initial

venv/lib/python3.12/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/python3.12/site-packages/cv2/LICENSE.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Olli-Pekka Heinisuo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

venv/lib/python3.12/site-packages/cv2/__init__.py

@@ -0,0 +1,181 @@
+'''
+OpenCV Python binary extension loader
+'''
+import os
+import importlib
+import sys
+
+__all__ = []
+
+try:
+    import numpy
+    import numpy.core.multiarray
+except ImportError:
+    print('OpenCV bindings requires "numpy" package.')
+    print('Install it via command:')
+    print('    pip install numpy')
+    raise
+
+# TODO
+# is_x64 = sys.maxsize > 2**32
+
+
+def __load_extra_py_code_for_module(base, name, enable_debug_print=False):
+    module_name = "{}.{}".format(__name__, name)
+    export_module_name = "{}.{}".format(base, name)
+    native_module = sys.modules.pop(module_name, None)
+    try:
+        py_module = importlib.import_module(module_name)
+    except ImportError as err:
+        if enable_debug_print:
+            print("Can't load Python code for module:", module_name,
+                  ". Reason:", err)
+        # Extension doesn't contain extra py code
+        return False
+
+    if base in sys.modules and not hasattr(sys.modules[base], name):
+        setattr(sys.modules[base], name, py_module)
+    sys.modules[export_module_name] = py_module
+    # If it is C extension module it is already loaded by cv2 package
+    if native_module:
+        setattr(py_module, "_native", native_module)
+        for k, v in filter(lambda kv: not hasattr(py_module, kv[0]),
+                           native_module.__dict__.items()):
+            if enable_debug_print: print('    symbol({}): {} = {}'.format(name, k, v))
+            setattr(py_module, k, v)
+    return True
+
+
+def __collect_extra_submodules(enable_debug_print=False):
+    def modules_filter(module):
+        return all((
+             # module is not internal
+             not module.startswith("_"),
+             not module.startswith("python-"),
+             # it is not a file
+             os.path.isdir(os.path.join(_extra_submodules_init_path, module))
+        ))
+    if sys.version_info[0] < 3:
+        if enable_debug_print:
+            print("Extra submodules is loaded only for Python 3")
+        return []
+
+    __INIT_FILE_PATH = os.path.abspath(__file__)
+    _extra_submodules_init_path = os.path.dirname(__INIT_FILE_PATH)
+    return filter(modules_filter, os.listdir(_extra_submodules_init_path))
+
+
+def bootstrap():
+    import sys
+
+    import copy
+    save_sys_path = copy.copy(sys.path)
+
+    if hasattr(sys, 'OpenCV_LOADER'):
+        print(sys.path)
+        raise ImportError('ERROR: recursion is detected during loading of "cv2" binary extensions. Check OpenCV installation.')
+    sys.OpenCV_LOADER = True
+
+    DEBUG = False
+    if hasattr(sys, 'OpenCV_LOADER_DEBUG'):
+        DEBUG = True
+
+    import platform
+    if DEBUG: print('OpenCV loader: os.name="{}"  platform.system()="{}"'.format(os.name, str(platform.system())))
+
+    LOADER_DIR = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
+
+    PYTHON_EXTENSIONS_PATHS = []
+    BINARIES_PATHS = []
+
+    g_vars = globals()
+    l_vars = locals().copy()
+
+    if sys.version_info[:2] < (3, 0):
+        from . load_config_py2 import exec_file_wrapper
+    else:
+        from . load_config_py3 import exec_file_wrapper
+
+    def load_first_config(fnames, required=True):
+        for fname in fnames:
+            fpath = os.path.join(LOADER_DIR, fname)
+            if not os.path.exists(fpath):
+                if DEBUG: print('OpenCV loader: config not found, skip: {}'.format(fpath))
+                continue
+            if DEBUG: print('OpenCV loader: loading config: {}'.format(fpath))
+            exec_file_wrapper(fpath, g_vars, l_vars)
+            return True
+        if required:
+            raise ImportError('OpenCV loader: missing configuration file: {}. Check OpenCV installation.'.format(fnames))
+
+    load_first_config(['config.py'], True)
+    load_first_config([
+        'config-{}.{}.py'.format(sys.version_info[0], sys.version_info[1]),
+        'config-{}.py'.format(sys.version_info[0])
+    ], True)
+
+    if DEBUG: print('OpenCV loader: PYTHON_EXTENSIONS_PATHS={}'.format(str(l_vars['PYTHON_EXTENSIONS_PATHS'])))
+    if DEBUG: print('OpenCV loader: BINARIES_PATHS={}'.format(str(l_vars['BINARIES_PATHS'])))
+
+    applySysPathWorkaround = False
+    if hasattr(sys, 'OpenCV_REPLACE_SYS_PATH_0'):
+        applySysPathWorkaround = True
+    else:
+        try:
+            BASE_DIR = os.path.dirname(LOADER_DIR)
+            if sys.path[0] == BASE_DIR or os.path.realpath(sys.path[0]) == BASE_DIR:
+                applySysPathWorkaround = True
+        except:
+            if DEBUG: print('OpenCV loader: exception during checking workaround for sys.path[0]')
+            pass  # applySysPathWorkaround is False
+
+    for p in reversed(l_vars['PYTHON_EXTENSIONS_PATHS']):
+        sys.path.insert(1 if not applySysPathWorkaround else 0, p)
+
+    if os.name == 'nt':
+        if sys.version_info[:2] >= (3, 8):  # https://github.com/python/cpython/pull/12302
+            for p in l_vars['BINARIES_PATHS']:
+                try:
+                    os.add_dll_directory(p)
+                except Exception as e:
+                    if DEBUG: print('Failed os.add_dll_directory(): '+ str(e))
+                    pass
+        os.environ['PATH'] = ';'.join(l_vars['BINARIES_PATHS']) + ';' + os.environ.get('PATH', '')
+        if DEBUG: print('OpenCV loader: PATH={}'.format(str(os.environ['PATH'])))
+    else:
+        # amending of LD_LIBRARY_PATH works for sub-processes only
+        os.environ['LD_LIBRARY_PATH'] = ':'.join(l_vars['BINARIES_PATHS']) + ':' + os.environ.get('LD_LIBRARY_PATH', '')
+
+    if DEBUG: print("Relink everything from native cv2 module to cv2 package")
+
+    py_module = sys.modules.pop("cv2")
+
+    native_module = importlib.import_module("cv2")
+
+    sys.modules["cv2"] = py_module
+    setattr(py_module, "_native", native_module)
+
+    for item_name, item in filter(lambda kv: kv[0] not in ("__file__", "__loader__", "__spec__",
+                                                           "__name__", "__package__"),
+                                  native_module.__dict__.items()):
+        if item_name not in g_vars:
+            g_vars[item_name] = item
+
+    sys.path = save_sys_path  # multiprocessing should start from bootstrap code (https://github.com/opencv/opencv/issues/18502)
+
+    try:
+        del sys.OpenCV_LOADER
+    except Exception as e:
+        if DEBUG:
+            print("Exception during delete OpenCV_LOADER:", e)
+
+    if DEBUG: print('OpenCV loader: binary extension... OK')
+
+    for submodule in __collect_extra_submodules(DEBUG):
+        if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
+            if DEBUG: print("Extra Python code for", submodule, "is loaded")
+
+    if DEBUG: print('OpenCV loader: DONE')
+
+
+bootstrap()

venv/lib/python3.12/site-packages/cv2/aruco/__init__.pyi

@@ -0,0 +1,392 @@
+__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]"""
+
+ARUCO_CCW_CENTER: int
+ARUCO_CW_TOP_LEFT_CORNER: int
+PatternPositionType = int
+"""One of [ARUCO_CCW_CENTER, ARUCO_CW_TOP_LEFT_CORNER]"""
+
+
+
+# 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]: ...
+
+
+class EstimateParameters:
+    pattern: PatternPositionType
+    useExtrinsicGuess: bool
+    solvePnPMethod: int
+
+    # Functions
+    def __init__(self) -> None: ...
+
+
+
+# Functions
+@_typing.overload
+def calibrateCameraAruco(corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike, counter: cv2.typing.MatLike, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: 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 calibrateCameraAruco(corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat, counter: cv2.UMat, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: 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 calibrateCameraArucoExtended(corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike, counter: cv2.typing.MatLike, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., stdDeviationsIntrinsics: cv2.typing.MatLike | None = ..., stdDeviationsExtrinsics: cv2.typing.MatLike | None = ..., perViewErrors: 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], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def calibrateCameraArucoExtended(corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat, counter: cv2.UMat, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., stdDeviationsIntrinsics: cv2.UMat | None = ..., stdDeviationsExtrinsics: cv2.UMat | None = ..., perViewErrors: cv2.UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat], cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def calibrateCameraCharuco(charucoCorners: _typing.Sequence[cv2.typing.MatLike], charucoIds: _typing.Sequence[cv2.typing.MatLike], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: 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 calibrateCameraCharuco(charucoCorners: _typing.Sequence[cv2.UMat], charucoIds: _typing.Sequence[cv2.UMat], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: 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 calibrateCameraCharucoExtended(charucoCorners: _typing.Sequence[cv2.typing.MatLike], charucoIds: _typing.Sequence[cv2.typing.MatLike], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., stdDeviationsIntrinsics: cv2.typing.MatLike | None = ..., stdDeviationsExtrinsics: cv2.typing.MatLike | None = ..., perViewErrors: 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], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def calibrateCameraCharucoExtended(charucoCorners: _typing.Sequence[cv2.UMat], charucoIds: _typing.Sequence[cv2.UMat], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., stdDeviationsIntrinsics: cv2.UMat | None = ..., stdDeviationsExtrinsics: cv2.UMat | None = ..., perViewErrors: cv2.UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat], cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def detectCharucoDiamond(image: cv2.typing.MatLike, markerCorners: _typing.Sequence[cv2.typing.MatLike], markerIds: cv2.typing.MatLike, squareMarkerLengthRate: float, diamondCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., diamondIds: cv2.typing.MatLike | None = ..., cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., dictionary: Dictionary = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
+@_typing.overload
+def detectCharucoDiamond(image: cv2.UMat, markerCorners: _typing.Sequence[cv2.UMat], markerIds: cv2.UMat, squareMarkerLengthRate: float, diamondCorners: _typing.Sequence[cv2.UMat] | None = ..., diamondIds: cv2.UMat | None = ..., cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., dictionary: Dictionary = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat]: ...
+
+@_typing.overload
+def detectMarkers(image: cv2.typing.MatLike, dictionary: Dictionary, corners: _typing.Sequence[cv2.typing.MatLike] | None = ..., ids: cv2.typing.MatLike | None = ..., parameters: DetectorParameters = ..., 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(image: cv2.UMat, dictionary: Dictionary, corners: _typing.Sequence[cv2.UMat] | None = ..., ids: cv2.UMat | None = ..., parameters: DetectorParameters = ..., rejectedImgPoints: _typing.Sequence[cv2.UMat] | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat]]: ...
+
+@_typing.overload
+def drawCharucoDiamond(dictionary: Dictionary, ids: cv2.typing.Vec4i, squareLength: int, markerLength: int, img: cv2.typing.MatLike | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawCharucoDiamond(dictionary: Dictionary, ids: cv2.typing.Vec4i, squareLength: int, markerLength: int, img: cv2.UMat | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.UMat: ...
+
+@_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: ...
+
+@_typing.overload
+def drawPlanarBoard(board: Board, outSize: cv2.typing.Size, marginSize: int, borderBits: int, img: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawPlanarBoard(board: Board, outSize: cv2.typing.Size, marginSize: int, borderBits: int, img: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def estimatePoseBoard(corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike, board: Board, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, useExtrinsicGuess: bool = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def estimatePoseBoard(corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat, board: Board, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvec: cv2.UMat, tvec: cv2.UMat, useExtrinsicGuess: bool = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def estimatePoseCharucoBoard(charucoCorners: cv2.typing.MatLike, charucoIds: cv2.typing.MatLike, board: CharucoBoard, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, useExtrinsicGuess: bool = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def estimatePoseCharucoBoard(charucoCorners: cv2.UMat, charucoIds: cv2.UMat, board: CharucoBoard, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvec: cv2.UMat, tvec: cv2.UMat, useExtrinsicGuess: bool = ...) -> tuple[bool, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def estimatePoseSingleMarkers(corners: _typing.Sequence[cv2.typing.MatLike], markerLength: float, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: cv2.typing.MatLike | None = ..., tvecs: cv2.typing.MatLike | None = ..., objPoints: cv2.typing.MatLike | None = ..., estimateParameters: EstimateParameters = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def estimatePoseSingleMarkers(corners: _typing.Sequence[cv2.UMat], markerLength: float, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: cv2.UMat | None = ..., tvecs: cv2.UMat | None = ..., objPoints: cv2.UMat | None = ..., estimateParameters: EstimateParameters = ...) -> tuple[cv2.UMat, cv2.UMat, 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: ...
+
+@_typing.overload
+def getBoardObjectAndImagePoints(board: Board, 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 getBoardObjectAndImagePoints(board: Board, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, objPoints: cv2.UMat | None = ..., imgPoints: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+def getPredefinedDictionary(dict: int) -> Dictionary: ...
+
+@_typing.overload
+def interpolateCornersCharuco(markerCorners: _typing.Sequence[cv2.typing.MatLike], markerIds: cv2.typing.MatLike, image: cv2.typing.MatLike, board: CharucoBoard, charucoCorners: cv2.typing.MatLike | None = ..., charucoIds: cv2.typing.MatLike | None = ..., cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., minMarkers: int = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def interpolateCornersCharuco(markerCorners: _typing.Sequence[cv2.UMat], markerIds: cv2.UMat, image: cv2.UMat, board: CharucoBoard, charucoCorners: cv2.UMat | None = ..., charucoIds: cv2.UMat | None = ..., cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., minMarkers: int = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def refineDetectedMarkers(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 = ..., minRepDistance: float = ..., errorCorrectionRate: float = ..., checkAllOrders: bool = ..., recoveredIdxs: cv2.typing.MatLike | None = ..., parameters: DetectorParameters = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
+@_typing.overload
+def refineDetectedMarkers(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 = ..., minRepDistance: float = ..., errorCorrectionRate: float = ..., checkAllOrders: bool = ..., recoveredIdxs: cv2.UMat | None = ..., parameters: DetectorParameters = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
+
+@_typing.overload
+def testCharucoCornersCollinear(board: CharucoBoard, charucoIds: cv2.typing.MatLike) -> bool: ...
+@_typing.overload
+def testCharucoCornersCollinear(board: CharucoBoard, charucoIds: cv2.UMat) -> bool: ...
+
+

venv/lib/python3.12/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/python3.12/site-packages/cv2/bgsegm/__init__.pyi

