PaddleX/paddlex/modules/instance_segmentation/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 json
import os
import os.path as osp
from collections import defaultdict, Counter
from pathlib import Path

from PIL import Image, ImageOps
from pycocotools.coco import COCO

from .utils.visualizer import draw_bbox, draw_mask
from .....utils.errors import DatasetFileNotFoundError
from .....utils.logging import info


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

    sample_cnts = dict()
    sample_paths = defaultdict(list)
    im_sizes = defaultdict(Counter)
    tags = ['instance_train', 'instance_val']
    for _, tag in enumerate(tags):
        file_list = osp.join(dataset_dir, f'annotations/{tag}.json')
        if not osp.exists(file_list):
            if tag in ('instance_train', 'instance_val'):
                # train and val file lists must exist
                raise DatasetFileNotFoundError(
                    file_path=file_list,
                    solution=f"Ensure that both `instance_train.json` and `instance_val.json` exist in \
{dataset_dir}/annotations")
            else:
                continue
        else:
            with open(file_list, 'r', encoding='utf-8') as f:
                jsondata = json.load(f)

            datanno = jsondata['annotations']
            sample_cnts[tag] = len(datanno)
            coco = COCO(file_list)
            num_class = len(coco.getCatIds())

            vis_save_dir = osp.join(output_dir, 'demo_img')

            image_info = jsondata['images']
            for i in range(sample_num):
                file_name = image_info[i]['file_name']
                img_id = image_info[i]['id']
                img_path = osp.join(dataset_dir, 'images', file_name)
                if not osp.exists(img_path):
                    raise DatasetFileNotFoundError(file_path=img_path)
                img = Image.open(img_path)
                img = ImageOps.exif_transpose(img)
                vis_im = draw_bbox(img, coco, img_id)
                vis_im = draw_mask(vis_im, coco, img_id)
                vis_path = osp.join(vis_save_dir, file_name)
                Path(vis_path).parent.mkdir(parents=True, exist_ok=True)
                vis_im.save(vis_path)
                sample_path = osp.join('check_dataset',
                                       os.path.relpath(vis_path, output_dir))
                sample_paths[tag].append(sample_path)

    attrs = {}
    attrs['num_classes'] = num_class
    attrs['train_samples'] = sample_cnts['instance_train']
    attrs['train_sample_paths'] = sample_paths['instance_train']

    attrs['val_samples'] = sample_cnts['instance_val']
    attrs['val_sample_paths'] = sample_paths['instance_val']
    return attrs