: ...
+
+    def setSimilarityThreshold(self, similarity_threshold: float) -> None: ...
+
+    def getSimilarityThreshold(self) -> float: ...
+
+
+class ChannelsCompensator(ExposureCompensator):
+    # Functions
+    def __init__(self, nr_feeds: int = ...) -> None: ...
+
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
+
+    def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+    def setNrFeeds(self, nr_feeds: int) -> None: ...
+
+    def getNrFeeds(self) -> int: ...
+
+    def setSimilarityThreshold(self, similarity_threshold: float) -> None: ...
+
+    def getSimilarityThreshold(self) -> float: ...
+
+
+class BlocksCompensator(ExposureCompensator):
+    # Functions
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
+
+    def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+    def setNrFeeds(self, nr_feeds: int) -> None: ...
+
+    def getNrFeeds(self) -> int: ...
+
+    def setSimilarityThreshold(self, similarity_threshold: float) -> None: ...
+
+    def getSimilarityThreshold(self) -> float: ...
+
+    @_typing.overload
+    def setBlockSize(self, width: int, height: int) -> None: ...
+    @_typing.overload
+    def setBlockSize(self, size: cv2.typing.Size) -> None: ...
+
+    def getBlockSize(self) -> cv2.typing.Size: ...
+
+    def setNrGainsFilteringIterations(self, nr_iterations: int) -> None: ...
+
+    def getNrGainsFilteringIterations(self) -> int: ...
+
+
+class BlocksGainCompensator(BlocksCompensator):
+    # Functions
+    @_typing.overload
+    def __init__(self, bl_width: int = ..., bl_height: int = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, bl_width: int, bl_height: int, nr_feeds: int) -> None: ...
+
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
+
+    def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+
+class BlocksChannelsCompensator(BlocksCompensator):
+    # Functions
+    def __init__(self, bl_width: int = ..., bl_height: int = ..., nr_feeds: int = ...) -> None: ...
+
+
+class ImageFeatures:
+    img_idx: int
+    img_size: cv2.typing.Size
+    keypoints: _typing.Sequence[cv2.KeyPoint]
+    descriptors: cv2.UMat
+
+    # Functions
+    def getKeypoints(self) -> _typing.Sequence[cv2.KeyPoint]: ...
+
+
+class MatchesInfo:
+    src_img_idx: int
+    dst_img_idx: int
+    matches: _typing.Sequence[cv2.DMatch]
+    inliers_mask: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]
+    num_inliers: int
+    H: cv2.typing.MatLike
+    confidence: float
+
+    # Functions
+    def getMatches(self) -> _typing.Sequence[cv2.DMatch]: ...
+
+    def getInliers(self) -> numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]: ...
+
+
+class FeaturesMatcher:
+    # Functions
+    def apply(self, features1: ImageFeatures, features2: ImageFeatures) -> MatchesInfo: ...
+
+    def apply2(self, features: _typing.Sequence[ImageFeatures], mask: cv2.UMat | None = ...) -> _typing.Sequence[MatchesInfo]: ...
+
+    def isThreadSafe(self) -> bool: ...
+
+    def collectGarbage(self) -> None: ...
+
+
+class BestOf2NearestMatcher(FeaturesMatcher):
+    # Functions
+    def __init__(self, try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ..., num_matches_thresh2: int = ..., matches_confindece_thresh: float = ...) -> None: ...
+
+    def collectGarbage(self) -> None: ...
+
+    @classmethod
+    def create(cls, try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ..., num_matches_thresh2: int = ..., matches_confindece_thresh: float = ...) -> BestOf2NearestMatcher: ...
+
+
+class BestOf2NearestRangeMatcher(BestOf2NearestMatcher):
+    # Functions
+    def __init__(self, range_width: int = ..., try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ..., num_matches_thresh2: int = ...) -> None: ...
+
+
+class AffineBestOf2NearestMatcher(BestOf2NearestMatcher):
+    # Functions
+    def __init__(self, full_affine: bool = ..., try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ...) -> None: ...
+
+
+class Estimator:
+    # Functions
+    def apply(self, features: _typing.Sequence[ImageFeatures], pairwise_matches: _typing.Sequence[MatchesInfo], cameras: _typing.Sequence[CameraParams]) -> tuple[bool, _typing.Sequence[CameraParams]]: ...
+
+
+class HomographyBasedEstimator(Estimator):
+    # Functions
+    def __init__(self, is_focals_estimated: bool = ...) -> None: ...
+
+
+class AffineBasedEstimator(Estimator):
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class BundleAdjusterBase(Estimator):
+    # Functions
+    def refinementMask(self) -> cv2.typing.MatLike: ...
+
+    def setRefinementMask(self, mask: cv2.typing.MatLike) -> None: ...
+
+    def confThresh(self) -> float: ...
+
+    def setConfThresh(self, conf_thresh: float) -> None: ...
+
+    def termCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, term_criteria: cv2.typing.TermCriteria) -> None: ...
+
+
+class NoBundleAdjuster(BundleAdjusterBase):
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class BundleAdjusterReproj(BundleAdjusterBase):
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class BundleAdjusterRay(BundleAdjusterBase):
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class BundleAdjusterAffine(BundleAdjusterBase):
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class BundleAdjusterAffinePartial(BundleAdjusterBase):
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class SeamFinder:
+    # Functions
+    def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+    @classmethod
+    def createDefault(cls, type: int) -> SeamFinder: ...
+
+
+class NoSeamFinder(SeamFinder):
+    # Functions
+    def find(self, arg1: _typing.Sequence[cv2.UMat], arg2: _typing.Sequence[cv2.typing.Point], arg3: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+
+class PairwiseSeamFinder(SeamFinder):
+    # Functions
+    def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+
+class VoronoiSeamFinder(PairwiseSeamFinder):
+    # Functions
+    def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+
+class DpSeamFinder(SeamFinder):
+    # Functions
+    def __init__(self, costFunc: str) -> None: ...
+
+    def setCostFunction(self, val: str) -> None: ...
+
+
+class GraphCutSeamFinder:
+    # Functions
+    def __init__(self, cost_type: str, terminal_cost: float = ..., bad_region_penalty: float = ...) -> None: ...
+
+    def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+
+class Timelapser:
+    # Functions
+    @classmethod
+    def createDefault(cls, type: int) -> Timelapser: ...
+
+    def initialize(self, corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> None: ...
+
+    @_typing.overload
+    def process(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
+    @_typing.overload
+    def process(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
+
+    def getDst(self) -> cv2.UMat: ...
+
+
+class TimelapserCrop(Timelapser):
+    ...
+
+class ProjectorBase:
+    ...
+
+class SphericalProjector(ProjectorBase):
+    # Functions
+    def mapForward(self, x: float, y: float, u: float, v: float) -> None: ...
+
+    def mapBackward(self, u: float, v: float, x: float, y: float) -> None: ...
+
+
+
+# Functions
+def calibrateRotatingCamera(Hs: _typing.Sequence[cv2.typing.MatLike], K: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ...
+
+@_typing.overload
+def computeImageFeatures(featuresFinder: cv2.Feature2D, images: _typing.Sequence[cv2.typing.MatLike], masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[ImageFeatures]: ...
+@_typing.overload
+def computeImageFeatures(featuresFinder: cv2.Feature2D, images: _typing.Sequence[cv2.UMat], masks: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[ImageFeatures]: ...
+
+@_typing.overload
+def computeImageFeatures2(featuresFinder: cv2.Feature2D, image: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> ImageFeatures: ...
+@_typing.overload
+def computeImageFeatures2(featuresFinder: cv2.Feature2D, image: cv2.UMat, mask: cv2.UMat | None = ...) -> ImageFeatures: ...
+
+@_typing.overload
+def createLaplacePyr(img: cv2.typing.MatLike, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+@_typing.overload
+def createLaplacePyr(img: cv2.UMat, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+@_typing.overload
+def createLaplacePyrGpu(img: cv2.typing.MatLike, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+@_typing.overload
+def createLaplacePyrGpu(img: cv2.UMat, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+@_typing.overload
+def createWeightMap(mask: cv2.typing.MatLike, sharpness: float, weight: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+@_typing.overload
+def createWeightMap(mask: cv2.UMat, sharpness: float, weight: cv2.UMat) -> cv2.UMat: ...
+
+def focalsFromHomography(H: cv2.typing.MatLike, f0: float, f1: float, f0_ok: bool, f1_ok: bool) -> None: ...
+
+def leaveBiggestComponent(features: _typing.Sequence[ImageFeatures], pairwise_matches: _typing.Sequence[MatchesInfo], conf_threshold: float) -> _typing.Sequence[int]: ...
+
+def matchesGraphAsString(paths: _typing.Sequence[str], pairwise_matches: _typing.Sequence[MatchesInfo], conf_threshold: float) -> str: ...
+
+@_typing.overload
+def normalizeUsingWeightMap(weight: cv2.typing.MatLike, src: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+@_typing.overload
+def normalizeUsingWeightMap(weight: cv2.UMat, src: cv2.UMat) -> cv2.UMat: ...
+
+def overlapRoi(tl1: cv2.typing.Point, tl2: cv2.typing.Point, sz1: cv2.typing.Size, sz2: cv2.typing.Size, roi: cv2.typing.Rect) -> bool: ...
+
+def restoreImageFromLaplacePyr(pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+def restoreImageFromLaplacePyrGpu(pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
+
+@_typing.overload
+def resultRoi(corners: _typing.Sequence[cv2.typing.Point], images: _typing.Sequence[cv2.UMat]) -> cv2.typing.Rect: ...
+@_typing.overload
+def resultRoi(corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> cv2.typing.Rect: ...
+
+def resultRoiIntersection(corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> cv2.typing.Rect: ...
+
+def resultTl(corners: _typing.Sequence[cv2.typing.Point]) -> cv2.typing.Point: ...
+
+def selectRandomSubset(count: int, size: int, subset: _typing.Sequence[int]) -> None: ...
+
+def stitchingLogLevel() -> int: ...
+
+@_typing.overload
+def strip(params: cv2.gapi.ie.PyParams) -> cv2.gapi.GNetParam: ...
+@_typing.overload
+def strip(params: cv2.gapi.onnx.PyParams) -> cv2.gapi.GNetParam: ...
+@_typing.overload
+def strip(params: cv2.gapi.ov.PyParams) -> cv2.gapi.GNetParam: ...
+
+def waveCorrect(rmats: _typing.Sequence[cv2.typing.MatLike], kind: WaveCorrectKind) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+

