mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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137 lines
4.4 KiB
Python
Executable File
137 lines
4.4 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import cv2
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import numpy as np
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import time
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import tools.infer.utility as utility
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from ppocr.data import create_operators, transform
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from ppocr.postprocess import build_post_process
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from ppocr.utils.logging import get_logger
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from ppocr.utils.utility import get_image_file_list, check_and_read_gif
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from ppstructure.utility import parse_args
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logger = get_logger()
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class TableStructurer(object):
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def __init__(self, args):
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pre_process_list = [{
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'ResizeTableImage': {
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'max_len': args.table_max_len
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}
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}, {
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'NormalizeImage': {
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'std': [0.229, 0.224, 0.225],
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'mean': [0.485, 0.456, 0.406],
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'scale': '1./255.',
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'order': 'hwc'
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}
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}, {
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'PaddingTableImage': None
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}, {
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'ToCHWImage': None
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}, {
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'KeepKeys': {
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'keep_keys': ['image']
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}
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}]
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postprocess_params = {
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'name': 'TableLabelDecode',
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"character_type": args.table_char_type,
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"character_dict_path": args.table_char_dict_path,
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}
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self.preprocess_op = create_operators(pre_process_list)
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self.postprocess_op = build_post_process(postprocess_params)
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self.predictor, self.input_tensor, self.output_tensors, self.config = \
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utility.create_predictor(args, 'table', logger)
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def __call__(self, img):
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ori_im = img.copy()
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data = {'image': img}
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data = transform(data, self.preprocess_op)
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img = data[0]
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if img is None:
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return None, 0
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img = np.expand_dims(img, axis=0)
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img = img.copy()
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starttime = time.time()
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self.input_tensor.copy_from_cpu(img)
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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preds = {}
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preds['structure_probs'] = outputs[1]
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preds['loc_preds'] = outputs[0]
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post_result = self.postprocess_op(preds)
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structure_str_list = post_result['structure_str_list']
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res_loc = post_result['res_loc']
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imgh, imgw = ori_im.shape[0:2]
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res_loc_final = []
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for rno in range(len(res_loc[0])):
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x0, y0, x1, y1 = res_loc[0][rno]
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left = max(int(imgw * x0), 0)
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top = max(int(imgh * y0), 0)
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right = min(int(imgw * x1), imgw - 1)
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bottom = min(int(imgh * y1), imgh - 1)
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res_loc_final.append([left, top, right, bottom])
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structure_str_list = structure_str_list[0][:-1]
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structure_str_list = ['<html>', '<body>', '<table>'] + structure_str_list + ['</table>', '</body>', '</html>']
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elapse = time.time() - starttime
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return (structure_str_list, res_loc_final), elapse
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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table_structurer = TableStructurer(args)
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count = 0
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total_time = 0
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for image_file in image_file_list:
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img, flag = check_and_read_gif(image_file)
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if not flag:
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img = cv2.imread(image_file)
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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structure_res, elapse = table_structurer(img)
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logger.info("result: {}".format(structure_res))
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if count > 0:
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total_time += elapse
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count += 1
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logger.info("Predict time of {}: {}".format(image_file, elapse))
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if __name__ == "__main__":
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main(parse_args())
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