fiftyone.utils.annotations#

Annotation utilities.

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

Functions:

`connect_to_api`([backend])	Returns an API instance connected to the annotation backend.
`annotate`(samples, anno_key[, label_schema, ...])	Exports the samples and optional label field(s) to the given annotation backend.
`load_annotations`(samples, anno_key[, ...])	Downloads the labels from the given annotation run from the annotation backend and merges them into the collection.
`draw_labeled_images`(samples, output_dir[, ...])	Renders annotated versions of the images in the collection with the specified label data overlaid to the given directory.
`draw_labeled_image`(sample, outpath[, ...])	Renders an annotated version of the sample's image with the specified label data overlaid to disk.
`draw_labeled_videos`(samples, output_dir[, ...])	Renders annotated versions of the videos in the collection with the specified label data overlaid to the given directory.
`draw_labeled_video`(sample, outpath[, ...])	Renders an annotated version of the sample's video with the specified label data overlaid to disk.

Classes:

`AnnotationBackendConfig`(name, label_schema)	Base class for configuring an `AnnotationBackend` instances.
`AnnotationBackend`(args, *kwargs)	Base class for annotation backends.
`AnnotationResults`(samples, config, anno_key, ...)	Base class for storing the intermediate results of an annotation run that has been initiated and is waiting for its results to be merged back into the FiftyOne dataset.
`AnnotationAPI`()	Base class for APIs that connect to annotation backends.
`DrawConfig`(d)	Configuration class that controls the look-and-feel of the annotations rendered on images/videos.

fiftyone.utils.annotations.connect_to_api(backend=None, **kwargs)#

Returns an API instance connected to the annotation backend.

Some annotation backends may not expose this functionality.

Parameters:

backend (None) – the annotation backend to use. The supported values are fiftyone.annotation_config.backends.keys() and the default is fiftyone.annotation_config.default_backend
**kwargs – keyword arguments for the AnnotationBackendConfig subclass of the backend being used

Returns:

an AnnotationAPI

fiftyone.utils.annotations.annotate(samples, anno_key, label_schema=None, label_field=None, label_type=None, classes=None, attributes=True, mask_targets=None, allow_additions=True, allow_deletions=True, allow_label_edits=True, allow_index_edits=True, allow_spatial_edits=True, media_field='filepath', backend=None, launch_editor=False, **kwargs)#

Exports the samples and optional label field(s) to the given annotation backend.

The backend parameter controls which annotation backend to use. Depending on the backend you use, you may want/need to provide extra keyword arguments to this function for the constructor of the backend’s AnnotationBackendConfig class.

The natively provided backends and their associated config classes are:

"cvat": fiftyone.utils.cvat.CVATBackendConfig
"labelstudio": fiftyone.utils.labelstudio.LabelStudioBackendConfig
"labelbox": fiftyone.utils.labelbox.LabelboxBackendConfig

