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FracAtlas_dataset.py

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+#!/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
+
+
+# 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_path": datasets.Value("string"),
+                  "image": datasets.Image(),
+                  "hand": datasets.Value("int32"),
+                  "leg":datasets.Value("int32"),
+                  "hip": datasets.Value("int32"),
+                  "shoulder": datasets.Value("int32"),
+                  "mixed": datasets.Value("int32"),
+                  "hardware": datasets.Value("int32"),
+                  "multiscan": datasets.Value("int32"),
+                  "fractured": datasets.Value("int32"),
+                  "fracture_count": datasets.Value("int32"),
+                  "frontal": datasets.Value("int32"),
+                  "lateral": datasets.Value("int32"),
+                  "oblique": datasets.Value("int32"),
+                  "segmentation": datasets.Sequence(datasets.Sequence(datasets.Value("float"))),
+                  "bbox": datasets.Sequence(datasets.Value("float")),
+                  "area": datasets.Value("float"),
+                  "width": datasets.Value("int32"),
+                  "height": datasets.Value("int32"),
+                  "depth": datasets.Value("int32"),
+                  "segmented": datasets.Value("int32")
+              }
+          ),
+          # 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)
+            
+            
+            if annotation:
+                segmentation = annotation.get('segmentation')  # Ensure a list of lists even if empty
+                bbox = annotation.get('bbox')  # Same as above
+                area = annotation.get('area')  # Default to 0.0 if missing
+            else:
+                segmentation, bbox, area = [[0.0]], [0.0], 0.0  # Default values for missing annotation
+            
+            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, segmented = parse_xml(xml_path)
+            
+            # Construct example data
+            example_data = {
+                "image_id": row['image_id'],
+                "image_path": image_path,
+                "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"],
+                "segmentation": segmentation,
+                "bbox": bbox,
+                "area": area,
+                "width": width,
+                "height": height,
+                "depth": depth,
+                "segmented": segmented
+            }
+            yield image_id, example_data
+