# 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 __dir__ = os.path.dirname(__file__) sys.path.append(os.path.join(__dir__, '')) import cv2 import numpy as np from pathlib import Path import tarfile import requests from tqdm import tqdm 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 tools.infer.utility import draw_ocr __all__ = ['PaddleOCR'] model_urls = { 'det': { 'ch': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar', 'en': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_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' } }, 'cls': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar' } SUPPORT_DET_MODEL = ['DB'] VERSION = '2.1' SUPPORT_REC_MODEL = ['CRNN'] BASE_DIR = os.path.expanduser("~/.paddleocr/") def download_with_progressbar(url, save_path): response = requests.get(url, stream=True) total_size_in_bytes = int(response.headers.get('content-length', 0)) block_size = 1024 # 1 Kibibyte progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) with open(save_path, 'wb') as file: for data in response.iter_content(block_size): progress_bar.update(len(data)) file.write(data) progress_bar.close() if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes: logger.error("Something went wrong while downloading models") sys.exit(0) def maybe_download(model_storage_directory, url): # using custom model tar_file_name_list = [ 'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel' ] if not os.path.exists( os.path.join(model_storage_directory, 'inference.pdiparams') ) or not os.path.exists( os.path.join(model_storage_directory, 'inference.pdmodel')): tmp_path = os.path.join(model_storage_directory, url.split('/')[-1]) print('download {} to {}'.format(url, tmp_path)) os.makedirs(model_storage_directory, exist_ok=True) download_with_progressbar(url, tmp_path) with tarfile.open(tmp_path, 'r') as tarObj: for member in tarObj.getmembers(): filename = None for tar_file_name in tar_file_name_list: if tar_file_name in member.name: filename = tar_file_name if filename is None: continue file = tarObj.extractfile(member) with open( os.path.join(model_storage_directory, filename), 'wb') as f: f.write(file.read()) os.remove(tmp_path) def parse_args(mMain=True, add_help=True): import argparse def str2bool(v): return v.lower() in ("true", "t", "1") if mMain: parser = argparse.ArgumentParser(add_help=add_help) # params for prediction engine parser.add_argument("--use_gpu", type=str2bool, default=True) parser.add_argument("--ir_optim", type=str2bool, default=True) parser.add_argument("--use_tensorrt", type=str2bool, default=False) parser.add_argument("--gpu_mem", type=int, default=500) # params for text detector parser.add_argument("--image_dir", type=str) parser.add_argument("--det_algorithm", type=str, default='DB') parser.add_argument("--det_model_dir", type=str, default=None) parser.add_argument("--det_limit_side_len", type=float, default=960) parser.add_argument("--det_limit_type", type=str, default='max') # DB parmas parser.add_argument("--det_db_thresh", type=float, default=0.3) parser.add_argument("--det_db_box_thresh", type=float, default=0.5) parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6) parser.add_argument("--use_dilation", type=bool, default=False) parser.add_argument("--det_db_score_mode", type=str, default="fast") # EAST parmas parser.add_argument("--det_east_score_thresh", type=float, default=0.8) parser.add_argument("--det_east_cover_thresh", type=float, default=0.1) parser.add_argument("--det_east_nms_thresh", type=float, default=0.2) # params for text recognizer parser.add_argument("--rec_algorithm", type=str, default='CRNN') parser.add_argument("--rec_model_dir", type=str, default=None) parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320") parser.add_argument("--rec_char_type", type=str, default='ch') parser.add_argument("--rec_batch_num", type=int, default=6) parser.add_argument("--max_text_length", type=int, default=25) parser.add_argument("--rec_char_dict_path", type=str, default=None) parser.add_argument("--use_space_char", type=bool, default=True) parser.add_argument("--drop_score", type=float, default=0.5) # params for text classifier parser.add_argument("--cls_model_dir", type=str, default=None) parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192") parser.add_argument("--label_list", type=list, default=['0', '180']) parser.add_argument("--cls_batch_num", type=int, default=6) parser.add_argument("--cls_thresh", type=float, default=0.9) parser.add_argument("--enable_mkldnn", type=bool, default=False) parser.add_argument("--use_zero_copy_run", type=bool, default=False) parser.add_argument("--use_pdserving", type=str2bool, default=False) 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("--use_angle_cls", type=str2bool, default=False) return parser.parse_args() else: return argparse.Namespace( use_gpu=True, ir_optim=True, use_tensorrt=False, gpu_mem=500, image_dir='', det_algorithm='DB', det_model_dir=None, det_limit_side_len=960, det_limit_type='max', det_db_thresh=0.3, det_db_box_thresh=0.5, det_db_unclip_ratio=1.6, use_dilation=False, det_db_score_mode="fast", det_east_score_thresh=0.8, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, rec_algorithm='CRNN', rec_model_dir=None, rec_image_shape="3, 32, 320", rec_char_type='ch', rec_batch_num=6, max_text_length=25, rec_char_dict_path=None, use_space_char=True, drop_score=0.5, cls_model_dir=None, cls_image_shape="3, 48, 192", label_list=['0', '180'], cls_batch_num=6, cls_thresh=0.9, enable_mkldnn=False, use_zero_copy_run=False, use_pdserving=False, lang='ch', det=True, rec=True, use_angle_cls=False) class PaddleOCR(predict_system.TextSystem): def __init__(self, **kwargs): """ paddleocr package args: **kwargs: other params show in paddleocr --help """ postprocess_params = parse_args(mMain=False, add_help=False) postprocess_params.__dict__.update(**kwargs) self.use_angle_cls = postprocess_params.use_angle_cls lang = postprocess_params.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[ 'rec'], 'param lang must in {}, but got {}'.format( model_urls['rec'].keys(), lang) if lang == "ch": det_lang = "ch" else: det_lang = "en" use_inner_dict = False if postprocess_params.rec_char_dict_path is None: use_inner_dict = True postprocess_params.rec_char_dict_path = model_urls['rec'][lang][ 'dict_path'] # init model dir if postprocess_params.det_model_dir is None: postprocess_params.det_model_dir = os.path.join(BASE_DIR, VERSION, 'det', det_lang) if postprocess_params.rec_model_dir is None: postprocess_params.rec_model_dir = os.path.join(BASE_DIR, VERSION, 'rec', lang) if postprocess_params.cls_model_dir is None: postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls') print(postprocess_params) # download model maybe_download(postprocess_params.det_model_dir, model_urls['det'][det_lang]) maybe_download(postprocess_params.rec_model_dir, model_urls['rec'][lang]['url']) maybe_download(postprocess_params.cls_model_dir, model_urls['cls']) if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL: logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL)) sys.exit(0) if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL: logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL)) sys.exit(0) if use_inner_dict: postprocess_params.rec_char_dict_path = str( Path(__file__).parent / postprocess_params.rec_char_dict_path) # init det_model and rec_model super().__init__(postprocess_params) def ocr(self, img, det=True, rec=True, cls=False): """ 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 """ 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 == False: self.use_angle_cls = False elif 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) 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: img, cls_res, elapse = self.text_classifier(img) if not rec: return cls_res rec_res, elapse = self.text_recognizer(img) return rec_res def main(): # for cmd args = parse_args(mMain=True) image_dir = args.image_dir if image_dir.startswith('http'): 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 ocr_engine = PaddleOCR(**(args.__dict__)) for img_path in image_file_list: logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10)) result = ocr_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)