First version of the CXR_Reports dataset

CXR_Reports.py

@@ -0,0 +1,50 @@
+import tensorflow as tf
+import datasets
+
+
+_DESCRIPTION = (
+    "This dataset consists 90k images of Chest-X-Ray from the Mimic-CXR dataset."
+    "For each image, we have a consise report obtain from de PRO-CXR dataset"
+    "All images have a size of 512x512 pixels."
+)
+
+
+_BASE_URL = "https://drive.google.com/file/d/1u27GCgIIRqDz8a5-VTcMJ1pEFQbGv_QB/view?usp=sharing"
+
+FEATURE_DESCRIPTION_TFRECORD = {
+  'report': tf.io.FixedLenFeature([], tf.string),
+  'image': tf.io.FixedLenFeature([], tf.string),
+}
+
+
+class ReportsCXR(datasets.GeneratorBasedBuilder):
+  def _info(self):
+    return datasets.DatasetInfo(
+        description = _DESCRIPTION,
+        features = datasets.Features(
+            'image': datasets.Image(),
+            'report': datasets.Value(dtype='string')
+        )
+    )
+  def _get_drive_url(self, url):
+        base_url = 'https://drive.google.com/uc?id='
+        split_url = url.split('/')
+        return base_url + split_url[5]
+
+  def _split_generators(self, dl_manager):
+    archive_path = dl_manager.download(self._get_drive_url(_BASE_URL))
+    return [
+        datasets.SplitGenerator(name='full', gen_kwargs={'filepath': dl_manager.iter_archive(archive_path)})
+    ]
+
+  def _parse_image_function(example_proto):
+    # Parse the input tf.train.Example proto using the dictionary above.
+    return tf.io.parse_single_example(example_proto, FEATURE_DESCRIPTION_TFRECORD)
+
+  def _generate_examples(self, filepath):
+    raw_image_dataset = tf.data.TFRecordDataset(filepath)
+    parsed_image_dataset = raw_image_dataset.map(self._parse_image_function)
+    for image_features in parsed_image_dataset:
+      image_raw = image_features['image'].numpy()
+      yield {'image': image_raw, 'report': image_features['report']}
+