@@ -0,0 +1,177 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+LSBP_CAMERA_MOTION_COMPENSATION_NONE: int
+LSBP_CAMERA_MOTION_COMPENSATION_LK: int
+LSBPCameraMotionCompensation = int
+"""One of [LSBP_CAMERA_MOTION_COMPENSATION_NONE, LSBP_CAMERA_MOTION_COMPENSATION_LK]"""
+
+
+
+# Classes
+class BackgroundSubtractorMOG(cv2.BackgroundSubtractor):
+    # Functions
+    def getHistory(self) -> int: ...
+
+    def setHistory(self, nframes: int) -> None: ...
+
+    def getNMixtures(self) -> int: ...
+
+    def setNMixtures(self, nmix: int) -> None: ...
+
+    def getBackgroundRatio(self) -> float: ...
+
+    def setBackgroundRatio(self, backgroundRatio: float) -> None: ...
+
+    def getNoiseSigma(self) -> float: ...
+
+    def setNoiseSigma(self, noiseSigma: float) -> None: ...
+
+
+class BackgroundSubtractorGMG(cv2.BackgroundSubtractor):
+    # Functions
+    @_typing.overload
+    def apply(self, image: cv2.typing.MatLike, fgmask: cv2.typing.MatLike | None = ..., learningRate: float = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, image: cv2.UMat, fgmask: cv2.UMat | None = ..., learningRate: float = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def getMaxFeatures(self) -> int: ...
+
+    def setMaxFeatures(self, maxFeatures: int) -> None: ...
+
+    def getDefaultLearningRate(self) -> float: ...
+
+    def setDefaultLearningRate(self, lr: float) -> None: ...
+
+    def getNumFrames(self) -> int: ...
+
+    def setNumFrames(self, nframes: int) -> None: ...
+
+    def getQuantizationLevels(self) -> int: ...
+
+    def setQuantizationLevels(self, nlevels: int) -> None: ...
+
+    def getBackgroundPrior(self) -> float: ...
+
+    def setBackgroundPrior(self, bgprior: float) -> None: ...
+
+    def getSmoothingRadius(self) -> int: ...
+
+    def setSmoothingRadius(self, radius: int) -> None: ...
+
+    def getDecisionThreshold(self) -> float: ...
+
+    def setDecisionThreshold(self, thresh: float) -> None: ...
+
+    def getUpdateBackgroundModel(self) -> bool: ...
+
+    def setUpdateBackgroundModel(self, update: bool) -> None: ...
+
+    def getMinVal(self) -> float: ...
+
+    def setMinVal(self, val: float) -> None: ...
+
+    def getMaxVal(self) -> float: ...
+
+    def setMaxVal(self, val: float) -> None: ...
+
+
+class BackgroundSubtractorCNT(cv2.BackgroundSubtractor):
+    # Functions
+    @_typing.overload
+    def apply(self, image: cv2.typing.MatLike, fgmask: cv2.typing.MatLike | None = ..., learningRate: float = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, image: cv2.UMat, fgmask: cv2.UMat | None = ..., learningRate: float = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def getMinPixelStability(self) -> int: ...
+
+    def setMinPixelStability(self, value: int) -> None: ...
+
+    def getMaxPixelStability(self) -> int: ...
+
+    def setMaxPixelStability(self, value: int) -> None: ...
+
+    def getUseHistory(self) -> bool: ...
+
+    def setUseHistory(self, value: bool) -> None: ...
+
+    def getIsParallel(self) -> bool: ...
+
+    def setIsParallel(self, value: bool) -> None: ...
+
+
+class BackgroundSubtractorGSOC(cv2.BackgroundSubtractor):
+    # Functions
+    @_typing.overload
+    def apply(self, image: cv2.typing.MatLike, fgmask: cv2.typing.MatLike | None = ..., learningRate: float = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, image: cv2.UMat, fgmask: cv2.UMat | None = ..., learningRate: float = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+
+class BackgroundSubtractorLSBP(cv2.BackgroundSubtractor):
+    # Functions
+    @_typing.overload
+    def apply(self, image: cv2.typing.MatLike, fgmask: cv2.typing.MatLike | None = ..., learningRate: float = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def apply(self, image: cv2.UMat, fgmask: cv2.UMat | None = ..., learningRate: float = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getBackgroundImage(self, backgroundImage: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+
+class BackgroundSubtractorLSBPDesc:
+    ...
+
+class SyntheticSequenceGenerator(cv2.Algorithm):
+    # Functions
+    @_typing.overload
+    def __init__(self, background: cv2.typing.MatLike, object: cv2.typing.MatLike, amplitude: float, wavelength: float, wavespeed: float, objspeed: float) -> None: ...
+    @_typing.overload
+    def __init__(self, background: cv2.UMat, object: cv2.UMat, amplitude: float, wavelength: float, wavespeed: float, objspeed: float) -> None: ...
+
+    @_typing.overload
+    def getNextFrame(self, frame: cv2.typing.MatLike | None = ..., gtMask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def getNextFrame(self, frame: cv2.UMat | None = ..., gtMask: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+
+
+# Functions
+def createBackgroundSubtractorCNT(minPixelStability: int = ..., useHistory: bool = ..., maxPixelStability: int = ..., isParallel: bool = ...) -> BackgroundSubtractorCNT: ...
+
+def createBackgroundSubtractorGMG(initializationFrames: int = ..., decisionThreshold: float = ...) -> BackgroundSubtractorGMG: ...
+
+def createBackgroundSubtractorGSOC(mc: int = ..., nSamples: int = ..., replaceRate: float = ..., propagationRate: float = ..., hitsThreshold: int = ..., alpha: float = ..., beta: float = ..., blinkingSupressionDecay: float = ..., blinkingSupressionMultiplier: float = ..., noiseRemovalThresholdFacBG: float = ..., noiseRemovalThresholdFacFG: float = ...) -> BackgroundSubtractorGSOC: ...
+
+def createBackgroundSubtractorLSBP(mc: int = ..., nSamples: int = ..., LSBPRadius: int = ..., Tlower: float = ..., Tupper: float = ..., Tinc: float = ..., Tdec: float = ..., Rscale: float = ..., Rincdec: float = ..., noiseRemovalThresholdFacBG: float = ..., noiseRemovalThresholdFacFG: float = ..., LSBPthreshold: int = ..., minCount: int = ...) -> BackgroundSubtractorLSBP: ...
+
+def createBackgroundSubtractorMOG(history: int = ..., nmixtures: int = ..., backgroundRatio: float = ..., noiseSigma: float = ...) -> BackgroundSubtractorMOG: ...
+
+@_typing.overload
+def createSyntheticSequenceGenerator(background: cv2.typing.MatLike, object: cv2.typing.MatLike, amplitude: float = ..., wavelength: float = ..., wavespeed: float = ..., objspeed: float = ...) -> SyntheticSequenceGenerator: ...
+@_typing.overload
+def createSyntheticSequenceGenerator(background: cv2.UMat, object: cv2.UMat, amplitude: float = ..., wavelength: float = ..., wavespeed: float = ..., objspeed: float = ...) -> SyntheticSequenceGenerator: ...
+
+

venv/lib/python3.12/site-packages/cv2/bioinspired/__init__.pyi

@@ -0,0 +1,121 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+RETINA_COLOR_RANDOM: int
+RETINA_COLOR_DIAGONAL: int
+RETINA_COLOR_BAYER: int
+
+
+
+# Classes
+class Retina(cv2.Algorithm):
+    # Functions
+    def getInputSize(self) -> cv2.typing.Size: ...
+
+    def getOutputSize(self) -> cv2.typing.Size: ...
+
+    def setup(self, retinaParameterFile: str = ..., applyDefaultSetupOnFailure: bool = ...) -> None: ...
+
+    def printSetup(self) -> str: ...
+
+    def write(self, fs: str) -> None: ...
+
+    def setupOPLandIPLParvoChannel(self, colorMode: bool = ..., normaliseOutput: bool = ..., photoreceptorsLocalAdaptationSensitivity: float = ..., photoreceptorsTemporalConstant: float = ..., photoreceptorsSpatialConstant: float = ..., horizontalCellsGain: float = ..., HcellsTemporalConstant: float = ..., HcellsSpatialConstant: float = ..., ganglionCellsSensitivity: float = ...) -> None: ...
+
+    def setupIPLMagnoChannel(self, normaliseOutput: bool = ..., parasolCells_beta: float = ..., parasolCells_tau: float = ..., parasolCells_k: float = ..., amacrinCellsTemporalCutFrequency: float = ..., V0CompressionParameter: float = ..., localAdaptintegration_tau: float = ..., localAdaptintegration_k: float = ...) -> None: ...
+
+    @_typing.overload
+    def run(self, inputImage: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def run(self, inputImage: cv2.UMat) -> None: ...
+
+    @_typing.overload
+    def applyFastToneMapping(self, inputImage: cv2.typing.MatLike, outputToneMappedImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def applyFastToneMapping(self, inputImage: cv2.UMat, outputToneMappedImage: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getParvo(self, retinaOutput_parvo: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getParvo(self, retinaOutput_parvo: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getParvoRAW(self, retinaOutput_parvo: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getParvoRAW(self, retinaOutput_parvo: cv2.UMat | None = ...) -> cv2.UMat: ...
+    @_typing.overload
+    def getParvoRAW(self) -> cv2.typing.MatLike: ...
+
+    @_typing.overload
+    def getMagno(self, retinaOutput_magno: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getMagno(self, retinaOutput_magno: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getMagnoRAW(self, retinaOutput_magno: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getMagnoRAW(self, retinaOutput_magno: cv2.UMat | None = ...) -> cv2.UMat: ...
+    @_typing.overload
+    def getMagnoRAW(self) -> cv2.typing.MatLike: ...
+
+    def setColorSaturation(self, saturateColors: bool = ..., colorSaturationValue: float = ...) -> None: ...
+
+    def clearBuffers(self) -> None: ...
+
+    def activateMovingContoursProcessing(self, activate: bool) -> None: ...
+
+    def activateContoursProcessing(self, activate: bool) -> None: ...
+
+    @classmethod
+    @_typing.overload
+    def create(cls, inputSize: cv2.typing.Size) -> Retina: ...
+    @classmethod
+    @_typing.overload
+    def create(cls, inputSize: cv2.typing.Size, colorMode: bool, colorSamplingMethod: int = ..., useRetinaLogSampling: bool = ..., reductionFactor: float = ..., samplingStrength: float = ...) -> Retina: ...
+
+
+class RetinaFastToneMapping(cv2.Algorithm):
+    # Functions
+    @_typing.overload
+    def applyFastToneMapping(self, inputImage: cv2.typing.MatLike, outputToneMappedImage: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def applyFastToneMapping(self, inputImage: cv2.UMat, outputToneMappedImage: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def setup(self, photoreceptorsNeighborhoodRadius: float = ..., ganglioncellsNeighborhoodRadius: float = ..., meanLuminanceModulatorK: float = ...) -> None: ...
+
+    @classmethod
+    def create(cls, inputSize: cv2.typing.Size) -> RetinaFastToneMapping: ...
+
+
+class TransientAreasSegmentationModule(cv2.Algorithm):
+    # Functions
+    def getSize(self) -> cv2.typing.Size: ...
+
+    def setup(self, segmentationParameterFile: str = ..., applyDefaultSetupOnFailure: bool = ...) -> None: ...
+
+    def printSetup(self) -> str: ...
+
+    def write(self, fs: str) -> None: ...
+
+    @_typing.overload
+    def run(self, inputToSegment: cv2.typing.MatLike, channelIndex: int = ...) -> None: ...
+    @_typing.overload
+    def run(self, inputToSegment: cv2.UMat, channelIndex: int = ...) -> None: ...
+
+    @_typing.overload
+    def getSegmentationPicture(self, transientAreas: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getSegmentationPicture(self, transientAreas: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def clearAllBuffers(self) -> None: ...
+
+    @classmethod
+    def create(cls, inputSize: cv2.typing.Size) -> TransientAreasSegmentationModule: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/ccm/__init__.pyi

@@ -0,0 +1,167 @@
+__all__: list[str] = []
+
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+CCM_3x3: int
+CCM_3X3: int
+CCM_4x3: int
+CCM_4X3: int
+CCM_TYPE = int
+"""One of [CCM_3x3, CCM_3X3, CCM_4x3, CCM_4X3]"""
+
+INITIAL_METHOD_WHITE_BALANCE: int
+INITIAL_METHOD_LEAST_SQUARE: int
+INITIAL_METHOD_TYPE = int
+"""One of [INITIAL_METHOD_WHITE_BALANCE, INITIAL_METHOD_LEAST_SQUARE]"""
+
+COLORCHECKER_Macbeth: int
+COLORCHECKER_MACBETH: int
+COLORCHECKER_Vinyl: int
+COLORCHECKER_VINYL: int
+COLORCHECKER_DigitalSG: int
+COLORCHECKER_DIGITAL_SG: int
+CONST_COLOR = int
+"""One of [COLORCHECKER_Macbeth, COLORCHECKER_MACBETH, COLORCHECKER_Vinyl, COLORCHECKER_VINYL, COLORCHECKER_DigitalSG, COLORCHECKER_DIGITAL_SG]"""
+
+COLOR_SPACE_sRGB: int
+COLOR_SPACE_S_RGB: int
+COLOR_SPACE_sRGBL: int
+COLOR_SPACE_S_RGBL: int
+COLOR_SPACE_AdobeRGB: int
+COLOR_SPACE_ADOBE_RGB: int
+COLOR_SPACE_AdobeRGBL: int
+COLOR_SPACE_ADOBE_RGBL: int
+COLOR_SPACE_WideGamutRGB: int
+COLOR_SPACE_WIDE_GAMUT_RGB: int
+COLOR_SPACE_WideGamutRGBL: int
+COLOR_SPACE_WIDE_GAMUT_RGBL: int
+COLOR_SPACE_ProPhotoRGB: int
+COLOR_SPACE_PRO_PHOTO_RGB: int
+COLOR_SPACE_ProPhotoRGBL: int
+COLOR_SPACE_PRO_PHOTO_RGBL: int
+COLOR_SPACE_DCI_P3_RGB: int
+COLOR_SPACE_DCI_P3_RGBL: int
+COLOR_SPACE_AppleRGB: int
+COLOR_SPACE_APPLE_RGB: int
+COLOR_SPACE_AppleRGBL: int
+COLOR_SPACE_APPLE_RGBL: int
+COLOR_SPACE_REC_709_RGB: int
+COLOR_SPACE_REC_709_RGBL: int
+COLOR_SPACE_REC_2020_RGB: int
+COLOR_SPACE_REC_2020_RGBL: int
+COLOR_SPACE_XYZ_D65_2: int
+COLOR_SPACE_XYZ_D65_10: int
+COLOR_SPACE_XYZ_D50_2: int
+COLOR_SPACE_XYZ_D50_10: int
+COLOR_SPACE_XYZ_A_2: int
+COLOR_SPACE_XYZ_A_10: int
+COLOR_SPACE_XYZ_D55_2: int
+COLOR_SPACE_XYZ_D55_10: int
+COLOR_SPACE_XYZ_D75_2: int
+COLOR_SPACE_XYZ_D75_10: int
+COLOR_SPACE_XYZ_E_2: int
+COLOR_SPACE_XYZ_E_10: int
+COLOR_SPACE_Lab_D65_2: int
+COLOR_SPACE_LAB_D65_2: int
+COLOR_SPACE_Lab_D65_10: int
+COLOR_SPACE_LAB_D65_10: int
+COLOR_SPACE_Lab_D50_2: int
+COLOR_SPACE_LAB_D50_2: int
+COLOR_SPACE_Lab_D50_10: int
+COLOR_SPACE_LAB_D50_10: int
+COLOR_SPACE_Lab_A_2: int
+COLOR_SPACE_LAB_A_2: int
+COLOR_SPACE_Lab_A_10: int
+COLOR_SPACE_LAB_A_10: int
+COLOR_SPACE_Lab_D55_2: int
+COLOR_SPACE_LAB_D55_2: int
+COLOR_SPACE_Lab_D55_10: int
+COLOR_SPACE_LAB_D55_10: int
+COLOR_SPACE_Lab_D75_2: int
+COLOR_SPACE_LAB_D75_2: int
+COLOR_SPACE_Lab_D75_10: int
+COLOR_SPACE_LAB_D75_10: int
+COLOR_SPACE_Lab_E_2: int
+COLOR_SPACE_LAB_E_2: int
+COLOR_SPACE_Lab_E_10: int
+COLOR_SPACE_LAB_E_10: int
+COLOR_SPACE = int
+"""One of [COLOR_SPACE_sRGB, COLOR_SPACE_S_RGB, COLOR_SPACE_sRGBL, COLOR_SPACE_S_RGBL, COLOR_SPACE_AdobeRGB, COLOR_SPACE_ADOBE_RGB, COLOR_SPACE_AdobeRGBL, COLOR_SPACE_ADOBE_RGBL, COLOR_SPACE_WideGamutRGB, COLOR_SPACE_WIDE_GAMUT_RGB, COLOR_SPACE_WideGamutRGBL, COLOR_SPACE_WIDE_GAMUT_RGBL, COLOR_SPACE_ProPhotoRGB, COLOR_SPACE_PRO_PHOTO_RGB, COLOR_SPACE_ProPhotoRGBL, COLOR_SPACE_PRO_PHOTO_RGBL, COLOR_SPACE_DCI_P3_RGB, COLOR_SPACE_DCI_P3_RGBL, COLOR_SPACE_AppleRGB, COLOR_SPACE_APPLE_RGB, COLOR_SPACE_AppleRGBL, COLOR_SPACE_APPLE_RGBL, COLOR_SPACE_REC_709_RGB, COLOR_SPACE_REC_709_RGBL, COLOR_SPACE_REC_2020_RGB, COLOR_SPACE_REC_2020_RGBL, COLOR_SPACE_XYZ_D65_2, COLOR_SPACE_XYZ_D65_10, COLOR_SPACE_XYZ_D50_2, COLOR_SPACE_XYZ_D50_10, COLOR_SPACE_XYZ_A_2, COLOR_SPACE_XYZ_A_10, COLOR_SPACE_XYZ_D55_2, COLOR_SPACE_XYZ_D55_10, COLOR_SPACE_XYZ_D75_2, COLOR_SPACE_XYZ_D75_10, COLOR_SPACE_XYZ_E_2, COLOR_SPACE_XYZ_E_10, COLOR_SPACE_Lab_D65_2, COLOR_SPACE_LAB_D65_2, COLOR_SPACE_Lab_D65_10, COLOR_SPACE_LAB_D65_10, COLOR_SPACE_Lab_D50_2, COLOR_SPACE_LAB_D50_2, COLOR_SPACE_Lab_D50_10, COLOR_SPACE_LAB_D50_10, COLOR_SPACE_Lab_A_2, COLOR_SPACE_LAB_A_2, COLOR_SPACE_Lab_A_10, COLOR_SPACE_LAB_A_10, COLOR_SPACE_Lab_D55_2, COLOR_SPACE_LAB_D55_2, COLOR_SPACE_Lab_D55_10, COLOR_SPACE_LAB_D55_10, COLOR_SPACE_Lab_D75_2, COLOR_SPACE_LAB_D75_2, COLOR_SPACE_Lab_D75_10, COLOR_SPACE_LAB_D75_10, COLOR_SPACE_Lab_E_2, COLOR_SPACE_LAB_E_2, COLOR_SPACE_Lab_E_10, COLOR_SPACE_LAB_E_10]"""
+
+LINEARIZATION_IDENTITY: int
+LINEARIZATION_GAMMA: int
+LINEARIZATION_COLORPOLYFIT: int
+LINEARIZATION_COLORLOGPOLYFIT: int
+LINEARIZATION_GRAYPOLYFIT: int
+LINEARIZATION_GRAYLOGPOLYFIT: int
+LINEAR_TYPE = int
+"""One of [LINEARIZATION_IDENTITY, LINEARIZATION_GAMMA, LINEARIZATION_COLORPOLYFIT, LINEARIZATION_COLORLOGPOLYFIT, LINEARIZATION_GRAYPOLYFIT, LINEARIZATION_GRAYLOGPOLYFIT]"""
+
+DISTANCE_CIE76: int
+DISTANCE_CIE94_GRAPHIC_ARTS: int
+DISTANCE_CIE94_TEXTILES: int
+DISTANCE_CIE2000: int
+DISTANCE_CMC_1TO1: int
+DISTANCE_CMC_2TO1: int
+DISTANCE_RGB: int
+DISTANCE_RGBL: int
+DISTANCE_TYPE = int
+"""One of [DISTANCE_CIE76, DISTANCE_CIE94_GRAPHIC_ARTS, DISTANCE_CIE94_TEXTILES, DISTANCE_CIE2000, DISTANCE_CMC_1TO1, DISTANCE_CMC_2TO1, DISTANCE_RGB, DISTANCE_RGBL]"""
+
+
+
+# Classes
+class ColorCorrectionModel:
+    # Functions
+    @_typing.overload
+    def __init__(self, src: cv2.typing.MatLike, constcolor: CONST_COLOR) -> None: ...
+    @_typing.overload
+    def __init__(self, src: cv2.typing.MatLike, colors: cv2.typing.MatLike, ref_cs: COLOR_SPACE) -> None: ...
+    @_typing.overload
+    def __init__(self, src: cv2.typing.MatLike, colors: cv2.typing.MatLike, ref_cs: COLOR_SPACE, colored: cv2.typing.MatLike) -> None: ...
+
+    def setColorSpace(self, cs: COLOR_SPACE) -> None: ...
+
+    def setCCM_TYPE(self, ccm_type: CCM_TYPE) -> None: ...
+
+    def setDistance(self, distance: DISTANCE_TYPE) -> None: ...
+
+    def setLinear(self, linear_type: LINEAR_TYPE) -> None: ...
+
+    def setLinearGamma(self, gamma: float) -> None: ...
+
+    def setLinearDegree(self, deg: int) -> None: ...
+
+    def setSaturatedThreshold(self, lower: float, upper: float) -> None: ...
+
+    def setWeightsList(self, weights_list: cv2.typing.MatLike) -> None: ...
+
+    def setWeightCoeff(self, weights_coeff: float) -> None: ...
+
+    def setInitialMethod(self, initial_method_type: INITIAL_METHOD_TYPE) -> None: ...
+
+    def setMaxCount(self, max_count: int) -> None: ...
+
+    def setEpsilon(self, epsilon: float) -> None: ...
+
+    def run(self) -> None: ...
+
+    def getCCM(self) -> cv2.typing.MatLike: ...
+
+    def getLoss(self) -> float: ...
+
+    def get_src_rgbl(self) -> cv2.typing.MatLike: ...
+
+    def get_dst_rgbl(self) -> cv2.typing.MatLike: ...
+
+    def getMask(self) -> cv2.typing.MatLike: ...
+
+    def getWeights(self) -> cv2.typing.MatLike: ...
+
+    def infer(self, img: cv2.typing.MatLike, islinear: bool = ...) -> cv2.typing.MatLike: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/colored_kinfu/__init__.pyi