.venv/lib/python3.11/site-packages/cv2/dnn/__init__.pyi

@@ -0,0 +1,534 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import numpy
+import sys
+import typing as _typing
+if sys.version_info >= (3, 8):
+    from typing import Protocol
+else:
+    from typing_extensions import Protocol
+
+
+# Enumerations
+DNN_BACKEND_DEFAULT: int
+DNN_BACKEND_HALIDE: int
+DNN_BACKEND_INFERENCE_ENGINE: int
+DNN_BACKEND_OPENCV: int
+DNN_BACKEND_VKCOM: int
+DNN_BACKEND_CUDA: int
+DNN_BACKEND_WEBNN: int
+DNN_BACKEND_TIMVX: int
+DNN_BACKEND_CANN: int
+Backend = int
+"""One of [DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV, DNN_BACKEND_VKCOM, DNN_BACKEND_CUDA, DNN_BACKEND_WEBNN, DNN_BACKEND_TIMVX, DNN_BACKEND_CANN]"""
+
+DNN_TARGET_CPU: int
+DNN_TARGET_OPENCL: int
+DNN_TARGET_OPENCL_FP16: int
+DNN_TARGET_MYRIAD: int
+DNN_TARGET_VULKAN: int
+DNN_TARGET_FPGA: int
+DNN_TARGET_CUDA: int
+DNN_TARGET_CUDA_FP16: int
+DNN_TARGET_HDDL: int
+DNN_TARGET_NPU: int
+DNN_TARGET_CPU_FP16: int
+Target = int
+"""One of [DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD, DNN_TARGET_VULKAN, DNN_TARGET_FPGA, DNN_TARGET_CUDA, DNN_TARGET_CUDA_FP16, DNN_TARGET_HDDL, DNN_TARGET_NPU, DNN_TARGET_CPU_FP16]"""
+
+DNN_LAYOUT_UNKNOWN: int
+DNN_LAYOUT_ND: int
+DNN_LAYOUT_NCHW: int
+DNN_LAYOUT_NCDHW: int
+DNN_LAYOUT_NHWC: int
+DNN_LAYOUT_NDHWC: int
+DNN_LAYOUT_PLANAR: int
+DataLayout = int
+"""One of [DNN_LAYOUT_UNKNOWN, DNN_LAYOUT_ND, DNN_LAYOUT_NCHW, DNN_LAYOUT_NCDHW, DNN_LAYOUT_NHWC, DNN_LAYOUT_NDHWC, DNN_LAYOUT_PLANAR]"""
+
+DNN_PMODE_NULL: int
+DNN_PMODE_CROP_CENTER: int
+DNN_PMODE_LETTERBOX: int
+ImagePaddingMode = int
+"""One of [DNN_PMODE_NULL, DNN_PMODE_CROP_CENTER, DNN_PMODE_LETTERBOX]"""
+
+SoftNMSMethod_SOFTNMS_LINEAR: int
+SOFT_NMSMETHOD_SOFTNMS_LINEAR: int
+SoftNMSMethod_SOFTNMS_GAUSSIAN: int
+SOFT_NMSMETHOD_SOFTNMS_GAUSSIAN: int
+SoftNMSMethod = int
+"""One of [SoftNMSMethod_SOFTNMS_LINEAR, SOFT_NMSMETHOD_SOFTNMS_LINEAR, SoftNMSMethod_SOFTNMS_GAUSSIAN, SOFT_NMSMETHOD_SOFTNMS_GAUSSIAN]"""
+
+
+
+# Classes
+class DictValue:
+    # Functions
+    @_typing.overload
+    def __init__(self, i: int) -> None: ...
+    @_typing.overload
+    def __init__(self, p: float) -> None: ...
+    @_typing.overload
+    def __init__(self, s: str) -> None: ...
+
+    def isInt(self) -> bool: ...
+
+    def isString(self) -> bool: ...
+
+    def isReal(self) -> bool: ...
+
+    def getIntValue(self, idx: int = ...) -> int: ...
+
+    def getRealValue(self, idx: int = ...) -> float: ...
+
+    def getStringValue(self, idx: int = ...) -> str: ...
+
+
+class Layer(cv2.Algorithm):
+    blobs: _typing.Sequence[cv2.typing.MatLike]
+    @property
+    def name(self) -> str: ...
+    @property
+    def type(self) -> str: ...
+    @property
+    def preferableTarget(self) -> int: ...
+
+    # Functions
+    @_typing.overload
+    def finalize(self, inputs: _typing.Sequence[cv2.typing.MatLike], outputs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+    @_typing.overload
+    def finalize(self, inputs: _typing.Sequence[cv2.UMat], outputs: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
+
+    def run(self, inputs: _typing.Sequence[cv2.typing.MatLike], internals: _typing.Sequence[cv2.typing.MatLike], outputs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
+
+    def outputNameToIndex(self, outputName: str) -> int: ...
+
+
+class Net:
+    # Functions
+    def __init__(self) -> None: ...
+
+    @classmethod
+    @_typing.overload
+    def readFromModelOptimizer(cls, xml: str, bin: str) -> Net: ...
+    @classmethod
+    @_typing.overload
+    def readFromModelOptimizer(cls, bufferModelConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferWeights: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
+
+    def empty(self) -> bool: ...
+
+    def dump(self) -> str: ...
+
+    def dumpToFile(self, path: str) -> None: ...
+
+    def dumpToPbtxt(self, path: str) -> None: ...
+
+    def addLayer(self, name: str, type: str, dtype: int, params: cv2.typing.LayerParams) -> int: ...
+
+    def addLayerToPrev(self, name: str, type: str, dtype: int, params: cv2.typing.LayerParams) -> int: ...
+
+    def getLayerId(self, layer: str) -> int: ...
+
+    def getLayerNames(self) -> _typing.Sequence[str]: ...
+
+    @_typing.overload
+    def getLayer(self, layerId: int) -> Layer: ...
+    @_typing.overload
+    def getLayer(self, layerName: str) -> Layer: ...
+    @_typing.overload
+    def getLayer(self, layerId: cv2.typing.LayerId) -> Layer: ...
+
+    def connect(self, outPin: str, inpPin: str) -> None: ...
+
+    def setInputsNames(self, inputBlobNames: _typing.Sequence[str]) -> None: ...
+
+    def setInputShape(self, inputName: str, shape: cv2.typing.MatShape) -> None: ...
+
+    @_typing.overload
+    def forward(self, outputName: str = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def forward(self, outputBlobs: _typing.Sequence[cv2.typing.MatLike] | None = ..., outputName: str = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+    @_typing.overload
+    def forward(self, outputBlobs: _typing.Sequence[cv2.UMat] | None = ..., outputName: str = ...) -> _typing.Sequence[cv2.UMat]: ...
+    @_typing.overload
+    def forward(self, outBlobNames: _typing.Sequence[str], outputBlobs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+    @_typing.overload
+    def forward(self, outBlobNames: _typing.Sequence[str], outputBlobs: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
+
+    def forwardAsync(self, outputName: str = ...) -> cv2.AsyncArray: ...
+
+    def forwardAndRetrieve(self, outBlobNames: _typing.Sequence[str]) -> _typing.Sequence[_typing.Sequence[cv2.typing.MatLike]]: ...
+
+    @_typing.overload
+    def quantize(self, calibData: _typing.Sequence[cv2.typing.MatLike], inputsDtype: int, outputsDtype: int, perChannel: bool = ...) -> Net: ...
+    @_typing.overload
+    def quantize(self, calibData: _typing.Sequence[cv2.UMat], inputsDtype: int, outputsDtype: int, perChannel: bool = ...) -> Net: ...
+
+    def getInputDetails(self) -> tuple[_typing.Sequence[float], _typing.Sequence[int]]: ...
+
+    def getOutputDetails(self) -> tuple[_typing.Sequence[float], _typing.Sequence[int]]: ...
+
+    def setHalideScheduler(self, scheduler: str) -> None: ...
+
+    def setPreferableBackend(self, backendId: int) -> None: ...
+
+    def setPreferableTarget(self, targetId: int) -> None: ...
+
+    @_typing.overload
+    def setInput(self, blob: cv2.typing.MatLike, name: str = ..., scalefactor: float = ..., mean: cv2.typing.Scalar = ...) -> None: ...
+    @_typing.overload
+    def setInput(self, blob: cv2.UMat, name: str = ..., scalefactor: float = ..., mean: cv2.typing.Scalar = ...) -> None: ...
+
+    @_typing.overload
+    def setParam(self, layer: int, numParam: int, blob: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def setParam(self, layerName: str, numParam: int, blob: cv2.typing.MatLike) -> None: ...
+
+    @_typing.overload
+    def getParam(self, layer: int, numParam: int = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getParam(self, layerName: str, numParam: int = ...) -> cv2.typing.MatLike: ...
+
+    def getUnconnectedOutLayers(self) -> _typing.Sequence[int]: ...
+
+    def getUnconnectedOutLayersNames(self) -> _typing.Sequence[str]: ...
+
+    @_typing.overload
+    def getLayersShapes(self, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> tuple[_typing.Sequence[int], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]]]: ...
+    @_typing.overload
+    def getLayersShapes(self, netInputShape: cv2.typing.MatShape) -> tuple[_typing.Sequence[int], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]]]: ...
+
+    @_typing.overload
+    def getFLOPS(self, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> int: ...
+    @_typing.overload
+    def getFLOPS(self, netInputShape: cv2.typing.MatShape) -> int: ...
+    @_typing.overload
+    def getFLOPS(self, layerId: int, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> int: ...
+    @_typing.overload
+    def getFLOPS(self, layerId: int, netInputShape: cv2.typing.MatShape) -> int: ...
+
+    def getLayerTypes(self) -> _typing.Sequence[str]: ...
+
+    def getLayersCount(self, layerType: str) -> int: ...
+
+    @_typing.overload
+    def getMemoryConsumption(self, netInputShape: cv2.typing.MatShape) -> tuple[int, int]: ...
+    @_typing.overload
+    def getMemoryConsumption(self, layerId: int, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> tuple[int, int]: ...
+    @_typing.overload
+    def getMemoryConsumption(self, layerId: int, netInputShape: cv2.typing.MatShape) -> tuple[int, int]: ...
+
+    def enableFusion(self, fusion: bool) -> None: ...
+
+    def enableWinograd(self, useWinograd: bool) -> None: ...
+
+    def getPerfProfile(self) -> tuple[int, _typing.Sequence[float]]: ...
+
+
+class Image2BlobParams:
+    scalefactor: cv2.typing.Scalar
+    size: cv2.typing.Size
+    mean: cv2.typing.Scalar
+    swapRB: bool
+    ddepth: int
+    datalayout: DataLayout
+    paddingmode: ImagePaddingMode
+    borderValue: cv2.typing.Scalar
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, scalefactor: cv2.typing.Scalar, size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., ddepth: int = ..., datalayout: DataLayout = ..., mode: ImagePaddingMode = ..., borderValue: cv2.typing.Scalar = ...) -> None: ...
+
+    def blobRectToImageRect(self, rBlob: cv2.typing.Rect, size: cv2.typing.Size) -> cv2.typing.Rect: ...
+
+    def blobRectsToImageRects(self, rBlob: _typing.Sequence[cv2.typing.Rect], size: cv2.typing.Size) -> _typing.Sequence[cv2.typing.Rect]: ...
+
+
+class Model:
+    # Functions
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+
+    @_typing.overload
+    def setInputSize(self, size: cv2.typing.Size) -> Model: ...
+    @_typing.overload
+    def setInputSize(self, width: int, height: int) -> Model: ...
+
+    def setInputMean(self, mean: cv2.typing.Scalar) -> Model: ...
+
+    def setInputScale(self, scale: cv2.typing.Scalar) -> Model: ...
+
+    def setInputCrop(self, crop: bool) -> Model: ...
+
+    def setInputSwapRB(self, swapRB: bool) -> Model: ...
+
+    def setOutputNames(self, outNames: _typing.Sequence[str]) -> Model: ...
+
+    def setInputParams(self, scale: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ...) -> None: ...
+
+    @_typing.overload
+    def predict(self, frame: cv2.typing.MatLike, outs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+    @_typing.overload
