# coding: utf-8 # [Pix2Text](https://github.com/breezedeus/pix2text): an Open-Source Alternative to Mathpix. # Copyright (C) 2022-2024, [Breezedeus](https://www.breezedeus.com). import string from typing import Sequence, List, Optional import numpy as np import cv2 from .utils import custom_deepcopy def clip(x, min_value, max_value): return min(max(x, min_value), max_value) class TextOcrEngine: """Text OCR Engine Wrapper""" name = 'unknown' def __init__(self, languages: Sequence[str], ocr_engine): self.languages = languages self.ocr_engine = ocr_engine def detect_only(self, img: np.ndarray, **kwargs): """ Only detect the texts from the input image. Args: img (np.ndarray): RGB image with shape: (height, width, 3) kwargs: more configs Returns: Dict[str, List[dict]]: The dictionary contains the following keys: * 'detected_texts': list, each element stores the information of a detected box, recorded in a dictionary, including the following values: 'position': The rectangular box corresponding to the detected text; np.ndarray, shape: (4, 2), representing the coordinates (x, y) of the 4 points of the box; Example: {'detected_texts': [{'position': array([[416, 77], [486, 13], [800, 325], [730, 390]], dtype=int32), }, ... ] } """ pass def recognize_only(self, img: np.ndarray, **kwargs): """ Only recognize the texts for cropped images, which are from bboxes detected by detect_only. Args: img (): RGB image with shape [height, width] or [height, width, channel]. channel should be 1 (gray image) or 3 (RGB formatted color image). scaled in [0, 255]; kwargs: more configs Returns: dict, with keys: - 'text' (str): The recognized text - 'score' (float): The score of the recognition result (confidence level), ranging from `[0, 1]`; the higher the score, the more reliable it is Example: ``` {'score': 0.8812797665596008, 'text': 'Current Line'} ``` """ pass def ocr(self, img: np.ndarray, rec_config: Optional[dict] = None, **kwargs): """ Detect texts first, and then recognize the texts for detected bbox patches. Args: img (np.ndarray): RGB image with shape [height, width] or [height, width, channel]. channel should be 1 (gray image) or 3 (RGB formatted color image). scaled in [0, 255]; rec_config (Optional[dict]): The config for recognition kwargs: more configs Returns: list of detected texts, which element is a dict, with keys: - 'text' (str): The recognized text - 'score' (float): The score of the recognition result (confidence level), ranging from `[0, 1]`; the higher the score, the more reliable it is - 'position' (np.ndarray): 4 x 2 array, representing the coordinates (x, y) of the 4 points of the box Example: ``` [{'score': 0.88, 'text': 'Line 1', 'position': array([[146, 22], [179, 22], [179, 60], [146, 60]], dtype=int32) }, {'score': 0.78, 'text': 'Line 2' 'position': array([[641, 115], [1180, 115], [1180, 244], [641, 244]], dtype=int32) }] ``` """ pass class CnOCREngine(TextOcrEngine): name = 'cnocr' def detect_only(self, img: np.ndarray, **kwargs): outs = self.ocr_engine.det_model.detect(img, **kwargs) for out in outs['detected_texts']: out['position'] = out.pop('box') return outs def recognize_only(self, img: np.ndarray, **kwargs): try: return self.ocr_engine.ocr_for_single_line(img) except: return {'text': '', 'score': 0.0} def ocr(self, img: np.ndarray, rec_config: Optional[dict] = None, **kwargs) -> str: rec_config = rec_config or {} outs = self.ocr_engine.ocr(img, **rec_config) return outs class EasyOCREngine(TextOcrEngine): name = 'easyocr' def detect_only(self, img: np.ndarray, **kwargs): if 'resized_shape' in kwargs: kwargs.pop('resized_shape') img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) height, width = img.shape[:2] horizontal_list, free_list = self.ocr_engine.detect(img, **kwargs) horizontal_list, free_list = horizontal_list[0], free_list[0] bboxes = [] for x1x2_y1y2 in horizontal_list: xmin, xmax, ymin, ymax = x1x2_y1y2 xmin = clip(xmin, 0, width) xmax = clip(xmax, 0, width) ymin = clip(ymin, 0, height) ymax = clip(ymax, 0, height) box = np.array([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]]) bboxes.append({'position': box}) for bbox in free_list: if bbox: bboxes.append({'position': np.array(bbox)}) return {'detected_texts': bboxes} def recognize_only(self, img: np.ndarray, **kwargs) -> dict: out = {'text': '', 'score': 0.0} try: img = cv2.cvtColor(img, cv2.COLOR_RGBA2GRAY) result = self.ocr_engine.recognize(img, **kwargs) if result: out = {'text': result[0][1], 'score': result[0][2]} except: pass return out def ocr( self, img: np.ndarray, rec_config: Optional[dict] = None, **kwargs ) -> List[dict]: rec_config = rec_config or {} img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) results = self.ocr_engine.readtext(img, **rec_config) outs = [] for result in results: outs.append( {'text': result[1], 'score': result[2], 'position': np.array(result[0])} ) return outs def prepare_ocr_engine(languages: Sequence[str], ocr_engine_config): ocr_engine_config = custom_deepcopy(ocr_engine_config) if ocr_engine_config else {} if len(set(languages).difference({'en', 'ch_sim'})) == 0: from cnocr import CnOcr # if 'ch_sim' not in languages and 'cand_alphabet' not in ocr_engine_config: # only recognize english characters # ocr_engine_config['cand_alphabet'] = list(string.printable) + [''] if tuple(languages) == ('en',): # only recognize english characters if 'det_model_name' not in ocr_engine_config: ocr_engine_config['det_model_name'] = 'en_PP-OCRv3_det' if 'rec_model_name' not in ocr_engine_config: ocr_engine_config['rec_model_name'] = 'en_PP-OCRv3' ocr_engine = CnOcr(**ocr_engine_config) engine_wrapper = CnOCREngine(languages, ocr_engine) else: try: from easyocr import Reader except: raise ImportError('Please install easyocr first: pip install easyocr') gpu = False if 'context' in ocr_engine_config: context = ocr_engine_config.pop('context').lower() gpu = 'gpu' in context or 'cuda' in context ocr_engine = Reader(lang_list=list(languages), gpu=gpu, **ocr_engine_config) engine_wrapper = EasyOCREngine(languages, ocr_engine) return engine_wrapper