| __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 |
|
|
|
|
| |
| 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]""" |
|
|
|
|
|
|
| |
| class DictValue: |
| |
| @_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: ... |
|
|
| |
| @_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: |
| |
| 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 |
|
|
| |
| @_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: |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| @_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): |
| |
| 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]: ... |
|
|
|
|
|
|
| |
| 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: ... |
|
|
|
|
|
|
