108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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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 cv2
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import glob
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import argparse
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from os.path import join
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from tqdm import tqdm
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def latexocr2paddleocr_train(image_path, math_unimernet_file, math_hwe_file, save_path):
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convert_f = open(save_path, "w")
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sub_dir = "UniMER-1M/images"
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img_sub_dir = os.path.join(image_path, sub_dir)
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with open(math_unimernet_file, "r") as f:
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lines = f.readlines()
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formula_num = len(lines)
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for i, line in tqdm(enumerate(lines), total=formula_num):
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image_name = "{0:07d}.png".format(i)
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math_gt = line.strip()
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image_p = os.path.join(img_sub_dir, image_name)
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img_name_subdir = os.path.join(sub_dir, image_name)
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if os.path.exists(image_p):
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convert_f.writelines("{}\t{}\n".format(img_name_subdir, math_gt))
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sub_dir = "HME100K/train_images"
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img_sub_dir = os.path.join(image_path, sub_dir)
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with open(math_hwe_file, "r") as f:
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lines = f.readlines()
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formula_num = len(lines)
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for i, line in tqdm(enumerate(lines), total=formula_num):
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img_name, math_gt = line.strip().split("\t")
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image_path = os.path.join(img_sub_dir, img_name)
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img_name_subdir = os.path.join(sub_dir, img_name)
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convert_f.writelines("{}\t{}\n".format(img_name_subdir, math_gt))
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convert_f.close()
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def unimernet2paddleocr_test(image_path, math_file, save_path):
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convert_f = open(save_path, "w")
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with open(math_file, "r") as f:
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# load maths which
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lines = f.readlines()
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formula_num = len(lines)
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for i, line in tqdm(enumerate(lines), total=formula_num):
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image_name = "{0:07d}.png".format(i)
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math_gt = line.strip()
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image_p = os.path.join(image_path, image_name)
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if os.path.exists(image_p):
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convert_f.writelines("{}\t{}\n".format(image_name, math_gt))
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convert_f.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--image_dir",
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type=str,
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default=".",
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help="Input_label or input path to be converted",
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)
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parser.add_argument(
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"--unimernet_txt_path",
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type=str,
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default="",
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help="Input_label or input path to be converted",
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)
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parser.add_argument(
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"--hme100k_txt_path",
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type=str,
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default="",
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help="Input_label or input path to be converted",
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)
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parser.add_argument(
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"--output_path", type=str, default="out_label.txt", help="Output file name"
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)
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parser.add_argument(
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"--datatype", type=str, default="out_label.txt", help="datatype"
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)
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args = parser.parse_args()
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if args.datatype == "unimernet_train":
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latexocr2paddleocr_train(
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args.image_dir,
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args.unimernet_txt_path,
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args.hme100k_txt_path,
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args.output_path,
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)
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elif args.datatype == "unimernet_test":
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unimernet2paddleocr_test(
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args.image_dir, args.unimernet_txt_path, args.output_path
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)
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else:
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raise NotImplementedError("the datatype is not supported")
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