# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys import importlib __dir__ = os.path.dirname(__file__) import paddle sys.path.append(os.path.join(__dir__, '')) import cv2 import logging import numpy as np from pathlib import Path tools = importlib.import_module('.', 'tools') ppocr = importlib.import_module('.', 'ppocr') ppstructure = importlib.import_module('.', 'ppstructure') from tools.infer import predict_system from ppocr.utils.logging import get_logger logger = get_logger() from ppocr.utils.utility import check_and_read_gif, get_image_file_list from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url from tools.infer.utility import draw_ocr, str2bool, check_gpu from ppstructure.utility import init_args, draw_structure_result from ppstructure.predict_system import OCRSystem, save_structure_res __all__ = [ 'PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res', 'download_with_progressbar' ] SUPPORT_DET_MODEL = ['DB'] VERSION = '2.4.0.3' SUPPORT_REC_MODEL = ['CRNN'] BASE_DIR = os.path.expanduser("~/.paddleocr/") DEFAULT_OCR_MODEL_VERSION = 'PP-OCR' SUPPORT_OCR_MODEL_VERSION = ['PP-OCR', 'PP-OCRv2'] DEFAULT_STRUCTURE_MODEL_VERSION = 'STRUCTURE' SUPPORT_STRUCTURE_MODEL_VERSION = ['STRUCTURE'] MODEL_URLS = { 'OCR': { 'PP-OCRv2': { 'det': { 'ch': { 'url': 'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar', }, }, 'rec': { 'ch': { 'url': 'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar', 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' } } }, DEFAULT_OCR_MODEL_VERSION: { 'det': { 'ch': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar', }, 'en': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar', }, 'structure': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar' } }, 'rec': { 'ch': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' }, 'en': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/en_dict.txt' }, 'french': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/french_dict.txt' }, 'german': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/german_dict.txt' }, 'korean': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/korean_dict.txt' }, 'japan': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/japan_dict.txt' }, 'chinese_cht': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt' }, 'ta': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/ta_dict.txt' }, 'te': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/te_dict.txt' }, 'ka': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/ka_dict.txt' }, 'latin': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/latin_dict.txt' }, 'arabic': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/arabic_dict.txt' }, 'cyrillic': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/cyrillic_dict.txt' }, 'devanagari': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar', 'dict_path': './ppocr/utils/dict/devanagari_dict.txt' }, 'structure': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar', 'dict_path': 'ppocr/utils/dict/table_dict.txt' } }, 'cls': { 'ch': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar', } }, } }, 'STRUCTURE': { DEFAULT_STRUCTURE_MODEL_VERSION: { 'table': { 'en': { 'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar', 'dict_path': 'ppocr/utils/dict/table_structure_dict.txt' } } } } } def parse_args(mMain=True): import argparse parser = init_args() parser.add_help = mMain parser.add_argument("--lang", type=str, default='ch') parser.add_argument("--det", type=str2bool, default=True) parser.add_argument("--rec", type=str2bool, default=True) parser.add_argument("--type", type=str, default='ocr') parser.add_argument( "--ocr_version", type=str, choices=SUPPORT_OCR_MODEL_VERSION, default='PP-OCRv2', help='OCR Model version, the current model support list is as follows: ' '1. PP-OCRv2 Support Chinese detection and recognition model. ' '2. