Using Sample Parsers ¶

This page describes how to use the SampleParser interface to add samples to your FiftyOne datasets.

The SampleParser interface provides native support for loading samples in a variety of common formats, and it can be easily extended to import datasets in custom formats, allowing you to automate the dataset loading process.

Warning

Using the SampleParser interface is not recommended. You’ll likely prefer adding samples manually or using dataset importers to load data into FiftyOne.

Adding samples to datasets ¶

Basic recipe ¶

The basic recipe for using the SampleParser interface to add samples to a Dataset is to create a parser of the appropriate type and then pass the parser along with an iterable of samples to the appropriate Dataset method.

Note

A typical use case is that samples in the above recipe is a torch.utils.data.Dataset or an iterable generated by tf.data.Dataset.as_numpy_iterator().

Adding unlabeled images ¶

FiftyOne provides a few convenient ways to add unlabeled images in FiftyOne datasets.

Adding a directory of images ¶

Use Dataset.add_images_dir() to add a directory of images to a dataset:

import fiftyone as fo

dataset = fo.Dataset()

# A directory of images to add
images_dir = "/path/to/images"

# Add images to the dataset
dataset.add_images_dir(images_dir)

Adding a glob pattern of images ¶

Use Dataset.add_images_patt() to add a glob pattern of images to a dataset:

import fiftyone as fo

dataset = fo.Dataset()

# A glob pattern of images to add
images_patt = "/path/to/images/*.jpg"

# Add images to the dataset
dataset.add_images_patt(images_patt)

Adding images using a SampleParser ¶

Use Dataset.add_images() to add an iterable of unlabeled images that can be parsed via a specified UnlabeledImageSampleParser to a dataset.

Example

FiftyOne provides an ImageSampleParser that handles samples that contain either an image that can be converted to numpy format via np.asarray() of the path to an image on disk.

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of images or image paths and the UnlabeledImageSampleParser
# to use to parse them
samples = ...
sample_parser = foud.ImageSampleParser

# Add images to the dataset
dataset.add_images(samples, sample_parser)

Adding labeled images ¶

Use Dataset.add_labeled_images() to add an iterable of samples that can be parsed via a specified LabeledImageSampleParser to a dataset.

Example

FiftyOne provides an ImageClassificationSampleParser that handles samples that contain (image_or_path, target) tuples, where:

• image_or_path is either an image that can be converted to numpy format via np.asarray() or the path to an image on disk

• target is either a class ID or a label string

The snippet below adds an iterable of image classification data in the above format to a dataset:

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of `(image_or_path, target)` tuples and the
# LabeledImageSampleParser to use to parse them
samples = ...
sample_parser = foud.ImageClassificationSampleParser

# Add labeled images to the dataset
dataset.add_labeled_images(samples, sample_parser)

Adding unlabeled videos ¶

FiftyOne provides a few convenient ways to add unlabeled videos in FiftyOne datasets.

Adding a directory of videos ¶

Use Dataset.add_videos_dir() to add a directory of videos to a dataset:

import fiftyone as fo

dataset = fo.Dataset()

# A directory of videos to add
videos_dir = "/path/to/videos"

# Add videos to the dataset
dataset.add_videos_dir(videos_dir)

Adding a glob pattern of videos ¶

Use Dataset.add_videos_patt() to add a glob pattern of videos to a dataset:

import fiftyone as fo

dataset = fo.Dataset()

# A glob pattern of videos to add
videos_patt = "/path/to/videos/*.mp4"

# Add videos to the dataset
dataset.add_videos_patt(videos_patt)

Adding videos using a SampleParser ¶

Use Dataset.add_videos() to add an iterable of unlabeled videos that can be parsed via a specified UnlabeledVideoSampleParser to a dataset.

Example

FiftyOne provides a VideoSampleParser that handles samples that directly contain the path to the video on disk.

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of video paths and the UnlabeledVideoSampleParser to use to
# parse them
samples = ...
sample_parser = foud.VideoSampleParser

# Add videos to the dataset
dataset.add_videos(samples, sample_parser)

Adding labeled videos ¶

Use Dataset.add_labeled_videos() to add an iterable of samples that can be parsed via a specified LabeledVideoSampleParser to a dataset.

