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