343 lines
14 KiB
Python
343 lines
14 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 numpy as np
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from pathlib import Path
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import tarfile
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import requests
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from tqdm import tqdm
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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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__all__ = ['PaddleOCR']
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model_urls = {
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'det':
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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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'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/dict/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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},
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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 = 2.0
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SUPPORT_REC_MODEL = ['CRNN']
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BASE_DIR = os.path.expanduser("~/.paddleocr/")
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def download_with_progressbar(url, save_path):
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response = requests.get(url, stream=True)
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total_size_in_bytes = int(response.headers.get('content-length', 0))
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block_size = 1024 # 1 Kibibyte
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progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
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with open(save_path, 'wb') as file:
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for data in response.iter_content(block_size):
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progress_bar.update(len(data))
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file.write(data)
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progress_bar.close()
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if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
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logger.error("Something went wrong while downloading models")
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sys.exit(0)
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def maybe_download(model_storage_directory, url):
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# using custom model
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tar_file_name_list = [
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'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel'
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]
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if not os.path.exists(
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os.path.join(model_storage_directory, 'inference.pdiparams')
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) or not os.path.exists(
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os.path.join(model_storage_directory, 'inference.pdmodel')):
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tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
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print('download {} to {}'.format(url, tmp_path))
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os.makedirs(model_storage_directory, exist_ok=True)
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download_with_progressbar(url, tmp_path)
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with tarfile.open(tmp_path, 'r') as tarObj:
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for member in tarObj.getmembers():
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filename = None
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for tar_file_name in tar_file_name_list:
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if tar_file_name in member.name:
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filename = tar_file_name
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if filename is None:
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continue
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file = tarObj.extractfile(member)
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with open(
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os.path.join(model_storage_directory, filename),
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'wb') as f:
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f.write(file.read())
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os.remove(tmp_path)
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def parse_args(mMain=True, add_help=True):
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import argparse
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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if mMain:
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parser = argparse.ArgumentParser(add_help=add_help)
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# params for prediction engine
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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parser.add_argument("--gpu_mem", type=int, default=8000)
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# params for text detector
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parser.add_argument("--image_dir", type=str)
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parser.add_argument("--det_algorithm", type=str, default='DB')
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parser.add_argument("--det_model_dir", type=str, default=None)
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parser.add_argument("--det_limit_side_len", type=float, default=960)
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parser.add_argument("--det_limit_type", type=str, default='max')
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# DB parmas
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parser.add_argument("--det_db_thresh", type=float, default=0.3)
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parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
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parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
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# EAST parmas
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parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
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parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
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parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
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# params for text recognizer
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parser.add_argument("--rec_algorithm", type=str, default='CRNN')
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parser.add_argument("--rec_model_dir", type=str, default=None)
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parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
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parser.add_argument("--rec_char_type", type=str, default='ch')
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parser.add_argument("--rec_batch_num", type=int, default=30)
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parser.add_argument("--max_text_length", type=int, default=25)
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parser.add_argument("--rec_char_dict_path", type=str, default=None)
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parser.add_argument("--use_space_char", type=bool, default=True)
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parser.add_argument("--drop_score", type=float, default=0.5)
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# params for text classifier
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parser.add_argument("--cls_model_dir", type=str, default=None)
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parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
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parser.add_argument("--label_list", type=list, default=['0', '180'])
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parser.add_argument("--cls_batch_num", type=int, default=30)
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parser.add_argument("--cls_thresh", type=float, default=0.9)
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parser.add_argument("--enable_mkldnn", type=bool, default=False)
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parser.add_argument("--use_zero_copy_run", type=bool, default=False)
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parser.add_argument("--use_pdserving", type=str2bool, default=False)
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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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parser.add_argument("--use_angle_cls", type=str2bool, default=False)
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return parser.parse_args()
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else:
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return argparse.Namespace(
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use_gpu=True,
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ir_optim=True,
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use_tensorrt=False,
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gpu_mem=8000,
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image_dir='',
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det_algorithm='DB',
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det_model_dir=None,
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det_limit_side_len=960,
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det_limit_type='max',
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det_db_thresh=0.3,
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det_db_box_thresh=0.5,
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det_db_unclip_ratio=2.0,
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det_east_score_thresh=0.8,
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det_east_cover_thresh=0.1,
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det_east_nms_thresh=0.2,
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rec_algorithm='CRNN',
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rec_model_dir=None,
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rec_image_shape="3, 32, 320",
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rec_char_type='ch',
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rec_batch_num=30,
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max_text_length=25,
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rec_char_dict_path=None,
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use_space_char=True,
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drop_score=0.5,
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cls_model_dir=None,
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cls_image_shape="3, 48, 192",
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label_list=['0', '180'],
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cls_batch_num=30,
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cls_thresh=0.9,
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enable_mkldnn=False,
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use_zero_copy_run=False,
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use_pdserving=False,
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lang='ch',
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det=True,
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rec=True,
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use_angle_cls=False)
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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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postprocess_params = parse_args(mMain=False, add_help=False)
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postprocess_params.__dict__.update(**kwargs)
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self.use_angle_cls = postprocess_params.use_angle_cls
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lang = postprocess_params.lang
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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 postprocess_params.rec_char_dict_path is None:
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postprocess_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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if postprocess_params.det_model_dir is None:
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postprocess_params.det_model_dir = os.path.join(
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BASE_DIR, '{}/det'.format(VERSION))
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if postprocess_params.rec_model_dir is None:
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postprocess_params.rec_model_dir = os.path.join(
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BASE_DIR, '{}/rec/{}'.format(VERSION, lang))
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if postprocess_params.cls_model_dir is None:
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postprocess_params.cls_model_dir = os.path.join(
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BASE_DIR, '{}/cls'.format(VERSION))
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print(postprocess_params)
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# download model
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maybe_download(postprocess_params.det_model_dir, model_urls['det'])
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maybe_download(postprocess_params.rec_model_dir,
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model_urls['rec'][lang]['url'])
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maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
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if postprocess_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 postprocess_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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postprocess_params.rec_char_dict_path = str(
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Path(__file__).parent / postprocess_params.rec_char_dict_path)
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# init det_model and rec_model
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super().__init__(postprocess_params)
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def ocr(self, img, det=True, rec=True, cls=False):
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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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self.use_angle_cls = cls
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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)
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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:
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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 image_dir.startswith('http'):
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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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