Add 'Lost and Found' Road Hazard Dataset

tensorflow_datasets/image/__init__.py

@@ -29,6 +29,7 @@
 from tensorflow_datasets.image.cifar import Cifar10
 from tensorflow_datasets.image.cifar import Cifar100
 from tensorflow_datasets.image.cifar10_corrupted import Cifar10Corrupted
+from tensorflow_datasets.image.cityscapes import Cityscapes
 from tensorflow_datasets.image.clevr import CLEVR
 from tensorflow_datasets.image.coco import Coco
 from tensorflow_datasets.image.coco2014_legacy import Coco2014
@@ -47,6 +48,7 @@
 from tensorflow_datasets.image.imagenet import Imagenet2012
 from tensorflow_datasets.image.imagenet2012_corrupted import Imagenet2012Corrupted
 from tensorflow_datasets.image.kitti import Kitti
+from tensorflow_datasets.image.lost_and_found import LostAndFound
 from tensorflow_datasets.image.lsun import Lsun
 from tensorflow_datasets.image.mnist import EMNIST
 from tensorflow_datasets.image.mnist import FashionMNIST

tensorflow_datasets/image/cityscapes.py

@@ -0,0 +1,280 @@
+'''Cityscapes Datasets.'''
+
+import math
+import os
+import re
+
+import tensorflow as tf
+import tensorflow_datasets.public_api as tfds
+from tensorflow_datasets.core import api_utils
+
+_CITATION = '''\
+@inproceedings{Cordts2016Cityscapes,
+  title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
+  author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
+  booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2016}
+}
+'''
+
+_DESCRIPTION = '''\
+  Cityscapes is a dataset consisting of diverse urban street scenes across 50 different cities
+  at varying times of the year as well as ground truths for several vision tasks including
+  semantic segmentation, instance level segmentation (TODO), and stereo pair disparity inference.
+
+
+  For segmentation tasks (default split, accessible via 'cityscapes/semantic_segmentation'), Cityscapes provides
+  dense pixel level annotations for 5000 images at 1024 * 2048 resolution pre-split into training (2975),
+  validation (500) and test (1525) sets. Label annotations for segmentation tasks span across 30+ classes
+  commonly encountered during driving scene perception. Detailed label information may be found here:
+  https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py#L52-L99
+
+  Cityscapes also provides coarse grain segmentation annotations (accessible via 'cityscapes/semantic_segmentation_extra')
+  for 19998 images in a 'train_extra' split which may prove useful for pretraining / data-heavy models.
+
+
+  Besides segmentation, cityscapes also provides stereo image pairs and ground truths for disparity inference
+  tasks on both the normal and extra splits (accessible via 'cityscapes/stereo_disparity' and 
+  'cityscapes/stereo_disparity_extra' respectively).
+
+  Ingored examples:
+  - For 'cityscapes/stereo_disparity_extra':
+    - troisdorf_000000_000073_{*} images (no disparity map present)
+
+  WARNING: this dataset requires users to setup a login and password in order to get the files.
+'''
+
+# TODO add instance ids (might need to import cityScapesScripts)
+
+class CityscapesConfig(tfds.core.BuilderConfig):
+  '''BuilderConfig for Cityscapes
+
+    Args:
+      right_images (bool): Enables right images for stereo image tasks.
+      segmentation_labels (bool): Enables image segmentation labels.
+      disparity_maps (bool): Enables disparity maps.
+      train_extra_split (bool): Enables train_extra split. This automatically
+          enables coarse grain segmentations, if segmentation labels are used.
+  '''
+
+  @api_utils.disallow_positional_args
+  def __init__(self, right_images=False, segmentation_labels=True,
+               disparity_maps=False, train_extra_split=False, **kwargs):
+    super().__init__(**kwargs)
+
+    self.right_images = right_images
+    self.segmentation_labels = segmentation_labels
+    self.disparity_maps = disparity_maps
+    self.train_extra_split = train_extra_split
+
+    self.ignored_ids = set()
+
+    # Setup required zips and their root dir names
+    self.zip_root = {}
+    self.zip_root['images_left'] =\
+      ('leftImg8bit_trainvaltest.zip', 'leftImg8bit')
+
+    if self.train_extra_split:
+      self.zip_root['images_left/extra'] =\
+        ('leftImg8bit_trainextra.zip', 'leftImg8bit')
+
+    if self.right_images:
+      self.zip_root['images_right'] =\
+        ('rightImg8bit_trainvaltest.zip', 'rightImg8bit')
+      if self.train_extra_split:
+        self.zip_root['images_right/extra'] =\
+          ('rightImg8bit_trainextra.zip', 'rightImg8bit')
+
+    if self.segmentation_labels:
+      if not self.train_extra_split:
+        self.zip_root['segmentation_labels'] =\
+          ('gtFine_trainvaltest.zip', 'gtFine')
+        self.label_suffix = 'gtFine_labelIds'
+      else:
+        # The 'train extra' split only has coarse labels unlike train and val.
+        # Therefore, for consistency across splits, we also enable coarse labels
+        # using the train_extra_split flag.
+        self.zip_root['segmentation_labels'] = ('gtCoarse.zip', 'gtCoarse')
+        self.zip_root['segmentation_labels/extra'] = \
+          ('gtCoarse.zip', 'gtCoarse')
+        self.label_suffix = 'gtCoarse_labelIds'
+
+    if self.disparity_maps:
+      self.zip_root['disparity_maps'] =\
+        ('disparity_trainvaltest.zip', 'disparity')
+      if self.train_extra_split:
+        self.zip_root['disparity_maps/extra'] =\
+          ('disparity_trainextra.zip', 'disparity')
+        self.ignored_ids.add('troisdorf_000000_000073') # No disparity for this file
+
+
+class Cityscapes(tfds.core.GeneratorBasedBuilder):
+  '''Base class for Cityscapes datasets'''
+
+  BUILDER_CONFIGS = [
+      CityscapesConfig(
+          name='semantic_segmentation',
+          description='Cityscapes semantic segmentation dataset.',
+          version="1.0.0",
+          right_images=False,