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.' ) parser.add_argument( "--structure_version", type=str, choices=SUPPORT_STRUCTURE_MODEL_VERSION, default='STRUCTURE', help='Model version, the current model support list is as follows:' ' 1. STRUCTURE Support en table structure model.') for action in parser._actions: if action.dest in ['rec_char_dict_path', 'table_char_dict_path']: action.default = None if mMain: return parser.parse_args() else: inference_args_dict = {} for action in parser._actions: inference_args_dict[action.dest] = action.default return argparse.Namespace(**inference_args_dict) def parse_lang(lang): latin_lang = [ 'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi' ] arabic_lang = ['ar', 'fa', 'ug', 'ur'] cyrillic_lang = [ 'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab' ] devanagari_lang = [ 'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom', 'sa', 'bgc' ] if lang in latin_lang: lang = "latin" elif lang in arabic_lang: lang = "arabic" elif lang in cyrillic_lang: lang = "cyrillic" elif lang in devanagari_lang: lang = "devanagari" assert lang in MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION][ 'rec'], 'param lang must in {}, but got {}'.format( MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang) if lang == "ch": det_lang = "ch" elif lang == 'structure': det_lang = 'structure' else: det_lang = "en" return lang, det_lang def get_model_config(type, version, model_type, lang): if type == 'OCR': DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION elif type == 'STRUCTURE': DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION else: raise NotImplementedError model_urls = MODEL_URLS[type] if version not in model_urls: version = DEFAULT_MODEL_VERSION if model_type not in model_urls[version]: if model_type in model_urls[DEFAULT_MODEL_VERSION]: version = DEFAULT_MODEL_VERSION else: logger.error('{} models is not support, we only support {}'.format( model_type, model_urls[DEFAULT_MODEL_VERSION].keys())) sys.exit(-1) if lang not in model_urls[version][model_type]: if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]: version = DEFAULT_MODEL_VERSION else: logger.error( 'lang {} is not support, we only support {} for {} models'. format(lang, model_urls[DEFAULT_MODEL_VERSION][model_type].keys( ), model_type)) sys.exit(-1) return model_urls[version][model_type][lang] class PaddleOCR(predict_system.TextSystem): def __init__(self, **kwargs): """ paddleocr package args: **kwargs: other params show in paddleocr --help """ params = parse_args(mMain=False) params.__dict__.update(**kwargs) assert params.ocr_version in SUPPORT_OCR_MODEL_VERSION, "ocr_version must in {}, but get {}".format( SUPPORT_OCR_MODEL_VERSION, params.ocr_version) params.use_gpu = check_gpu(params.use_gpu) if not params.show_log: logger.setLevel(logging.INFO) self.use_angle_cls = params.use_angle_cls lang, det_lang = parse_lang(params.lang) # init model dir det_model_config = get_model_config('OCR', params.ocr_version, 'det', det_lang) params.det_model_dir, det_url = confirm_model_dir_url( params.det_model_dir, os.path.join(BASE_DIR, 'whl', 'det', det_lang), det_model_config['url']) rec_model_config = get_model_config('OCR', params.ocr_version, 'rec', lang) params.rec_model_dir, rec_url = confirm_model_dir_url( params.rec_model_dir, os.path.join(BASE_DIR, 'whl', 'rec', lang), rec_model_config['url']) cls_model_config = get_model_config('OCR', params.ocr_version, 'cls', 'ch') params.cls_model_dir, cls_url = confirm_model_dir_url( params.cls_model_dir, os.path.join(BASE_DIR, 'whl', 'cls'), cls_model_config['url']) # download model maybe_download(params.det_model_dir, det_url) maybe_download(params.rec_model_dir, rec_url) maybe_download(params.cls_model_dir, cls_url) if params.det_algorithm not in SUPPORT_DET_MODEL: logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL)) sys.exit(0) if params.rec_algorithm not in SUPPORT_REC_MODEL: logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL)) sys.exit(0) if params.rec_char_dict_path is None: params.rec_char_dict_path = str( Path(__file__).parent / rec_model_config['dict_path']) logger.debug(params) # init det_model and rec_model super().