See this page for more information about using this method, including how to define label schemas and how to configure login credentials for your annotation provider.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
anno_key – a string key to use to refer to this annotation run
label_schema (None) – a dictionary defining the label schema to use. If this argument is provided, it takes precedence over the other schema-related arguments
label_field (None) – a string indicating a new or existing label field to annotate
label_type (None) –
a string indicating the type of labels to annotate. The possible values are:
- "classification": a single classification stored in fiftyone.core.labels.Classification fields
- "classifications": multilabel classifications stored in fiftyone.core.labels.Classifications fields
- "detections": object detections stored in fiftyone.core.labels.Detections fields
- "instances": instance segmentations stored in fiftyone.core.labels.Detections fields with their mask attributes populated
- "polylines": polylines stored in fiftyone.core.labels.Polylines fields with their filled attributes set to False
- "polygons": polygons stored in fiftyone.core.labels.Polylines fields with their filled attributes set to True
- "keypoints": keypoints stored in fiftyone.core.labels.Keypoints fields
- "segmentation": semantic segmentations stored in fiftyone.core.labels.Segmentation fields
- "scalar": scalar labels stored in fiftyone.core.fields.IntField, fiftyone.core.fields.FloatField, fiftyone.core.fields.StringField, or fiftyone.core.fields.BooleanField fields
All new label fields must have their type specified via this argument or in label_schema. Note that annotation backends may not support all label types
classes (None) – a list of strings indicating the class options for label_field or all fields in label_schema without classes specified. All new label fields must have a class list provided via one of the supported methods. For existing label fields, if classes are not provided by this argument nor label_schema, the observed labels on your dataset are used
attributes (True) –
specifies the label attributes of each label field to include (other than their label, which is always included) in the annotation export. Can be any of the following:
- True: export all label attributes
- False: don’t export any custom label attributes
- an attribute or list of attributes to export
- a dict mapping attribute names to dicts specifying the details of the attribute field
If a label_schema is also provided, this parameter determines which attributes are included for all fields that do not explicitly define their per-field attributes (in addition to any per-class attributes)
mask_targets (None) – a dict mapping pixel values to semantic label strings. Only applicable when annotating semantic segmentations. All new label fields must have mask targets provided via one of the supported methods. For existing label fields, if mask targets are not provided by this argument nor label_schema, any applicable mask targets stored on your dataset will be used, if available
allow_additions (True) – whether to allow new labels to be added. Only applicable when editing existing label fields
allow_deletions (True) – whether to allow labels to be deleted. Only applicable when editing existing label fields
allow_label_edits (True) – whether to allow the label attribute of existing labels to be modified. Only applicable when editing existing fields with label attributes
allow_index_edits (True) – whether to allow the index attribute of existing video tracks to be modified. Only applicable when editing existing frame fields with index attributes
allow_spatial_edits (True) – whether to allow edits to the spatial properties (bounding boxes, vertices, keypoints, masks, etc) of labels. Only applicable when editing existing spatial label fields
media_field ("filepath") – the field containing the paths to the media files to upload
backend (None) – the annotation backend to use. The supported values are fiftyone.annotation_config.backends.keys() and the default is fiftyone.annotation_config.default_backend
launch_editor (False) – whether to launch the annotation backend’s editor after uploading the samples
**kwargs – keyword arguments for the AnnotationBackendConfig subclass of the backend being used

Returns:

an AnnotationResults

fiftyone.utils.annotations.load_annotations(samples, anno_key, dest_field=None, unexpected='prompt', cleanup=False, progress=None, **kwargs)#

Downloads the labels from the given annotation run from the annotation backend and merges them into the collection.

See this page for more information about using this method to import annotations that you have scheduled by calling annotate().

Parameters:

samples – a fiftyone.core.collections.SampleCollection
anno_key – an annotation key
dest_field (None) – an optional name of a new destination field into which to load the annotations, or a dict mapping field names in the run’s label schema to new destination field names
unexpected ("prompt") –
how to deal with any unexpected labels that don’t match the run’s label schema when importing. The supported values are:
- "prompt": present an interactive prompt to direct/discard unexpected labels
- "ignore": automatically ignore any unexpected labels
- "keep": automatically keep all unexpected labels in a field whose name matches the the label type
- "return": return a dict containing all unexpected labels, or None if there aren’t any
cleanup (False) – whether to delete any informtation regarding this run from the annotation backend after loading the annotations
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
**kwargs – keyword arguments for the run’s fiftyone.core.annotation.AnnotationMethodConfig.load_credentials() method

Returns:

None, unless unexpected=="return" and unexpected labels are found, in which case a dict containing the extra labels is returned

class fiftyone.utils.annotations.AnnotationBackendConfig(name, label_schema, media_field='filepath', **kwargs)#

Bases: AnnotationMethodConfig

Base class for configuring an AnnotationBackend instances.

Subclasses are free to define additional keyword arguments if they desire.

Parameters:

name – the name of the backend
label_schema – a dictionary containing the description of label fields, classes and attributes to annotate
media_field ("filepath") – string field name containing the paths to media files on disk to upload
**kwargs – any leftover keyword arguments after subclasses have done their parsing

Attributes:

`method`	The name of the annotation backend.
`cls`	The fully-qualified name of this `BaseRunConfig` class.
`run_cls`	The `BaseRun` class associated with this config.
`type`	The type of run.

Methods:

`load_credentials`(**kwargs)	Loads any necessary credentials from the given keyword arguments or the relevant FiftyOne config.
`serialize`(args, *kwargs)	Serializes the object into a dictionary.
`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.
`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 method#: The name of the annotation backend.

load_credentials(**kwargs)#

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

Parameters:: **kwargs – subclass-specific credentials

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

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.