+    def predict(self, frame: cv2.UMat, outs: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
+
+    def setPreferableBackend(self, backendId: Backend) -> Model: ...
+
+    def setPreferableTarget(self, targetId: Target) -> Model: ...
+
+    def enableWinograd(self, useWinograd: bool) -> Model: ...
+
+
+class ClassificationModel(Model):
+    # Functions
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+
+    def setEnableSoftmaxPostProcessing(self, enable: bool) -> ClassificationModel: ...
+
+    def getEnableSoftmaxPostProcessing(self) -> bool: ...
+
+    @_typing.overload
+    def classify(self, frame: cv2.typing.MatLike) -> tuple[int, float]: ...
+    @_typing.overload
+    def classify(self, frame: cv2.UMat) -> tuple[int, float]: ...
+
+
+class KeypointsModel(Model):
+    # Functions
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+
+    @_typing.overload
+    def estimate(self, frame: cv2.typing.MatLike, thresh: float = ...) -> _typing.Sequence[cv2.typing.Point2f]: ...
+    @_typing.overload
+    def estimate(self, frame: cv2.UMat, thresh: float = ...) -> _typing.Sequence[cv2.typing.Point2f]: ...
+
+
+class SegmentationModel(Model):
+    # Functions
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+
+    @_typing.overload
+    def segment(self, frame: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def segment(self, frame: cv2.UMat, mask: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+
+class DetectionModel(Model):
+    # Functions
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+
+    def setNmsAcrossClasses(self, value: bool) -> DetectionModel: ...
+
+    def getNmsAcrossClasses(self) -> bool: ...
+
+    @_typing.overload
+    def detect(self, frame: cv2.typing.MatLike, confThreshold: float = ..., nmsThreshold: float = ...) -> tuple[_typing.Sequence[int], _typing.Sequence[float], _typing.Sequence[cv2.typing.Rect]]: ...
+    @_typing.overload
+    def detect(self, frame: cv2.UMat, confThreshold: float = ..., nmsThreshold: float = ...) -> tuple[_typing.Sequence[int], _typing.Sequence[float], _typing.Sequence[cv2.typing.Rect]]: ...
+
+
+class TextRecognitionModel(Model):
+    # Functions
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+
+    def setDecodeType(self, decodeType: str) -> TextRecognitionModel: ...
+
+    def getDecodeType(self) -> str: ...
+
+    def setDecodeOptsCTCPrefixBeamSearch(self, beamSize: int, vocPruneSize: int = ...) -> TextRecognitionModel: ...
+
+    def setVocabulary(self, vocabulary: _typing.Sequence[str]) -> TextRecognitionModel: ...
+
+    def getVocabulary(self) -> _typing.Sequence[str]: ...
+
+    @_typing.overload
+    def recognize(self, frame: cv2.typing.MatLike) -> str: ...
+    @_typing.overload
+    def recognize(self, frame: cv2.UMat) -> str: ...
+    @_typing.overload
+    def recognize(self, frame: cv2.typing.MatLike, roiRects: _typing.Sequence[cv2.typing.MatLike]) -> _typing.Sequence[str]: ...
+    @_typing.overload
+    def recognize(self, frame: cv2.UMat, roiRects: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[str]: ...
+
+
+class TextDetectionModel(Model):
+    # Functions
+    @_typing.overload
+    def detect(self, frame: cv2.typing.MatLike) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[float]]: ...
+    @_typing.overload
+    def detect(self, frame: cv2.UMat) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[float]]: ...
+    @_typing.overload
+    def detect(self, frame: cv2.typing.MatLike) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ...
+    @_typing.overload
+    def detect(self, frame: cv2.UMat) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ...
+
+    @_typing.overload
+    def detectTextRectangles(self, frame: cv2.typing.MatLike) -> tuple[_typing.Sequence[cv2.typing.RotatedRect], _typing.Sequence[float]]: ...
+    @_typing.overload
+    def detectTextRectangles(self, frame: cv2.UMat) -> tuple[_typing.Sequence[cv2.typing.RotatedRect], _typing.Sequence[float]]: ...
+    @_typing.overload
+    def detectTextRectangles(self, frame: cv2.typing.MatLike) -> _typing.Sequence[cv2.typing.RotatedRect]: ...
+    @_typing.overload
+    def detectTextRectangles(self, frame: cv2.UMat) -> _typing.Sequence[cv2.typing.RotatedRect]: ...
+
+
+class TextDetectionModel_EAST(TextDetectionModel):
+    # Functions
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+
+    def setConfidenceThreshold(self, confThreshold: float) -> TextDetectionModel_EAST: ...
+
+    def getConfidenceThreshold(self) -> float: ...
+
+    def setNMSThreshold(self, nmsThreshold: float) -> TextDetectionModel_EAST: ...
+
+    def getNMSThreshold(self) -> float: ...
+
+
+class TextDetectionModel_DB(TextDetectionModel):
+    # Functions
+    @_typing.overload
+    def __init__(self, network: Net) -> None: ...
+    @_typing.overload
+    def __init__(self, model: str, config: str = ...) -> None: ...
+
+    def setBinaryThreshold(self, binaryThreshold: float) -> TextDetectionModel_DB: ...
+
+    def getBinaryThreshold(self) -> float: ...
+
+    def setPolygonThreshold(self, polygonThreshold: float) -> TextDetectionModel_DB: ...
+
+    def getPolygonThreshold(self) -> float: ...
+
+    def setUnclipRatio(self, unclipRatio: float) -> TextDetectionModel_DB: ...
+
+    def getUnclipRatio(self) -> float: ...
+
+    def setMaxCandidates(self, maxCandidates: int) -> TextDetectionModel_DB: ...
+
+    def getMaxCandidates(self) -> int: ...
+
+
+class LayerProtocol(Protocol):
+    # Functions
+    def __init__(self, params: dict[str, DictValue], blobs: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+
+    def getMemoryShapes(self, inputs: _typing.Sequence[_typing.Sequence[int]]) -> _typing.Sequence[_typing.Sequence[int]]: ...
+
+    def forward(self, inputs: _typing.Sequence[cv2.typing.MatLike]) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+
+
+# Functions
+def NMSBoxes(bboxes: _typing.Sequence[cv2.typing.Rect2d], scores: _typing.Sequence[float], score_threshold: float, nms_threshold: float, eta: float = ..., top_k: int = ...) -> _typing.Sequence[int]: ...
+
+def NMSBoxesBatched(bboxes: _typing.Sequence[cv2.typing.Rect2d], scores: _typing.Sequence[float], class_ids: _typing.Sequence[int], score_threshold: float, nms_threshold: float, eta: float = ..., top_k: int = ...) -> _typing.Sequence[int]: ...
+
+def NMSBoxesRotated(bboxes: _typing.Sequence[cv2.typing.RotatedRect], scores: _typing.Sequence[float], score_threshold: float, nms_threshold: float, eta: float = ..., top_k: int = ...) -> _typing.Sequence[int]: ...
+
+@_typing.overload
+def blobFromImage(image: cv2.typing.MatLike, scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImage(image: cv2.UMat, scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
+
+@_typing.overload
+def blobFromImageWithParams(image: cv2.typing.MatLike, param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImageWithParams(image: cv2.UMat, param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImageWithParams(image: cv2.typing.MatLike, blob: cv2.typing.MatLike | None = ..., param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImageWithParams(image: cv2.UMat, blob: cv2.UMat | None = ..., param: Image2BlobParams = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def blobFromImages(images: _typing.Sequence[cv2.typing.MatLike], scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImages(images: _typing.Sequence[cv2.UMat], scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
+
+@_typing.overload
+def blobFromImagesWithParams(images: _typing.Sequence[cv2.typing.MatLike], param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImagesWithParams(images: _typing.Sequence[cv2.UMat], param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImagesWithParams(images: _typing.Sequence[cv2.typing.MatLike], blob: cv2.typing.MatLike | None = ..., param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blobFromImagesWithParams(images: _typing.Sequence[cv2.UMat], blob: cv2.UMat | None = ..., param: Image2BlobParams = ...) -> cv2.UMat: ...
+
+def getAvailableTargets(be: Backend) -> _typing.Sequence[Target]: ...
+
+@_typing.overload
+def imagesFromBlob(blob_: cv2.typing.MatLike, images_: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+@_typing.overload
+def imagesFromBlob(blob_: cv2.typing.MatLike, images_: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
+
+@_typing.overload
+def readNet(model: str, config: str = ..., framework: str = ...) -> Net: ...
+@_typing.overload
+def readNet(framework: str, bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
+
+@_typing.overload
+def readNetFromCaffe(prototxt: str, caffeModel: str = ...) -> Net: ...
+@_typing.overload
+def readNetFromCaffe(bufferProto: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
+
+@_typing.overload
+def readNetFromDarknet(cfgFile: str, darknetModel: str = ...) -> Net: ...
+@_typing.overload
+def readNetFromDarknet(bufferCfg: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
+
+@_typing.overload
+def readNetFromModelOptimizer(xml: str, bin: str = ...) -> Net: ...
+@_typing.overload
+def readNetFromModelOptimizer(bufferModelConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferWeights: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
+
+@_typing.overload
+def readNetFromONNX(onnxFile: str) -> Net: ...
+@_typing.overload
+def readNetFromONNX(buffer: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
+
+@_typing.overload
+def readNetFromTFLite(model: str) -> Net: ...
+@_typing.overload
+def readNetFromTFLite(bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
+
+@_typing.overload
+def readNetFromTensorflow(model: str, config: str = ...) -> Net: ...
+@_typing.overload
+def readNetFromTensorflow(bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
+
+def readNetFromTorch(model: str, isBinary: bool = ..., evaluate: bool = ...) -> Net: ...
+
+def readTensorFromONNX(path: str) -> cv2.typing.MatLike: ...
+
+def readTorchBlob(filename: str, isBinary: bool = ...) -> cv2.typing.MatLike: ...
+
+def shrinkCaffeModel(src: str, dst: str, layersTypes: _typing.Sequence[str] = ...) -> None: ...
+
+def softNMSBoxes(bboxes: _typing.Sequence[cv2.typing.Rect], scores: _typing.Sequence[float], score_threshold: float, nms_threshold: float, top_k: int = ..., sigma: float = ..., method: SoftNMSMethod = ...) -> tuple[_typing.Sequence[float], _typing.Sequence[int]]: ...
+
+def writeTextGraph(model: str, output: str) -> None: ...
+
+

