Datasets:

yh0701
/

FracAtlas_dataset

@@ -1,257 +0,0 @@
-#!/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_fracture','hand_fracture']),
- "leg": datasets.ClassLabel(num_classes=2,names=['no_leg_fracture','leg_fracture']),
- "hip": datasets.ClassLabel(num_classes=2,names=['no_hip_fracture','hip_fracture']),
- "shoulder": datasets.ClassLabel(num_classes=2,names=['no_shoulder_fracture','shoulder_fracture']),
- "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