fiftyone.utils.labelbox#

Utilities for working with annotations in Labelbox format.

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

Classes:

LabelboxExportVersion()

Enum for Labelbox export formats and API versions.

LabelboxBackendConfig(name, label_schema[, ...])

Class for configuring LabelboxBackend instances.

LabelboxBackend(*args, **kwargs)

Class for interacting with the Labelbox annotation backend.

LabelboxAnnotationAPI(name, url[, api_key, ...])

A class to facilitate connection to and management of projects in Labelbox.

LabelboxAnnotationResults(samples, config, ...)

Class that stores all relevant information needed to monitor the progress of an annotation run sent to Labelbox and download the results.

Functions:

import_from_labelbox(dataset, json_path[, ...])

Imports the labels from the Labelbox project into the FiftyOne dataset.

export_to_labelbox(sample_collection, ...[, ...])

Exports labels from the FiftyOne samples to Labelbox format.

download_labels_from_labelbox(labelbox_project)

Downloads the labels for the given Labelbox project.

upload_media_to_labelbox(labelbox_dataset, ...)

Uploads the raw media for the FiftyOne samples to Labelbox.

upload_labels_to_labelbox(labelbox_project, ...)

Uploads labels to a Labelbox project.

convert_labelbox_export_to_import(inpath[, ...])

Converts a Labelbox NDJSON export generated by export_to_labelbox() into the format expected by import_from_labelbox().
class fiftyone.utils.labelbox.LabelboxExportVersion#

Bases: object

Enum for Labelbox export formats and API versions.

Attributes:

V1

V2

V1 = 'v1'#
V2 = 'v2'#
class fiftyone.utils.labelbox.LabelboxBackendConfig(name, label_schema, media_field='filepath', url=None, api_key=None, project_name=None, members=None, classes_as_attrs=True, export_version='v2', **kwargs)#

Bases: AnnotationBackendConfig

Class for configuring LabelboxBackend instances.

Parameters:

  • name – the name of the backend

  • label_schema – a dictionary containing the description of label fields, classes and attribute to annotate

  • media_field ("filepath") – string field name containing the paths to media files on disk to upload

  • url (None) – the url of the Labelbox server

  • api_key (None) – the Labelbox API key

  • project_name (None) – a name for the Labelbox project that will be created. The default is "FiftyOne_<dataset_name>"

  • members (None) – an optional list of (email, role) tuples specifying the email addresses and roles of users to add to the project. If a user is not a member of the project’s organization, an email invitation will be sent to them. The supported roles are ["LABELER", "REVIEWER", "TEAM_MANAGER", "ADMIN"]

  • classes_as_attrs (True) – whether to show every object class at the top level of the editor (False) or whether to show the label field at the top level and annotate the class as a required attribute of each object (True)

  • export_version ("v2") – the Labelbox export format and API version to use. Supported values are ("v1", "v2")

Attributes:

api_key

cls

The fully-qualified name of this BaseRunConfig class.

method

The name of the annotation backend.

run_cls

The BaseRun class associated with this config.

type

The type of run.

Methods:

load_credentials([url, api_key])

Loads any necessary credentials from the given keyword arguments or the relevant FiftyOne config.

attributes()

Returns the list of class attributes that will be serialized by serialize().

base_config_cls(type)

Returns the config class for the given run type.

build()

Builds the BaseRun instance associated with this config.

builder()

Returns a ConfigBuilder instance for this class.

copy()

Returns a deep copy of the object.

custom_attributes([dynamic, private])

Returns a customizable list of class attributes.

default()

Returns the default config instance.

from_dict(d)

Constructs a BaseRunConfig from a serialized JSON dict representation of it.

from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

from_kwargs(**kwargs)

Constructs a Config object from keyword arguments.

from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

get_class_name()

Returns the fully-qualified class name string of this object.

load_default()

Loads the default config instance from file.

parse_array(d, key[, default])

Parses a raw array attribute.

parse_bool(d, key[, default])

Parses a boolean value.

parse_categorical(d, key, choices[, default])

Parses a categorical JSON field, which must take a value from among the given choices.

parse_dict(d, key[, default])

Parses a dictionary attribute.

parse_int(d, key[, default])

Parses an integer attribute.

parse_mutually_exclusive_fields(fields)

Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.

parse_number(d, key[, default])

Parses a number attribute.

parse_object(d, key, cls[, default])

Parses an object attribute.

parse_object_array(d, key, cls[, default])

Parses an array of objects.

parse_object_dict(d, key, cls[, default])

Parses a dictionary whose values are objects.

parse_path(d, key[, default])

Parses a path attribute.

parse_raw(d, key[, default])

Parses a raw (arbitrary) JSON field.

parse_string(d, key[, default])

Parses a string attribute.

serialize(*args, **kwargs)

Serializes the object into a dictionary.

to_str([pretty_print])

Returns a string representation of this object.

validate_all_or_nothing_fields(fields)

Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.

write_json(path[, pretty_print])

Serializes the object and writes it to disk.
property api_key#
load_credentials(url=None, api_key=None)#

Loads any necessary credentials from the given keyword arguments or the relevant FiftyOne config.

