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