298 lines
12 KiB
Python
298 lines
12 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(os.path.join(__dir__, ''))
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import cv2
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import logging
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import numpy as np
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from pathlib import Path
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from tools.infer import predict_system
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from ppocr.utils.logging import get_logger
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logger = get_logger()
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from ppocr.utils.utility import check_and_read_gif, get_image_file_list
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from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
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from tools.infer.utility import draw_ocr, init_args, str2bool
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__all__ = ['PaddleOCR']
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model_urls = {
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'det': {
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'ch':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
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'en':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
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},
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'rec': {
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'ch': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
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},
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'en': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/en_dict.txt'
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},
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'french': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/french_dict.txt'
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},
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'german': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/german_dict.txt'
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},
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'korean': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/korean_dict.txt'
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},
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'japan': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/japan_dict.txt'
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},
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'chinese_cht': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
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},
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'ta': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/ta_dict.txt'
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},
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'te': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/te_dict.txt'
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},
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'ka': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/ka_dict.txt'
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},
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'latin': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/latin_dict.txt'
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},
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'arabic': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/arabic_dict.txt'
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},
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'cyrillic': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
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},
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'devanagari': {
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'url':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
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'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
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}
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},
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'cls':
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'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
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}
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SUPPORT_DET_MODEL = ['DB']
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VERSION = '2.1'
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SUPPORT_REC_MODEL = ['CRNN']
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BASE_DIR = os.path.expanduser("~/.paddleocr/")
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def parse_args(mMain=True):
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import argparse
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parser = init_args()
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parser.add_help = mMain
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parser.add_argument("--lang", type=str, default='ch')
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parser.add_argument("--det", type=str2bool, default=True)
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parser.add_argument("--rec", type=str2bool, default=True)
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for action in parser._actions:
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if action.dest == 'rec_char_dict_path':
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action.default = None
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if mMain:
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return parser.parse_args()
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else:
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inference_args_dict = {}
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for action in parser._actions:
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inference_args_dict[action.dest] = action.default
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return argparse.Namespace(**inference_args_dict)
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class PaddleOCR(predict_system.TextSystem):
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def __init__(self, **kwargs):
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"""
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paddleocr package
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args:
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**kwargs: other params show in paddleocr --help
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"""
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params = parse_args(mMain=False)
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params.__dict__.update(**kwargs)
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if not params.show_log:
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logger.setLevel(logging.INFO)
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self.use_angle_cls = params.use_angle_cls
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lang = params.lang
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latin_lang = [
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'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
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'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
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'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
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'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
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]
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arabic_lang = ['ar', 'fa', 'ug', 'ur']
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cyrillic_lang = [
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'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
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'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
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]
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devanagari_lang = [
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'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
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'gom', 'sa', 'bgc'
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]
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if lang in latin_lang:
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lang = "latin"
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elif lang in arabic_lang:
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lang = "arabic"
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elif lang in cyrillic_lang:
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lang = "cyrillic"
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elif lang in devanagari_lang:
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lang = "devanagari"
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assert lang in model_urls[
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'rec'], 'param lang must in {}, but got {}'.format(
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model_urls['rec'].keys(), lang)
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if lang == "ch":
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det_lang = "ch"
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else:
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det_lang = "en"
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use_inner_dict = False
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if params.rec_char_dict_path is None:
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use_inner_dict = True
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params.rec_char_dict_path = model_urls['rec'][lang][
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'dict_path']
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# init model dir
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params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
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os.path.join(BASE_DIR, VERSION, 'det', det_lang),
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model_urls['det'][det_lang])
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params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
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os.path.join(BASE_DIR, VERSION, 'rec', lang),
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model_urls['rec'][lang]['url'])
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params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
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os.path.join(BASE_DIR, VERSION, 'cls'),
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model_urls['cls'])
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# download model
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maybe_download(params.det_model_dir, det_url)
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maybe_download(params.rec_model_dir, rec_url)
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maybe_download(params.cls_model_dir, cls_url)
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if params.det_algorithm not in SUPPORT_DET_MODEL:
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logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
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sys.exit(0)
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if params.rec_algorithm not in SUPPORT_REC_MODEL:
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logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
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sys.exit(0)
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if use_inner_dict:
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params.rec_char_dict_path = str(
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Path(__file__).parent / params.rec_char_dict_path)
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print(params)
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# init det_model and rec_model
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super().__init__(params)
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def ocr(self, img, det=True, rec=True, cls=True):
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"""
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ocr with paddleocr
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args：
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img: img for ocr, support ndarray, img_path and list or ndarray
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det: use text detection or not, if false, only rec will be exec. default is True
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rec: use text recognition or not, if false, only det will be exec. default is True
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"""
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assert isinstance(img, (np.ndarray, list, str))
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if isinstance(img, list) and det == True:
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logger.error('When input a list of images, det must be false')
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exit(0)
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if cls == True and self.use_angle_cls == False:
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logger.warning(
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'Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process'
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)
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if isinstance(img, str):
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# download net image
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if img.startswith('http'):
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download_with_progressbar(img, 'tmp.jpg')
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img = 'tmp.jpg'
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image_file = img
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img, flag = check_and_read_gif(image_file)
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if not flag:
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with open(image_file, 'rb') as f:
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np_arr = np.frombuffer(f.read(), dtype=np.uint8)
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img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
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if img is None:
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logger.error("error in loading image:{}".format(image_file))
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return None
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if isinstance(img, np.ndarray) and len(img.shape) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if det and rec:
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dt_boxes, rec_res = self.__call__(img, cls)
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return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
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elif det and not rec:
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dt_boxes, elapse = self.text_detector(img)
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if dt_boxes is None:
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return None
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return [box.tolist() for box in dt_boxes]
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else:
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if not isinstance(img, list):
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img = [img]
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if self.use_angle_cls and cls:
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img, cls_res, elapse = self.text_classifier(img)
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if not rec:
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return cls_res
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rec_res, elapse = self.text_recognizer(img)
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return rec_res
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def main():
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# for cmd
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args = parse_args(mMain=True)
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image_dir = args.image_dir
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if is_link(image_dir):
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download_with_progressbar(image_dir, 'tmp.jpg')
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image_file_list = ['tmp.jpg']
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else:
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image_file_list = get_image_file_list(args.image_dir)
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if len(image_file_list) == 0:
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logger.error('no images find in {}'.format(args.image_dir))
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return
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ocr_engine = PaddleOCR(**(args.__dict__))
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for img_path in image_file_list:
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logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
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result = ocr_engine.ocr(img_path,
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det=args.det,
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rec=args.rec,
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cls=args.use_angle_cls)
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if result is not None:
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for line in result:
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logger.info(line)
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