@@ -0,0 +1,96 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.kinfu
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class Params:
+    frameSize: cv2.typing.Size
+    rgb_frameSize: cv2.typing.Size
+    volumeType: cv2.kinfu.VolumeType
+    intr: cv2.typing.Matx33f
+    rgb_intr: cv2.typing.Matx33f
+    depthFactor: float
+    bilateral_sigma_depth: float
+    bilateral_sigma_spatial: float
+    bilateral_kernel_size: int
+    pyramidLevels: int
+    volumeDims: cv2.typing.Vec3i
+    voxelSize: float
+    tsdf_min_camera_movement: float
+    tsdf_trunc_dist: float
+    tsdf_max_weight: int
+    raycast_step_factor: float
+    lightPose: cv2.typing.Vec3f
+    icpDistThresh: float
+    icpAngleThresh: float
+    icpIterations: _typing.Sequence[int]
+    truncateThreshold: float
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, volumeInitialPoseRot: cv2.typing.Matx33f, volumeInitialPoseTransl: cv2.typing.Vec3f) -> None: ...
+    @_typing.overload
+    def __init__(self, volumeInitialPose: cv2.typing.Matx44f) -> None: ...
+
+    @_typing.overload
+    def setInitialVolumePose(self, R: cv2.typing.Matx33f, t: cv2.typing.Vec3f) -> None: ...
+    @_typing.overload
+    def setInitialVolumePose(self, homogen_tf: cv2.typing.Matx44f) -> None: ...
+
+    @classmethod
+    def defaultParams(cls) -> Params: ...
+
+    @classmethod
+    def coarseParams(cls) -> Params: ...
+
+    @classmethod
+    def hashTSDFParams(cls, isCoarse: bool) -> Params: ...
+
+    @classmethod
+    def coloredTSDFParams(cls, isCoarse: bool) -> Params: ...
+
+
+class ColoredKinFu:
+    # Functions
+    @classmethod
+    def create(cls, _params: Params) -> ColoredKinFu: ...
+
+    @_typing.overload
+    def render(self, image: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, image: cv2.UMat | None = ...) -> cv2.UMat: ...
+    @_typing.overload
+    def render(self, cameraPose: cv2.typing.Matx44f, image: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, cameraPose: cv2.typing.Matx44f, image: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getCloud(self, points: cv2.typing.MatLike | None = ..., normals: cv2.typing.MatLike | None = ..., colors: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def getCloud(self, points: cv2.UMat | None = ..., normals: cv2.UMat | None = ..., colors: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def getPoints(self, points: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getPoints(self, points: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getNormals(self, points: cv2.typing.MatLike, normals: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getNormals(self, points: cv2.UMat, normals: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def reset(self) -> None: ...
+
+    @_typing.overload
+    def update(self, depth: cv2.typing.MatLike, rgb: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def update(self, depth: cv2.UMat, rgb: cv2.UMat) -> bool: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/config-3.py

@@ -0,0 +1,24 @@
+PYTHON_EXTENSIONS_PATHS = [
+    LOADER_DIR
+] + PYTHON_EXTENSIONS_PATHS
+
+ci_and_not_headless = False
+
+try:
+    from .version import ci_build, headless
+
+    ci_and_not_headless = ci_build and not headless
+except:
+    pass
+
+# the Qt plugin is included currently only in the pre-built wheels
+if sys.platform.startswith("linux") and ci_and_not_headless:
+    os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)), "qt", "plugins"
+    )
+
+# Qt will throw warning on Linux if fonts are not found
+if sys.platform.startswith("linux") and ci_and_not_headless:
+    os.environ["QT_QPA_FONTDIR"] = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)), "qt", "fonts"
+    )

venv/lib/python3.12/site-packages/cv2/config.py

@@ -0,0 +1,5 @@
+import os
+
+BINARIES_PATHS = [
+    os.path.join(os.path.join(LOADER_DIR, '../../'), 'lib64')
+] + BINARIES_PATHS

venv/lib/python3.12/site-packages/cv2/cuda/__init__.pyi

@@ -0,0 +1,551 @@
+__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]"""
+
+SURF_CUDA_X_ROW: int
+SURF_CUDA_Y_ROW: int
+SURF_CUDA_LAPLACIAN_ROW: int
+SURF_CUDA_OCTAVE_ROW: int
+SURF_CUDA_SIZE_ROW: int
+SURF_CUDA_ANGLE_ROW: int
+SURF_CUDA_HESSIAN_ROW: int
+SURF_CUDA_ROWS_COUNT: int
+SURF_CUDA_KeypointLayout = int
+"""One of [SURF_CUDA_X_ROW, SURF_CUDA_Y_ROW, SURF_CUDA_LAPLACIAN_ROW, SURF_CUDA_OCTAVE_ROW, SURF_CUDA_SIZE_ROW, SURF_CUDA_ANGLE_ROW, SURF_CUDA_HESSIAN_ROW, SURF_CUDA_ROWS_COUNT]"""
+
+
+# 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: ...
+
+
+class SURF_CUDA:
+    @property
+    def hessianThreshold(self) -> float: ...
+    @property
+    def nOctaves(self) -> int: ...
+    @property
+    def nOctaveLayers(self) -> int: ...
+    @property
+    def extended(self) -> bool: ...
+    @property
+    def upright(self) -> bool: ...
+    @property
+    def keypointsRatio(self) -> float: ...
+
+    # Functions
+    @classmethod
+    def create(cls, _hessianThreshold: float, _nOctaves: int = ..., _nOctaveLayers: int = ..., _extended: bool = ..., _keypointsRatio: float = ..., _upright: bool = ...) -> SURF_CUDA: ...
+
+    def descriptorSize(self) -> int: ...
+
+    def defaultNorm(self) -> int: ...
+
+    def downloadKeypoints(self, keypointsGPU: GpuMat) -> _typing.Sequence[cv2.KeyPoint]: ...
+
+    def detect(self, img: GpuMat, mask: GpuMat, keypoints: GpuMat | None = ...) -> GpuMat: ...
+
+    def detectWithDescriptors(self, img: GpuMat, mask: GpuMat, keypoints: GpuMat | None = ..., descriptors: GpuMat | None = ..., useProvidedKeypoints: bool = ...) -> tuple[GpuMat, GpuMat]: ...
+
+
+
+# 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/python3.12/site-packages/cv2/data/__init__.py

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

venv/lib/python3.12/site-packages/cv2/datasets/__init__.pyi

@@ -0,0 +1,80 @@
+__all__: list[str] = []
+
+# Enumerations
+male: int
+MALE: int
+female: int
+FEMALE: int
+none: int
+NONE: int
+genderType = int
+"""One of [male, MALE, female, FEMALE, none, NONE]"""
+
+circle: int
+CIRCLE: int
+triangle: int
+TRIANGLE: int
+updown: int
+UPDOWN: int
+rightleft: int
+RIGHTLEFT: int
+wave: int
+WAVE: int
+z: int
+Z: int
+cross: int
+CROSS: int
+comehere: int
+COMEHERE: int
+turnaround: int
+TURNAROUND: int
+pat: int
+PAT: int
+actionType = int
+"""One of [circle, CIRCLE, triangle, TRIANGLE, updown, UPDOWN, rightleft, RIGHTLEFT, wave, WAVE, z, Z, cross, CROSS, comehere, COMEHERE, turnaround, TURNAROUND, pat, PAT]"""
+
+fist: int
+FIST: int
+index: int
+INDEX: int
+flat: int
+FLAT: int
+poseType = int
+"""One of [fist, FIST, index, INDEX, flat, FLAT]"""
+
+light: int
+LIGHT: int
+dark: int
+DARK: int
+illuminationType = int
+"""One of [light, LIGHT, dark, DARK]"""
+
+woodenBoard: int
+WOODEN_BOARD: int
+whitePaper: int
+WHITE_PAPER: int
+paperWithCharacters: int
+PAPER_WITH_CHARACTERS: int
+backgroundType = int
+"""One of [woodenBoard, WOODEN_BOARD, whitePaper, WHITE_PAPER, paperWithCharacters, PAPER_WITH_CHARACTERS]"""
+
+humaneva_1: int
+HUMANEVA_1: int
+humaneva_2: int
+HUMANEVA_2: int
+datasetType = int
+"""One of [humaneva_1, HUMANEVA_1, humaneva_2, HUMANEVA_2]"""
+
+POS: int
+NEG: int
+sampleType = int
+"""One of [POS, NEG]"""
+
+LEFT: int
+RIGHT: int
+LADYBUG: int
+imageType = int
+"""One of [LEFT, RIGHT, LADYBUG]"""
+
+
+

venv/lib/python3.12/site-packages/cv2/detail/__init__.pyi

@@ -0,0 +1,627 @@
+__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]"""
+
+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]"""
+
+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]"""
+
+
+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
+
+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]"""
+
+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]"""
+
+CvFeatureParams_HAAR: int
+CV_FEATURE_PARAMS_HAAR: int
+CvFeatureParams_LBP: int
+CV_FEATURE_PARAMS_LBP: int
+CvFeatureParams_HOG: int
+CV_FEATURE_PARAMS_HOG: int
+CvFeatureParams_FeatureType = int
+"""One of [CvFeatureParams_HAAR, CV_FEATURE_PARAMS_HAAR, CvFeatureParams_LBP, CV_FEATURE_PARAMS_LBP, CvFeatureParams_HOG, CV_FEATURE_PARAMS_HOG]"""
+
+TrackerContribSamplerCSC_MODE_INIT_POS: int
+TRACKER_CONTRIB_SAMPLER_CSC_MODE_INIT_POS: int
+TrackerContribSamplerCSC_MODE_INIT_NEG: int
+TRACKER_CONTRIB_SAMPLER_CSC_MODE_INIT_NEG: int
+TrackerContribSamplerCSC_MODE_TRACK_POS: int
+TRACKER_CONTRIB_SAMPLER_CSC_MODE_TRACK_POS: int
+TrackerContribSamplerCSC_MODE_TRACK_NEG: int
+TRACKER_CONTRIB_SAMPLER_CSC_MODE_TRACK_NEG: int
+TrackerContribSamplerCSC_MODE_DETECT: int
+TRACKER_CONTRIB_SAMPLER_CSC_MODE_DETECT: int
+
+TrackerSamplerCS_MODE_POSITIVE: int
+TRACKER_SAMPLER_CS_MODE_POSITIVE: int
+TrackerSamplerCS_MODE_NEGATIVE: int
+TRACKER_SAMPLER_CS_MODE_NEGATIVE: int
+TrackerSamplerCS_MODE_CLASSIFY: int
+TRACKER_SAMPLER_CS_MODE_CLASSIFY: int
+
+
+# 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/python3.12/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/python3.12/site-packages/cv2/dnn_superres/__init__.pyi

@@ -0,0 +1,37 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class DnnSuperResImpl:
+    # Functions
+    @classmethod
+    def create(cls) -> DnnSuperResImpl: ...
+
+    def readModel(self, path: str) -> None: ...
+
+    def setModel(self, algo: str, scale: int) -> None: ...
+
+    def setPreferableBackend(self, backendId: int) -> None: ...
+
+    def setPreferableTarget(self, targetId: int) -> None: ...
+
+    @_typing.overload
+    def upsample(self, img: cv2.typing.MatLike, result: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def upsample(self, img: cv2.UMat, result: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def upsampleMultioutput(self, img: cv2.typing.MatLike, imgs_new: _typing.Sequence[cv2.typing.MatLike], scale_factors: _typing.Sequence[int], node_names: _typing.Sequence[str]) -> None: ...
+    @_typing.overload
+    def upsampleMultioutput(self, img: cv2.UMat, imgs_new: _typing.Sequence[cv2.typing.MatLike], scale_factors: _typing.Sequence[int], node_names: _typing.Sequence[str]) -> None: ...
+
+    def getScale(self) -> int: ...
+
+    def getAlgorithm(self) -> str: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/dpm/__init__.pyi

@@ -0,0 +1,10 @@
+__all__: list[str] = []
+
+# Classes
+class DPMDetector:
+    # Classes
+    class ObjectDetection:
+        ...
+
+
+

venv/lib/python3.12/site-packages/cv2/dynafu/__init__.pyi

@@ -0,0 +1,43 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.kinfu
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class DynaFu:
+    # Functions
+    @classmethod
+    def create(cls, _params: cv2.kinfu.Params) -> DynaFu: ...
+
+    @_typing.overload
+    def render(self, image: cv2.typing.MatLike | None = ..., cameraPose: cv2.typing.Matx44f = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, image: cv2.UMat | None = ..., cameraPose: cv2.typing.Matx44f = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getCloud(self, points: cv2.typing.MatLike | None = ..., normals: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def getCloud(self, points: cv2.UMat | None = ..., normals: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def getPoints(self, points: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getPoints(self, points: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getNormals(self, points: cv2.typing.MatLike, normals: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getNormals(self, points: cv2.UMat, normals: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def reset(self) -> None: ...
+
+    @_typing.overload
+    def update(self, depth: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def update(self, depth: cv2.UMat) -> bool: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/face/__init__.pyi

