PaddleX/paddlex/modules/image_classification/dataset_checker/dataset_src/check_dataset.py

# !/usr/bin/env python3
# -*- coding: UTF-8 -*-
################################################################################
#
# Copyright (c) 2024 Baidu.com, Inc. All Rights Reserved
#
################################################################################
"""
Author: PaddlePaddle Authors
"""

import os
import os.path as osp
import random
from PIL import Image, ImageOps
from collections import defaultdict

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


def check(dataset_dir, output, sample_num=10):
    """ check dataset """
    dataset_dir = osp.abspath(dataset_dir)
    # Custom dataset
    if not osp.exists(dataset_dir) or not osp.isdir(dataset_dir):
        raise DatasetFileNotFoundError(file_path=dataset_dir)

    tags = ['train', 'val']
    delim = ' '
    valid_num_parts = 2

    sample_cnts = dict()
    label_map_dict = dict()
    sample_paths = defaultdict(list)
    labels = []

    label_file = osp.join(dataset_dir, 'label.txt')
    if not osp.exists(label_file):
        raise DatasetFileNotFoundError(
            file_path=label_file,
            solution=f"Ensure that `label.txt` exist in {dataset_dir}")

    with open(label_file, 'r', encoding='utf-8') as f:
        all_lines = f.readlines()
        for line in all_lines:
            substr = line.strip("\n").split(delim, 1)
            try:
                label_idx = int(substr[0])
                labels.append(label_idx)
                label_map_dict[label_idx] = str(substr[1])
            except:
                raise CheckFailedError(
                    f"Ensure that the first number in each line in {label_file} should be int."
                )
    if min(labels) != 0:
        raise CheckFailedError(
            f"Ensure that the index starts from 0 in `{label_file}`.")

    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()
                random.seed(123)
                random.shuffle(all_lines)
                sample_cnts[tag] = len(all_lines)
                for line in all_lines:
                    substr = line.strip("\n").split(delim)
                    if len(substr) != valid_num_parts:
                        raise CheckFailedError(
                            f"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]
                    label = substr[1]

                    img_path = osp.join(dataset_dir, file_name)

                    if not osp.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_im = draw_label(img, label, label_map_dict)
                        vis_path = osp.join(vis_save_dir,
                                            osp.basename(file_name))
                        vis_im.save(vis_path)
                        sample_path = osp.join(
                            'check_dataset', os.path.relpath(vis_path, output))
                        sample_paths[tag].append(sample_path)

                    try:
                        label = int(label)
                    except (ValueError, TypeError) as e:
                        raise CheckFailedError(
                            f"Ensure that the second number in each line in {label_file} should be int."
                        ) from e

    num_classes = max(labels) + 1

    attrs = {}
    attrs['label_file'] = osp.relpath(label_file, output)
    attrs['num_classes'] = num_classes
    attrs['train_samples'] = sample_cnts['train']
    attrs['train_sample_paths'] = sample_paths['train']

    attrs['val_samples'] = sample_cnts['val']
    attrs['val_sample_paths'] = sample_paths['val']

    return attrs