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