@@ -0,0 +1,219 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class FaceRecognizer(cv2.Algorithm):
+    # Functions
+    @_typing.overload
+    def train(self, src: _typing.Sequence[cv2.typing.MatLike], labels: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def train(self, src: _typing.Sequence[cv2.UMat], labels: cv2.UMat) -> None: ...
+
+    @_typing.overload
+    def update(self, src: _typing.Sequence[cv2.typing.MatLike], labels: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def update(self, src: _typing.Sequence[cv2.UMat], labels: cv2.UMat) -> None: ...
+
+    @_typing.overload
+    def predict_label(self, src: cv2.typing.MatLike) -> int: ...
+    @_typing.overload
+    def predict_label(self, src: cv2.UMat) -> int: ...
+
+    @_typing.overload
+    def predict(self, src: cv2.typing.MatLike) -> tuple[int, float]: ...
+    @_typing.overload
+    def predict(self, src: cv2.UMat) -> tuple[int, float]: ...
+
+    @_typing.overload
+    def predict_collect(self, src: cv2.typing.MatLike, collector: PredictCollector) -> None: ...
+    @_typing.overload
+    def predict_collect(self, src: cv2.UMat, collector: PredictCollector) -> None: ...
+
+    def write(self, filename: str) -> None: ...
+
+    def read(self, filename: str) -> None: ...
+
+    def setLabelInfo(self, label: int, strInfo: str) -> None: ...
+
+    def getLabelInfo(self, label: int) -> str: ...
+
+    def getLabelsByString(self, str: str) -> _typing.Sequence[int]: ...
+
+
+class BIF(cv2.Algorithm):
+    # Functions
+    def getNumBands(self) -> int: ...
+
+    def getNumRotations(self) -> int: ...
+
+    @_typing.overload
+    def compute(self, image: cv2.typing.MatLike, features: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def compute(self, image: cv2.UMat, features: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @classmethod
+    def create(cls, num_bands: int = ..., num_rotations: int = ...) -> BIF: ...
+
+
+class FacemarkKazemi(Facemark):
+    ...
+
+class Facemark(cv2.Algorithm):
+    # Functions
+    def loadModel(self, model: str) -> None: ...
+
+    @_typing.overload
+    def fit(self, image: cv2.typing.MatLike, faces: cv2.typing.MatLike, landmarks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[bool, _typing.Sequence[cv2.typing.MatLike]]: ...
+    @_typing.overload
+    def fit(self, image: cv2.UMat, faces: cv2.UMat, landmarks: _typing.Sequence[cv2.UMat] | None = ...) -> tuple[bool, _typing.Sequence[cv2.UMat]]: ...
+
+
+class FacemarkAAM(FacemarkTrain):
+    ...
+
+class FacemarkTrain(Facemark):
+    ...
+
+class FacemarkLBF(FacemarkTrain):
+    ...
+
+class BasicFaceRecognizer(FaceRecognizer):
+    # Functions
+    def getNumComponents(self) -> int: ...
+
+    def setNumComponents(self, val: int) -> None: ...
+
+    def getThreshold(self) -> float: ...
+
+    def setThreshold(self, val: float) -> None: ...
+
+    def getProjections(self) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def getLabels(self) -> cv2.typing.MatLike: ...
+
+    def getEigenValues(self) -> cv2.typing.MatLike: ...
+
+    def getEigenVectors(self) -> cv2.typing.MatLike: ...
+
+    def getMean(self) -> cv2.typing.MatLike: ...
+
+
+class EigenFaceRecognizer(BasicFaceRecognizer):
+    # Functions
+    @classmethod
+    def create(cls, num_components: int = ..., threshold: float = ...) -> EigenFaceRecognizer: ...
+
+
+class FisherFaceRecognizer(BasicFaceRecognizer):
+    # Functions
+    @classmethod
+    def create(cls, num_components: int = ..., threshold: float = ...) -> FisherFaceRecognizer: ...
+
+
+class LBPHFaceRecognizer(FaceRecognizer):
+    # Functions
+    def getGridX(self) -> int: ...
+
+    def setGridX(self, val: int) -> None: ...
+
+    def getGridY(self) -> int: ...
+
+    def setGridY(self, val: int) -> None: ...
+
+    def getRadius(self) -> int: ...
+
+    def setRadius(self, val: int) -> None: ...
+
+    def getNeighbors(self) -> int: ...
+
+    def setNeighbors(self, val: int) -> None: ...
+
+    def getThreshold(self) -> float: ...
+
+    def setThreshold(self, val: float) -> None: ...
+
+    def getHistograms(self) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    def getLabels(self) -> cv2.typing.MatLike: ...
+
+    @classmethod
+    def create(cls, radius: int = ..., neighbors: int = ..., grid_x: int = ..., grid_y: int = ..., threshold: float = ...) -> LBPHFaceRecognizer: ...
+
+
+class MACE(cv2.Algorithm):
+    # Functions
+    def salt(self, passphrase: str) -> None: ...
+
+    @_typing.overload
+    def train(self, images: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
+    @_typing.overload
+    def train(self, images: _typing.Sequence[cv2.UMat]) -> None: ...
+
+    @_typing.overload
+    def same(self, query: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def same(self, query: cv2.UMat) -> bool: ...
+
+    @classmethod
+    def load(cls, filename: str, objname: str = ...) -> MACE: ...
+
+    @classmethod
+    def create(cls, IMGSIZE: int = ...) -> MACE: ...
+
+
+class PredictCollector:
+    ...
+
+class StandardCollector(PredictCollector):
+    # Functions
+    def getMinLabel(self) -> int: ...
+
+    def getMinDist(self) -> float: ...
+
+    def getResults(self, sorted: bool = ...) -> _typing.Sequence[tuple[int, float]]: ...
+
+    @classmethod
+    def create(cls, threshold: float = ...) -> StandardCollector: ...
+
+
+
+# Functions
+def createFacemarkAAM() -> Facemark: ...
+
+def createFacemarkKazemi() -> Facemark: ...
+
+def createFacemarkLBF() -> Facemark: ...
+
+@_typing.overload
+def drawFacemarks(image: cv2.typing.MatLike, points: cv2.typing.MatLike, color: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawFacemarks(image: cv2.UMat, points: cv2.UMat, color: cv2.typing.Scalar = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def getFacesHAAR(image: cv2.typing.MatLike, face_cascade_name: str, faces: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ...
+@_typing.overload
+def getFacesHAAR(image: cv2.UMat, face_cascade_name: str, faces: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat]: ...
+
+def loadDatasetList(imageList: str, annotationList: str, images: _typing.Sequence[str], annotations: _typing.Sequence[str]) -> bool: ...
+
+@_typing.overload
+def loadFacePoints(filename: str, points: cv2.typing.MatLike | None = ..., offset: float = ...) -> tuple[bool, cv2.typing.MatLike]: ...
+@_typing.overload
+def loadFacePoints(filename: str, points: cv2.UMat | None = ..., offset: float = ...) -> tuple[bool, cv2.UMat]: ...
+
+@_typing.overload
+def loadTrainingData(filename: str, images: _typing.Sequence[str], facePoints: cv2.typing.MatLike | None = ..., delim: str = ..., offset: float = ...) -> tuple[bool, cv2.typing.MatLike]: ...
+@_typing.overload
+def loadTrainingData(filename: str, images: _typing.Sequence[str], facePoints: cv2.UMat | None = ..., delim: str = ..., offset: float = ...) -> tuple[bool, cv2.UMat]: ...
+@_typing.overload
+def loadTrainingData(imageList: str, groundTruth: str, images: _typing.Sequence[str], facePoints: cv2.typing.MatLike | None = ..., offset: float = ...) -> tuple[bool, cv2.typing.MatLike]: ...
+@_typing.overload
+def loadTrainingData(imageList: str, groundTruth: str, images: _typing.Sequence[str], facePoints: cv2.UMat | None = ..., offset: float = ...) -> tuple[bool, cv2.UMat]: ...
+@_typing.overload
+def loadTrainingData(filename: _typing.Sequence[str], trainlandmarks: _typing.Sequence[_typing.Sequence[cv2.typing.Point2f]], trainimages: _typing.Sequence[str]) -> bool: ...
+
+

venv/lib/python3.12/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/python3.12/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/python3.12/site-packages/cv2/ft/__init__.pyi

@@ -0,0 +1,98 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+LINEAR: int
+SINUS: int
+ONE_STEP: int
+MULTI_STEP: int
+ITERATIVE: int
+
+
+
+# Functions
+@_typing.overload
+def FT02D_FL_process(matrix: cv2.typing.MatLike, radius: int, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT02D_FL_process(matrix: cv2.UMat, radius: int, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT02D_FL_process_float(matrix: cv2.typing.MatLike, radius: int, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT02D_FL_process_float(matrix: cv2.UMat, radius: int, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT02D_components(matrix: cv2.typing.MatLike, kernel: cv2.typing.MatLike, components: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT02D_components(matrix: cv2.UMat, kernel: cv2.UMat, components: cv2.UMat | None = ..., mask: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT02D_inverseFT(components: cv2.typing.MatLike, kernel: cv2.typing.MatLike, width: int, height: int, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT02D_inverseFT(components: cv2.UMat, kernel: cv2.UMat, width: int, height: int, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT02D_iteration(matrix: cv2.typing.MatLike, kernel: cv2.typing.MatLike, mask: cv2.typing.MatLike, firstStop: bool, output: cv2.typing.MatLike | None = ..., maskOutput: cv2.typing.MatLike | None = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def FT02D_iteration(matrix: cv2.UMat, kernel: cv2.UMat, mask: cv2.UMat, firstStop: bool, output: cv2.UMat | None = ..., maskOutput: cv2.UMat | None = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def FT02D_process(matrix: cv2.typing.MatLike, kernel: cv2.typing.MatLike, output: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT02D_process(matrix: cv2.UMat, kernel: cv2.UMat, output: cv2.UMat | None = ..., mask: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT12D_components(matrix: cv2.typing.MatLike, kernel: cv2.typing.MatLike, components: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT12D_components(matrix: cv2.UMat, kernel: cv2.UMat, components: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT12D_createPolynomMatrixHorizontal(radius: int, chn: int, matrix: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT12D_createPolynomMatrixHorizontal(radius: int, chn: int, matrix: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT12D_createPolynomMatrixVertical(radius: int, chn: int, matrix: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT12D_createPolynomMatrixVertical(radius: int, chn: int, matrix: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT12D_inverseFT(components: cv2.typing.MatLike, kernel: cv2.typing.MatLike, width: int, height: int, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT12D_inverseFT(components: cv2.UMat, kernel: cv2.UMat, width: int, height: int, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def FT12D_polynomial(matrix: cv2.typing.MatLike, kernel: cv2.typing.MatLike, c00: cv2.typing.MatLike | None = ..., c10: cv2.typing.MatLike | None = ..., c01: cv2.typing.MatLike | None = ..., components: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def FT12D_polynomial(matrix: cv2.UMat, kernel: cv2.UMat, c00: cv2.UMat | None = ..., c10: cv2.UMat | None = ..., c01: cv2.UMat | None = ..., components: cv2.UMat | None = ..., mask: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def FT12D_process(matrix: cv2.typing.MatLike, kernel: cv2.typing.MatLike, output: cv2.typing.MatLike | None = ..., mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def FT12D_process(matrix: cv2.UMat, kernel: cv2.UMat, output: cv2.UMat | None = ..., mask: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def createKernel(function: int, radius: int, chn: int, kernel: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def createKernel(function: int, radius: int, chn: int, kernel: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def createKernel1(A: cv2.typing.MatLike, B: cv2.typing.MatLike, chn: int, kernel: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def createKernel1(A: cv2.UMat, B: cv2.UMat, chn: int, kernel: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def filter(image: cv2.typing.MatLike, kernel: cv2.typing.MatLike, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def filter(image: cv2.UMat, kernel: cv2.UMat, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def inpaint(image: cv2.typing.MatLike, mask: cv2.typing.MatLike, radius: int, function: int, algorithm: int, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def inpaint(image: cv2.UMat, mask: cv2.UMat, radius: int, function: int, algorithm: int, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+

venv/lib/python3.12/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/python3.12/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/python3.12/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/python3.12/site-packages/cv2/gapi/core/cpu/__init__.pyi

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

venv/lib/python3.12/site-packages/cv2/gapi/core/fluid/__init__.pyi

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

venv/lib/python3.12/site-packages/cv2/gapi/core/ocl/__init__.pyi

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

venv/lib/python3.12/site-packages/cv2/gapi/ie/__init__.pyi

@@ -0,0 +1,51 @@
+__all__: list[str] = []
+
+import cv2.typing
+import typing as _typing
+
+
+from cv2.gapi.ie import detail as detail
+
+
+# Enumerations
+TraitAs_TENSOR: int
+TRAIT_AS_TENSOR: int
+TraitAs_IMAGE: int
+TRAIT_AS_IMAGE: int
+TraitAs = int
+"""One of [TraitAs_TENSOR, TRAIT_AS_TENSOR, TraitAs_IMAGE, TRAIT_AS_IMAGE]"""
+
+Sync: int
+SYNC: int
+Async: int
+ASYNC: int
+InferMode = int
+"""One of [Sync, SYNC, Async, ASYNC]"""
+
+
+
+# Classes
+class PyParams:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, tag: str, model: str, weights: str, device: str) -> None: ...
+    @_typing.overload
+    def __init__(self, tag: str, model: str, device: str) -> None: ...
+
+    def constInput(self, layer_name: str, data: cv2.typing.MatLike, hint: TraitAs = ...) -> PyParams: ...
+
+    def cfgNumRequests(self, nireq: int) -> PyParams: ...
+
+    def cfgBatchSize(self, size: int) -> PyParams: ...
+
+
+
+# Functions
+@_typing.overload
+def params(tag: str, model: str, weights: str, device: str) -> PyParams: ...
+@_typing.overload
+def params(tag: str, model: str, device: str) -> PyParams: ...
+
+

venv/lib/python3.12/site-packages/cv2/gapi/ie/detail/__init__.pyi

@@ -0,0 +1,12 @@
+__all__: list[str] = []
+
+ParamDesc_Kind_Load: int
+PARAM_DESC_KIND_LOAD: int
+ParamDesc_Kind_Import: int
+PARAM_DESC_KIND_IMPORT: int
+ParamDesc_Kind = int
+"""One of [ParamDesc_Kind_Load, PARAM_DESC_KIND_LOAD, ParamDesc_Kind_Import, PARAM_DESC_KIND_IMPORT]"""
+
+
+# Classes
+

venv/lib/python3.12/site-packages/cv2/gapi/imgproc/__init__.pyi

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

venv/lib/python3.12/site-packages/cv2/gapi/imgproc/fluid/__init__.pyi

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

venv/lib/python3.12/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/python3.12/site-packages/cv2/gapi/onnx/__init__.pyi

@@ -0,0 +1,55 @@
+__all__: list[str] = []
+
+import cv2.gapi.onnx.ep
+import cv2.typing
+import typing as _typing
+
+
+from cv2.gapi.onnx import ep as ep
+
+
+# Enumerations
+TraitAs_TENSOR: int
+TRAIT_AS_TENSOR: int
+TraitAs_IMAGE: int
+TRAIT_AS_IMAGE: int
+TraitAs = int
+"""One of [TraitAs_TENSOR, TRAIT_AS_TENSOR, TraitAs_IMAGE, TRAIT_AS_IMAGE]"""
+
+
+
+# Classes
+class PyParams:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, tag: str, model_path: str) -> None: ...
+
+    def cfgMeanStd(self, layer_name: str, m: cv2.typing.Scalar, s: cv2.typing.Scalar) -> PyParams: ...
+
+    def cfgNormalize(self, layer_name: str, flag: bool) -> PyParams: ...
+
+    @_typing.overload
+    def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.OpenVINO) -> PyParams: ...
+    @_typing.overload
+    def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.DirectML) -> PyParams: ...
+    @_typing.overload
+    def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.CoreML) -> PyParams: ...
+    @_typing.overload
+    def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.CUDA) -> PyParams: ...
+    @_typing.overload
+    def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.TensorRT) -> PyParams: ...
+
+    def cfgDisableMemPattern(self) -> PyParams: ...
+
+    def cfgSessionOptions(self, options: cv2.typing.map_string_and_string) -> PyParams: ...
+
+    def cfgOptLevel(self, opt_level: int) -> PyParams: ...
+
+
+
+# Functions
+def params(tag: str, model_path: str) -> PyParams: ...
+
+

venv/lib/python3.12/site-packages/cv2/gapi/onnx/ep/__init__.pyi

@@ -0,0 +1,63 @@
+__all__: list[str] = []
+
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class CoreML:
+    # Functions
+    def __init__(self) -> None: ...
+
+    def cfgUseCPUOnly(self) -> CoreML: ...
+
+    def cfgEnableOnSubgraph(self) -> CoreML: ...
+
+    def cfgEnableOnlyNeuralEngine(self) -> CoreML: ...
+
+
+class CUDA:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, dev_id: int) -> None: ...
+
+
+class TensorRT:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, dev_id: int) -> None: ...
+
+
+class OpenVINO:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, dev_type: str) -> None: ...
+    @_typing.overload
+    def __init__(self, params: cv2.typing.map_string_and_string) -> None: ...
+
+    def cfgCacheDir(self, dir: str) -> OpenVINO: ...
+
+    def cfgNumThreads(self, nthreads: int) -> OpenVINO: ...
+
+    def cfgEnableOpenCLThrottling(self) -> OpenVINO: ...
+
+    def cfgEnableDynamicShapes(self) -> OpenVINO: ...
+
+
+class DirectML:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, device_id: int) -> None: ...
+    @_typing.overload
+    def __init__(self, adapter_name: str) -> None: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/gapi/ot/__init__.pyi

@@ -0,0 +1,32 @@
+__all__: list[str] = []
+
+import cv2
+import typing as _typing
+
+
+from cv2.gapi.ot import cpu as cpu
+
+
+# Enumerations
+NEW: int
+TRACKED: int
+LOST: int
+TrackingStatus = int
+"""One of [NEW, TRACKED, LOST]"""
+
+
+
+# Classes
+class ObjectTrackerParams:
+    max_num_objects: int
+    input_image_format: int
+    tracking_per_class: bool
+
+
+# Functions
+@_typing.overload
+def track(mat: cv2.GMat, detected_rects: cv2.GArrayT, detected_class_labels: cv2.GArrayT, delta: float) -> tuple[cv2.GArrayT, cv2.GArrayT, cv2.GArrayT, cv2.GArrayT]: ...
+@_typing.overload
+def track(frame: cv2.GFrame, detected_rects: cv2.GArrayT, detected_class_labels: cv2.GArrayT, delta: float) -> tuple[cv2.GArrayT, cv2.GArrayT, cv2.GArrayT, cv2.GArrayT]: ...
+
+

venv/lib/python3.12/site-packages/cv2/gapi/ot/cpu/__init__.pyi

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

venv/lib/python3.12/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/python3.12/site-packages/cv2/gapi/own/__init__.pyi

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

venv/lib/python3.12/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/python3.12/site-packages/cv2/gapi/render/__init__.pyi

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

venv/lib/python3.12/site-packages/cv2/gapi/render/ocv/__init__.pyi

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

venv/lib/python3.12/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/python3.12/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/python3.12/site-packages/cv2/gapi/wip/__init__.pyi

