# 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 time import json 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 ppstructure.utility import parse_args logger = get_logger() def build_pre_process_list(args): resize_op = {'ResizeTableImage': {'max_len': args.table_max_len, }} pad_op = { 'PaddingTableImage': { 'size': [args.table_max_len, args.table_max_len] } } normalize_op = { 'NormalizeImage': { 'std': [0.229, 0.224, 0.225] if args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], 'mean': [0.485, 0.456, 0.406] if args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5], 'scale': '1./255.', 'order': 'hwc' } } to_chw_op = {'ToCHWImage': None} keep_keys_op = {'KeepKeys': {'keep_keys': ['image', 'shape']}} if args.table_algorithm not in ['TableMaster']: pre_process_list = [ resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op ] else: pre_process_list = [ resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op ] return pre_process_list class TableStructurer(object): def __init__(self, args): pre_process_list = build_pre_process_list(args) if args.table_algorithm not in ['TableMaster']: postprocess_params = { 'name': 'TableLabelDecode', "character_dict_path": args.table_char_dict_path, } else: postprocess_params = { 'name': 'TableMasterLabelDecode', "character_dict_path": args.table_char_dict_path, 'box_shape': 'pad' } 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, 'table', 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] shape_list = np.expand_dims(data[-1], axis=0) post_result = self.postprocess_op(preds, [shape_list]) structure_str_list = post_result['structure_batch_list'][0] bbox_list = post_result['bbox_batch_list'][0] structure_str_list = structure_str_list[0] structure_str_list = [ '', '', '' ] + structure_str_list + ['

', '', ''] elapse = time.time() - starttime return structure_str_list, bbox_list, elapse def draw_rectangle(img_path, boxes, use_xywh=False): img = cv2.imread(img_path) img_show = img.copy() for box in boxes.astype(int): if use_xywh: x, y, w, h = box x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2 else: x1, y1, x2, y2 = box cv2.rectangle(img_show, (x1, y1), (x2, y2), (255, 0, 0), 2) return img_show def main(args): image_file_list = get_image_file_list(args.image_dir) table_structurer = TableStructurer(args) count = 0 total_time = 0 use_xywh = args.table_algorithm in ['TableMaster'] os.makedirs(args.output, exist_ok=True) with open( os.path.join(args.output, 'infer.txt'), mode='w', encoding='utf-8') as f_w: 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_str_list, bbox_list, elapse = table_structurer(img) bbox_list_str = json.dumps(bbox_list.tolist()) logger.info("result: {}, {}".format(structure_str_list, bbox_list_str)) f_w.write("result: {}, {}\n".format(structure_str_list, bbox_list_str)) img = draw_rectangle(image_file, bbox_list, use_xywh) img_save_path = os.path.join(args.output, os.path.basename(image_file)) cv2.imwrite(img_save_path, img) logger.info("save vis result to {}".format(img_save_path)) if count > 0: total_time += elapse count += 1 logger.info("Predict time of {}: {}".format(image_file, elapse)) if __name__ == "__main__": main(parse_args())