.venv/lib/python3.11/site-packages/cv2/fisheye/__init__.pyi

@@ -0,0 +1,83 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+CALIB_USE_INTRINSIC_GUESS: int
+CALIB_RECOMPUTE_EXTRINSIC: int
+CALIB_CHECK_COND: int
+CALIB_FIX_SKEW: int
+CALIB_FIX_K1: int
+CALIB_FIX_K2: int
+CALIB_FIX_K3: int
+CALIB_FIX_K4: int
+CALIB_FIX_INTRINSIC: int
+CALIB_FIX_PRINCIPAL_POINT: int
+CALIB_ZERO_DISPARITY: int
+CALIB_FIX_FOCAL_LENGTH: int
+
+
+
+# Functions
+@_typing.overload
+def calibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], image_size: cv2.typing.Size, K: cv2.typing.MatLike, D: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
+@_typing.overload
+def calibrate(objectPoints: _typing.Sequence[cv2.UMat], imagePoints: _typing.Sequence[cv2.UMat], image_size: cv2.typing.Size, K: cv2.UMat, D: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat]]: ...
+
+@_typing.overload
+def distortPoints(undistorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, distorted: cv2.typing.MatLike | None = ..., alpha: float = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def distortPoints(undistorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, distorted: cv2.UMat | None = ..., alpha: float = ...) -> cv2.UMat: ...
+@_typing.overload
+def distortPoints(undistorted: cv2.typing.MatLike, Kundistorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, distorted: cv2.typing.MatLike | None = ..., alpha: float = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def distortPoints(undistorted: cv2.UMat, Kundistorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, distorted: cv2.UMat | None = ..., alpha: float = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def estimateNewCameraMatrixForUndistortRectify(K: cv2.typing.MatLike, D: cv2.typing.MatLike, image_size: cv2.typing.Size, R: cv2.typing.MatLike, P: cv2.typing.MatLike | None = ..., balance: float = ..., new_size: cv2.typing.Size = ..., fov_scale: float = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def estimateNewCameraMatrixForUndistortRectify(K: cv2.UMat, D: cv2.UMat, image_size: cv2.typing.Size, R: cv2.UMat, P: cv2.UMat | None = ..., balance: float = ..., new_size: cv2.typing.Size = ..., fov_scale: float = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def initUndistortRectifyMap(K: cv2.typing.MatLike, D: cv2.typing.MatLike, R: cv2.typing.MatLike, P: cv2.typing.MatLike, size: cv2.typing.Size, m1type: int, map1: cv2.typing.MatLike | None = ..., map2: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def initUndistortRectifyMap(K: cv2.UMat, D: cv2.UMat, R: cv2.UMat, P: cv2.UMat, size: cv2.typing.Size, m1type: int, map1: cv2.UMat | None = ..., map2: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def projectPoints(objectPoints: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike | None = ..., alpha: float = ..., jacobian: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def projectPoints(objectPoints: cv2.UMat, rvec: cv2.UMat, tvec: cv2.UMat, K: cv2.UMat, D: cv2.UMat, imagePoints: cv2.UMat | None = ..., alpha: float = ..., jacobian: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def solvePnP(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike | None = ..., tvec: cv2.typing.MatLike | None = ..., useExtrinsicGuess: bool = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def solvePnP(objectPoints: cv2.UMat, imagePoints: cv2.UMat, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvec: cv2.UMat | None = ..., tvec: cv2.UMat | None = ..., useExtrinsicGuess: bool = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[bool, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def stereoCalibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], K1: cv2.typing.MatLike, D1: cv2.typing.MatLike, K2: cv2.typing.MatLike, D2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike | None = ..., T: cv2.typing.MatLike | None = ..., rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
+@_typing.overload
+def stereoCalibrate(objectPoints: _typing.Sequence[cv2.UMat], imagePoints1: _typing.Sequence[cv2.UMat], imagePoints2: _typing.Sequence[cv2.UMat], K1: cv2.UMat, D1: cv2.UMat, K2: cv2.UMat, D2: cv2.UMat, imageSize: cv2.typing.Size, R: cv2.UMat | None = ..., T: cv2.UMat | None = ..., rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat]]: ...
+@_typing.overload
+def stereoCalibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], K1: cv2.typing.MatLike, D1: cv2.typing.MatLike, K2: cv2.typing.MatLike, D2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike | None = ..., T: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def stereoCalibrate(objectPoints: _typing.Sequence[cv2.UMat], imagePoints1: _typing.Sequence[cv2.UMat], imagePoints2: _typing.Sequence[cv2.UMat], K1: cv2.UMat, D1: cv2.UMat, K2: cv2.UMat, D2: cv2.UMat, imageSize: cv2.typing.Size, R: cv2.UMat | None = ..., T: cv2.UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def stereoRectify(K1: cv2.typing.MatLike, D1: cv2.typing.MatLike, K2: cv2.typing.MatLike, D2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike, tvec: cv2.typing.MatLike, flags: int, R1: cv2.typing.MatLike | None = ..., R2: cv2.typing.MatLike | None = ..., P1: cv2.typing.MatLike | None = ..., P2: cv2.typing.MatLike | None = ..., Q: cv2.typing.MatLike | None = ..., newImageSize: cv2.typing.Size = ..., balance: float = ..., fov_scale: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def stereoRectify(K1: cv2.UMat, D1: cv2.UMat, K2: cv2.UMat, D2: cv2.UMat, imageSize: cv2.typing.Size, R: cv2.UMat, tvec: cv2.UMat, flags: int, R1: cv2.UMat | None = ..., R2: cv2.UMat | None = ..., P1: cv2.UMat | None = ..., P2: cv2.UMat | None = ..., Q: cv2.UMat | None = ..., newImageSize: cv2.typing.Size = ..., balance: float = ..., fov_scale: float = ...) -> tuple[cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def undistortImage(distorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, undistorted: cv2.typing.MatLike | None = ..., Knew: cv2.typing.MatLike | None = ..., new_size: cv2.typing.Size = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def undistortImage(distorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, undistorted: cv2.UMat | None = ..., Knew: cv2.UMat | None = ..., new_size: cv2.typing.Size = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def undistortPoints(distorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, undistorted: cv2.typing.MatLike | None = ..., R: cv2.typing.MatLike | None = ..., P: cv2.typing.MatLike | None = ..., criteria: cv2.typing.TermCriteria = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def undistortPoints(distorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, undistorted: cv2.UMat | None = ..., R: cv2.UMat | None = ..., P: cv2.UMat | None = ..., criteria: cv2.typing.TermCriteria = ...) -> cv2.UMat: ...
+
+