Parameters:

**kwargs – subclass-specific credentials

attributes()#

Returns the list of class attributes that will be serialized by serialize().

Returns:

a list of attributes

static base_config_cls(type)#

Returns the config class for the given run type.

Parameters:

type – a BaseRunConfig.type

Returns:

a BaseRunConfig subclass

build()#

Builds the BaseRun instance associated with this config.

Returns:

a BaseRun instance

classmethod builder()#

Returns a ConfigBuilder instance for this class.

property cls#

The fully-qualified name of this BaseRunConfig class.

copy()#

Returns a deep copy of the object.

Returns:

a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

  • dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

  • private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

classmethod default()#

Returns the default config instance.

By default, this method instantiates the class from an empty dictionary, which will only succeed if all attributes are optional. Otherwise, subclasses should override this method to provide the desired default configuration.

classmethod from_dict(d)#

Constructs a BaseRunConfig from a serialized JSON dict representation of it.

Parameters:

d – a JSON dict

Returns:

a BaseRunConfig

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

  • path – the path to the JSON file on disk

  • *args – optional positional arguments for self.from_dict()

  • **kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_kwargs(**kwargs)#

Constructs a Config object from keyword arguments.

Parameters:

**kwargs – keyword arguments that define the fields expected by cls

Returns:

an instance of cls

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

  • s – a JSON string representation of a Serializable object

  • *args – optional positional arguments for self.from_dict()

  • **kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#

Returns the fully-qualified class name string of this object.

classmethod load_default()#

Loads the default config instance from file.

Subclasses must implement this method if they intend to support default instances.

property method#

The name of the annotation backend.

static parse_array(d, key, default=<eta.core.config.NoDefault object>)#

Parses a raw array attribute.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default list to return if key is not present

Returns:

a list of raw (untouched) values

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_bool(d, key, default=<eta.core.config.NoDefault object>)#

Parses a boolean value.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default bool to return if key is not present

Returns:

True/False

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_categorical(d, key, choices, default=<eta.core.config.NoDefault object>)#

Parses a categorical JSON field, which must take a value from among the given choices.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • choices – either an iterable of possible values or an enum-like class whose attributes define the possible values

  • default – a default value to return if key is not present

Returns:

the raw (untouched) value of the given field, which is equal to a value from choices

Raises:

ConfigError – if the key was present in the dictionary but its value was not an allowed choice, or if no default value was provided and the key was not found in the dictionary

static parse_dict(d, key, default=<eta.core.config.NoDefault object>)#

Parses a dictionary attribute.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default dict to return if key is not present

Returns:

a dictionary

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_int(d, key, default=<eta.core.config.NoDefault object>)#

Parses an integer attribute.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default integer value to return if key is not present

Returns:

an int

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_mutually_exclusive_fields(fields)#

Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.

Parameters:

fields – a dictionary of pre-parsed fields

Returns:

the (field, value) that was set

Raises:

ConfigError – if zero or more than one truthy value was found

static parse_number(d, key, default=<eta.core.config.NoDefault object>)#

Parses a number attribute.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default numeric value to return if key is not present

Returns:

a number (e.g. int, float)

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses an object attribute.

The value of d[key] can be either an instance of cls or a serialized dict from an instance of cls.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • cls – the class of d[key]

  • default – a default cls instance to return if key is not present

Returns:

an instance of cls

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_array(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses an array of objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • cls – the class of the elements of list d[key]

  • default – the default list to return if key is not present

Returns:

a list of cls instances

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_dict(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses a dictionary whose values are objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • cls – the class of the values of dictionary d[key]

  • default – the default dict of cls instances to return if key is not present

Returns:

a dictionary whose values are cls instances

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_path(d, key, default=<eta.core.config.NoDefault object>)#

Parses a path attribute.

The path is converted to an absolute path if necessary via os.path.abspath(os.path.expanduser(value)).