@@ -0,0 +1,41 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.gapi
+import cv2.gapi.wip.gst
+import cv2.typing
+import typing as _typing
+
+
+from cv2.gapi.wip import draw as draw
+from cv2.gapi.wip import gst as gst
+from cv2.gapi.wip import onevpl as onevpl
+
+
+# Classes
+class GOutputs:
+    # Functions
+    def getGMat(self) -> cv2.GMat: ...
+
+    def getGScalar(self) -> cv2.GScalar: ...
+
+    def getGArray(self, type: cv2.gapi.ArgType) -> cv2.GArrayT: ...
+
+    def getGOpaque(self, type: cv2.gapi.ArgType) -> cv2.GOpaqueT: ...
+
+
+class IStreamSource:
+    ...
+
+
+# Functions
+def get_streaming_source(pipeline: cv2.gapi.wip.gst.GStreamerPipeline, appsinkName: str, outputType: cv2.gapi.wip.gst.GStreamerSource_OutputType = ...) -> IStreamSource: ...
+
+@_typing.overload
+def make_capture_src(path: str, properties: cv2.typing.map_int_and_double = ...) -> IStreamSource: ...
+@_typing.overload
+def make_capture_src(id: int, properties: cv2.typing.map_int_and_double = ...) -> IStreamSource: ...
+
+def make_gst_src(pipeline: str, outputType: cv2.gapi.wip.gst.GStreamerSource_OutputType = ...) -> IStreamSource: ...
+
+

venv/lib/python3.12/site-packages/cv2/gapi/wip/draw/__init__.pyi

@@ -0,0 +1,119 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class Text:
+    text: str
+    org: cv2.typing.Point
+    ff: int
+    fs: float
+    color: cv2.typing.Scalar
+    thick: int
+    lt: int
+    bottom_left_origin: bool
+
+    # Functions
+    @_typing.overload
+    def __init__(self, text_: str, org_: cv2.typing.Point, ff_: int, fs_: float, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., bottom_left_origin_: bool = ...) -> None: ...
+    @_typing.overload
+    def __init__(self) -> None: ...
+
+
+class Rect:
+    rect: cv2.typing.Rect
+    color: cv2.typing.Scalar
+    thick: int
+    lt: int
+    shift: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, rect_: cv2.typing.Rect2i, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
+
+
+class Circle:
+    center: cv2.typing.Point
+    radius: int
+    color: cv2.typing.Scalar
+    thick: int
+    lt: int
+    shift: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self, center_: cv2.typing.Point, radius_: int, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
+    @_typing.overload
+    def __init__(self) -> None: ...
+
+
+class Line:
+    pt1: cv2.typing.Point
+    pt2: cv2.typing.Point
+    color: cv2.typing.Scalar
+    thick: int
+    lt: int
+    shift: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self, pt1_: cv2.typing.Point, pt2_: cv2.typing.Point, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
+    @_typing.overload
+    def __init__(self) -> None: ...
+
+
+class Mosaic:
+    mos: cv2.typing.Rect
+    cellSz: int
+    decim: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, mos_: cv2.typing.Rect2i, cellSz_: int, decim_: int) -> None: ...
+
+
+class Image:
+    org: cv2.typing.Point
+    img: cv2.typing.MatLike
+    alpha: cv2.typing.MatLike
+
+    # Functions
+    @_typing.overload
+    def __init__(self, org_: cv2.typing.Point, img_: cv2.typing.MatLike, alpha_: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def __init__(self) -> None: ...
+
+
+class Poly:
+    points: _typing.Sequence[cv2.typing.Point]
+    color: cv2.typing.Scalar
+    thick: int
+    lt: int
+    shift: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self, points_: _typing.Sequence[cv2.typing.Point], color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
+    @_typing.overload
+    def __init__(self) -> None: ...
+
+
+
+# Functions
+@_typing.overload
+def render(bgr: cv2.typing.MatLike, prims: _typing.Sequence[cv2.typing.Prim], args: _typing.Sequence[cv2.GCompileArg] = ...) -> None: ...
+@_typing.overload
+def render(y_plane: cv2.typing.MatLike, uv_plane: cv2.typing.MatLike, prims: _typing.Sequence[cv2.typing.Prim], args: _typing.Sequence[cv2.GCompileArg] = ...) -> None: ...
+
+def render3ch(src: cv2.GMat, prims: cv2.GArrayT) -> cv2.GMat: ...
+
+def renderNV12(y: cv2.GMat, uv: cv2.GMat, prims: cv2.GArrayT) -> tuple[cv2.GMat, cv2.GMat]: ...
+
+

venv/lib/python3.12/site-packages/cv2/gapi/wip/gst/__init__.pyi

@@ -0,0 +1,17 @@
+__all__: list[str] = []
+
+GStreamerSource_OutputType_FRAME: int
+GSTREAMER_SOURCE_OUTPUT_TYPE_FRAME: int
+GStreamerSource_OutputType_MAT: int
+GSTREAMER_SOURCE_OUTPUT_TYPE_MAT: int
+GStreamerSource_OutputType = int
+"""One of [GStreamerSource_OutputType_FRAME, GSTREAMER_SOURCE_OUTPUT_TYPE_FRAME, GStreamerSource_OutputType_MAT, GSTREAMER_SOURCE_OUTPUT_TYPE_MAT]"""
+
+
+# Classes
+class GStreamerPipeline:
+    # Functions
+    def __init__(self, pipeline: str) -> None: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/gapi/wip/onevpl/__init__.pyi

@@ -0,0 +1,16 @@
+__all__: list[str] = []
+
+# Enumerations
+AccelType_HOST: int
+ACCEL_TYPE_HOST: int
+AccelType_DX11: int
+ACCEL_TYPE_DX11: int
+AccelType_VAAPI: int
+ACCEL_TYPE_VAAPI: int
+AccelType_LAST_VALUE: int
+ACCEL_TYPE_LAST_VALUE: int
+AccelType = int
+"""One of [AccelType_HOST, ACCEL_TYPE_HOST, AccelType_DX11, ACCEL_TYPE_DX11, AccelType_VAAPI, ACCEL_TYPE_VAAPI, AccelType_LAST_VALUE, ACCEL_TYPE_LAST_VALUE]"""
+
+
+

venv/lib/python3.12/site-packages/cv2/hfs/__init__.pyi

@@ -0,0 +1,53 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class HfsSegment(cv2.Algorithm):
+    # Functions
+    def setSegEgbThresholdI(self, c: float) -> None: ...
+
+    def getSegEgbThresholdI(self) -> float: ...
+
+    def setMinRegionSizeI(self, n: int) -> None: ...
+
+    def getMinRegionSizeI(self) -> int: ...
+
+    def setSegEgbThresholdII(self, c: float) -> None: ...
+
+    def getSegEgbThresholdII(self) -> float: ...
+
+    def setMinRegionSizeII(self, n: int) -> None: ...
+
+    def getMinRegionSizeII(self) -> int: ...
+
+    def setSpatialWeight(self, w: float) -> None: ...
+
+    def getSpatialWeight(self) -> float: ...
+
+    def setSlicSpixelSize(self, n: int) -> None: ...
+
+    def getSlicSpixelSize(self) -> int: ...
+
+    def setNumSlicIter(self, n: int) -> None: ...
+
+    def getNumSlicIter(self) -> int: ...
+
+    @_typing.overload
+    def performSegmentGpu(self, src: cv2.typing.MatLike, ifDraw: bool = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def performSegmentGpu(self, src: cv2.UMat, ifDraw: bool = ...) -> cv2.typing.MatLike: ...
+
+    @_typing.overload
+    def performSegmentCpu(self, src: cv2.typing.MatLike, ifDraw: bool = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def performSegmentCpu(self, src: cv2.UMat, ifDraw: bool = ...) -> cv2.typing.MatLike: ...
+
+    @classmethod
+    def create(cls, height: int, width: int, segEgbThresholdI: float = ..., minRegionSizeI: int = ..., segEgbThresholdII: float = ..., minRegionSizeII: int = ..., spatialWeight: float = ..., slicSpixelSize: int = ..., numSlicIter: int = ...) -> HfsSegment: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/img_hash/__init__.pyi

@@ -0,0 +1,116 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+BLOCK_MEAN_HASH_MODE_0: int
+BLOCK_MEAN_HASH_MODE_1: int
+BlockMeanHashMode = int
+"""One of [BLOCK_MEAN_HASH_MODE_0, BLOCK_MEAN_HASH_MODE_1]"""
+
+
+
+# Classes
+class AverageHash(ImgHashBase):
+    # Functions
+    @classmethod
+    def create(cls) -> AverageHash: ...
+
+
+class ImgHashBase(cv2.Algorithm):
+    # Functions
+    @_typing.overload
+    def compute(self, inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def compute(self, inputArr: cv2.UMat, outputArr: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def compare(self, hashOne: cv2.typing.MatLike, hashTwo: cv2.typing.MatLike) -> float: ...
+    @_typing.overload
+    def compare(self, hashOne: cv2.UMat, hashTwo: cv2.UMat) -> float: ...
+
+
+class BlockMeanHash(ImgHashBase):
+    # Functions
+    def setMode(self, mode: int) -> None: ...
+
+    def getMean(self) -> _typing.Sequence[float]: ...
+
+    @classmethod
+    def create(cls, mode: int = ...) -> BlockMeanHash: ...
+
+
+class ColorMomentHash(ImgHashBase):
+    # Functions
+    @classmethod
+    def create(cls) -> ColorMomentHash: ...
+
+
+class MarrHildrethHash(ImgHashBase):
+    # Functions
+    def getAlpha(self) -> float: ...
+
+    def getScale(self) -> float: ...
+
+    def setKernelParam(self, alpha: float, scale: float) -> None: ...
+
+    @classmethod
+    def create(cls, alpha: float = ..., scale: float = ...) -> MarrHildrethHash: ...
+
+
+class PHash(ImgHashBase):
+    # Functions
+    @classmethod
+    def create(cls) -> PHash: ...
+
+
+class RadialVarianceHash(ImgHashBase):
+    # Functions
+    @classmethod
+    def create(cls, sigma: float = ..., numOfAngleLine: int = ...) -> RadialVarianceHash: ...
+
+    def getNumOfAngleLine(self) -> int: ...
+
+    def getSigma(self) -> float: ...
+
+    def setNumOfAngleLine(self, value: int) -> None: ...
+
+    def setSigma(self, value: float) -> None: ...
+
+
+
+# Functions
+@_typing.overload
+def averageHash(inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def averageHash(inputArr: cv2.UMat, outputArr: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def blockMeanHash(inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ..., mode: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def blockMeanHash(inputArr: cv2.UMat, outputArr: cv2.UMat | None = ..., mode: int = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def colorMomentHash(inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def colorMomentHash(inputArr: cv2.UMat, outputArr: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def marrHildrethHash(inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ..., alpha: float = ..., scale: float = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def marrHildrethHash(inputArr: cv2.UMat, outputArr: cv2.UMat | None = ..., alpha: float = ..., scale: float = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def pHash(inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def pHash(inputArr: cv2.UMat, outputArr: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def radialVarianceHash(inputArr: cv2.typing.MatLike, outputArr: cv2.typing.MatLike | None = ..., sigma: float = ..., numOfAngleLine: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def radialVarianceHash(inputArr: cv2.UMat, outputArr: cv2.UMat | None = ..., sigma: float = ..., numOfAngleLine: int = ...) -> cv2.UMat: ...
+
+

venv/lib/python3.12/site-packages/cv2/intensity_transform/__init__.pyi

@@ -0,0 +1,27 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Functions
+@_typing.overload
+def BIMEF(input: cv2.typing.MatLike, output: cv2.typing.MatLike | None = ..., mu: float = ..., a: float = ..., b: float = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def BIMEF(input: cv2.UMat, output: cv2.UMat | None = ..., mu: float = ..., a: float = ..., b: float = ...) -> cv2.UMat: ...
+
+@_typing.overload
+def BIMEF2(input: cv2.typing.MatLike, k: float, mu: float, a: float, b: float, output: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def BIMEF2(input: cv2.UMat, k: float, mu: float, a: float, b: float, output: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+def autoscaling(input: cv2.typing.MatLike, output: cv2.typing.MatLike) -> None: ...
+
+def contrastStretching(input: cv2.typing.MatLike, output: cv2.typing.MatLike, r1: int, s1: int, r2: int, s2: int) -> None: ...
+
+def gammaCorrection(input: cv2.typing.MatLike, output: cv2.typing.MatLike, gamma: float) -> None: ...
+
+def logTransform(input: cv2.typing.MatLike, output: cv2.typing.MatLike) -> None: ...
+
+

venv/lib/python3.12/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/python3.12/site-packages/cv2/kinfu/__init__.pyi

@@ -0,0 +1,133 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+from cv2.kinfu import detail as detail
+
+
+# Enumerations
+VolumeType_TSDF: int
+VOLUME_TYPE_TSDF: int
+VolumeType_HASHTSDF: int
+VOLUME_TYPE_HASHTSDF: int
+VolumeType_COLOREDTSDF: int
+VOLUME_TYPE_COLOREDTSDF: int
+VolumeType = int
+"""One of [VolumeType_TSDF, VOLUME_TYPE_TSDF, VolumeType_HASHTSDF, VOLUME_TYPE_HASHTSDF, VolumeType_COLOREDTSDF, VOLUME_TYPE_COLOREDTSDF]"""
+
+
+
+# Classes
+class Params:
+    frameSize: cv2.typing.Size
+    volumeType: VolumeType
+    intr: cv2.typing.Matx33f
+    rgb_intr: cv2.typing.Matx33f
+    depthFactor: float
+    bilateral_sigma_depth: float
+    bilateral_sigma_spatial: float
+    bilateral_kernel_size: int
+    pyramidLevels: int
+    volumeDims: cv2.typing.Vec3i
+    voxelSize: float
+    tsdf_min_camera_movement: float
+    tsdf_trunc_dist: float
+    tsdf_max_weight: int
+    raycast_step_factor: float
+    lightPose: cv2.typing.Vec3f
+    icpDistThresh: float
+    icpAngleThresh: float
+    icpIterations: _typing.Sequence[int]
+    truncateThreshold: float
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, volumeInitialPoseRot: cv2.typing.Matx33f, volumeInitialPoseTransl: cv2.typing.Vec3f) -> None: ...
+    @_typing.overload
+    def __init__(self, volumeInitialPose: cv2.typing.Matx44f) -> None: ...
+
+    @_typing.overload
+    def setInitialVolumePose(self, R: cv2.typing.Matx33f, t: cv2.typing.Vec3f) -> None: ...
+    @_typing.overload
+    def setInitialVolumePose(self, homogen_tf: cv2.typing.Matx44f) -> None: ...
+
+    @classmethod
+    def defaultParams(cls) -> Params: ...
+
+    @classmethod
+    def coarseParams(cls) -> Params: ...
+
+    @classmethod
+    def hashTSDFParams(cls, isCoarse: bool) -> Params: ...
+
+    @classmethod
+    def coloredTSDFParams(cls, isCoarse: bool) -> Params: ...
+
+
+class KinFu:
+    # Functions
+    @classmethod
+    def create(cls, _params: Params) -> KinFu: ...
+
+    @_typing.overload
+    def render(self, image: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, image: cv2.UMat | None = ...) -> cv2.UMat: ...
+    @_typing.overload
+    def render(self, cameraPose: cv2.typing.Matx44f, image: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, cameraPose: cv2.typing.Matx44f, image: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getCloud(self, points: cv2.typing.MatLike | None = ..., normals: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def getCloud(self, points: cv2.UMat | None = ..., normals: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def getPoints(self, points: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getPoints(self, points: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getNormals(self, points: cv2.typing.MatLike, normals: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getNormals(self, points: cv2.UMat, normals: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def reset(self) -> None: ...
+
+    @_typing.overload
+    def update(self, depth: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def update(self, depth: cv2.UMat) -> bool: ...
+
+
+class Volume:
+    ...
+
+class VolumeParams:
+    type: VolumeType
+    resolution: cv2.typing.Vec3i
+    voxelSize: float
+    tsdfTruncDist: float
+    maxWeight: int
+    depthTruncThreshold: float
+    raycastStepFactor: float
+
+    # Functions
+    @classmethod
+    def defaultParams(cls, _volumeType: VolumeType) -> VolumeParams: ...
+
+    @classmethod
+    def coarseParams(cls, _volumeType: VolumeType) -> VolumeParams: ...
+
+
+
+# Functions
+def makeVolume(_volumeType: VolumeType, _voxelSize: float, _pose: cv2.typing.Matx44f, _raycastStepFactor: float, _truncDist: float, _maxWeight: int, _truncateThreshold: float, _resolution: cv2.typing.Vec3i) -> Volume: ...
+
+

venv/lib/python3.12/site-packages/cv2/kinfu/detail/__init__.pyi

@@ -0,0 +1,7 @@
+__all__: list[str] = []
+
+# Classes
+class PoseGraph:
+    ...
+
+

venv/lib/python3.12/site-packages/cv2/large_kinfu/__init__.pyi

@@ -0,0 +1,73 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class Params:
+    frameSize: cv2.typing.Size
+    intr: cv2.typing.Matx33f
+    rgb_intr: cv2.typing.Matx33f
+    depthFactor: float
+    bilateral_sigma_depth: float
+    bilateral_sigma_spatial: float
+    bilateral_kernel_size: int
+    pyramidLevels: int
+    tsdf_min_camera_movement: float
+    lightPose: cv2.typing.Vec3f
+    icpDistThresh: float
+    icpAngleThresh: float
+    icpIterations: _typing.Sequence[int]
+    truncateThreshold: float
+
+    # Functions
+    @classmethod
+    def defaultParams(cls) -> Params: ...
+
+    @classmethod
+    def coarseParams(cls) -> Params: ...
+
+    @classmethod
+    def hashTSDFParams(cls, isCoarse: bool) -> Params: ...
+
+
+class LargeKinfu:
+    # Functions
+    @classmethod
+    def create(cls, _params: Params) -> LargeKinfu: ...
+
+    @_typing.overload
+    def render(self, image: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, image: cv2.UMat | None = ...) -> cv2.UMat: ...
+    @_typing.overload
+    def render(self, cameraPose: cv2.typing.Matx44f, image: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def render(self, cameraPose: cv2.typing.Matx44f, image: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getCloud(self, points: cv2.typing.MatLike | None = ..., normals: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def getCloud(self, points: cv2.UMat | None = ..., normals: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def getPoints(self, points: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getPoints(self, points: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    @_typing.overload
+    def getNormals(self, points: cv2.typing.MatLike, normals: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getNormals(self, points: cv2.UMat, normals: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def reset(self) -> None: ...
+
+    @_typing.overload
+    def update(self, depth: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def update(self, depth: cv2.UMat) -> bool: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/legacy/__init__.pyi

