Added xcat (#4)

tensorflow_datasets/image/__init__.py

@@ -104,3 +104,4 @@
 from tensorflow_datasets.image_classification.uc_merced import UcMerced
 from tensorflow_datasets.image_classification.vgg_face2 import VggFace2
 from tensorflow_datasets.image_classification.visual_domain_decathlon import VisualDomainDecathlon
+from tensorflow_datasets.image.xcat import Xcat  # TODO(xcat) Sort alphabetically

tensorflow_datasets/image/xcat.py

@@ -0,0 +1,85 @@
+"""xcat dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow_datasets.public_api as tfds
+import tensorflow as tf
+import csv
+import os
+
+# TODO(xcat): BibTeX citation
+_CITATION = """
+"""
+
+# TODO(xcat):
+_DESCRIPTION = """
+"""
+
+
+class Xcat(tfds.core.GeneratorBasedBuilder):
+  """TODO(xcat): Short description of my dataset."""
+
+  VERSION = tfds.core.Version('0.1.0')
+  MANUAL_DOWNLOAD_INSTRUCTIONS = "Internal Gradient Dataset"
+  def _info(self):
+    return tfds.core.DatasetInfo(
+        builder=self,
+        description=_DESCRIPTION,
+        features=tfds.features.FeaturesDict({
+            'xray': tfds.features.Image(shape=(2048,2048,1), dtype=tf.uint16, encoding_format='png'),
+            'bse': tfds.features.Image(shape=(2048,2048,1), dtype=tf.uint16, encoding_format='png')
+        }),
+        supervised_keys=('xray', 'bse'),
+        homepage='https://dataset-homepage/',
+        citation=_CITATION
+    )
+
+  def _split_generators(self, dl_manager):
+    """Returns SplitGenerators."""
+    return [
+        tfds.core.SplitGenerator(
+            name=tfds.Split.TRAIN,
+            gen_kwargs={
+                'manual_dir': dl_manager.manual_dir,
+                'file': 'train_body_c7.csv',
+                'data_dir': 'projections_body_c7'
+            }
+        ),
+        tfds.core.SplitGenerator(
+            name=tfds.Split.TEST,
+            gen_kwargs={
+                'manual_dir': dl_manager.manual_dir,
+                'file': 'test_body_c7.csv',
+                'data_dir': 'projections_body_c7'
+            }
+        )
+    ]
+
+  def _generate_examples(self, manual_dir=None, file=None, data_dir=None):
+    with tf.io.gfile.GFile(os.path.join(manual_dir, file)) as f:
+        reader = csv.DictReader(f)
+        for row in reader:           
+            bse_file = row['filename']
+            xray_file = row['filename'].split('.')
+            xray_file[2] = 'bone'
+            xray_file = ".".join(xray_file)
+
+            key = row['filename'].split('.')[0]
+
+            bse_data = tf.io.read_file(os.path.join(manual_dir, data_dir, bse_file))
+            xray_data = tf.io.read_file(os.path.join(manual_dir, data_dir, xray_file))
+            bse = tf.reshape((tf.io.decode_raw(bse_data, tf.float32)), (2048,2048,1))
+            xray = tf.reshape((tf.io.decode_raw(xray_data, tf.float32)), (2048,2048,1))
+
+            bse = (bse - tf.reduce_min(bse))/(tf.reduce_max(bse)-tf.reduce_min(bse))
+            xray = (xray - tf.reduce_min(xray))/(tf.reduce_max(xray)-tf.reduce_min(xray))
+
+            bse = tf.cast(tf.round(bse*65534), tf.uint16)
+            xray = tf.cast(tf.round(xray*65534), tf.uint16)
+
+            yield key, {
+                'xray': xray.numpy(),
+                'bse': bse.numpy()
+            }

tensorflow_datasets/image/xcat_test.py

@@ -0,0 +1,28 @@
+"""xcat dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow_datasets.public_api as tfds
+from tensorflow_datasets.image import xcat
+
+
+class XcatTest(tfds.testing.DatasetBuilderTestCase):
+  # TODO(xcat):
+  DATASET_CLASS = xcat.Xcat
+  SPLITS = {
+      "train": 3,  # Number of fake train example
+      "test": 1,  # Number of fake test example
+  }
+
+  # If you are calling `download/download_and_extract` with a dict, like:
+  #   dl_manager.download({'some_key': 'http://a.org/out.txt', ...})
+  # then the tests needs to provide the fake output paths relative to the
+  # fake data directory
+  # DL_EXTRACT_RESULT = {'some_key': 'output_file1.txt', ...}
+
+
+if __name__ == "__main__":
+  tfds.testing.test_main()
+

tensorflow_datasets/testing/test_data/fake_examples/xcat/TODO-add_fake_data_in_this_directory.txt

@@ -0,0 +1 @@
+TODO(xcat): Add fake data in this directory

tensorflow_datasets/url_checksums/xcat.txt

@@ -0,0 +1,3 @@
+# TODO(xcat): If your dataset downloads files, then the checksums will be
+# automatically added here when running the download_and_prepare script
+# with --register_checksums.