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default string to return if key is not present

Returns:

a path string

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_raw(d, key, default=<eta.core.config.NoDefault object>)#

Parses a raw (arbitrary) JSON field.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default value to return if key is not present

Returns:

the raw (untouched) value of the given field

Raises:

ConfigError – if no default value was provided and the key was not found in the dictionary

static parse_string(d, key, default=<eta.core.config.NoDefault object>)#

Parses a string attribute.

Parameters:

  • d – a JSON dictionary

  • key – the key to parse

  • default – a default string to return if key is not present

Returns:

a string

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

property run_cls#

The BaseRun class associated with this config.

serialize(*args, **kwargs)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns:

a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

  • pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

  • **kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

property type#

The type of run.

static validate_all_or_nothing_fields(fields)#

Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.

Parameters:

fields – a dictionary of pre-parsed fields

Raises:

ConfigError – if some values are truth and some are not

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

  • path – the output path

  • pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

  • **kwargs – optional keyword arguments for self.serialize()

class fiftyone.utils.labelbox.LabelboxBackend(*args, **kwargs)#

Bases: AnnotationBackend

Class for interacting with the Labelbox annotation backend.

Attributes:

supported_media_types

The list of media types that this backend supports.

supported_label_types

The list of label types supported by the backend.

supported_scalar_types

The list of scalar field types supported by the backend.

supported_attr_types

The list of attribute types supported by the backend.

supports_keyframes

Whether this backend supports uploading only keyframes when editing existing video track annotations.

supports_video_sample_fields

Whether this backend supports annotating video labels at a sample-level.

requires_label_schema

Whether this backend requires a pre-defined label schema for its annotation runs.

supports_clips_views

Whether this backend supports annotating clips views.

Methods:

recommend_attr_tool(name, value)

Recommends an attribute tool for an attribute with the given name and value.

requires_attr_values(attr_type)

Determines whether the list of possible values are required for attributes of the given type.

upload_annotations(samples, anno_key[, ...])

Uploads the samples and relevant existing labels from the label schema to the annotation backend.

download_annotations(results)

Downloads the annotations from the annotation backend for the given results.

cleanup(samples, key)

Cleans up the results of the run with the given key from the collection.

connect_to_api()

Returns an API instance connected to the annotation backend.

delete_run(samples, key[, cleanup])

Deletes the results associated with the given run key from the collection.

delete_runs(samples[, cleanup])

Deletes all runs from the collection.

ensure_requirements()

Ensures that any necessary packages to execute this run are installed.

ensure_usage_requirements()

Ensures that any necessary packages to use existing results for this run are installed.

from_config(config)

Instantiates a Configurable class from a <cls>Config instance.

from_dict(d)

Instantiates a Configurable class from a <cls>Config dict.

from_json(json_path)

Instantiates a Configurable class from a <cls>Config JSON file.

from_kwargs(**kwargs)

Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.

get_fields(samples, anno_key)

Gets the fields that were involved in the given run.

get_run_info(samples, key)

Gets the BaseRunInfo for the given key on the collection.

has_cached_run_results(samples, key)

Determines whether BaseRunResults for the given key are cached on the collection.

list_runs(samples[, type, method])

Returns the list of run keys on the given collection.

load_run_results(samples, key[, cache, ...])

Loads the BaseRunResults for the given key on the collection.

load_run_view(samples, key[, select_fields])

Loads the view on which the specified run was performed.

parse(class_name[, module_name])

Parses a Configurable subclass name string.

register_run(samples, key[, overwrite, cleanup])

Registers a run of this method under the given key on the given collection.

rename(samples, key, new_key)

Performs any necessary operations required to rename this run's key.

run_info_cls()

The BaseRunInfo class associated with this class.

save_run_info(samples, run_info[, ...])

Saves the run information on the collection.

save_run_results(samples, key, run_results)

Saves the run results on the collection.

update_run_config(samples, key, config)

Updates the BaseRunConfig for the given run on the collection.

update_run_key(samples, key, new_key)

Replaces the key for the given run with a new key.

use_api(api)

Registers an API instance to use for subsequent operations.

validate(config)

Validates that the given config is an instance of <cls>Config.

validate_run(samples, key[, overwrite])

Validates that the collection can accept this run.
property supported_media_types#

The list of media types that this backend supports.

For example, CVAT supports ["image", "video"].

property supported_label_types#

The list of label types supported by the backend.

Backends may support any subset of the following label types:

  • "classification"

  • "classifications"

  • "detection"

  • "detections"

  • "instance"

  • "instances"

  • "polyline"

  • "polylines"

  • "polygon"

  • "polygons"

  • "keypoint"

  • "keypoints"

  • "segmentation"

  • "scalar"

property supported_scalar_types#

The list of scalar field types supported by the backend.

