316 lines
13 KiB
Python
316 lines
13 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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import subprocess
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../')))
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import cv2
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import json
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import numpy as np
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import time
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import logging
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from copy import deepcopy
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from ppocr.utils.utility import get_image_file_list, check_and_read
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from ppocr.utils.logging import get_logger
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from ppocr.utils.visual import draw_ser_results, draw_re_results
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from tools.infer.predict_system import TextSystem
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from ppstructure.layout.predict_layout import LayoutPredictor
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from ppstructure.table.predict_table import TableSystem, to_excel
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from ppstructure.utility import parse_args, draw_structure_result
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logger = get_logger()
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class StructureSystem(object):
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def __init__(self, args):
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self.mode = args.mode
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self.recovery = args.recovery
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self.image_orientation_predictor = None
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if args.image_orientation:
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import paddleclas
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self.image_orientation_predictor = paddleclas.PaddleClas(
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model_name="text_image_orientation")
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if self.mode == 'structure':
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if not args.show_log:
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logger.setLevel(logging.INFO)
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if args.layout == False and args.ocr == True:
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args.ocr = False
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logger.warning(
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"When args.layout is false, args.ocr is automatically set to false"
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)
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args.drop_score = 0
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# init model
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self.layout_predictor = None
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self.text_system = None
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self.table_system = None
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if args.layout:
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self.layout_predictor = LayoutPredictor(args)
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if args.ocr:
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self.text_system = TextSystem(args)
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if args.table:
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if self.text_system is not None:
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self.table_system = TableSystem(
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args, self.text_system.text_detector,
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self.text_system.text_recognizer)
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else:
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self.table_system = TableSystem(args)
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elif self.mode == 'kie':
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from ppstructure.kie.predict_kie_token_ser_re import SerRePredictor
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self.kie_predictor = SerRePredictor(args)
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def __call__(self, img, return_ocr_result_in_table=False, img_idx=0):
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time_dict = {
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'image_orientation': 0,
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'layout': 0,
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'table': 0,
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'table_match': 0,
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'det': 0,
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'rec': 0,
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'kie': 0,
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'all': 0
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}
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start = time.time()
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if self.image_orientation_predictor is not None:
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tic = time.time()
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cls_result = self.image_orientation_predictor.predict(
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input_data=img)
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cls_res = next(cls_result)
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angle = cls_res[0]['label_names'][0]
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cv_rotate_code = {
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'90': cv2.ROTATE_90_COUNTERCLOCKWISE,
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'180': cv2.ROTATE_180,
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'270': cv2.ROTATE_90_CLOCKWISE
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}
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if angle in cv_rotate_code:
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img = cv2.rotate(img, cv_rotate_code[angle])
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toc = time.time()
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time_dict['image_orientation'] = toc - tic
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if self.mode == 'structure':
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ori_im = img.copy()
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if self.layout_predictor is not None:
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layout_res, elapse = self.layout_predictor(img)
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time_dict['layout'] += elapse
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else:
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h, w = ori_im.shape[:2]
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layout_res = [dict(bbox=None, label='table')]
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res_list = []
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for region in layout_res:
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res = ''
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if region['bbox'] is not None:
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x1, y1, x2, y2 = region['bbox']
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x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
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roi_img = ori_im[y1:y2, x1:x2, :]
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else:
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x1, y1, x2, y2 = 0, 0, w, h
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roi_img = ori_im
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if region['label'] == 'table':
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if self.table_system is not None:
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res, table_time_dict = self.table_system(
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roi_img, return_ocr_result_in_table)
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time_dict['table'] += table_time_dict['table']
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time_dict['table_match'] += table_time_dict['match']
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time_dict['det'] += table_time_dict['det']
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time_dict['rec'] += table_time_dict['rec']
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else:
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if self.text_system is not None:
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if self.recovery:
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wht_im = np.ones(ori_im.shape, dtype=ori_im.dtype)
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wht_im[y1:y2, x1:x2, :] = roi_img
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filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(
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wht_im)
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else:
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filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(
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roi_img)
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time_dict['det'] += ocr_time_dict['det']
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time_dict['rec'] += ocr_time_dict['rec']
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# remove style char,
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# when using the recognition model trained on the PubtabNet dataset,
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# it will recognize the text format in the table, such as <b>
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style_token = [
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'<strike>', '<strike>', '<sup>', '</sub>', '<b>',
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'</b>', '<sub>', '</sup>', '<overline>',
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'</overline>', '<underline>', '</underline>', '<i>',
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'</i>'
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]
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res = []
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for box, rec_res in zip(filter_boxes, filter_rec_res):
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rec_str, rec_conf = rec_res
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for token in style_token:
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if token in rec_str:
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rec_str = rec_str.replace(token, '')
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if not self.recovery:
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box += [x1, y1]