__init__(params) def ocr(self, img, det=True, rec=True, cls=True): """ ocr with paddleocr args： img: img for ocr, support ndarray, img_path and list or ndarray det: use text detection or not. If false, only rec will be exec. Default is True rec: use text recognition or not. If false, only det will be exec. Default is True cls: use angle classifier or not. Default is True. If true, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False. """ assert isinstance(img, (np.ndarray, list, str)) if isinstance(img, list) and det == True: logger.error('When input a list of images, det must be false') exit(0) if cls == True and self.use_angle_cls == False: logger.warning( 'Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process' ) if isinstance(img, str): # download net image if img.startswith('http'): download_with_progressbar(img, 'tmp.jpg') img = 'tmp.jpg' image_file = img img, flag = check_and_read_gif(image_file) if not flag: with open(image_file, 'rb') as f: np_arr = np.frombuffer(f.read(), dtype=np.uint8) img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) if img is None: logger.error("error in loading image:{}".format(image_file)) return None if isinstance(img, np.ndarray) and len(img.shape) == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) if det and rec: dt_boxes, rec_res = self.__call__(img, cls) return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)] elif det and not rec: dt_boxes, elapse = self.text_detector(img) if dt_boxes is None: return None return [box.tolist() for box in dt_boxes] else: if not isinstance(img, list): img = [img] if self.use_angle_cls and cls: img, cls_res, elapse = self.text_classifier(img) if not rec: return cls_res rec_res, elapse = self.text_recognizer(img) return rec_res class PPStructure(OCRSystem): def __init__(self, **kwargs): params = parse_args(mMain=False) params.__dict__.update(**kwargs) assert params.structure_version in SUPPORT_STRUCTURE_MODEL_VERSION, "structure_version must in {}, but get {}".format( SUPPORT_STRUCTURE_MODEL_VERSION, params.structure_version) params.use_gpu = check_gpu(params.use_gpu) if not params.show_log: logger.setLevel(logging.INFO) lang, det_lang = parse_lang(params.lang) # init model dir det_model_config = get_model_config('OCR', params.ocr_version, 'det', det_lang) params.det_model_dir, det_url = confirm_model_dir_url( params.det_model_dir, os.path.join(BASE_DIR, 'whl', 'det', det_lang), det_model_config['url']) rec_model_config = get_model_config('OCR', params.ocr_version, 'rec', lang) params.rec_model_dir, rec_url = confirm_model_dir_url( params.rec_model_dir, os.path.join(BASE_DIR, 'whl', 'rec', lang), rec_model_config['url']) table_model_config = get_model_config( 'STRUCTURE', params.structure_version, 'table', 'en') params.table_model_dir, table_url = confirm_model_dir_url( params.table_model_dir, os.path.join(BASE_DIR, 'whl', 'table'), table_model_config['url']) # download model maybe_download(params.det_model_dir, det_url) maybe_download(params.rec_model_dir, rec_url) maybe_download(params.table_model_dir, table_url) if params.rec_char_dict_path is None: params.rec_char_dict_path = str( Path(__file__).parent / rec_model_config['dict_path']) if params.table_char_dict_path is None: params.table_char_dict_path = str( Path(__file__).parent / table_model_config['dict_path']) logger.debug(params) super().__init__(params) def __call__(self, img): if isinstance(img, str): # download net image if img.startswith('http'): download_with_progressbar(img, 'tmp.jpg') img = 'tmp.jpg' image_file = img img, flag = check_and_read_gif(image_file) if not flag: with open(image_file, 'rb') as f: np_arr = np.frombuffer(f.read(), dtype=np.uint8) img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) if img is None: logger.error("error in loading image:{}".format(image_file)) return None if isinstance(img, np.ndarray) and len(img.shape) == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) res = super().__call__(img) return res def main(): # for cmd args = parse_args(mMain=True) image_dir = args.image_dir if is_link(image_dir): download_with_progressbar(image_dir, 'tmp.jpg') image_file_list = ['tmp.jpg'] else: image_file_list = get_image_file_list(args.image_dir) if len(image_file_list) == 0: logger.error('no images find in {}'.format(args.image_dir)) return if args.type == 'ocr': engine = PaddleOCR(**(args.__dict__)) elif args.type == 'structure': engine = PPStructure(**(args.__dict__)) else: raise NotImplementedError for img_path in image_file_list: img_name = os.path.basename(img_path).split('.')[0] logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10)) if args.type == 'ocr': result = engine.ocr(img_path, det=args.det, rec=args.rec, cls=args.use_angle_cls) if result is not None: for line in result: logger.info(line) elif args.type == 'structure': result = engine(img_path) save_structure_res(result, args.output, img_name) for item in result: item.pop('img') logger.info(item)