For example, CVAT supports the following types:

property supported_attr_types#

The list of attribute types supported by the backend.

This list defines the valid string values for the type field of an attributes dict of the label schema provided to the backend.

For example, CVAT supports ["text", "select", "radio", "checkbox"].

property supports_keyframes#

Whether this backend supports uploading only keyframes when editing existing video track annotations.

property supports_video_sample_fields#

Whether this backend supports annotating video labels at a sample-level.

property requires_label_schema#

Whether this backend requires a pre-defined label schema for its annotation runs.

recommend_attr_tool(name, value)#

Recommends an attribute tool for an attribute with the given name and value.

For example, a backend may recommend a tool as follows for a boolean value:

{
    "type": "radio",
    "values": [False, True],
}

or a tool as follows for a generic value:

{"type": "text"}
Parameters:

  • name – the name of the attribute

  • value – the attribute value, which may be None

Returns:

an attribute type dict

requires_attr_values(attr_type)#

Determines whether the list of possible values are required for attributes of the given type.

Parameters:

attr_type – the attribute type string

Returns:

True/False

upload_annotations(samples, anno_key, launch_editor=False)#

Uploads the samples and relevant existing labels from the label schema to the annotation backend.

Parameters:
Returns:

an AnnotationResults

download_annotations(results)#

Downloads the annotations from the annotation backend for the given results.

The returned labels should be represented as either scalar values or fiftyone.core.labels.Label instances.

For image datasets, the return dictionary should have the following nested structure:

# Scalar fields
results[label_type][sample_id] = scalar

# Label fields
results[label_type][sample_id][label_id] = label

For video datasets, the returned labels dictionary should have the following nested structure:

# Scalar fields
results[label_type][sample_id][frame_id] = scalar

# Label fields
results[label_type][sample_id][frame_id][label_id] = label

The valid values for label_type are:

  • “classifications”: single or multilabel classifications

  • “detections”: detections or instance segmentations

  • “polylines”: polygons or polylines

  • “segmentation”: semantic segmentations

  • “scalar”: scalar values

Parameters:

results – an AnnotationResults

Returns:

the labels results dict

cleanup(samples, key)#

Cleans up the results of the run with the given key from the collection.

Parameters:
connect_to_api()#

Returns an API instance connected to the annotation backend.

Existing API instances are reused, if available.

Some annotation backends may not expose this functionality.

Returns:

an AnnotationAPI, or None if the backend does not expose an API

classmethod delete_run(samples, key, cleanup=True)#

Deletes the results associated with the given run key from the collection.

Parameters:
classmethod delete_runs(samples, cleanup=True)#

Deletes all runs from the collection.

Parameters:
ensure_requirements()#

Ensures that any necessary packages to execute this run are installed.

Runs should respect fiftyone.config.requirement_error_level when handling errors.

ensure_usage_requirements()#

Ensures that any necessary packages to use existing results for this run are installed.

Runs should respect fiftyone.config.requirement_error_level when handling errors.

classmethod from_config(config)#

Instantiates a Configurable class from a <cls>Config instance.

classmethod from_dict(d)#

Instantiates a Configurable class from a <cls>Config dict.

Parameters:

d – a dict to construct a <cls>Config

Returns:

an instance of cls

classmethod from_json(json_path)#

Instantiates a Configurable class from a <cls>Config JSON file.

Parameters:

json_path – path to a JSON file for type <cls>Config

Returns:

an instance of cls

classmethod from_kwargs(**kwargs)#

Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.

Parameters:

**kwargs – keyword arguments that define the fields of a <cls>Config dict

Returns:

an instance of cls

get_fields(samples, anno_key)#

Gets the fields that were involved in the given run.

Parameters:
Returns:

a list of fields

classmethod get_run_info(samples, key)#

Gets the BaseRunInfo for the given key on the collection.

Parameters:
Returns:

a BaseRunInfo

classmethod has_cached_run_results(samples, key)#

Determines whether BaseRunResults for the given key are cached on the collection.

Parameters:
Returns:

True/False

classmethod list_runs(samples, type=None, method=None, **kwargs)#

Returns the list of run keys on the given collection.

Parameters:
Returns:

a list of run keys

classmethod load_run_results(samples, key, cache=True, load_view=True, **kwargs)#

Loads the BaseRunResults for the given key on the collection.

Parameters:

  • samples – a fiftyone.core.collections.SampleCollection

  • key – a run key

  • cache (True) – whether to cache the results on the collection

  • load_view (True) – whether to load the run view in the results (True) or the full dataset (False)

  • **kwargs – keyword arguments for the run’s BaseRunConfig.load_credentials() method

Returns:

a BaseRunResults, or None if the run did not save results

classmethod load_run_view(samples, key, select_fields=False)#

Loads the view on which the specified run was performed.

