Format and improve readability

NIH-Chest-X-ray-dataset.py

@@ -1,77 +1,89 @@
-import os
+# Copyright 2022 Cristóbal Alcázar
+#
+# 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.
+"""NIH Chest X-ray Dataset"""
+
 
+import os
 import datasets
 from datasets.tasks import ImageClassification
-
 from requests import get
 from pandas import read_csv
 
 logger = datasets.logging.get_logger(__name__)
 
-
-_HOMEPAGE = "https://nihcc.app.box.com/v/ChestXray-NIHCC"
-
 _CITATION = """\
-@ONLINE {beansdata,
-    author="Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summer",
-    title="ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases",
-    month="January",
-    year="2017",
-    url="https://nihcc.app.box.com/v/ChestXray-NIHCC"
+@inproceedings{Wang_2017,
+	doi = {10.1109/cvpr.2017.369},
+	url = {https://doi.org/10.1109%2Fcvpr.2017.369},
+	year = 2017,
+	month = {jul},
+	publisher = {{IEEE}
+},
+	author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
+	title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
+	booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}
 }
 """
 
+
 _DESCRIPTION = """\
 The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format.
 
 The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
 """
 
-_IMAGE_URLS2 = [
-    'https://nihcc.box.com/shared/static/vfk49d74nhbxq3nqjg0900w5nvkorp5c.gz',
-    'https://nihcc.box.com/shared/static/i28rlmbvmfjbl8p2n3ril0pptcmcu9d1.gz',
-    'https://nihcc.box.com/shared/static/f1t00wrtdk94satdfb9olcolqx20z2jp.gz',
-    'https://nihcc.box.com/shared/static/0aowwzs5lhjrceb3qp67ahp0rd1l1etg.gz',
-    'https://nihcc.box.com/shared/static/v5e3goj22zr6h8tzualxfsqlqaygfbsn.gz',
-    'https://nihcc.box.com/shared/static/asi7ikud9jwnkrnkj99jnpfkjdes7l6l.gz',
-    'https://nihcc.box.com/shared/static/jn1b4mw4n6lnh74ovmcjb8y48h8xj07n.gz',
-    'https://nihcc.box.com/shared/static/tvpxmn7qyrgl0w8wfh9kqfjskv6nmm1j.gz',
-    'https://nihcc.box.com/shared/static/upyy3ml7qdumlgk2rfcvlb9k6gvqq2pj.gz',
-    'https://nihcc.box.com/shared/static/l6nilvfa9cg3s28tqv1qc1olm3gnz54p.gz',
-    'https://nihcc.box.com/shared/static/hhq8fkdgvcari67vfhs7ppg2w6ni4jze.gz',
-    'https://nihcc.box.com/shared/static/ioqwiy20ihqwyr8pf4c24eazhh281pbu.gz'
-]
+
+_HOMEPAGE = "https://nihcc.app.box.com/v/chestxray-nihcc"
+
+
+_REPO = "https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data"
+
 
 _IMAGE_URLS = [
-	"https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data/images/images_001.zip",
-	"https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data/images/images_003.zip",
-	"https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data/images/images_004.zip"
+	f"{_REPO}/images/images_001.zip",
+	f"{_repo}/images/images_003.zip",
+	f"{_REPO}/images/images_004.zip",
+	f"{_REPO}/images/images_005.zip"
 	#'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_001.tar.gz',
 	#'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_002.tar.gz'
 ]
 
+
 _URLS = {
-	'train_val_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/train_val_list.txt',
-	'test_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/test_list.txt',
-	'labels': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/Data_Entry_2017_v2020.csv',
-	'image_urls': _IMAGE_URLS
+	"train_val_list": f"{_REPO}/train_val_list.txt",
+	"test_list": f"{_REPO}/test_list.txt",
+	"labels": f"{_REPO}/Data_Entry_2017_v2020.csv",
+	"image_urls": _IMAGE_URLS
 }
 