.venv/lib/python3.11/site-packages/cv2/flann/__init__.pyi

@@ -0,0 +1,64 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+FLANN_INDEX_TYPE_8U: int
+FLANN_INDEX_TYPE_8S: int
+FLANN_INDEX_TYPE_16U: int
+FLANN_INDEX_TYPE_16S: int
+FLANN_INDEX_TYPE_32S: int
+FLANN_INDEX_TYPE_32F: int
+FLANN_INDEX_TYPE_64F: int
+FLANN_INDEX_TYPE_STRING: int
+FLANN_INDEX_TYPE_BOOL: int
+FLANN_INDEX_TYPE_ALGORITHM: int
+LAST_VALUE_FLANN_INDEX_TYPE: int
+FlannIndexType = int
+"""One of [FLANN_INDEX_TYPE_8U, FLANN_INDEX_TYPE_8S, FLANN_INDEX_TYPE_16U, FLANN_INDEX_TYPE_16S, FLANN_INDEX_TYPE_32S, FLANN_INDEX_TYPE_32F, FLANN_INDEX_TYPE_64F, FLANN_INDEX_TYPE_STRING, FLANN_INDEX_TYPE_BOOL, FLANN_INDEX_TYPE_ALGORITHM, LAST_VALUE_FLANN_INDEX_TYPE]"""
+
+
+
+# Classes
+class Index:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, features: cv2.typing.MatLike, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
+    @_typing.overload
+    def __init__(self, features: cv2.UMat, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
+
+    @_typing.overload
+    def build(self, features: cv2.typing.MatLike, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
+    @_typing.overload
+    def build(self, features: cv2.UMat, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
+
+    @_typing.overload
+    def knnSearch(self, query: cv2.typing.MatLike, knn: int, indices: cv2.typing.MatLike | None = ..., dists: cv2.typing.MatLike | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def knnSearch(self, query: cv2.UMat, knn: int, indices: cv2.UMat | None = ..., dists: cv2.UMat | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def radiusSearch(self, query: cv2.typing.MatLike, radius: float, maxResults: int, indices: cv2.typing.MatLike | None = ..., dists: cv2.typing.MatLike | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def radiusSearch(self, query: cv2.UMat, radius: float, maxResults: int, indices: cv2.UMat | None = ..., dists: cv2.UMat | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
+
+    def save(self, filename: str) -> None: ...
+
+    @_typing.overload
+    def load(self, features: cv2.typing.MatLike, filename: str) -> bool: ...
+    @_typing.overload
+    def load(self, features: cv2.UMat, filename: str) -> bool: ...
+
+    def release(self) -> None: ...
+
+    def getDistance(self) -> int: ...
+
+    def getAlgorithm(self) -> int: ...
+
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/__init__.py

@@ -0,0 +1,323 @@
+__all__ = ['op', 'kernel']
+
+import sys
+import cv2 as cv
+
+# NB: Register function in specific module
+def register(mname):
+    def parameterized(func):
+        sys.modules[mname].__dict__[func.__name__] = func
+        return func
+    return parameterized
+
+
+@register('cv2.gapi')
+def networks(*args):
+    return cv.gapi_GNetPackage(list(map(cv.detail.strip, args)))
+
+
+@register('cv2.gapi')
+def compile_args(*args):
+    return list(map(cv.GCompileArg, args))
+
+
+@register('cv2')
+def GIn(*args):
+    return [*args]
+
+
+@register('cv2')
+def GOut(*args):
+    return [*args]
+
+
+@register('cv2')
+def gin(*args):
+    return [*args]
+
+
+@register('cv2.gapi')
+def descr_of(*args):
+    return [*args]
+
+
+@register('cv2')
+class GOpaque():
+    # NB: Inheritance from c++ class cause segfault.
+    # So just aggregate cv.GOpaqueT instead of inheritance
+    def __new__(cls, argtype):
+        return cv.GOpaqueT(argtype)
+
+    class Bool():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_BOOL)
+
+    class Int():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_INT)
+
+    class Int64():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_INT64)
+
+    class UInt64():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_UINT64)
+
+    class Double():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_DOUBLE)
+
+    class Float():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_FLOAT)
+
+    class String():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_STRING)
+
+    class Point():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_POINT)
+
+    class Point2f():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_POINT2F)
+
+    class Point3f():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_POINT3F)
+
+    class Size():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_SIZE)
+
+    class Rect():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_RECT)
+
+    class Prim():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_DRAW_PRIM)
+
+    class Any():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_ANY)
+
+@register('cv2')
+class GArray():
+    # NB: Inheritance from c++ class cause segfault.
+    # So just aggregate cv.GArrayT instead of inheritance
+    def __new__(cls, argtype):
+        return cv.GArrayT(argtype)
+
+    class Bool():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_BOOL)
+
+    class Int():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_INT)
+
+    class Int64():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_INT64)
+
+    class UInt64():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_UINT64)
+
+    class Double():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_DOUBLE)
+
+    class Float():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_FLOAT)
+
+    class String():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_STRING)
+
+    class Point():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_POINT)
+
+    class Point2f():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_POINT2F)
+
+    class Point3f():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_POINT3F)
+
+    class Size():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_SIZE)
+
+    class Rect():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_RECT)
+
+    class Scalar():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_SCALAR)
+
+    class Mat():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_MAT)
+
+    class GMat():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_GMAT)
+
+    class Prim():
+        def __new__(self):
+            return cv.GArray(cv.gapi.CV_DRAW_PRIM)
+
+    class Any():
+        def __new__(self):
+            return cv.GArray(cv.gapi.CV_ANY)
+
+
+# NB: Top lvl decorator takes arguments
+def op(op_id, in_types, out_types):
+
+    garray_types= {
+            cv.GArray.Bool:    cv.gapi.CV_BOOL,
+            cv.GArray.Int:     cv.gapi.CV_INT,
+            cv.GArray.Int64:   cv.gapi.CV_INT64,
+            cv.GArray.UInt64:  cv.gapi.CV_UINT64,
+            cv.GArray.Double:  cv.gapi.CV_DOUBLE,
+            cv.GArray.Float:   cv.gapi.CV_FLOAT,
+            cv.GArray.String:  cv.gapi.CV_STRING,
+            cv.GArray.Point:   cv.gapi.CV_POINT,
+            cv.GArray.Point2f: cv.gapi.CV_POINT2F,
+            cv.GArray.Point3f: cv.gapi.CV_POINT3F,
+            cv.GArray.Size:    cv.gapi.CV_SIZE,
+            cv.GArray.Rect:    cv.gapi.CV_RECT,
+            cv.GArray.Scalar:  cv.gapi.CV_SCALAR,
+            cv.GArray.Mat:     cv.gapi.CV_MAT,
+            cv.GArray.GMat:    cv.gapi.CV_GMAT,
+            cv.GArray.Prim:    cv.gapi.CV_DRAW_PRIM,
+            cv.GArray.Any:     cv.gapi.CV_ANY
+    }
+
+    gopaque_types= {
+            cv.GOpaque.Size:    cv.gapi.CV_SIZE,
+            cv.GOpaque.Rect:    cv.gapi.CV_RECT,
+            cv.GOpaque.Bool:    cv.gapi.CV_BOOL,
+            cv.GOpaque.Int:     cv.gapi.CV_INT,
+            cv.GOpaque.Int64:   cv.gapi.CV_INT64,
+            cv.GOpaque.UInt64:  cv.gapi.CV_UINT64,
+            cv.GOpaque.Double:  cv.gapi.CV_DOUBLE,
+            cv.GOpaque.Float:   cv.gapi.CV_FLOAT,
+            cv.GOpaque.String:  cv.gapi.CV_STRING,
+            cv.GOpaque.Point:   cv.gapi.CV_POINT,
+            cv.GOpaque.Point2f: cv.gapi.CV_POINT2F,
+            cv.GOpaque.Point3f: cv.gapi.CV_POINT3F,
+            cv.GOpaque.Size:    cv.gapi.CV_SIZE,
+            cv.GOpaque.Rect:    cv.gapi.CV_RECT,
+            cv.GOpaque.Prim:    cv.gapi.CV_DRAW_PRIM,
+            cv.GOpaque.Any:     cv.gapi.CV_ANY
+    }
+
+    type2str = {
+        cv.gapi.CV_BOOL:      'cv.gapi.CV_BOOL' ,
+        cv.gapi.CV_INT:       'cv.gapi.CV_INT' ,
+        cv.gapi.CV_INT64:     'cv.gapi.CV_INT64' ,
+        cv.gapi.CV_UINT64:    'cv.gapi.CV_UINT64' ,
+        cv.gapi.CV_DOUBLE:    'cv.gapi.CV_DOUBLE' ,
+        cv.gapi.CV_FLOAT:     'cv.gapi.CV_FLOAT' ,
+        cv.gapi.CV_STRING:    'cv.gapi.CV_STRING' ,
+        cv.gapi.CV_POINT:     'cv.gapi.CV_POINT' ,
+        cv.gapi.CV_POINT2F:   'cv.gapi.CV_POINT2F' ,
+        cv.gapi.CV_POINT3F:   'cv.gapi.CV_POINT3F' ,
+        cv.gapi.CV_SIZE:      'cv.gapi.CV_SIZE',
+        cv.gapi.CV_RECT:      'cv.gapi.CV_RECT',
+        cv.gapi.CV_SCALAR:    'cv.gapi.CV_SCALAR',
+        cv.gapi.CV_MAT:       'cv.gapi.CV_MAT',
+        cv.gapi.CV_GMAT:      'cv.gapi.CV_GMAT',
+        cv.gapi.CV_DRAW_PRIM: 'cv.gapi.CV_DRAW_PRIM'
+    }
+
+    # NB: Second lvl decorator takes class to decorate
+    def op_with_params(cls):
+        if not in_types:
+            raise Exception('{} operation should have at least one input!'.format(cls.__name__))
+
+        if not out_types:
+            raise Exception('{} operation should have at least one output!'.format(cls.__name__))
+
+        for i, t in enumerate(out_types):
+            if t not in [cv.GMat, cv.GScalar, *garray_types, *gopaque_types]:
+                   raise Exception('{} unsupported output type: {} in position: {}'
+                           .format(cls.__name__, t.__name__, i))
+
+        def on(*args):
+            if len(in_types) != len(args):
+                raise Exception('Invalid number of input elements!\nExpected: {}, Actual: {}'
+                        .format(len(in_types), len(args)))
+
+            for i, (t, a) in enumerate(zip(in_types, args)):
+                if t in garray_types:
+                    if not isinstance(a, cv.GArrayT):
+                        raise Exception("{} invalid type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, cv.GArrayT.__name__, type(a).__name__))
+
+                    elif a.type() != garray_types[t]:
+                        raise Exception("{} invalid GArrayT type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, type2str[garray_types[t]], type2str[a.type()]))
+
+                elif t in gopaque_types:
+                    if not isinstance(a, cv.GOpaqueT):
+                        raise Exception("{} invalid type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, cv.GOpaqueT.__name__, type(a).__name__))
+
+                    elif a.type() != gopaque_types[t]:
+                        raise Exception("{} invalid GOpaque type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, type2str[gopaque_types[t]], type2str[a.type()]))
+
+                else:
+                    if t != type(a):
+                        raise Exception('{} invalid input type for argument {}.\nExpected: {}, Actual: {}'
+                                .format(cls.__name__, i, t.__name__, type(a).__name__))
+
+            op = cv.gapi.__op(op_id, cls.outMeta, *args)
+
+            out_protos = []
+            for i, out_type in enumerate(out_types):
+                if out_type == cv.GMat:
+                    out_protos.append(op.getGMat())
+                elif out_type == cv.GScalar:
+                    out_protos.append(op.getGScalar())
+                elif out_type in gopaque_types:
+                    out_protos.append(op.getGOpaque(gopaque_types[out_type]))
+                elif out_type in garray_types:
+                    out_protos.append(op.getGArray(garray_types[out_type]))
+                else:
+                    raise Exception("""In {}: G-API operation can't produce the output with type: {} in position: {}"""
+                            .format(cls.__name__, out_type.__name__, i))
+
+            return tuple(out_protos) if len(out_protos) != 1 else out_protos[0]
+
+        # NB: Extend operation class
+        cls.id = op_id
+        cls.on = staticmethod(on)
+        return cls
+
+    return op_with_params
+
+
+def kernel(op_cls):
+    # NB: Second lvl decorator takes class to decorate
+    def kernel_with_params(cls):
+        # NB: Add new members to kernel class
+        cls.id      = op_cls.id
+        cls.outMeta = op_cls.outMeta
+        return cls
+
+    return kernel_with_params
+
+
+cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline