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.

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.annotations.AnnotationBackend(*args, **kwargs)#

Bases: AnnotationMethod

Base class for annotation backends.

Parameters:: config – an AnnotationBackendConfig

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_clips_views`	Whether this backend supports annotating clips views.
`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.

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.
`connect_to_api`()	Returns an API instance connected to the annotation backend.
`use_api`(api)	Registers an API instance to use for subsequent operations.
`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.
`get_fields`(samples, anno_key)	Gets the fields that were involved in the given run.
`cleanup`(samples, key)	Cleans up the results of the run with the given key from the collection.
`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_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.
`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_clips_views#: Whether this backend supports annotating clips views.

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

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

use_api(api)#

Registers an API instance to use for subsequent operations.

Parameters:: api – an AnnotationAPI

upload_annotations(samples, anno_key, launch_editor=False)#

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

Parameters:

samples – a fiftyone.core.collections.SampleCollection
anno_key – the annotation key
launch_editor (False) – whether to launch the annotation backend’s editor after uploading the samples

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

get_fields(samples, anno_key)#

Gets the fields that were involved in the given run.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key

Returns:

a list of fields

cleanup(samples, key)#

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

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key

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

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

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key
cleanup (True) – whether to execute the run’s BaseRun.cleanup() method

classmethod delete_runs(samples, cleanup=True)#

Deletes all runs from the collection.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
cleanup (True) – whether to execute the run’s BaseRun.cleanup() methods

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

classmethod get_run_info(samples, key)#

Gets the BaseRunInfo for the given key on the collection.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key

Returns:

a BaseRunInfo

classmethod has_cached_run_results(samples, key)#

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

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key

Returns:

True/False

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

Returns the list of run keys on the given collection.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
type (None) –
a specific run type to match, which can be:
- a string fiftyone.core.runs.BaseRunConfig.type
- a fiftyone.core.runs.BaseRun class or its fully-qualified class name string
method (None) – a specific fiftyone.core.runs.BaseRunConfig.method string to match
**kwargs – optional config parameters to match

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:

samples – a fiftyone.core.collections.SampleCollection
key – a run key
select_fields (False) – whether to exclude fields involved in other runs of the same type

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:

samples – a fiftyone.core.collections.SampleCollection
key – a run key
new_key – a new run key

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

classmethod update_run_config(samples, key, config)#

Updates the BaseRunConfig for the given run on the collection.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key
config – a BaseRunConfig

classmethod update_run_key(samples, key, new_key)#

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

Parameters:

samples – a fiftyone.core.collections.SampleCollection
key – a run key
new_key – a new run key

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:

samples – a fiftyone.core.collections.SampleCollection
key – a run key
overwrite (True) – whether to allow overwriting an existing run of the same type

Raises:

ValueError – if the run is invalid

class fiftyone.utils.annotations.AnnotationResults(samples, config, anno_key, id_map, backend=None)#

Bases: AnnotationResults

Base class for storing the intermediate results of an annotation run that has been initiated and is waiting for its results to be merged back into the FiftyOne dataset.

The id_map dictionary must record the IDs of any existing labels that are being edited by the annotation run. Any new label fields do not need to have keys in this dictionary.

For image datasets, id_map should have the following format:

{
    "<label-field>": {
        "<sample-id>": "label-id" or ["label-id", ...],
        ...
    },
    ...
}

For video datasets, id_map should have the following format:

{
    "<label-field>": {
        "<sample-id>": {
            "<frame-id>": label-id" or ["label-id", ...],
            ...
        },
        ...
    },
    ...
}

When editing scalar fields, set the dictionary values corresponding to uploaded scalars to True (since scalars do not have IDs).

If a particular sample or frame was included in the annotation run but no labels/scalars were uploaded for editing, the corresponding entry in id_map can be either missing or have a value of None.

Note

This class is serialized for storage in the database by calling serialize().

Any public attributes of this class are included in the representation generated by serialize(), so they must be JSON serializable.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
config – an AnnotationBackendConfig
anno_key – the annotation key
id_map – a dictionary recording the existing label IDs, in the format described above
backend (None) – an AnnotationBackend

Methods:

`connect_to_api`()	Returns an API instance connected to the annotation backend.
`use_api`(api)	Registers an API instance to use for subsequent operations.
`launch_editor`()	Launches the annotation backend's editor for these results.
`cleanup`()	Deletes all information for this run from the annotation backend.
`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.
`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.
`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.