Parameters:
Returns:

a fiftyone.core.collections.SampleCollection

static parse(class_name, module_name=None)#

Parses a Configurable subclass name string.

Assumes both the Configurable class and the Config class are defined in the same module. The module containing the classes will be loaded if necessary.

Parameters:

  • class_name – a string containing the name of the Configurable class, e.g. “ClassName”, or a fully-qualified class name, e.g. “eta.core.config.ClassName”

  • module_name – a string containing the fully-qualified module name, e.g. “eta.core.config”, or None if class_name includes the module name. Set module_name = __name__ to load a class from the calling module

Returns:

the Configurable class config_cls: the Config class associated with cls

Return type:

cls

register_run(samples, key, overwrite=True, cleanup=True)#

Registers a run of this method under the given key on the given collection.

Parameters:

  • samples – a fiftyone.core.collections.SampleCollection

  • key – a run key

  • overwrite (True) – whether to allow overwriting an existing run of the same type

  • cleanup (True) – whether to execute an existing run’s BaseRun.cleanup() method when overwriting it

rename(samples, key, new_key)#

Performs any necessary operations required to rename this run’s key.

Parameters:
classmethod run_info_cls()#

The BaseRunInfo class associated with this class.

classmethod save_run_info(samples, run_info, overwrite=True, cleanup=True)#

Saves the run information on the collection.

Parameters:

  • samples – a fiftyone.core.collections.SampleCollection

  • run_info – a BaseRunInfo

  • overwrite (True) – whether to overwrite an existing run with the same key

  • cleanup (True) – whether to execute an existing run’s BaseRun.cleanup() method when overwriting it

classmethod save_run_results(samples, key, run_results, overwrite=True, cache=True)#

Saves the run results on the collection.

Parameters:

  • samples – a fiftyone.core.collections.SampleCollection

  • key – a run key

  • run_results – a BaseRunResults, or None

  • overwrite (True) – whether to overwrite an existing result with the same key

  • cache (True) – whether to cache the results on the collection

property supports_clips_views#

Whether this backend supports annotating clips views.

classmethod update_run_config(samples, key, config)#

Updates the BaseRunConfig for the given run on the collection.

Parameters:
classmethod update_run_key(samples, key, new_key)#

Replaces the key for the given run with a new key.

Parameters:
use_api(api)#

Registers an API instance to use for subsequent operations.

Parameters:

api – an AnnotationAPI

classmethod validate(config)#

Validates that the given config is an instance of <cls>Config.

Raises:

ConfigurableError – if config is not an instance of <cls>Config

validate_run(samples, key, overwrite=True)#

Validates that the collection can accept this run.

The run may be invalid if, for example, a run of a different type has already been run under the same key and thus overwriting it would cause ambiguity on how to cleanup the results.

Parameters:
Raises:

ValueError – if the run is invalid

class fiftyone.utils.labelbox.LabelboxAnnotationAPI(name, url, api_key=None, export_version='v2', _experimental=False)#

Bases: AnnotationAPI

A class to facilitate connection to and management of projects in Labelbox.

On initialization, this class constructs a client based on the provided server url and credentials.

This API provides methods to easily upload, download, create, and delete projects and data through the formatted urls specified by the Labelbox API.

Additionally, samples and label schemas can be uploaded and annotations downloaded through this class.

Parameters:

  • name – the name of the backend

  • url – url of the Labelbox server

  • api_key (None) – the Labelbox API key

  • export_version ("v2") – the Labelbox export format and API version to use. Supported values are ("v1", "v2")

Attributes:

roles

attr_type_map

attr_list_types

base_api_url

base_graphql_url

projects_url

Methods:

project_url(project_id)

editor_url(project_id)

get_project_users([project, project_id])

Returns a list of users that are assigned to the given project.

add_member(project, email, role)

Adds a member to the given Labelbox project with the given project-level role.

list_datasets()

Retrieves the list of datasets in your Labelbox account.

delete_datasets(dataset_ids[, progress])

Deletes the given datasets from the Labelbox server.

list_projects()

Retrieves the list of projects in your Labelbox account.

get_project(project_id)

Retrieves the labelbox.schema.project.Project for the project with the given ID.

delete_project(project_id[, delete_batches, ...])