@@ -0,0 +1,93 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class Tracker(cv2.Algorithm):
+    # Functions
+    @_typing.overload
+    def init(self, image: cv2.typing.MatLike, boundingBox: cv2.typing.Rect2d) -> bool: ...
+    @_typing.overload
+    def init(self, image: cv2.UMat, boundingBox: cv2.typing.Rect2d) -> bool: ...
+
+    @_typing.overload
+    def update(self, image: cv2.typing.MatLike) -> tuple[bool, cv2.typing.Rect2d]: ...
+    @_typing.overload
+    def update(self, image: cv2.UMat) -> tuple[bool, cv2.typing.Rect2d]: ...
+
+
+class TrackerMIL(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerMIL: ...
+
+
+class TrackerBoosting(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerBoosting: ...
+
+
+class TrackerMedianFlow(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerMedianFlow: ...
+
+
+class TrackerTLD(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerTLD: ...
+
+
+class TrackerKCF(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerKCF: ...
+
+
+class TrackerMOSSE(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerMOSSE: ...
+
+
+class MultiTracker(cv2.Algorithm):
+    # Functions
+    def __init__(self) -> None: ...
+
+    @_typing.overload
+    def add(self, newTracker: Tracker, image: cv2.typing.MatLike, boundingBox: cv2.typing.Rect2d) -> bool: ...
+    @_typing.overload
+    def add(self, newTracker: Tracker, image: cv2.UMat, boundingBox: cv2.typing.Rect2d) -> bool: ...
+
+    @_typing.overload
+    def update(self, image: cv2.typing.MatLike) -> tuple[bool, _typing.Sequence[cv2.typing.Rect2d]]: ...
+    @_typing.overload
+    def update(self, image: cv2.UMat) -> tuple[bool, _typing.Sequence[cv2.typing.Rect2d]]: ...
+
+    def getObjects(self) -> _typing.Sequence[cv2.typing.Rect2d]: ...
+
+    @classmethod
+    def create(cls) -> MultiTracker: ...
+
+
+class TrackerCSRT(Tracker):
+    # Functions
+    @classmethod
+    def create(cls) -> TrackerCSRT: ...
+
+    @_typing.overload
+    def setInitialMask(self, mask: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def setInitialMask(self, mask: cv2.UMat) -> None: ...
+
+
+
+# Functions
+def upgradeTrackingAPI(legacy_tracker: Tracker) -> Tracker: ...
+
+

venv/lib/python3.12/site-packages/cv2/line_descriptor/__init__.pyi

@@ -0,0 +1,112 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class KeyLine:
+    angle: float
+    class_id: int
+    octave: int
+    pt: cv2.typing.Point2f
+    response: float
+    size: float
+    startPointX: float
+    startPointY: float
+    endPointX: float
+    endPointY: float
+    sPointInOctaveX: float
+    sPointInOctaveY: float
+    ePointInOctaveX: float
+    ePointInOctaveY: float
+    lineLength: float
+    numOfPixels: int
+
+    # Functions
+    def __init__(self) -> None: ...
+
+    def getStartPoint(self) -> cv2.typing.Point2f: ...
+
+    def getEndPoint(self) -> cv2.typing.Point2f: ...
+
+    def getStartPointInOctave(self) -> cv2.typing.Point2f: ...
+
+    def getEndPointInOctave(self) -> cv2.typing.Point2f: ...
+
+
+class BinaryDescriptor(cv2.Algorithm):
+    # Functions
+    @classmethod
+    def createBinaryDescriptor(cls) -> BinaryDescriptor: ...
+
+    def getNumOfOctaves(self) -> int: ...
+
+    def setNumOfOctaves(self, octaves: int) -> None: ...
+
+    def getWidthOfBand(self) -> int: ...
+
+    def setWidthOfBand(self, width: int) -> None: ...
+
+    def getReductionRatio(self) -> int: ...
+
+    def setReductionRatio(self, rRatio: int) -> None: ...
+
+    def detect(self, image: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> _typing.Sequence[KeyLine]: ...
+
+    def compute(self, image: cv2.typing.MatLike, keylines: _typing.Sequence[KeyLine], descriptors: cv2.typing.MatLike | None = ..., returnFloatDescr: bool = ...) -> tuple[_typing.Sequence[KeyLine], cv2.typing.MatLike]: ...
+
+
+class LSDParam:
+    scale: float
+    sigma_scale: float
+    quant: float
+    ang_th: float
+    log_eps: float
+    density_th: float
+    n_bins: int
+
+    # Functions
+    def __init__(self) -> None: ...
+
+
+class LSDDetector(cv2.Algorithm):
+    # Functions
+    def __init__(self, _params: LSDParam) -> None: ...
+
+    @classmethod
+    def createLSDDetector(cls) -> LSDDetector: ...
+
+    @classmethod
+    def createLSDDetectorWithParams(cls, params: LSDParam) -> LSDDetector: ...
+
+    @_typing.overload
+    def detect(self, image: cv2.typing.MatLike, scale: int, numOctaves: int, mask: cv2.typing.MatLike | None = ...) -> _typing.Sequence[KeyLine]: ...
+    @_typing.overload
+    def detect(self, images: _typing.Sequence[cv2.typing.MatLike], keylines: _typing.Sequence[_typing.Sequence[KeyLine]], scale: int, numOctaves: int, masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> None: ...
+
+
+class BinaryDescriptorMatcher(cv2.Algorithm):
+    # Functions
+    def __init__(self) -> None: ...
+
+    def match(self, queryDescriptors: cv2.typing.MatLike, trainDescriptors: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> _typing.Sequence[cv2.DMatch]: ...
+
+    def matchQuery(self, queryDescriptors: cv2.typing.MatLike, masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.DMatch]: ...
+
+    def knnMatch(self, queryDescriptors: cv2.typing.MatLike, trainDescriptors: cv2.typing.MatLike, k: int, mask: cv2.typing.MatLike | None = ..., compactResult: bool = ...) -> _typing.Sequence[_typing.Sequence[cv2.DMatch]]: ...
+
+    def knnMatchQuery(self, queryDescriptors: cv2.typing.MatLike, matches: _typing.Sequence[_typing.Sequence[cv2.DMatch]], k: int, masks: _typing.Sequence[cv2.typing.MatLike] | None = ..., compactResult: bool = ...) -> None: ...
+
+
+class DrawLinesMatchesFlags:
+    ...
+
+
+# Functions
+def drawKeylines(image: cv2.typing.MatLike, keylines: _typing.Sequence[KeyLine], outImage: cv2.typing.MatLike | None = ..., color: cv2.typing.Scalar = ..., flags: int = ...) -> cv2.typing.MatLike: ...
+
+def drawLineMatches(img1: cv2.typing.MatLike, keylines1: _typing.Sequence[KeyLine], img2: cv2.typing.MatLike, keylines2: _typing.Sequence[KeyLine], matches1to2: _typing.Sequence[cv2.DMatch], outImg: cv2.typing.MatLike | None = ..., matchColor: cv2.typing.Scalar = ..., singleLineColor: cv2.typing.Scalar = ..., matchesMask: _typing.Sequence[str] = ..., flags: int = ...) -> cv2.typing.MatLike: ...
+
+

venv/lib/python3.12/site-packages/cv2/linemod/__init__.pyi

@@ -0,0 +1,151 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Classes
+class Feature:
+    x: int
+    y: int
+    label: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, x: int, y: int, label: int) -> None: ...
+
+
+class Template:
+    @property
+    def width(self) -> int: ...
+    @property
+    def height(self) -> int: ...
+    @property
+    def pyramid_level(self) -> int: ...
+    @property
+    def features(self) -> _typing.Sequence[Feature]: ...
+
+class QuantizedPyramid:
+    # Functions
+    def quantize(self, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+
+    def extractTemplate(self) -> tuple[bool, Template]: ...
+
+    def pyrDown(self) -> None: ...
+
+
+class Modality:
+    # Functions
+    def process(self, src: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> QuantizedPyramid: ...
+
+    def name(self) -> str: ...
+
+    def read(self, fn: cv2.FileNode) -> None: ...
+
+    @classmethod
+    @_typing.overload
+    def create(cls, modality_type: str) -> Modality: ...
+    @classmethod
+    @_typing.overload
+    def create(cls, fn: cv2.FileNode) -> Modality: ...
+
+
+class ColorGradient(Modality):
+    @property
+    def weak_threshold(self) -> float: ...
+    @property
+    def num_features(self) -> int: ...
+    @property
+    def strong_threshold(self) -> float: ...
+
+    # Functions
+    @classmethod
+    def create(cls, weak_threshold: float, num_features: int, strong_threshold: float) -> ColorGradient: ...
+
+
+class DepthNormal(Modality):
+    @property
+    def distance_threshold(self) -> int: ...
+    @property
+    def difference_threshold(self) -> int: ...
+    @property
+    def num_features(self) -> int: ...
+    @property
+    def extract_threshold(self) -> int: ...
+
+    # Functions
+    @classmethod
+    def create(cls, distance_threshold: int, difference_threshold: int, num_features: int, extract_threshold: int) -> DepthNormal: ...
+
+
+class Match:
+    x: int
+    y: int
+    similarity: float
+    class_id: str
+    template_id: int
+
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, x: int, y: int, similarity: float, class_id: str, template_id: int) -> None: ...
+
+
+class Detector:
+    # Functions
+    @_typing.overload
+    def __init__(self) -> None: ...
+    @_typing.overload
+    def __init__(self, modalities: _typing.Sequence[Modality], T_pyramid: _typing.Sequence[int]) -> None: ...
+
+    @_typing.overload
+    def match(self, sources: _typing.Sequence[cv2.typing.MatLike], threshold: float, class_ids: _typing.Sequence[str] = ..., quantized_images: _typing.Sequence[cv2.typing.MatLike] | None = ..., masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[Match], _typing.Sequence[cv2.typing.MatLike]]: ...
+    @_typing.overload
+    def match(self, sources: _typing.Sequence[cv2.typing.MatLike], threshold: float, class_ids: _typing.Sequence[str] = ..., quantized_images: _typing.Sequence[cv2.UMat] | None = ..., masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[Match], _typing.Sequence[cv2.UMat]]: ...
+
+    def addTemplate(self, sources: _typing.Sequence[cv2.typing.MatLike], class_id: str, object_mask: cv2.typing.MatLike) -> tuple[int, cv2.typing.Rect]: ...
+
+    def addSyntheticTemplate(self, templates: _typing.Sequence[Template], class_id: str) -> int: ...
+
+    def getModalities(self) -> _typing.Sequence[Modality]: ...
+
+    def getT(self, pyramid_level: int) -> int: ...
+
+    def pyramidLevels(self) -> int: ...
+
+    def getTemplates(self, class_id: str, template_id: int) -> _typing.Sequence[Template]: ...
+
+    @_typing.overload
+    def numTemplates(self) -> int: ...
+    @_typing.overload
+    def numTemplates(self, class_id: str) -> int: ...
+
+    def numClasses(self) -> int: ...
+
+    def classIds(self) -> _typing.Sequence[str]: ...
+
+    def read(self, fn: cv2.FileNode) -> None: ...
+
+    def readClasses(self, class_ids: _typing.Sequence[str], format: str = ...) -> None: ...
+
+    def writeClasses(self, format: str = ...) -> None: ...
+
+
+
+# Functions
+def colormap(quantized: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+
+@_typing.overload
+def drawFeatures(img: cv2.typing.MatLike, templates: _typing.Sequence[Template], tl: cv2.typing.Point2i, size: int = ...) -> cv2.typing.MatLike: ...
+@_typing.overload
+def drawFeatures(img: cv2.UMat, templates: _typing.Sequence[Template], tl: cv2.typing.Point2i, size: int = ...) -> cv2.UMat: ...
+
+def getDefaultLINE() -> Detector: ...
+
+def getDefaultLINEMOD() -> Detector: ...
+
+

venv/lib/python3.12/site-packages/cv2/load_config_py2.py

@@ -0,0 +1,6 @@
+# flake8: noqa
+import sys
+
+if sys.version_info[:2] < (3, 0):
+    def exec_file_wrapper(fpath, g_vars, l_vars):
+        execfile(fpath, g_vars, l_vars)

venv/lib/python3.12/site-packages/cv2/load_config_py3.py

@@ -0,0 +1,9 @@
+# flake8: noqa
+import os
+import sys
+
+if sys.version_info[:2] >= (3, 0):
+    def exec_file_wrapper(fpath, g_vars, l_vars):
+        with open(fpath) as f:
+            code = compile(f.read(), os.path.basename(fpath), 'exec')
+            exec(code, g_vars, l_vars)

venv/lib/python3.12/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)

venv/lib/python3.12/site-packages/cv2/mcc/__init__.pyi

@@ -0,0 +1,109 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.dnn
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+MCC24: int
+SG140: int
+VINYL18: int
+TYPECHART = int
+"""One of [MCC24, SG140, VINYL18]"""
+
+
+
+# Classes
+class DetectorParameters:
+    adaptiveThreshWinSizeMin: int
+    adaptiveThreshWinSizeMax: int
+    adaptiveThreshWinSizeStep: int
+    adaptiveThreshConstant: float
+    minContoursAreaRate: float
+    minContoursArea: float
+    confidenceThreshold: float
+    minContourSolidity: float
+    findCandidatesApproxPolyDPEpsMultiplier: float
+    borderWidth: int
+    B0factor: float
+    maxError: float
+    minContourPointsAllowed: int
+    minContourLengthAllowed: int
+    minInterContourDistance: int
+    minInterCheckerDistance: int
+    minImageSize: int
+    minGroupSize: int
+
+    # Functions
+    @classmethod
+    def create(cls) -> DetectorParameters: ...
+
+
+class CCheckerDetector(cv2.Algorithm):
+    # Functions
+    def setNet(self, net: cv2.dnn.Net) -> bool: ...
+
+    @_typing.overload
+    def processWithROI(self, image: cv2.typing.MatLike, chartType: TYPECHART, regionsOfInterest: _typing.Sequence[cv2.typing.Rect], nc: int = ..., useNet: bool = ..., params: DetectorParameters = ...) -> bool: ...
+    @_typing.overload
+    def processWithROI(self, image: cv2.UMat, chartType: TYPECHART, regionsOfInterest: _typing.Sequence[cv2.typing.Rect], nc: int = ..., useNet: bool = ..., params: DetectorParameters = ...) -> bool: ...
+
+    @_typing.overload
+    def process(self, image: cv2.typing.MatLike, chartType: TYPECHART, nc: int = ..., useNet: bool = ..., params: DetectorParameters = ...) -> bool: ...
+    @_typing.overload
+    def process(self, image: cv2.UMat, chartType: TYPECHART, nc: int = ..., useNet: bool = ..., params: DetectorParameters = ...) -> bool: ...
+
+    def getBestColorChecker(self) -> CChecker: ...
+
+    def getListColorChecker(self) -> _typing.Sequence[CChecker]: ...
+
+    @classmethod
+    def create(cls) -> CCheckerDetector: ...
+
+
+class CChecker:
+    # Functions
+    @classmethod
+    def create(cls) -> CChecker: ...
+
+    def setTarget(self, _target: TYPECHART) -> None: ...
+
+    def setBox(self, _box: _typing.Sequence[cv2.typing.Point2f]) -> None: ...
+
+    def setChartsRGB(self, _chartsRGB: cv2.typing.MatLike) -> None: ...
+
+    def setChartsYCbCr(self, _chartsYCbCr: cv2.typing.MatLike) -> None: ...
+
+    def setCost(self, _cost: float) -> None: ...
+
+    def setCenter(self, _center: cv2.typing.Point2f) -> None: ...
+
+    def getTarget(self) -> TYPECHART: ...
+
+    def getBox(self) -> _typing.Sequence[cv2.typing.Point2f]: ...
+
+    def getColorCharts(self) -> _typing.Sequence[cv2.typing.Point2f]: ...
+
+    def getChartsRGB(self) -> cv2.typing.MatLike: ...
+
+    def getChartsYCbCr(self) -> cv2.typing.MatLike: ...
+
+    def getCost(self) -> float: ...
+
+    def getCenter(self) -> cv2.typing.Point2f: ...
+
+
+class CCheckerDraw:
+    # Functions
+    @_typing.overload
+    def draw(self, img: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def draw(self, img: cv2.UMat) -> cv2.UMat: ...
+
+    @classmethod
+    def create(cls, pChecker: CChecker, color: cv2.typing.Scalar = ..., thickness: int = ...) -> CCheckerDraw: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/misc/__init__.py

