# coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors. # # 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. # Lint as: python3 """Lost and Found Road Hazard Dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from os import path import re import tensorflow.compat.v2 as tf import tensorflow_datasets.public_api as tfds _CITATION = """ @inproceedings{pinggera2016lost, title={Lost and found: detecting small road hazards for self-driving vehicles}, author={Pinggera, Peter and Ramos, Sebastian and Gehrig, Stefan and Franke, Uwe and Rother, Carsten and Mester, Rudolf}, booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year={2016} } """ _DESCRIPTION = """ The LostAndFound Dataset addresses the problem of detecting unexpected small obstacles on the road often caused by lost cargo. The dataset comprises 112 stereo video sequences with 2104 annotated frames (picking roughly every tenth frame from the recorded data). The dataset is designed analogous to the 'Cityscapes' dataset. The datset provides: - stereo image pairs in either 8 or 16 bit color resolution - precomputed disparity maps - coarse semantic labels for objects and street Descriptions of the labels are given here: http://www.6d-vision.com/laf_table.pdf """ class LostAndFoundConfig(tfds.core.BuilderConfig): """BuilderConfig for 'Lost and Found'. Args: right_images (bool): Enables right images for stereo image tasks. segmentation_labels (bool): Enables image segmentation labels. instance_ids (bool): Enables instance-id labels. disparity_maps (bool): Enables disparity maps. use_16bit (bool): Loads 16 bit (rgb) images instead of 8bit. """ def __init__(self, right_images=False, segmentation_labels=False, instance_ids=False, disparity_maps=False, use_16bit=False, **kwargs): super(LostAndFoundConfig, self).__init__(**kwargs) self.features = ['image_left'] if right_images: self.features.append('image_right') if segmentation_labels: self.features.append('segmentation_label') if instance_ids: self.features.append('instance_id') if disparity_maps: self.features.append('disparity_map') self.left_image_string = 'leftImg{}bit'.format('16' if use_16bit else '8') self.right_image_string = 'rightImg{}bit'.format('16' if use_16bit else '8') class LostAndFound(tfds.core.GeneratorBasedBuilder): """Lost and Found Road Hazard Dataset.""" VERSION = tfds.core.Version('1.0.0') BUILDER_CONFIGS = [ LostAndFoundConfig( name='semantic_segmentation', description='Lost and Found semantic segmentation dataset.', version='1.0.0', right_images=False, segmentation_labels=True, instance_ids=False, disparity_maps=False, use_16bit=False, ), LostAndFoundConfig( name='stereo_disparity', description='Lost and Found stereo images and disparity maps.', version='1.0.0', right_images=True, segmentation_labels=False, instance_ids=False, disparity_maps=True, use_16bit=False, ), LostAndFoundConfig( name='full', description='Full Lost and Found dataset.', version='1.0.0', right_images=True, segmentation_labels=True, instance_ids=True, disparity_maps=True, use_16bit=False, ), LostAndFoundConfig( name='full_16bit', description='Full Lost and Found dataset.', version='1.0.0', right_images=True, segmentation_labels=True, instance_ids=True, disparity_maps=True, use_16bit=True, ) ] def _info(self): possible_features = { 'image_left': tfds.features.Image(shape=(1024, 2048, 3), encoding_format='png'), 'image_right': tfds.features.Image(shape=(1024, 2048, 3), encoding_format='png'), 'segmentation_label': tfds.features.Image(shape=(1024, 2048, 1), encoding_format='png'), 'instance_id': tfds.features.Image(shape=(1024, 2048, 1), encoding_format='png'), 'disparity_map': tfds.features.Image(shape=(1024, 2048, 1), encoding_format='png') } features = { feat: possible_features[feat] for feat in self.builder_config.features } features['image_id'] = tfds.features.Text() features = tfds.features.FeaturesDict(features) return tfds.core.DatasetInfo( builder=self, # This is the description that will appear on the datasets page. description=_DESCRIPTION, # tfds.features.FeatureConnectors features=features, # Homepage of the dataset for documentation homepage='http://www.6d-vision.com/lostandfounddataset', citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" base_url = 'http://www.dhbw-stuttgart.de/~sgehrig/lostAndFoundDataset/{}.zip' # For each feature, this is the name of the zipfile and # root-directory in the archive zip_file_names = { 'image_left': self.builder_config.left_image_string, 'image_right': self.builder_config.right_image_string, 'segmentation_label': 'gtCoarse', 'instance_id': 'gtCoarse', 'disparity_map': 'disparity' } download_urls = { feat: base_url.format(zip_file_names[feat]) for feat in self.builder_config.features } # Split download and extract in two functions such that mock-data can # replace the result of the download function and is still used as input to # extract. Therefore, fake_data can be compressed zip archives. dl_paths = dl_manager.extract(dl_manager.download(download_urls)) return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ feat: path.join(dl_paths[feat], zip_file_names[feat], 'train') for feat in self.builder_config.features }, ), tfds.core.SplitGenerator( name=tfds.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ feat: path.join(dl_paths[feat], zip_file_names[feat], 'test') for feat in self.builder_config.features }, ) ] def _generate_examples(self, **paths): """Yields examples.""" # different file-suffixes dependent on the feature to load file_suffix = { 'image_left': self.builder_config.left_image_string, 'image_right': self.builder_config.right_image_string, 'segmentation_label': 'gtCoarse_labelIds', 'instance_id': 'gtCoarse_instanceIds', 'disparity_map': 'disparity' } for scene_id in tf.io.gfile.listdir(paths['image_left']): paths_city_root = { feat: path.join(feat_dir, scene_id) for feat, feat_dir in paths.items() } left_city_root = paths_city_root['image_left'] for left_img in tf.io.gfile.listdir(left_city_root): image_id = _get_id_from_left_image(left_img) features = { feat: path.join(paths_city_root[feat], '{}_{}.png'.format(image_id, file_suffix[feat])) for feat in paths } features['image_id'] = image_id yield image_id, features # Helper functions LEFT_IMAGE_FILE_RE = re.compile(r'(.+)_leftImg(?:8|16)bit\.png') def _get_id_from_left_image(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' Args: left_image: left image file name. Returns: id of the image. """ return LEFT_IMAGE_FILE_RE.match(left_image).group(1)