.venv/lib/python3.11/site-packages/cv2/gapi/__init__.pyi

@@ -0,0 +1,349 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+from cv2.gapi import core as core
+from cv2.gapi import ie as ie
+from cv2.gapi import imgproc as imgproc
+from cv2.gapi import oak as oak
+from cv2.gapi import onnx as onnx
+from cv2.gapi import ot as ot
+from cv2.gapi import ov as ov
+from cv2.gapi import own as own
+from cv2.gapi import render as render
+from cv2.gapi import streaming as streaming
+from cv2.gapi import video as video
+from cv2.gapi import wip as wip
+
+
+# Enumerations
+StereoOutputFormat_DEPTH_FLOAT16: int
+STEREO_OUTPUT_FORMAT_DEPTH_FLOAT16: int
+StereoOutputFormat_DEPTH_FLOAT32: int
+STEREO_OUTPUT_FORMAT_DEPTH_FLOAT32: int
+StereoOutputFormat_DISPARITY_FIXED16_11_5: int
+STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_11_5: int
+StereoOutputFormat_DISPARITY_FIXED16_12_4: int
+STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_12_4: int
+StereoOutputFormat_DEPTH_16F: int
+STEREO_OUTPUT_FORMAT_DEPTH_16F: int
+StereoOutputFormat_DEPTH_32F: int
+STEREO_OUTPUT_FORMAT_DEPTH_32F: int
+StereoOutputFormat_DISPARITY_16Q_10_5: int
+STEREO_OUTPUT_FORMAT_DISPARITY_16Q_10_5: int
+StereoOutputFormat_DISPARITY_16Q_11_4: int
+STEREO_OUTPUT_FORMAT_DISPARITY_16Q_11_4: int
+StereoOutputFormat = int
+"""One of [StereoOutputFormat_DEPTH_FLOAT16, STEREO_OUTPUT_FORMAT_DEPTH_FLOAT16, StereoOutputFormat_DEPTH_FLOAT32, STEREO_OUTPUT_FORMAT_DEPTH_FLOAT32, StereoOutputFormat_DISPARITY_FIXED16_11_5, STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_11_5, StereoOutputFormat_DISPARITY_FIXED16_12_4, STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_12_4, StereoOutputFormat_DEPTH_16F, STEREO_OUTPUT_FORMAT_DEPTH_16F, StereoOutputFormat_DEPTH_32F, STEREO_OUTPUT_FORMAT_DEPTH_32F, StereoOutputFormat_DISPARITY_16Q_10_5, STEREO_OUTPUT_FORMAT_DISPARITY_16Q_10_5, StereoOutputFormat_DISPARITY_16Q_11_4, STEREO_OUTPUT_FORMAT_DISPARITY_16Q_11_4]"""
+
+CV_BOOL: int
+CV_INT: int
+CV_INT64: int
+CV_UINT64: int
+CV_DOUBLE: int
+CV_FLOAT: int
+CV_STRING: int
+CV_POINT: int
+CV_POINT2F: int
+CV_POINT3F: int
+CV_SIZE: int
+CV_RECT: int
+CV_SCALAR: int
+CV_MAT: int
+CV_GMAT: int
+CV_DRAW_PRIM: int
+CV_ANY: int
+ArgType = int
+"""One of [CV_BOOL, CV_INT, CV_INT64, CV_UINT64, CV_DOUBLE, CV_FLOAT, CV_STRING, CV_POINT, CV_POINT2F, CV_POINT3F, CV_SIZE, CV_RECT, CV_SCALAR, CV_MAT, CV_GMAT, CV_DRAW_PRIM, CV_ANY]"""
+
+
+
+# Classes
+class GNetParam:
+    ...
+
+class GNetPackage:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, nets: _typing.Sequence[GNetParam]) -> None: ...
+
+
+
+# Functions
+def BGR2Gray(src: cv2.GMat) -> cv2.GMat: ...
+
+def BGR2I420(src: cv2.GMat) -> cv2.GMat: ...
+
+def BGR2LUV(src: cv2.GMat) -> cv2.GMat: ...
+
+def BGR2RGB(src: cv2.GMat) -> cv2.GMat: ...
+
+def BGR2YUV(src: cv2.GMat) -> cv2.GMat: ...
+
+def BayerGR2RGB(src_gr: cv2.GMat) -> cv2.GMat: ...
+
+def Canny(image: cv2.GMat, threshold1: float, threshold2: float, apertureSize: int = ..., L2gradient: bool = ...) -> cv2.GMat: ...
+
+def I4202BGR(src: cv2.GMat) -> cv2.GMat: ...
+
+def I4202RGB(src: cv2.GMat) -> cv2.GMat: ...
+
+def LUT(src: cv2.GMat, lut: cv2.typing.MatLike) -> cv2.GMat: ...
+
+def LUV2BGR(src: cv2.GMat) -> cv2.GMat: ...
+
+def Laplacian(src: cv2.GMat, ddepth: int, ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> cv2.GMat: ...
+
+def NV12toBGR(src_y: cv2.GMat, src_uv: cv2.GMat) -> cv2.GMat: ...
+
+def NV12toGray(src_y: cv2.GMat, src_uv: cv2.GMat) -> cv2.GMat: ...
+
+def NV12toRGB(src_y: cv2.GMat, src_uv: cv2.GMat) -> cv2.GMat: ...
+
+@_typing.overload
+def RGB2Gray(src: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def RGB2Gray(src: cv2.GMat, rY: float, gY: float, bY: float) -> cv2.GMat: ...
+
+def RGB2HSV(src: cv2.GMat) -> cv2.GMat: ...
+
+def RGB2I420(src: cv2.GMat) -> cv2.GMat: ...
+
+def RGB2Lab(src: cv2.GMat) -> cv2.GMat: ...
+
+def RGB2YUV(src: cv2.GMat) -> cv2.GMat: ...
+
+def RGB2YUV422(src: cv2.GMat) -> cv2.GMat: ...
+
+def Sobel(src: cv2.GMat, ddepth: int, dx: int, dy: int, ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def SobelXY(src: cv2.GMat, ddepth: int, order: int, ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
+
+def YUV2BGR(src: cv2.GMat) -> cv2.GMat: ...
+
+def YUV2RGB(src: cv2.GMat) -> cv2.GMat: ...
+
+def absDiff(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+
+def absDiffC(src: cv2.GMat, c: cv2.GScalar) -> cv2.GMat: ...
+
+def add(src1: cv2.GMat, src2: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
+
+@_typing.overload
+def addC(src1: cv2.GMat, c: cv2.GScalar, ddepth: int = ...) -> cv2.GMat: ...
+@_typing.overload
+def addC(c: cv2.GScalar, src1: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
+
+def addWeighted(src1: cv2.GMat, alpha: float, src2: cv2.GMat, beta: float, gamma: float, ddepth: int = ...) -> cv2.GMat: ...
+
+def bilateralFilter(src: cv2.GMat, d: int, sigmaColor: float, sigmaSpace: float, borderType: int = ...) -> cv2.GMat: ...
+
+@_typing.overload
+def bitwise_and(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def bitwise_and(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+def bitwise_not(src: cv2.GMat) -> cv2.GMat: ...
+
+@_typing.overload
+def bitwise_or(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def bitwise_or(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def bitwise_xor(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def bitwise_xor(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+def blur(src: cv2.GMat, ksize: cv2.typing.Size, anchor: cv2.typing.Point = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+@_typing.overload
+def boundingRect(src: cv2.GMat) -> cv2.GOpaqueT: ...
+@_typing.overload
+def boundingRect(src: cv2.GArrayT) -> cv2.GOpaqueT: ...
+@_typing.overload
+def boundingRect(src: cv2.GArrayT) -> cv2.GOpaqueT: ...
+
+def boxFilter(src: cv2.GMat, dtype: int, ksize: cv2.typing.Size, anchor: cv2.typing.Point = ..., normalize: bool = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def cartToPolar(x: cv2.GMat, y: cv2.GMat, angleInDegrees: bool = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
+
+@_typing.overload
+def cmpEQ(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def cmpEQ(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def cmpGE(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def cmpGE(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def cmpGT(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def cmpGT(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def cmpLE(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def cmpLE(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def cmpLT(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def cmpLT(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def cmpNE(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def cmpNE(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
+
+def combine(lhs: cv2.GKernelPackage, rhs: cv2.GKernelPackage) -> cv2.GKernelPackage: ...
+
+@_typing.overload
+def concatHor(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def concatHor(v: _typing.Sequence[cv2.GMat]) -> cv2.GMat: ...
+
+@_typing.overload
+def concatVert(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+@_typing.overload
+def concatVert(v: _typing.Sequence[cv2.GMat]) -> cv2.GMat: ...
+
+def convertTo(src: cv2.GMat, rdepth: int, alpha: float = ..., beta: float = ...) -> cv2.GMat: ...
+
+def copy(in_: cv2.GMat) -> cv2.GMat: ...
+