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res.append({
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'text': rec_str,
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'confidence': float(rec_conf),
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'text_region': box.tolist()
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})
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res_list.append({
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'type': region['label'].lower(),
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'bbox': [x1, y1, x2, y2],
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'img': roi_img,
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'res': res,
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'img_idx': img_idx
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})
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end = time.time()
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time_dict['all'] = end - start
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return res_list, time_dict
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elif self.mode == 'kie':
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re_res, elapse = self.kie_predictor(img)
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time_dict['kie'] = elapse
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time_dict['all'] = elapse
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return re_res[0], time_dict
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return None, None
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def save_structure_res(res, save_folder, img_name, img_idx=0):
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excel_save_folder = os.path.join(save_folder, img_name)
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os.makedirs(excel_save_folder, exist_ok=True)
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res_cp = deepcopy(res)
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# save res
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with open(
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os.path.join(excel_save_folder, 'res_{}.txt'.format(img_idx)),
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'w',
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encoding='utf8') as f:
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for region in res_cp:
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roi_img = region.pop('img')
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f.write('{}\n'.format(json.dumps(region)))
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if region['type'].lower() == 'table' and len(region[
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'res']) > 0 and 'html' in region['res']:
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excel_path = os.path.join(
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excel_save_folder,
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'{}_{}.xlsx'.format(region['bbox'], img_idx))
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to_excel(region['res']['html'], excel_path)
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elif region['type'].lower() == 'figure':
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img_path = os.path.join(
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excel_save_folder,
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'{}_{}.jpg'.format(region['bbox'], img_idx))
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cv2.imwrite(img_path, roi_img)
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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image_file_list = image_file_list
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image_file_list = image_file_list[args.process_id::args.total_process_num]
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if not args.use_pdf2docx_api:
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structure_sys = StructureSystem(args)
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save_folder = os.path.join(args.output, structure_sys.mode)
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os.makedirs(save_folder, exist_ok=True)
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img_num = len(image_file_list)
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for i, image_file in enumerate(image_file_list):
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logger.info("[{}/{}] {}".format(i, img_num, image_file))
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img, flag_gif, flag_pdf = check_and_read(image_file)
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img_name = os.path.basename(image_file).split('.')[0]
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if args.recovery and args.use_pdf2docx_api and flag_pdf:
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from pdf2docx.converter import Converter
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os.makedirs(args.output, exist_ok=True)
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docx_file = os.path.join(args.output,
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'{}_api.docx'.format(img_name))
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cv = Converter(image_file)
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cv.convert(docx_file)
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cv.close()
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logger.info('docx save to {}'.format(docx_file))
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continue
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if not flag_gif and not flag_pdf:
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img = cv2.imread(image_file)
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if not flag_pdf:
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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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continue
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imgs = [img]
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else:
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imgs = img
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all_res = []
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for index, img in enumerate(imgs):
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res, time_dict = structure_sys(img, img_idx=index)
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img_save_path = os.path.join(save_folder, img_name,
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'show_{}.jpg'.format(index))
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os.makedirs(os.path.join(save_folder, img_name), exist_ok=True)
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if structure_sys.mode == 'structure' and res != []:
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draw_img = draw_structure_result(img, res, args.vis_font_path)
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save_structure_res(res, save_folder, img_name, index)
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elif structure_sys.mode == 'kie':
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if structure_sys.kie_predictor.predictor is not None:
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draw_img = draw_re_results(
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img, res, font_path=args.vis_font_path)
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else:
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draw_img = draw_ser_results(
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img, res, font_path=args.vis_font_path)
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with open(
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os.path.join(save_folder, img_name,
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'res_{}_kie.txt'.format(index)),
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'w',
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encoding='utf8') as f:
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res_str = '{}\t{}\n'.format(
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image_file,
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json.dumps(
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{
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"ocr_info": res
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}, ensure_ascii=False))
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f.write(res_str)
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if res != []:
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cv2.imwrite(img_save_path, draw_img)
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logger.info('result save to {}'.format(img_save_path))
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if args.recovery and res != []:
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from ppstructure.recovery.recovery_to_doc import sorted_layout_boxes, convert_info_docx
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h, w, _ = img.shape
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res = sorted_layout_boxes(res, w)
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all_res += res
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if args.recovery and all_res != []:
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try:
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convert_info_docx(img, all_res, save_folder, img_name)
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except Exception as ex:
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logger.error("error in layout recovery image:{}, err msg: {}".
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format(image_file, ex))
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continue
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logger.info("Predict time : {:.3f}s".format(time_dict['all']))
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if __name__ == "__main__":
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args = parse_args()
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if args.use_mp:
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p_list = []
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total_process_num = args.total_process_num
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for process_id in range(total_process_num):
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cmd = [sys.executable, "-u"] + sys.argv + [
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"--process_id={}".format(process_id),
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"--use_mp={}".format(False)
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]
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p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
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p_list.append(p)
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for p in p_list:
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p.wait()
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else:
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main(args)
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