@@ -0,0 +1 @@
+from .version import get_ocv_version

venv/lib/python3.12/site-packages/cv2/misc/version.py

@@ -0,0 +1,5 @@
+import cv2
+
+
+def get_ocv_version():
+    return getattr(cv2, "__version__", "unavailable")

venv/lib/python3.12/site-packages/cv2/ml/__init__.pyi

@@ -0,0 +1,695 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Enumerations
+VAR_NUMERICAL: int
+VAR_ORDERED: int
+VAR_CATEGORICAL: int
+VariableTypes = int
+"""One of [VAR_NUMERICAL, VAR_ORDERED, VAR_CATEGORICAL]"""
+
+TEST_ERROR: int
+TRAIN_ERROR: int
+ErrorTypes = int
+"""One of [TEST_ERROR, TRAIN_ERROR]"""
+
+ROW_SAMPLE: int
+COL_SAMPLE: int
+SampleTypes = int
+"""One of [ROW_SAMPLE, COL_SAMPLE]"""
+
+
+StatModel_UPDATE_MODEL: int
+STAT_MODEL_UPDATE_MODEL: int
+StatModel_RAW_OUTPUT: int
+STAT_MODEL_RAW_OUTPUT: int
+StatModel_COMPRESSED_INPUT: int
+STAT_MODEL_COMPRESSED_INPUT: int
+StatModel_PREPROCESSED_INPUT: int
+STAT_MODEL_PREPROCESSED_INPUT: int
+StatModel_Flags = int
+"""One of [StatModel_UPDATE_MODEL, STAT_MODEL_UPDATE_MODEL, StatModel_RAW_OUTPUT, STAT_MODEL_RAW_OUTPUT, StatModel_COMPRESSED_INPUT, STAT_MODEL_COMPRESSED_INPUT, StatModel_PREPROCESSED_INPUT, STAT_MODEL_PREPROCESSED_INPUT]"""
+
+KNearest_BRUTE_FORCE: int
+KNEAREST_BRUTE_FORCE: int
+KNearest_KDTREE: int
+KNEAREST_KDTREE: int
+KNearest_Types = int
+"""One of [KNearest_BRUTE_FORCE, KNEAREST_BRUTE_FORCE, KNearest_KDTREE, KNEAREST_KDTREE]"""
+
+SVM_C_SVC: int
+SVM_NU_SVC: int
+SVM_ONE_CLASS: int
+SVM_EPS_SVR: int
+SVM_NU_SVR: int
+SVM_Types = int
+"""One of [SVM_C_SVC, SVM_NU_SVC, SVM_ONE_CLASS, SVM_EPS_SVR, SVM_NU_SVR]"""
+
+SVM_CUSTOM: int
+SVM_LINEAR: int
+SVM_POLY: int
+SVM_RBF: int
+SVM_SIGMOID: int
+SVM_CHI2: int
+SVM_INTER: int
+SVM_KernelTypes = int
+"""One of [SVM_CUSTOM, SVM_LINEAR, SVM_POLY, SVM_RBF, SVM_SIGMOID, SVM_CHI2, SVM_INTER]"""
+
+SVM_C: int
+SVM_GAMMA: int
+SVM_P: int
+SVM_NU: int
+SVM_COEF: int
+SVM_DEGREE: int
+SVM_ParamTypes = int
+"""One of [SVM_C, SVM_GAMMA, SVM_P, SVM_NU, SVM_COEF, SVM_DEGREE]"""
+
+EM_COV_MAT_SPHERICAL: int
+EM_COV_MAT_DIAGONAL: int
+EM_COV_MAT_GENERIC: int
+EM_COV_MAT_DEFAULT: int
+EM_Types = int
+"""One of [EM_COV_MAT_SPHERICAL, EM_COV_MAT_DIAGONAL, EM_COV_MAT_GENERIC, EM_COV_MAT_DEFAULT]"""
+
+EM_DEFAULT_NCLUSTERS: int
+EM_DEFAULT_MAX_ITERS: int
+EM_START_E_STEP: int
+EM_START_M_STEP: int
+EM_START_AUTO_STEP: int
+
+DTrees_PREDICT_AUTO: int
+DTREES_PREDICT_AUTO: int
+DTrees_PREDICT_SUM: int
+DTREES_PREDICT_SUM: int
+DTrees_PREDICT_MAX_VOTE: int
+DTREES_PREDICT_MAX_VOTE: int
+DTrees_PREDICT_MASK: int
+DTREES_PREDICT_MASK: int
+DTrees_Flags = int
+"""One of [DTrees_PREDICT_AUTO, DTREES_PREDICT_AUTO, DTrees_PREDICT_SUM, DTREES_PREDICT_SUM, DTrees_PREDICT_MAX_VOTE, DTREES_PREDICT_MAX_VOTE, DTrees_PREDICT_MASK, DTREES_PREDICT_MASK]"""
+
+Boost_DISCRETE: int
+BOOST_DISCRETE: int
+Boost_REAL: int
+BOOST_REAL: int
+Boost_LOGIT: int
+BOOST_LOGIT: int
+Boost_GENTLE: int
+BOOST_GENTLE: int
+Boost_Types = int
+"""One of [Boost_DISCRETE, BOOST_DISCRETE, Boost_REAL, BOOST_REAL, Boost_LOGIT, BOOST_LOGIT, Boost_GENTLE, BOOST_GENTLE]"""
+
+ANN_MLP_BACKPROP: int
+ANN_MLP_RPROP: int
+ANN_MLP_ANNEAL: int
+ANN_MLP_TrainingMethods = int
+"""One of [ANN_MLP_BACKPROP, ANN_MLP_RPROP, ANN_MLP_ANNEAL]"""
+
+ANN_MLP_IDENTITY: int
+ANN_MLP_SIGMOID_SYM: int
+ANN_MLP_GAUSSIAN: int
+ANN_MLP_RELU: int
+ANN_MLP_LEAKYRELU: int
+ANN_MLP_ActivationFunctions = int
+"""One of [ANN_MLP_IDENTITY, ANN_MLP_SIGMOID_SYM, ANN_MLP_GAUSSIAN, ANN_MLP_RELU, ANN_MLP_LEAKYRELU]"""
+
+ANN_MLP_UPDATE_WEIGHTS: int
+ANN_MLP_NO_INPUT_SCALE: int
+ANN_MLP_NO_OUTPUT_SCALE: int
+ANN_MLP_TrainFlags = int
+"""One of [ANN_MLP_UPDATE_WEIGHTS, ANN_MLP_NO_INPUT_SCALE, ANN_MLP_NO_OUTPUT_SCALE]"""
+
+LogisticRegression_REG_DISABLE: int
+LOGISTIC_REGRESSION_REG_DISABLE: int
+LogisticRegression_REG_L1: int
+LOGISTIC_REGRESSION_REG_L1: int
+LogisticRegression_REG_L2: int
+LOGISTIC_REGRESSION_REG_L2: int
+LogisticRegression_RegKinds = int
+"""One of [LogisticRegression_REG_DISABLE, LOGISTIC_REGRESSION_REG_DISABLE, LogisticRegression_REG_L1, LOGISTIC_REGRESSION_REG_L1, LogisticRegression_REG_L2, LOGISTIC_REGRESSION_REG_L2]"""
+
+LogisticRegression_BATCH: int
+LOGISTIC_REGRESSION_BATCH: int
+LogisticRegression_MINI_BATCH: int
+LOGISTIC_REGRESSION_MINI_BATCH: int
+LogisticRegression_Methods = int
+"""One of [LogisticRegression_BATCH, LOGISTIC_REGRESSION_BATCH, LogisticRegression_MINI_BATCH, LOGISTIC_REGRESSION_MINI_BATCH]"""
+
+SVMSGD_SGD: int
+SVMSGD_ASGD: int
+SVMSGD_SvmsgdType = int
+"""One of [SVMSGD_SGD, SVMSGD_ASGD]"""
+
+SVMSGD_SOFT_MARGIN: int
+SVMSGD_HARD_MARGIN: int
+SVMSGD_MarginType = int
+"""One of [SVMSGD_SOFT_MARGIN, SVMSGD_HARD_MARGIN]"""
+
+
+# Classes
+class ParamGrid:
+    minVal: float
+    maxVal: float
+    logStep: float
+
+    # Functions
+    @classmethod
+    def create(cls, minVal: float = ..., maxVal: float = ..., logstep: float = ...) -> ParamGrid: ...
+
+
+class TrainData:
+    # Functions
+    def getLayout(self) -> int: ...
+
+    def getNTrainSamples(self) -> int: ...
+
+    def getNTestSamples(self) -> int: ...
+
+    def getNSamples(self) -> int: ...
+
+    def getNVars(self) -> int: ...
+
+    def getNAllVars(self) -> int: ...
+
+    @_typing.overload
+    def getSample(self, varIdx: cv2.typing.MatLike, sidx: int, buf: float) -> None: ...
+    @_typing.overload
+    def getSample(self, varIdx: cv2.UMat, sidx: int, buf: float) -> None: ...
+
+    def getSamples(self) -> cv2.typing.MatLike: ...
+
+    def getMissing(self) -> cv2.typing.MatLike: ...
+
+    def getTrainSamples(self, layout: int = ..., compressSamples: bool = ..., compressVars: bool = ...) -> cv2.typing.MatLike: ...
+
+    def getTrainResponses(self) -> cv2.typing.MatLike: ...
+
+    def getTrainNormCatResponses(self) -> cv2.typing.MatLike: ...
+
+    def getTestResponses(self) -> cv2.typing.MatLike: ...
+
+    def getTestNormCatResponses(self) -> cv2.typing.MatLike: ...
+
+    def getResponses(self) -> cv2.typing.MatLike: ...
+
+    def getNormCatResponses(self) -> cv2.typing.MatLike: ...
+
+    def getSampleWeights(self) -> cv2.typing.MatLike: ...
+
+    def getTrainSampleWeights(self) -> cv2.typing.MatLike: ...
+
+    def getTestSampleWeights(self) -> cv2.typing.MatLike: ...
+
+    def getVarIdx(self) -> cv2.typing.MatLike: ...
+
+    def getVarType(self) -> cv2.typing.MatLike: ...
+
+    def getVarSymbolFlags(self) -> cv2.typing.MatLike: ...
+
+    def getResponseType(self) -> int: ...
+
+    def getTrainSampleIdx(self) -> cv2.typing.MatLike: ...
+
+    def getTestSampleIdx(self) -> cv2.typing.MatLike: ...
+
+    @_typing.overload
+    def getValues(self, vi: int, sidx: cv2.typing.MatLike, values: float) -> None: ...
+    @_typing.overload
+    def getValues(self, vi: int, sidx: cv2.UMat, values: float) -> None: ...
+
+    def getDefaultSubstValues(self) -> cv2.typing.MatLike: ...
+
+    def getCatCount(self, vi: int) -> int: ...
+
+    def getClassLabels(self) -> cv2.typing.MatLike: ...
+
+    def getCatOfs(self) -> cv2.typing.MatLike: ...
+
+    def getCatMap(self) -> cv2.typing.MatLike: ...
+
+    def setTrainTestSplit(self, count: int, shuffle: bool = ...) -> None: ...
+
+    def setTrainTestSplitRatio(self, ratio: float, shuffle: bool = ...) -> None: ...
+
+    def shuffleTrainTest(self) -> None: ...
+
+    def getTestSamples(self) -> cv2.typing.MatLike: ...
+
+    def getNames(self, names: _typing.Sequence[str]) -> None: ...
+
+    @staticmethod
+    def getSubVector(vec: cv2.typing.MatLike, idx: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
+
+    @staticmethod
+    def getSubMatrix(matrix: cv2.typing.MatLike, idx: cv2.typing.MatLike, layout: int) -> cv2.typing.MatLike: ...
+
+    @classmethod
+    @_typing.overload
+    def create(cls, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike, varIdx: cv2.typing.MatLike | None = ..., sampleIdx: cv2.typing.MatLike | None = ..., sampleWeights: cv2.typing.MatLike | None = ..., varType: cv2.typing.MatLike | None = ...) -> TrainData: ...
+    @classmethod
+    @_typing.overload
+    def create(cls, samples: cv2.UMat, layout: int, responses: cv2.UMat, varIdx: cv2.UMat | None = ..., sampleIdx: cv2.UMat | None = ..., sampleWeights: cv2.UMat | None = ..., varType: cv2.UMat | None = ...) -> TrainData: ...
+
+
+class StatModel(cv2.Algorithm):
+    # Functions
+    def getVarCount(self) -> int: ...
+
+    def empty(self) -> bool: ...
+
+    def isTrained(self) -> bool: ...
+
+    def isClassifier(self) -> bool: ...
+
+    @_typing.overload
+    def train(self, trainData: TrainData, flags: int = ...) -> bool: ...
+    @_typing.overload
+    def train(self, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike) -> bool: ...
+    @_typing.overload
+    def train(self, samples: cv2.UMat, layout: int, responses: cv2.UMat) -> bool: ...
+
+    @_typing.overload
+    def calcError(self, data: TrainData, test: bool, resp: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def calcError(self, data: TrainData, test: bool, resp: cv2.UMat | None = ...) -> tuple[float, cv2.UMat]: ...
+
+    @_typing.overload
+    def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
+
+
+class NormalBayesClassifier(StatModel):
+    # Functions
+    @_typing.overload
+    def predictProb(self, inputs: cv2.typing.MatLike, outputs: cv2.typing.MatLike | None = ..., outputProbs: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def predictProb(self, inputs: cv2.UMat, outputs: cv2.UMat | None = ..., outputProbs: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat, cv2.UMat]: ...
+
+    @classmethod
+    def create(cls) -> NormalBayesClassifier: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> NormalBayesClassifier: ...
+
+
+class KNearest(StatModel):
+    # Functions
+    def getDefaultK(self) -> int: ...
+
+    def setDefaultK(self, val: int) -> None: ...
+
+    def getIsClassifier(self) -> bool: ...
+
+    def setIsClassifier(self, val: bool) -> None: ...
+
+    def getEmax(self) -> int: ...
+
+    def setEmax(self, val: int) -> None: ...
+
+    def getAlgorithmType(self) -> int: ...
+
+    def setAlgorithmType(self, val: int) -> None: ...
+
+    @_typing.overload
+    def findNearest(self, samples: cv2.typing.MatLike, k: int, results: cv2.typing.MatLike | None = ..., neighborResponses: cv2.typing.MatLike | None = ..., dist: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def findNearest(self, samples: cv2.UMat, k: int, results: cv2.UMat | None = ..., neighborResponses: cv2.UMat | None = ..., dist: cv2.UMat | None = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+    @classmethod
+    def create(cls) -> KNearest: ...
+
+    @classmethod
+    def load(cls, filepath: str) -> KNearest: ...
+
+
+class SVM(StatModel):
+    # Functions
+    def getType(self) -> int: ...
+
+    def setType(self, val: int) -> None: ...
+
+    def getGamma(self) -> float: ...
+
+    def setGamma(self, val: float) -> None: ...
+
+    def getCoef0(self) -> float: ...
+
+    def setCoef0(self, val: float) -> None: ...
+
+    def getDegree(self) -> float: ...
+
+    def setDegree(self, val: float) -> None: ...
+
+    def getC(self) -> float: ...
+
+    def setC(self, val: float) -> None: ...
+
+    def getNu(self) -> float: ...
+
+    def setNu(self, val: float) -> None: ...
+
+    def getP(self) -> float: ...
+
+    def setP(self, val: float) -> None: ...
+
+    def getClassWeights(self) -> cv2.typing.MatLike: ...
+
+    def setClassWeights(self, val: cv2.typing.MatLike) -> None: ...
+
+    def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
+
+    def getKernelType(self) -> int: ...
+
+    def setKernel(self, kernelType: int) -> None: ...
+
+    @_typing.overload
+    def trainAuto(self, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike, kFold: int = ..., Cgrid: ParamGrid = ..., gammaGrid: ParamGrid = ..., pGrid: ParamGrid = ..., nuGrid: ParamGrid = ..., coeffGrid: ParamGrid = ..., degreeGrid: ParamGrid = ..., balanced: bool = ...) -> bool: ...
+    @_typing.overload
+    def trainAuto(self, samples: cv2.UMat, layout: int, responses: cv2.UMat, kFold: int = ..., Cgrid: ParamGrid = ..., gammaGrid: ParamGrid = ..., pGrid: ParamGrid = ..., nuGrid: ParamGrid = ..., coeffGrid: ParamGrid = ..., degreeGrid: ParamGrid = ..., balanced: bool = ...) -> bool: ...
+
+    def getSupportVectors(self) -> cv2.typing.MatLike: ...
+
+    def getUncompressedSupportVectors(self) -> cv2.typing.MatLike: ...
+
+    @_typing.overload
+    def getDecisionFunction(self, i: int, alpha: cv2.typing.MatLike | None = ..., svidx: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def getDecisionFunction(self, i: int, alpha: cv2.UMat | None = ..., svidx: cv2.UMat | None = ...) -> tuple[float, cv2.UMat, cv2.UMat]: ...
+
+    @staticmethod
+    def getDefaultGridPtr(param_id: int) -> ParamGrid: ...
+
+    @classmethod
+    def create(cls) -> SVM: ...
+
+    @classmethod
+    def load(cls, filepath: str) -> SVM: ...
+
+
+class EM(StatModel):
+    # Functions
+    def getClustersNumber(self) -> int: ...
+
+    def setClustersNumber(self, val: int) -> None: ...
+
+    def getCovarianceMatrixType(self) -> int: ...
+
+    def setCovarianceMatrixType(self, val: int) -> None: ...
+
+    def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
+
+    def getWeights(self) -> cv2.typing.MatLike: ...
+
+    def getMeans(self) -> cv2.typing.MatLike: ...
+
+    def getCovs(self, covs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
+
+    @_typing.overload
+    def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
+
+    @_typing.overload
+    def predict2(self, sample: cv2.typing.MatLike, probs: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Vec2d, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def predict2(self, sample: cv2.UMat, probs: cv2.UMat | None = ...) -> tuple[cv2.typing.Vec2d, cv2.UMat]: ...
+
+    @_typing.overload
+    def trainEM(self, samples: cv2.typing.MatLike, logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def trainEM(self, samples: cv2.UMat, logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def trainE(self, samples: cv2.typing.MatLike, means0: cv2.typing.MatLike, covs0: cv2.typing.MatLike | None = ..., weights0: cv2.typing.MatLike | None = ..., logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def trainE(self, samples: cv2.UMat, means0: cv2.UMat, covs0: cv2.UMat | None = ..., weights0: cv2.UMat | None = ..., logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+    @_typing.overload
+    def trainM(self, samples: cv2.typing.MatLike, probs0: cv2.typing.MatLike, logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def trainM(self, samples: cv2.UMat, probs0: cv2.UMat, logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
+
+    @classmethod
+    def create(cls) -> EM: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> EM: ...
+
+
+class DTrees(StatModel):
+    # Functions
+    def getMaxCategories(self) -> int: ...
+
+    def setMaxCategories(self, val: int) -> None: ...
+
+    def getMaxDepth(self) -> int: ...
+
+    def setMaxDepth(self, val: int) -> None: ...
+
+    def getMinSampleCount(self) -> int: ...
+
+    def setMinSampleCount(self, val: int) -> None: ...
+
+    def getCVFolds(self) -> int: ...
+
+    def setCVFolds(self, val: int) -> None: ...
+
+    def getUseSurrogates(self) -> bool: ...
+
+    def setUseSurrogates(self, val: bool) -> None: ...
+
+    def getUse1SERule(self) -> bool: ...
+
+    def setUse1SERule(self, val: bool) -> None: ...
+
+    def getTruncatePrunedTree(self) -> bool: ...
+
+    def setTruncatePrunedTree(self, val: bool) -> None: ...
+
+    def getRegressionAccuracy(self) -> float: ...
+
+    def setRegressionAccuracy(self, val: float) -> None: ...
+
+    def getPriors(self) -> cv2.typing.MatLike: ...
+
+    def setPriors(self, val: cv2.typing.MatLike) -> None: ...
+
+    @classmethod
+    def create(cls) -> DTrees: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> DTrees: ...
+
+
+class RTrees(DTrees):
+    # Functions
+    def getCalculateVarImportance(self) -> bool: ...
+
+    def setCalculateVarImportance(self, val: bool) -> None: ...
+
+    def getActiveVarCount(self) -> int: ...
+
+    def setActiveVarCount(self, val: int) -> None: ...
+
+    def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
+
+    def getVarImportance(self) -> cv2.typing.MatLike: ...
+
+    @_typing.overload
+    def getVotes(self, samples: cv2.typing.MatLike, flags: int, results: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
+    @_typing.overload
+    def getVotes(self, samples: cv2.UMat, flags: int, results: cv2.UMat | None = ...) -> cv2.UMat: ...
+
+    def getOOBError(self) -> float: ...
+
+    @classmethod
+    def create(cls) -> RTrees: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> RTrees: ...
+
+
+class Boost(DTrees):
+    # Functions
+    def getBoostType(self) -> int: ...
+
+    def setBoostType(self, val: int) -> None: ...
+
+    def getWeakCount(self) -> int: ...
+
+    def setWeakCount(self, val: int) -> None: ...
+
+    def getWeightTrimRate(self) -> float: ...
+
+    def setWeightTrimRate(self, val: float) -> None: ...
+
+    @classmethod
+    def create(cls) -> Boost: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> Boost: ...
+
+
+class ANN_MLP(StatModel):
+    # Functions
+    def setTrainMethod(self, method: int, param1: float = ..., param2: float = ...) -> None: ...
+
+    def getTrainMethod(self) -> int: ...
+
+    def setActivationFunction(self, type: int, param1: float = ..., param2: float = ...) -> None: ...
+
+    @_typing.overload
+    def setLayerSizes(self, _layer_sizes: cv2.typing.MatLike) -> None: ...
+    @_typing.overload
+    def setLayerSizes(self, _layer_sizes: cv2.UMat) -> None: ...
+
+    def getLayerSizes(self) -> cv2.typing.MatLike: ...
+
+    def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
+
+    def getBackpropWeightScale(self) -> float: ...
+
+    def setBackpropWeightScale(self, val: float) -> None: ...
+
+    def getBackpropMomentumScale(self) -> float: ...
+
+    def setBackpropMomentumScale(self, val: float) -> None: ...
+
+    def getRpropDW0(self) -> float: ...
+
+    def setRpropDW0(self, val: float) -> None: ...
+
+    def getRpropDWPlus(self) -> float: ...
+
+    def setRpropDWPlus(self, val: float) -> None: ...
+
+    def getRpropDWMinus(self) -> float: ...
+
+    def setRpropDWMinus(self, val: float) -> None: ...
+
+    def getRpropDWMin(self) -> float: ...
+
+    def setRpropDWMin(self, val: float) -> None: ...
+
+    def getRpropDWMax(self) -> float: ...
+
+    def setRpropDWMax(self, val: float) -> None: ...
+
+    def getAnnealInitialT(self) -> float: ...
+
+    def setAnnealInitialT(self, val: float) -> None: ...
+
+    def getAnnealFinalT(self) -> float: ...
+
+    def setAnnealFinalT(self, val: float) -> None: ...
+
+    def getAnnealCoolingRatio(self) -> float: ...
+
+    def setAnnealCoolingRatio(self, val: float) -> None: ...
+
+    def getAnnealItePerStep(self) -> int: ...
+
+    def setAnnealItePerStep(self, val: int) -> None: ...
+
+    def getWeights(self, layerIdx: int) -> cv2.typing.MatLike: ...
+
+    @classmethod
+    def create(cls) -> ANN_MLP: ...
+
+    @classmethod
+    def load(cls, filepath: str) -> ANN_MLP: ...
+
+
+class LogisticRegression(StatModel):
+    # Functions
+    def getLearningRate(self) -> float: ...
+
+    def setLearningRate(self, val: float) -> None: ...
+
+    def getIterations(self) -> int: ...
+
+    def setIterations(self, val: int) -> None: ...
+
+    def getRegularization(self) -> int: ...
+
+    def setRegularization(self, val: int) -> None: ...
+
+    def getTrainMethod(self) -> int: ...
+
+    def setTrainMethod(self, val: int) -> None: ...
+
+    def getMiniBatchSize(self) -> int: ...
+
+    def setMiniBatchSize(self, val: int) -> None: ...
+
+    def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
+
+    @_typing.overload
+    def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
+    @_typing.overload
+    def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
+
+    def get_learnt_thetas(self) -> cv2.typing.MatLike: ...
+
+    @classmethod
+    def create(cls) -> LogisticRegression: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> LogisticRegression: ...
+
+
+class SVMSGD(StatModel):
+    # Functions
+    def getWeights(self) -> cv2.typing.MatLike: ...
+
+    def getShift(self) -> float: ...
+
+    @classmethod
+    def create(cls) -> SVMSGD: ...
+
+    @classmethod
+    def load(cls, filepath: str, nodeName: str = ...) -> SVMSGD: ...
+
+    def setOptimalParameters(self, svmsgdType: int = ..., marginType: int = ...) -> None: ...
+
+    def getSvmsgdType(self) -> int: ...
+
+    def setSvmsgdType(self, svmsgdType: int) -> None: ...
+
+    def getMarginType(self) -> int: ...
+
+    def setMarginType(self, marginType: int) -> None: ...
+
+    def getMarginRegularization(self) -> float: ...
+
+    def setMarginRegularization(self, marginRegularization: float) -> None: ...
+
+    def getInitialStepSize(self) -> float: ...
+
+    def setInitialStepSize(self, InitialStepSize: float) -> None: ...
+
+    def getStepDecreasingPower(self) -> float: ...
+
+    def setStepDecreasingPower(self, stepDecreasingPower: float) -> None: ...
+
+    def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
+
+    def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
+
+
+

