PaddleX/paddlex/modules/text_recognition/dataset_checker/dataset_src/check_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 os.path as osp
from collections import defaultdict

from PIL import Image, ImageOps
import json
import numpy as np

from .....utils.errors import DatasetFileNotFoundError, CheckFailedError


def check(
    dataset_dir, output, dataset_type="MSTextRecDataset", mode="fast", sample_num=10
):
    """check dataset"""
    if dataset_type == "SimpleDataSet" or "MSTextRecDataset" or "LaTeXOCRDataset":
        # Custom dataset
        if not osp.exists(dataset_dir) or not osp.isdir(dataset_dir):
            raise DatasetFileNotFoundError(file_path=dataset_dir)
        tags = ["train", "val"]
        delim = "\t"
        valid_num_parts = 2
        max_recorded_sample_cnts = 50
        sample_cnts = dict()
        sample_paths = defaultdict(list)
        if dataset_type == "LaTeXOCRDataset":
            dict_file = osp.join(dataset_dir, "latex_ocr_tokenizer.json")
            if not osp.exists(dict_file):
                raise DatasetFileNotFoundError(
                    file_path=dict_file,
                    solution=f"Ensure that `latex_ocr_tokenizer.json` exist in {dataset_dir}",
                )
        else:
            dict_file = osp.join(dataset_dir, "dict.txt")
            if not osp.exists(dict_file):
                raise DatasetFileNotFoundError(
                    file_path=dict_file,
                    solution=f"Ensure that `dict.txt` exist in {dataset_dir}",
                )
        for tag in tags:
            file_list = osp.join(dataset_dir, f"{tag}.txt")
            if not osp.exists(file_list):
                if tag in ("train", "val"):
                    # train and val file lists must exist
                    raise DatasetFileNotFoundError(
                        file_path=file_list,
                        solution=f"Ensure that both `train.txt` and `val.txt` exist in {dataset_dir}",
                    )
                else:
                    # tag == 'test'
                    continue
            else:
                with open(file_list, "r", encoding="utf-8") as f:
                    all_lines = f.readlines()
                    sample_cnts[tag] = len(all_lines)
                    for line in all_lines:
                        substr = line.strip("\n").split(delim)
                        if len(line.strip("\n")) < 1:
                            continue
                        if len(substr) != valid_num_parts and len(line.strip("\n")) > 1:
                            raise CheckFailedError(
                                f"Error in {line}, The number of delimiter-separated items in each row "
                                "in {file_list} should be {valid_num_parts} (current delimiter is '{delim}')."
                            )
                        file_name = substr[0]
                        img_path = osp.join(dataset_dir, file_name)

                        if not os.path.exists(img_path):
                            raise DatasetFileNotFoundError(file_path=img_path)
                        vis_save_dir = osp.join(output, "demo_img")
                        if not osp.exists(vis_save_dir):
                            os.makedirs(vis_save_dir)
                        if len(sample_paths[tag]) < sample_num:
                            img = Image.open(img_path)
                            img = ImageOps.exif_transpose(img)
                            vis_path = osp.join(vis_save_dir, osp.basename(file_name))
                            img.save(vis_path)
                            sample_path = osp.join(
                                "check_dataset", os.path.relpath(vis_path, output)
                            )
                            sample_paths[tag].append(sample_path)

        meta = {}
        meta["train_samples"] = sample_cnts["train"]
        meta["train_sample_paths"] = sample_paths["train"][:sample_num]

        meta["val_samples"] = sample_cnts["val"]
        meta["val_sample_paths"] = sample_paths["val"][:sample_num]

        # meta['dict_file'] = dict_file

        return meta