# fiftyone.core.dataset FiftyOne datasets. Copyright 2017-2026, Voxel51, Inc.
[voxel51.com](https://voxel51.com/)

**Exceptions:** | [`DatasetNotFoundError`](#fiftyone.core.dataset.DatasetNotFoundError)(name) | Exception raised when a dataset is not found. | |-------------------------------------------------------------------------------|-------------------------------------------------| **Functions:** | [`list_datasets`](#fiftyone.core.dataset.list_datasets)([glob_patt, tags, info]) | Lists the available FiftyOne datasets. | |------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------| | [`dataset_exists`](#fiftyone.core.dataset.dataset_exists)(name) | Checks if the dataset exists. | | [`load_dataset`](#fiftyone.core.dataset.load_dataset)(name[, create_if_necessary, reload]) | Loads the FiftyOne dataset with the given name. | | [`get_default_dataset_name`](#fiftyone.core.dataset.get_default_dataset_name)() | Returns a default dataset name based on the current time. | | [`make_unique_dataset_name`](#fiftyone.core.dataset.make_unique_dataset_name)(root) | Makes a unique dataset name with the given root name. | | [`get_default_dataset_dir`](#fiftyone.core.dataset.get_default_dataset_dir)(name) | Returns the default dataset directory for the dataset with the given name. | | [`delete_dataset`](#fiftyone.core.dataset.delete_dataset)(name[, verbose]) | Deletes the FiftyOne dataset with the given name. | | [`delete_datasets`](#fiftyone.core.dataset.delete_datasets)(glob_patt[, verbose]) | Deletes all FiftyOne datasets whose names match the given glob pattern. | | [`delete_non_persistent_datasets`](#fiftyone.core.dataset.delete_non_persistent_datasets)([verbose]) | Deletes all non-persistent datasets. | | [`register_extras_cloner`](#fiftyone.core.dataset.register_extras_cloner)(cloner) | Registers a callable to be invoked during `_clone_extras()`. | **Classes:** | [`Dataset`](#fiftyone.core.dataset.Dataset)([name, \_create, \_reload]) | A FiftyOne dataset. | |---------------------------------------------------------------------------|-----------------------| ### *exception* fiftyone.core.dataset.DatasetNotFoundError(name) Bases: `ValueError` Exception raised when a dataset is not found. **Methods:** | [`add_note`](#fiftyone.core.dataset.DatasetNotFoundError.add_note)(object, /) | Exception.add_note(note) -- add a note to the exception | |-------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------| | [`with_traceback`](#fiftyone.core.dataset.DatasetNotFoundError.with_traceback)(object, /) | Exception.with_traceback(tb) -- set self._\_traceback_\_ to tb and return self. | **Attributes:** | [`args`](#fiftyone.core.dataset.DatasetNotFoundError.args) | | |--------------------------------------------------------------|----| #### add_note(object,) Exception.add_note(note) – add a note to the exception #### args #### with_traceback(object,) Exception.with_traceback(tb) – set self._\_traceback_\_ to tb and return self. ### fiftyone.core.dataset.list_datasets(glob_patt=None, tags=None, info=False) Lists the available FiftyOne datasets. * **Parameters:** * **glob_patt** (*None*) – an optional glob pattern of names to return * **tags** (*None*) – only include datasets that have the specified tag or list of tags * **info** (*False*) – whether to return info dicts describing each dataset rather than just their names * **Returns:** a list of dataset names or info dicts ### fiftyone.core.dataset.dataset_exists(name) Checks if the dataset exists. * **Parameters:** **name** – the name of the dataset * **Returns:** True/False ### fiftyone.core.dataset.load_dataset(name, create_if_necessary=False, reload=False) Loads the FiftyOne dataset with the given name. To create a new dataset, use the [`Dataset`](#fiftyone.core.dataset.Dataset) constructor. #### NOTE [`Dataset`](#fiftyone.core.dataset.Dataset) instances are singletons keyed by their name, so all calls to this method with a given dataset `name` in a program will return the same object. * **Parameters:** * **name** – the name of the dataset * **create_if_necessary** (*False*) – if no dataset exists, create an empty one * **reload** (*False*) – whether to reload the dataset if necessary * **Raises:** [**DatasetNotFoundError**](#fiftyone.core.dataset.DatasetNotFoundError) – if the dataset does not exist and `create_if_necessary` is False * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) ### fiftyone.core.dataset.get_default_dataset_name() Returns a default dataset name based on the current time. * **Returns:** a dataset name ### fiftyone.core.dataset.make_unique_dataset_name(root) Makes a unique dataset name with the given root name. * **Parameters:** **root** – the root name for the dataset * **Returns:** the dataset name ### fiftyone.core.dataset.get_default_dataset_dir(name) Returns the default dataset directory for the dataset with the given name. * **Parameters:** **name** – the dataset name * **Returns:** the default directory for the dataset ### fiftyone.core.dataset.delete_dataset(name, verbose=False) Deletes the FiftyOne dataset with the given name. * **Parameters:** * **name** – the name of the dataset * **verbose** (*False*) – whether to log the name of the deleted dataset ### fiftyone.core.dataset.delete_datasets(glob_patt, verbose=False) Deletes all FiftyOne datasets whose names match the given glob pattern. * **Parameters:** * **glob_patt** – a glob pattern of datasets to delete * **verbose** (*False*) – whether to log the names of deleted datasets ### fiftyone.core.dataset.delete_non_persistent_datasets(verbose=False) Deletes all non-persistent datasets. * **Parameters:** **verbose** (*False*) – whether to log the names of deleted datasets ### *class* fiftyone.core.dataset.Dataset(name=None, \_create=True, \_reload=False, \*args, \*\*kwargs) Bases: [`SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) A FiftyOne dataset. Datasets represent an ordered collection of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instances that describe a particular type of raw media (e.g., images or videos) together with a user-defined set of fields. FiftyOne datasets ingest and store the labels for all samples internally; raw media is stored on disk and the dataset provides paths to the data. See [this page](../user_guide/using_datasets.md#using-datasets) for an overview of working with FiftyOne datasets. * **Parameters:** * **name** (*None*) – the name of the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name **Attributes:** | [`media_type`](#fiftyone.core.dataset.Dataset.media_type) | The media type of the dataset. | |-------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [`media_sources`](#fiftyone.core.dataset.Dataset.media_sources) | The media sources this dataset records, one per referenced source. | | [`group_field`](#fiftyone.core.dataset.Dataset.group_field) | The group field of the dataset, or None if the dataset is not grouped. | | [`group_slice`](#fiftyone.core.dataset.Dataset.group_slice) | The current group slice of the dataset, or None if the dataset is not grouped. | | [`group_slices`](#fiftyone.core.dataset.Dataset.group_slices) | The list of group slices of the dataset, or None if the dataset is not grouped. | | [`group_media_types`](#fiftyone.core.dataset.Dataset.group_media_types) | A dict mapping group slices to media types, or None if the dataset is not grouped. | | [`default_group_slice`](#fiftyone.core.dataset.Dataset.default_group_slice) | The default group slice of the dataset, or None if the dataset is not grouped. | | [`version`](#fiftyone.core.dataset.Dataset.version) | The version of the `fiftyone` package for which the dataset is formatted. | | [`name`](#fiftyone.core.dataset.Dataset.name) | The name of the dataset. | | [`slug`](#fiftyone.core.dataset.Dataset.slug) | The slug of the dataset. | | [`created_at`](#fiftyone.core.dataset.Dataset.created_at) | The datetime that the dataset was created. | | [`last_modified_at`](#fiftyone.core.dataset.Dataset.last_modified_at) | The datetime that dataset-level metadata was last modified. | | [`last_deletion_at`](#fiftyone.core.dataset.Dataset.last_deletion_at) | The datetime that a sample was last deleted from the dataset, or `None` if no samples have been deleted. | | [`last_loaded_at`](#fiftyone.core.dataset.Dataset.last_loaded_at) | The datetime that the dataset was last loaded. | | [`persistent`](#fiftyone.core.dataset.Dataset.persistent) | Whether the dataset persists in the database after a session is terminated. | | [`tags`](#fiftyone.core.dataset.Dataset.tags) | A list of tags on the dataset. | | [`description`](#fiftyone.core.dataset.Dataset.description) | A string description on the dataset. | | [`info`](#fiftyone.core.dataset.Dataset.info) | A user-facing dictionary of information about the dataset. | | [`app_config`](#fiftyone.core.dataset.Dataset.app_config) | A [`fiftyone.core.odm.dataset.DatasetAppConfig`](fiftyone.core.odm.dataset.md#fiftyone.core.odm.dataset.DatasetAppConfig) that customizes how this dataset is visualized in the [FiftyOne App](../user_guide/app.md#fiftyone-app). | | [`classes`](#fiftyone.core.dataset.Dataset.classes) | A dict mapping field names to list of class label strings for the corresponding fields of the dataset. | | [`default_classes`](#fiftyone.core.dataset.Dataset.default_classes) | A list of class label strings for all [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields of this dataset that do not have customized classes defined in [`classes()`](#fiftyone.core.dataset.Dataset.classes). | | [`mask_targets`](#fiftyone.core.dataset.Dataset.mask_targets) | A dict mapping field names to mask target dicts, each of which defines a mapping between pixel values (2D masks) or RGB hex strings (3D masks) and label strings for the segmentation masks in the corresponding field of the dataset. | | [`default_mask_targets`](#fiftyone.core.dataset.Dataset.default_mask_targets) | A dict defining a default mapping between pixel values (2D masks) or RGB hex strings (3D masks) and label strings for the segmentation masks of all [`fiftyone.core.labels.Segmentation`](fiftyone.core.labels.md#fiftyone.core.labels.Segmentation) fields of this dataset that do not have customized mask targets defined in [`mask_targets()`](#fiftyone.core.dataset.Dataset.mask_targets). | | [`skeletons`](#fiftyone.core.dataset.Dataset.skeletons) | A dict mapping field names to [`fiftyone.core.odm.dataset.KeypointSkeleton`](fiftyone.core.odm.dataset.md#fiftyone.core.odm.dataset.KeypointSkeleton) instances, each of which defines the semantic labels and point connectivity for the [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) instances in the corresponding field of the dataset. | | [`default_skeleton`](#fiftyone.core.dataset.Dataset.default_skeleton) | A default [`fiftyone.core.odm.dataset.KeypointSkeleton`](fiftyone.core.odm.dataset.md#fiftyone.core.odm.dataset.KeypointSkeleton) defining the semantic labels and point connectivity for all [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) fields of this dataset that do not have customized skeletons defined in `skeleton()`. | | [`camera_intrinsics`](#fiftyone.core.dataset.Dataset.camera_intrinsics) | A dict mapping sensor/camera names to [`fiftyone.core.camera.CameraIntrinsics`](fiftyone.core.camera.md#fiftyone.core.camera.CameraIntrinsics) instances that define the intrinsic parameters for each camera in the dataset. | | [`static_transforms`](#fiftyone.core.dataset.Dataset.static_transforms) | A dict mapping frame relationships to [`fiftyone.core.camera.StaticTransform`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransform) instances that define the transformation parameters (poses) for sensors in the dataset. | | [`deleted`](#fiftyone.core.dataset.Dataset.deleted) | Whether the dataset is deleted. | | [`active_label_schemas`](#fiftyone.core.dataset.Dataset.active_label_schemas) | The list of active fields in the dataset's [label schemas](../integrations/annotation.md#annotation-label-schema). | | [`label_schemas`](#fiftyone.core.dataset.Dataset.label_schemas) | The dataset's [label schemas](../integrations/annotation.md#annotation-label-schema) that define its App annotation UX and constraints. | | [`has_saved_views`](#fiftyone.core.dataset.Dataset.has_saved_views) | Whether this dataset has any saved views. | | [`has_workspaces`](#fiftyone.core.dataset.Dataset.has_workspaces) | Whether this dataset has any saved workspaces. | | [`has_annotation_runs`](#fiftyone.core.dataset.Dataset.has_annotation_runs) | Whether this collection has any annotation runs. | | [`has_brain_runs`](#fiftyone.core.dataset.Dataset.has_brain_runs) | Whether this collection has any brain runs. | | [`has_evaluations`](#fiftyone.core.dataset.Dataset.has_evaluations) | Whether this collection has any evaluation results. | | [`has_runs`](#fiftyone.core.dataset.Dataset.has_runs) | Whether this collection has any runs. | **Methods:** | [`add_intrinsics`](#fiftyone.core.dataset.Dataset.add_intrinsics)(name, intrinsics) | Adds camera intrinsics to the dataset. | |-------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [`add_static_transform`](#fiftyone.core.dataset.Dataset.add_static_transform)(transform) | Adds a static transform to the dataset. | | [`resolve_intrinsics`](#fiftyone.core.dataset.Dataset.resolve_intrinsics)(sample) | Resolves camera intrinsics for the given sample. | | [`resolve_transformation`](#fiftyone.core.dataset.Dataset.resolve_transformation)(sample[, ...]) | Resolves a static transform for the given sample. | | [`get_transform_chain`](#fiftyone.core.dataset.Dataset.get_transform_chain)(source_frame, target_frame) | Computes a composed transformation between coordinate frames. | | [`set_label_schemas`](#fiftyone.core.dataset.Dataset.set_label_schemas)(label_schemas) | Set the dataset's [label schemas](../integrations/annotation.md#annotation-label-schema) that defines its App annotation UX and constraints. | | [`update_label_schema`](#fiftyone.core.dataset.Dataset.update_label_schema)(field, label_schema[, ...]) | Update an individual field's [label schema](../integrations/annotation.md#annotation-label-schema). | | [`delete_label_schemas`](#fiftyone.core.dataset.Dataset.delete_label_schemas)([fields]) | Deletes one or more [label schemas](../integrations/annotation.md#annotation-label-schema). | | [`activate_label_schemas`](#fiftyone.core.dataset.Dataset.activate_label_schemas)([fields, prepend]) | Activate [`label_schemas()`](#fiftyone.core.dataset.Dataset.label_schemas). | | [`deactivate_label_schemas`](#fiftyone.core.dataset.Dataset.deactivate_label_schemas)([fields]) | Deactivate [`label_schemas()`](#fiftyone.core.dataset.Dataset.label_schemas). | | [`summary`](#fiftyone.core.dataset.Dataset.summary)() | Returns a string summary of the dataset. | | [`stats`](#fiftyone.core.dataset.Dataset.stats)([include_media, include_indexes, ...]) | Returns stats about the dataset on disk. | | [`first`](#fiftyone.core.dataset.Dataset.first)() | Returns the first sample in the dataset. | | [`last`](#fiftyone.core.dataset.Dataset.last)() | Returns the last sample in the dataset. | | [`head`](#fiftyone.core.dataset.Dataset.head)([num_samples]) | Returns a list of the first few samples in the dataset. | | [`tail`](#fiftyone.core.dataset.Dataset.tail)([num_samples]) | Returns a list of the last few samples in the dataset. | | [`one`](#fiftyone.core.dataset.Dataset.one)(expr[, exact]) | Returns a single sample in this dataset matching the expression. | | [`view`](#fiftyone.core.dataset.Dataset.view)() | Returns a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) containing the entire dataset. | | [`get_field_schema`](#fiftyone.core.dataset.Dataset.get_field_schema)([ftype, embedded_doc_type, ...]) | Returns a schema dictionary describing the fields of the samples in the dataset. | | [`get_frame_field_schema`](#fiftyone.core.dataset.Dataset.get_frame_field_schema)([ftype, ...]) | Returns a schema dictionary describing the fields of the frames of the samples in the dataset. | | [`add_sample_field`](#fiftyone.core.dataset.Dataset.add_sample_field)(field_name, ftype[, ...]) | Adds a new sample field or embedded field to the dataset, if necessary. | | [`add_dynamic_sample_fields`](#fiftyone.core.dataset.Dataset.add_dynamic_sample_fields)([fields, ...]) | Adds dynamic sample fields to the dataset's schema. | | [`add_frame_field`](#fiftyone.core.dataset.Dataset.add_frame_field)(field_name, ftype[, ...]) | Adds a new frame-level field or embedded field to the dataset, if necessary. | | [`list_summary_fields`](#fiftyone.core.dataset.Dataset.list_summary_fields)() | Lists the summary fields on the dataset. | | [`create_summary_field`](#fiftyone.core.dataset.Dataset.create_summary_field)(path[, field_name, ...]) | Populates a sample-level field that records the unique values or numeric ranges that appear in the specified field on each sample in the dataset. | | [`check_summary_fields`](#fiftyone.core.dataset.Dataset.check_summary_fields)() | Returns a list of summary fields that **may** need to be updated. | | [`update_summary_field`](#fiftyone.core.dataset.Dataset.update_summary_field)(field_name) | Updates the summary field based on the current values of its source field. | | [`delete_summary_field`](#fiftyone.core.dataset.Dataset.delete_summary_field)(field_name[, error_level]) | Deletes the summary field from all samples in the dataset. | | [`delete_summary_fields`](#fiftyone.core.dataset.Dataset.delete_summary_fields)(field_names[, error_level]) | Deletes the summary fields from all samples in the dataset. | | [`add_dynamic_frame_fields`](#fiftyone.core.dataset.Dataset.add_dynamic_frame_fields)([fields, ...]) | Adds dynamic frame fields to the dataset's schema. | | [`add_group_field`](#fiftyone.core.dataset.Dataset.add_group_field)(field_name[, default, ...]) | Adds a group field to the dataset, if necessary. | | [`rename_sample_field`](#fiftyone.core.dataset.Dataset.rename_sample_field)(field_name, new_field_name) | Renames the sample field to the given new name. | | [`rename_sample_fields`](#fiftyone.core.dataset.Dataset.rename_sample_fields)(field_mapping) | Renames the sample fields to the given new names. | | [`rename_frame_field`](#fiftyone.core.dataset.Dataset.rename_frame_field)(field_name, new_field_name) | Renames the frame-level field to the given new name. | | [`rename_frame_fields`](#fiftyone.core.dataset.Dataset.rename_frame_fields)(field_mapping) | Renames the frame-level fields to the given new names. | | [`clone_sample_field`](#fiftyone.core.dataset.Dataset.clone_sample_field)(field_name, new_field_name) | Clones the given sample field into a new field of the dataset. | | [`clone_sample_fields`](#fiftyone.core.dataset.Dataset.clone_sample_fields)(field_mapping) | Clones the given sample fields into new fields of the dataset. | | [`clone_frame_field`](#fiftyone.core.dataset.Dataset.clone_frame_field)(field_name, new_field_name) | Clones the frame-level field into a new field. | | [`clone_frame_fields`](#fiftyone.core.dataset.Dataset.clone_frame_fields)(field_mapping) | Clones the frame-level fields into new fields. | | [`clear_sample_field`](#fiftyone.core.dataset.Dataset.clear_sample_field)(field_name) | Clears the values of the field from all samples in the dataset. | | [`clear_sample_fields`](#fiftyone.core.dataset.Dataset.clear_sample_fields)(field_names) | Clears the values of the fields from all samples in the dataset. | | [`clear_frame_field`](#fiftyone.core.dataset.Dataset.clear_frame_field)(field_name) | Clears the values of the frame-level field from all samples in the dataset. | | [`clear_frame_fields`](#fiftyone.core.dataset.Dataset.clear_frame_fields)(field_names) | Clears the values of the frame-level fields from all samples in the dataset. | | [`delete_sample_field`](#fiftyone.core.dataset.Dataset.delete_sample_field)(field_name[, error_level]) | Deletes the field from all samples in the dataset. | | [`delete_sample_fields`](#fiftyone.core.dataset.Dataset.delete_sample_fields)(field_names[, error_level]) | Deletes the fields from all samples in the dataset. | | [`remove_dynamic_sample_field`](#fiftyone.core.dataset.Dataset.remove_dynamic_sample_field)(field_name[, ...]) | Removes the dynamic embedded sample field from the dataset's schema. | | [`remove_dynamic_sample_fields`](#fiftyone.core.dataset.Dataset.remove_dynamic_sample_fields)(field_names[, ...]) | Removes the dynamic embedded sample fields from the dataset's schema. | | [`delete_frame_field`](#fiftyone.core.dataset.Dataset.delete_frame_field)(field_name[, error_level]) | Deletes the frame-level field from all samples in the dataset. | | [`delete_frame_fields`](#fiftyone.core.dataset.Dataset.delete_frame_fields)(field_names[, error_level]) | Deletes the frame-level fields from all samples in the dataset. | | [`remove_dynamic_frame_field`](#fiftyone.core.dataset.Dataset.remove_dynamic_frame_field)(field_name[, ...]) | Removes the dynamic embedded frame field from the dataset's schema. | | [`remove_dynamic_frame_fields`](#fiftyone.core.dataset.Dataset.remove_dynamic_frame_fields)(field_names[, ...]) | Removes the dynamic embedded frame fields from the dataset's schema. | | [`add_group_slice`](#fiftyone.core.dataset.Dataset.add_group_slice)(name, media_type) | Adds a group slice with the given media type to the dataset, if necessary. | | [`rename_group_slice`](#fiftyone.core.dataset.Dataset.rename_group_slice)(name, new_name) | Renames the group slice with the given name. | | [`delete_group_slice`](#fiftyone.core.dataset.Dataset.delete_group_slice)(name) | Deletes all samples in the given group slice from the dataset. | | [`iter_samples`](#fiftyone.core.dataset.Dataset.iter_samples)([progress, autosave, ...]) | Returns an iterator over the samples in the dataset. | | [`iter_groups`](#fiftyone.core.dataset.Dataset.iter_groups)([group_slices, progress, ...]) | Returns an iterator over the groups in the dataset. | | [`get_group`](#fiftyone.core.dataset.Dataset.get_group)(group_id[, group_slices]) | Returns a dict containing the samples for the given group ID. | | [`add_sample`](#fiftyone.core.dataset.Dataset.add_sample)(sample[, expand_schema, dynamic, ...]) | Adds the given sample to the dataset. | | [`add_samples`](#fiftyone.core.dataset.Dataset.add_samples)(samples[, expand_schema, ...]) | Adds the given samples to the dataset. | | [`add_collection`](#fiftyone.core.dataset.Dataset.add_collection)(sample_collection[, ...]) | Adds the contents of the given collection to the dataset. | | [`merge_sample`](#fiftyone.core.dataset.Dataset.merge_sample)(sample[, key_field, ...]) | Merges the fields of the given sample into this dataset. | | [`merge_samples`](#fiftyone.core.dataset.Dataset.merge_samples)(samples[, key_field, key_fcn, ...]) | Merges the given samples into this dataset. | | [`delete_samples`](#fiftyone.core.dataset.Dataset.delete_samples)(samples_or_ids) | Deletes the given sample(s) from the dataset. | | [`delete_frames`](#fiftyone.core.dataset.Dataset.delete_frames)(frames_or_ids) | Deletes the given frames(s) from the dataset. | | [`delete_groups`](#fiftyone.core.dataset.Dataset.delete_groups)(groups_or_ids) | Deletes the given groups(s) from the dataset. | | [`delete_labels`](#fiftyone.core.dataset.Dataset.delete_labels)([labels, ids, instance_ids, ...]) | Deletes the specified labels from the dataset. | | [`save`](#fiftyone.core.dataset.Dataset.save)() | Saves the dataset to the database. | | [`has_saved_view`](#fiftyone.core.dataset.Dataset.has_saved_view)(name) | Whether this dataset has a saved view with the given name. | | [`list_saved_views`](#fiftyone.core.dataset.Dataset.list_saved_views)([info]) | List saved views on this dataset. | | [`save_view`](#fiftyone.core.dataset.Dataset.save_view)(name, view[, description, color, ...]) | Saves the given view into this dataset under the given name so it can be loaded later via [`load_saved_view()`](#fiftyone.core.dataset.Dataset.load_saved_view). | | [`get_saved_view_info`](#fiftyone.core.dataset.Dataset.get_saved_view_info)(name) | Loads the editable information about the saved view with the given name. | | [`update_saved_view_info`](#fiftyone.core.dataset.Dataset.update_saved_view_info)(name, info) | Updates the editable information for the saved view with the given name. | | [`load_saved_view`](#fiftyone.core.dataset.Dataset.load_saved_view)(name) | Loads the saved view with the given name. | | [`delete_saved_view`](#fiftyone.core.dataset.Dataset.delete_saved_view)(name) | Deletes the saved view with the given name. | | [`delete_saved_views`](#fiftyone.core.dataset.Dataset.delete_saved_views)() | Deletes all saved views from this dataset. | | [`has_workspace`](#fiftyone.core.dataset.Dataset.has_workspace)(name) | Whether this dataset has a saved workspace with the given name. | | [`list_workspaces`](#fiftyone.core.dataset.Dataset.list_workspaces)([info]) | List saved workspaces on this dataset. | | [`save_workspace`](#fiftyone.core.dataset.Dataset.save_workspace)(name, workspace[, ...]) | Saves a workspace into this dataset under the given name so it can be loaded later via [`load_workspace()`](#fiftyone.core.dataset.Dataset.load_workspace). | | [`load_workspace`](#fiftyone.core.dataset.Dataset.load_workspace)(name) | Loads the saved workspace with the given name. | | [`get_workspace_info`](#fiftyone.core.dataset.Dataset.get_workspace_info)(name) | Gets the information about the workspace with the given name. | | [`update_workspace_info`](#fiftyone.core.dataset.Dataset.update_workspace_info)(name, info) | Updates the editable information for the saved view with the given name. | | [`delete_workspace`](#fiftyone.core.dataset.Dataset.delete_workspace)(name) | Deletes the saved workspace with the given name. | | [`delete_workspaces`](#fiftyone.core.dataset.Dataset.delete_workspaces)() | Deletes all saved workspaces from this dataset. | | [`clone`](#fiftyone.core.dataset.Dataset.clone)([name, persistent, include_indexes]) | Creates a copy of the dataset. | | [`clear`](#fiftyone.core.dataset.Dataset.clear)() | Removes all samples from the dataset. | | [`clear_frames`](#fiftyone.core.dataset.Dataset.clear_frames)() | Removes all frame labels from the dataset. | | [`ensure_frames`](#fiftyone.core.dataset.Dataset.ensure_frames)() | Ensures that the video dataset contains frame instances for every frame of each sample's source video. | | [`delete`](#fiftyone.core.dataset.Dataset.delete)() | Deletes the dataset. | | [`add_dir`](#fiftyone.core.dataset.Dataset.add_dir)([dataset_dir, dataset_type, ...]) | Adds the contents of the given directory to the dataset. | | [`merge_dir`](#fiftyone.core.dataset.Dataset.merge_dir)([dataset_dir, dataset_type, ...]) | Merges the contents of the given directory into the dataset. | | [`add_archive`](#fiftyone.core.dataset.Dataset.add_archive)(archive_path[, dataset_type, ...]) | Adds the contents of the given archive to the dataset. | | [`merge_archive`](#fiftyone.core.dataset.Dataset.merge_archive)(archive_path[, dataset_type, ...]) | Merges the contents of the given archive into the dataset. | | [`add_importer`](#fiftyone.core.dataset.Dataset.add_importer)(dataset_importer[, ...]) | Adds the samples from the given [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) to the dataset. | | [`merge_importer`](#fiftyone.core.dataset.Dataset.merge_importer)(dataset_importer[, ...]) | Merges the samples from the given [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) into the dataset. | | [`add_images`](#fiftyone.core.dataset.Dataset.add_images)(paths_or_samples[, ...]) | Adds the given images to the dataset. | | [`add_labeled_images`](#fiftyone.core.dataset.Dataset.add_labeled_images)(samples, sample_parser[, ...]) | Adds the given labeled images to the dataset. | | [`add_images_dir`](#fiftyone.core.dataset.Dataset.add_images_dir)(images_dir[, tags, ...]) | Adds the given directory of images to the dataset. | | [`add_images_patt`](#fiftyone.core.dataset.Dataset.add_images_patt)(images_patt[, tags, ...]) | Adds the given glob pattern of images to the dataset. | | [`ingest_images`](#fiftyone.core.dataset.Dataset.ingest_images)(paths_or_samples[, ...]) | Ingests the given iterable of images into the dataset. | | [`ingest_labeled_images`](#fiftyone.core.dataset.Dataset.ingest_labeled_images)(samples, sample_parser) | Ingests the given iterable of labeled image samples into the dataset. | | [`add_videos`](#fiftyone.core.dataset.Dataset.add_videos)(paths_or_samples[, ...]) | Adds the given videos to the dataset. | | [`add_labeled_videos`](#fiftyone.core.dataset.Dataset.add_labeled_videos)(samples, sample_parser[, ...]) | Adds the given labeled videos to the dataset. | | [`add_videos_dir`](#fiftyone.core.dataset.Dataset.add_videos_dir)(videos_dir[, tags, ...]) | Adds the given directory of videos to the dataset. | | [`add_videos_patt`](#fiftyone.core.dataset.Dataset.add_videos_patt)(videos_patt[, tags, ...]) | Adds the given glob pattern of videos to the dataset. | | [`ingest_videos`](#fiftyone.core.dataset.Dataset.ingest_videos)(paths_or_samples[, ...]) | Ingests the given iterable of videos into the dataset. | | [`ingest_labeled_videos`](#fiftyone.core.dataset.Dataset.ingest_labeled_videos)(samples, sample_parser) | Ingests the given iterable of labeled video samples into the dataset. | | [`from_dir`](#fiftyone.core.dataset.Dataset.from_dir)([dataset_dir, dataset_type, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the contents of the given directory. | | [`from_archive`](#fiftyone.core.dataset.Dataset.from_archive)(archive_path[, dataset_type, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the contents of the given archive. | | [`from_importer`](#fiftyone.core.dataset.Dataset.from_importer)(dataset_importer[, name, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) by importing the samples in the given [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter). | | [`from_images`](#fiftyone.core.dataset.Dataset.from_images)(paths_or_samples[, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given images. | | [`from_labeled_images`](#fiftyone.core.dataset.Dataset.from_labeled_images)(samples, sample_parser) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given labeled images. | | [`from_images_dir`](#fiftyone.core.dataset.Dataset.from_images_dir)(images_dir[, name, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given directory of images. | | [`from_images_patt`](#fiftyone.core.dataset.Dataset.from_images_patt)(images_patt[, name, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given glob pattern of images. | | [`from_videos`](#fiftyone.core.dataset.Dataset.from_videos)(paths_or_samples[, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given videos. | | [`from_labeled_videos`](#fiftyone.core.dataset.Dataset.from_labeled_videos)(samples, sample_parser) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given labeled videos. | | [`from_videos_dir`](#fiftyone.core.dataset.Dataset.from_videos_dir)(videos_dir[, name, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given directory of videos. | | [`from_videos_patt`](#fiftyone.core.dataset.Dataset.from_videos_patt)(videos_patt[, name, ...]) | Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given glob pattern of videos. | | [`from_dict`](#fiftyone.core.dataset.Dataset.from_dict)(d[, name, persistent, overwrite, ...]) | Loads a [`Dataset`](#fiftyone.core.dataset.Dataset) from a JSON dictionary generated by [`fiftyone.core.collections.SampleCollection.to_dict()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.to_dict). | | [`from_json`](#fiftyone.core.dataset.Dataset.from_json)(path_or_str[, name, persistent, ...]) | Loads a [`Dataset`](#fiftyone.core.dataset.Dataset) from JSON generated by [`fiftyone.core.collections.SampleCollection.write_json()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.write_json) or [`fiftyone.core.collections.SampleCollection.to_json()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.to_json). | | [`reload`](#fiftyone.core.dataset.Dataset.reload)() | Reloads the dataset and any in-memory samples from the database. | | [`clear_cache`](#fiftyone.core.dataset.Dataset.clear_cache)() | Clears the dataset's in-memory cache. | | [`add_stage`](#fiftyone.core.dataset.Dataset.add_stage)(stage) | Applies the given [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) to the collection. | | [`aggregate`](#fiftyone.core.dataset.Dataset.aggregate)(aggregations[, \_mongo]) | Aggregates one or more [`fiftyone.core.aggregations.Aggregation`](fiftyone.core.aggregations.md#fiftyone.core.aggregations.Aggregation) instances. | | [`annotate`](#fiftyone.core.dataset.Dataset.annotate)(anno_key[, label_schema, ...]) | Exports the samples and optional label field(s) in this collection to the given annotation backend. | | [`apply_model`](#fiftyone.core.dataset.Dataset.apply_model)(model[, label_field, ...]) | Applies the model to the samples in the collection. | | [`bounds`](#fiftyone.core.dataset.Dataset.bounds)(field_or_expr[, expr, safe]) | Computes the bounds of a numeric field of the collection. | | [`compute_embeddings`](#fiftyone.core.dataset.Dataset.compute_embeddings)(model[, ...]) | Computes embeddings for the samples in the collection using the given model. | | [`compute_metadata`](#fiftyone.core.dataset.Dataset.compute_metadata)([overwrite, num_workers, ...]) | Populates the `metadata` field of all samples in the collection. | | [`compute_patch_embeddings`](#fiftyone.core.dataset.Dataset.compute_patch_embeddings)(model, patches_field) | Computes embeddings for the image patches defined by `patches_field` of the samples in the collection using the given model. | | [`concat`](#fiftyone.core.dataset.Dataset.concat)(samples) | Concatenates the contents of the given `SampleCollection` to this collection. | | [`count`](#fiftyone.core.dataset.Dataset.count)([field_or_expr, expr, safe]) | Counts the number of field values in the collection. | | [`count_label_tags`](#fiftyone.core.dataset.Dataset.count_label_tags)([label_fields]) | Counts the occurrences of all label tags in the specified label field(s) of this collection. | | [`count_sample_tags`](#fiftyone.core.dataset.Dataset.count_sample_tags)() | Counts the occurrences of sample tags in this collection. | | [`count_values`](#fiftyone.core.dataset.Dataset.count_values)(field_or_expr[, expr, safe]) | Counts the occurrences of field values in the collection. | | [`create_index`](#fiftyone.core.dataset.Dataset.create_index)(field_or_spec[, unique, force, ...]) | Creates an index on the given field or with the given specification, if necessary. | | [`delete_annotation_run`](#fiftyone.core.dataset.Dataset.delete_annotation_run)(anno_key) | Deletes the annotation run with the given key from this collection. | | [`delete_annotation_runs`](#fiftyone.core.dataset.Dataset.delete_annotation_runs)() | Deletes all annotation runs from this collection. | | [`delete_brain_run`](#fiftyone.core.dataset.Dataset.delete_brain_run)(brain_key) | Deletes the brain method run with the given key from this collection. | | [`delete_brain_runs`](#fiftyone.core.dataset.Dataset.delete_brain_runs)() | Deletes all brain method runs from this collection. | | [`delete_evaluation`](#fiftyone.core.dataset.Dataset.delete_evaluation)(eval_key) | Deletes the evaluation results associated with the given evaluation key from this collection. | | [`delete_evaluations`](#fiftyone.core.dataset.Dataset.delete_evaluations)() | Deletes all evaluation results from this collection. | | [`delete_run`](#fiftyone.core.dataset.Dataset.delete_run)(run_key) | Deletes the run with the given key from this collection. | | [`delete_runs`](#fiftyone.core.dataset.Dataset.delete_runs)() | Deletes all runs from this collection. | | [`distinct`](#fiftyone.core.dataset.Dataset.distinct)(field_or_expr[, expr, safe]) | Computes the distinct values of a field in the collection. | | [`draw_labels`](#fiftyone.core.dataset.Dataset.draw_labels)(output_dir[, rel_dir, ...]) | Renders annotated versions of the media in the collection with the specified label data overlaid to the given directory. | | [`drop_index`](#fiftyone.core.dataset.Dataset.drop_index)(field_or_name) | Drops the index for the given field or name, if necessary. | | [`evaluate_classifications`](#fiftyone.core.dataset.Dataset.evaluate_classifications)(pred_field[, ...]) | Evaluates the classification