-_LABEL2IDX = {'No Finding': 0,
-	     'Atelectasis': 1,
-	     'Cardiomegaly': 2,
-	     'Effusion': 3,
-	     'Infiltration': 4,
-	     'Mass': 5,
-	     'Nodule': 6,
-	     'Pneumonia': 7,
-	     'Pneumothorax': 8,
-  	     'Consolidation': 9,
-	     'Edema': 10,
-	     'Emphysema': 11,
-	     'Fibrosis': 12,
-	     'Pleural_Thickening': 13,
-	     'Hernia': 14}
+
+_LABEL2IDX = {"No Finding": 0,
+	     "Atelectasis": 1,
+	     "Cardiomegaly": 2,
+	     "Effusion": 3,
+	     "Infiltration": 4,
+	     "Mass": 5,
+	     "Nodule": 6,
+	     "Pneumonia": 7,
+	     "Pneumothorax": 8,
+  	     "Consolidation": 9,
+	     "Edema": 10,
+	     "Emphysema": 11,
+	     "Fibrosis": 12,
+	     "Pleural_Thickening": 13,
+	     "Hernia": 14}
+
 
 _NAMES = list(_LABEL2IDX.keys())
 
@@ -79,7 +91,7 @@ _NAMES = list(_LABEL2IDX.keys())
 class XChest(datasets.GeneratorBasedBuilder):
 	"""NIH Image Chest X-ray dataset."""
 
-	VERSION = datasets.Version("0.0.0")
+	VERSION = datasets.Version("1.0.0")
 
 	def _info(self):
 		return datasets.DatasetInfo(
@@ -88,7 +100,6 @@ class XChest(datasets.GeneratorBasedBuilder):
 			{
 			"image_file_path": datasets.Value("string"),
 			"image": datasets.Image(),
-			#"labels": datasets.features.ClassLabel(names=_NAMES),
 			"labels": datasets.features.Sequence(
 			   datasets.features.ClassLabel(num_classes=len(_NAMES), 
 							names=_NAMES)
@@ -98,8 +109,6 @@ class XChest(datasets.GeneratorBasedBuilder):
 		    supervised_keys=("image", "labels"),
 		    homepage=_HOMEPAGE,
 		    citation=_CITATION,
-		    #task_templates=[ImageClassification(image_column="image",
-		    #					label_column="labels")],
 		)
 
 
@@ -115,7 +124,7 @@ class XChest(datasets.GeneratorBasedBuilder):
 		test_files = []
 		
 		# Download batches
-		data_files = dl_manager.download_and_extract(_URLS['image_urls'])
+		data_files = dl_manager.download_and_extract(_URLS["image_urls"])
 
 		# Iterate trought image folder and check if they belong to
 		# the trainset or testset
@@ -133,26 +142,26 @@ class XChest(datasets.GeneratorBasedBuilder):
 		    datasets.SplitGenerator(
 			name=datasets.Split.TRAIN,
 			gen_kwargs={
-				'files': iter(train_files)
+				"files": iter(train_files)
 			}
 
 		    ),
 		    datasets.SplitGenerator(
 			name=datasets.Split.TEST,
 			gen_kwargs={
-				'files': iter(test_files)
+				"files": iter(test_files)
 			}
 		    )
 		]
 
 	def _generate_examples(self, files):
 		# Read csv with image labels
-		label_csv = read_csv(_URLS['labels'])
+		label_csv = read_csv(_URLS["labels"])
 		for i, path in enumerate(files):
 		    file_name = os.path.basename(path)
 		    # Get image id to filter the respective row of the csv 	
 		    image_id = file_name.split('/')[-1]
-		    image_labels = label_csv[label_csv['Image Index'] == image_id]['Finding Labels'].values[0].split('|')
+		    image_labels = label_csv[label_csv["Image Index"] == image_id]["Finding Labels"].values[0].split("|")
 		    if file_name.endswith(".png"):
 		        yield i, {
 			    "image_file_path": path,