Deletes the given project from the Labelbox server.

delete_projects(project_ids[, delete_batches])

Deletes the given projects from the Labelbox server.

delete_unused_ontologies()

Deletes unused ontologies from the Labelbox server.

launch_editor([url])

Launches the Labelbox editor in your default web browser.

upload_data(samples, dataset_name[, media_field])

Uploads the media for the given samples to Labelbox.

upload_samples(samples, anno_key, backend)

Uploads the given samples to Labelbox according to the given backend's annotation and server configuration.

get_data_row_ids(sample_ids)

download_annotations(results)

Downloads the annotations from the Labelbox server for the given results instance and parses them into the appropriate FiftyOne types.

close()

Closes the API session.
property roles#
property attr_type_map#
property attr_list_types#
property base_api_url#
property base_graphql_url#
property projects_url#
project_url(project_id)#
editor_url(project_id)#
get_project_users(project=None, project_id=None)#

Returns a list of users that are assigned to the given project.

Provide either project or project_id to this method.

Parameters:

  • project – a labelbox.schema.project.Project

  • project_id – the project ID

Returns:

a list of labelbox.schema.user.User objects

add_member(project, email, role)#

Adds a member to the given Labelbox project with the given project-level role.

If the user is not a member of the project’s parent organization, an email invitivation will be sent.

Parameters:

  • project – the labelbox.schema.project.Project

  • email – the email of the user

  • role – the role for the user. Supported values are ["LABELER", "REVIEWER", "TEAM_MANAGER", "ADMIN"]

list_datasets()#

Retrieves the list of datasets in your Labelbox account.

Returns:

a list of dataset IDs

delete_datasets(dataset_ids, progress=None)#

Deletes the given datasets from the Labelbox server.

Parameters:

  • dataset_ids – an iterable of dataset IDs

  • progress (None) – whether to render a progress bar (True/False), use the default value fiftyone.config.show_progress_bars (None), or a progress callback function to invoke instead

list_projects()#

Retrieves the list of projects in your Labelbox account.

Returns:

a list of project IDs

get_project(project_id)#

Retrieves the labelbox.schema.project.Project for the project with the given ID.

Parameters:

project_id – the project ID

Returns:

a labelbox.schema.project.Project

delete_project(project_id, delete_batches=False, delete_ontologies=True)#

Deletes the given project from the Labelbox server.

Parameters:

  • project_id – the project ID

  • delete_batches (False) – whether to delete the attached batches as well

  • delete_ontologies (True) – whether to delete the attached ontologies as well

delete_projects(project_ids, delete_batches=False)#

Deletes the given projects from the Labelbox server.

Parameters:

  • project_ids – an iterable of project IDs

  • delete_batches (False) – whether to delete the attached batches as well

delete_unused_ontologies()#

Deletes unused ontologies from the Labelbox server.

launch_editor(url=None)#

Launches the Labelbox editor in your default web browser.

Parameters:

url (None) – an optional URL to open. By default, the base URL of the server is opened

upload_data(samples, dataset_name, media_field='filepath')#

Uploads the media for the given samples to Labelbox.

This method uses labelbox.schema.dataset.Dataset.create_data_rows() to add data in batches, and sets the global key of each DataRow to the ID of the corresponding sample.

Parameters:

  • samples – a fiftyone.core.collections.SampleCollection containing the media to upload

  • dataset_name – the name of the Labelbox dataset created if data needs to be uploaded

  • media_field ("filepath") – string field name containing the paths to media files on disk to upload

upload_samples(samples, anno_key, backend)#

Uploads the given samples to Labelbox according to the given backend’s annotation and server configuration.

Parameters:
Returns:

a LabelboxAnnotationResults

get_data_row_ids(sample_ids)#
download_annotations(results)#

Downloads the annotations from the Labelbox server for the given results instance and parses them into the appropriate FiftyOne types.

Parameters:

results – a LabelboxAnnotationResults

Returns:

the annotations dict

close()#

Closes the API session.

class fiftyone.utils.labelbox.LabelboxAnnotationResults(samples, config, anno_key, id_map, project_id, frame_id_map, backend=None)#

Bases: AnnotationResults

Class that stores all relevant information needed to monitor the progress of an annotation run sent to Labelbox and download the results.

Methods:

launch_editor()

Launches the Labelbox editor and loads the project for this annotation run.

get_status()

Gets the status of the annotation run.

print_status()

Prints the status of the annotation run.

cleanup()

Deletes the project associated with this annotation run from the Labelbox server.

attributes()

Returns the list of class attributes that will be serialized by serialize().

base_results_cls(type)

Returns the results class for the given run type.

connect_to_api()

Returns an API instance connected to the annotation backend.

copy()

Returns a deep copy of the object.

custom_attributes([dynamic, private])

Returns a customizable list of class attributes.

from_dict(d, samples, config, key)

Builds a BaseRunResults from a JSON dict representation of it.

from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

get_class_name()

Returns the fully-qualified class name string of this object.

save()

Saves the results to the database.

save_config()

Saves these results config to the database.

serialize([reflective])

Serializes the object into a dictionary.

to_str([pretty_print])

Returns a string representation of this object.

use_api(api)

Registers an API instance to use for subsequent operations.

write_json(path[, pretty_print])

Serializes the object and writes it to disk.