+          segmentation_labels=True,
+          disparity_maps=False,
+          train_extra_split=False,
+      ),
+      CityscapesConfig(
+          name='semantic_segmentation_extra',
+          description='Cityscapes semantic segmentation dataset with train_extra split and coarse labels.', # pylint: disable=line-too-long
+          version="1.0.0",
+          right_images=False,
+          segmentation_labels=True,
+          disparity_maps=False,
+          train_extra_split=True,
+      ),
+      CityscapesConfig(
+          name='stereo_disparity',
+          description='Cityscapes stereo image and disparity maps dataset.',
+          version="1.0.0",
+          right_images=True,
+          segmentation_labels=False,
+          disparity_maps=True,
+          train_extra_split=False,
+      ),
+      CityscapesConfig(
+          name='stereo_disparity_extra',
+          description='Cityscapes stereo image and disparity maps dataset with train_extra split.', # pylint: disable=line-too-long
+          version="1.0.0",
+          right_images=True,
+          segmentation_labels=False,
+          disparity_maps=True,
+          train_extra_split=True,
+      ),
+  ]
+
+  VERSION = tfds.core.Version('1.0.0')
+
+  def _info(self):
+    # Enable features as necessary
+    features = {}
+    features['image_id'] = tfds.features.Text()
+    features['image_left'] =\
+      tfds.features.Image(shape=(1024, 2048, 3), encoding_format='png')
+
+    if self.builder_config.right_images:
+      features['image_right'] =\
+        tfds.features.Image(shape=(1024, 2048, 3), encoding_format='png')
+
+    if self.builder_config.segmentation_labels:
+      features['segmentation_label'] =\
+        tfds.features.Image(shape=(1024, 2048, 1), encoding_format='png')
+
+    if self.builder_config.disparity_maps:
+      features['disparity_map'] =\
+        tfds.features.Image(shape=(1024, 2048, 1), encoding_format='png')
+
+    return tfds.core.DatasetInfo(
+        builder=self,
+        description=(_DESCRIPTION),
+        features=tfds.features.FeaturesDict(features),
+        urls=['https://www.cityscapes-dataset.com', 'https://github.com/mcordts/cityscapesScripts'],
+        citation=_CITATION,
+    )
+
+  def _split_generators(self, dl_manager):
+    paths = {}
+    for split, (zip_file, zip_root) in self.builder_config.zip_root.items():
+      paths[split] = os.path.join(dl_manager.manual_dir, zip_file)
+
+    if any(not os.path.exists(z) for z in paths.values()):
+      msg = 'You must download the dataset files manually and place them in: '
+      msg += ', '.join(paths.values())
+      raise AssertionError(msg)
+
+    for split, (_, zip_root) in self.builder_config.zip_root.items():
+      paths[split] = os.path.join(dl_manager.extract(paths[split]), zip_root)
+
+    splits = [
+        tfds.core.SplitGenerator(
+            name=tfds.Split.TRAIN,
+            gen_kwargs={
+                feat_dir: os.path.join(path, 'train')
+                for feat_dir, path in paths.items()
+                if not feat_dir.endswith('/extra')
+            },
+        ),
+        tfds.core.SplitGenerator(
+            name=tfds.Split.VALIDATION,
+            gen_kwargs={
+                feat_dir: os.path.join(path, 'val')
+                for feat_dir, path in paths.items()
+                if not feat_dir.endswith('/extra')
+            },
+        ),
+    ]
+
+    # Test split does not exist in coarse dataset
+    if not self.builder_config.train_extra_split:
+      splits.append(tfds.core.SplitGenerator(
+          name=tfds.Split.TEST,
+          gen_kwargs={
+              feat_dir: os.path.join(path, 'test')
+              for feat_dir, path in paths.items()
+              if not feat_dir.endswith('/extra')
+          },
+      ))
+    else:
+      splits.append(tfds.core.SplitGenerator(
+          name='train_extra',
+          gen_kwargs={
+              feat_dir.replace('/extra', ''): os.path.join(path, 'train_extra')
+              for feat_dir, path in paths.items()
+              if feat_dir.endswith('/extra')
+          },
+      ))
+    return splits
+
+  def _generate_examples(self, **paths):
+    left_imgs_root = paths['images_left']
+    for city_id in tf.io.gfile.listdir(left_imgs_root):
+      paths_city_root = {feat_dir: os.path.join(path, city_id)
+                         for feat_dir, path in paths.items()}
+
+      left_city_root = paths_city_root['images_left']
+      for left_img in tf.io.gfile.listdir(left_city_root):
+        left_img_path = os.path.join(left_city_root, left_img)
+        image_id = _get_left_image_id(left_img)
+
+        if image_id in self.builder_config.ignored_ids:
+          continue
+
+        features = {
+            'image_id': image_id,
+            'image_left': left_img_path
+        }
+
+        if self.builder_config.right_images:
+          features['image_right'] = os.path.join(
+              paths_city_root['images_right'], f'{image_id}_rightImg8bit.png')
+
+        if self.builder_config.segmentation_labels:
+          features['segmentation_label'] = os.path.join(
+              paths_city_root['segmentation_labels'],
+              f'{image_id}_{self.builder_config.label_suffix}.png')
+
+        if self.builder_config.disparity_maps:
+          features['disparity_map'] = os.path.join(
+              paths_city_root['disparity_maps'], f'{image_id}_disparity.png')
+
+        yield image_id, features
+
+# Helper functions
+
+LEFT_IMAGE_FILE_RE = re.compile(r'([a-z\-]+)_(\d+)_(\d+)_leftImg8bit\.png')
+
+def _get_left_image_id(left_image):
+  '''Returns the id of an image file. Used to associate an image file
+  with its corresponding label.
+  Example:
+    'bonn_000001_000019_leftImg8bit' -> 'bonn_000001_000019'
+  '''
+  match = LEFT_IMAGE_FILE_RE.match(left_image)
+  return f'{match.group(1)}_{match.group(2)}_{match.group(3)}'

