PaddleX/paddlex/modules/text_recognition/dataset_checker/dataset_src/convert_dataset.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# 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 shutil
import json
import random
import math
import pickle
from tqdm import tqdm
from collections import defaultdict
import imagesize
from .....utils.errors import ConvertFailedError
from .....utils.logging import info, warning


def check_src_dataset(root_dir, dataset_type):
    """check src dataset format validity"""
    if dataset_type in ("MSTextRecDataset"):
        anno_suffix = ".txt"
    else:
        raise ConvertFailedError(
            message=f"数据格式转换失败！不支持{dataset_type}格式数据集。当前仅支持 MSTextRecDataset 格式。"
        )

    err_msg_prefix = f"数据格式转换失败！请参考上述`{dataset_type}格式数据集示例`检查待转换数据集格式。"

    for anno in ["train.txt", "val.txt", "latex_ocr_tokenizer.json"]:
        src_anno_path = os.path.join(root_dir, anno)
        if not os.path.exists(src_anno_path):
            raise ConvertFailedError(
                message=f"{err_msg_prefix}保证{src_anno_path}文件存在。"
            )
    return None


def convert(dataset_type, input_dir):
    """convert dataset to pkl format"""
    # check format validity
    check_src_dataset(input_dir, dataset_type)
    if dataset_type in ("MSTextRecDataset"):
        convert_pkl_dataset(input_dir)
    else:
        raise ConvertFailedError(
            message=f"数据格式转换失败！不支持{dataset_type}格式数据集。当前仅支持 MSTextRecDataset 格式。"
        )


def convert_pkl_dataset(root_dir):
    for anno in ["train.txt", "val.txt"]:
        src_img_dir = root_dir
        src_anno_path = os.path.join(root_dir, anno)
        txt2pickle(src_img_dir, src_anno_path, root_dir)


def txt2pickle(images, equations, save_dir):
    phase = os.path.basename(equations).replace(".txt", "")
    save_p = os.path.join(save_dir, "latexocr_{}.pkl".format(phase))
    min_dimensions = (32, 32)
    max_dimensions = (672, 192)
    max_length = 512
    data = defaultdict(lambda: [])
    pic_num = 0
    if images is not None and equations is not None:
        with open(equations, "r") as f:
            lines = f.readlines()
            for l in tqdm(lines, total=len(lines)):
                l = l.strip()
                img_name, equation = l.split("\t")
                img_path = os.path.join(images, img_name)
                width, height = imagesize.get(img_path)
                if (
                    min_dimensions[0] <= width <= max_dimensions[0]
                    and min_dimensions[1] <= height <= max_dimensions[1]
                ):
                    divide_h = math.ceil(height / 16) * 16
                    divide_w = math.ceil(width / 16) * 16
                    data[(divide_w, divide_h)].append((equation, img_name))
                    pic_num += 1
        data = dict(data)
        with open(save_p, "wb") as file:
            pickle.dump(data, file)