Example

FiftyOne provides a VideoLabelsSampleParser that handles samples that contain (video_path, video_labels_or_path) tuples, where:

• video_path is the path to a video on disk

• video_labels_or_path is an eta.core.video.VideoLabels instance, a serialized dict representation of one, or the path to one on disk

The snippet below adds an iterable of labeled video samples in the above format to a dataset:

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of `(video_path, video_labels_or_path)` tuples and the
# LabeledVideoSampleParser to use to parse them
samples = ...
sample_parser = foud.VideoLabelsSampleParser

# Add labeled videos to the dataset
dataset.add_labeled_videos(samples, sample_parser)

Ingesting samples into datasets ¶

Creating FiftyOne datasets typically does not create copies of the source media, since Sample instances store the filepath to the media, not the media itself.

However, in certain circumstances, such as loading data from binary sources like TFRecords or creating a FiftyOne dataset from unorganized and/or temporary files on disk, it can be desirable to ingest the raw media for each sample into a common backing location.

FiftyOne provides support for ingesting samples and their underlying source media in both common formats and can be extended to import datasets in custom formats.

Basic recipe ¶

The basic recipe for ingesting samples and their source media into a Dataset is to create a SampleParser of the appropriate type of sample that you’re loading and then pass the parser along with an iterable of samples to the appropriate Dataset method.

Note

A typical use case is that samples in the above recipe is a torch.utils.data.Dataset or an iterable generated by tf.data.Dataset.as_numpy_iterator().

Ingesting unlabeled images ¶

Use Dataset.ingest_images() to ingest an iterable of unlabeled images that can be parsed via a specified UnlabeledImageSampleParser into a dataset.

The has_image_path property of the parser may either be True or False. If the parser provides image paths, the source images will be directly copied from their source locations into the backing directory for the dataset; otherwise, the image will be read in-memory via get_image() and then written to the backing directory.

Example

FiftyOne provides an ImageSampleParser that handles samples that contain either an image that can be converted to numpy format via np.asarray() of the path to an image on disk.

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of images or image paths and the UnlabeledImageSampleParser
# to use to parse them
samples = ...
sample_parser = foud.ImageSampleParser

# A directory in which the images will be written; If `None`, a default directory
# based on the dataset's `name` will be used
dataset_dir = ...

# Ingest the images into the dataset
# The source images are copied into `dataset_dir`
dataset.ingest_images(samples, sample_parser, dataset_dir=dataset_dir)

Ingesting labeled images ¶

Use Dataset.ingest_labeled_images() to ingest an iterable of samples that can be parsed via a specified LabeledImageSampleParser into a dataset.

The has_image_path property of the parser may either be True or False. If the parser provides image paths, the source images will be directly copied from their source locations into the backing directory for the dataset; otherwise, the image will be read in-memory via get_image() and then written to the backing directory.

Example

FiftyOne provides an ImageClassificationSampleParser that handles samples that contain (image_or_path, target) tuples, where:

• image_or_path is either an image that can be converted to numpy format via np.asarray() or the path to an image on disk

• target is either a class ID or a label string

The snippet below ingests an iterable of image classification data in the above format intoa a FiftyOne dataset:

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of `(image_or_path, target)` tuples and the
# LabeledImageSampleParser to use to parse them
samples = ...
sample_parser = foud.ImageClassificationSampleParser  # for example

# A directory in which the images will be written; If `None`, a default directory
# based on the dataset's `name` will be used
dataset_dir = ...

# Ingest the labeled images into the dataset
# The source images are copied into `dataset_dir`
dataset.ingest_labeled_images(samples, sample_parser, dataset_dir=dataset_dir)

Ingesting unlabeled videos ¶

Use Dataset.ingest_videos() to ingest an iterable of unlabeled videos that can be parsed via a specified UnlabeledVideoSampleParser into a dataset.

