# 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.append(os.path.abspath(os.path.join(__dir__, '..'))) os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import numpy as np import time import logging 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.table.predict_table import TableSystem, to_excel from ppstructure.vqa.infer_ser_e2e import SerPredictor, draw_ser_results from ppstructure.utility import parse_args, draw_structure_result logger = get_logger() class OCRSystem(object): def __init__(self, args): self.mode = args.mode if self.mode == 'structure': import layoutparser as lp # args.det_limit_type = 'resize_long' args.drop_score = 0 if not args.show_log: logger.setLevel(logging.INFO) self.text_system = TextSystem(args) self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer) config_path = None model_path = None if os.path.isdir(args.layout_path_model): model_path = args.layout_path_model else: config_path = args.layout_path_model self.table_layout = lp.PaddleDetectionLayoutModel( config_path=config_path, model_path=model_path, threshold=0.5, enable_mkldnn=args.enable_mkldnn, enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads) self.use_angle_cls = args.use_angle_cls self.drop_score = args.drop_score elif self.mode == 'vqa': self.vqa_engine = SerPredictor(args) def __call__(self, img): if self.mode == 'structure': ori_im = img.copy() layout_res = self.table_layout.detect(img[..., ::-1]) res_list = [] for region in layout_res: x1, y1, x2, y2 = region.coordinates x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) roi_img = ori_im[y1:y2, x1:x2, :] if region.type == 'Table': res = self.table_system(roi_img) else: filter_boxes, filter_rec_res = self.text_system(roi_img) filter_boxes = [x + [x1, y1] for x in filter_boxes] filter_boxes = [ x.reshape(-1).tolist() for x in filter_boxes ] # remove style char style_token = [ '', '', '', '', '', '', '', '', '', '', '', '', '', '' ] filter_rec_res_tmp = [] for rec_res in 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, '') filter_rec_res_tmp.append((rec_str, rec_conf)) res = (filter_boxes, filter_rec_res_tmp) res_list.append({ 'type': region.type, 'bbox': [x1, y1, x2, y2], 'img': roi_img, 'res': res }) elif self.mode == 'vqa': res_list, _ = self.vqa_engine(img) return res_list 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) # save res with open( os.path.join(excel_save_folder, 'res.txt'), 'w', encoding='utf8') as f: for region in res: if region['type'] == 'Table': excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox'])) to_excel(region['res'], excel_path) if region['type'] == 'Figure': roi_img = region['img'] img_path = os.path.join(excel_save_folder, '{}.jpg'.format(region['bbox'])) cv2.imwrite(img_path, roi_img) else: for box, rec_res in zip(region['res'][0], region['res'][1]): f.write('{}\t{}\n'.format( np.array(box).reshape(-1).tolist(), rec_res)) 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 = OCRSystem(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 starttime = time.time() res = 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': 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)) elapse = time.time() - starttime logger.info("Predict time : {:.3f}s".format(elapse)) 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)