+def countNonZero(src: cv2.GMat) -> cv2.GOpaqueT: ...
+
+def crop(src: cv2.GMat, rect: cv2.typing.Rect) -> cv2.GMat: ...
+
+def dilate(src: cv2.GMat, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def dilate3x3(src: cv2.GMat, iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def div(src1: cv2.GMat, src2: cv2.GMat, scale: float, ddepth: int = ...) -> cv2.GMat: ...
+
+def divC(src: cv2.GMat, divisor: cv2.GScalar, scale: float, ddepth: int = ...) -> cv2.GMat: ...
+
+def divRC(divident: cv2.GScalar, src: cv2.GMat, scale: float, ddepth: int = ...) -> cv2.GMat: ...
+
+def equalizeHist(src: cv2.GMat) -> cv2.GMat: ...
+
+def erode(src: cv2.GMat, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def erode3x3(src: cv2.GMat, iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def filter2D(src: cv2.GMat, ddepth: int, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., delta: cv2.typing.Scalar = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def flip(src: cv2.GMat, flipCode: int) -> cv2.GMat: ...
+
+def gaussianBlur(src: cv2.GMat, ksize: cv2.typing.Size, sigmaX: float, sigmaY: float = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def goodFeaturesToTrack(image: cv2.GMat, maxCorners: int, qualityLevel: float, minDistance: float, mask: cv2.typing.MatLike | None = ..., blockSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> cv2.GArrayT: ...
+
+def inRange(src: cv2.GMat, threshLow: cv2.GScalar, threshUp: cv2.GScalar) -> cv2.GMat: ...
+
+@_typing.overload
+def infer(name: str, inputs: cv2.GInferInputs) -> cv2.GInferOutputs: ...
+@_typing.overload
+def infer(name: str, roi: cv2.GOpaqueT, inputs: cv2.GInferInputs) -> cv2.GInferOutputs: ...
+@_typing.overload
+def infer(name: str, rois: cv2.GArrayT, inputs: cv2.GInferInputs) -> cv2.GInferListOutputs: ...
+
+def infer2(name: str, in_: cv2.GMat, inputs: cv2.GInferListInputs) -> cv2.GInferListOutputs: ...
+
+def integral(src: cv2.GMat, sdepth: int = ..., sqdepth: int = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
+
+@_typing.overload
+def kmeans(data: cv2.GMat, K: int, bestLabels: cv2.GMat, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GMat, cv2.GMat]: ...
+@_typing.overload
+def kmeans(data: cv2.GMat, K: int, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GMat, cv2.GMat]: ...
+@_typing.overload
+def kmeans(data: cv2.GArrayT, K: int, bestLabels: cv2.GArrayT, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GArrayT, cv2.GArrayT]: ...
+@_typing.overload
+def kmeans(data: cv2.GArrayT, K: int, bestLabels: cv2.GArrayT, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GArrayT, cv2.GArrayT]: ...
+
+def mask(src: cv2.GMat, mask: cv2.GMat) -> cv2.GMat: ...
+
+def max(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+
+def mean(src: cv2.GMat) -> cv2.GScalar: ...
+
+def medianBlur(src: cv2.GMat, ksize: int) -> cv2.GMat: ...
+
+def merge3(src1: cv2.GMat, src2: cv2.GMat, src3: cv2.GMat) -> cv2.GMat: ...
+
+def merge4(src1: cv2.GMat, src2: cv2.GMat, src3: cv2.GMat, src4: cv2.GMat) -> cv2.GMat: ...
+
+def min(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
+
+def morphologyEx(src: cv2.GMat, op: cv2.MorphTypes, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: cv2.BorderTypes = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def mul(src1: cv2.GMat, src2: cv2.GMat, scale: float = ..., ddepth: int = ...) -> cv2.GMat: ...
+
+@_typing.overload
+def mulC(src: cv2.GMat, multiplier: float, ddepth: int = ...) -> cv2.GMat: ...
+@_typing.overload
+def mulC(src: cv2.GMat, multiplier: cv2.GScalar, ddepth: int = ...) -> cv2.GMat: ...
+@_typing.overload
+def mulC(multiplier: cv2.GScalar, src: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
+
+def normInf(src: cv2.GMat) -> cv2.GScalar: ...
+
+def normL1(src: cv2.GMat) -> cv2.GScalar: ...
+
+def normL2(src: cv2.GMat) -> cv2.GScalar: ...
+
+def normalize(src: cv2.GMat, alpha: float, beta: float, norm_type: int, ddepth: int = ...) -> cv2.GMat: ...
+
+@_typing.overload
+def parseSSD(in_: cv2.GMat, inSz: cv2.GOpaqueT, confidenceThreshold: float = ..., filterLabel: int = ...) -> tuple[cv2.GArrayT, cv2.GArrayT]: ...
+@_typing.overload
+def parseSSD(in_: cv2.GMat, inSz: cv2.GOpaqueT, confidenceThreshold: float, alignmentToSquare: bool, filterOutOfBounds: bool) -> cv2.GArrayT: ...
+
+def parseYolo(in_: cv2.GMat, inSz: cv2.GOpaqueT, confidenceThreshold: float = ..., nmsThreshold: float = ..., anchors: _typing.Sequence[float] = ...) -> tuple[cv2.GArrayT, cv2.GArrayT]: ...
+
+def phase(x: cv2.GMat, y: cv2.GMat, angleInDegrees: bool = ...) -> cv2.GMat: ...
+
+def polarToCart(magnitude: cv2.GMat, angle: cv2.GMat, angleInDegrees: bool = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
+
+def remap(src: cv2.GMat, map1: cv2.typing.MatLike, map2: cv2.typing.MatLike, interpolation: int, borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def resize(src: cv2.GMat, dsize: cv2.typing.Size, fx: float = ..., fy: float = ..., interpolation: int = ...) -> cv2.GMat: ...
+
+def select(src1: cv2.GMat, src2: cv2.GMat, mask: cv2.GMat) -> cv2.GMat: ...
+
+def sepFilter(src: cv2.GMat, ddepth: int, kernelX: cv2.typing.MatLike, kernelY: cv2.typing.MatLike, anchor: cv2.typing.Point, delta: cv2.typing.Scalar, borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def split3(src: cv2.GMat) -> tuple[cv2.GMat, cv2.GMat, cv2.GMat]: ...
+
+def split4(src: cv2.GMat) -> tuple[cv2.GMat, cv2.GMat, cv2.GMat, cv2.GMat]: ...
+
+def sqrt(src: cv2.GMat) -> cv2.GMat: ...
+
+def sub(src1: cv2.GMat, src2: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
+
+def subC(src: cv2.GMat, c: cv2.GScalar, ddepth: int = ...) -> cv2.GMat: ...
+
+def subRC(c: cv2.GScalar, src: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
+
+def sum(src: cv2.GMat) -> cv2.GScalar: ...
+
+@_typing.overload
+def threshold(src: cv2.GMat, thresh: cv2.GScalar, maxval: cv2.GScalar, type: int) -> cv2.GMat: ...
+@_typing.overload
+def threshold(src: cv2.GMat, maxval: cv2.GScalar, type: int) -> tuple[cv2.GMat, cv2.GScalar]: ...
+
+def transpose(src: cv2.GMat) -> cv2.GMat: ...
+
+def warpAffine(src: cv2.GMat, M: cv2.typing.MatLike, dsize: cv2.typing.Size, flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+def warpPerspective(src: cv2.GMat, M: cv2.typing.MatLike, dsize: cv2.typing.Size, flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/core/__init__.pyi

@@ -0,0 +1,7 @@
+__all__: list[str] = []
+
+from cv2.gapi.core import cpu as cpu
+from cv2.gapi.core import fluid as fluid
+from cv2.gapi.core import ocl as ocl
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/core/cpu/__init__.pyi

@@ -0,0 +1,9 @@
+__all__: list[str] = []
+
+import cv2
+
+
+# Functions
+def kernels() -> cv2.GKernelPackage: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/core/fluid/__init__.pyi

@@ -0,0 +1,9 @@
+__all__: list[str] = []
+
+import cv2
+
+
+# Functions
+def kernels() -> cv2.GKernelPackage: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/core/ocl/__init__.pyi

@@ -0,0 +1,9 @@
+__all__: list[str] = []
+
+import cv2
+
+
+# Functions
+def kernels() -> cv2.GKernelPackage: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/imgproc/__init__.pyi

@@ -0,0 +1,5 @@
+__all__: list[str] = []
+
+from cv2.gapi.imgproc import fluid as fluid
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/imgproc/fluid/__init__.pyi

@@ -0,0 +1,9 @@
+__all__: list[str] = []
+
+import cv2
+
+
+# Functions
+def kernels() -> cv2.GKernelPackage: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/oak/__init__.pyi

