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- # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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
- from collections import defaultdict
- from .....utils.file_interface import custom_open
- def simple_analyse(dataset_path):
- """
- Analyse the dataset samples by return image path and label path
- Args:
- dataset_path (str): dataset path
- Returns:
- tuple: tuple of sample number, image path and label path for train, val and text subdataset.
- """
- tags = ["train", "val", "test"]
- sample_cnts = defaultdict(int)
- defaultdict(list)
- res = [None] * 6
- for tag in tags:
- file_list = os.path.join(dataset_path, f"{tag}.txt")
- if not os.path.exists(file_list):
- if tag in ("train", "val"):
- res.insert(0, "数据集不符合规范，请先通过数据校准")
- return res
- else:
- continue
- else:
- with custom_open(file_list, "r") as f:
- all_lines = f.readlines()
- # Each line corresponds to a sample
- sample_cnts[tag] = len(all_lines)
- # img_paths[tag] = images_dict[tag]
- return f"训练数据样本数: {sample_cnts[tags[0]]}\t评估数据样本数: {sample_cnts[tags[1]]}"
- def deep_analyse(dataset_path, output=None):
- """class analysis for dataset"""
- return simple_analyse(dataset_path)
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