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

use_api(api)#

Registers an API instance to use for subsequent operations.

Parameters:: api – an AnnotationAPI

launch_editor()#: Launches the annotation backend’s editor for these results.

cleanup()#: Deletes all information for this run from the annotation backend.

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.

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:

d – a JSON dict
samples – the fiftyone.core.collections.SampleCollection for the run
config – the BaseRunConfig for the run
key – the run key

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

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.annotations.AnnotationAPI#

Bases: object

Base class for APIs that connect to annotation backends.

Methods:

close()

Closes the API session.

close()#: Closes the API session.

class fiftyone.utils.annotations.DrawConfig(d)#

Bases: AnnotationConfig

Configuration class that controls the look-and-feel of the annotations rendered on images/videos.

Parameters:

show_frame_attr_names – (True) whether to render video/frame attribute names, if available
show_frame_attr_confidences – (False) whether to render video/frame attribute confidences, if available
frame_attrs_box_gap – (“1%”) the gap between the frame attributes box and the upper left corner of the image. This value is parsed by eta.core.image.Width(frame_attrs_box_gap)
show_object_boxes – (True) whether to render object bounding boxes, if available. If this is False, labels, confidences, attributes, etc. are also hidden
show_object_names – (True) whether to render object names, if available
show_object_labels – (True) whether to render object labels, if available
show_object_attrs – (True) whether to render object attributes, if available
show_object_confidences – (False) whether to render object label confidences, if available
per_object_name_colors – (True) whether to render boxes for objects with different names in different colors
per_object_label_colors – (True) whether to render boxes for objects with different labels in different colors
per_object_index_colors – (True) whether to render boxes for objects with different indexes in different colors
show_object_attr_names – (True) whether to render object attribute names, if available
show_object_attr_confidences – (False) whether to render object attribute confidences, if available
show_object_indices – (True) whether to render object indices, if available
show_object_masks – (True) whether to render object segmentation masks, if available
occluded_object_attr – (“occluded”) the name of the boolean attribute indicating whether an object is occluded
hide_occluded_objects – (False) whether to hide objects when they are occluded
show_event_boxes – (True) whether to render event bounding boxes, if available. If this is False, all attributes, confidences, etc. are also hidden
show_event_labels – (True) whether to render event labels, if available
show_event_attrs – (True) whether to render event attributes, if available
show_event_names – (True) whether to render event names, if available
show_event_confidences – (False) whether to render event label confidences, if available
per_event_name_colors – (True) whether to render boxes for events with different names in different colors
per_event_label_colors – (True) whether to render boxes for events with different labels in different colors
per_event_index_colors – (True) whether to render boxes for events with different indexes in different colors
show_event_attr_names – (True) whether to render event attribute names, if available
show_event_attr_confidences – (False) whether to render event attribute confidences, if available
show_event_indices – (True) whether to render event indices, if available. By default, this is True
show_event_masks – (True) whether to render event segmentation masks, if available
show_event_label_on_objects – (True) whether to render event labels as attributes on objects that belong to events
show_event_objects_in_same_color – (True) whether to render objects that belong to events in the same color as their parent event
occluded_event_attr – (“occluded”) the name of the boolean attribute indicating whether an event is occluded
hide_occluded_events – (False) whether to hide events when they are occluded
bbox_alpha – (0.75) the transparency of bounding boxes
bbox_label_text_pad_pixels – (2) the padding, in pixels, around the text in bounding box labels
bbox_linewidth –
1. the linewidth, in pixels, of bounding boxes
mask_border_thickness – (2) the thickness, in pixels, to use when drawing the borders of segmentation masks
mask_fill_alpha – (0.7) the transparency of segmentation masks
show_frame_mask_semantics – (True) whether to render semantic labels for frame mask regions, when mask indexes are available
attrs_box_render_method – (“panel”) the method used to render object attributes
attrs_box_bg_color – (“#000000”) the background color for attributes boxes
attrs_box_bg_alpha – (0.5) the transparency of attribute panel boxes
attrs_box_text_pad_pixels – (5) the padding, in pixels, around the text in attribute boxes
attrs_box_text_line_spacing_pixels – (1) the padding, in pixels, between each line of text in attribute boxes
show_keypoints_names – (False) whether to render keypoints names, if available
show_keypoints_labels – (False) whether to render keypoints labels, if available
show_keypoints_attrs – (False) whether to render keypoints attributes, if available
show_keypoints_attr_names – (False) whether to render keypoint attribute names, if available
show_keypoints_attr_confidences – (False) whether to render keypoint attribute confidences, if available
per_keypoints_name_colors – (True) whether to render keypoints with different names in different colors
per_keypoints_label_colors – (True) whether to render keypoints with different labels in different colors
keypoints_size –
1. the size to render keypoints
keypoints_alpha – (0.75) the transparency of keypoints

