fiftyone.utils.kitti#

Utilities for working with datasets in KITTI format.

Copyright 2017-2025, Voxel51, Inc.
voxel51.com

Classes:

`KITTIDetectionDatasetImporter`([dataset_dir, ...])	Importer for KITTI detection datasets stored on disk.
`KITTIDetectionDatasetExporter`([export_dir, ...])	Exporter that writes KITTI detection datasets to disk.
`KITTIAnnotationWriter`()	Class for writing annotations in KITTI detection format.

Functions:

`load_kitti_detection_annotations`(txt_path, ...)	Loads the KITTI detection annotations from the given TXT file.
`download_kitti_multiview_dataset`(dataset_dir)	Downloads and prepares the multiview KITTI dataset.
`download_kitti_detection_dataset`(dataset_dir)	Downloads the KITTI object detection dataset from the web.

class fiftyone.utils.kitti.KITTIDetectionDatasetImporter(dataset_dir=None, data_path=None, labels_path=None, include_all_data=False, extra_attrs=True, shuffle=False, seed=None, max_samples=None)#

Bases: LabeledImageDatasetImporter, ImportPathsMixin

Importer for KITTI detection datasets stored on disk.

See this page for format details.

Parameters:

dataset_dir (None) – the dataset directory. If omitted, data_path and/or labels_path must be provided
data_path (None) –
an optional parameter that enables explicit control over the location of the media. Can be any of the following:
- a folder name like "data" or "data/" specifying a subfolder of dataset_dir where the media files reside
- an absolute directory path where the media files reside. In this case, the dataset_dir has no effect on the location of the data
- a filename like "data.json" specifying the filename of the JSON data manifest file in dataset_dir
- an absolute filepath specifying the location of the JSON data manifest. In this case, dataset_dir has no effect on the location of the data
- a dict mapping filenames to absolute filepaths
If None, this parameter will default to whichever of data/ or data.json exists in the dataset directory
labels_path (None) –
an optional parameter that enables explicit control over the location of the labels. Can be any of the following:
- a folder name like "labels" or "labels/" specifying the location of the labels in dataset_dir
- an absolute folder path to the labels. In this case, dataset_dir has no effect on the location of the labels
If None, the parameter will default to labels/
include_all_data (False) – whether to generate samples for all images in the data directory (True) rather than only creating samples for images with label entries (False)
extra_attrs (True) –
whether to load extra annotation attributes onto the imported labels. Supported values are:
- True: load all extra attributes found
- False: do not load extra attributes
- a name or list of names of specific attributes to load
shuffle (False) – whether to randomly shuffle the order in which the samples are imported
seed (None) – a random seed to use when shuffling
max_samples (None) – a maximum number of samples to import. By default, all samples are imported

Attributes:

`has_dataset_info`	Whether this importer produces a dataset info dictionary.
`has_image_metadata`	Whether this importer produces `fiftyone.core.metadata.ImageMetadata` instances for each image.
`label_cls`	The `fiftyone.core.labels.Label` class(es) returned by this importer.

Methods:

`setup`()	Performs any necessary setup before importing the first sample in the dataset.
`close`(*args)	Performs any necessary actions after the last sample has been imported.
`get_dataset_info`()	Returns the dataset info for the dataset.

property has_dataset_info#: Whether this importer produces a dataset info dictionary.

property has_image_metadata#: Whether this importer produces fiftyone.core.metadata.ImageMetadata instances for each image.

property label_cls#

The fiftyone.core.labels.Label class(es) returned by this importer.

This can be any of the following:

a fiftyone.core.labels.Label class. In this case, the importer is guaranteed to return labels of this type
a list or tuple of fiftyone.core.labels.Label classes. In this case, the importer can produce a single label field of any of these types
a dict mapping keys to fiftyone.core.labels.Label classes. In this case, the importer will return label dictionaries with keys and value-types specified by this dictionary. Not all keys need be present in the imported labels
None. In this case, the importer makes no guarantees about the labels that it may return

setup()#

Performs any necessary setup before importing the first sample in the dataset.

This method is called when the importer’s context manager interface is entered, DatasetImporter.__enter__().

close(*args)#

Performs any necessary actions after the last sample has been imported.

