Yuhan Hou
commited on
Commit
•
a500017
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Parent(s):
02ffd0b
My commit
Browse files- FracAtlas_dataset.py +257 -0
FracAtlas_dataset.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sun Feb 18 23:13:51 2024
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@author: houyuhan
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"""
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#Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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FracAtlas Dataset Loader
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This script provides a Hugging Face `datasets` loader for the FracAtlas dataset, a comprehensive collection
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of musculoskeletal radiographs aimed at advancing research in fracture classification, localization, and segmentation.
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The dataset includes high-quality X-Ray images accompanied by detailed annotations in COCO JSON format for segmentation
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and bounding box information, as well as PASCAL VOC XML files for additional localization data.
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The loader handles downloading and preparing the dataset, making it readily available for machine learning models and analysis
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tasks in medical imaging, especially focusing on the detection and understanding of bone fractures.
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License: CC-BY 4.0
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"""
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import csv
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import json
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import os
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from typing import List
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import datasets
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import logging
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import shutil
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import xml.etree.ElementTree as ET
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from datasets import load_dataset
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{huggingface:yh0701/FracAtlas_dataset,
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title = {FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs},
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author={Abedeen, Iftekharul; Rahman, Md. Ashiqur; Zohra Prottyasha, Fatema; Ahmed, Tasnim; Mohmud Chowdhury, Tareque; Shatabda, Swakkhar},
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year={2023}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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The "FracAtlas" dataset is a collection of musculoskeletal radiographs for fracture classification, localization, and segmentation.
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It includes 4,083 X-Ray images with annotations in multiple formats.The annotations include bbox, segmentations, and etc.
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The dataset is intended for use in deep learning tasks in medical imaging, specifically targeting the understanding of bone fractures.
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It is freely available under a CC-BY 4.0 license.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = "https://figshare.com/articles/dataset/The_dataset/22363012"
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = "The dataset is licensed under a CC-BY 4.0 license."
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URL = "https://figshare.com/ndownloader/files/43283628"
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class FracAtlasDataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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_URL = _URL
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"image_id": datasets.Value("string"),
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"image": datasets.Image(),
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"hand": datasets.ClassLabel(num_classes=2,names=['no_hand','hand']),
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"leg": datasets.ClassLabel(num_classes=2,names=['no_leg','leg']),
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"hip": datasets.ClassLabel(num_classes=2,names=['no_hip','hip']),
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"shoulder": datasets.ClassLabel(num_classes=2,names=['no_shoulder','shoulder']),
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"mixed": datasets.ClassLabel(num_classes=2,names=['not_mixed','mixed']),
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"hardware": datasets.ClassLabel(num_classes=2,names=['no_hardware','hardware']),
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"multiscan": datasets.ClassLabel(num_classes=2,names=['not_multiscan','multiscan']),
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"fractured": datasets.ClassLabel(num_classes=2,names=['not_fractured','fractured']),
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"fracture_count": datasets.Value("int32"),
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"frontal": datasets.ClassLabel(num_classes=2,names=['not_frontal','frontal']),
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"lateral": datasets.ClassLabel(num_classes=2,names=['not_lateral','lateral']),
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"oblique": datasets.ClassLabel(num_classes=2,names=['not_oblique','oblique']),
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"localization_metadata": datasets.Features({
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"width": datasets.Value("int32"),
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"height": datasets.Value("int32"),
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"depth": datasets.Value("int32"),
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}),
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"segmentation_metadata": datasets.Features({
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"segmentation": datasets.Sequence(datasets.Sequence(datasets.Value("float"))),
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"bbox": datasets.Sequence(datasets.Value("float")),
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"area": datasets.Value("float")
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}) or None
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}
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),
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# No default supervised_keys (as we have to pass both question
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# and context as input).