The source videos will be directly copied from their source locations into the backing directory for the dataset.

Example

FiftyOne provides a VideoSampleParser that handles samples that directly contain the paths to videos on disk.

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of videos or video paths and the UnlabeledVideoSampleParser
# to use to parse them
samples = ...
sample_parser = foud.VideoSampleParser

# A directory in which the videos will be written; If `None`, a default directory
# based on the dataset's `name` will be used
dataset_dir = ...

# Ingest the videos into the dataset
# The source videos are copied into `dataset_dir`
dataset.ingest_videos(samples, sample_parser, dataset_dir=dataset_dir)

Ingesting labeled videos ¶

Use Dataset.ingest_labeled_videos() to ingest an iterable of samples that can be parsed via a specified LabeledVideoSampleParser into a dataset.

The source videos will be directly copied from their source locations into the backing directory for the dataset.

Example

FiftyOne provides a VideoLabelsSampleParser that handles samples that contain (video_path, video_labels_or_path) tuples, where:

• video_path is the path to a video on disk

• video_labels_or_path is an eta.core.video.VideoLabels instance, a serialized dict representation of one, or the path to one on disk

The snippet below ingests an iterable of labeled videos in the above format into a FiftyOne dataset:

import fiftyone as fo
import fiftyone.utils.data as foud

dataset = fo.Dataset()

# An iterable of `(video_path, video_labels_or_path)` tuples and the
# LabeledVideoSampleParser to use to parse them
samples = ...
sample_parser = foud.VideoLabelsSampleParser  # for example

# A directory in which the videos will be written; If `None`, a default directory
# based on the dataset's `name` will be used
dataset_dir = ...

# Ingest the labeled videos into the dataset
# The source videos are copied into `dataset_dir`
dataset.ingest_labeled_videos(samples, sample_parser, dataset_dir=dataset_dir)

Built-in SampleParser classes ¶

The table below lists the common data formats for which FiftyOne provides built-in SampleParser implementations. You can also write a custom SampleParser to automate the parsing of samples in your own custom data format.

You can use a SampleParser to add samples to datasets and ingest samples into datasets.

SampleParser	Description
ImageSampleParser	A sample parser that parses raw image samples.
VideoSampleParser	A sample parser that parses raw video samples.
ImageClassificationSampleParser	Generic parser for image classification samples whose labels are represented as Classification instances.
ImageDetectionSampleParser	Generic parser for image detection samples whose labels are represented as Detections instances.
ImageLabelsSampleParser	Generic parser for image detection samples whose labels are stored in ETA ImageLabels format.
FiftyOneImageClassificationSampleParser	Parser for samples in FiftyOne image classification datasets. See FiftyOneImageClassificationDataset for format details.
FiftyOneImageDetectionSampleParser	Parser for samples in FiftyOne image detection datasets. See FiftyOneImageDetectionDataset for format details.
FiftyOneImageLabelsSampleParser	Parser for samples in FiftyOne image labels datasets. See FiftyOneImageLabelsDataset for format details.
FiftyOneVideoLabelsSampleParser	Parser for samples in FiftyOne video labels datasets. See FiftyOneVideoLabelsDataset for format details.
TFImageClassificationSampleParser	Parser for image classification samples stored as TFRecords.
TFObjectDetectionSampleParser	Parser for image detection samples stored in TF Object Detection API format.

Writing a custom SampleParser ¶

FiftyOne provides a variety of built-in SampleParser classes to parse data in common formats. However, if your samples are stored in a custom format, you can provide a custom SampleParser class and provide it to FiftyOne when adding or ingesting samples into your datasets.

The SampleParser interface provides a mechanism for defining methods that parse a data sample that is stored in a particular (external to FiftyOne) format and return various elements of the sample in a format that FiftyOne understands.

SampleParser itself is an abstract interface; the concrete interface that you should implement is determined by the type of samples that you are importing. For example, LabeledImageSampleParser defines an interface for parsing information from a labeled image sample, such as the path to the image on disk, the image itself, metadata about the image, and the label (e.g., classification or object detections) associated with the image.