tensorflow_datasets/image/cityscapes_test.py

@@ -0,0 +1,53 @@
+
+'''Tests for Cityscapes dataset module.'''
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow_datasets import testing
+from tensorflow_datasets.image import cityscapes
+
+# TODO add tests for features and files per configuration
+class CityscapesSegmentationTest(testing.DatasetBuilderTestCase):
+  DATASET_CLASS = cityscapes.Cityscapes
+  BUILDER_CONFIG_NAMES_TO_TEST = ['semantic_segmentation']
+  SPLITS = {
+      'train': 3,
+      'validation': 1,
+      'test': 2,
+  }
+
+
+class CityscapesSegmentationExtraTest(testing.DatasetBuilderTestCase):
+  DATASET_CLASS = cityscapes.Cityscapes
+  BUILDER_CONFIG_NAMES_TO_TEST = ['semantic_segmentation_extra']
+  SPLITS = {
+      'train': 3,
+      'train_extra': 4,
+      'validation': 1,
+  }
+
+
+class CityscapesStereoDisparityTest(testing.DatasetBuilderTestCase):
+  DATASET_CLASS = cityscapes.Cityscapes
+  BUILDER_CONFIG_NAMES_TO_TEST = ['stereo_disparity']
+  SPLITS = {
+      'train': 3,
+      'validation': 1,
+      'test': 2,
+  }
+
+
+class CityscapesStereoDisparityExtraTest(testing.DatasetBuilderTestCase):
+  DATASET_CLASS = cityscapes.Cityscapes
+  BUILDER_CONFIG_NAMES_TO_TEST = ['stereo_disparity_extra']
+  SPLITS = {
+      'train': 3,
+      'train_extra': 4,
+      'validation': 1,
+  }
+
+
+if __name__ == '__main__':
+  testing.test_main()