This method is called when the importer’s context manager interface is exited, DatasetImporter.__exit__().

Parameters:: *args – the arguments to DatasetImporter.__exit__()

get_dataset_info()#

Returns the dataset info for the dataset.

By convention, this method should be called after all samples in the dataset have been imported.

Returns:: a dict of dataset info

class fiftyone.utils.kitti.KITTIDetectionDatasetExporter(export_dir=None, data_path=None, labels_path=None, export_media=None, rel_dir=None, image_format=None)#

Bases: LabeledImageDatasetExporter, ExportPathsMixin

Exporter that writes KITTI detection datasets to disk.

See this page for format details.

Parameters:

export_dir (None) – the directory to write the export. This has no effect if data_path and labels_path are absolute paths
data_path (None) –
an optional parameter that enables explicit control over the location of the exported media. Can be any of the following:
- a folder name like "data" or "data/" specifying a subfolder of export_dir in which to export the media
- an absolute directory path in which to export the media. In this case, the export_dir has no effect on the location of the data
- a JSON filename like "data.json" specifying the filename of the manifest file in export_dir generated when export_media is "manifest"
- an absolute filepath specifying the location to write the JSON manifest file when export_media is "manifest". In this case, export_dir has no effect on the location of the data
If None, the default value of this parameter will be chosen based on the value of the export_media parameter
labels_path (None) –
an optional parameter that enables explicit control over the location of the exported labels. Can be any of the following:
- a folder name like "labels" or "labels/" specifying the location in export_dir in which to export the labels
- an absolute folder path to which to export the labels. In this case, the export_dir has no effect on the location of the labels
If None, the labels will be exported into export_dir using the default folder name
export_media (None) –
controls how to export the raw media. The supported values are:
- True: copy all media files into the output directory
- False: don’t export media
- "move": move all media files into the output directory
- "symlink": create symlinks to the media files in the output directory
- "manifest": create a data.json in the output directory that maps UUIDs used in the labels files to the filepaths of the source media, rather than exporting the actual media
If None, the default value of this parameter will be chosen based on the value of the data_path parameter
rel_dir (None) – an optional relative directory to strip from each input filepath to generate a unique identifier for each image. When exporting media, this identifier is joined with data_path and labels_path to generate output paths for each exported image and labels file. This argument allows for populating nested subdirectories that match the shape of the input paths. The path is converted to an absolute path (if necessary) via fiftyone.core.storage.normalize_path()
image_format (None) – the image format to use when writing in-memory images to disk. By default, fiftyone.config.default_image_ext is used

Attributes:

`requires_image_metadata`	Whether this exporter requires `fiftyone.core.metadata.ImageMetadata` instances for each sample being exported.
`label_cls`	The `fiftyone.core.labels.Label` class(es) exported by this exporter.

Methods:

`setup`()	Performs any necessary setup before exporting the first sample in the dataset.
`export_sample`(image_or_path, detections[, ...])	Exports the given sample to the dataset.
`close`(*args)	Performs any necessary actions after the last sample has been exported.
`log_collection`(sample_collection)	Logs any relevant information about the `fiftyone.core.collections.SampleCollection` whose samples will be exported.

property requires_image_metadata#: Whether this exporter requires fiftyone.core.metadata.ImageMetadata instances for each sample being exported.

property label_cls#

The fiftyone.core.labels.Label class(es) exported by this exporter.

This can be any of the following:

a fiftyone.core.labels.Label class. In this case, the exporter directly exports labels of this type
a list or tuple of fiftyone.core.labels.Label classes. In this case, the exporter can export a single label field of any of these types
a dict mapping keys to fiftyone.core.labels.Label classes. In this case, the exporter can handle label dictionaries with value-types specified by this dictionary. Not all keys need be present in the exported label dicts
None. In this case, the exporter makes no guarantees about the labels that it can export

setup()#

Performs any necessary setup before exporting the first sample in the dataset.

This method is called when the exporter’s context manager interface is entered, DatasetExporter.__enter__().

export_sample(image_or_path, detections, metadata=None)#

Exports the given sample to the dataset.

