# 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 __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 math import time import traceback import paddle import tools.infer.utility as utility from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read_gif from test1.utility import parse_args logger = get_logger() class TableStructurer(object): def __init__(self, args): pre_process_list = [{ 'ResizeTableImage': { 'max_len': args.structure_max_len } }, { 'NormalizeImage': { 'std': [0.229, 0.224, 0.225], 'mean': [0.485, 0.456, 0.406], 'scale': '1./255.', 'order': 'hwc' } }, { 'PaddingTableImage': None }, { 'ToCHWImage': None }, { 'KeepKeys': { 'keep_keys': ['image'] } }] postprocess_params = { 'name': 'TableLabelDecode', "character_type": args.structure_char_type, "character_dict_path": args.structure_char_dict_path, } self.preprocess_op = create_operators(pre_process_list) self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = \ utility.create_predictor(args, 'structure', logger) def __call__(self, img): ori_im = img.copy() data = {'image': img} data = transform(data, self.preprocess_op) img = data[0] if img is None: return None, 0 img = np.expand_dims(img, axis=0) img = img.copy() starttime = time.time() self.input_tensor.copy_from_cpu(img) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = {} preds['structure_probs'] = outputs[1] preds['loc_preds'] = outputs[0] post_result = self.postprocess_op(preds) structure_str_list = post_result['structure_str_list'] res_loc = post_result['res_loc'] imgh, imgw = ori_im.shape[0:2] res_loc_final = [] for rno in range(len(res_loc[0])): x0, y0, x1, y1 = res_loc[0][rno] left = max(int(imgw * x0), 0) top = max(int(imgh * y0), 0) right = min(int(imgw * x1), imgw - 1) bottom = min(int(imgh * y1), imgh - 1) res_loc_final.append([left, top, right, bottom]) structure_str_list = structure_str_list[0][:-1] structure_str_list = ['', '', ''] + structure_str_list + ['

', '', ''] elapse = time.time() - starttime return (structure_str_list, res_loc_final), elapse def main(args): image_file_list = get_image_file_list(args.image_dir) table_structurer = TableStructurer(args) count = 0 total_time = 0 for image_file in image_file_list: img, flag = check_and_read_gif(image_file) if not flag: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue structure_res, elapse = table_structurer(img) logger.info("result: {}".format(structure_res)) if count > 0: total_time += elapse count += 1 logger.info("Predict time of {}: {}".format(image_file, elapse)) if __name__ == "__main__": main(parse_args())