__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 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 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: ...