Parameters:

image_or_path – an image or the path to the image on disk
label – an instance of label_cls(), or a dictionary mapping field names to fiftyone.core.labels.Label instances, or None if the sample is unlabeled
metadata (None) – a fiftyone.core.metadata.ImageMetadata instance for the sample. Only required when requires_image_metadata() is True

close(*args)#

Performs any necessary actions after the last sample has been exported.

This method is called when the exporter’s context manager interface is exited, DatasetExporter.__exit__().

Parameters:: *args – the arguments to DatasetExporter.__exit__()

log_collection(sample_collection)#

Logs any relevant information about the fiftyone.core.collections.SampleCollection whose samples will be exported.

Subclasses can optionally implement this method if their export format can record information such as the fiftyone.core.collections.SampleCollection.info() of the collection being exported.

By convention, this method must be optional; i.e., if it is not called before the first call to export_sample(), then the exporter must make do without any information about the fiftyone.core.collections.SampleCollection (which may not be available, for example, if the samples being exported are not stored in a collection).

Parameters:: sample_collection – the fiftyone.core.collections.SampleCollection whose samples will be exported

class fiftyone.utils.kitti.KITTIAnnotationWriter#

Bases: object

Class for writing annotations in KITTI detection format.

See this page for format details.

Methods:

write(detections, metadata, txt_path)

Writes the detections to disk.

write(detections, metadata, txt_path)#

Writes the detections to disk.

Parameters:

detections – a fiftyone.core.labels.Detections instance
metadata – a fiftyone.core.metadata.ImageMetadata instance
txt_path – the path to write the annotation TXT file

fiftyone.utils.kitti.load_kitti_detection_annotations(txt_path, frame_size, extra_attrs=True)#

Loads the KITTI detection annotations from the given TXT file.

See this page for format details.

Parameters:

txt_path – the path to the annotations TXT file
frame_size – the (width, height) of the image
extra_attrs (True) –
whether to load extra annotation attributes onto the imported labels. Supported values are:
- True: load all extra attributes found
- False: do not load extra attributes
- a name or list of names of specific attributes to load

Returns:

a fiftyone.core.detections.Detections instance

fiftyone.utils.kitti.download_kitti_multiview_dataset(dataset_dir, splits=None, scratch_dir=None, overwrite=False, cleanup=False, num_workers=None)#

Downloads and prepares the multiview KITTI dataset.

The dataset will be organized on disk in as follows, with each split stored in FiftyOneDataset format:

dataset_dir/
    train/
        labels/
            000000.txt
            000001.txt
            ...
        calib/
            000000.txt
            000001.txt
            ...
        left/
            000000.png
            000001.png
            ...
        right/
            000000.png
            000001.png
            ...
        velodyne/
            000000.bin
            000001.bin
            ...
        pcd/
            000000.pcd
            000001.pcd
            ...
        metadata.json
        samples.json
    test/
        ...

Parameters:

dataset_dir – the directory in which to construct the dataset
splits (None) – the split or list of splits to download. Supported values are ("train", "test")
scratch_dir (None) – a scratch directory to use to download any necessary temporary files
overwrite (False) – whether to redownload/regenerate files if they already exist
cleanup (False) – whether to delete the downloaded zips and scratch directory
num_workers (None) – a suggested number of processes to use when converting LiDAR to PCD

fiftyone.utils.kitti.download_kitti_detection_dataset(dataset_dir, splits=None, scratch_dir=None, overwrite=False, cleanup=False)#

Downloads the KITTI object detection dataset from the web.

The dataset will be organized on disk in as follows:

dataset_dir/
    train/
        data/
            000000.png
            000001.png
            ...
        labels/
            000000.txt
            000001.txt
            ...
    test/
        data/
            000000.png
            000001.png
            ...

Parameters:

dataset_dir – the directory in which to construct the dataset
splits (None) – the split or list of splits to download. Supported values are ("train", "test")
scratch_dir (None) – a scratch directory to use to download any necessary temporary files
overwrite (False) – whether to redownload the zips if they already exist
cleanup (False) – whether to delete the downloaded zips and scratch directory