Attributes:

backend

The BaseRun for these results.

cls

The fully-qualified name of this BaseRunResults class.

config

The BaseRunConfig for these results.

key

The run key for these results.

samples

The fiftyone.core.collections.SampleCollection associated with these results.
launch_editor()#

Launches the Labelbox editor and loads the project for this annotation run.

get_status()#

Gets the status of the annotation run.

Returns:

a dict of status information

print_status()#

Prints the status of the annotation run.

cleanup()#

Deletes the project associated with this annotation run from the Labelbox server.

attributes()#

Returns the list of class attributes that will be serialized by serialize().

Returns:

a list of attributes

property backend#

The BaseRun for these results.

static base_results_cls(type)#

Returns the results class for the given run type.

Parameters:

type – a BaseRunConfig.type

Returns:

a BaseRunResults subclass

property cls#

The fully-qualified name of this BaseRunResults class.

property config#

The BaseRunConfig for these results.

connect_to_api()#

Returns an API instance connected to the annotation backend.

Existing API instances are reused, if available.

Some annotation backends may not expose this functionality.

Returns:

an AnnotationAPI, or None if the backend does not expose an API

copy()#

Returns a deep copy of the object.

Returns:

a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

  • dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

  • private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

classmethod from_dict(d, samples, config, key)#

Builds a BaseRunResults from a JSON dict representation of it.

Parameters:
Returns:

a BaseRunResults

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

  • path – the path to the JSON file on disk

  • *args – optional positional arguments for self.from_dict()

  • **kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

  • s – a JSON string representation of a Serializable object

  • *args – optional positional arguments for self.from_dict()

  • **kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#

Returns the fully-qualified class name string of this object.

property key#

The run key for these results.

property samples#

The fiftyone.core.collections.SampleCollection associated with these results.

save()#

Saves the results to the database.

save_config()#

Saves these results config to the database.

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns:

a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

  • pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

  • **kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

use_api(api)#

Registers an API instance to use for subsequent operations.

Parameters:

api – an AnnotationAPI

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

  • path – the output path

  • pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

  • **kwargs – optional keyword arguments for self.serialize()

fiftyone.utils.labelbox.import_from_labelbox(dataset, json_path, label_prefix=None, download_dir=None, labelbox_id_field='labelbox_id', progress=None)#

Imports the labels from the Labelbox project into the FiftyOne dataset.

The labelbox_id_field of the FiftyOne samples are used to associate the corresponding Labelbox labels.

If a download_dir is provided, any Labelbox IDs with no matching FiftyOne sample are added to the FiftyOne dataset, and their media is downloaded into download_dir.

The provided json_path should contain a JSON file in the following format:

[
    {
        "DataRow ID": <labelbox-id>,
        "Labeled Data": <url-or-None>,
        "Label": {...}
    }
]

When importing image labels, the Label field should contain a dict of Labelbox image labels:

{
    "objects": [...],
    "classifications": [...]
}

When importing video labels, the Label field should contain a dict as follows:

{
    "frames": <url-or-filepath>
}

where the frames field can either contain a URL, in which case the file is downloaded from the web, or the path to NDJSON file on disk of Labelbox video labels:

{"frameNumber": 1, "objects": [...], "classifications": [...]}
{"frameNumber": 2, "objects": [...], "classifications": [...]}
...
Parameters:

  • dataset – a fiftyone.core.dataset.Dataset

  • json_path – the path to the Labelbox JSON export to load

  • label_prefix (None) – a prefix to prepend to the sample label field(s) that are created, separated by an underscore

  • download_dir (None) – a directory into which to download the media for any Labelbox IDs with no corresponding sample with the matching labelbox_id_field value. This can be omitted if all IDs are already present or you do not wish to download media and add new samples

  • labelbox_id_field ("labelbox_id") – the sample field to lookup/store the IDs of the Labelbox DataRows

  • progress (None) – whether to render a progress bar (True/False), use the default value fiftyone.config.show_progress_bars (None), or a progress callback function to invoke instead

fiftyone.utils.labelbox.export_to_labelbox(sample_collection, ndjson_path, video_labels_dir=None, labelbox_id_field='labelbox_id', label_field=None, frame_labels_field=None, progress=None)#

Exports labels from the FiftyOne samples to Labelbox format.

