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rattatwinko
203
venv/lib/python3.12/site-packages/cv2/text/__init__.pyi
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203
venv/lib/python3.12/site-packages/cv2/text/__init__.pyi
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__all__: list[str] = []
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import cv2
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import cv2.typing
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import typing as _typing
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# Enumerations
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ERFILTER_NM_RGBLGrad: int
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ERFILTER_NM_RGBLGRAD: int
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ERFILTER_NM_IHSGrad: int
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ERFILTER_NM_IHSGRAD: int
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OCR_LEVEL_WORD: int
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OCR_LEVEL_TEXTLINE: int
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ERGROUPING_ORIENTATION_HORIZ: int
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ERGROUPING_ORIENTATION_ANY: int
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erGrouping_Modes = int
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"""One of [ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY]"""
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PSM_OSD_ONLY: int
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PSM_AUTO_OSD: int
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PSM_AUTO_ONLY: int
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PSM_AUTO: int
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PSM_SINGLE_COLUMN: int
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PSM_SINGLE_BLOCK_VERT_TEXT: int
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PSM_SINGLE_BLOCK: int
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PSM_SINGLE_LINE: int
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PSM_SINGLE_WORD: int
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PSM_CIRCLE_WORD: int
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PSM_SINGLE_CHAR: int
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page_seg_mode = int
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"""One of [PSM_OSD_ONLY, PSM_AUTO_OSD, PSM_AUTO_ONLY, PSM_AUTO, PSM_SINGLE_COLUMN, PSM_SINGLE_BLOCK_VERT_TEXT, PSM_SINGLE_BLOCK, PSM_SINGLE_LINE, PSM_SINGLE_WORD, PSM_CIRCLE_WORD, PSM_SINGLE_CHAR]"""
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OEM_TESSERACT_ONLY: int
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OEM_CUBE_ONLY: int
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OEM_TESSERACT_CUBE_COMBINED: int
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OEM_DEFAULT: int
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ocr_engine_mode = int
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"""One of [OEM_TESSERACT_ONLY, OEM_CUBE_ONLY, OEM_TESSERACT_CUBE_COMBINED, OEM_DEFAULT]"""
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OCR_DECODER_VITERBI: int
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decoder_mode = int
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"""One of [OCR_DECODER_VITERBI]"""
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OCR_KNN_CLASSIFIER: int
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OCR_CNN_CLASSIFIER: int
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classifier_type = int
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"""One of [OCR_KNN_CLASSIFIER, OCR_CNN_CLASSIFIER]"""
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# Classes
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class ERFilter(cv2.Algorithm):
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# Classes
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class Callback:
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...
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class BaseOCR:
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...
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class OCRTesseract(BaseOCR):
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# Functions
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@_typing.overload
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def run(self, image: cv2.typing.MatLike, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.UMat, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.typing.MatLike, mask: cv2.typing.MatLike, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.UMat, mask: cv2.UMat, min_confidence: int, component_level: int = ...) -> str: ...
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def setWhiteList(self, char_whitelist: str) -> None: ...
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@classmethod
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def create(cls, datapath: str = ..., language: str = ..., char_whitelist: str = ..., oem: int = ..., psmode: int = ...) -> OCRTesseract: ...
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class OCRHMMDecoder(BaseOCR):
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# Classes
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class ClassifierCallback:
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...
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# Functions
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@_typing.overload
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def run(self, image: cv2.typing.MatLike, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.UMat, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.typing.MatLike, mask: cv2.typing.MatLike, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.UMat, mask: cv2.UMat, min_confidence: int, component_level: int = ...) -> str: ...
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@classmethod
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@_typing.overload
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def create(cls, classifier: OCRHMMDecoder.ClassifierCallback, vocabulary: str, transition_probabilities_table: cv2.typing.MatLike, emission_probabilities_table: cv2.typing.MatLike, mode: int = ...) -> OCRHMMDecoder: ...
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@classmethod
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@_typing.overload
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def create(cls, classifier: OCRHMMDecoder.ClassifierCallback, vocabulary: str, transition_probabilities_table: cv2.UMat, emission_probabilities_table: cv2.UMat, mode: int = ...) -> OCRHMMDecoder: ...
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@classmethod
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@_typing.overload
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def create(cls, filename: str, vocabulary: str, transition_probabilities_table: cv2.typing.MatLike, emission_probabilities_table: cv2.typing.MatLike, mode: int = ..., classifier: int = ...) -> OCRHMMDecoder: ...
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@classmethod
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@_typing.overload
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def create(cls, filename: str, vocabulary: str, transition_probabilities_table: cv2.UMat, emission_probabilities_table: cv2.UMat, mode: int = ..., classifier: int = ...) -> OCRHMMDecoder: ...
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class OCRBeamSearchDecoder(BaseOCR):
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# Classes
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class ClassifierCallback:
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...
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# Functions
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@_typing.overload
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def run(self, image: cv2.typing.MatLike, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.UMat, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.typing.MatLike, mask: cv2.typing.MatLike, min_confidence: int, component_level: int = ...) -> str: ...
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@_typing.overload
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def run(self, image: cv2.UMat, mask: cv2.UMat, min_confidence: int, component_level: int = ...) -> str: ...
