110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
# Copyright (c) 2022 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.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
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import cv2
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import pickle
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import paddle
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from tqdm import tqdm
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from ppstructure.table.table_metric import TEDS
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from ppstructure.table.predict_table import TableSystem
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from ppstructure.utility import init_args
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from ppocr.utils.logging import get_logger
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logger = get_logger()
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def parse_args():
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parser = init_args()
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parser.add_argument("--gt_path", type=str)
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return parser.parse_args()
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def load_txt(txt_path):
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pred_html_dict = {}
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if not os.path.exists(txt_path):
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return pred_html_dict
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with open(txt_path, encoding='utf-8') as f:
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lines = f.readlines()
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for line in lines:
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line = line.strip().split('\t')
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img_name, pred_html = line
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pred_html_dict[img_name] = pred_html
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return pred_html_dict
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def load_result(path):
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data = {}
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if os.path.exists(path):
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data = pickle.load(open(path, 'rb'))
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return data
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def save_result(path, data):
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old_data = load_result(path)
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old_data.update(data)
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with open(path, 'wb') as f:
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pickle.dump(old_data, f)
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def main(gt_path, img_root, args):
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os.makedirs(args.output, exist_ok=True)
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# init TableSystem
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text_sys = TableSystem(args)
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# load gt and preds html result
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gt_html_dict = load_txt(gt_path)
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ocr_result = load_result(os.path.join(args.output, 'ocr.pickle'))
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structure_result = load_result(
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os.path.join(args.output, 'structure.pickle'))
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pred_htmls = []
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gt_htmls = []
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for img_name, gt_html in tqdm(gt_html_dict.items()):
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img = cv2.imread(os.path.join(img_root, img_name))
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# run ocr and save result
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if img_name not in ocr_result:
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dt_boxes, rec_res, _, _ = text_sys._ocr(img)
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ocr_result[img_name] = [dt_boxes, rec_res]
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save_result(os.path.join(args.output, 'ocr.pickle'), ocr_result)
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# run structure and save result
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if img_name not in structure_result:
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structure_res, _ = text_sys._structure(img)
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structure_result[img_name] = structure_res
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save_result(
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os.path.join(args.output, 'structure.pickle'), structure_result)
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dt_boxes, rec_res = ocr_result[img_name]
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structure_res = structure_result[img_name]
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# match ocr and structure
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pred_html = text_sys.match(structure_res, dt_boxes, rec_res)
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pred_htmls.append(pred_html)
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gt_htmls.append(gt_html)
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# compute teds
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teds = TEDS(n_jobs=16)
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scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)
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logger.info('teds: {}'.format(sum(scores) / len(scores)))
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if __name__ == '__main__':
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args = parse_args()
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main(args.gt_path, args.image_dir, args)
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