vp_suite.datasets.kitti_raw

class KITTIRawDataset(split, **dataset_kwargs)

Bases: vp_suite.base.base_dataset.VPDataset

Dataset class for the “raw data” regime of the “KITTI Vision Benchmark Suite”, as described in “Vision meets Robotics: The KITTI Dataset” by Geiger et al. (http://www.cvlibs.net/publications/Geiger2013IJRR.pdf).

Each sequence shows a short clip of ‘driving around the mid-size city of Karlsruhe, in rural areas and on highways’.

ACTION_SIZE: int = 0

The size of the action vector per frame (If the dataset provides no actions, this value is 0).

AVAILABLE_CAMERAS = ['image_00', 'image_01', 'image_02', 'image_03']

[greyscale_left, greyscale_right, color_left, color_right].

Type

Available cameras

DATASET_FRAME_SHAPE: (<class 'int'>, <class 'int'>, <class 'int'>) = (375, 1242, 3)

Shape of a single frame in the dataset (height, width, channels).

DEFAULT_DATA_DIR: pathlib.Path = PosixPath('/home/runner/work/vp-suite/vp-suite/vp-suite-data/data/kitti_raw')

The default save location of the dataset files.

FPS = 10

Frames per Second.

IS_DOWNLOADABLE: str = 'With Registered Account'

A string identifying whether the dataset can be (freely) downloaded.

MIN_SEQ_LEN: int = 994

Minimum number of frames across all sequences (6349 in longest).

NAME: str = 'KITTI raw'

The dataset’s name.

REFERENCE: str = 'http://www.cvlibs.net/datasets/kitti/raw_data.php'

The reference (publication) where the original dataset is introduced.

VALID_SPLITS = ['train', 'val', 'test']

The valid arguments for specifying splits.

__init__(split, **dataset_kwargs)

Initializes the dataset loader by determining its split and extracting and processing all dataset attributes from the parameters given in dataset_kwargs.

Parameters

• split (str) – The dataset’s split identifier (i.e. whether it’s a training/validation/test dataset)

• **dataset_kwargs (Any) – Optional dataset arguments for image transformation, value_range, splitting etc.

camera = 'image_02'

Chosen camera, can be set to any of the AVAILABLE_CAMERAS.

classmethod download_and_prepare_dataset()

Downloads the specific dataset, prepares it for the video prediction task (if needed) and stores it in a default location in the ‘data/’ folder. Implemented by the derived dataset classes.

train_to_val_ratio: float = 0.9

The ratio of files that will be training data (rest will be validation data). For bigger datasets, this ratio can be set closer to 1.

trainval_test_seed = 1234

The random seed used to separate training/validation and testing data.

trainval_to_test_ratio = 0.8

The ratio of files that will be training/validation data (rest will be test data).