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@classmethod
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@_typing.overload
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def create(cls, classifier: OCRBeamSearchDecoder.ClassifierCallback, vocabulary: str, transition_probabilities_table: cv2.typing.MatLike, emission_probabilities_table: cv2.typing.MatLike, mode: decoder_mode = ..., beam_size: int = ...) -> OCRBeamSearchDecoder: ...
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@classmethod
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@_typing.overload
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def create(cls, classifier: OCRBeamSearchDecoder.ClassifierCallback, vocabulary: str, transition_probabilities_table: cv2.UMat, emission_probabilities_table: cv2.UMat, mode: decoder_mode = ..., beam_size: int = ...) -> OCRBeamSearchDecoder: ...
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class TextDetector:
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# Functions
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@_typing.overload
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def detect(self, inputImage: cv2.typing.MatLike) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]: ...
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@_typing.overload
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def detect(self, inputImage: cv2.UMat) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]: ...
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class TextDetectorCNN(TextDetector):
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# Functions
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@_typing.overload
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def detect(self, inputImage: cv2.typing.MatLike) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]: ...
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@_typing.overload
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def detect(self, inputImage: cv2.UMat) -> tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]: ...
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@classmethod
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def create(cls, modelArchFilename: str, modelWeightsFilename: str) -> TextDetectorCNN: ...
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# Functions
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@_typing.overload
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def computeNMChannels(_src: cv2.typing.MatLike, _channels: _typing.Sequence[cv2.typing.MatLike] | None = ..., _mode: int = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
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@_typing.overload
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def computeNMChannels(_src: cv2.UMat, _channels: _typing.Sequence[cv2.UMat] | None = ..., _mode: int = ...) -> _typing.Sequence[cv2.UMat]: ...
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@_typing.overload
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def createERFilterNM1(cb: ERFilter.Callback, thresholdDelta: int = ..., minArea: float = ..., maxArea: float = ..., minProbability: float = ..., nonMaxSuppression: bool = ..., minProbabilityDiff: float = ...) -> ERFilter: ...
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@_typing.overload
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def createERFilterNM1(filename: str, thresholdDelta: int = ..., minArea: float = ..., maxArea: float = ..., minProbability: float = ..., nonMaxSuppression: bool = ..., minProbabilityDiff: float = ...) -> ERFilter: ...
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@_typing.overload
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def createERFilterNM2(cb: ERFilter.Callback, minProbability: float = ...) -> ERFilter: ...
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@_typing.overload
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def createERFilterNM2(filename: str, minProbability: float = ...) -> ERFilter: ...
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def createOCRHMMTransitionsTable(vocabulary: str, lexicon: _typing.Sequence[str]) -> cv2.typing.MatLike: ...
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@_typing.overload
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def detectRegions(image: cv2.typing.MatLike, er_filter1: ERFilter, er_filter2: ERFilter) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ...
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@_typing.overload
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def detectRegions(image: cv2.UMat, er_filter1: ERFilter, er_filter2: ERFilter) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ...
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@_typing.overload
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def detectRegions(image: cv2.typing.MatLike, er_filter1: ERFilter, er_filter2: ERFilter, method: int = ..., filename: str = ..., minProbability: float = ...) -> _typing.Sequence[cv2.typing.Rect]: ...
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@_typing.overload
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def detectRegions(image: cv2.UMat, er_filter1: ERFilter, er_filter2: ERFilter, method: int = ..., filename: str = ..., minProbability: float = ...) -> _typing.Sequence[cv2.typing.Rect]: ...
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@_typing.overload
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def detectTextSWT(input: cv2.typing.MatLike, dark_on_light: bool, draw: cv2.typing.MatLike | None = ..., chainBBs: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], cv2.typing.MatLike, cv2.typing.MatLike]: ...
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@_typing.overload
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def detectTextSWT(input: cv2.UMat, dark_on_light: bool, draw: cv2.UMat | None = ..., chainBBs: cv2.UMat | None = ...) -> tuple[_typing.Sequence[cv2.typing.Rect], cv2.UMat, cv2.UMat]: ...
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@_typing.overload
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def erGrouping(image: cv2.typing.MatLike, channel: cv2.typing.MatLike, regions: _typing.Sequence[_typing.Sequence[cv2.typing.Point]], method: int = ..., filename: str = ..., minProbablity: float = ...) -> _typing.Sequence[cv2.typing.Rect]: ...
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@_typing.overload
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def erGrouping(image: cv2.UMat, channel: cv2.UMat, regions: _typing.Sequence[_typing.Sequence[cv2.typing.Point]], method: int = ..., filename: str = ..., minProbablity: float = ...) -> _typing.Sequence[cv2.typing.Rect]: ...
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def loadClassifierNM1(filename: str) -> ERFilter.Callback: ...
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def loadClassifierNM2(filename: str) -> ERFilter.Callback: ...
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def loadOCRBeamSearchClassifierCNN(filename: str) -> OCRBeamSearchDecoder.ClassifierCallback: ...
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def loadOCRHMMClassifier(filename: str, classifier: int) -> OCRHMMDecoder.ClassifierCallback: ...
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def loadOCRHMMClassifierCNN(filename: str) -> OCRHMMDecoder.ClassifierCallback: ...
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def loadOCRHMMClassifierNM(filename: str) -> OCRHMMDecoder.ClassifierCallback: ...
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