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