This function is useful for loading predictions into Labelbox for model-assisted labeling.

You can use upload_labels_to_labelbox() to upload the exported labels to a Labelbox project.

You can use upload_media_to_labelbox() to upload sample media to Labelbox and populate the labelbox_id_field field, if necessary.

The IDs of the Labelbox DataRows corresponding to each sample must be stored in the labelbox_id_field of the samples. Any samples with no value in labelbox_id_field will be skipped.

When exporting frame labels for video datasets, the frames key of the exported labels will contain the paths on disk to per-sample NDJSON files that are written to video_labels_dir as follows:

video_labels_dir/
    <labelbox-id1>.json
    <labelbox-id2>.json
    ...

where each NDJSON file contains the frame labels for the video with the corresponding Labelbox ID.

Parameters:

  • sample_collection – a fiftyone.core.collections.SampleCollection

  • ndjson_path – the path to write an NDJSON export of the labels

  • video_labels_dir (None) – a directory to write the per-sample video labels. Only applicable for video datasets

  • labelbox_id_field ("labelbox_id") – the sample field to lookup/store the IDs of the Labelbox DataRows

  • label_field (None) –

    optional label field(s) to export. Can be any of the following:

    • the name of a label field to export

    • a glob pattern of label field(s) to export

    • a list or tuple of label field(s) to export

    • a dictionary mapping label field names to keys to use when constructing the exported labels

    By default, no labels are exported

  • frame_labels_field (None) –

    optional frame label field(s) to export. Only applicable to video datasets. Can be any of the following:

    • the name of a frame label field to export

    • a glob pattern of frame label field(s) to export

    • a list or tuple of frame label field(s) to export

    • a dictionary mapping frame label field names to keys to use when constructing the exported frame labels

    By default, no frame labels are exported

  • progress (None) – whether to render a progress bar (True/False), use the default value fiftyone.config.show_progress_bars (None), or a progress callback function to invoke instead

fiftyone.utils.labelbox.download_labels_from_labelbox(labelbox_project, outpath=None, export_version='v2')#

Downloads the labels for the given Labelbox project.

Parameters:

  • labelbox_project – a labelbox.schema.project.Project

  • outpath (None) – the path to write the JSON export on disk

  • export_version ("v2") – the Labelbox export format and API version to use. Supported values are ("v1", "v2")

Returns:

None if an outpath is provided, or the loaded JSON itself if no outpath is provided

fiftyone.utils.labelbox.upload_media_to_labelbox(labelbox_dataset, sample_collection, labelbox_id_field='labelbox_id', progress=None)#

Uploads the raw media for the FiftyOne samples to Labelbox.

The IDs of the Labelbox DataRows that are created are stored in the labelbox_id_field of the samples.

Parameters:

  • labelbox_dataset – a labelbox.schema.dataset.Dataset to which to add the media

  • sample_collection – a fiftyone.core.collections.SampleCollection

  • labelbox_id_field ("labelbox_id") – the sample field in which to store the IDs of the Labelbox DataRows

  • progress (None) – whether to render a progress bar (True/False), use the default value fiftyone.config.show_progress_bars (None), or a progress callback function to invoke instead

fiftyone.utils.labelbox.upload_labels_to_labelbox(labelbox_project, annos_or_ndjson_path, batch_size=None)#

Uploads labels to a Labelbox project.

Use this function to load predictions into Labelbox for model-assisted labeling.

Use export_to_labelbox() to export annotations in the format expected by this method.

Parameters:

  • labelbox_project – a labelbox.schema.project.Project

  • annos_or_ndjson_path – a list of annotation dicts or the path to an NDJSON file on disk containing annotations

  • batch_size (None) – an optional batch size to use when uploading the annotations. By default, annos_or_ndjson_path is passed directly to labelbox_project.upload_annotations()

fiftyone.utils.labelbox.convert_labelbox_export_to_import(inpath, outpath=None, video_outdir=None)#

Converts a Labelbox NDJSON export generated by export_to_labelbox() into the format expected by import_from_labelbox().

The output JSON file will have the same format that is generated when exporting a Labelbox project’s labels.

The Labeled Data fields of the output labels will be None.

Parameters:

  • inpath – the path to an NDJSON file generated (for example) by export_to_labelbox()

  • outpath (None) – the path to write a JSON file containing the converted labels. If omitted, the input file will be overwritten

  • video_outdir (None) – a directory to write the converted video frame labels (if applicable). If omitted, the input frame label files will be overwritten

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