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supervised_keys=None,
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homepage=_HOMEPAGE,
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citation=_CITATION
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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url_to_download = self._URL
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downloaded_files = dl_manager.download_and_extract(url_to_download)
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# Adjusted path to include 'FracAtlas' directory
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base_path = os.path.join(downloaded_files, 'FracAtlas')
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# Split the dataset to train/test/validation by 0.7,0.15,0.15
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df = pd.read_csv(os.path.join(base_path, 'dataset.csv'))
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train_df, test_df = train_test_split(df, test_size=0.3)
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validation_df, test_df = train_test_split(test_df, test_size=0.5)
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# store them back as csv
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train_df.to_csv(os.path.join(base_path, 'train_dataset.csv'), index=False)
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validation_df.to_csv(os.path.join(base_path, 'validation_dataset.csv'), index=False)
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test_df.to_csv(os.path.join(base_path, 'test_dataset.csv'), index=False)
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annotations_path = os.path.join(base_path, 'Annotations/COCO JSON/COCO_fracture_masks.json')
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images_path = os.path.join(base_path, 'images')
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localization_path = os.path.join(base_path, 'Annotations/PASCAL VOC')
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"dataset_csv_path": os.path.join(base_path, 'train_dataset.csv'),
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"images_path": images_path,
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"annotations_path": annotations_path,
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"localization_path":localization_path
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}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"dataset_csv_path": os.path.join(base_path, 'validation_dataset.csv'),
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"images_path": images_path,
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"annotations_path": annotations_path,
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"localization_path":localization_path
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}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"dataset_csv_path": os.path.join(base_path, 'test_dataset.csv'),
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"images_path": images_path,
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"annotations_path": annotations_path,
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"localization_path":localization_path
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})
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]
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def _generate_examples(self, annotations_path, images_path, dataset_csv_path,localization_path):
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logging.info("Generating examples from = %s", dataset_csv_path)
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split_df = pd.read_csv(dataset_csv_path) # Load the DataFrame for the current split
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# Function to convert numeric ID to formatted string
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def format_image_id(numeric_id):
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return f"IMG{numeric_id:07d}.jpg" # Adjust format as needed
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# Function to extract information from xml files
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def parse_xml(xml_path):
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tree = ET.parse(xml_path)
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root = tree.getroot()
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# Extract the necessary information
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width = int(root.find("./size/width").text)
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height = int(root.find("./size/height").text)
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depth = int(root.find("./size/depth").text)
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segmented = int(root.find("./segmented").text)
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return width, height, depth, segmented
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# Load annotations
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with open(annotations_path) as file:
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annotations_json = json.load(file)
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for item in annotations_json['annotations']:
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item['image_id'] = format_image_id(item['image_id'])
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annotations = {item['image_id']: item for item in annotations_json['annotations']}
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# Iterate through each row in the split DataFrame
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for _, row in split_df.iterrows():
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image_id = row['image_id']
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# Determine the folder based on the 'fractured' column
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folder = 'Fractured' if row['fractured'] == 1 else 'Non_fractured'
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# Check if the formatted_image_id exists in annotations
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annotation = annotations.get(image_id)
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image_path = os.path.join(images_path, folder, image_id)
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# Initialize variables
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segmentation, bbox, area = None, None, None
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segmentation_metadata = None
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if annotation:
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segmentation = annotation.get('segmentation')
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bbox = annotation.get('bbox')
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area = annotation.get('area')
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segmentation_metadata = {
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'segmentation': segmentation,
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'bbox':bbox,
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'area': area
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}
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else:
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segmentation_metadata = None # Default if not present
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xml_file_name = f"{image_id.split('.')[0]}.xml"
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xml_path = os.path.join(localization_path, xml_file_name)
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# Parse the XML file
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width, height, depth, _ = parse_xml(xml_path)
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localization_metadata = {
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'width': width,
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"height":height,
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'depth': depth
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}
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# Construct example data
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example_data = {
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"image_id": row['image_id'],
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"image":image_path,
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"hand": row["hand"],
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"leg": row["leg"],
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"hip": row["hip"],
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"shoulder": row["shoulder"],
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"mixed": row["mixed"],
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"hardware": row["hardware"],
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"multiscan": row["multiscan"],
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"fractured": row["fractured"],
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"fracture_count": row["fracture_count"],
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"frontal": row["frontal"],
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"lateral": row["lateral"],
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"oblique": row["oblique"],
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"localization_metadata": localization_metadata,
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'segmentation_metadata': segmentation_metadata
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}
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yield image_id, example_data
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