# 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 copy import numpy as np import time import tools.infer.utility as utility import tools.infer.predict_rec as predict_rec import tools.infer.predict_det as predict_det import ppstructure.table.predict_structure as predict_strture from ppocr.utils.utility import get_image_file_list, check_and_read_gif from ppocr.utils.logging import get_logger from ppstructure.table.matcher import distance, compute_iou logger = get_logger() def expand(pix, det_box, shape): x0, y0, x1, y1 = det_box # print(shape) h, w, c = shape tmp_x0 = x0 - pix tmp_x1 = x1 + pix tmp_y0 = y0 - pix tmp_y1 = y1 + pix x0_ = tmp_x0 if tmp_x0 >= 0 else 0 x1_ = tmp_x1 if tmp_x1 <= w else w y0_ = tmp_y0 if tmp_y0 >= 0 else 0 y1_ = tmp_y1 if tmp_y1 <= h else h return x0_, y0_, x1_, y1_ class TableSystem(object): def __init__(self, args): self.text_detector = predict_det.TextDetector(args) self.text_recognizer = predict_rec.TextRecognizer(args) self.table_structurer = predict_strture.TableStructurer(args) self.use_angle_cls = args.use_angle_cls self.drop_score = args.drop_score def __call__(self, img): ori_im = img.copy() structure_res, elapse = self.table_structurer(copy.deepcopy(img)) dt_boxes, elapse = self.text_detector(copy.deepcopy(img)) dt_boxes = sorted_boxes(dt_boxes) r_boxes = [] for box in dt_boxes: x_min = box[:, 0].min() - 1 x_max = box[:, 0].max() + 1 y_min = box[:, 1].min() - 1 y_max = box[:, 1].max() + 1 box = [x_min, y_min, x_max, y_max] r_boxes.append(box) dt_boxes = np.array(r_boxes) # logger.info("dt_boxes num : {}, elapse : {}".format( # len(dt_boxes), elapse)) if dt_boxes is None: return None, None img_crop_list = [] for i in range(len(dt_boxes)): det_box = dt_boxes[i] x0, y0, x1, y1 = expand(2, det_box, ori_im.shape) text_rect = ori_im[int(y0):int(y1), int(x0):int(x1), :] img_crop_list.append(text_rect) rec_res, elapse = self.text_recognizer(img_crop_list) # logger.info("rec_res num : {}, elapse : {}".format( # len(rec_res), elapse)) pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res) return pred_html def rebuild_table(self, structure_res, dt_boxes, rec_res): pred_structures, pred_bboxes = structure_res matched_index = self.match_result(dt_boxes, pred_bboxes) pred_html, pred = self.get_pred_html(pred_structures, matched_index, rec_res) return pred_html, pred def match_result(self, dt_boxes, pred_bboxes): matched = {} for i, gt_box in enumerate(dt_boxes): # gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])] distances = [] for j, pred_box in enumerate(pred_bboxes): distances.append( (distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) # 获取两两cell之间的L1距离和 1- IOU sorted_distances = distances.copy() # 根据距离和IOU挑选最"近"的cell sorted_distances = sorted(sorted_distances, key=lambda item: (item[1], item[0])) if distances.index(sorted_distances[0]) not in matched.keys(): matched[distances.index(sorted_distances[0])] = [i] else: matched[distances.index(sorted_distances[0])].append(i) return matched def get_pred_html(self, pred_structures, matched_index, ocr_contents): end_html = [] td_index = 0 for tag in pred_structures: if '' in tag: if td_index in matched_index.keys(): b_with = False if '' in ocr_contents[matched_index[td_index][0]] and len(matched_index[td_index]) > 1: b_with = True end_html.extend('') for i, td_index_index in enumerate(matched_index[td_index]): content = ocr_contents[td_index_index][0] if len(matched_index[td_index]) > 1: if len(content) == 0: continue if content[0] == ' ': content = content[1:] if '' in content: content = content[3:] if '' in content: content = content[:-4] if len(content) == 0: continue if i != len(matched_index[td_index]) - 1 and ' ' != content[-1]: content += ' ' end_html.extend(content) if b_with: end_html.extend('') end_html.append(tag) td_index += 1 else: end_html.append(tag) return ''.join(end_html), end_html def sorted_boxes(dt_boxes): """ Sort text boxes in order from top to bottom, left to right args: dt_boxes(array):detected text boxes with shape [4, 2] return: sorted boxes(array) with shape [4, 2] """ num_boxes = dt_boxes.shape[0] sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) _boxes = list(sorted_boxes) for i in range(num_boxes - 1): if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \ (_boxes[i + 1][0][0] < _boxes[i][0][0]): tmp = _boxes[i] _boxes[i] = _boxes[i + 1] _boxes[i + 1] = tmp return _boxes def to_excel(html_table, excel_path): from tablepyxl import tablepyxl tablepyxl.document_to_xl(html_table, excel_path) def main(args): image_file_list = get_image_file_list(args.image_dir) image_file_list = image_file_list[args.process_id::args.total_process_num] excel_save_folder = 'output/table' os.makedirs(excel_save_folder, exist_ok=True) text_sys = TableSystem(args) img_num = len(image_file_list) 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) excel_path = os.path.join(excel_save_folder, os.path.basename(image_file).split('.')[0] + '.xlsx') if not flag: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue starttime = time.time() pred_html = text_sys(img) to_excel(pred_html, excel_path) logger.info('excel saved to {}'.format(excel_path)) logger.info(pred_html) elapse = time.time() - starttime logger.info("Predict time : {:.3f}s".format(elapse)) if __name__ == "__main__": args = utility.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)