@@ -0,0 +1,37 @@
+__all__: list[str] = []
+
+EncoderConfig_RateControlMode_CBR: int
+ENCODER_CONFIG_RATE_CONTROL_MODE_CBR: int
+EncoderConfig_RateControlMode_VBR: int
+ENCODER_CONFIG_RATE_CONTROL_MODE_VBR: int
+EncoderConfig_RateControlMode = int
+"""One of [EncoderConfig_RateControlMode_CBR, ENCODER_CONFIG_RATE_CONTROL_MODE_CBR, EncoderConfig_RateControlMode_VBR, ENCODER_CONFIG_RATE_CONTROL_MODE_VBR]"""
+
+EncoderConfig_Profile_H264_BASELINE: int
+ENCODER_CONFIG_PROFILE_H264_BASELINE: int
+EncoderConfig_Profile_H264_HIGH: int
+ENCODER_CONFIG_PROFILE_H264_HIGH: int
+EncoderConfig_Profile_H264_MAIN: int
+ENCODER_CONFIG_PROFILE_H264_MAIN: int
+EncoderConfig_Profile_H265_MAIN: int
+ENCODER_CONFIG_PROFILE_H265_MAIN: int
+EncoderConfig_Profile_MJPEG: int
+ENCODER_CONFIG_PROFILE_MJPEG: int
+EncoderConfig_Profile = int
+"""One of [EncoderConfig_Profile_H264_BASELINE, ENCODER_CONFIG_PROFILE_H264_BASELINE, EncoderConfig_Profile_H264_HIGH, ENCODER_CONFIG_PROFILE_H264_HIGH, EncoderConfig_Profile_H264_MAIN, ENCODER_CONFIG_PROFILE_H264_MAIN, EncoderConfig_Profile_H265_MAIN, ENCODER_CONFIG_PROFILE_H265_MAIN, EncoderConfig_Profile_MJPEG, ENCODER_CONFIG_PROFILE_MJPEG]"""
+
+ColorCameraParams_BoardSocket_RGB: int
+COLOR_CAMERA_PARAMS_BOARD_SOCKET_RGB: int
+ColorCameraParams_BoardSocket_BGR: int
+COLOR_CAMERA_PARAMS_BOARD_SOCKET_BGR: int
+ColorCameraParams_BoardSocket = int
+"""One of [ColorCameraParams_BoardSocket_RGB, COLOR_CAMERA_PARAMS_BOARD_SOCKET_RGB, ColorCameraParams_BoardSocket_BGR, COLOR_CAMERA_PARAMS_BOARD_SOCKET_BGR]"""
+
+ColorCameraParams_Resolution_THE_1080_P: int
+COLOR_CAMERA_PARAMS_RESOLUTION_THE_1080_P: int
+ColorCameraParams_Resolution = int
+"""One of [ColorCameraParams_Resolution_THE_1080_P, COLOR_CAMERA_PARAMS_RESOLUTION_THE_1080_P]"""
+
+
+# Classes
+

.venv/lib/python3.11/site-packages/cv2/gapi/ov/__init__.pyi

@@ -0,0 +1,74 @@
+__all__: list[str] = []
+
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class PyParams:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, tag: str, model_path: str, bin_path: str, device: str) -> None: ...
+    @_typing.overload
+    def __init__(self, tag: str, blob_path: str, device: str) -> None: ...
+
+    def cfgPluginConfig(self, config: cv2.typing.map_string_and_string) -> PyParams: ...
+
+    @_typing.overload
+    def cfgInputTensorLayout(self, tensor_layout: str) -> PyParams: ...
+    @_typing.overload
+    def cfgInputTensorLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
+
+    @_typing.overload
+    def cfgInputModelLayout(self, tensor_layout: str) -> PyParams: ...
+    @_typing.overload
+    def cfgInputModelLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
+
+    @_typing.overload
+    def cfgOutputTensorLayout(self, tensor_layout: str) -> PyParams: ...
+    @_typing.overload
+    def cfgOutputTensorLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
+
+    @_typing.overload
+    def cfgOutputModelLayout(self, tensor_layout: str) -> PyParams: ...
+    @_typing.overload
+    def cfgOutputModelLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
+
+    @_typing.overload
+    def cfgOutputTensorPrecision(self, precision: int) -> PyParams: ...
+    @_typing.overload
+    def cfgOutputTensorPrecision(self, precision_map: cv2.typing.map_string_and_int) -> PyParams: ...
+
+    @_typing.overload
+    def cfgReshape(self, new_shape: _typing.Sequence[int]) -> PyParams: ...
+    @_typing.overload
+    def cfgReshape(self, new_shape_map: cv2.typing.map_string_and_vector_size_t) -> PyParams: ...
+
+    def cfgNumRequests(self, nireq: int) -> PyParams: ...
+
+    @_typing.overload
+    def cfgMean(self, mean_values: _typing.Sequence[float]) -> PyParams: ...
+    @_typing.overload
+    def cfgMean(self, mean_map: cv2.typing.map_string_and_vector_float) -> PyParams: ...
+
+    @_typing.overload
+    def cfgScale(self, scale_values: _typing.Sequence[float]) -> PyParams: ...
+    @_typing.overload
+    def cfgScale(self, scale_map: cv2.typing.map_string_and_vector_float) -> PyParams: ...
+
+    @_typing.overload
+    def cfgResize(self, interpolation: int) -> PyParams: ...
+    @_typing.overload
+    def cfgResize(self, interpolation: cv2.typing.map_string_and_int) -> PyParams: ...
+
+
+
+# Functions
+@_typing.overload
+def params(tag: str, model_path: str, weights: str, device: str) -> PyParams: ...
+@_typing.overload
+def params(tag: str, bin_path: str, device: str) -> PyParams: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/own/__init__.pyi

@@ -0,0 +1,5 @@
+__all__: list[str] = []
+
+from cv2.gapi.own import detail as detail
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/own/detail/__init__.pyi

@@ -0,0 +1,10 @@
+__all__: list[str] = []
+
+MatHeader_AUTO_STEP: int
+MAT_HEADER_AUTO_STEP: int
+MatHeader_TYPE_MASK: int
+MAT_HEADER_TYPE_MASK: int
+
+
+# Classes
+

.venv/lib/python3.11/site-packages/cv2/gapi/render/__init__.pyi

@@ -0,0 +1,5 @@
+__all__: list[str] = []
+
+from cv2.gapi.render import ocv as ocv
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/render/ocv/__init__.pyi

@@ -0,0 +1,9 @@
+__all__: list[str] = []
+
+import cv2
+
+
+# Functions
+def kernels() -> cv2.GKernelPackage: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/streaming/__init__.pyi

@@ -0,0 +1,42 @@
+__all__: list[str] = []
+
+import cv2
+import typing as _typing
+
+
+# Enumerations
+sync_policy_dont_sync: int
+SYNC_POLICY_DONT_SYNC: int
+sync_policy_drop: int
+SYNC_POLICY_DROP: int
+sync_policy = int
+"""One of [sync_policy_dont_sync, SYNC_POLICY_DONT_SYNC, sync_policy_drop, SYNC_POLICY_DROP]"""
+
+
+
+# Classes
+class queue_capacity:
+    capacity: int
+
+    # Functions
+    def __init__(self, cap: int = ...) -> None: ...
+
+
+
+# Functions
+def desync(g: cv2.GMat) -> cv2.GMat: ...
+
+def seqNo(arg1: cv2.GMat) -> cv2.GOpaqueT: ...
+
+def seq_id(arg1: cv2.GMat) -> cv2.GOpaqueT: ...
+
+@_typing.overload
+def size(src: cv2.GMat) -> cv2.GOpaqueT: ...
+@_typing.overload
+def size(r: cv2.GOpaqueT) -> cv2.GOpaqueT: ...
+@_typing.overload
+def size(src: cv2.GFrame) -> cv2.GOpaqueT: ...
+
+def timestamp(arg1: cv2.GMat) -> cv2.GOpaqueT: ...
+
+

.venv/lib/python3.11/site-packages/cv2/gapi/video/__init__.pyi

@@ -0,0 +1,10 @@
+__all__: list[str] = []
+
+# Enumerations
+TYPE_BS_MOG2: int
+TYPE_BS_KNN: int
+BackgroundSubtractorType = int
+"""One of [TYPE_BS_MOG2, TYPE_BS_KNN]"""
+
+
+

.venv/lib/python3.11/site-packages/cv2/ipp/__init__.pyi

@@ -0,0 +1,14 @@
+__all__: list[str] = []
+
+# Functions
+def getIppVersion() -> str: ...
+
+def setUseIPP(flag: bool) -> None: ...
+
+def setUseIPP_NotExact(flag: bool) -> None: ...
+
+def useIPP() -> bool: ...
+
+def useIPP_NotExact() -> bool: ...
+
+

.venv/lib/python3.11/site-packages/cv2/mat_wrapper/__init__.py

@@ -0,0 +1,40 @@
+__all__ = []
+
+import numpy as np
+import cv2 as cv
+from typing import TYPE_CHECKING, Any
+
+# Same as cv2.typing.NumPyArrayNumeric, but avoids circular dependencies
+if TYPE_CHECKING:
+    _NumPyArrayNumeric = np.ndarray[Any, np.dtype[np.integer[Any] | np.floating[Any]]]
+else:
+    _NumPyArrayNumeric = np.ndarray
+
+# NumPy documentation: https://numpy.org/doc/stable/user/basics.subclassing.html
+
+
+class Mat(_NumPyArrayNumeric):
+    '''
+    cv.Mat wrapper for numpy array.
+
+    Stores extra metadata information how to interpret and process of numpy array for underlying C++ code.
+    '''
+
+    def __new__(cls, arr, **kwargs):
+        obj = arr.view(Mat)
+        return obj
+
+    def __init__(self, arr, **kwargs):
+        self.wrap_channels = kwargs.pop('wrap_channels', getattr(arr, 'wrap_channels', False))
+        if len(kwargs) > 0:
+            raise TypeError('Unknown parameters: {}'.format(repr(kwargs)))
+
+    def __array_finalize__(self, obj):
+        if obj is None:
+            return
+        self.wrap_channels = getattr(obj, 'wrap_channels', None)
+
+
+Mat.__module__ = cv.__name__
+cv.Mat = Mat
+cv._registerMatType(Mat)