keypoints_skeleton –

(None) an optional keypoint skeleton dictionary of the following form:

{
    "labels": [
        "left hand" "left shoulder", "right shoulder",
        "right hand", "left eye", "right eye", "mouth"
    ],
    "edges": [[0, 1, 2, 3], [4, 5, 6]],
}

draw_keypoints_skeletons – (True) whether to render keypoint skeletons, if available
keypoints_edge_linewidth (2) – the linewidth, in pixels, of keypoint skeleton edges
keypoints_edge_alpha – (0.75) the transparency of keypoint skeleton edges
show_polyline_names – (True) whether to render polyline names, if available
show_polyline_labels – (True) whether to render polyline labels, if available
show_polyline_attrs – (True) whether to render polyline attributes, if available
show_polyline_attr_names – (True) whether to render polyline attribute names, if available
show_polyline_attr_confidences – (False) whether to render polyline attribute confidences, if available
hide_non_filled_polyline_annos – (False) whether to override other settings and hide the annotation panels for non-filled polylines (those with filled == False)
per_polyline_name_colors – (True) whether to render polylines with different names in different colors
per_polyline_label_colors – (True) whether to render polylines with different labels in different colors
polyline_alpha – (0.75) the transparency of polylines
polyline_linewidth – (3) the linewidth, in pixels, of non-filled polylines
fill_polylines – (True) whether to draw polylines as filled, when possible
show_all_names – (False) whether to render all names, if available. If set to True, this overrides all other name flags
hide_all_names – (False) whether to hide all names, if available. If set to True, this overrides all other name flags
show_name_only_titles – (False) whether to render titles that only contain the name of the entity (i.e., no label, confidence, index, etc)
show_all_confidences – (False) whether to render all confidences, if available. If set to True, this overrides all other confidence flags
hide_all_confidences – (False) whether to hide all confidences, if available. If set to True, this overrides all other confidence flags
labels_whitelist – (None) an optional whitelist of labels (of any kind). If provided, only entities whose label is in this list will be rendered
labels_blacklist – (None) an optional list of labels (of any kind) to not render
attr_names_blacklist – (None) an optional list of attribute names (of any kind) to not render
attr_values_blacklist – (None) an optional list of attribute values (of any kind) to not render
hide_false_boolean_attrs – (False) whether to hide attributes (of any kind) when they are False
confidence_scaled_alpha – (False) whether to scale alpha values of objects and events based on their associated confidences
colormap_config – (None) the eta.core.annotations.ColormapConfig to use to select colors for objects/event boxes
text_color – (“#FFFFFF”) the annotation text color
font_path – (eta.core.constants.DEFAULT_FONT_PATH) the path to the PIL.ImageFont to use
font_size –
1. the font size to use
scale_by_media_height – (True) whether to scale font sizes and linewidths according to the height of the media (relative to a height of 720 pixels)
add_logo – (False) whether to add a logo to the frames
logo_config – (None) the eta.core.logo.LogoConfig describing the logo to use

Methods:

`attributes`()	Returns a list of class attributes to be serialized.
`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 Config object from a JSON dictionary.
`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`([reflective])	Serializes the object into a dictionary.
`set_media_size`([frame_size, shape, img])	Sets the size of the media to the given value.
`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.

Attributes:

`colormap`
`font`
`linewidth`
`logo`
`media_height`
`scale_factor`

attributes()#

Returns a list of class attributes to be serialized.

This method is called internally by serialize() to determine the class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in vars(self) are returned, minus private attributes, i.e., those starting with “_”. The order of the attributes in this list is preserved when serializing objects, so a common pattern is for subclasses to override this method if they want their JSON files to be organized in a particular way.