venv/lib/python3.12/site-packages/cv2/motempl/__init__.pyi

@@ -0,0 +1,29 @@
+__all__: list[str] = []
+
+import cv2
+import cv2.typing
+import typing as _typing
+
+
+# Functions
+@_typing.overload
+def calcGlobalOrientation(orientation: cv2.typing.MatLike, mask: cv2.typing.MatLike, mhi: cv2.typing.MatLike, timestamp: float, duration: float) -> float: ...
+@_typing.overload
+def calcGlobalOrientation(orientation: cv2.UMat, mask: cv2.UMat, mhi: cv2.UMat, timestamp: float, duration: float) -> float: ...
+
+@_typing.overload
+def calcMotionGradient(mhi: cv2.typing.MatLike, delta1: float, delta2: float, mask: cv2.typing.MatLike | None = ..., orientation: cv2.typing.MatLike | None = ..., apertureSize: int = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
+@_typing.overload
+def calcMotionGradient(mhi: cv2.UMat, delta1: float, delta2: float, mask: cv2.UMat | None = ..., orientation: cv2.UMat | None = ..., apertureSize: int = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
+
+@_typing.overload
+def segmentMotion(mhi: cv2.typing.MatLike, timestamp: float, segThresh: float, segmask: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, _typing.Sequence[cv2.typing.Rect]]: ...
+@_typing.overload
+def segmentMotion(mhi: cv2.UMat, timestamp: float, segThresh: float, segmask: cv2.UMat | None = ...) -> tuple[cv2.UMat, _typing.Sequence[cv2.typing.Rect]]: ...
+
+@_typing.overload
+def updateMotionHistory(silhouette: cv2.typing.MatLike, mhi: cv2.typing.MatLike, timestamp: float, duration: float) -> cv2.typing.MatLike: ...
+@_typing.overload
+def updateMotionHistory(silhouette: cv2.UMat, mhi: cv2.UMat, timestamp: float, duration: float) -> cv2.UMat: ...
+
+

venv/lib/python3.12/site-packages/cv2/multicalib/__init__.pyi

@@ -0,0 +1,10 @@
+__all__: list[str] = []
+
+MultiCameraCalibration_PINHOLE: int
+MULTI_CAMERA_CALIBRATION_PINHOLE: int
+MultiCameraCalibration_OMNIDIRECTIONAL: int
+MULTI_CAMERA_CALIBRATION_OMNIDIRECTIONAL: int
+
+
+# Classes
+

venv/lib/python3.12/site-packages/cv2/ocl/__init__.pyi

@@ -0,0 +1,252 @@
+__all__: list[str] = []
+
+# Enumerations
+OCL_VECTOR_OWN: int
+OCL_VECTOR_MAX: int
+OCL_VECTOR_DEFAULT: int
+OclVectorStrategy = int
+"""One of [OCL_VECTOR_OWN, OCL_VECTOR_MAX, OCL_VECTOR_DEFAULT]"""
+
+
+Device_TYPE_DEFAULT: int
+DEVICE_TYPE_DEFAULT: int
+Device_TYPE_CPU: int
+DEVICE_TYPE_CPU: int
+Device_TYPE_GPU: int
+DEVICE_TYPE_GPU: int
+Device_TYPE_ACCELERATOR: int
+DEVICE_TYPE_ACCELERATOR: int
+Device_TYPE_DGPU: int
+DEVICE_TYPE_DGPU: int
+Device_TYPE_IGPU: int
+DEVICE_TYPE_IGPU: int
+Device_TYPE_ALL: int
+DEVICE_TYPE_ALL: int
+Device_FP_DENORM: int
+DEVICE_FP_DENORM: int
+Device_FP_INF_NAN: int
+DEVICE_FP_INF_NAN: int
+Device_FP_ROUND_TO_NEAREST: int
+DEVICE_FP_ROUND_TO_NEAREST: int
+Device_FP_ROUND_TO_ZERO: int
+DEVICE_FP_ROUND_TO_ZERO: int
+Device_FP_ROUND_TO_INF: int
+DEVICE_FP_ROUND_TO_INF: int
+Device_FP_FMA: int
+DEVICE_FP_FMA: int
+Device_FP_SOFT_FLOAT: int
+DEVICE_FP_SOFT_FLOAT: int
+Device_FP_CORRECTLY_ROUNDED_DIVIDE_SQRT: int
+DEVICE_FP_CORRECTLY_ROUNDED_DIVIDE_SQRT: int
+Device_EXEC_KERNEL: int
+DEVICE_EXEC_KERNEL: int
+Device_EXEC_NATIVE_KERNEL: int
+DEVICE_EXEC_NATIVE_KERNEL: int
+Device_NO_CACHE: int
+DEVICE_NO_CACHE: int
+Device_READ_ONLY_CACHE: int
+DEVICE_READ_ONLY_CACHE: int
+Device_READ_WRITE_CACHE: int
+DEVICE_READ_WRITE_CACHE: int
+Device_NO_LOCAL_MEM: int
+DEVICE_NO_LOCAL_MEM: int
+Device_LOCAL_IS_LOCAL: int
+DEVICE_LOCAL_IS_LOCAL: int
+Device_LOCAL_IS_GLOBAL: int
+DEVICE_LOCAL_IS_GLOBAL: int
+Device_UNKNOWN_VENDOR: int
+DEVICE_UNKNOWN_VENDOR: int
+Device_VENDOR_AMD: int
+DEVICE_VENDOR_AMD: int
+Device_VENDOR_INTEL: int
+DEVICE_VENDOR_INTEL: int
+Device_VENDOR_NVIDIA: int
+DEVICE_VENDOR_NVIDIA: int
+
+KernelArg_LOCAL: int
+KERNEL_ARG_LOCAL: int
+KernelArg_READ_ONLY: int
+KERNEL_ARG_READ_ONLY: int
+KernelArg_WRITE_ONLY: int
+KERNEL_ARG_WRITE_ONLY: int
+KernelArg_READ_WRITE: int
+KERNEL_ARG_READ_WRITE: int
+KernelArg_CONSTANT: int
+KERNEL_ARG_CONSTANT: int
+KernelArg_PTR_ONLY: int
+KERNEL_ARG_PTR_ONLY: int
+KernelArg_NO_SIZE: int
+KERNEL_ARG_NO_SIZE: int
+
+
+# Classes
+class Device:
+    # Functions
+    def __init__(self) -> None: ...
+
+    def name(self) -> str: ...
+
+    def extensions(self) -> str: ...
+
+    def isExtensionSupported(self, extensionName: str) -> bool: ...
+
+    def version(self) -> str: ...
+
+    def vendorName(self) -> str: ...
+
+    def OpenCL_C_Version(self) -> str: ...
+
+    def OpenCLVersion(self) -> str: ...
+
+    def deviceVersionMajor(self) -> int: ...
+
+    def deviceVersionMinor(self) -> int: ...
+
+    def driverVersion(self) -> str: ...
+
+    def type(self) -> int: ...
+
+    def addressBits(self) -> int: ...
+
+    def available(self) -> bool: ...
+
+    def compilerAvailable(self) -> bool: ...
+
+    def linkerAvailable(self) -> bool: ...
+
+    def doubleFPConfig(self) -> int: ...
+
+    def singleFPConfig(self) -> int: ...
+
+    def halfFPConfig(self) -> int: ...
+
+    def hasFP64(self) -> bool: ...
+
+    def hasFP16(self) -> bool: ...
+
+    def endianLittle(self) -> bool: ...
+
+    def errorCorrectionSupport(self) -> bool: ...
+
+    def executionCapabilities(self) -> int: ...
+
+    def globalMemCacheSize(self) -> int: ...
+
+    def globalMemCacheType(self) -> int: ...
+
+    def globalMemCacheLineSize(self) -> int: ...
+
+    def globalMemSize(self) -> int: ...
+
+    def localMemSize(self) -> int: ...
+
+    def localMemType(self) -> int: ...
+
+    def hostUnifiedMemory(self) -> bool: ...
+
+    def imageSupport(self) -> bool: ...
+
+    def imageFromBufferSupport(self) -> bool: ...
+
+    def intelSubgroupsSupport(self) -> bool: ...
+
+    def image2DMaxWidth(self) -> int: ...
+
+    def image2DMaxHeight(self) -> int: ...
+
+    def image3DMaxWidth(self) -> int: ...
+
+    def image3DMaxHeight(self) -> int: ...
+
+    def image3DMaxDepth(self) -> int: ...
+
+    def imageMaxBufferSize(self) -> int: ...
+
+    def imageMaxArraySize(self) -> int: ...
+
+    def vendorID(self) -> int: ...
+
+    def isAMD(self) -> bool: ...
+
+    def isIntel(self) -> bool: ...
+
+    def isNVidia(self) -> bool: ...
+
+    def maxClockFrequency(self) -> int: ...
+
+    def maxComputeUnits(self) -> int: ...
+
+    def maxConstantArgs(self) -> int: ...
+
+    def maxConstantBufferSize(self) -> int: ...
+
+    def maxMemAllocSize(self) -> int: ...
+
+    def maxParameterSize(self) -> int: ...
+
+    def maxReadImageArgs(self) -> int: ...
+
+    def maxWriteImageArgs(self) -> int: ...
+
+    def maxSamplers(self) -> int: ...
+
+    def maxWorkGroupSize(self) -> int: ...
+
+    def maxWorkItemDims(self) -> int: ...
+
+    def memBaseAddrAlign(self) -> int: ...
+
+    def nativeVectorWidthChar(self) -> int: ...
+
+    def nativeVectorWidthShort(self) -> int: ...
+
+    def nativeVectorWidthInt(self) -> int: ...
+
+    def nativeVectorWidthLong(self) -> int: ...
+
+    def nativeVectorWidthFloat(self) -> int: ...
+
+    def nativeVectorWidthDouble(self) -> int: ...
+
+    def nativeVectorWidthHalf(self) -> int: ...
+
+    def preferredVectorWidthChar(self) -> int: ...
+
+    def preferredVectorWidthShort(self) -> int: ...
+
+    def preferredVectorWidthInt(self) -> int: ...
+
+    def preferredVectorWidthLong(self) -> int: ...
+
+    def preferredVectorWidthFloat(self) -> int: ...
+
+    def preferredVectorWidthDouble(self) -> int: ...
+
+    def preferredVectorWidthHalf(self) -> int: ...
+
+    def printfBufferSize(self) -> int: ...
+
+    def profilingTimerResolution(self) -> int: ...
+
+    @classmethod
+    def getDefault(cls) -> Device: ...
+
+
+class OpenCLExecutionContext:
+    ...
+
+
+# Functions
+def finish() -> None: ...
+
+def haveAmdBlas() -> bool: ...
+
+def haveAmdFft() -> bool: ...
+
+def haveOpenCL() -> bool: ...
+
+def setUseOpenCL(flag: bool) -> None: ...
+
+def useOpenCL() -> bool: ...
+
+