#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Feb 18 23:13:51 2024 @author: houyuhan """ #Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. """ FracAtlas Dataset Loader This script provides a Hugging Face `datasets` loader for the FracAtlas dataset, a comprehensive collection of musculoskeletal radiographs aimed at advancing research in fracture classification, localization, and segmentation. The dataset includes high-quality X-Ray images accompanied by detailed annotations in COCO JSON format for segmentation and bounding box information, as well as PASCAL VOC XML files for additional localization data. The loader handles downloading and preparing the dataset, making it readily available for machine learning models and analysis tasks in medical imaging, especially focusing on the detection and understanding of bone fractures. License: CC-BY 4.0 """ import csv import json import os from typing import List import datasets import logging import pandas as pd from sklearn.model_selection import train_test_split import shutil import xml.etree.ElementTree as ET from datasets import load_dataset # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:yh0701/FracAtlas_dataset, title = {FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs}, author={Abedeen, Iftekharul; Rahman, Md. Ashiqur; Zohra Prottyasha, Fatema; Ahmed, Tasnim; Mohmud Chowdhury, Tareque; Shatabda, Swakkhar}, year={2023} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ The "FracAtlas" dataset is a collection of musculoskeletal radiographs for fracture classification, localization, and segmentation. It includes 4,083 X-Ray images with annotations in multiple formats.The annotations include bbox, segmentations, and etc. The dataset is intended for use in deep learning tasks in medical imaging, specifically targeting the understanding of bone fractures. It is freely available under a CC-BY 4.0 license. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://figshare.com/articles/dataset/The_dataset/22363012" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "The dataset is licensed under a CC-BY 4.0 license." # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URL = "https://figshare.com/ndownloader/files/43283628" # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class FracAtlasDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" _URL = _URL VERSION = datasets.Version("1.1.0") def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "image_id": datasets.Value("string"), "image": datasets.Image(), "hand": datasets.ClassLabel(num_classes=2,names=['no_hand','hand']), "leg": datasets.ClassLabel(num_classes=2,names=['no_leg','leg']), "hip": datasets.ClassLabel(num_classes=2,names=['no_hip','hip']), "shoulder": datasets.ClassLabel(num_classes=2,names=['no_shoulder','shoulder']), "mixed": datasets.ClassLabel(num_classes=2,names=['not_mixed','mixed']), "hardware": datasets.ClassLabel(num_classes=2,names=['no_hardware','hardware']), "multiscan": datasets.ClassLabel(num_classes=2,names=['not_multiscan','multiscan']), "fractured": datasets.ClassLabel(num_classes=2,names=['not_fractured','fractured']), "fracture_count": datasets.Value("int32"), "frontal": datasets.ClassLabel(num_classes=2,names=['not_frontal','frontal']), "lateral": datasets.ClassLabel(num_classes=2,names=['not_lateral','lateral']), "oblique": datasets.ClassLabel(num_classes=2,names=['not_oblique','oblique']), "localization_metadata": datasets.Features({ "width": datasets.Value("int32"), "height": datasets.Value("int32"), "depth": datasets.Value("int32"), }), "segmentation_metadata": datasets.Features({ "segmentation": datasets.Sequence(datasets.Sequence(datasets.Value("float"))), "bbox": datasets.Sequence(datasets.Value("float")), "area": datasets.Value("float") }) or None } ), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: url_to_download = self._URL downloaded_files = dl_manager.download_and_extract(url_to_download) # Adjusted path to include 'FracAtlas' directory base_path = os.path.join(downloaded_files, 'FracAtlas') # Split the dataset to train/test/validation by 0.7,0.15,0.15 df = pd.read_csv(os.path.join(base_path, 'dataset.csv')) train_df, test_df = train_test_split(df, test_size=0.3) validation_df, test_df = train_test_split(test_df, test_size=0.5) # store them back as csv train_df.to_csv(os.path.join(base_path, 'train_dataset.csv'), index=False) validation_df.to_csv(os.path.join(base_path, 'validation_dataset.csv'), index=False) test_df.to_csv(os.path.join(base_path, 'test_dataset.csv'), index=False) annotations_path = os.path.join(base_path, 'Annotations/COCO JSON/COCO_fracture_masks.json') images_path = os.path.join(base_path, 'images') localization_path = os.path.join(base_path, 'Annotations/PASCAL VOC') return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"dataset_csv_path": os.path.join(base_path, 'train_dataset.csv'), "images_path": images_path, "annotations_path": annotations_path, "localization_path":localization_path }), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"dataset_csv_path": os.path.join(base_path, 'validation_dataset.csv'), "images_path": images_path, "annotations_path": annotations_path, "localization_path":localization_path }), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"dataset_csv_path": os.path.join(base_path, 'test_dataset.csv'), "images_path": images_path, "annotations_path": annotations_path, "localization_path":localization_path }) ] def _generate_examples(self, annotations_path, images_path, dataset_csv_path,localization_path): logging.info("Generating examples from = %s", dataset_csv_path) split_df = pd.read_csv(dataset_csv_path) # Load the DataFrame for the current split # Function to convert numeric ID to formatted string def format_image_id(numeric_id): return f"IMG{numeric_id:07d}.jpg" # Adjust format as needed # Function to extract information from xml files def parse_xml(xml_path): tree = ET.parse(xml_path) root = tree.getroot() # Extract the necessary information width = int(root.find("./size/width").text) height = int(root.find("./size/height").text) depth = int(root.find("./size/depth").text) segmented = int(root.find("./segmented").text) return width, height, depth, segmented # Load annotations with open(annotations_path) as file: annotations_json = json.load(file) for item in annotations_json['annotations']: item['image_id'] = format_image_id(item['image_id']) annotations = {item['image_id']: item for item in annotations_json['annotations']} # Iterate through each row in the split DataFrame for _, row in split_df.iterrows(): image_id = row['image_id'] # Determine the folder based on the 'fractured' column folder = 'Fractured' if row['fractured'] == 1 else 'Non_fractured' # Check if the formatted_image_id exists in annotations annotation = annotations.get(image_id) image_path = os.path.join(images_path, folder, image_id) # Initialize variables segmentation, bbox, area = None, None, None segmentation_metadata = None if annotation: segmentation = annotation.get('segmentation') bbox = annotation.get('bbox') area = annotation.get('area') segmentation_metadata = { 'segmentation': segmentation, 'bbox':bbox, 'area': area } else: segmentation_metadata = None # Default if not present xml_file_name = f"{image_id.split('.')[0]}.xml" xml_path = os.path.join(localization_path, xml_file_name) # Parse the XML file width, height, depth, _ = parse_xml(xml_path) localization_metadata = { 'width': width, "height":height, 'depth': depth } # Construct example data example_data = { "image_id": row['image_id'], "image":image_path, "hand": row["hand"], "leg": row["leg"], "hip": row["hip"], "shoulder": row["shoulder"], "mixed": row["mixed"], "hardware": row["hardware"], "multiscan": row["multiscan"], "fractured": row["fractured"], "fracture_count": row["fracture_count"], "frontal": row["frontal"], "lateral": row["lateral"], "oblique": row["oblique"], "localization_metadata": localization_metadata, 'segmentation_metadata': segmentation_metadata } yield image_id, example_data