Returns:: a list of class attributes to be serialized

classmethod builder()#: Returns a ConfigBuilder instance for this class.

property colormap#

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.

property font#

classmethod from_dict(d)#

Constructs a Config object from a JSON dictionary.

Config subclass constructors accept JSON dictionaries, so this method simply passes the dictionary to cls().

Parameters:: d – a dict of fields expected by cls
Returns:: an instance of cls

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.

property linewidth#

classmethod load_default()#

Loads the default config instance from file.

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

property logo#

property media_height#

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 scale_factor#

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

set_media_size(frame_size=None, shape=None, img=None)#

Sets the size of the media to the given value. This allows for optimizing font sizes, linewidths, and logo resolutions to suit the dimensions of the media being annotated.

Exactly one keyword argument must be provided.

Parameters:

frame_size – the (width, height) of the image/video frame
shape – the (height, width, …) of the image/video frame, e.g. from img.shape
img – an example image/video frame

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

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()

fiftyone.utils.annotations.draw_labeled_images(samples, output_dir, rel_dir=None, label_fields=None, config=None, progress=None, **kwargs)#

Renders annotated versions of the images in the collection with the specified label data overlaid to the given directory.

The filenames of the sample images are maintained, unless a name conflict would occur in output_dir, in which case an index of the form "-%d" % count is appended to the base filename.

The images are written in format fo.config.default_image_ext.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
output_dir – the directory to write the annotated images
rel_dir (None) – an optional relative directory to strip from each input filepath to generate a unique identifier that is joined with output_dir to generate an output path for each annotated image. This argument allows for populating nested subdirectories in output_dir that match the shape of the input paths. The path is converted to an absolute path (if necessary) via fiftyone.core.storage.normalize_path()
label_fields (None) – a label field or list of label fields to render. If omitted, all compatible fields are rendered
config (None) – an optional DrawConfig configuring how to draw the labels
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
**kwargs – optional keyword arguments specifying parameters of the default DrawConfig to override

Returns:

the list of paths to the labeled images

fiftyone.utils.annotations.draw_labeled_image(sample, outpath, label_fields=None, config=None, **kwargs)#

Renders an annotated version of the sample’s image with the specified label data overlaid to disk.

Parameters:

sample – a fiftyone.core.sample.Sample
outpath – the path to write the annotated image
label_fields (None) – a label field or list of label fields to render. If omitted, all compatible fields are rendered
config (None) – an optional DrawConfig configuring how to draw the labels
**kwargs – optional keyword arguments specifying parameters of the default DrawConfig to override

fiftyone.utils.annotations.draw_labeled_videos(samples, output_dir, rel_dir=None, label_fields=None, config=None, progress=None, **kwargs)#

Renders annotated versions of the videos in the collection with the specified label data overlaid to the given directory.

The filenames of the videos are maintained, unless a name conflict would occur in output_dir, in which case an index of the form "-%d" % count is appended to the base filename.

The videos are written in format fo.config.default_video_ext.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
output_dir – the directory to write the annotated videos
rel_dir (None) – an optional relative directory to strip from each input filepath to generate a unique identifier that is joined with output_dir to generate an output path for each annotated video. This argument allows for populating nested subdirectories in output_dir that match the shape of the input paths. The path is converted to an absolute path (if necessary) via fiftyone.core.storage.normalize_path()
label_fields (None) – a label field or list of label fields to render. If omitted, all compatible fields are rendered
config (None) – an optional DrawConfig configuring how to draw the labels
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
**kwargs – optional keyword arguments specifying parameters of the default DrawConfig to override

Returns:

the list of paths to the labeled videos

fiftyone.utils.annotations.draw_labeled_video(sample, outpath, support=None, label_fields=None, config=None, **kwargs)#

Renders an annotated version of the sample’s video with the specified label data overlaid to disk.

Parameters:

sample – a fiftyone.core.sample.Sample
outpath – the path to write the annotated image
support (None) – an optional [first, last] range of frames to render
label_fields (None) – a label field or list of label fields to render. If omitted, all compatible fields are rendered
config (None) – an optional DrawConfig configuring how to draw the labels
**kwargs – optional keyword arguments specifying parameters of the default DrawConfig to override