predictions in this collection with respect to the specified ground truth labels. | | [`evaluate_detections`](#fiftyone.core.dataset.Dataset.evaluate_detections)(pred_field[, gt_field, ...]) | Evaluates the specified predicted detections in this collection with respect to the specified ground truth detections. | | [`evaluate_regressions`](#fiftyone.core.dataset.Dataset.evaluate_regressions)(pred_field[, gt_field, ...]) | Evaluates the regression predictions in this collection with respect to the specified ground truth values. | | [`evaluate_segmentations`](#fiftyone.core.dataset.Dataset.evaluate_segmentations)(pred_field[, ...]) | Evaluates the specified semantic segmentation masks in this collection with respect to the specified ground truth masks. | | [`exclude`](#fiftyone.core.dataset.Dataset.exclude)(sample_ids) | Excludes the samples with the given IDs from the collection. | | [`exclude_by`](#fiftyone.core.dataset.Dataset.exclude_by)(field, values) | Excludes the samples with the given field values from the collection. | | [`exclude_fields`](#fiftyone.core.dataset.Dataset.exclude_fields)([field_names, meta_filter, ...]) | Excludes the fields with the given names from the samples in the collection. | | [`exclude_frames`](#fiftyone.core.dataset.Dataset.exclude_frames)(frame_ids[, omit_empty]) | Excludes the frames with the given IDs from the video collection. | | [`exclude_group_slices`](#fiftyone.core.dataset.Dataset.exclude_group_slices)([slices, media_type]) | Excludes the specified group slice(s) from the grouped collection. | | [`exclude_groups`](#fiftyone.core.dataset.Dataset.exclude_groups)(group_ids) | Excludes the groups with the given IDs from the grouped collection. | | [`exclude_labels`](#fiftyone.core.dataset.Dataset.exclude_labels)([labels, ids, instance_ids, ...]) | Excludes the specified labels from the collection. | | [`exists`](#fiftyone.core.dataset.Dataset.exists)(field[, bool]) | Returns a view containing the samples in the collection that have (or do not have) a non-`None` value for the given field or embedded field. | | [`export`](#fiftyone.core.dataset.Dataset.export)([export_dir, dataset_type, ...]) | Exports the samples in the collection to disk. | | [`filter_field`](#fiftyone.core.dataset.Dataset.filter_field)(field, filter[, only_matches]) | Filters the values of a field or embedded field of each sample in the collection. | | [`filter_keypoints`](#fiftyone.core.dataset.Dataset.filter_keypoints)(field[, filter, labels, ...]) | Filters the individual [`fiftyone.core.labels.Keypoint.points`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint.points) elements in the specified keypoints field of each sample in the collection. | | [`filter_labels`](#fiftyone.core.dataset.Dataset.filter_labels)(field, filter[, only_matches, ...]) | Filters the [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) field of each sample in the collection. | | [`flatten`](#fiftyone.core.dataset.Dataset.flatten)([stages]) | Returns a flattened view that contains all samples in the dynamic grouped collection. | | [`generate_label_schemas`](#fiftyone.core.dataset.Dataset.generate_label_schemas)([fields, scan_samples]) | Generates label schemas for the [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection). | | [`geo_near`](#fiftyone.core.dataset.Dataset.geo_near)(point[, location_field, ...]) | Sorts the samples in the collection by their proximity to a specified geolocation. | | [`geo_within`](#fiftyone.core.dataset.Dataset.geo_within)(boundary[, location_field, ...]) | Filters the samples in this collection to only include samples whose geolocation is within a specified boundary. | | [`get_annotation_info`](#fiftyone.core.dataset.Dataset.get_annotation_info)(anno_key) | Returns information about the annotation run with the given key on this collection. | | [`get_brain_info`](#fiftyone.core.dataset.Dataset.get_brain_info)(brain_key) | Returns information about the brain method run with the given key on this collection. | | [`get_classes`](#fiftyone.core.dataset.Dataset.get_classes)(field) | Gets the classes list for the given field, or None if no classes are available. | | [`get_dynamic_field_schema`](#fiftyone.core.dataset.Dataset.get_dynamic_field_schema)([fields, recursive]) | Returns a schema dictionary describing the dynamic fields of the samples in the collection. | | [`get_dynamic_frame_field_schema`](#fiftyone.core.dataset.Dataset.get_dynamic_frame_field_schema)([fields, ...]) | Returns a schema dictionary describing the dynamic fields of the frames in the collection. | | [`get_evaluation_info`](#fiftyone.core.dataset.Dataset.get_evaluation_info)(eval_key) | Returns information about the evaluation with the given key on this collection. | | [`get_field`](#fiftyone.core.dataset.Dataset.get_field)(path[, ftype, embedded_doc_type, ...]) | Returns the field instance of the provided path, or `None` if one does not exist. | | [`get_index_information`](#fiftyone.core.dataset.Dataset.get_index_information)([include_stats, ...]) | Returns a dictionary of information about the indexes on this collection. | | [`get_mask_targets`](#fiftyone.core.dataset.Dataset.get_mask_targets)(field) | Gets the mask targets for the given field, or None if no mask targets are available. | | [`get_run_info`](#fiftyone.core.dataset.Dataset.get_run_info)(run_key) | Returns information about the run with the given key on this collection. | | [`get_skeleton`](#fiftyone.core.dataset.Dataset.get_skeleton)(field) | Gets the keypoint skeleton for the given field, or None if no skeleton is available. | | [`group_by`](#fiftyone.core.dataset.Dataset.group_by)(field_or_expr[, order_by, reverse, ...]) | Creates a view that groups the samples in the collection by a specified field or expression. | | [`has_annotation_run`](#fiftyone.core.dataset.Dataset.has_annotation_run)(anno_key) | Whether this collection has an annotation run with the given key. | | [`has_brain_run`](#fiftyone.core.dataset.Dataset.has_brain_run)(brain_key) | Whether this collection has a brain method run with the given key. | | [`has_classes`](#fiftyone.core.dataset.Dataset.has_classes)(field) | Determines whether this collection has a classes list for the given field. | | [`has_evaluation`](#fiftyone.core.dataset.Dataset.has_evaluation)(eval_key) | Whether this collection has an evaluation with the given key. | | [`has_field`](#fiftyone.core.dataset.Dataset.has_field)(path) | Determines whether the collection has a field with the given name. | | [`has_frame_field`](#fiftyone.core.dataset.Dataset.has_frame_field)(path) | Determines whether the collection has a frame-level field with the given name. | | [`has_mask_targets`](#fiftyone.core.dataset.Dataset.has_mask_targets)(field) | Determines whether this collection has mask targets for the given field. | | [`has_run`](#fiftyone.core.dataset.Dataset.has_run)(run_key) | Whether this collection has a run with the given key. | | [`has_sample_field`](#fiftyone.core.dataset.Dataset.has_sample_field)(path) | Determines whether the collection has a sample field with the given name. | | [`has_skeleton`](#fiftyone.core.dataset.Dataset.has_skeleton)(field) | Determines whether this collection has a keypoint skeleton for the given field. | | [`histogram_values`](#fiftyone.core.dataset.Dataset.histogram_values)(field_or_expr[, expr, ...]) | Computes a histogram of the field values in the collection. | | [`init_run`](#fiftyone.core.dataset.Dataset.init_run)(\*\*kwargs) | Initializes a config instance for a new run. | | [`init_run_results`](#fiftyone.core.dataset.Dataset.init_run_results)(run_key, \*\*kwargs) | Initializes a results instance for the run with the given key. | | [`limit`](#fiftyone.core.dataset.Dataset.limit)(limit) | Returns a view with at most the given number of samples. | | [`limit_labels`](#fiftyone.core.dataset.Dataset.limit_labels)(field, limit) | Limits the number of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instances in the specified labels list field of each sample in the collection. | | [`list_aggregations`](#fiftyone.core.dataset.Dataset.list_aggregations)() | Returns a list of all available methods on this collection that apply [`fiftyone.core.aggregations.Aggregation`](fiftyone.core.aggregations.md#fiftyone.core.aggregations.Aggregation) operations to this collection. | | [`list_annotation_runs`](#fiftyone.core.dataset.Dataset.list_annotation_runs)([type, method]) | Returns a list of annotation keys on this collection. | | [`list_brain_runs`](#fiftyone.core.dataset.Dataset.list_brain_runs)([type, method]) | Returns a list of brain keys on this collection. | | [`list_evaluations`](#fiftyone.core.dataset.Dataset.list_evaluations)([type, method]) | Returns a list of evaluation keys on this collection. | | [`list_indexes`](#fiftyone.core.dataset.Dataset.list_indexes)() | Returns the list of index names on this collection. | | [`list_runs`](#fiftyone.core.dataset.Dataset.list_runs)(\*\*kwargs) | Returns a list of run keys on this collection. | | [`list_schema`](#fiftyone.core.dataset.Dataset.list_schema)(field_or_expr[, expr]) | Extracts the value type(s) in a specified list field across all samples in the collection. | | [`list_view_stages`](#fiftyone.core.dataset.Dataset.list_view_stages)() | Returns a list of all available methods on this collection that apply [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) operations to this collection. | | [`load_annotation_results`](#fiftyone.core.dataset.Dataset.load_annotation_results)(anno_key[, cache]) | Loads the results for the annotation run with the given key on this collection. | | [`load_annotation_view`](#fiftyone.core.dataset.Dataset.load_annotation_view)(anno_key[, select_fields]) | Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified annotation run was performed on this collection. | | [`load_annotations`](#fiftyone.core.dataset.Dataset.load_annotations)(anno_key[, dest_field, ...]) | Downloads the labels from the given annotation run from the annotation backend and merges them into this collection. | | [`load_brain_results`](#fiftyone.core.dataset.Dataset.load_brain_results)(brain_key[, cache, load_view]) | Loads the results for the brain method run with the given key on this collection. | | [`load_brain_view`](#fiftyone.core.dataset.Dataset.load_brain_view)(brain_key[, select_fields]) | Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified brain method run was performed on this collection. | | [`load_evaluation_results`](#fiftyone.core.dataset.Dataset.load_evaluation_results)(eval_key[, cache]) | Loads the results for the evaluation with the given key on this collection. | | [`load_evaluation_view`](#fiftyone.core.dataset.Dataset.load_evaluation_view)(eval_key[, select_fields]) | Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified evaluation was performed on this collection. | | [`load_run_results`](#fiftyone.core.dataset.Dataset.load_run_results)(run_key[, cache, load_view]) | Loads the results for the run with the given key on this collection. | | [`load_run_view`](#fiftyone.core.dataset.Dataset.load_run_view)(run_key[, select_fields]) | Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified run was performed on this collection. | | [`make_unique_field_name`](#fiftyone.core.dataset.Dataset.make_unique_field_name)([root]) | Makes a unique field name with the given root name for the collection. | | [`map_labels`](#fiftyone.core.dataset.Dataset.map_labels)(field, map) | Maps the `label` values of a [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) field to new values for each sample in the collection. | | [`map_samples`](#fiftyone.core.dataset.Dataset.map_samples)(map_fcn[, save, skip_failures, ...]) | Applies the given function to each sample in the collection and returns the results as a generator. | | [`map_values`](#fiftyone.core.dataset.Dataset.map_values)(field, map) | Maps the values in the given field to new values for each sample in the collection. | | [`match`](#fiftyone.core.dataset.Dataset.match)(filter) | Filters the samples in the collection by the given filter. | | [`match_frames`](#fiftyone.core.dataset.Dataset.match_frames)(filter[, omit_empty]) | Filters the frames in the video collection by the given filter. | | [`match_labels`](#fiftyone.core.dataset.Dataset.match_labels)([labels, ids, instance_ids, ...]) | Selects the samples from the collection that contain (or do not contain) at least one label that matches the specified criteria. | | [`match_tags`](#fiftyone.core.dataset.Dataset.match_tags)(tags[, bool, all]) | Returns a view containing the samples in the collection that have or don't have any/all of the given tag(s). | | [`max`](#fiftyone.core.dataset.Dataset.max)(field_or_expr[, expr, safe]) | Computes the maximum of a numeric field of the collection. | | [`mean`](#fiftyone.core.dataset.Dataset.mean)(field_or_expr[, expr, safe]) | Computes the arithmetic mean of the field values of the collection. | | [`merge_labels`](#fiftyone.core.dataset.Dataset.merge_labels)(in_field, out_field) | Merges the labels from the given input field into the given output field of the collection. | | [`min`](#fiftyone.core.dataset.Dataset.min)(field_or_expr[, expr, safe]) | Computes the minimum of a numeric field of the collection. | | [`mongo`](#fiftyone.core.dataset.Dataset.mongo)(pipeline[, \_needs_frames, \_group_slices]) | Adds a view stage defined by a raw MongoDB aggregation pipeline. | | [`quantiles`](#fiftyone.core.dataset.Dataset.quantiles)(field_or_expr, quantiles[, expr, safe]) | Computes the quantile(s) of the field values of a collection. | | [`register_run`](#fiftyone.core.dataset.Dataset.register_run)(run_key, config[, results, ...]) | Registers a run under the given key on this collection. | | [`rename_annotation_run`](#fiftyone.core.dataset.Dataset.rename_annotation_run)(anno_key, new_anno_key) | Replaces the key for the given annotation run with a new key. | | [`rename_brain_run`](#fiftyone.core.dataset.Dataset.rename_brain_run)(brain_key, new_brain_key) | Replaces the key for the given brain run with a new key. | | [`rename_evaluation`](#fiftyone.core.dataset.Dataset.rename_evaluation)(eval_key, new_eval_key) | Replaces the key for the given evaluation with a new key. | | [`rename_run`](#fiftyone.core.dataset.Dataset.rename_run)(run_key, new_run_key) | Replaces the key for the given run with a new key. | | [`save_context`](#fiftyone.core.dataset.Dataset.save_context)([batch_size, batching_strategy]) | Returns a context that can be used to save samples from this collection according to a configurable batching strategy. | | [`save_run_results`](#fiftyone.core.dataset.Dataset.save_run_results)(run_key, results[, ...]) | Saves run results for the run with the given key. | | [`schema`](#fiftyone.core.dataset.Dataset.schema)(field_or_expr[, expr, dynamic_only, ...]) | Extracts the names and types of the attributes of a specified embedded document field across all samples in the collection. | | [`select`](#fiftyone.core.dataset.Dataset.select)(sample_ids[, ordered]) | Selects the samples with the given IDs from the collection. | | [`select_by`](#fiftyone.core.dataset.Dataset.select_by)(field, values[, ordered]) | Selects the samples with the given field values from the collection. | | [`select_fields`](#fiftyone.core.dataset.Dataset.select_fields)([field_names, meta_filter, ...]) | Selects only the fields with the given names from the samples in the collection. | | [`select_frames`](#fiftyone.core.dataset.Dataset.select_frames)(frame_ids[, omit_empty]) | Selects the frames with the given IDs from the video collection. | | [`select_group_slices`](#fiftyone.core.dataset.Dataset.select_group_slices)([slices, media_type, ...]) | Selects the specified group slice(s) from the grouped collection. | | [`select_groups`](#fiftyone.core.dataset.Dataset.select_groups)(group_ids[, ordered]) | Selects the groups with the given IDs from the grouped collection. | | [`select_labels`](#fiftyone.core.dataset.Dataset.select_labels)([labels, ids, instance_ids, ...]) | Selects only the specified labels from the collection. | | [`set_field`](#fiftyone.core.dataset.Dataset.set_field)(field, expr[, \_allow_missing]) | Sets a field or embedded field on each sample in a collection by evaluating the given expression. | | [`set_label_values`](#fiftyone.core.dataset.Dataset.set_label_values)(field_name, values[, ...]) | Sets the fields of the specified labels in the collection to the given values. | | [`set_values`](#fiftyone.core.dataset.Dataset.set_values)(field_name, values[, key_field, ...]) | Sets the field or embedded field on each sample or frame in the collection to the given values. | | [`shuffle`](#fiftyone.core.dataset.Dataset.shuffle)([seed]) | Randomly shuffles the samples in the collection. | | [`skip`](#fiftyone.core.dataset.Dataset.skip)(skip) | Omits the given number of samples from the head of the collection. | | [`sort_by`](#fiftyone.core.dataset.Dataset.sort_by)(field_or_expr[, reverse, create_index]) | Sorts the samples in the collection by the given field(s) or expression(s). | | [`sort_by_similarity`](#fiftyone.core.dataset.Dataset.sort_by_similarity)(query[, k, reverse, ...]) | Sorts the collection by similarity to a specified query. | | [`split_labels`](#fiftyone.core.dataset.Dataset.split_labels)(in_field, out_field[, filter]) | Splits the labels from the given input field into the given output field of the collection. | | [`std`](#fiftyone.core.dataset.Dataset.std)(field_or_expr[, expr, safe, sample]) | Computes the standard deviation of the field values of the collection. | | [`sum`](#fiftyone.core.dataset.Dataset.sum)(field_or_expr[, expr, safe]) | Computes the sum of the field values of the collection. | | [`sync_last_modified_at`](#fiftyone.core.dataset.Dataset.sync_last_modified_at)([include_frames]) | Syncs the `last_modified_at` property(s) of the dataset. | | [`tag_labels`](#fiftyone.core.dataset.Dataset.tag_labels)(tags[, label_fields]) | Adds the tag(s) to all labels in the specified label field(s) of this collection, if necessary. | | [`tag_samples`](#fiftyone.core.dataset.Dataset.tag_samples)(tags) | Adds the tag(s) to all samples in this collection, if necessary. | | [`take`](#fiftyone.core.dataset.Dataset.take)(size[, seed]) | Randomly samples the given number of samples from the collection. | | [`to_clips`](#fiftyone.core.dataset.Dataset.to_clips)(field_or_expr, \*\*kwargs) | Creates a view that contains one sample per clip defined by the given field or expression in the video collection. | | [`to_dict`](#fiftyone.core.dataset.Dataset.to_dict)([rel_dir, include_private, ...]) | Returns a JSON dictionary representation of the collection. | | [`to_evaluation_patches`](#fiftyone.core.dataset.Dataset.to_evaluation_patches)(eval_key, \*\*kwargs) | Creates a view based on the results of the evaluation with the given key that contains one sample for each true positive, false positive, and false negative example in the collection, respectively. | | [`to_frames`](#fiftyone.core.dataset.Dataset.to_frames)(\*\*kwargs) | Creates a view that contains one sample per frame in the video collection. | | [`to_json`](#fiftyone.core.dataset.Dataset.to_json)([rel_dir, include_private, ...]) | Returns a JSON string representation of the collection. | | [`to_patches`](#fiftyone.core.dataset.Dataset.to_patches)(field, \*\*kwargs) | Creates a view that contains one sample per object patch in the specified field of the collection. | | [`to_torch`](#fiftyone.core.dataset.Dataset.to_torch)(get_item, \*[, index_field, ...]) | Constructs a [`torch.utils.data.Dataset`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) that loads data from this collection via the provided [`fiftyone.utils.torch.GetItem`](fiftyone.utils.torch.md#fiftyone.utils.torch.GetItem) instance. | | [`to_trajectories`](#fiftyone.core.dataset.Dataset.to_trajectories)(field, \*\*kwargs) | Creates a view that contains one clip for each unique object trajectory defined by their `(label, index)` in a frame-level field of a video collection. | | [`untag_labels`](#fiftyone.core.dataset.Dataset.untag_labels)(tags[, label_fields]) | Removes the tag from all labels in the specified label field(s) of this collection, if necessary. | | [`untag_samples`](#fiftyone.core.dataset.Dataset.untag_samples)(tags) | Removes the tag(s) from all samples in this collection, if necessary. | | [`update_run_config`](#fiftyone.core.dataset.Dataset.update_run_config)(run_key, config) | Updates the run config for the run with the given key. | | [`update_samples`](#fiftyone.core.dataset.Dataset.update_samples)(update_fcn[, skip_failures, ...]) | Applies the given function to each sample in the collection and saves the resulting sample edits. | | [`validate_field_type`](#fiftyone.core.dataset.Dataset.validate_field_type)(path[, ftype, ...]) | Validates that the collection has a field of the given type. | | [`validate_fields_exist`](#fiftyone.core.dataset.Dataset.validate_fields_exist)(fields[, include_private]) | Validates that the collection has field(s) with the given name(s). | | [`values`](#fiftyone.core.dataset.Dataset.values)(field_or_expr[, expr, missing_value, ...]) | Extracts the values of a field from all samples in the collection. | | [`write_json`](#fiftyone.core.dataset.Dataset.write_json)(json_path[, rel_dir, ...]) | Writes the collection to disk in JSON format. | #### *property* media_type The media type of the dataset. #### *property* media_sources The media sources this dataset records, one per referenced source. Each is a dict describing where a source is and what every episode of it shares. A sample’s `media_reference` is `/` and names one of them. The list is a copy; use an importer to record sources. #### *property* group_field The group field of the dataset, or None if the dataset is not grouped. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") print(dataset.group_field) # group ``` #### *property* group_slice The current group slice of the dataset, or None if the dataset is not grouped. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") print(dataset.group_slices) # ['left', 'right', 'pcd'] print(dataset.group_slice) # left # Change the current group slice dataset.group_slice = "right" print(dataset.group_slice) # right ``` #### *property* group_slices The list of group slices of the dataset, or None if the dataset is not grouped. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") print(dataset.group_slices) # ['left', 'right', 'pcd'] ``` #### *property* group_media_types A dict mapping group slices to media types, or None if the dataset is not grouped. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") print(dataset.group_media_types) # {'left': 'image', 'right': 'image', 'pcd': 'point-cloud'} ``` #### *property* default_group_slice The default group slice of the dataset, or None if the dataset is not grouped. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") print(dataset.default_group_slice) # left # Change the default group slice dataset.default_group_slice = "right" print(dataset.default_group_slice) # right ``` #### *property* version The version of the `fiftyone` package for which the dataset is formatted. #### *property* name The name of the dataset. #### *property* slug The slug of the dataset. #### *property* created_at The datetime that the dataset was created. #### *property* last_modified_at The datetime that dataset-level metadata was last modified. This property is incremented in the following cases: - when properties such as [`name`](#fiftyone.core.dataset.Dataset.name), [`persistent`](#fiftyone.core.dataset.Dataset.persistent), [`tags`](#fiftyone.core.dataset.Dataset.tags), [`description`](#fiftyone.core.dataset.Dataset.description), [`info`](#fiftyone.core.dataset.Dataset.info), and [`app_config`](#fiftyone.core.dataset.Dataset.app_config) are edited - when fields are added or deleted from the dataset’s schema - when group slices are added or deleted from the dataset’s schema - when saved views or workspaces are added, edited, or deleted - when annotation, brain, evaluation, or custom runs are added, edited, or deleted This property is **not** updated when samples are added, edited, or deleted. Use [`max("last_modified_at")`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.max) to determine when samples were last added or edited, and use [`last_deletion_at`](#fiftyone.core.dataset.Dataset.last_deletion_at) to determine when samples were last deleted. #### *property* last_deletion_at The datetime that a sample was last deleted from the dataset, or `None` if no samples have been deleted. #### *property* last_loaded_at The datetime that the dataset was last loaded. #### *property* persistent Whether the dataset persists in the database after a session is terminated. #### *property* tags A list of tags on the dataset. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Add some tags dataset.tags = ["test", "projectA"] # Edit the tags dataset.tags.pop() dataset.tags.append("projectB") dataset.save() # must save after edits ``` #### *property* description A string description on the dataset. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Store a description on the dataset dataset.description = "Your description here" ``` #### *property* info A user-facing dictionary of information about the dataset. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Store a class list in the dataset's info dataset.info = {"classes": ["cat", "dog"]} # Edit the info dataset.info["other_classes"] = ["bird", "plane"] dataset.save() # must save after edits ``` #### *property* app_config A [`fiftyone.core.odm.dataset.DatasetAppConfig`](fiftyone.core.odm.dataset.md#fiftyone.core.odm.dataset.DatasetAppConfig) that customizes how this dataset is visualized in the [FiftyOne App](../user_guide/app.md#fiftyone-app). Examples: ```default import fiftyone as fo import fiftyone.utils.image as foui import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") # View the dataset's current App config print(dataset.app_config) # Generate some thumbnail images foui.transform_images( dataset, size=(-1, 32), output_field="thumbnail_path", output_dir="/tmp/thumbnails", ) # Modify the dataset's App config dataset.app_config.media_fields = ["filepath", "thumbnail_path"] dataset.app_config.grid_media_field = "thumbnail_path" dataset.save() # must save after edits session = fo.launch_app(dataset) ``` #### *property* classes A dict mapping field names to list of class label strings for the corresponding fields of the dataset. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Set classes for the `ground_truth` and `predictions` fields dataset.classes = { "ground_truth": ["cat", "dog"], "predictions": ["cat", "dog", "other"], } # Edit an existing classes list dataset.classes["ground_truth"].append("other") dataset.save() # must save after edits ``` #### *property* default_classes A list of class label strings for all [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields of this dataset that do not have customized classes defined in [`classes()`](#fiftyone.core.dataset.Dataset.classes). Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Set default classes dataset.default_classes = ["cat", "dog"] # Edit the default classes dataset.default_classes.append("rabbit") dataset.save() # must save after edits ``` #### *property* mask_targets A dict mapping field names to mask target dicts, each of which defines a mapping between pixel values (2D masks) or RGB hex strings (3D masks) and label strings for the segmentation masks in the corresponding field of the dataset. Examples: ```default import fiftyone as fo # # 2D masks # dataset = fo.Dataset() # Set mask targets for the `ground_truth` and `predictions` fields dataset.mask_targets = { "ground_truth": {1: "cat", 2: "dog"}, "predictions": {1: "cat", 2: "dog", 255: "other"}, } # Or, for RGB mask targets dataset.mask_targets = { "segmentations": {"#3f0a44": "road", "#eeffee": "building", "#ffffff": "other"} } # Edit an existing mask target dataset.mask_targets["ground_truth"][255] = "other" dataset.save() # must save after edits # # 3D masks # dataset = fo.Dataset() # Set mask targets for the `ground_truth` and `predictions` fields dataset.mask_targets = { "ground_truth": {"#499CEF": "cat", "#6D04FF": "dog"}, "predictions": { "#499CEF": "cat", "#6D04FF": "dog", "#FF6D04": "person" }, } # Edit an existing mask target dataset.mask_targets["ground_truth"]["#FF6D04"] = "person" dataset.save() # must save after edits ``` #### *property* default_mask_targets A dict defining a default mapping between pixel values (2D masks) or RGB hex strings (3D masks) and label strings for the segmentation masks of all [`fiftyone.core.labels.Segmentation`](fiftyone.core.labels.md#fiftyone.core.labels.Segmentation) fields of this dataset that do not have customized mask targets defined in [`mask_targets()`](#fiftyone.core.dataset.Dataset.mask_targets). Examples: ```default import fiftyone as fo # # 2D masks # dataset = fo.Dataset() # Set default mask targets dataset.default_mask_targets = {1: "cat", 2: "dog"} # Or, for RGB mask targets dataset.default_mask_targets = {"#3f0a44": "road", "#eeffee": "building", "#ffffff": "other"} # Edit the default mask targets dataset.default_mask_targets[255] = "other" dataset.save() # must save after edits # # 3D masks # dataset = fo.Dataset() # Set default mask targets dataset.default_mask_targets = {"#499CEF": "cat", "#6D04FF": "dog"} # Edit the default mask targets dataset.default_mask_targets["#FF6D04"] = "person" dataset.save() # must save after edits ``` #### *property* skeletons A dict mapping field names to [`fiftyone.core.odm.dataset.KeypointSkeleton`](fiftyone.core.odm.dataset.md#fiftyone.core.odm.dataset.KeypointSkeleton) instances, each of which defines the semantic labels and point connectivity for the [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) instances in the corresponding field of the dataset. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Set keypoint skeleton for the `ground_truth` field dataset.skeletons = { "ground_truth": fo.KeypointSkeleton( labels=[ "left hand" "left shoulder", "right shoulder", "right hand", "left eye", "right eye", "mouth", ], edges=[[0, 1, 2, 3], [4, 5, 6]], ) } # Edit an existing skeleton dataset.skeletons["ground_truth"].labels[-1] = "lips" dataset.save() # must save after edits ``` #### *property* default_skeleton A default [`fiftyone.core.odm.dataset.KeypointSkeleton`](fiftyone.core.odm.dataset.md#fiftyone.core.odm.dataset.KeypointSkeleton) defining the semantic labels and point connectivity for all [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) fields of this dataset that do not have customized skeletons defined in `skeleton()`. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Set default keypoint skeleton dataset.default_skeleton = fo.KeypointSkeleton( labels=[ "left hand" "left shoulder", "right shoulder", "right hand", "left eye", "right eye", "mouth", ], edges=[[0, 1, 2, 3], [4, 5, 6]], ) # Edit the default skeleton dataset.default_skeleton.labels[-1] = "lips" dataset.save() # must save after edits ``` #### *property* camera_intrinsics A dict mapping sensor/camera names to [`fiftyone.core.camera.CameraIntrinsics`](fiftyone.core.camera.md#fiftyone.core.camera.CameraIntrinsics) instances that define the intrinsic parameters for each camera in the dataset. Intrinsics are keyed by sensor name (typically matching group slice names for grouped datasets). Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Set intrinsics for cameras dataset.camera_intrinsics = { "camera_front": fo.OpenCVCameraIntrinsics( fx=1000.0, fy=1000.0, cx=960.0, cy=540.0, k1=-0.1, k2=0.05, ), "camera_rear": fo.PinholeCameraIntrinsics( fx=800.0, fy=800.0, cx=640.0, cy=360.0, ), } # Edit existing intrinsics dataset.camera_intrinsics["camera_front"].k1 = -0.12 dataset.save() # must save after edits ``` #### add_intrinsics(name, intrinsics) Adds camera intrinsics to the dataset. This method provides a convenient way to add intrinsics for a specific camera/sensor without replacing the entire intrinsics dict. * **Parameters:** * **name** – the sensor/camera name (typically matching group slice names for grouped datasets) * **intrinsics** – a [`fiftyone.core.camera.CameraIntrinsics`](fiftyone.core.camera.md#fiftyone.core.camera.CameraIntrinsics) instance Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Add intrinsics for front camera dataset.add_intrinsics( "camera_front", fo.OpenCVCameraIntrinsics( fx=1000.0, fy=1000.0, cx=960.0, cy=540.0, k1=-0.1, k2=0.05, ), ) # Add intrinsics for rear camera dataset.add_intrinsics( "camera_rear", fo.PinholeCameraIntrinsics( fx=800.0, fy=800.0, cx=640.0, cy=360.0, ), ) # Verify intrinsics were added print(dataset.camera_intrinsics.keys()) # dict_keys(['camera_front', 'camera_rear']) ``` #### *property* static_transforms A dict mapping frame relationships to [`fiftyone.core.camera.StaticTransform`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransform) instances that define the transformation parameters (poses) for sensors in the dataset. Transforms are keyed by `"source_frame::target_frame"` (e.g., `"camera_front::ego"`) or just `"source_frame"` which implies transformation to the `"world"` frame. Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Set transforms for sensors dataset.static_transforms = { "camera_front::ego": fo.StaticTransform( translation=[1.5, 0.0, 1.2], quaternion=[0.0, 0.0, 0.0, 1.0], source_frame="camera_front", target_frame="ego", ), "lidar::ego": fo.StaticTransform( translation=[0.0, 0.0, 2.0], quaternion=[0.0, 0.0, 0.0, 1.0], source_frame="lidar", target_frame="ego", ), } # Edit existing transforms dataset.static_transforms["camera_front::ego"].translation[0] = 1.6 dataset.save() # must save after edits ``` #### add_static_transform(transform) Adds a static transform to the dataset. This method provides a convenient way to add transforms without manually constructing the `"source_frame::target_frame"` key. * **Parameters:** **transform** – a [`fiftyone.core.camera.StaticTransform`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransform) instance with `source_frame` set (`target_frame` defaults to “world” if not specified) Examples: ```default import fiftyone as fo dataset = fo.Dataset() # Add transform for camera to ego transformation dataset.add_static_transform( fo.StaticTransform( translation=[1.5, 0.0, 1.2], quaternion=[0.0, 0.0, 0.0, 1.0], source_frame="camera_front", target_frame="ego", ) ) # Add transform for lidar (target_frame defaults to "world") dataset.add_static_transform( fo.StaticTransform( translation=[0.0, 0.0, 2.0], quaternion=[0.0, 0.0, 0.0, 1.0], source_frame="lidar", ) ) # Verify transforms were added print(dataset.static_transforms.keys()) # dict_keys(['camera_front::ego', 'lidar::world']) ``` #### resolve_intrinsics(sample) Resolves camera intrinsics for the given sample. Resolution precedence: : 1. If sample has a field with a [`fiftyone.core.camera.CameraIntrinsics`](fiftyone.core.camera.md#fiftyone.core.camera.CameraIntrinsics) value, use it 2. If sample has a field with a [`fiftyone.core.camera.CameraIntrinsicsRef`](fiftyone.core.camera.md#fiftyone.core.camera.CameraIntrinsicsRef), look up in `dataset.camera_intrinsics[ref]` 3. If sample is from a grouped dataset, infer from group slice name and look up in `dataset.camera_intrinsics[slice_name]` * **Parameters:** **sample** – a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) * **Returns:** a [`fiftyone.core.camera.CameraIntrinsics`](fiftyone.core.camera.md#fiftyone.core.camera.CameraIntrinsics), or None if not found #### resolve_transformation(sample, source_frame=None, target_frame=None, chain_via=None) Resolves a static transform for the given sample. Resolution precedence: > 1. If sample has a field with a > [`fiftyone.core.camera.StaticTransform`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransform) or > [`fiftyone.core.camera.StaticTransformRef`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransformRef) value (or a > list of them) with matching source/target frames, use it > 2. Look up in `dataset.static_transforms` using the key format > `"source_frame::target_frame"` or `"source_frame"` (implies > target is `"world"`) > 3. If sample is from a grouped dataset and `source_frame` is None, > infer source_frame from group slice name and look up in > `dataset.static_transforms` > 4. If `chain_via` is provided and no direct match is found, > chain transforms through the intermediate frames > 5. If no direct match is found, `chain_via` is not provided, > and `target_frame` is `"world"`, attempt automatic chaining > through a bounded number of intermediate frames (default 2). > This is only used when exactly one valid path exists * **Parameters:** * **sample** – a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) * **source_frame** (*None*) – the source coordinate frame name. If None and sample is from a grouped dataset, inferred from group slice name * **target_frame** (*None*) – the target coordinate frame name. If None, defaults to `"world"` * **chain_via** (*None*) – optional list of intermediate frame names to chain through if a direct transform is not found. For example, `chain_via=["ego"]` would attempt to resolve `source_frame -> ego -> target_frame` by composing individual transforms * **Returns:** a [`fiftyone.core.camera.StaticTransform`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransform), or None if not found Example: ```default # Direct lookup transform = dataset.resolve_transformation(sample, "camera", "world") # Chain through intermediate frame transform = dataset.resolve_transformation( sample, "camera", "world", chain_via=["ego"] ) ``` #### get_transform_chain(source_frame, target_frame, intermediate_frames=None) Computes a composed transformation between coordinate frames. This method chains multiple static transforms to compute the transformation from `source_frame` to `target_frame`, optionally through intermediate frames. * **Parameters:** * **source_frame** – the source coordinate frame name * **target_frame** – the target coordinate frame name * **intermediate_frames** (*None*) – optional list of intermediate frame names to chain through (e.g., [“ego”]) * **Returns:** a [`fiftyone.core.camera.StaticTransform`](fiftyone.core.camera.md#fiftyone.core.camera.StaticTransform) representing the composed transformation, or None if the chain cannot be resolved Example: ```default # Get transform from camera_front to world via ego transform = dataset.get_transform_chain( "camera_front", "world", intermediate_frames=["ego"] ) ``` #### *property* deleted Whether the dataset is deleted. #### *property* active_label_schemas The list of active fields in the dataset’s [label schemas](../integrations/annotation.md#annotation-label-schema). #### *property* label_schemas The dataset’s [label schemas](../integrations/annotation.md#annotation-label-schema) that define its App annotation UX and constraints. See [`generate_label_schemas()`](#fiftyone.core.dataset.Dataset.generate_label_schemas) for ways in which to generate label schemas. * **Returns:** the dataset’s label schemas `dict` #### set_label_schemas(label_schemas) Set the dataset’s [label schemas](../integrations/annotation.md#annotation-label-schema) that defines its App annotation UX and constraints. See [`generate_label_schemas()`](#fiftyone.core.dataset.Dataset.generate_label_schemas) for ways in which to generate a label schema. Example: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") # Generate and assign a label schemas for all supported fields with # populated constraints and other settings, e.g. 'values', and # 'range'. Requires all samples in the dataset dataset.set_label_schemas(dataset.generate_label_schemas()) # Generate a bare label schemas for all supported fields dataset.set_label_schemas( dataset.generate_label_schemas(scan_samples=False) ) ``` * **Parameters:** **label_schemas** – a label schemas `dict` #### update_label_schema(field, label_schema, allow_new_attrs=False, allow_new_fields=False) Update an individual field’s [label schema](../integrations/annotation.md#annotation-label-schema). Example:: : dataset.update_label_schema( : “ground_truth”, dataset.generate_label_schemas(“ground_truth”)
) * **Parameters:** * **field** – the field name * **label_schema** – the field’s label schema * **allow_new_attrs** (*False*) – whether to allow label attributes that do not yet exist on the field schema * **allow_new_fields** (*False*) – whether to allow label schemas for fields that do not yet exist on the dataset * **Raises:** **ExceptionGroup** – if the label schema is invalid #### delete_label_schemas(fields=None) Deletes one or more [label schemas](../integrations/annotation.md#annotation-label-schema). If no fields are provided, all label schemas are deleted. * **Parameters:** **fields** (*None*) – a field name, `embedded.field.name` or iterable of such values * **Raises:** **ValueError** – if the label schema or schemas do not exist #### activate_label_schemas(fields=None, prepend=False) Activate [`label_schemas()`](#fiftyone.core.dataset.Dataset.label_schemas). If no fields are provided, all label schemas are activated. * **Parameters:** * **fields** (*None*) – a field name, `embedded.field.name` or iterable of such values * **prepend** (*False*) – whether to prepend the fields to the beginning of the active list (True) or append to the end (False) * **Raises:** **ValueError** – if any fields are not in the label schema #### deactivate_label_schemas(fields=None) Deactivate [`label_schemas()`](#fiftyone.core.dataset.Dataset.label_schemas). If no fields are provided, all label schemas are deactivated. * **Parameters:** **fields** (*None*) – a field name, `embedded.field.name` or iterable of such values * **Raises:** **ValueError** – if any fields are not in the label schema, or any field is not currently active #### summary() Returns a string summary of the dataset. * **Returns:** a string summary #### stats(include_media=False, include_indexes=False, compressed=False) Returns stats about the dataset on disk. The `samples` keys refer to the sample documents stored in the database. For video datasets, the `frames` keys refer to the frame documents stored in the database. The `media` keys refer to the raw media associated with each sample on disk. The `index[es]` keys refer to the indexes associated with the dataset. Note that dataset-level metadata such as annotation runs are not included in this computation. * **Parameters:** * **include_media** (*False*) – whether to include stats about the size of the raw media in the dataset * **include_indexes** (*False*) – whether to include stats on the dataset’s indexes * **compressed** (*False*) – whether to return the sizes of collections in their compressed form on disk (True) or the logical uncompressed size of the collections (False) * **Returns:** a stats dict #### first() Returns the first sample in the dataset. * **Returns:** a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) #### last() Returns the last sample in the dataset. * **Returns:** a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) #### head(num_samples=3) Returns a list of the first few samples in the dataset. If fewer than `num_samples` samples are in the dataset, only the available samples are returned. * **Parameters:** **num_samples** (*3*) – the number of samples * **Returns:** a list of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) objects #### tail(num_samples=3) Returns a list of the last few samples in the dataset. If fewer than `num_samples` samples are in the dataset, only the available samples are returned. * **Parameters:** **num_samples** (*3*) – the number of samples * **Returns:** a list of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) objects #### one(expr, exact=False) Returns a single sample in this dataset matching the expression. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Get a sample by filepath # # A random filepath in the dataset filepath = dataset.take(1).first().filepath # Get sample by filepath sample = dataset.one(F("filepath") == filepath) # # Dealing with multiple matches # # Get a sample whose image is JPEG sample = dataset.one(F("filepath").ends_with(".jpg")) # Raises an error since there are multiple JPEGs dataset.one(F("filepath").ends_with(".jpg"), exact=True) ``` * **Parameters:** * **expr** – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that evaluates to `True` for the sample to match * **exact** (*False*) – whether to raise an error if multiple samples match the expression * **Returns:** a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) #### view() Returns a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) containing the entire dataset. * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### get_field_schema(ftype=None, embedded_doc_type=None, subfield=None, read_only=None, info_keys=None, created_after=None, include_private=False, flat=False, unwind=True, mode=None) Returns a schema dictionary describing the fields of the samples in the dataset. * **Parameters:** * **ftype** (*None*) – an optional field type or iterable of types to which to restrict the returned schema. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **embedded_doc_type** (*None*) – an optional embedded document type or iterable of types to which to restrict the returned schema. Must be subclass(es) of [`fiftyone.core.odm.BaseEmbeddedDocument`](fiftyone.core.odm.md#fiftyone.core.odm.BaseEmbeddedDocument) * **subfield** (*None*) – an optional subfield type or iterable of subfield types to which to restrict the returned schema. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **read_only** (*None*) – whether to restrict to (True) or exclude (False) read-only fields. By default, all fields are included * **info_keys** (*None*) – an optional key or list of keys that must be in the field’s `info` dict * **created_after** (*None*) – an optional `datetime` specifying a minimum creation date * **include_private** (*False*) – whether to include fields that start with `_` in the returned schema * **flat** (*False*) – whether to return a flattened schema where all embedded document fields are included as top-level keys * **unwind** (*True*) – whether to traverse into list fields. Only applicable when `flat=True` * **mode** (*None*) – whether to apply the above constraints before and/or after flattening the schema. Only applicable when `flat=True`. Supported values are `("before", "after", "both")`. The default is `"after"` * **Returns:** a dict mapping field names to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances #### get_frame_field_schema(ftype=None, embedded_doc_type=None, subfield=None, read_only=None, info_keys=None, created_after=None, include_private=False, flat=False, unwind=True, mode=None) Returns a schema dictionary describing the fields of the frames of the samples in the dataset. Only applicable for datasets that contain videos. * **Parameters:** * **ftype** (*None*) – an optional field type or iterable of types to which to restrict the returned schema. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **embedded_doc_type** (*None*) – an optional embedded document type or iterable of types to which to restrict the returned schema. Must be subclass(es) of [`fiftyone.core.odm.BaseEmbeddedDocument`](fiftyone.core.odm.md#fiftyone.core.odm.BaseEmbeddedDocument) * **subfield** (*None*) – an optional subfield type or iterable of subfield types to which to restrict the returned schema. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **read_only** (*None*) – whether to restrict to (True) or exclude (False) read-only fields. By default, all fields are included * **info_keys** (*None*) – an optional key or list of keys that must be in the field’s `info` dict * **created_after** (*None*) – an optional `datetime` specifying a minimum creation date * **include_private** (*False*) – whether to include fields that start with `_` in the returned schema * **flat** (*False*) – whether to return a flattened schema where all embedded document fields are included as top-level keys * **unwind** (*True*) – whether to traverse into list fields. Only applicable when `flat=True` * **mode** (*None*) – whether to apply the above constraints before and/or after flattening the schema. Only applicable when `flat=True`. Supported values are `("before", "after", "both")`. The default is `"after"` * **Returns:** a dict mapping field names to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances, or `None` if the dataset does not contain videos #### add_sample_field(field_name, ftype, embedded_doc_type=None, subfield=None, fields=None, description=None, info=None, read_only=False, \*\*kwargs) Adds a new sample field or embedded field to the dataset, if necessary. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **ftype** – the field type to create. Must be a subclass of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **embedded_doc_type** (*None*) – the [`fiftyone.core.odm.BaseEmbeddedDocument`](fiftyone.core.odm.md#fiftyone.core.odm.BaseEmbeddedDocument) type of the field. Only applicable when `ftype` is [`fiftyone.core.fields.EmbeddedDocumentField`](fiftyone.core.fields.md#fiftyone.core.fields.EmbeddedDocumentField) * **subfield** (*None*) – the [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) type of the contained field. Only applicable when `ftype` is [`fiftyone.core.fields.ListField`](fiftyone.core.fields.md#fiftyone.core.fields.ListField) or [`fiftyone.core.fields.DictField`](fiftyone.core.fields.md#fiftyone.core.fields.DictField) * **fields** (*None*) – a list of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances defining embedded document attributes. Only applicable when `ftype` is [`fiftyone.core.fields.EmbeddedDocumentField`](fiftyone.core.fields.md#fiftyone.core.fields.EmbeddedDocumentField) * **description** (*None*) – an optional description * **info** (*None*) – an optional info dict * **read_only** (*False*) – whether the field should be read-only * **Raises:** **ValueError** – if a field of the same name already exists and it is not compliant with the specified values #### add_dynamic_sample_fields(fields=None, recursive=True, add_mixed=False) Adds dynamic sample fields to the dataset’s schema. Dynamic fields are embedded document fields with at least one non-None value that have not been declared on the dataset’s schema. #### NOTE You can use [`get_dynamic_field_schema()`](#fiftyone.core.dataset.Dataset.get_dynamic_field_schema) to determine what dynamic fields exist before adding them to your dataset’s schema via this method. * **Parameters:** * **fields** (*None*) – an optional parent field or iterable of parent fields to scope the search for dynamic fields, e.g. `"ground_truth"` or `"ground_truth.detections"`. All dynamic fields discovered within the given parents are added. Specifying a candidate dynamic path itself (e.g. `"ground_truth.detections.my_new_field"`) is not supported; use the parent path instead. You may also pass a pre-computed dict mapping paths to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances generated by [`get_dynamic_field_schema()`](#fiftyone.core.dataset.Dataset.get_dynamic_field_schema). By default, all fields are considered * **recursive** (*True*) – whether to recursively inspect nested lists and embedded documents for dynamic fields * **add_mixed** (*False*) – whether to declare fields that contain values of mixed types as generic [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances (True) or to skip such fields (False) #### add_frame_field(field_name, ftype, embedded_doc_type=None, subfield=None, fields=None, description=None, info=None, read_only=False, \*\*kwargs) Adds a new frame-level field or embedded field to the dataset, if necessary. Only applicable to datasets that contain videos. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **ftype** – the field type to create. Must be a subclass of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **embedded_doc_type** (*None*) – the [`fiftyone.core.odm.BaseEmbeddedDocument`](fiftyone.core.odm.md#fiftyone.core.odm.BaseEmbeddedDocument) type of the field. Only applicable when `ftype` is [`fiftyone.core.fields.EmbeddedDocumentField`](fiftyone.core.fields.md#fiftyone.core.fields.EmbeddedDocumentField) * **subfield** (*None*) – the [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) type of the contained field. Only applicable when `ftype` is [`fiftyone.core.fields.ListField`](fiftyone.core.fields.md#fiftyone.core.fields.ListField) or [`fiftyone.core.fields.DictField`](fiftyone.core.fields.md#fiftyone.core.fields.DictField) * **fields** (*None*) – a list of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances defining embedded document attributes. Only applicable when `ftype` is [`fiftyone.core.fields.EmbeddedDocumentField`](fiftyone.core.fields.md#fiftyone.core.fields.EmbeddedDocumentField) * **description** (*None*) – an optional description * **info** (*None*) – an optional info dict * **read_only** (*False*) – whether the field should be read-only * **Raises:** **ValueError** – if a field of the same name already exists and it is not compliant with the specified values #### list_summary_fields() Lists the summary fields on the dataset. Use [`create_summary_field()`](#fiftyone.core.dataset.Dataset.create_summary_field) to create summary fields, and use [`delete_summary_field()`](#fiftyone.core.dataset.Dataset.delete_summary_field) to delete them. * **Returns:** a list of summary field names #### create_summary_field(path, field_name=None, sidebar_group=None, include_counts=False, group_by=None, read_only=True, create_index=True) Populates a sample-level field that records the unique values or numeric ranges that appear in the specified field on each sample in the dataset. This method is particularly useful for summarizing frame-level fields of video datasets, in which case the sample-level field records the unique values or numeric ranges that appear in the specified frame-level field across all frames of that sample. This summary field can then be efficiently queried to retrieve samples that contain specific values of interest in at least one frame. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart-video") dataset.set_field("frames.detections.detections.confidence", F.rand()).save() # Generate a summary field for object labels dataset.create_summary_field("frames.detections.detections.label") # Generate a summary field for [min, max] confidences dataset.create_summary_field("frames.detections.detections.confidence") # Generate a summary field for object labels and counts dataset.create_summary_field( "frames.detections.detections.label", field_name="frames_detections_label2", include_counts=True, ) # Generate a summary field for per-label [min, max] confidences dataset.create_summary_field( "frames.detections.detections.confidence", field_name="frames_detections_confidence2", group_by="label", ) print(dataset.list_summary_fields()) ``` * **Parameters:** * **path** – an input field path * **field_name** (*None*) – the sample-level field in which to store the summary data. By default, a suitable name is derived from the given `path` * **sidebar_group** (*None*) – the name of a [App sidebar group](../user_guide/app.md#app-sidebar-groups) to which to add the summary field. By default, all summary fields are added to a `"summaries"` group. You can pass `False` to skip sidebar group modification * **include_counts** (*False*) – whether to include per-value counts when summarizing categorical fields * **group_by** (*None*) – an optional attribute to group by when `path` is a numeric field to generate per-attribute `[min, max]` ranges. This may either be an absolute path or an attribute name that is interpreted relative to `path` * **read_only** (*True*) – whether to mark the summary field as read-only * **create_index** (*True*) – whether to create database index(es) for the summary field * **Returns:** the summary field name #### check_summary_fields() Returns a list of summary fields that **may** need to be updated. Summary fields may need to be updated whenever there have been modifications to the dataset’s samples since the summaries were last generated. Note that inclusion in this list is only a heuristic, as any sample modifications may not have affected the summary’s source field. * **Returns:** list of summary field names #### update_summary_field(field_name) Updates the summary field based on the current values of its source field. * **Parameters:** **field_name** – the summary field #### delete_summary_field(field_name, error_level=0) Deletes the summary field from all samples in the dataset. * **Parameters:** * **field_name** – the summary field * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a summary field cannot be deleted * **1** ( *-*) – log warning if a summary field cannot be deleted * **2** ( *-*) – ignore summary fields that cannot be deleted #### delete_summary_fields(field_names, error_level=0) Deletes the summary fields from all samples in the dataset. * **Parameters:** * **field_names** – the summary field or iterable of summary fields * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a summary field cannot be deleted * **1** ( *-*) – log warning if a summary field cannot be deleted * **2** ( *-*) – ignore summary fields that cannot be deleted #### add_dynamic_frame_fields(fields=None, recursive=True, add_mixed=False) Adds dynamic frame fields to the dataset’s schema. Dynamic fields are embedded document fields with at least one non-None value that have not been declared on the dataset’s schema. #### NOTE You can use [`get_dynamic_frame_field_schema()`](#fiftyone.core.dataset.Dataset.get_dynamic_frame_field_schema) to determine what dynamic frame fields exist before adding them to your dataset’s schema via this method. * **Parameters:** * **fields** (*None*) – an optional frame field or iterable of frame fields for which to add dynamic fields, or a pre-computed dict mapping paths to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances generated by [`get_dynamic_frame_field_schema()`](#fiftyone.core.dataset.Dataset.get_dynamic_frame_field_schema) to use. By default, all frame fields are considered * **recursive** (*True*) – whether to recursively inspect nested lists and embedded documents for dynamic fields * **add_mixed** (*False*) – whether to declare fields that contain values of mixed types as generic [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances (True) or to skip such fields (False) #### add_group_field(field_name, default=None, description=None, info=None, read_only=False) Adds a group field to the dataset, if necessary. * **Parameters:** * **field_name** – the field name * **default** (*None*) – a default group slice for the field * **description** (*None*) – an optional description * **info** (*None*) – an optional info dict * **read_only** (*False*) – whether the field should be read-only * **Raises:** **ValueError** – if a group field with another name already exists #### rename_sample_field(field_name, new_field_name) Renames the sample field to the given new name. You can use dot notation (`embedded.field.name`) to rename embedded fields. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **new_field_name** – the new field name or `embedded.field.name` #### rename_sample_fields(field_mapping) Renames the sample fields to the given new names. You can use dot notation (`embedded.field.name`) to rename embedded fields. * **Parameters:** **field_mapping** – a dict mapping field names to new field names #### rename_frame_field(field_name, new_field_name) Renames the frame-level field to the given new name. You can use dot notation (`embedded.field.name`) to rename embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **new_field_name** – the new field name or `embedded.field.name` #### rename_frame_fields(field_mapping) Renames the frame-level fields to the given new names. You can use dot notation (`embedded.field.name`) to rename embedded frame fields. * **Parameters:** **field_mapping** – a dict mapping field names to new field names #### clone_sample_field(field_name, new_field_name) Clones the given sample field into a new field of the dataset. You can use dot notation (`embedded.field.name`) to clone embedded fields. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **new_field_name** – the new field name or `embedded.field.name` #### clone_sample_fields(field_mapping) Clones the given sample fields into new fields of the dataset. You can use dot notation (`embedded.field.name`) to clone embedded fields. * **Parameters:** **field_mapping** – a dict mapping field names to new field names into which to clone each field #### clone_frame_field(field_name, new_field_name) Clones the frame-level field into a new field. You can use dot notation (`embedded.field.name`) to clone embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **new_field_name** – the new field name or `embedded.field.name` #### clone_frame_fields(field_mapping) Clones the frame-level fields into new fields. You can use dot notation (`embedded.field.name`) to clone embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** **field_mapping** – a dict mapping field names to new field names into which to clone each field #### clear_sample_field(field_name) Clears the values of the field from all samples in the dataset. The field will remain in the dataset’s schema, and all samples will have the value `None` for the field. You can use dot notation (`embedded.field.name`) to clear embedded fields. * **Parameters:** **field_name** – the field name or `embedded.field.name` #### clear_sample_fields(field_names) Clears the values of the fields from all samples in the dataset. The field will remain in the dataset’s schema, and all samples will have the value `None` for the field. You can use dot notation (`embedded.field.name`) to clear embedded fields. * **Parameters:** **field_names** – the field name or iterable of field names #### clear_frame_field(field_name) Clears the values of the frame-level field from all samples in the dataset. The field will remain in the dataset’s frame schema, and all frames will have the value `None` for the field. You can use dot notation (`embedded.field.name`) to clear embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** **field_name** – the field name or `embedded.field.name` #### clear_frame_fields(field_names) Clears the values of the frame-level fields from all samples in the dataset. The fields will remain in the dataset’s frame schema, and all frames will have the value `None` for the field. You can use dot notation (`embedded.field.name`) to clear embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** **field_names** – the field name or iterable of field names #### delete_sample_field(field_name, error_level=0) Deletes the field from all samples in the dataset. You can use dot notation (`embedded.field.name`) to delete embedded fields. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be deleted * **1** ( *-*) – log warning if a top-level field cannot be deleted * **2** ( *-*) – ignore top-level fields that cannot be deleted #### delete_sample_fields(field_names, error_level=0) Deletes the fields from all samples in the dataset. You can use dot notation (`embedded.field.name`) to delete embedded fields. * **Parameters:** * **field_names** – the field name or iterable of field names * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be deleted * **1** ( *-*) – log warning if a top-level field cannot be deleted * **2** ( *-*) – ignore top-level fields that cannot be deleted #### remove_dynamic_sample_field(field_name, error_level=0) Removes the dynamic embedded sample field from the dataset’s schema. The underlying data is **not** deleted from the samples. * **Parameters:** * **field_name** – the `embedded.field.name` * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be removed * **1** ( *-*) – log warning if a top-level field cannot be removed * **2** ( *-*) – ignore top-level fields that cannot be removed #### remove_dynamic_sample_fields(field_names, error_level=0) Removes the dynamic embedded sample fields from the dataset’s schema. The underlying data is **not** deleted from the samples. * **Parameters:** * **field_names** – the `embedded.field.name` or iterable of field names * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be removed * **1** ( *-*) – log warning if a top-level field cannot be removed * **2** ( *-*) – ignore top-level fields that cannot be removed #### delete_frame_field(field_name, error_level=0) Deletes the frame-level field from all samples in the dataset. You can use dot notation (`embedded.field.name`) to delete embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** * **field_name** – the field name or `embedded.field.name` * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be deleted * **1** ( *-*) – log warning if a top-level field cannot be deleted * **2** ( *-*) – ignore top-level fields that cannot be deleted #### delete_frame_fields(field_names, error_level=0) Deletes the frame-level fields from all samples in the dataset. You can use dot notation (`embedded.field.name`) to delete embedded frame fields. Only applicable to datasets that contain videos. * **Parameters:** * **field_names** – a field name or iterable of field names * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be deleted * **1** ( *-*) – log warning if a top-level field cannot be deleted * **2** ( *-*) – ignore top-level fields that cannot be deleted #### remove_dynamic_frame_field(field_name, error_level=0) Removes the dynamic embedded frame field from the dataset’s schema. The underlying data is **not** deleted from the frames. * **Parameters:** * **field_name** – the `embedded.field.name` * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be removed * **1** ( *-*) – log warning if a top-level field cannot be removed * **2** ( *-*) – ignore top-level fields that cannot be removed #### remove_dynamic_frame_fields(field_names, error_level=0) Removes the dynamic embedded frame fields from the dataset’s schema. The underlying data is **not** deleted from the frames. * **Parameters:** * **field_names** – the `embedded.field.name` or iterable of field names * **error_level** (*0*) – the error level to use. Valid values are: * **0** ( *-*) – raise error if a top-level field cannot be removed * **1** ( *-*) – log warning if a top-level field cannot be removed * **2** ( *-*) – ignore top-level fields that cannot be removed #### add_group_slice(name, media_type) Adds a group slice with the given media type to the dataset, if necessary. * **Parameters:** * **name** – a group slice name * **media_type** – the media type of the slice #### rename_group_slice(name, new_name) Renames the group slice with the given name. * **Parameters:** * **name** – the group slice name * **new_name** – the new group slice name #### delete_group_slice(name) Deletes all samples in the given group slice from the dataset. * **Parameters:** **name** – a group slice name #### iter_samples(progress=False, autosave=False, batch_size=None, batching_strategy=None) Returns an iterator over the samples in the dataset. Examples: ```default import random as r import string as s import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("cifar10", split="test") def make_label(): return "".join(r.choice(s.ascii_letters) for i in range(10)) # No save context for sample in dataset.iter_samples(progress=True): sample.ground_truth.label = make_label() sample.save() # Save using default batching strategy for sample in dataset.iter_samples(progress=True, autosave=True): sample.ground_truth.label = make_label() # Save in batches of 10 for sample in dataset.iter_samples( progress=True, autosave=True, batch_size=10 ): sample.ground_truth.label = make_label() # Save every 0.5 seconds for sample in dataset.iter_samples( progress=True, autosave=True, batch_size=0.5 ): sample.ground_truth.label = make_label() ``` * **Parameters:** * **progress** (*False*) – 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 * **autosave** (*False*) – whether to automatically save changes to samples emitted by this iterator * **batch_size** (*None*) – the batch size to use when autosaving samples. If a `batching_strategy` is provided, this parameter configures the strategy as described below. If no `batching_strategy` is provided, this can either be an integer specifying the number of samples to save in a batch (in which case `batching_strategy` is implicitly set to `"static"`) or a float number of seconds between batched saves (in which case `batching_strategy` is implicitly set to `"latency"`) * **batching_strategy** (*None*) – the batching strategy to use for each save operation when autosaving samples. Supported values are: - `"static"`: a fixed sample batch size for each save - `"size"`: a target batch size, in bytes, for each save - `"latency"`: a target latency, in seconds, between saves By default, `fo.config.default_batcher` is used * **Returns:** an iterator over [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instances #### iter_groups(group_slices=None, progress=False, autosave=False, batch_size=None, batching_strategy=None) Returns an iterator over the groups in the dataset. Examples: ```default import random as r import string as s import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") def make_label(): return "".join(r.choice(s.ascii_letters) for i in range(10)) # No save context for group in dataset.iter_groups(progress=True): for sample in group.values(): sample["test"] = make_label() sample.save() # Save using default batching strategy for group in dataset.iter_groups(progress=True, autosave=True): for sample in group.values(): sample["test"] = make_label() # Save in batches of 10 for group in dataset.iter_groups( progress=True, autosave=True, batch_size=10 ): for sample in group.values(): sample["test"] = make_label() # Save every 0.5 seconds for group in dataset.iter_groups( progress=True, autosave=True, batch_size=0.5 ): for sample in group.values(): sample["test"] = make_label() ``` * **Parameters:** * **group_slices** (*None*) – an optional subset of group slices to load * **progress** (*False*) – 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 * **autosave** (*False*) – whether to automatically save changes to samples emitted by this iterator * **batch_size** (*None*) – the batch size to use when autosaving samples. If a `batching_strategy` is provided, this parameter configures the strategy as described below. If no `batching_strategy` is provided, this can either be an integer specifying the number of samples to save in a batch (in which case `batching_strategy` is implicitly set to `"static"`) or a float number of seconds between batched saves (in which case `batching_strategy` is implicitly set to `"latency"`) * **batching_strategy** (*None*) – the batching strategy to use for each save operation when autosaving samples. Supported values are: - `"static"`: a fixed sample batch size for each save - `"size"`: a target batch size, in bytes, for each save - `"latency"`: a target latency, in seconds, between saves By default, `fo.config.default_batcher` is used * **Returns:** an iterator that emits dicts mapping group slice names to [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instances, one per group #### get_group(group_id, group_slices=None) Returns a dict containing the samples for the given group ID. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") group_id = dataset.take(1).first().group.id group = dataset.get_group(group_id) print(group.keys()) # ['left', 'right', 'pcd'] ``` * **Parameters:** * **group_id** – a group ID * **group_slices** (*None*) – an optional subset of group slices to load * **Returns:** a dict mapping group names to [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instances * **Raises:** **KeyError** – if the group ID is not found #### add_sample(sample, expand_schema=True, dynamic=False, validate=True) Adds the given sample to the dataset. If the sample instance does not belong to a dataset, it is updated in-place to reflect its membership in this dataset. If the sample instance belongs to another dataset, it is not modified. * **Parameters:** * **sample** – a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if the sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **validate** (*True*) – whether to validate that the fields of the sample are compliant with the dataset schema before adding it * **Returns:** the ID of the sample in the dataset #### add_samples(samples, expand_schema=True, dynamic=False, validate=True, batcher=None, generator=False, progress=None, num_samples=None) Adds the given samples to the dataset. Any sample instances that do not belong to a dataset are updated in-place to reflect membership in this dataset. Any sample instances that belong to other datasets are not modified. * **Parameters:** * **samples** – an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instances or a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **validate** (*True*) – whether to validate that the fields of each sample are compliant with the dataset schema before adding it * **batcher** (*None*) – an optional [`fiftyone.core.utils.Batcher`](fiftyone.core.utils.md#fiftyone.core.utils.Batcher) class to use to batch the samples, or `False` to add all samples in a single batch. By default, `fiftyone.config.default_batcher` is used * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **num_samples** (*None*) – the number of samples in `samples`. If not provided, this is computed (if possible) via `len(samples)` if needed for progress tracking * **Returns:** a list of IDs of the samples in the dataset #### add_collection(sample_collection, include_info=True, overwrite_info=False, new_ids=False, progress=None) Adds the contents of the given collection to the dataset. This method is a special case of [`Dataset.merge_samples()`](#fiftyone.core.dataset.Dataset.merge_samples) that adds samples with new IDs to this dataset and omits any samples with existing IDs (the latter would only happen in rare cases). Use [`Dataset.merge_samples()`](#fiftyone.core.dataset.Dataset.merge_samples) if you have multiple datasets whose samples refer to the same source media. * **Parameters:** * **sample_collection** – a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **include_info** (*True*) – whether to merge dataset-level information such as `info` and `classes` * **overwrite_info** (*False*) – whether to overwrite existing dataset-level information. Only applicable when `include_info` is True * **new_ids** (*False*) – whether to generate new sample/frame/group IDs. By default, the IDs of the input collection are retained * **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 * **Returns:** a list of IDs of the samples that were added to this dataset #### merge_sample(sample, key_field=None, skip_existing=False, insert_new=True, fields=None, omit_fields=None, merge_lists=True, merge_embedded_docs=False, overwrite=True, expand_schema=True, validate=True, dynamic=False) Merges the fields of the given sample into this dataset. By default, the sample is merged with an existing sample with the same filepath or media reference key, if one exists. Otherwise a new sample is inserted. You can customize this behavior via the `key_field`, `skip_existing`, and `insert_new` parameters. The behavior of this method is highly customizable. By default, all top-level fields from the provided sample are merged in, overwriting any existing values for those fields, with the exception of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields), in which case the elements of the lists themselves are merged. In the case of label list fields, labels with the same `id` in both samples are updated rather than duplicated. To avoid confusion between missing fields and fields whose value is `None`, `None`-valued fields are always treated as missing while merging. This method can be configured in numerous ways, including: - Whether new fields can be added to the dataset schema - Whether list fields should be treated as ordinary fields and merged as a whole rather than merging their elements - Whether to merge only specific fields, or all but certain fields - Mapping input sample fields to different field names of this sample * **Parameters:** * **sample** – a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) * **key_field** (*None*) – the sample field to use to decide whether to join with an existing sample. By default, `filepath` or `media_reference` is used according to the dataset’s media identity * **skip_existing** (*False*) – whether to skip existing samples (True) or merge them (False) * **insert_new** (*True*) – whether to insert new samples (True) or skip them (False) * **fields** (*None*) – an optional field or iterable of fields to which to restrict the merge. May contain frame fields for video samples. This can also be a dict mapping field names of the input sample to field names of this dataset * **omit_fields** (*None*) – an optional field or iterable of fields to exclude from the merge. May contain frame fields for video samples * **merge_lists** (*True*) – whether to merge the elements of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields) rather than merging the entire top-level field like other field types. For label lists fields, existing `fiftyone.core.label.Label` elements are either replaced (when `overwrite` is True) or kept (when `overwrite` is False) when their `id` matches a label from the provided sample * **merge_embedded_docs** (*False*) – whether to merge the attributes of embedded documents (True) rather than merging the entire top-level field (False) * **overwrite** (*True*) – whether to overwrite (True) or skip (False) existing fields and label elements * **expand_schema** (*True*) – whether to dynamically add new fields encountered to the dataset schema. If False, an error is raised if any fields are not in the dataset schema * **validate** (*True*) – whether to validate values for existing fields * **dynamic** (*False*) – whether to declare dynamic embedded document fields #### merge_samples(samples, key_field=None, key_fcn=None, skip_existing=False, insert_new=True, fields=None, omit_fields=None, merge_lists=True, merge_embedded_docs=False, overwrite=True, expand_schema=True, dynamic=False, include_info=True, overwrite_info=False, progress=None, num_samples=None) Merges the given samples into this dataset. #### NOTE This method requires the ability to create *unique* indexes on the `key_field` of each collection, if they don’t already exist. See [`add_collection()`](#fiftyone.core.dataset.Dataset.add_collection) if you want to add samples from one collection to another dataset without a uniqueness constraint. By default, samples with the same filepath or media reference key are merged, but you can customize this behavior via the `key_field` and `key_fcn` parameters. For example, you could set `key_fcn = lambda sample: os.path.basename(sample.filepath)` to merge samples with the same base filename. The behavior of this method is highly customizable. By default, all top-level fields from the provided samples are merged in, overwriting any existing values for those fields, with the exception of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields), in which case the elements of the lists themselves are merged. In the case of label list fields, labels with the same `id` in both collections are updated rather than duplicated. To avoid confusion between missing fields and fields whose value is `None`, `None`-valued fields are always treated as missing while merging. This method can be configured in numerous ways, including: - Whether existing samples should be modified or skipped - Whether new samples should be added or omitted - Whether new fields can be added to the dataset schema - Whether list fields should be treated as ordinary fields and merged as a whole rather than merging their elements - Whether to merge only specific fields, or all but certain fields - Mapping input fields to different field names of this dataset * **Parameters:** * **samples** – a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) or iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instances * **key_field** (*None*) – the sample field to use to decide whether to join with an existing sample. By default, `filepath` or `media_reference` is used according to the dataset’s media identity * **key_fcn** (*None*) – a function that accepts a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instance and computes a key to decide if two samples should be merged. If a `key_fcn` is provided, `key_field` is ignored * **skip_existing** (*False*) – whether to skip existing samples (True) or merge them (False) * **insert_new** (*True*) – whether to insert new samples (True) or skip them (False) * **fields** (*None*) – an optional field or iterable of fields to which to restrict the merge. If provided, fields other than these are omitted from `samples` when merging or adding samples. The active media source is always included when adding new samples, since it is required. This can also be a dict mapping field names of the input collection to field names of this dataset * **omit_fields** (*None*) – an optional field or iterable of fields to exclude from the merge. If provided, these fields are omitted from `samples`, if present, when merging or adding samples. The active media source is always included when adding new samples, since it is required * **merge_lists** (*True*) – whether to merge the elements of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields) rather than merging the entire top-level field like other field types. For label lists fields, existing `fiftyone.core.label.Label` elements are either replaced (when `overwrite` is True) or kept (when `overwrite` is False) when their `id` matches a label from the provided samples * **merge_embedded_docs** (*False*) – whether to merge the attributes of embedded documents (True) rather than merging the entire top-level field (False) * **overwrite** (*True*) – whether to overwrite (True) or skip (False) existing fields and label elements * **expand_schema** (*True*) – whether to dynamically add new fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered. Only applicable when `samples` is not a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **include_info** (*True*) – whether to merge dataset-level information such as `info` and `classes`. Only applicable when `samples` is a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **overwrite_info** (*False*) – whether to overwrite existing dataset-level information. Only applicable when `samples` is a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) and `include_info` is True * **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 * **num_samples** (*None*) – the number of samples in `samples`. If not provided, this is computed (if possible) via `len(samples)` if needed for progress tracking #### delete_samples(samples_or_ids) Deletes the given sample(s) from the dataset. If reference to a sample exists in memory, the sample will be updated such that `sample.in_dataset` is False. * **Parameters:** **samples_or_ids** – the sample(s) to delete. Can be any of the following: - a sample ID - an iterable of sample IDs - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) #### delete_frames(frames_or_ids) Deletes the given frames(s) from the dataset. If reference to a frame exists in memory, the frame will be updated such that `frame.in_dataset` is False. * **Parameters:** **frames_or_ids** – the frame(s) to delete. Can be any of the following: - a frame ID - an iterable of frame IDs - a [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) or [`fiftyone.core.frame.FrameView`](fiftyone.core.frame.md#fiftyone.core.frame.FrameView) - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) whose frames to delete - an iterable of [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) or [`fiftyone.core.frame.FrameView`](fiftyone.core.frame.md#fiftyone.core.frame.FrameView) instances - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances whose frames to delete - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) whose frames to delete #### delete_groups(groups_or_ids) Deletes the given groups(s) from the dataset. If reference to a sample exists in memory, the sample will be updated such that `sample.in_dataset` is False. * **Parameters:** **groups_or_ids** – the group(s) to delete. Can be any of the following: - a group ID - an iterable of group IDs - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) - a group dict returned by [`get_group()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_group) - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances - an iterable of group dicts returned by [`get_group()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_group) - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) #### delete_labels(labels=None, ids=None, instance_ids=None, tags=None, view=None, fields=None) Deletes the specified labels from the dataset. You can specify the labels to delete via any of the following methods: - Provide the `labels` argument, which should contain a list of dicts in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels) - Provide the `ids` argument to specify the labels to delete via their IDs - Provide the `instance_ids` argument to specify the labels to delete via their instance IDs - Provide the `tags` argument to specify the labels to delete via their tags - Provide the `view` argument to delete all of the labels in a view into this dataset. This syntax is useful if you have constructed a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) defining the labels to delete Additionally, you can specify the `fields` argument to restrict deletion to specific field(s), either for efficiency or to ensure that labels from other fields are not deleted if their contents are included in the other arguments. * **Parameters:** * **labels** (*None*) – a list of dicts specifying the labels to delete in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels) * **ids** (*None*) – an ID or iterable of IDs of the labels to delete * **instance_ids** (*None*) – an instance ID or iterable of instance IDs of the labels to delete * **tags** (*None*) – a tag or iterable of tags of the labels to delete * **view** (*None*) – a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) into this dataset containing the labels to delete * **fields** (*None*) – a field or iterable of fields from which to delete labels #### save() Saves the dataset to the database. This only needs to be called when dataset-level information such as its [`Dataset.info()`](#fiftyone.core.dataset.Dataset.info) is modified. #### *property* has_saved_views Whether this dataset has any saved views. #### has_saved_view(name) Whether this dataset has a saved view with the given name. * **Parameters:** **name** – a saved view name * **Returns:** True/False #### list_saved_views(info=False) List saved views on this dataset. * **Parameters:** **info** (*False*) – whether to return info dicts describing each saved view rather than just their names * **Returns:** a list of saved view names or info dicts #### save_view(name, view, description=None, color=None, overwrite=False) Saves the given view into this dataset under the given name so it can be loaded later via [`load_saved_view()`](#fiftyone.core.dataset.Dataset.load_saved_view). Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") view = dataset.filter_labels("ground_truth", F("label") == "cat") dataset.save_view("cats", view) also_view = dataset.load_saved_view("cats") assert view == also_view ``` * **Parameters:** * **name** – a name for the saved view * **view** – a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) * **description** (*None*) – an optional string description * **color** (*None*) – an optional RGB hex string like `'#FF6D04'` * **overwrite** (*False*) – whether to overwrite an existing saved view with the same name #### get_saved_view_info(name) Loads the editable information about the saved view with the given name. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") view = dataset.limit(10) dataset.save_view("test", view) print(dataset.get_saved_view_info("test")) ``` * **Parameters:** **name** – the name of a saved view * **Returns:** a dict of editable info #### update_saved_view_info(name, info) Updates the editable information for the saved view with the given name. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") view = dataset.limit(10) dataset.save_view("test", view) # Update the saved view's name and add a description info = dict( name="a new name", description="a description", ) dataset.update_saved_view_info("test", info) ``` * **Parameters:** * **name** – the name of a saved view * **info** – a dict whose keys are a subset of the keys returned by [`get_saved_view_info()`](#fiftyone.core.dataset.Dataset.get_saved_view_info) #### load_saved_view(name) Loads the saved view with the given name. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") view = dataset.filter_labels("ground_truth", F("label") == "cat") dataset.save_view("cats", view) also_view = dataset.load_saved_view("cats") assert view == also_view ``` * **Parameters:** **name** – the name of a saved view * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### delete_saved_view(name) Deletes the saved view with the given name. * **Parameters:** **name** – the name of a saved view #### delete_saved_views() Deletes all saved views from this dataset. #### *property* has_workspaces Whether this dataset has any saved workspaces. #### has_workspace(name) Whether this dataset has a saved workspace with the given name. * **Parameters:** **name** – a saved workspace name * **Returns:** True/False #### list_workspaces(info=False) List saved workspaces on this dataset. * **Parameters:** **info** (*False*) – whether to return info dicts describing each saved workspace rather than just their names * **Returns:** a list of saved workspace names or info dicts #### save_workspace(name, workspace, description=None, color=None, overwrite=False) Saves a workspace into this dataset under the given name so it can be loaded later via [`load_workspace()`](#fiftyone.core.dataset.Dataset.load_workspace). Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") embeddings_panel = fo.Panel( type="Embeddings", state=dict( brainResult="img_viz", colorByField="metadata.size_bytes" ), ) workspace = fo.Space(children=[embeddings_panel]) workspace_name = "embeddings-workspace" description = "Show embeddings only" dataset.save_workspace( workspace_name, workspace, description=description ) assert dataset.has_workspace(workspace_name) also_workspace = dataset.load_workspace(workspace_name) assert workspace == also_workspace ``` * **Parameters:** * **name** – a name for the saved workspace * **workspace** – a [`fiftyone.core.odm.workspace.Space`](fiftyone.core.odm.workspace.md#fiftyone.core.odm.workspace.Space) * **description** (*None*) – an optional string description * **color** (*None*) – an optional RGB hex string like `'#FF6D04'` * **overwrite** (*False*) – whether to overwrite an existing workspace with the same name * **Raises:** **ValueError** – if `overwrite==False` and workspace with `name` already exists #### load_workspace(name) Loads the saved workspace with the given name. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") embeddings_panel = fo.Panel( type="Embeddings", state=dict(brainResult="img_viz", colorByField="metadata.size_bytes"), ) workspace = fo.Space(children=[embeddings_panel]) workspace_name = "embeddings-workspace" dataset.save_workspace(workspace_name, workspace) # Some time later ... load the workspace loaded_workspace = dataset.load_workspace(workspace_name) assert workspace == loaded_workspace # Launch app with the loaded workspace! session = fo.launch_app(dataset, spaces=loaded_workspace) # Or set via session later on session.spaces = loaded_workspace ``` * **Parameters:** **name** – the name of a saved workspace * **Returns:** a [`fiftyone.core.odm.workspace.Space`](fiftyone.core.odm.workspace.md#fiftyone.core.odm.workspace.Space) * **Raises:** **ValueError** – if `name` is not a saved workspace #### get_workspace_info(name) Gets the information about the workspace with the given name. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") workspace = fo.Space() description = "A really cool (apparently empty?) workspace" dataset.save_workspace("test", workspace, description=description) print(dataset.get_workspace_info("test")) ``` * **Parameters:** **name** – the name of a saved view * **Returns:** a dict of editable info #### update_workspace_info(name, info) Updates the editable information for the saved view with the given name. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") workspace = fo.Space() dataset.save_workspace("test", view) # Update the workspace's name and add a description, color info = dict( name="a new name", color="#FF6D04", description="a description", ) dataset.update_workspace_info("test", info) ``` * **Parameters:** * **name** – the name of a saved workspace * **info** – a dict whose keys are a subset of the keys returned by [`get_workspace_info()`](#fiftyone.core.dataset.Dataset.get_workspace_info) #### delete_workspace(name) Deletes the saved workspace with the given name. * **Parameters:** **name** – the name of a saved workspace * **Raises:** **ValueError** – if `name` is not a saved workspace #### delete_workspaces() Deletes all saved workspaces from this dataset. #### clone(name=None, persistent=False, include_indexes=True) Creates a copy of the dataset. Dataset clones contain deep copies of all samples and dataset-level information in the source dataset. The source *media files*, however, are not copied. * **Parameters:** * **name** (*None*) – a name for the cloned dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the cloned dataset should be persistent * **include_indexes** (*True*) – whether to recreate any custom indexes on the new dataset (True) or a list of specific indexes or index prefixes to recreate. By default, all custom indexes are recreated * **Returns:** the new [`Dataset`](#fiftyone.core.dataset.Dataset) #### clear() Removes all samples from the dataset. If reference to a sample exists in memory, the sample will be updated such that `sample.in_dataset` is False. #### clear_frames() Removes all frame labels from the dataset. If reference to a frame exists in memory, the frame will be updated such that `frame.in_dataset` is False. #### ensure_frames() Ensures that the video dataset contains frame instances for every frame of each sample’s source video. Empty frames will be inserted for missing frames, and already existing frames are left unchanged. #### delete() Deletes the dataset. Once deleted, only the `name` and `deleted` attributes of a dataset may be accessed. If reference to a sample exists in memory, the sample will be updated such that `sample.in_dataset` is False. #### add_dir(dataset_dir=None, dataset_type=None, data_path=None, labels_path=None, label_field=None, tags=None, expand_schema=True, dynamic=False, add_info=True, generator=False, progress=None, \*\*kwargs) Adds the contents of the given directory to the dataset. You can perform imports with this method via the following basic patterns: 1. Provide `dataset_dir` and `dataset_type` to import the contents of a directory that is organized in the default layout for the dataset type as documented in [this guide](../user_guide/import_datasets.md#loading-common-datasets) 2. Provide `dataset_type` along with `data_path`, `labels_path`, or other type-specific parameters to perform a customized import. This syntax provides the flexibility to, for example, perform labels-only imports or imports where the source media lies in a different location than the labels In either workflow, the remaining parameters of this method can be provided to further configure the import. See [this guide](../user_guide/import_datasets.md#loading-common-datasets) for example usages of this method and descriptions of the available dataset types. * **Parameters:** * **dataset_dir** (*None*) – the dataset directory. This can be omitted for certain dataset formats if you provide arguments such as `data_path` and `labels_path` * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type of the dataset * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the media for certain dataset types. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `dataset_dir` in which the media lies - an absolute directory path in which the media lies. In this case, the `dataset_dir` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `dataset_dir` that maps UUIDs to media filepaths. Files of this format are generated when passing the `export_media="manifest"` option to [`fiftyone.core.collections.SampleCollection.export()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.export) - an absolute filepath to a JSON manifest file. In this case, `dataset_dir` has no effect on the location of the data - a dict mapping filenames to absolute filepaths By default, it is assumed that the data can be located in the default location within `dataset_dir` for the dataset type * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the labels. Only applicable when importing certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `dataset_dir` of the labels file(s) - an absolute directory or filepath containing the labels file(s). In this case, `dataset_dir` has no effect on the location of the labels For labeled datasets, this parameter defaults to the location in `dataset_dir` of the labels for the default layout of the dataset type being imported * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **add_info** (*True*) – whether to add dataset info from the importer (if any) to the dataset’s `info` * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 to pass to the constructor of the [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) for the specified `dataset_type` * **Returns:** a list of IDs of the samples that were added to the dataset #### merge_dir(dataset_dir=None, dataset_type=None, data_path=None, labels_path=None, label_field=None, tags=None, key_field=None, key_fcn=None, skip_existing=False, insert_new=True, fields=None, omit_fields=None, merge_lists=True, merge_embedded_docs=False, overwrite=True, expand_schema=True, dynamic=False, add_info=True, progress=None, \*\*kwargs) Merges the contents of the given directory into the dataset. #### NOTE This method requires the ability to create *unique* indexes on the `key_field` of each collection. See [`add_dir()`](#fiftyone.core.dataset.Dataset.add_dir) if you want to add samples without a uniqueness constraint. You can perform imports with this method via the following basic patterns: 1. Provide `dataset_dir` and `dataset_type` to import the contents of a directory that is organized in the default layout for the dataset type as documented in [this guide](../user_guide/import_datasets.md#loading-common-datasets) 2. Provide `dataset_type` along with `data_path`, `labels_path`, or other type-specific parameters to perform a customized import. This syntax provides the flexibility to, for example, perform labels-only imports or imports where the source media lies in a different location than the labels In either workflow, the remaining parameters of this method can be provided to further configure the import. See [this guide](../user_guide/import_datasets.md#loading-common-datasets) for example usages of this method and descriptions of the available dataset types. By default, samples with the same filepath or media reference key are merged, but you can customize this behavior via the `key_field` and `key_fcn` parameters. For example, you could set `key_fcn = lambda sample: os.path.basename(sample.filepath)` to merge samples with the same base filename. The behavior of this method is highly customizable. By default, all top-level fields from the imported samples are merged in, overwriting any existing values for those fields, with the exception of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields), in which case the elements of the lists themselves are merged. In the case of label list fields, labels with the same `id` in both collections are updated rather than duplicated. To avoid confusion between missing fields and fields whose value is `None`, `None`-valued fields are always treated as missing while merging. This method can be configured in numerous ways, including: - Whether existing samples should be modified or skipped - Whether new samples should be added or omitted - Whether new fields can be added to the dataset schema - Whether list fields should be treated as ordinary fields and merged as a whole rather than merging their elements - Whether to merge only specific fields, or all but certain fields - Mapping input fields to different field names of this dataset * **Parameters:** * **dataset_dir** (*None*) – the dataset directory. This can be omitted for certain dataset formats if you provide arguments such as `data_path` and `labels_path` * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type of the dataset * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the media for certain dataset types. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `dataset_dir` in which the media lies - an absolute directory path in which the media lies. In this case, the `dataset_dir` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `dataset_dir` that maps UUIDs to media filepaths. Files of this format are generated when passing the `export_media="manifest"` option to [`fiftyone.core.collections.SampleCollection.export()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.export) - an absolute filepath to a JSON manifest file. In this case, `dataset_dir` has no effect on the location of the data - a dict mapping filenames to absolute filepaths By default, it is assumed that the data can be located in the default location within `dataset_dir` for the dataset type * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the labels. Only applicable when importing certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `dataset_dir` of the labels file(s) - an absolute directory or filepath containing the labels file(s). In this case, `dataset_dir` has no effect on the location of the labels For labeled datasets, this parameter defaults to the location in `dataset_dir` of the labels for the default layout of the dataset type being imported * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **key_field** (*None*) – the sample field to use to decide whether to join with an existing sample. By default, `filepath` or `media_reference` is used according to the media source of the samples * **key_fcn** (*None*) – a function that accepts a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instance and computes a key to decide if two samples should be merged. If a `key_fcn` is provided, `key_field` is ignored * **skip_existing** (*False*) – whether to skip existing samples (True) or merge them (False) * **insert_new** (*True*) – whether to insert new samples (True) or skip them (False) * **fields** (*None*) – an optional field or iterable of fields to which to restrict the merge. If provided, fields other than these are omitted from `samples` when merging or adding samples. The active media source is always included when adding new samples, since it is required. This can also be a dict mapping field names of the input collection to field names of this dataset * **omit_fields** (*None*) – an optional field or iterable of fields to exclude from the merge. If provided, these fields are omitted from imported samples, if present. The active media source is always included when adding new samples, since it is required * **merge_lists** (*True*) – whether to merge the elements of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields) rather than merging the entire top-level field like other field types. For label lists fields, existing `fiftyone.core.label.Label` elements are either replaced (when `overwrite` is True) or kept (when `overwrite` is False) when their `id` matches a label from the provided samples * **merge_embedded_docs** (*False*) – whether to merge the attributes of embedded documents (True) rather than merging the entire top-level field (False) * **overwrite** (*True*) – whether to overwrite (True) or skip (False) existing fields and label elements * **expand_schema** (*True*) – whether to dynamically add new fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **add_info** (*True*) – whether to add dataset info from the importer (if any) to the dataset * **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 to pass to the constructor of the [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) for the specified `dataset_type` #### add_archive(archive_path, dataset_type=None, data_path=None, labels_path=None, label_field=None, tags=None, expand_schema=True, dynamic=False, add_info=True, cleanup=True, generator=False, progress=None, \*\*kwargs) Adds the contents of the given archive to the dataset. If a directory with the same root name as `archive_path` exists, it is assumed that this directory contains the extracted contents of the archive, and thus the archive is not re-extracted. See [this guide](../user_guide/import_datasets.md#loading-common-datasets) for example usages of this method and descriptions of the available dataset types. #### NOTE The following archive formats are explicitly supported: ```default .zip, .tar, .tar.gz, .tgz, .tar.bz, .tbz ``` If an archive *not* in the above list is found, extraction will be attempted via the `patool` package, which supports many formats but may require that additional system packages be installed. * **Parameters:** * **archive_path** – the path to an archive of a dataset directory * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type of the dataset in `archive_path` * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the media for certain dataset types. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `dataset_dir` in which the media lies - an absolute directory path in which the media lies. In this case, the `archive_path` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `archive_path` that maps UUIDs to media filepaths. Files of this format are generated when passing the `export_media="manifest"` option to [`fiftyone.core.collections.SampleCollection.export()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.export) - an absolute filepath to a JSON manifest file. In this case, `archive_path` has no effect on the location of the data - a dict mapping filenames to absolute filepaths By default, it is assumed that the data can be located in the default location within `archive_path` for the dataset type * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the labels. Only applicable when importing certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `archive_path` of the labels file(s) - an absolute directory or filepath containing the labels file(s). In this case, `archive_path` has no effect on the location of the labels For labeled datasets, this parameter defaults to the location in `archive_path` of the labels for the default layout of the dataset type being imported * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **add_info** (*True*) – whether to add dataset info from the importer (if any) to the dataset’s `info` * **cleanup** (*True*) – whether to delete the archive after extracting it * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 to pass to the constructor of the [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) for the specified `dataset_type` * **Returns:** a list of IDs of the samples that were added to the dataset #### merge_archive(archive_path, dataset_type=None, data_path=None, labels_path=None, label_field=None, tags=None, key_field=None, key_fcn=None, skip_existing=False, insert_new=True, fields=None, omit_fields=None, merge_lists=True, merge_embedded_docs=False, overwrite=True, expand_schema=True, dynamic=False, add_info=True, cleanup=True, progress=None, \*\*kwargs) Merges the contents of the given archive into the dataset. #### NOTE This method requires the ability to create *unique* indexes on the `key_field` of each collection. See [`add_archive()`](#fiftyone.core.dataset.Dataset.add_archive) if you want to add samples without a uniqueness constraint. If a directory with the same root name as `archive_path` exists, it is assumed that this directory contains the extracted contents of the archive, and thus the archive is not re-extracted. See [this guide](../user_guide/import_datasets.md#loading-common-datasets) for example usages of this method and descriptions of the available dataset types. #### NOTE The following archive formats are explicitly supported: ```default .zip, .tar, .tar.gz, .tgz, .tar.bz, .tbz ``` If an archive *not* in the above list is found, extraction will be attempted via the `patool` package, which supports many formats but may require that additional system packages be installed. By default, samples with the same filepath or media reference key are merged, but you can customize this behavior via the `key_field` and `key_fcn` parameters. For example, you could set `key_fcn = lambda sample: os.path.basename(sample.filepath)` to merge samples with the same base filename. The behavior of this method is highly customizable. By default, all top-level fields from the imported samples are merged in, overwriting any existing values for those fields, with the exception of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields), in which case the elements of the lists themselves are merged. In the case of label list fields, labels with the same `id` in both collections are updated rather than duplicated. To avoid confusion between missing fields and fields whose value is `None`, `None`-valued fields are always treated as missing while merging. This method can be configured in numerous ways, including: - Whether existing samples should be modified or skipped - Whether new samples should be added or omitted - Whether new fields can be added to the dataset schema - Whether list fields should be treated as ordinary fields and merged as a whole rather than merging their elements - Whether to merge only specific fields, or all but certain fields - Mapping input fields to different field names of this dataset * **Parameters:** * **archive_path** – the path to an archive of a dataset directory * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type of the dataset in `archive_path` * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the media for certain dataset types. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `dataset_dir` in which the media lies - an absolute directory path in which the media lies. In this case, the `archive_path` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `archive_path` that maps UUIDs to media filepaths. Files of this format are generated when passing the `export_media="manifest"` option to [`fiftyone.core.collections.SampleCollection.export()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.export) - an absolute filepath to a JSON manifest file. In this case, `archive_path` has no effect on the location of the data - a dict mapping filenames to absolute filepaths By default, it is assumed that the data can be located in the default location within `archive_path` for the dataset type * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the labels. Only applicable when importing certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `archive_path` of the labels file(s) - an absolute directory or filepath containing the labels file(s). In this case, `archive_path` has no effect on the location of the labels For labeled datasets, this parameter defaults to the location in `archive_path` of the labels for the default layout of the dataset type being imported * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **key_field** (*None*) – the sample field to use to decide whether to join with an existing sample. By default, `filepath` or `media_reference` is used according to the media source of the samples * **key_fcn** (*None*) – a function that accepts a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instance and computes a key to decide if two samples should be merged. If a `key_fcn` is provided, `key_field` is ignored * **skip_existing** (*False*) – whether to skip existing samples (True) or merge them (False) * **insert_new** (*True*) – whether to insert new samples (True) or skip them (False) * **fields** (*None*) – an optional field or iterable of fields to which to restrict the merge. If provided, fields other than these are omitted from `samples` when merging or adding samples. The active media source is always included when adding new samples, since it is required. This can also be a dict mapping field names of the input collection to field names of this dataset * **omit_fields** (*None*) – an optional field or iterable of fields to exclude from the merge. If provided, these fields are omitted from imported samples, if present. The active media source is always included when adding new samples, since it is required * **merge_lists** (*True*) – whether to merge the elements of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields) rather than merging the entire top-level field like other field types. For label lists fields, existing `fiftyone.core.label.Label` elements are either replaced (when `overwrite` is True) or kept (when `overwrite` is False) when their `id` matches a label from the provided samples * **merge_embedded_docs** (*False*) – whether to merge the attributes of embedded documents (True) rather than merging the entire top-level field (False) * **overwrite** (*True*) – whether to overwrite (True) or skip (False) existing fields and label elements * **expand_schema** (*True*) – whether to dynamically add new fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **add_info** (*True*) – whether to add dataset info from the importer (if any) to the dataset * **cleanup** (*True*) – whether to delete the archive after extracting it * **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 to pass to the constructor of the [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) for the specified `dataset_type` #### add_importer(dataset_importer, label_field=None, tags=None, expand_schema=True, dynamic=False, add_info=True, generator=False, progress=None) Adds the samples from the given [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) to the dataset. See [this guide](../user_guide/import_datasets.md#custom-dataset-importer) for more details about importing datasets in custom formats by defining your own [`DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter). * **Parameters:** * **dataset_importer** – a [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **add_info** (*True*) – whether to add dataset info from the importer (if any) to the dataset’s `info` * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples that were added to the dataset #### merge_importer(dataset_importer, label_field=None, tags=None, key_field=None, key_fcn=None, skip_existing=False, insert_new=True, fields=None, omit_fields=None, merge_lists=True, merge_embedded_docs=False, overwrite=True, expand_schema=True, dynamic=False, add_info=True, progress=None) Merges the samples from the given [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) into the dataset. #### NOTE This method requires the ability to create *unique* indexes on the `key_field` of each collection. See [`add_importer()`](#fiftyone.core.dataset.Dataset.add_importer) if you want to add samples without a uniqueness constraint. See [this guide](../user_guide/import_datasets.md#custom-dataset-importer) for more details about importing datasets in custom formats by defining your own [`DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter). By default, samples with the same filepath or media reference key are merged, but you can customize this behavior via the `key_field` and `key_fcn` parameters. For example, you could set `key_fcn = lambda sample: os.path.basename(sample.filepath)` to merge samples with the same base filename. The behavior of this method is highly customizable. By default, all top-level fields from the imported samples are merged in, overwriting any existing values for those fields, with the exception of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields), in which case the elements of the lists themselves are merged. In the case of label list fields, labels with the same `id` in both collections are updated rather than duplicated. To avoid confusion between missing fields and fields whose value is `None`, `None`-valued fields are always treated as missing while merging. This method can be configured in numerous ways, including: - Whether existing samples should be modified or skipped - Whether new samples should be added or omitted - Whether new fields can be added to the dataset schema - Whether list fields should be treated as ordinary fields and merged as a whole rather than merging their elements - Whether to merge only specific fields, or all but certain fields - Mapping input fields to different field names of this dataset * **Parameters:** * **dataset_importer** – a [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **key_field** (*None*) – the sample field to use to decide whether to join with an existing sample. By default, `filepath` or `media_reference` is used according to the media source of the samples * **key_fcn** (*None*) – a function that accepts a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) instance and computes a key to decide if two samples should be merged. If a `key_fcn` is provided, `key_field` is ignored * **skip_existing** (*False*) – whether to skip existing samples (True) or merge them (False) * **insert_new** (*True*) – whether to insert new samples (True) or skip them (False) * **fields** (*None*) – an optional field or iterable of fields to which to restrict the merge. If provided, fields other than these are omitted from `samples` when merging or adding samples. The active media source is always included when adding new samples, since it is required. This can also be a dict mapping field names of the input collection to field names of this dataset * **omit_fields** (*None*) – an optional field or iterable of fields to exclude from the merge. If provided, these fields are omitted from imported samples, if present. The active media source is always included when adding new samples, since it is required * **merge_lists** (*True*) – whether to merge the elements of list fields (e.g., `tags`) and label list fields (e.g., [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields) rather than merging the entire top-level field like other field types. For label lists fields, existing `fiftyone.core.label.Label` elements are either replaced (when `overwrite` is True) or kept (when `overwrite` is False) when their `id` matches a label from the provided samples * **merge_embedded_docs** (*False*) – whether to merge the attributes of embedded documents (True) rather than merging the entire top-level field (False) * **overwrite** (*True*) – whether to overwrite (True) or skip (False) existing fields and label elements * **expand_schema** (*True*) – whether to dynamically add new fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **add_info** (*True*) – whether to add dataset info from the importer (if any) to the dataset * **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 #### add_images(paths_or_samples, sample_parser=None, tags=None, generator=False, progress=None) Adds the given images to the dataset. This operation does not read the images. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about adding images to a dataset by defining your own [`UnlabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledImageSampleParser). * **Parameters:** * **paths_or_samples** – an iterable of data. If no `sample_parser` is provided, this must be an iterable of image paths. If a `sample_parser` is provided, this can be an arbitrary iterable whose elements can be parsed by the sample parser * **sample_parser** (*None*) – a [`fiftyone.utils.data.parsers.UnlabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledImageSampleParser) instance to use to parse the samples * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples that were added to the dataset #### add_labeled_images(samples, sample_parser, label_field=None, tags=None, expand_schema=True, dynamic=False, generator=False, progress=None) Adds the given labeled images to the dataset. This operation will iterate over all provided samples, but the images will not be read (unless the sample parser requires it in order to compute image metadata). See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about adding labeled images to a dataset by defining your own [`LabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledImageSampleParser). * **Parameters:** * **samples** – an iterable of data * **sample_parser** – a [`fiftyone.utils.data.parsers.LabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledImageSampleParser) instance to use to parse the samples * **label_field** (*None*) – controls the field(s) in which imported labels are stored. If the parser produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the parser produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples that were added to the dataset #### add_images_dir(images_dir, tags=None, recursive=True, generator=False, progress=None) Adds the given directory of images to the dataset. See [`fiftyone.types.ImageDirectory`](fiftyone.types.md#fiftyone.types.ImageDirectory) for format details. In particular, note that files with non-image MIME types are omitted. This operation does not read the images. * **Parameters:** * **images_dir** – a directory of images * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **recursive** (*True*) – whether to recursively traverse subdirectories * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### add_images_patt(images_patt, tags=None, generator=False, progress=None) Adds the given glob pattern of images to the dataset. This operation does not read the images. * **Parameters:** * **images_patt** – a glob pattern of images like `/path/to/images/*.jpg` * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### ingest_images(paths_or_samples, sample_parser=None, tags=None, dataset_dir=None, image_format=None, generator=False, progress=None) Ingests the given iterable of images into the dataset. The images are read in-memory and written to `dataset_dir`. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about ingesting images into a dataset by defining your own [`UnlabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledImageSampleParser). * **Parameters:** * **paths_or_samples** – an iterable of data. If no `sample_parser` is provided, this must be an iterable of image paths. If a `sample_parser` is provided, this can be an arbitrary iterable whose elements can be parsed by the sample parser * **sample_parser** (*None*) – a [`fiftyone.utils.data.parsers.UnlabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledImageSampleParser) instance to use to parse the samples * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dataset_dir** (*None*) – the directory in which the images will be written. By default, [`get_default_dataset_dir()`](#fiftyone.core.dataset.get_default_dataset_dir) is used * **image_format** (*None*) – the image format to use to write the images to disk. By default, `fiftyone.config.default_image_ext` is used * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### ingest_labeled_images(samples, sample_parser, label_field=None, tags=None, expand_schema=True, dynamic=False, dataset_dir=None, image_format=None, generator=False, progress=None) Ingests the given iterable of labeled image samples into the dataset. The images are read in-memory and written to `dataset_dir`. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about ingesting labeled images into a dataset by defining your own [`LabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledImageSampleParser). * **Parameters:** * **samples** – an iterable of data * **sample_parser** – a [`fiftyone.utils.data.parsers.LabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledImageSampleParser) instance to use to parse the samples * **label_field** (*None*) – controls the field(s) in which imported labels are stored. If the parser produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the parser produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if the sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **dataset_dir** (*None*) – the directory in which the images will be written. By default, [`get_default_dataset_dir()`](#fiftyone.core.dataset.get_default_dataset_dir) is used * **image_format** (*None*) – the image format to use to write the images to disk. By default, `fiftyone.config.default_image_ext` is used * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### add_videos(paths_or_samples, sample_parser=None, tags=None, generator=False, progress=None) Adds the given videos to the dataset. This operation does not read the videos. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about adding videos to a dataset by defining your own [`UnlabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledVideoSampleParser). * **Parameters:** * **paths_or_samples** – an iterable of data. If no `sample_parser` is provided, this must be an iterable of video paths. If a `sample_parser` is provided, this can be an arbitrary iterable whose elements can be parsed by the sample parser * **sample_parser** (*None*) – a [`fiftyone.utils.data.parsers.UnlabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledVideoSampleParser) instance to use to parse the samples * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples that were added to the dataset #### add_labeled_videos(samples, sample_parser, label_field=None, tags=None, expand_schema=True, dynamic=False, generator=False, progress=None) Adds the given labeled videos to the dataset. This operation will iterate over all provided samples, but the videos will not be read/decoded/etc. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about adding labeled videos to a dataset by defining your own [`LabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledVideoSampleParser). * **Parameters:** * **samples** – an iterable of data * **sample_parser** – a [`fiftyone.utils.data.parsers.LabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledVideoSampleParser) instance to use to parse the samples * **label_field** ( *"ground_truth"*) – controls the field(s) in which imported labels are stored. If the parser produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the parser produces a dictionary of labels per sample/frame, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **label_field** – the name (or root name) of the frame field(s) to use for the labels * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if a sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples that were added to the dataset #### add_videos_dir(videos_dir, tags=None, recursive=True, generator=False, progress=None) Adds the given directory of videos to the dataset. See [`fiftyone.types.VideoDirectory`](fiftyone.types.md#fiftyone.types.VideoDirectory) for format details. In particular, note that files with non-video MIME types are omitted. This operation does not read/decode the videos. * **Parameters:** * **videos_dir** – a directory of videos * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **recursive** (*True*) – whether to recursively traverse subdirectories * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### add_videos_patt(videos_patt, tags=None, generator=False, progress=None) Adds the given glob pattern of videos to the dataset. This operation does not read/decode the videos. * **Parameters:** * **videos_patt** – a glob pattern of videos like `/path/to/videos/*.mp4` * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### ingest_videos(paths_or_samples, sample_parser=None, tags=None, dataset_dir=None, generator=False, progress=None) Ingests the given iterable of videos into the dataset. The videos are copied to `dataset_dir`. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about ingesting videos into a dataset by defining your own [`UnlabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledVideoSampleParser). * **Parameters:** * **paths_or_samples** – an iterable of data. If no `sample_parser` is provided, this must be an iterable of video paths. If a `sample_parser` is provided, this can be an arbitrary iterable whose elements can be parsed by the sample parser * **sample_parser** (*None*) – a [`fiftyone.utils.data.parsers.UnlabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledVideoSampleParser) instance to use to parse the samples * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dataset_dir** (*None*) – the directory in which the videos will be written. By default, [`get_default_dataset_dir()`](#fiftyone.core.dataset.get_default_dataset_dir) is used * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### ingest_labeled_videos(samples, sample_parser, tags=None, expand_schema=True, dynamic=False, dataset_dir=None, generator=False, progress=None) Ingests the given iterable of labeled video samples into the dataset. The videos are copied to `dataset_dir`. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about ingesting labeled videos into a dataset by defining your own [`LabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledVideoSampleParser). * **Parameters:** * **samples** – an iterable of data * **sample_parser** – a [`fiftyone.utils.data.parsers.LabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledVideoSampleParser) instance to use to parse the samples * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **expand_schema** (*True*) – whether to dynamically add new sample fields encountered to the dataset schema. If False, an error is raised if the sample’s schema is not a subset of the dataset schema * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **dataset_dir** (*None*) – the directory in which the videos will be written. By default, [`get_default_dataset_dir()`](#fiftyone.core.dataset.get_default_dataset_dir) is used * **generator** (*False*) – whether to yield ID batches as a generator as samples are added to the dataset * **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 * **Returns:** a list of IDs of the samples in the dataset #### *classmethod* from_dir(dataset_dir=None, dataset_type=None, data_path=None, labels_path=None, name=None, persistent=False, overwrite=False, label_field=None, tags=None, dynamic=False, progress=None, \*\*kwargs) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the contents of the given directory. You can create datasets with this method via the following basic patterns: 1. Provide `dataset_dir` and `dataset_type` to import the contents of a directory that is organized in the default layout for the dataset type as documented in [this guide](../user_guide/import_datasets.md#loading-common-datasets) 2. Provide `dataset_type` along with `data_path`, `labels_path`, or other type-specific parameters to perform a customized import. This syntax provides the flexibility to, for example, perform labels-only imports or imports where the source media lies in a different location than the labels In either workflow, the remaining parameters of this method can be provided to further configure the import. See [this guide](../user_guide/import_datasets.md#loading-common-datasets) for example usages of this method and descriptions of the available dataset types. * **Parameters:** * **dataset_dir** (*None*) – the dataset directory. This can be omitted if you provide arguments such as `data_path` and `labels_path` * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type of the dataset * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the media for certain dataset types. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `dataset_dir` in which the media lies - an absolute directory path in which the media lies. In this case, the `dataset_dir` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `dataset_dir` that maps UUIDs to media filepaths. Files of this format are generated when passing the `export_media="manifest"` option to [`fiftyone.core.collections.SampleCollection.export()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.export) - an absolute filepath to a JSON manifest file. In this case, `dataset_dir` has no effect on the location of the data - a dict mapping filenames to absolute filepaths By default, it is assumed that the data can be located in the default location within `dataset_dir` for the dataset type * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the labels. Only applicable when importing certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `dataset_dir` of the labels file(s) - an absolute directory or filepath containing the labels file(s). In this case, `dataset_dir` has no effect on the location of the labels For labeled datasets, this parameter defaults to the location in `dataset_dir` of the labels for the default layout of the dataset type being imported * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **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 to pass to the constructor of the [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) for the specified `dataset_type` * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_archive(archive_path, dataset_type=None, data_path=None, labels_path=None, name=None, persistent=False, overwrite=False, label_field=None, tags=None, dynamic=False, cleanup=True, progress=None, \*\*kwargs) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the contents of the given archive. If a directory with the same root name as `archive_path` exists, it is assumed that this directory contains the extracted contents of the archive, and thus the archive is not re-extracted. See [this guide](../user_guide/import_datasets.md#loading-common-datasets) for example usages of this method and descriptions of the available dataset types. #### NOTE The following archive formats are explicitly supported: ```default .zip, .tar, .tar.gz, .tgz, .tar.bz, .tbz ``` If an archive *not* in the above list is found, extraction will be attempted via the `patool` package, which supports many formats but may require that additional system packages be installed. * **Parameters:** * **archive_path** – the path to an archive of a dataset directory * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type of the dataset in `archive_path` * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the media for certain dataset types. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `dataset_dir` in which the media lies - an absolute directory path in which the media lies. In this case, the `archive_path` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `archive_path` that maps UUIDs to media filepaths. Files of this format are generated when passing the `export_media="manifest"` option to [`fiftyone.core.collections.SampleCollection.export()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.export) - an absolute filepath to a JSON manifest file. In this case, `archive_path` has no effect on the location of the data - a dict mapping filenames to absolute filepaths By default, it is assumed that the data can be located in the default location within `archive_path` for the dataset type * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the labels. Only applicable when importing certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `archive_path` of the labels file(s) - an absolute directory or filepath containing the labels file(s). In this case, `archive_path` has no effect on the location of the labels For labeled datasets, this parameter defaults to the location in `archive_path` of the labels for the default layout of the dataset type being imported * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **cleanup** (*True*) – whether to delete the archive after extracting it * **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 to pass to the constructor of the [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) for the specified `dataset_type` * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_importer(dataset_importer, name=None, persistent=False, overwrite=False, label_field=None, tags=None, dynamic=False, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) by importing the samples in the given [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter). See [this guide](../user_guide/import_datasets.md#custom-dataset-importer) for more details about providing a custom [`DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) to import datasets into FiftyOne. * **Parameters:** * **dataset_importer** – a [`fiftyone.utils.data.importers.DatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.DatasetImporter) * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **label_field** (*None*) – controls the field(s) in which imported labels are stored. Only applicable if `dataset_importer` is a [`fiftyone.utils.data.importers.LabeledImageDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledImageDatasetImporter) or [`fiftyone.utils.data.importers.LabeledVideoDatasetImporter`](fiftyone.utils.data.importers.md#fiftyone.utils.data.importers.LabeledVideoDatasetImporter). If the importer produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the importer produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_images(paths_or_samples, sample_parser=None, name=None, persistent=False, overwrite=False, tags=None, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given images. This operation does not read the images. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about providing a custom [`UnlabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledImageSampleParser) to load image samples into FiftyOne. * **Parameters:** * **paths_or_samples** – an iterable of data. If no `sample_parser` is provided, this must be an iterable of image paths. If a `sample_parser` is provided, this can be an arbitrary iterable whose elements can be parsed by the sample parser * **sample_parser** (*None*) – a [`fiftyone.utils.data.parsers.UnlabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledImageSampleParser) instance to use to parse the samples * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_labeled_images(samples, sample_parser, name=None, persistent=False, overwrite=False, label_field=None, tags=None, dynamic=False, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given labeled images. This operation will iterate over all provided samples, but the images will not be read. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about providing a custom [`LabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledImageSampleParser) to load labeled image samples into FiftyOne. * **Parameters:** * **samples** – an iterable of data * **sample_parser** – a [`fiftyone.utils.data.parsers.LabeledImageSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledImageSampleParser) instance to use to parse the samples * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **label_field** (*None*) – controls the field(s) in which imported labels are stored. If the parser produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the parser produces a dictionary of labels per sample, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_images_dir(images_dir, name=None, persistent=False, overwrite=False, tags=None, recursive=True, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given directory of images. This operation does not read the images. * **Parameters:** * **images_dir** – a directory of images * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **recursive** (*True*) – whether to recursively traverse subdirectories * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_images_patt(images_patt, name=None, persistent=False, overwrite=False, tags=None, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given glob pattern of images. This operation does not read the images. * **Parameters:** * **images_patt** – a glob pattern of images like `/path/to/images/*.jpg` * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_videos(paths_or_samples, sample_parser=None, name=None, persistent=False, overwrite=False, tags=None, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given videos. This operation does not read/decode the videos. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about providing a custom [`UnlabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledVideoSampleParser) to load video samples into FiftyOne. * **Parameters:** * **paths_or_samples** – an iterable of data. If no `sample_parser` is provided, this must be an iterable of video paths. If a `sample_parser` is provided, this can be an arbitrary iterable whose elements can be parsed by the sample parser * **sample_parser** (*None*) – a [`fiftyone.utils.data.parsers.UnlabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.UnlabeledVideoSampleParser) instance to use to parse the samples * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_labeled_videos(samples, sample_parser, name=None, persistent=False, overwrite=False, label_field=None, tags=None, dynamic=False, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given labeled videos. This operation will iterate over all provided samples, but the videos will not be read/decoded/etc. See [this guide](../user_guide/sample_parsers.md#custom-sample-parser) for more details about providing a custom [`LabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledVideoSampleParser) to load labeled video samples into FiftyOne. * **Parameters:** * **samples** – an iterable of data * **sample_parser** – a [`fiftyone.utils.data.parsers.LabeledVideoSampleParser`](fiftyone.utils.data.parsers.md#fiftyone.utils.data.parsers.LabeledVideoSampleParser) instance to use to parse the samples * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **label_field** (*None*) – controls the field(s) in which imported labels are stored. If the parser produces a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instance per sample/frame, this argument specifies the name of the field to use; the default is `"ground_truth"`. If the parser produces a dictionary of labels per sample/frame, this argument can be either a string prefix to prepend to each label key or a dict mapping label keys to field names; the default in this case is to directly use the keys of the imported label dictionaries as field names * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_videos_dir(videos_dir, name=None, persistent=False, overwrite=False, tags=None, recursive=True, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given directory of videos. This operation does not read/decode the videos. * **Parameters:** * **videos_dir** – a directory of videos * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **recursive** (*True*) – whether to recursively traverse subdirectories * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_videos_patt(videos_patt, name=None, persistent=False, overwrite=False, tags=None, progress=None) Creates a [`Dataset`](#fiftyone.core.dataset.Dataset) from the given glob pattern of videos. This operation does not read/decode the videos. * **Parameters:** * **videos_patt** – a glob pattern of videos like `/path/to/videos/*.mp4` * **name** (*None*) – a name for the dataset. By default, [`get_default_dataset_name()`](#fiftyone.core.dataset.get_default_dataset_name) is used * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **tags** (*None*) – an optional tag or iterable of tags to attach to each sample * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_dict(d, name=None, persistent=False, overwrite=False, rel_dir=None, frame_labels_dir=None, progress=None) Loads a [`Dataset`](#fiftyone.core.dataset.Dataset) from a JSON dictionary generated by [`fiftyone.core.collections.SampleCollection.to_dict()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.to_dict). The JSON dictionary can contain an export of any [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection), e.g., [`Dataset`](#fiftyone.core.dataset.Dataset) or [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView). * **Parameters:** * **d** – a JSON dictionary * **name** (*None*) – a name for the new dataset * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **rel_dir** (*None*) – a relative directory to prepend to the `filepath` of each sample if the filepath is not absolute (begins with a path separator). The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path) * **frame_labels_dir** (*None*) – a directory of per-sample JSON files containing the frame labels for video samples. If omitted, it is assumed that the frame labels are included directly in the provided JSON dict. Only applicable to datasets that contain videos * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### *classmethod* from_json(path_or_str, name=None, persistent=False, overwrite=False, rel_dir=None, frame_labels_dir=None, progress=None) Loads a [`Dataset`](#fiftyone.core.dataset.Dataset) from JSON generated by [`fiftyone.core.collections.SampleCollection.write_json()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.write_json) or [`fiftyone.core.collections.SampleCollection.to_json()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.to_json). The JSON file can contain an export of any [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection), e.g., [`Dataset`](#fiftyone.core.dataset.Dataset) or [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView). * **Parameters:** * **path_or_str** – the path to a JSON file on disk or a JSON string * **name** (*None*) – a name for the new dataset * **persistent** (*False*) – whether the dataset should persist in the database after the session terminates * **overwrite** (*False*) – whether to overwrite an existing dataset of the same name * **rel_dir** (*None*) – a relative directory to prepend to the `filepath` of each sample, if the filepath is not absolute (begins with a path separator). The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path) * **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 * **Returns:** a [`Dataset`](#fiftyone.core.dataset.Dataset) #### reload() Reloads the dataset and any in-memory samples from the database. #### clear_cache() Clears the dataset’s in-memory cache. Dataset caches may contain sample/frame singletons and annotation/brain/evaluation/custom runs. #### add_stage(stage) Applies the given [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) to the collection. * **Parameters:** **stage** – a [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### aggregate(aggregations, \_mongo=False) Aggregates one or more [`fiftyone.core.aggregations.Aggregation`](fiftyone.core.aggregations.md#fiftyone.core.aggregations.Aggregation) instances. Note that it is best practice to group aggregations into a single call to [`aggregate()`](#fiftyone.core.dataset.Dataset.aggregate), as this will be more efficient than performing multiple aggregations in series. * **Parameters:** **aggregations** – an [`fiftyone.core.aggregations.Aggregation`](fiftyone.core.aggregations.md#fiftyone.core.aggregations.Aggregation) or iterable of [`fiftyone.core.aggregations.Aggregation`](fiftyone.core.aggregations.md#fiftyone.core.aggregations.Aggregation) instances * **Returns:** Aggregation result(s) corresponding to the input aggregation(s). Returns a single result for a single aggregation, a list of results for multiple aggregations, or a generator for lazy aggregations #### annotate(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) in this collection 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 [`fiftyone.utils.annotations.AnnotationBackendConfig`](fiftyone.utils.annotations.md#fiftyone.utils.annotations.AnnotationBackendConfig) class. The natively provided backends and their associated config classes are: - `"cvat"`: [`fiftyone.utils.cvat.CVATBackendConfig`](fiftyone.utils.cvat.md#fiftyone.utils.cvat.CVATBackendConfig) - `"labelstudio"`: [`fiftyone.utils.labelstudio.LabelStudioBackendConfig`](fiftyone.utils.labelstudio.md#fiftyone.utils.labelstudio.LabelStudioBackendConfig) - `"labelbox"`: [`fiftyone.utils.labelbox.LabelboxBackendConfig`](fiftyone.utils.labelbox.md#fiftyone.utils.labelbox.LabelboxBackendConfig) See [this page](../integrations/annotation.md#requesting-annotations) for more information about using this method, including how to define label schemas and how to configure login credentials for your annotation provider. * **Parameters:** * **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`](fiftyone.core.labels.md#fiftyone.core.labels.Classification) fields - `"classifications"`: multilabel classifications stored in [`fiftyone.core.labels.Classifications`](fiftyone.core.labels.md#fiftyone.core.labels.Classifications) fields - `"detections"`: object detections stored in [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields - `"instances"`: instance segmentations stored in [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) fields with their [`mask`](fiftyone.core.labels.md#fiftyone.core.labels.Detection.mask) attributes populated - `"polylines"`: polylines stored in [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) fields with their [`filled`](fiftyone.core.labels.md#fiftyone.core.labels.Polyline.filled) attributes set to `False` - `"polygons"`: polygons stored in [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) fields with their [`filled`](fiftyone.core.labels.md#fiftyone.core.labels.Polyline.filled) attributes set to `True` - `"keypoints"`: keypoints stored in [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) fields - `"segmentation"`: semantic segmentations stored in [`fiftyone.core.labels.Segmentation`](fiftyone.core.labels.md#fiftyone.core.labels.Segmentation) fields - `"scalar"`: scalar labels stored in [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField), [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField), [`fiftyone.core.fields.StringField`](fiftyone.core.fields.md#fiftyone.core.fields.StringField), or [`fiftyone.core.fields.BooleanField`](fiftyone.core.fields.md#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`, they are retrieved from [`get_classes()`](#fiftyone.core.dataset.Dataset.get_classes) if possible, or else 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 - a list of label attributes to export - a dict mapping attribute names to dicts specifying the `type`, `values`, and `default` for each attribute 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 (2D masks) or RGB hex strings (3D masks) 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 [`fiftyone.utils.annotations.AnnotationBackendConfig`](fiftyone.utils.annotations.md#fiftyone.utils.annotations.AnnotationBackendConfig) * **Returns:** an `fiftyone.utils.annotations.AnnnotationResults` #### apply_model(model, label_field='predictions', confidence_thresh=None, classes=None, store_logits=False, batch_size=None, num_workers=None, skip_failures=True, output_dir=None, rel_dir=None, progress=None, \*\*kwargs) Applies the model to the samples in the collection. This method supports all of the following cases: - Applying an image model to an image collection - Applying an image model to the frames of a video collection - Applying a video model to a video collection * **Parameters:** * **model** – a [`fiftyone.core.models.Model`](fiftyone.core.models.md#fiftyone.core.models.Model), Hugging Face transformers model, Ultralytics model, SuperGradients model, or Lightning Flash model * **label_field** ( *"predictions"*) – the name of the field in which to store the model predictions. When performing inference on video frames, the “frames.” prefix is optional * **confidence_thresh** (*None*) – an optional confidence threshold to apply to any applicable labels generated by the model * **classes** (*None*) – an optional iterable of classes to which to restrict any applicable labels generated by the model * **store_logits** (*False*) – whether to store logits for the model predictions. This is only supported when the provided `model` has logits, `model.has_logits == True` * **batch_size** (*None*) – an optional batch size to use, if the model supports batching * **num_workers** (*None*) – the number of workers for the [`torch.utils.data.DataLoader`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) to use. Only applicable for Torch-based models * **skip_failures** (*True*) – whether to gracefully continue without raising an error if predictions cannot be generated for a sample. Only applicable to [`fiftyone.core.models.Model`](fiftyone.core.models.md#fiftyone.core.models.Model) instances * **output_dir** (*None*) – an optional output directory in which to write segmentation images. Only applicable if the model generates segmentations. If none is provided, the segmentations are stored in the database * **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 segmentation 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()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path) * **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 model-specific keyword arguments passed through to the underlying inference implementation #### bounds(field_or_expr, expr=None, safe=False) Computes the bounds of a numeric field of the collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* or *date* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) - [`fiftyone.core.fields.DateField`](fiftyone.core.fields.md#fiftyone.core.fields.DateField) - [`fiftyone.core.fields.DateTimeField`](fiftyone.core.fields.md#fiftyone.core.fields.DateTimeField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the bounds of a numeric field # bounds = dataset.bounds("numeric_field") print(bounds) # (min, max) # # Compute the bounds of a numeric list field # bounds = dataset.bounds("numeric_list_field") print(bounds) # (min, max) # # Compute the bounds of a transformation of a numeric field # bounds = dataset.bounds(2 * (F("numeric_field") + 1)) print(bounds) # (min, max) ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the `(min, max)` bounds #### compute_embeddings(model, embeddings_field=None, batch_size=None, num_workers=None, skip_failures=True, progress=None, \*\*kwargs) Computes embeddings for the samples in the collection using the given model. This method supports all the following cases: - Using an image model to compute embeddings for an image collection - Using an image model to compute frame embeddings for a video collection - Using a video model to compute embeddings for a video collection The `model` must expose embeddings, i.e., [`fiftyone.core.models.Model.has_embeddings()`](fiftyone.core.models.md#fiftyone.core.models.Model.has_embeddings) must return `True`. If an `embeddings_field` is provided, the embeddings are saved to the samples; otherwise, the embeddings are returned in-memory. * **Parameters:** * **model** – a [`fiftyone.core.models.Model`](fiftyone.core.models.md#fiftyone.core.models.Model), Hugging Face Transformers model, Ultralytics model, SuperGradients model, or Lightning Flash model * **embeddings_field** (*None*) – the name of a field in which to store the embeddings. When computing video frame embeddings, the “frames.” prefix is optional * **batch_size** (*None*) – an optional batch size to use, if the model supports batching * **num_workers** (*None*) – the number of workers for the [`torch.utils.data.DataLoader`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) to use. Only applicable for Torch-based models * **skip_failures** (*True*) – whether to gracefully continue without raising an error if embeddings cannot be generated for a sample. Only applicable to [`fiftyone.core.models.Model`](fiftyone.core.models.md#fiftyone.core.models.Model) instances * **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 model-specific keyword arguments passed through to the underlying inference implementation * **Returns:** - `None`, if an `embeddings_field` is provided - a `num_samples x num_dim` array of embeddings, when computing embeddings for image/video collections with image/video models, respectively, and no `embeddings_field` is provided. If `skip_failures` is `True` and any errors are detected, a list of length `num_samples` is returned instead containing all successfully computed embedding vectors along with `None` entries for samples for which embeddings could not be computed - a dictionary mapping sample IDs to `num_frames x num_dim` arrays of embeddings, when computing frame embeddings for video collections using an image model. If `skip_failures` is `True` and any errors are detected, the values of this dictionary will contain arrays of embeddings for all frames 1, 2, … until the error occurred, or `None` if no embeddings were computed at all * **Return type:** one of the following #### compute_metadata(overwrite=False, num_workers=None, skip_failures=True, warn_failures=True, progress=None) Populates the `metadata` field of all samples in the collection. Any samples with existing metadata are skipped, unless `overwrite == True`. * **Parameters:** * **overwrite** (*False*) – whether to overwrite existing metadata * **num_workers** (*None*) – a suggested number of threads to use * **skip_failures** (*True*) – whether to gracefully continue without raising an error if metadata cannot be computed for a sample * **warn_failures** (*True*) – whether to log a warning if metadata cannot be computed for a sample * **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 #### compute_patch_embeddings(model, patches_field, embeddings_field=None, force_square=False, alpha=None, handle_missing='skip', batch_size=None, num_workers=None, skip_failures=True, progress=None) Computes embeddings for the image patches defined by `patches_field` of the samples in the collection using the given model. This method supports all the following cases: - Using an image model to compute patch embeddings for an image collection - Using an image model to compute frame patch embeddings for a video collection The `model` must expose embeddings, i.e., [`fiftyone.core.models.Model.has_embeddings()`](fiftyone.core.models.md#fiftyone.core.models.Model.has_embeddings) must return `True`. If an `embeddings_field` is provided, the embeddings are saved to the samples; otherwise, the embeddings are returned in-memory. * **Parameters:** * **model** – a [`fiftyone.core.models.Model`](fiftyone.core.models.md#fiftyone.core.models.Model), Hugging Face Transformers model, Ultralytics model, SuperGradients model, or Lightning Flash model * **patches_field** – the name of the field defining the image patches in each sample to embed. Must be of type [`fiftyone.core.labels.Detection`](fiftyone.core.labels.md#fiftyone.core.labels.Detection), [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Polyline`](fiftyone.core.labels.md#fiftyone.core.labels.Polyline), or [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines). When computing video frame embeddings, the “frames.” prefix is optional * **embeddings_field** (*None*) – the name of a label attribute in which to store the embeddings * **force_square** (*False*) – whether to minimally manipulate the patch bounding boxes into squares prior to extraction * **alpha** (*None*) – an optional expansion/contraction to apply to the patches before extracting them, in `[-1, inf)`. If provided, the length and width of the box are expanded (or contracted, when `alpha < 0`) by `(100 * alpha)%`. For example, set `alpha = 0.1` to expand the boxes by 10%, and set `alpha = -0.1` to contract the boxes by 10% * **handle_missing** ( *"skip"*) – how to handle images with no patches. Supported values are: - ”skip”: skip the image and assign its embedding as `None` - ”image”: use the whole image as a single patch - ”error”: raise an error * **batch_size** (*None*) – an optional batch size to use, if the model supports batching * **num_workers** (*None*) – the number of workers for the [`torch.utils.data.DataLoader`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) to use. Only applicable for Torch-based models * **skip_failures** (*True*) – whether to gracefully continue without raising an error if embeddings cannot be generated for a sample * **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 * **Returns:** - `None`, if an `embeddings_field` is provided - a dict mapping sample IDs to `num_patches x num_dim` arrays of patch embeddings, when computing patch embeddings for image collections and no `embeddings_field` is provided. If `skip_failures` is `True` and any errors are detected, this dictionary will contain `None` values for any samples for which embeddings could not be computed - a dict of dicts mapping sample IDs to frame numbers to `num_patches x num_dim` arrays of patch embeddings, when computing patch embeddings for the frames of video collections and no `embeddings_field` is provided. If `skip_failures` is `True` and any errors are detected, this nested dict will contain missing or `None` values to indicate uncomputable embeddings * **Return type:** one of the following #### concat(samples) Concatenates the contents of the given `SampleCollection` to this collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Concatenate two views # view1 = dataset.match(F("uniqueness") < 0.2) view2 = dataset.match(F("uniqueness") > 0.7) view = view1.concat(view2) print(view1) print(view2) print(view) # # Concatenate two patches views # gt_objects = dataset.to_patches("ground_truth") patches1 = gt_objects[:50] patches2 = gt_objects[-50:] patches = patches1.concat(patches2) print(patches1) print(patches2) print(patches) ``` * **Parameters:** **samples** – a `SampleCollection` whose contents to append to this collection * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### count(field_or_expr=None, expr=None, safe=False) Counts the number of field values in the collection. `None`-valued fields are ignored. If no field is provided, the samples themselves are counted. Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Detections( detections=[ fo.Detection(label="cat"), fo.Detection(label="dog"), ] ), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Detections( detections=[ fo.Detection(label="cat"), fo.Detection(label="rabbit"), fo.Detection(label="squirrel"), ] ), ), fo.Sample( filepath="/path/to/image3.png", predictions=None, ), ] ) # # Count the number of samples in the dataset # count = dataset.count() print(count) # the count # # Count the number of samples with `predictions` # count = dataset.count("predictions") print(count) # the count # # Count the number of objects in the `predictions` field # count = dataset.count("predictions.detections") print(count) # the count # # Count the number of objects in samples with > 2 predictions # count = dataset.count( (F("predictions.detections").length() > 2).if_else( F("predictions.detections"), None ) ) print(count) # the count ``` * **Parameters:** * **field_or_expr** (*None*) – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. If neither `field_or_expr` or `expr` is provided, the samples themselves are counted. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the count #### count_label_tags(label_fields=None) Counts the occurrences of all label tags in the specified label field(s) of this collection. * **Parameters:** **label_fields** (*None*) – an optional name or iterable of names of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields. By default, all label fields are used * **Returns:** a dict mapping tags to counts #### count_sample_tags() Counts the occurrences of sample tags in this collection. * **Returns:** a dict mapping tags to counts #### count_values(field_or_expr, expr=None, safe=False) Counts the occurrences of field values in the collection. This aggregation is typically applied to *countable* field types (or lists of such types): - [`fiftyone.core.fields.BooleanField`](fiftyone.core.fields.md#fiftyone.core.fields.BooleanField) - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.StringField`](fiftyone.core.fields.md#fiftyone.core.fields.StringField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", tags=["sunny"], predictions=fo.Detections( detections=[ fo.Detection(label="cat"), fo.Detection(label="dog"), ] ), ), fo.Sample( filepath="/path/to/image2.png", tags=["cloudy"], predictions=fo.Detections( detections=[ fo.Detection(label="cat"), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image3.png", predictions=None, ), ] ) # # Compute the tag counts in the dataset # counts = dataset.count_values("tags") print(counts) # dict mapping values to counts # # Compute the predicted label counts in the dataset # counts = dataset.count_values("predictions.detections.label") print(counts) # dict mapping values to counts # # Compute the predicted label counts after some normalization # counts = dataset.count_values( F("predictions.detections.label").map_values( {"cat": "pet", "dog": "pet"} ).upper() ) print(counts) # dict mapping values to counts ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to treat nan/inf values as None when dealing with floating point values * **Returns:** a dict mapping values to counts #### create_index(field_or_spec, unique=False, force=False, wait=True, \*\*kwargs) Creates an index on the given field or with the given specification, if necessary. Indexes enable efficient sorting, merging, and other such operations. Frame-level fields can be indexed by prepending `"frames."` to the field name. #### NOTE If a matching non-unique index exists and you request a unique index, the existing index will be converted to a unique index. If a matching unique index exists and you request a non-unique index, the existing index will **only** be converted to a non-unique index if you specify `force=True`. #### NOTE If an index with the same field(s) but different order(s) already exists, the existing index will **only** be replaced with a new index if you specify `force=True`. * **Parameters:** * **field_or_spec** – the field name, `embedded.field.name`, or index specification list. See [`pymongo.collection.Collection.create_index()`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.create_index) for supported values * **unique** (*False*) – whether to add a uniqueness constraint to the index * **force** (*False*) – whether to convert an existing unique index to a non-unique index or replace an existing index with different orderings with a new index. By default, existing indexes will not be modified in these cases * **wait** (*True*) – whether to wait for index creation to finish * **\*\*kwargs** – optional keyword arguments for [`pymongo.collection.Collection.create_index()`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.create_index) * **Returns:** the name of the index #### delete_annotation_run(anno_key) Deletes the annotation run with the given key from this collection. Calling this method only deletes the **record** of the annotation run from the collection; it will not delete any annotations loaded onto your dataset via [`load_annotations()`](#fiftyone.core.dataset.Dataset.load_annotations), nor will it delete any associated information from the annotation backend. Use [`load_annotation_results()`](#fiftyone.core.dataset.Dataset.load_annotation_results) to programmatically manage/delete a run from the annotation backend. * **Parameters:** **anno_key** – an annotation key #### delete_annotation_runs() Deletes all annotation runs from this collection. Calling this method only deletes the **records** of the annotation runs from this collection; it will not delete any annotations loaded onto your dataset via [`load_annotations()`](#fiftyone.core.dataset.Dataset.load_annotations), nor will it delete any associated information from the annotation backend. Use [`load_annotation_results()`](#fiftyone.core.dataset.Dataset.load_annotation_results) to programmatically manage/delete runs in the annotation backend. #### delete_brain_run(brain_key) Deletes the brain method run with the given key from this collection. * **Parameters:** **brain_key** – a brain key #### delete_brain_runs() Deletes all brain method runs from this collection. #### delete_evaluation(eval_key) Deletes the evaluation results associated with the given evaluation key from this collection. * **Parameters:** **eval_key** – an evaluation key #### delete_evaluations() Deletes all evaluation results from this collection. #### delete_run(run_key) Deletes the run with the given key from this collection. * **Parameters:** **run_key** – a run key #### delete_runs() Deletes all runs from this collection. #### distinct(field_or_expr, expr=None, safe=False) Computes the distinct values of a field in the collection. `None`-valued fields are ignored. This aggregation is typically applied to *countable* field types (or lists of such types): - [`fiftyone.core.fields.BooleanField`](fiftyone.core.fields.md#fiftyone.core.fields.BooleanField) - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.StringField`](fiftyone.core.fields.md#fiftyone.core.fields.StringField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", tags=["sunny"], predictions=fo.Detections( detections=[ fo.Detection(label="cat"), fo.Detection(label="dog"), ] ), ), fo.Sample( filepath="/path/to/image2.png", tags=["sunny", "cloudy"], predictions=fo.Detections( detections=[ fo.Detection(label="cat"), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image3.png", predictions=None, ), ] ) # # Get the distinct tags in a dataset # values = dataset.distinct("tags") print(values) # list of distinct values # # Get the distinct predicted labels in a dataset # values = dataset.distinct("predictions.detections.label") print(values) # list of distinct values # # Get the distinct predicted labels after some normalization # values = dataset.distinct( F("predictions.detections.label").map_values( {"cat": "pet", "dog": "pet"} ).upper() ) print(values) # list of distinct values ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** a sorted list of distinct values #### draw_labels(output_dir, rel_dir=None, label_fields=None, overwrite=False, config=None, progress=None, \*\*kwargs) Renders annotated versions of the media in the collection with the specified label data overlaid to the given directory. The filenames of the sample media 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. Images are written in format `fo.config.default_image_ext`, and videos are written in format `fo.config.default_video_ext`. * **Parameters:** * **output_dir** – the directory to write the annotated media * **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 media. 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()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path) * **label_fields** (*None*) – a label field or list of label fields to render. By default, all [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields are drawn * **overwrite** (*False*) – whether to delete `output_dir` if it exists before rendering * **config** (*None*) – an optional [`fiftyone.utils.annotations.DrawConfig`](fiftyone.utils.annotations.md#fiftyone.utils.annotations.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 [`fiftyone.utils.annotations.DrawConfig`](fiftyone.utils.annotations.md#fiftyone.utils.annotations.DrawConfig) to override * **Returns:** the list of paths to the rendered media #### drop_index(field_or_name) Drops the index for the given field or name, if necessary. * **Parameters:** **field_or_name** – a field name, `embedded.field.name`, or compound index name. Use [`list_indexes()`](#fiftyone.core.dataset.Dataset.list_indexes) to see the available indexes #### evaluate_classifications(pred_field, gt_field='ground_truth', eval_key=None, classes=None, missing=None, method=None, progress=None, \*\*kwargs) Evaluates the classification predictions in this collection with respect to the specified ground truth labels. By default, this method simply compares the ground truth and prediction for each sample, but other strategies such as binary evaluation and top-k matching can be configured via the `method` parameter. You can customize the evaluation method by passing additional parameters for the method’s config class as `kwargs`. The natively provided `method` values and their associated configs are: - `"simple"`: [`fiftyone.utils.eval.classification.SimpleEvaluationConfig`](fiftyone.utils.eval.classification.md#fiftyone.utils.eval.classification.SimpleEvaluationConfig) - `"top-k"`: [`fiftyone.utils.eval.classification.TopKEvaluationConfig`](fiftyone.utils.eval.classification.md#fiftyone.utils.eval.classification.TopKEvaluationConfig) - `"binary"`: [`fiftyone.utils.eval.classification.BinaryEvaluationConfig`](fiftyone.utils.eval.classification.md#fiftyone.utils.eval.classification.BinaryEvaluationConfig) If an `eval_key` is specified, then this method will record some statistics on each sample: - When evaluating sample-level fields, an `eval_key` field will be populated on each sample recording whether that sample’s prediction is correct. - When evaluating frame-level fields, an `eval_key` field will be populated on each frame recording whether that frame’s prediction is correct. In addition, an `eval_key` field will be populated on each sample that records the average accuracy of the frame predictions of the sample. * **Parameters:** * **pred_field** – the name of the field containing the predicted [`fiftyone.core.labels.Classification`](fiftyone.core.labels.md#fiftyone.core.labels.Classification) instances * **gt_field** ( *"ground_truth"*) – the name of the field containing the ground truth [`fiftyone.core.labels.Classification`](fiftyone.core.labels.md#fiftyone.core.labels.Classification) instances * **eval_key** (*None*) – a string key to use to refer to this evaluation * **classes** (*None*) – the list of possible classes. If not provided, the observed ground truth/predicted labels are used * **missing** (*None*) – a missing label string. Any None-valued labels are given this label for results purposes * **method** (*None*) – a string specifying the evaluation method to use. The supported values are `fo.evaluation_config.classification_backends.keys()` and the default is `fo.evaluation_config.classification_default_backend` * **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 for the constructor of the [`fiftyone.utils.eval.classification.ClassificationEvaluationConfig`](fiftyone.utils.eval.classification.md#fiftyone.utils.eval.classification.ClassificationEvaluationConfig) being used * **Returns:** a [`fiftyone.utils.eval.classification.ClassificationResults`](fiftyone.utils.eval.classification.md#fiftyone.utils.eval.classification.ClassificationResults) #### evaluate_detections(pred_field, gt_field='ground_truth', eval_key=None, classes=None, missing=None, method=None, iou=0.5, use_masks=False, use_boxes=False, classwise=True, dynamic=True, progress=None, \*\*kwargs) Evaluates the specified predicted detections in this collection with respect to the specified ground truth detections. This method supports evaluating the following spatial data types: - Object detections in [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) format - Instance segmentations in [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) format with their `mask` attributes populated - Polygons in [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) format - Keypoints in [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) format - Temporal detections in [`fiftyone.core.labels.TemporalDetections`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetections) format For spatial object detection evaluation, this method uses COCO-style evaluation by default. When evaluating keypoints, “IoUs” are computed via [object keypoint similarity](https://cocodataset.org/#keypoints-eval). You can pass `keypoint_sigmas` to customize the per-keypoint OKS falloff. For temporal segment detection, this method uses ActivityNet-style evaluation by default. You can use the `method` parameter to select a different method, and you can optionally customize the method by passing additional parameters for the method’s config class as `kwargs`. The natively provided `method` values and their associated configs are: - `"coco"`: [`fiftyone.utils.eval.coco.COCOEvaluationConfig`](fiftyone.utils.eval.coco.md#fiftyone.utils.eval.coco.COCOEvaluationConfig) - `"open-images"`: [`fiftyone.utils.eval.openimages.OpenImagesEvaluationConfig`](fiftyone.utils.eval.openimages.md#fiftyone.utils.eval.openimages.OpenImagesEvaluationConfig) - `"activitynet"`: [`fiftyone.utils.eval.activitynet.ActivityNetEvaluationConfig`](fiftyone.utils.eval.activitynet.md#fiftyone.utils.eval.activitynet.ActivityNetEvaluationConfig) If an `eval_key` is provided, a number of fields are populated at the object- and sample-level recording the results of the evaluation: - True positive (TP), false positive (FP), and false negative (FN) counts for the each sample are saved in top-level fields of each sample: ```default TP: sample._tp FP: sample._fp FN: sample._fn ``` In addition, when evaluating frame-level objects, TP/FP/FN counts are recorded for each frame: ```default TP: frame._tp FP: frame._fp FN: frame._fn ``` - The fields listed below are populated on each individual object; these fields tabulate the TP/FP/FN status of the object, the ID of the matching object (if any), and the matching IoU: ```default TP/FP/FN: object. ID: object._id IoU: object._iou ``` * **Parameters:** * **pred_field** – the name of the field containing the predicted [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines), [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints), or [`fiftyone.core.labels.TemporalDetections`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetections) * **gt_field** ( *"ground_truth"*) – the name of the field containing the ground truth [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines), [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints), or [`fiftyone.core.labels.TemporalDetections`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetections) * **eval_key** (*None*) – a string key to use to refer to this evaluation * **classes** (*None*) – the list of possible classes. If not provided, the observed ground truth/predicted labels are used * **missing** (*None*) – a missing label string. Any unmatched objects are given this label for results purposes * **method** (*None*) – a string specifying the evaluation method to use. The supported values are `fo.evaluation_config.detection_backends.keys()` and the default is `fo.evaluation_config.detection_default_backend` * **iou** (*0.50*) – the IoU threshold to use to determine matches * **use_masks** (*False*) – whether to compute IoUs using the instances masks in the `mask` attribute of the provided objects, which must be [`fiftyone.core.labels.Detection`](fiftyone.core.labels.md#fiftyone.core.labels.Detection) instances * **use_boxes** (*False*) – whether to compute IoUs using the bounding boxes of the provided [`fiftyone.core.labels.Polyline`](fiftyone.core.labels.md#fiftyone.core.labels.Polyline) instances rather than using their actual geometries * **classwise** (*True*) – whether to only match objects with the same class label (True) or allow matches between classes (False) * **dynamic** (*True*) – whether to declare the dynamic object-level attributes that are populated on the dataset’s schema * **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 for the constructor of the [`fiftyone.utils.eval.detection.DetectionEvaluationConfig`](fiftyone.utils.eval.detection.md#fiftyone.utils.eval.detection.DetectionEvaluationConfig) being used * **Returns:** a [`fiftyone.utils.eval.detection.DetectionResults`](fiftyone.utils.eval.detection.md#fiftyone.utils.eval.detection.DetectionResults) #### evaluate_regressions(pred_field, gt_field='ground_truth', eval_key=None, missing=None, method=None, progress=None, \*\*kwargs) Evaluates the regression predictions in this collection with respect to the specified ground truth values. You can customize the evaluation method by passing additional parameters for the method’s config class as `kwargs`. The natively provided `method` values and their associated configs are: - `"simple"`: [`fiftyone.utils.eval.regression.SimpleEvaluationConfig`](fiftyone.utils.eval.regression.md#fiftyone.utils.eval.regression.SimpleEvaluationConfig) If an `eval_key` is specified, then this method will record some statistics on each sample: - When evaluating sample-level fields, an `eval_key` field will be populated on each sample recording the error of that sample’s prediction. - When evaluating frame-level fields, an `eval_key` field will be populated on each frame recording the error of that frame’s prediction. In addition, an `eval_key` field will be populated on each sample that records the average error of the frame predictions of the sample. * **Parameters:** * **pred_field** – the name of the field containing the predicted [`fiftyone.core.labels.Regression`](fiftyone.core.labels.md#fiftyone.core.labels.Regression) instances * **gt_field** ( *"ground_truth"*) – the name of the field containing the ground truth [`fiftyone.core.labels.Regression`](fiftyone.core.labels.md#fiftyone.core.labels.Regression) instances * **eval_key** (*None*) – a string key to use to refer to this evaluation * **missing** (*None*) – a missing value. Any None-valued regressions are given this value for results purposes * **method** (*None*) – a string specifying the evaluation method to use. The supported values are `fo.evaluation_config.regression_backends.keys()` and the default is `fo.evaluation_config.regression_default_backend` * **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 for the constructor of the [`fiftyone.utils.eval.regression.RegressionEvaluationConfig`](fiftyone.utils.eval.regression.md#fiftyone.utils.eval.regression.RegressionEvaluationConfig) being used * **Returns:** a [`fiftyone.utils.eval.regression.RegressionResults`](fiftyone.utils.eval.regression.md#fiftyone.utils.eval.regression.RegressionResults) #### evaluate_segmentations(pred_field, gt_field='ground_truth', eval_key=None, mask_targets=None, method=None, progress=None, \*\*kwargs) Evaluates the specified semantic segmentation masks in this collection with respect to the specified ground truth masks. If the size of a predicted mask does not match the ground truth mask, it is resized to match the ground truth. By default, this method simply performs pixelwise evaluation of the full masks, but other strategies such as boundary-only evaluation can be configured by passing additional parameters for the method’s config class as `kwargs`. The natively provided `method` values and their associated configs are: - `"simple"`: [`fiftyone.utils.eval.segmentation.SimpleEvaluationConfig`](fiftyone.utils.eval.segmentation.md#fiftyone.utils.eval.segmentation.SimpleEvaluationConfig) If an `eval_key` is provided, the accuracy, precision, and recall of each sample is recorded in top-level fields of each sample: ```default Accuracy: sample._accuracy Precision: sample._precision Recall: sample._recall ``` In addition, when evaluating frame-level masks, the accuracy, precision, and recall of each frame if recorded in the following frame-level fields: ```default Accuracy: frame._accuracy Precision: frame._precision Recall: frame._recall ``` #### NOTE The mask values `0` and `#000000` are treated as a background class for the purposes of computing evaluation metrics like precision and recall. * **Parameters:** * **pred_field** – the name of the field containing the predicted [`fiftyone.core.labels.Segmentation`](fiftyone.core.labels.md#fiftyone.core.labels.Segmentation) instances * **gt_field** ( *"ground_truth"*) – the name of the field containing the ground truth [`fiftyone.core.labels.Segmentation`](fiftyone.core.labels.md#fiftyone.core.labels.Segmentation) instances * **eval_key** (*None*) – a string key to use to refer to this evaluation * **mask_targets** (*None*) – a dict mapping pixel values (2D masks) or RGB hex strings (3D masks) to semantic label strings. If not provided, the observed values are used as labels * **method** (*None*) – a string specifying the evaluation method to use. The supported values are `fo.evaluation_config.segmentation_backends.keys()` and the default is `fo.evaluation_config.segmentation_default_backend` * **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 for the constructor of the [`fiftyone.utils.eval.segmentation.SegmentationEvaluationConfig`](fiftyone.utils.eval.segmentation.md#fiftyone.utils.eval.segmentation.SegmentationEvaluationConfig) being used * **Returns:** a [`fiftyone.utils.eval.segmentation.SegmentationResults`](fiftyone.utils.eval.segmentation.md#fiftyone.utils.eval.segmentation.SegmentationResults) #### exclude(sample_ids) Excludes the samples with the given IDs from the collection. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample(filepath="/path/to/image1.png"), fo.Sample(filepath="/path/to/image2.png"), fo.Sample(filepath="/path/to/image3.png"), ] ) # # Exclude the first sample from the dataset # sample_id = dataset.first().id view = dataset.exclude(sample_id) # # Exclude the first and last samples from the dataset # sample_ids = [dataset.first().id, dataset.last().id] view = dataset.exclude(sample_ids) ``` * **Parameters:** **sample_ids** – the samples to exclude. Can be any of the following: - a sample ID - an iterable of sample IDs - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exclude_by(field, values) Excludes the samples with the given field values from the collection. This stage is typically used to work with categorical fields (strings, ints, and bools). If you want to exclude samples based on floating point fields, use [`match()`](#fiftyone.core.dataset.Dataset.match). Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample(filepath="image%d.jpg" % i, int=i, str=str(i)) for i in range(10) ] ) # # Create a view excluding samples whose `int` field have the given # values # view = dataset.exclude_by("int", [1, 9, 3, 7, 5]) print(view.head(5)) # # Create a view excluding samples whose `str` field have the given # values # view = dataset.exclude_by("str", ["1", "9", "3", "7", "5"]) print(view.head(5)) ``` * **Parameters:** * **field** – a field or `embedded.field.name` * **values** – a value or iterable of values to exclude by * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exclude_fields(field_names=None, meta_filter=None, \_allow_missing=False) Excludes the fields with the given names from the samples in the collection. Note that default fields cannot be excluded. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), predictions=fo.Classification( label="cat", confidence=0.9, mood="surly", ), ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), predictions=fo.Classification( label="dog", confidence=0.8, mood="happy", ), ), fo.Sample( filepath="/path/to/image3.png", ), ] ) # # Exclude the `predictions` field from all samples # view = dataset.exclude_fields("predictions") # # Exclude the `mood` attribute from all classifications in the # `predictions` field # view = dataset.exclude_fields("predictions.mood") ``` * **Parameters:** * **field_names** (*None*) – a field name or iterable of field names to exclude. May contain `embedded.field.name` as well * **meta_filter** (*None*) – a filter that dynamically excludes fields in the collection’s schema according to the specified rule, which can be matched against the field’s `name`, `type`, `description`, and/or `info`. For example: - Use `meta_filter="2023"` or `meta_filter={"any": "2023"}` to exclude fields that have the string “2023” anywhere in their name, type, description, or info - Use `meta_filter={"type": "StringField"}` or `meta_filter={"type": "Classification"}` to exclude all string or classification fields, respectively - Use `meta_filter={"description": "my description"}` to exclude fields whose description contains the string “my description” - Use `meta_filter={"info": "2023"}` to exclude fields that have the string “2023” anywhere in their info - Use `meta_filter={"info.key": "value"}}` to exclude fields that have a specific key/value pair in their info - Include `meta_filter={"include_nested_fields": True, ...}` in your meta filter to include all nested fields in the filter * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exclude_frames(frame_ids, omit_empty=True) Excludes the frames with the given IDs from the video collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-video") # # Exclude some specific frames # frame_ids = [ dataset.first().frames.first().id, dataset.last().frames.last().id, ] view = dataset.exclude_frames(frame_ids) print(dataset.count("frames")) print(view.count("frames")) ``` * **Parameters:** * **frame_ids** – the frames to exclude. Can be any of the following: - a frame ID - an iterable of frame IDs - a [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) or [`fiftyone.core.frame.FrameView`](fiftyone.core.frame.md#fiftyone.core.frame.FrameView) - an iterable of [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) or [`fiftyone.core.frame.FrameView`](fiftyone.core.frame.md#fiftyone.core.frame.FrameView) instances - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) whose frames to exclude * **omit_empty** (*True*) – whether to omit samples that have no frames after excluding the specified frames * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exclude_group_slices(slices=None, media_type=None) Excludes the specified group slice(s) from the grouped collection. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_group_field("group", default="ego") group1 = fo.Group() group2 = fo.Group() dataset.add_samples( [ fo.Sample( filepath="/path/to/left-image1.jpg", group=group1.element("left"), ), fo.Sample( filepath="/path/to/video1.mp4", group=group1.element("ego"), ), fo.Sample( filepath="/path/to/right-image1.jpg", group=group1.element("right"), ), fo.Sample( filepath="/path/to/left-image2.jpg", group=group2.element("left"), ), fo.Sample( filepath="/path/to/video2.mp4", group=group2.element("ego"), ), fo.Sample( filepath="/path/to/right-image2.jpg", group=group2.element("right"), ), ] ) # # Exclude the samples from the "ego" group slice # view = dataset.exclude_group_slices("ego") # # Exclude the samples from the "left" or "right" group slices # view = dataset.exclude_group_slices(["left", "right"]) # # Exclude all image slices # view = dataset.exclude_group_slices(media_type="image") ``` * **Parameters:** * **slices** (*None*) – a group slice or iterable of group slices to exclude * **media_type** (*None*) – a media type or iterable of media types whose slice(s) to exclude * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exclude_groups(group_ids) Excludes the groups with the given IDs from the grouped collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") # # Exclude some specific groups by ID # view = dataset.take(2) group_ids = view.values("group.id") other_groups = dataset.exclude_groups(group_ids) assert len(set(group_ids) & set(other_groups.values("group.id"))) == 0 ``` * **Parameters:** **groups_ids** – the groups to exclude. Can be any of the following: - a group ID - an iterable of group IDs - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) - a group dict returned by [`get_group()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_group) - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances - an iterable of group dicts returned by [`get_group()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_group) - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exclude_labels(labels=None, ids=None, instance_ids=None, tags=None, fields=None, omit_empty=True) Excludes the specified labels from the collection. The returned view will omit samples, sample fields, and individual labels that do not match the specified selection criteria. You can perform an exclusion via one or more of the following methods: - Provide the `labels` argument, which should contain a list of dicts in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels), to exclude specific labels - Provide the `ids` argument to exclude labels with specific IDs - Provide the `instance_ids` argument to exclude labels with specific instance IDs - Provide the `tags` argument to exclude labels with specific tags If multiple criteria are specified, labels must match all of them in order to be excluded. By default, the exclusion is applied to all [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields, but you can provide the `fields` argument to explicitly define the field(s) in which to exclude. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") # # Exclude the labels currently selected in the App # session = fo.launch_app(dataset) # Select some labels in the App... view = dataset.exclude_labels(labels=session.selected_labels) # # Exclude labels with the specified IDs # # Grab some label IDs ids = [ dataset.first().ground_truth.detections[0].id, dataset.last().predictions.detections[0].id, ] view = dataset.exclude_labels(ids=ids) print(dataset.count("ground_truth.detections")) print(view.count("ground_truth.detections")) print(dataset.count("predictions.detections")) print(view.count("predictions.detections")) # # Exclude labels with the specified tags # # Grab some label IDs ids = [ dataset.first().ground_truth.detections[0].id, dataset.last().predictions.detections[0].id, ] # Give the labels a "test" tag dataset = dataset.clone() # create copy since we're modifying data dataset.select_labels(ids=ids).tag_labels("test") print(dataset.count_values("ground_truth.detections.tags")) print(dataset.count_values("predictions.detections.tags")) # Exclude the labels via their tag view = dataset.exclude_labels(tags="test") print(dataset.count("ground_truth.detections")) print(view.count("ground_truth.detections")) print(dataset.count("predictions.detections")) print(view.count("predictions.detections")) ``` * **Parameters:** * **labels** (*None*) – a list of dicts specifying the labels to exclude in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels) * **ids** (*None*) – an ID or iterable of IDs of the labels to exclude * **instance_ids** (*None*) – an instance ID or iterable of instance IDs of the labels to exclude * **tags** (*None*) – a tag or iterable of tags of labels to exclude * **fields** (*None*) – a field or iterable of fields from which to exclude * **omit_empty** (*True*) – whether to omit samples that have no labels after filtering * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### exists(field, bool=None) Returns a view containing the samples in the collection that have (or do not have) a non-`None` value for the given field or embedded field. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), predictions=fo.Classification(label="cat", confidence=0.9), ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), predictions=fo.Classification(label="dog", confidence=0.8), ), fo.Sample( filepath="/path/to/image3.png", ground_truth=fo.Classification(label="dog"), predictions=fo.Classification(label="dog"), ), fo.Sample( filepath="/path/to/image4.png", ground_truth=None, predictions=None, ), fo.Sample(filepath="/path/to/image5.png"), ] ) # # Only include samples that have a value in their `predictions` # field # view = dataset.exists("predictions") # # Only include samples that do NOT have a value in their # `predictions` field # view = dataset.exists("predictions", False) # # Only include samples that have prediction confidences # view = dataset.exists("predictions.confidence") ``` * **Parameters:** * **field** – the field name or `embedded.field.name` * **bool** (*None*) – whether to check if the field exists (None or True) or does not exist (False) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### export(export_dir=None, dataset_type=None, data_path=None, labels_path=None, export_media=None, rel_dir=None, dataset_exporter=None, label_field=None, frame_labels_field=None, overwrite=False, progress=None, \*\*kwargs) Exports the samples in the collection to disk. You can perform exports with this method via the following basic patterns: 1. Provide `export_dir` and `dataset_type` to export the content to a directory in the default layout for the specified format, as documented in [this page](../user_guide/export_datasets.md#exporting-datasets) 2. Provide `dataset_type` along with `data_path`, `labels_path`, and/or `export_media` to directly specify where to export the source media and/or labels (if applicable) in your desired format. This syntax provides the flexibility to, for example, perform workflows like labels-only exports 3. Provide a `dataset_exporter` to which to feed samples to perform a fully-customized export In all workflows, the remaining parameters of this method can be provided to further configure the export. See [this page](../user_guide/export_datasets.md#exporting-datasets) for more information about the available export formats and examples of using this method. See [this guide](../user_guide/export_datasets.md#custom-dataset-exporter) for more details about exporting datasets in custom formats by defining your own [`fiftyone.utils.data.exporters.DatasetExporter`](fiftyone.utils.data.exporters.md#fiftyone.utils.data.exporters.DatasetExporter). This method will automatically coerce the data to match the requested export in the following cases: - When exporting in either an unlabeled image or image classification format, if a spatial label field is provided ([`fiftyone.core.labels.Detection`](fiftyone.core.labels.md#fiftyone.core.labels.Detection), [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Polyline`](fiftyone.core.labels.md#fiftyone.core.labels.Polyline), or [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines)), then the **image patches** of the provided samples will be exported - When exporting in labeled image dataset formats that expect list-type labels ([`fiftyone.core.labels.Classifications`](fiftyone.core.labels.md#fiftyone.core.labels.Classifications), [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints), or [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines)), if a label field contains labels in non-list format (e.g., [`fiftyone.core.labels.Classification`](fiftyone.core.labels.md#fiftyone.core.labels.Classification)), the labels will be automatically upgraded to single-label lists - When exporting in labeled image dataset formats that expect [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) labels, if a [`fiftyone.core.labels.Classification`](fiftyone.core.labels.md#fiftyone.core.labels.Classification) field is provided, the labels will be automatically upgraded to detections that span the entire images * **Parameters:** * **export_dir** (*None*) – the directory to which to export the samples in format `dataset_type`. This parameter may be omitted if you have provided appropriate values for the `data_path` and/or `labels_path` parameters. Alternatively, this can also be an archive path with one of the following extensions: ```default .zip, .tar, .tar.gz, .tgz, .tar.bz, .tbz ``` If an archive path is specified, the export is performed in a directory of same name (minus extension) and then automatically archived and the directory then deleted * **dataset_type** (*None*) – the [`fiftyone.types.Dataset`](fiftyone.types.md#fiftyone.types.Dataset) type to write. If not specified, the default type for `label_field` is used * **data_path** (*None*) – an optional parameter that enables explicit control over the location of the exported media for certain export formats. Can be any of the following: - a folder name like `"data"` or `"data/"` specifying a subfolder of `export_dir` in which to export the media - an absolute directory path in which to export the media. In this case, the `export_dir` has no effect on the location of the data - a filename like `"data.json"` specifying the filename of a JSON manifest file in `export_dir` generated when `export_media` is `"manifest"` - an absolute filepath specifying the location to write the JSON manifest file when `export_media` is `"manifest"`. In this case, `export_dir` has no effect on the location of the data If None, a default value of this parameter will be chosen based on the value of the `export_media` parameter. Note that this parameter is not applicable to certain export formats such as binary types like TF records * **labels_path** (*None*) – an optional parameter that enables explicit control over the location of the exported labels. Only applicable when exporting in certain labeled dataset formats. Can be any of the following: - a type-specific folder name like `"labels"` or `"labels/"` or a filename like `"labels.json"` or `"labels.xml"` specifying the location in `export_dir` in which to export the labels - an absolute directory or filepath in which to export the labels. In this case, the `export_dir` has no effect on the location of the labels For labeled datasets, the default value of this parameter will be chosen based on the export format so that the labels will be exported into `export_dir` * **export_media** (*None*) – controls how to export the raw media. The supported values are: - `True`: copy all media files into the output directory - `False`: don’t export media. This option is only useful when exporting labeled datasets whose label format stores sufficient information to locate the associated media - `"move"`: move all media files into the output directory - `"symlink"`: create symlinks to the media files in the output directory - `"manifest"`: create a `data.json` in the output directory that maps UUIDs used in the labels files to the filepaths of the source media, rather than exporting the actual media If None, an appropriate default value of this parameter will be chosen based on the value of the `data_path` parameter. Note that some dataset formats may not support certain values for this parameter (e.g., when exporting in binary formats such as TF records, “symlink” is not an option) * **rel_dir** (*None*) – an optional relative directory to strip from each input filepath to generate a unique identifier for each media. When exporting media, this identifier is joined with `data_path` to generate an output path for each exported media. This argument allows for populating nested subdirectories that match the shape of the input paths. The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path) * **dataset_exporter** (*None*) – a [`fiftyone.utils.data.exporters.DatasetExporter`](fiftyone.utils.data.exporters.md#fiftyone.utils.data.exporters.DatasetExporter) to use to export the samples. When provided, parameters such as `export_dir`, `dataset_type`, `data_path`, and `labels_path` have no effect * **label_field** (*None*) – controls the label field(s) to export. Only applicable to labeled datasets. 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 label dictionaries to pass to the exporter Note that multiple fields can only be specified when the exporter used can handle dictionaries of labels. By default, the first field of compatible type for the exporter is used. When exporting labeled video datasets, this argument may contain frame fields prefixed by `"frames."` * **frame_labels_field** (*None*) – controls the frame label field(s) to export. The `"frames."` prefix is optional. Only applicable to labeled 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 frame label dictionaries to pass to the exporter Note that multiple fields can only be specified when the exporter used can handle dictionaries of frame labels. By default, the first field of compatible type for the exporter is used * **overwrite** (*False*) – whether to delete existing directories before performing the export (True) or to merge the export with existing files and directories (False) * **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 to pass to the dataset exporter’s constructor. If you are exporting image patches, this can also contain keyword arguments for [`fiftyone.utils.patches.ImagePatchesExtractor`](fiftyone.utils.patches.md#fiftyone.utils.patches.ImagePatchesExtractor) #### filter_field(field, filter, only_matches=True) Filters the values of a field or embedded field of each sample in the collection. Values of `field` for which `filter` returns `False` are replaced with `None`. Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), predictions=fo.Classification(label="cat", confidence=0.9), numeric_field=1.0, ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), predictions=fo.Classification(label="dog", confidence=0.8), numeric_field=-1.0, ), fo.Sample( filepath="/path/to/image3.png", ground_truth=None, predictions=None, numeric_field=None, ), ] ) # # Only include classifications in the `predictions` field # whose `label` is "cat" # view = dataset.filter_field("predictions", F("label") == "cat") # # Only include samples whose `numeric_field` value is positive # view = dataset.filter_field("numeric_field", F() > 0) ``` * **Parameters:** * **field** – the field name or `embedded.field.name` * **filter** – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that returns a boolean describing the filter to apply * **only_matches** (*True*) – whether to only include samples that match the filter (True) or include all samples (False) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### filter_keypoints(field, filter=None, labels=None, only_matches=True) Filters the individual [`fiftyone.core.labels.Keypoint.points`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint.points) elements in the specified keypoints field of each sample in the collection. #### NOTE Use [`filter_labels()`](#fiftyone.core.dataset.Dataset.filter_labels) if you simply want to filter entire [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) objects in a field. Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Keypoints( keypoints=[ fo.Keypoint( label="person", points=[(0.1, 0.1), (0.1, 0.9), (0.9, 0.9), (0.9, 0.1)], confidence=[0.7, 0.8, 0.95, 0.99], ) ] ) ), fo.Sample(filepath="/path/to/image2.png"), ] ) dataset.default_skeleton = fo.KeypointSkeleton( labels=["nose", "left eye", "right eye", "left ear", "right ear"], edges=[[0, 1, 2, 0], [0, 3], [0, 4]], ) # # Only include keypoints in the `predictions` field whose # `confidence` is greater than 0.9 # view = dataset.filter_keypoints( "predictions", filter=F("confidence") > 0.9 ) # # Only include keypoints in the `predictions` field with less than # four points # view = dataset.filter_keypoints( "predictions", labels=["left eye", "right eye"] ) ``` * **Parameters:** * **field** – the [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) or [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) field to filter * **filter** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that returns a boolean, like `F("confidence") > 0.5` or `F("occluded") == False`, to apply elementwise to the specified field, which must be a list of same length as [`fiftyone.core.labels.Keypoint.points`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint.points) * **labels** (*None*) – a label or iterable of keypoint skeleton labels to keep * **only_matches** (*True*) – whether to only include keypoints/samples with at least one point after filtering (True) or include all keypoints/samples (False) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### filter_labels(field, filter, only_matches=True, trajectories=False) Filters the [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) field of each sample in the collection. If the specified `field` is a single [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) type, fields for which `filter` returns `False` are replaced with `None`: - [`fiftyone.core.labels.Classification`](fiftyone.core.labels.md#fiftyone.core.labels.Classification) - [`fiftyone.core.labels.Detection`](fiftyone.core.labels.md#fiftyone.core.labels.Detection) - [`fiftyone.core.labels.Polyline`](fiftyone.core.labels.md#fiftyone.core.labels.Polyline) - [`fiftyone.core.labels.Keypoint`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoint) If the specified `field` is a [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) list type, the label elements for which `filter` returns `False` are omitted from the view: - [`fiftyone.core.labels.Classifications`](fiftyone.core.labels.md#fiftyone.core.labels.Classifications) - [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) - [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) - [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) Classifications Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Classification(label="cat", confidence=0.9), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Classification(label="dog", confidence=0.8), ), fo.Sample( filepath="/path/to/image3.png", predictions=fo.Classification(label="rabbit"), ), fo.Sample( filepath="/path/to/image4.png", predictions=None, ), ] ) # # Only include classifications in the `predictions` field whose # `confidence` is greater than 0.8 # view = dataset.filter_labels("predictions", F("confidence") > 0.8) # # Only include classifications in the `predictions` field whose # `label` is "cat" or "dog" # view = dataset.filter_labels( "predictions", F("label").is_in(["cat", "dog"]) ) ``` Detections Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.5, 0.5], confidence=0.9, ), fo.Detection( label="dog", bounding_box=[0.2, 0.2, 0.3, 0.3], confidence=0.8, ), ] ), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.5, 0.5, 0.4, 0.4], confidence=0.95, ), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image3.png", predictions=fo.Detections( detections=[ fo.Detection( label="squirrel", bounding_box=[0.25, 0.25, 0.5, 0.5], confidence=0.5, ), ] ), ), fo.Sample( filepath="/path/to/image4.png", predictions=None, ), ] ) # # Only include detections in the `predictions` field whose # `confidence` is greater than 0.8 # view = dataset.filter_labels("predictions", F("confidence") > 0.8) # # Only include detections in the `predictions` field whose `label` # is "cat" or "dog" # view = dataset.filter_labels( "predictions", F("label").is_in(["cat", "dog"]) ) # # Only include detections in the `predictions` field whose bounding # box area is smaller than 0.2 # # Bboxes are in [top-left-x, top-left-y, width, height] format bbox_area = F("bounding_box")[2] * F("bounding_box")[3] view = dataset.filter_labels("predictions", bbox_area < 0.2) ``` Polylines Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Polylines( polylines=[ fo.Polyline( label="lane", points=[[(0.1, 0.1), (0.1, 0.6)]], filled=False, ), fo.Polyline( label="road", points=[[(0.2, 0.2), (0.5, 0.5), (0.2, 0.5)]], filled=True, ), ] ), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Polylines( polylines=[ fo.Polyline( label="lane", points=[[(0.4, 0.4), (0.9, 0.4)]], filled=False, ), fo.Polyline( label="road", points=[[(0.6, 0.6), (0.9, 0.9), (0.6, 0.9)]], filled=True, ), ] ), ), fo.Sample( filepath="/path/to/image3.png", predictions=None, ), ] ) # # Only include polylines in the `predictions` field that are filled # view = dataset.filter_labels("predictions", F("filled") == True) # # Only include polylines in the `predictions` field whose `label` # is "lane" # view = dataset.filter_labels("predictions", F("label") == "lane") # # Only include polylines in the `predictions` field with at least # 3 vertices # num_vertices = F("points").map(F().length()).sum() view = dataset.filter_labels("predictions", num_vertices >= 3) ``` Keypoints Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Keypoint( label="house", points=[(0.1, 0.1), (0.1, 0.9), (0.9, 0.9), (0.9, 0.1)], ), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Keypoint( label="window", points=[(0.4, 0.4), (0.5, 0.5), (0.6, 0.6)], ), ), fo.Sample( filepath="/path/to/image3.png", predictions=None, ), ] ) # # Only include keypoints in the `predictions` field whose `label` # is "house" # view = dataset.filter_labels("predictions", F("label") == "house") # # Only include keypoints in the `predictions` field with less than # four points # view = dataset.filter_labels("predictions", F("points").length() < 4) ``` * **Parameters:** * **field** – the label field to filter * **filter** – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that returns a boolean describing the filter to apply * **only_matches** (*True*) – whether to only include samples with at least one label after filtering (True) or include all samples (False) * **trajectories** (*False*) – whether to match entire object trajectories for which the object matches the given filter on at least one frame. Only applicable to datasets that contain videos and frame-level label fields whose objects have their `index` attributes populated * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### flatten(stages=None) Returns a flattened view that contains all samples in the dynamic grouped collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("cifar10", split="test") # Group samples by ground truth label grouped_view = dataset.take(1000).group_by("ground_truth.label") print(len(grouped_view)) # 10 # Return a flat view that contains 10 samples from each class flat_view = grouped_view.flatten(fo.Limit(10)) print(len(flat_view)) # 100 ``` * **Parameters:** **stages** (*None*) – a [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) or list of [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) instances to apply to each group’s samples while flattening * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### generate_label_schemas(fields=None, scan_samples=True) Generates label schemas for the [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection). A label schema is defined by a `type` and `component` with respect to a field. Further settings depend on the `type` and `component` combination as outlined below. The `type` value for a field is inferred from the collection’s field schema. See [`fiftyone.core.collections.SampleCollection.get_field_schema()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_field_schema) Currently supported media types for the collection are `image` and `3d`. See [`fiftyone.core.collections.SampleCollection.media_type`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.media_type) **Primitives and components** Supported primitive types are: > - `bool`: [`fiftyone.core.fields.BooleanField`](fiftyone.core.fields.md#fiftyone.core.fields.BooleanField) > - `date`: [`fiftyone.core.fields.DateField`](fiftyone.core.fields.md#fiftyone.core.fields.DateField) > - `datetime`: [`fiftyone.core.fields.DateTimeField`](fiftyone.core.fields.md#fiftyone.core.fields.DateTimeField) > - `dict`: [`fiftyone.core.fields.DictField`](fiftyone.core.fields.md#fiftyone.core.fields.DictField) > - `float`: [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) > - `id`: [`fiftyone.core.fields.ObjectIdField`](fiftyone.core.fields.md#fiftyone.core.fields.ObjectIdField) or > [`fiftyone.core.fields.UUIDField`](fiftyone.core.fields.md#fiftyone.core.fields.UUIDField) > - `int`: [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) or > [`fiftyone.core.fields.FrameNumberField`](fiftyone.core.fields.md#fiftyone.core.fields.FrameNumberField) > - `list`: [`fiftyone.core.fields.ListField`](fiftyone.core.fields.md#fiftyone.core.fields.ListField) of > [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) > - `list`: [`fiftyone.core.fields.ListField`](fiftyone.core.fields.md#fiftyone.core.fields.ListField) of > [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) > - `list`: [`fiftyone.core.fields.ListField`](fiftyone.core.fields.md#fiftyone.core.fields.ListField) of > [`fiftyone.core.fields.StringField`](fiftyone.core.fields.md#fiftyone.core.fields.StringField) > - `str`: [`fiftyone.core.fields.StringField`](fiftyone.core.fields.md#fiftyone.core.fields.StringField) Supported `bool` components are: > - `checkbox` > - `toggle` - the default `date` and `datetime` only support the `datepicker` component. `dict` only supports the `json` component. Supported `float` and `int` components are: > - `dropdown` > - `radio` > - `slider`: the default when `scan_samples` is `True` and > distinct finite bounds are found that define a `range` > - `text`: the default when `scan_samples` is `False` or > distinct finite bounds are not found Supported `list` and `list` components are: > - `checkboxes` > - `dropdown` > - `text` - the default Supported `list` components are: > - `checkboxes`: the default if `<=5` values are scanned > - `dropdown`: the default if `>5` and `<=1000` values are > scanned > - `text`: the default if `0` values or `>1000` values are > scanned, or `scan_samples` is `False` Supported `str` type components are: > - `dropdown`: the default if `>5` and `<=1000` values are > scanned > - `radio`: the default if `<=5` values are scanned > - `text`: the default if `0` values or `>1000` values are > scanned, or `scan_samples` is `False` `float` types support a `precision` setting when a `text` component is configured for the number of digits to allow after the decimal. All types support a `read_only` flag. `id` types must be `read_only`. If a field is `read_only` in the field schema, then the `read_only` label schema setting must be `True`, e.g. `created_at` and `last_modified_at` must be read only. All components support `values` except `json`, `slider`, and `toggle` excepting `id` restrictions. `checkboxes` and `dropdown` require the `values` setting. `slider` requires the `range: [min, max]` setting. **Labels** The `label` subfield of all label types are configured via `classes` and support the same settings as a `str` type. See the example output below for `detections` fields in the quickstart dataset. If the label type has a visual representation, that field is handled by the App’s builtin annotation UI, e.g. `bounding_box` for a `detection`. Primitive attributes of label types are configured via the `attributes` list. When a label is marked as `read_only`, all its attributes inherit the setting as well. All [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) types are resolved by this method except [`fiftyone.core.labels.GeoLocation`](fiftyone.core.labels.md#fiftyone.core.labels.GeoLocation), [`fiftyone.core.labels.GeoLocations`](fiftyone.core.labels.md#fiftyone.core.labels.GeoLocations), [`fiftyone.core.labels.TemporalDetection`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetection), and [`fiftyone.core.labels.TemporalDetections`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetections) when provided in the `fields` argument, otherwise only App supported fields are resolved. For label types supported by the App for annotation, see [`fiftyone.core.annotation.utils.get_supported_app_annotation_fields()`](fiftyone.core.annotation.utils.md#fiftyone.core.annotation.utils.get_supported_app_annotation_fields). All attributes and the label class itself support a `default` setting that applies when creating a new label. **Embedded documents** One level of nesting is supported via `dot.notation` for `fiftyone.core.fields.EmbeddedDocumentField`` fields for the default `metadata` field and the `fiftyone.core.odm.embedded_document.DynamicEmbeddedDocument`` document type. All label and primitive types are supported. See [here](../user_guide/using_datasets.md#dynamic-attributes) for more details on adding dynamic attributes. Example: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") dataset.compute_metadata() fo.pprint(dataset.generate_label_schemas(scan_samples=False)) ``` Output: ```default { "created_at": { "type": "datetime", "component": "datepicker", "read_only": True, }, "filepath": {"type": "str", "component": "text"}, "ground_truth": { "attributes": [ { "name": "attributes", "type": "dict", "component": "json", }, { "name": "confidence", "type": "float", "component": "text", }, { "name": "id", "type": "id", "component": "text", "read_only": True, }, {"name": "index", "type": "int", "component": "text"}, { "name": "mask_path", "type": "str", "component": "text", }, { "name": "tags", "type": "list", "component": "text", }, ], "component": "text", "type": "detections", }, "id": {"type": "id", "component": "text", "read_only": True}, "last_modified_at": { "type": "datetime", "component": "datepicker", "read_only": True, }, "metadata.height": {"type": "int", "component": "text"}, "metadata.mime_type": {"type": "str", "component": "text"}, "metadata.num_channels": {"type": "int", "component": "text"}, "metadata.size_bytes": {"type": "int", "component": "text"}, "metadata.width": {"type": "int", "component": "text"}, "predictions": { "attributes": [ { "name": "attributes", "type": "dict", "component": "json", }, { "name": "confidence", "type": "float", "component": "text", }, { "name": "id", "type": "id", "component": "text", "read_only": True, }, {"name": "index", "type": "int", "component": "text"}, { "name": "mask_path", "type": "str", "component": "text", }, { "name": "tags", "type": "list", "component": "text", }, ], "component": "text", "type": "detections", }, "tags": {"type": "list", "component": "text"}, "uniqueness": {"type": "float", "component": "text"}, } ``` * **Parameters:** * **fields** (*None*) – a field name, `embedded.field.name` or iterable of such values * **scan_samples** (*True*) – whether to scan the collection to populate component settings based on actual field values (ranges, values, etc). If False, the label schema is generated from *only* the statically available information in the dataset’s field schema * **Raises:** **ValueError** – if the sample collection or field is not supported * **Returns:** a label schemas `dict`, or an individual field’s label schema `dict` if only one field is provided #### geo_near(point, location_field=None, min_distance=None, max_distance=None, query=None, create_index=False) Sorts the samples in the collection by their proximity to a specified geolocation. #### NOTE This stage must be the **first stage** in any [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) in which it appears, and it **requires** a spherical index on the specified location field. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz TIMES_SQUARE = [-73.9855, 40.7580] dataset = foz.load_zoo_dataset("quickstart-geo") # # Sort the samples by their proximity to Times Square # view = dataset.geo_near(TIMES_SQUARE, create_index=True) # # Sort the samples by their proximity to Times Square, and only # include samples within 5km # view = dataset.geo_near( TIMES_SQUARE, max_distance=5000, create_index=True, ) # # Sort the samples by their proximity to Times Square, and only # include samples that are in Manhattan # import fiftyone.utils.geojson as foug in_manhattan = foug.geo_within( "location.point", [ [ [-73.949701, 40.834487], [-73.896611, 40.815076], [-73.998083, 40.696534], [-74.031751, 40.715273], [-73.949701, 40.834487], ] ] ) view = dataset.geo_near( TIMES_SQUARE, location_field="location", query=in_manhattan, create_index=True, ) ``` * **Parameters:** * **point** – the reference point to compute distances to. Can be any of the following: - A `[longitude, latitude]` list - A GeoJSON dict with `Point` type - A [`fiftyone.core.labels.GeoLocation`](fiftyone.core.labels.md#fiftyone.core.labels.GeoLocation) instance whose `point` attribute contains the point * **location_field** (*None*) – the location data of each sample to use. Can be any of the following: - The name of a `fiftyone.core.fields.GeoLocation` field whose `point` attribute to use as location data - An `embedded.field.name` containing GeoJSON data to use as location data - `None`, in which case there must be a single `fiftyone.core.fields.GeoLocation` field on the samples, which is used by default * **min_distance** (*None*) – filter samples that are less than this distance (in meters) from `point` * **max_distance** (*None*) – filter samples that are greater than this distance (in meters) from `point` * **query** (*None*) – an optional dict defining a [MongoDB read query](https://docs.mongodb.com/manual/tutorial/query-documents/#read-operations-query-argument) that samples must match in order to be included in this view * **create_index** (*False*) – whether to create the required spherical index, if necessary * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### geo_within(boundary, location_field=None, strict=True, create_index=False) Filters the samples in this collection to only include samples whose geolocation is within a specified boundary. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz MANHATTAN = [ [ [-73.949701, 40.834487], [-73.896611, 40.815076], [-73.998083, 40.696534], [-74.031751, 40.715273], [-73.949701, 40.834487], ] ] dataset = foz.load_zoo_dataset("quickstart-geo") # # Create a view that only contains samples in Manhattan # view = dataset.geo_within(MANHATTAN) ``` * **Parameters:** * **boundary** – a [`fiftyone.core.labels.GeoLocation`](fiftyone.core.labels.md#fiftyone.core.labels.GeoLocation), [`fiftyone.core.labels.GeoLocations`](fiftyone.core.labels.md#fiftyone.core.labels.GeoLocations), GeoJSON dict, or list of coordinates that define a `Polygon` or `MultiPolygon` to search within * **location_field** (*None*) – the location data of each sample to use. Can be any of the following: - The name of a `fiftyone.core.fields.GeoLocation` field whose `point` attribute to use as location data - An `embedded.field.name` that directly contains the GeoJSON location data to use - `None`, in which case there must be a single `fiftyone.core.fields.GeoLocation` field on the samples, which is used by default * **strict** (*True*) – whether a sample’s location data must strictly fall within boundary (True) in order to match, or whether any intersection suffices (False) * **create_index** (*False*) – whether to create a spherical index, if necessary, to optimize the query * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### get_annotation_info(anno_key) Returns information about the annotation run with the given key on this collection. * **Parameters:** **anno_key** – an annotation key * **Returns:** a [`fiftyone.core.annotation.AnnotationInfo`](fiftyone.core.annotation.md#fiftyone.core.annotation.AnnotationInfo) #### get_brain_info(brain_key) Returns information about the brain method run with the given key on this collection. * **Parameters:** **brain_key** – a brain key * **Returns:** a [`fiftyone.core.brain.BrainInfo`](fiftyone.core.brain.md#fiftyone.core.brain.BrainInfo) #### get_classes(field) Gets the classes list for the given field, or None if no classes are available. Classes are first retrieved from [`classes()`](#fiftyone.core.dataset.Dataset.classes) if they exist, otherwise from [`default_classes()`](#fiftyone.core.dataset.Dataset.default_classes). * **Parameters:** **field** – a field name * **Returns:** a list of classes, or None #### get_dynamic_field_schema(fields=None, recursive=True) Returns a schema dictionary describing the dynamic fields of the samples in the collection. Dynamic fields are embedded document fields with at least one non-None value that have not been declared on the dataset’s schema. * **Parameters:** * **fields** (*None*) – an optional field or iterable of fields for which to return dynamic fields. By default, all fields are considered * **recursive** (*True*) – whether to recursively inspect nested lists and embedded documents * **Returns:** a dict mapping field paths to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances or lists of them #### get_dynamic_frame_field_schema(fields=None, recursive=True) Returns a schema dictionary describing the dynamic fields of the frames in the collection. Dynamic fields are embedded document fields with at least one non-None value that have not been declared on the dataset’s schema. * **Parameters:** * **fields** (*None*) – an optional field or iterable of fields for which to return dynamic fields. By default, all fields are considered * **recursive** (*True*) – whether to recursively inspect nested lists and embedded documents * **Returns:** a dict mapping field paths to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances or lists of them, or `None` if the collection does not contain videos #### get_evaluation_info(eval_key) Returns information about the evaluation with the given key on this collection. * **Parameters:** **eval_key** – an evaluation key * **Returns:** an [`fiftyone.core.evaluation.EvaluationInfo`](fiftyone.core.evaluation.md#fiftyone.core.evaluation.EvaluationInfo) #### get_field(path, ftype=None, embedded_doc_type=None, subfield=None, read_only=None, include_private=False, leaf=False) Returns the field instance of the provided path, or `None` if one does not exist. * **Parameters:** * **path** – a field path * **ftype** (*None*) – an optional field type or iterable of types to enforce. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **embedded_doc_type** (*None*) – an optional embedded document type or iterable of types to enforce. Must be subclass(es) of [`fiftyone.core.odm.BaseEmbeddedDocument`](fiftyone.core.odm.md#fiftyone.core.odm.BaseEmbeddedDocument) * **subfield** (*None*) – an optional subfield type or iterable of subfield types to enforce. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **read_only** (*None*) – whether to optionally enforce that the field is read-only (True) or not read-only (False) * **include_private** (*False*) – whether to include fields that start with `_` in the returned schema * **leaf** (*False*) – whether to return the subfield of list fields * **Returns:** a [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instance or `None` * **Raises:** **ValueError** – if the field does not match provided constraints #### get_index_information(include_stats=False, \_keep_index_names=False) Returns a dictionary of information about the indexes on this collection. See [`pymongo.collection.Collection.index_information()`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.index_information) for details on the structure of this dictionary. * **Parameters:** **include_stats** (*False*) – whether to include the size, usage, and build status of each index * **Returns:** a dict mapping index names to info dicts #### get_mask_targets(field) Gets the mask targets for the given field, or None if no mask targets are available. Mask targets are first retrieved from [`mask_targets()`](#fiftyone.core.dataset.Dataset.mask_targets) if they exist, otherwise from [`default_mask_targets()`](#fiftyone.core.dataset.Dataset.default_mask_targets). * **Parameters:** **field** – a field name * **Returns:** a list of classes, or None #### get_run_info(run_key) Returns information about the run with the given key on this collection. * **Parameters:** **run_key** – a run key * **Returns:** a [`fiftyone.core.runs.RunInfo`](fiftyone.core.runs.md#fiftyone.core.runs.RunInfo) #### get_skeleton(field) Gets the keypoint skeleton for the given field, or None if no skeleton is available. Skeletons are first retrieved from [`skeletons()`](#fiftyone.core.dataset.Dataset.skeletons) if they exist, otherwise from [`default_skeleton()`](#fiftyone.core.dataset.Dataset.default_skeleton). * **Parameters:** **field** – a field name * **Returns:** a list of classes, or None #### group_by(field_or_expr, order_by=None, reverse=False, flat=False, match_expr=None, sort_expr=None, create_index=False, order_by_key=None) Creates a view that groups the samples in the collection by a specified field or expression. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("cifar10", split="test") # # Take 1000 samples at random and group them by ground truth label # view = dataset.take(1000).group_by("ground_truth.label") for group in view.iter_dynamic_groups(): group_value = group.first().ground_truth.label print("%s: %d" % (group_value, len(group))) # # Variation of above operation that arranges the groups in # decreasing order of size and immediately flattens them # from itertools import groupby view = dataset.take(1000).group_by( "ground_truth.label", flat=True, sort_expr=F().length(), reverse=True, ) rle = lambda v: [(k, len(list(g))) for k, g in groupby(v)] for label, count in rle(view.values("ground_truth.label")): print("%s: %d" % (label, count)) ``` * **Parameters:** * **field_or_expr** – the field or `embedded.field.name` to group by, or a list of field names defining a compound group key, or a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB aggregation expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that defines the value to group by * **order_by** (*None*) – an optional field by which to order the samples in each group * **reverse** (*False*) – whether to return the results in descending order Applies both to `order_by` and `sort_expr` * **flat** (*False*) – whether to return a grouped collection (False) or a flattened collection (True) * **match_expr** (*None*) – an optional [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB aggregation expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that defines which groups to include in the output view. If provided, this expression will be evaluated on the list of samples in each group. Only applicable when `flat=True` * **sort_expr** (*None*) – an optional [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB aggregation expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that defines how to sort the groups in the output view. If provided, this expression will be evaluated on the list of samples in each group. Only applicable when `flat=True` * **create_index** (*False*) – whether to create an index, if necessary, to optimize the grouping. Only applicable when grouping by field(s), not expressions * **order_by_key** (*None*) – an optional fixed `order_by` value representing the first sample in a group. Required for optimized performance. See [this guide](../user_guide/app.md#app-query-performant-stages) for more details * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### has_annotation_run(anno_key) Whether this collection has an annotation run with the given key. * **Parameters:** **anno_key** – an annotation key * **Returns:** True/False #### *property* has_annotation_runs Whether this collection has any annotation runs. #### has_brain_run(brain_key) Whether this collection has a brain method run with the given key. * **Parameters:** **brain_key** – a brain key * **Returns:** True/False #### *property* has_brain_runs Whether this collection has any brain runs. #### has_classes(field) Determines whether this collection has a classes list for the given field. Classes may be defined either in [`classes()`](#fiftyone.core.dataset.Dataset.classes) or [`default_classes()`](#fiftyone.core.dataset.Dataset.default_classes). * **Parameters:** **field** – a field name * **Returns:** True/False #### has_evaluation(eval_key) Whether this collection has an evaluation with the given key. * **Parameters:** **eval_key** – an evaluation key * **Returns:** True/False #### *property* has_evaluations Whether this collection has any evaluation results. #### has_field(path) Determines whether the collection has a field with the given name. * **Parameters:** **path** – the field name or `embedded.field.name` * **Returns:** True/False #### has_frame_field(path) Determines whether the collection has a frame-level field with the given name. * **Parameters:** **path** – the field name or `embedded.field.name` * **Returns:** True/False #### has_mask_targets(field) Determines whether this collection has mask targets for the given field. Mask targets may be defined either in [`mask_targets()`](#fiftyone.core.dataset.Dataset.mask_targets) or [`default_mask_targets()`](#fiftyone.core.dataset.Dataset.default_mask_targets). * **Parameters:** **field** – a field name * **Returns:** True/False #### has_run(run_key) Whether this collection has a run with the given key. * **Parameters:** **run_key** – a run key * **Returns:** True/False #### *property* has_runs Whether this collection has any runs. #### has_sample_field(path) Determines whether the collection has a sample field with the given name. * **Parameters:** **path** – the field name or `embedded.field.name` * **Returns:** True/False #### has_skeleton(field) Determines whether this collection has a keypoint skeleton for the given field. Keypoint skeletons may be defined either in [`skeletons()`](#fiftyone.core.dataset.Dataset.skeletons) or [`default_skeleton()`](#fiftyone.core.dataset.Dataset.default_skeleton). * **Parameters:** **field** – a field name * **Returns:** True/False #### histogram_values(field_or_expr, expr=None, bins=None, range=None, auto=False) Computes a histogram of the field values in the collection. This aggregation is typically applied to *numeric* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) Examples: ```default import numpy as np import matplotlib.pyplot as plt import fiftyone as fo from fiftyone import ViewField as F samples = [] for idx in range(100): samples.append( fo.Sample( filepath="/path/to/image%d.png" % idx, numeric_field=np.random.randn(), numeric_list_field=list(np.random.randn(10)), ) ) dataset = fo.Dataset() dataset.add_samples(samples) def plot_hist(counts, edges): counts = np.asarray(counts) edges = np.asarray(edges) left_edges = edges[:-1] widths = edges[1:] - edges[:-1] plt.bar(left_edges, counts, width=widths, align="edge") # # Compute a histogram of a numeric field # counts, edges, other = dataset.histogram_values( "numeric_field", bins=50, range=(-4, 4) ) plot_hist(counts, edges) plt.show(block=False) # # Compute the histogram of a numeric list field # counts, edges, other = dataset.histogram_values( "numeric_list_field", bins=50 ) plot_hist(counts, edges) plt.show(block=False) # # Compute the histogram of a transformation of a numeric field # counts, edges, other = dataset.histogram_values( 2 * (F("numeric_field") + 1), bins=50 ) plot_hist(counts, edges) plt.show(block=False) ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **bins** (*None*) – can be either an integer number of bins to generate or a monotonically increasing sequence specifying the bin edges to use. By default, 10 bins are created. If `bins` is an integer and no `range` is specified, bin edges are automatically distributed in an attempt to evenly distribute the counts in each bin * **range** (*None*) – a `(lower, upper)` tuple specifying a range in which to generate equal-width bins. Only applicable when `bins` is an integer * **auto** (*False*) – whether to automatically choose bin edges in an attempt to evenly distribute the counts in each bin. If this option is chosen, `bins` will only be used if it is an integer, and the `range` parameter is ignored * **Returns:** a tuple of - counts: a list of counts in each bin - edges: an increasing list of bin edges of length `len(counts) + 1`. Note that each bin is treated as having an inclusive lower boundary and exclusive upper boundary, `[lower, upper)`, including the rightmost bin - other: the number of items outside the bins #### init_run(\*\*kwargs) Initializes a config instance for a new run. * **Parameters:** **\*\*kwargs** – JSON serializable config parameters * **Returns:** a [`fiftyone.core.runs.RunConfig`](fiftyone.core.runs.md#fiftyone.core.runs.RunConfig) #### init_run_results(run_key, \*\*kwargs) Initializes a results instance for the run with the given key. * **Parameters:** * **run_key** – a run key * **\*\*kwargs** – JSON serializable data * **Returns:** a [`fiftyone.core.runs.RunResults`](fiftyone.core.runs.md#fiftyone.core.runs.RunResults) #### limit(limit) Returns a view with at most the given number of samples. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), ), fo.Sample( filepath="/path/to/image3.png", ground_truth=None, ), ] ) # # Only include the first 2 samples in the view # view = dataset.limit(2) ``` * **Parameters:** **limit** – the maximum number of samples to return. If a non-positive number is provided, an empty view is returned * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### limit_labels(field, limit) Limits the number of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instances in the specified labels list field of each sample in the collection. The specified `field` must be one of the following types: - [`fiftyone.core.labels.Classifications`](fiftyone.core.labels.md#fiftyone.core.labels.Classifications) - [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) - [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) - [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.5, 0.5], confidence=0.9, ), fo.Detection( label="dog", bounding_box=[0.2, 0.2, 0.3, 0.3], confidence=0.8, ), ] ), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.5, 0.5, 0.4, 0.4], confidence=0.95, ), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image4.png", predictions=None, ), ] ) # # Only include the first detection in the `predictions` field of # each sample # view = dataset.limit_labels("predictions", 1) ``` * **Parameters:** * **field** – the labels list field to filter * **limit** – the maximum number of labels to include in each labels list. If a non-positive number is provided, all lists will be empty * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### *classmethod* list_aggregations() Returns a list of all available methods on this collection that apply [`fiftyone.core.aggregations.Aggregation`](fiftyone.core.aggregations.md#fiftyone.core.aggregations.Aggregation) operations to this collection. * **Returns:** a list of `SampleCollection` method names #### list_annotation_runs(type=None, method=None, \*\*kwargs) Returns a list of annotation keys on this collection. * **Parameters:** * **type** (*None*) – a specific annotation run type to match, which can be: - a string `fiftyone.core.annotations.AnnotationMethodConfig.type` - a `fiftyone.core.annotations.AnnotationMethod` class or its fully-qualified class name string * **method** (*None*) – a specific `fiftyone.core.annotations.AnnotationMethodConfig.method` string to match * **\*\*kwargs** – optional config parameters to match * **Returns:** a list of annotation keys #### list_brain_runs(type=None, method=None, \*\*kwargs) Returns a list of brain keys on this collection. * **Parameters:** * **type** (*None*) – a specific brain run type to match, which can be: - a string [`fiftyone.core.brain.BrainMethodConfig.type`](fiftyone.core.brain.md#fiftyone.core.brain.BrainMethodConfig.type) - a [`fiftyone.core.brain.BrainMethod`](fiftyone.core.brain.md#fiftyone.core.brain.BrainMethod) class or its fully-qualified class name string * **method** (*None*) – a specific [`fiftyone.core.brain.BrainMethodConfig.method`](fiftyone.core.brain.md#fiftyone.core.brain.BrainMethodConfig.method) string to match * **\*\*kwargs** – optional config parameters to match * **Returns:** a list of brain keys #### list_evaluations(type=None, method=None, \*\*kwargs) Returns a list of evaluation keys on this collection. * **Parameters:** * **type** (*None*) – a specific evaluation type to match, which can be: - a string `fiftyone.core.evaluations.EvaluationMethodConfig.type` - a `fiftyone.core.evaluations.EvaluationMethod` class or its fully-qualified class name string * **method** (*None*) – a specific `fiftyone.core.evaluations.EvaluationMethodConfig.method` string to match * **\*\*kwargs** – optional config parameters to match * **Returns:** a list of evaluation keys #### list_indexes() Returns the list of index names on this collection. Single-field indexes are referenced by their field name, while compound indexes are referenced by more complicated strings. See [`pymongo.collection.Collection.index_information()`](https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html#pymongo.collection.Collection.index_information) for details on the compound format. * **Returns:** the list of index names #### list_runs(\*\*kwargs) Returns a list of run keys on this collection. * **Parameters:** **\*\*kwargs** – optional config parameters to match * **Returns:** a list of run keys #### list_schema(field_or_expr, expr=None) Extracts the value type(s) in a specified list field across all samples in the collection. Examples: ```default from datetime import datetime import fiftyone as fo dataset = fo.Dataset() sample1 = fo.Sample( filepath="image1.png", ground_truth=fo.Classification( label="cat", info=[ fo.DynamicEmbeddedDocument( task="initial_annotation", author="Alice", timestamp=datetime(1970, 1, 1), notes=["foo", "bar"], ), fo.DynamicEmbeddedDocument( task="editing_pass", author="Bob", timestamp=datetime.utcnow(), ), ], ), ) sample2 = fo.Sample( filepath="image2.png", ground_truth=fo.Classification( label="dog", info=[ fo.DynamicEmbeddedDocument( task="initial_annotation", author="Bob", timestamp=datetime(2018, 10, 18), notes=["spam", "eggs"], ), ], ), ) dataset.add_samples([sample1, sample2]) # Determine that `ground_truth.info` contains embedded documents print(dataset.list_schema("ground_truth.info")) # fo.EmbeddedDocumentField # Determine the fields of the embedded documents in the list print(dataset.schema("ground_truth.info[]")) # {'task': StringField, ..., 'notes': ListField} # Determine the type of the values in the nested `notes` list field # Since `ground_truth.info` is not yet declared on the dataset's # schema, we must manually include `[]` to unwind the info lists print(dataset.list_schema("ground_truth.info[].notes")) # fo.StringField # Declare the `ground_truth.info` field dataset.add_sample_field( "ground_truth.info", fo.ListField, subfield=fo.EmbeddedDocumentField, embedded_doc_type=fo.DynamicEmbeddedDocument, ) # Now we can inspect the nested `notes` field without unwinding print(dataset.list_schema("ground_truth.info.notes")) # fo.StringField ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **Returns:** a [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) or list of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances describing the value type(s) in the list #### *classmethod* list_view_stages() Returns a list of all available methods on this collection that apply [`fiftyone.core.stages.ViewStage`](fiftyone.core.stages.md#fiftyone.core.stages.ViewStage) operations to this collection. * **Returns:** a list of `SampleCollection` method names #### load_annotation_results(anno_key, cache=True, \*\*kwargs) Loads the results for the annotation run with the given key on this collection. The [`fiftyone.utils.annotations.AnnotationResults`](fiftyone.utils.annotations.md#fiftyone.utils.annotations.AnnotationResults) object returned by this method will provide a variety of backend-specific methods allowing you to perform actions such as checking the status and deleting this run from the annotation backend. Use [`load_annotations()`](#fiftyone.core.dataset.Dataset.load_annotations) to load the labels from an annotation run onto your FiftyOne dataset. * **Parameters:** * **anno_key** – an annotation key * **cache** (*True*) – whether to cache the results on the collection * **\*\*kwargs** – keyword arguments for run’s [`fiftyone.core.annotation.AnnotationMethodConfig.load_credentials()`](fiftyone.core.annotation.md#fiftyone.core.annotation.AnnotationMethodConfig.load_credentials) method * **Returns:** a [`fiftyone.utils.annotations.AnnotationResults`](fiftyone.utils.annotations.md#fiftyone.utils.annotations.AnnotationResults) #### load_annotation_view(anno_key, select_fields=False) Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified annotation run was performed on this collection. * **Parameters:** * **anno_key** – an annotation key * **select_fields** (*False*) – whether to exclude fields involved in other annotation runs * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### load_annotations(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 this collection. See [this page](../integrations/annotation.md#loading-annotations) for more information about using this method to import annotations that you have scheduled by calling [`annotate()`](#fiftyone.core.dataset.Dataset.annotate). * **Parameters:** * **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 label type - `"return"`: return a dict containing all unexpected labels, or `None` if there aren’t any * **cleanup** (*False*) – whether to delete any information 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()`](fiftyone.core.annotation.md#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 #### load_brain_results(brain_key, cache=True, load_view=True, \*\*kwargs) Loads the results for the brain method run with the given key on this collection. * **Parameters:** * **brain_key** – a brain key * **cache** (*True*) – whether to cache the results on the collection * **load_view** (*True*) – whether to load the view on which the results were computed (True) or the full dataset (False) * **\*\*kwargs** – keyword arguments for the run’s [`fiftyone.core.brain.BrainMethodConfig.load_credentials()`](fiftyone.core.brain.md#fiftyone.core.brain.BrainMethodConfig.load_credentials) method * **Returns:** a [`fiftyone.core.brain.BrainResults`](fiftyone.core.brain.md#fiftyone.core.brain.BrainResults) #### load_brain_view(brain_key, select_fields=False) Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified brain method run was performed on this collection. * **Parameters:** * **brain_key** – a brain key * **select_fields** (*False*) – whether to exclude fields involved in other brain method runs * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### load_evaluation_results(eval_key, cache=True, \*\*kwargs) Loads the results for the evaluation with the given key on this collection. * **Parameters:** * **eval_key** – an evaluation key * **cache** (*True*) – whether to cache the results on the collection * **\*\*kwargs** – keyword arguments for the run’s [`fiftyone.core.evaluation.EvaluationMethodConfig.load_credentials()`](fiftyone.core.evaluation.md#fiftyone.core.evaluation.EvaluationMethodConfig.load_credentials) method * **Returns:** a [`fiftyone.core.evaluation.EvaluationResults`](fiftyone.core.evaluation.md#fiftyone.core.evaluation.EvaluationResults) #### load_evaluation_view(eval_key, select_fields=False) Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified evaluation was performed on this collection. * **Parameters:** * **eval_key** – an evaluation key * **select_fields** (*False*) – whether to exclude fields involved in other evaluations * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### load_run_results(run_key, cache=True, load_view=True, \*\*kwargs) Loads the results for the run with the given key on this collection. * **Parameters:** * **run_key** – a run key * **cache** (*True*) – whether to cache the results on the collection * **load_view** (*True*) – whether to load the view on which the results were computed (True) or the full dataset (False) * **\*\*kwargs** – keyword arguments for the run’s [`fiftyone.core.runs.RunConfig.load_credentials()`](fiftyone.core.runs.md#fiftyone.core.runs.RunConfig.load_credentials) method * **Returns:** a [`fiftyone.core.runs.RunResults`](fiftyone.core.runs.md#fiftyone.core.runs.RunResults) #### load_run_view(run_key, select_fields=False) Loads the [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) on which the specified run was performed on this collection. * **Parameters:** * **run_key** – a run key * **select_fields** (*False*) – whether to exclude fields involved in other runs * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### make_unique_field_name(root='') Makes a unique field name with the given root name for the collection. * **Parameters:** **root** – an optional root for the output field name * **Returns:** the field name #### map_labels(field, map) Maps the `label` values of a [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) field to new values for each sample in the collection. Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", weather=fo.Classification(label="sunny"), predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.5, 0.5], confidence=0.9, ), fo.Detection( label="dog", bounding_box=[0.2, 0.2, 0.3, 0.3], confidence=0.8, ), ] ), ), fo.Sample( filepath="/path/to/image2.png", weather=fo.Classification(label="cloudy"), predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.5, 0.5, 0.4, 0.4], confidence=0.95, ), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image3.png", weather=fo.Classification(label="partly cloudy"), predictions=fo.Detections( detections=[ fo.Detection( label="squirrel", bounding_box=[0.25, 0.25, 0.5, 0.5], confidence=0.5, ), ] ), ), fo.Sample( filepath="/path/to/image4.png", predictions=None, ), ] ) # # Map the "partly cloudy" weather label to "cloudy" # view = dataset.map_labels("weather", {"partly cloudy": "cloudy"}) # # Map "rabbit" and "squirrel" predictions to "other" # view = dataset.map_labels( "predictions", {"rabbit": "other", "squirrel": "other"} ) ``` * **Parameters:** * **field** – the labels field to map * **map** – a dict mapping label values to new label values * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### map_samples(map_fcn, save=False, skip_failures=False, parallelize_method=None, num_workers=None, batch_method=None, batch_size=None, progress=None) Applies the given function to each sample in the collection and returns the results as a generator. By default, a multiprocessing pool is used to parallelize the work, unless this method is called in a daemon process (subprocess), in which case no workers are used. This function effectively performs the following map operation with the outer loop in parallel: ```default for batch_view in fou.iter_slices(sample_collection, batch_size): for sample in batch_view.iter_samples(autosave=save): sample_output = map_fcn(sample) yield sample.id, sample_output ``` Example: ```default from collections import Counter import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("cifar10", split="train") view = dataset.select_fields("ground_truth") def map_fcn(sample): return sample.ground_truth.label.upper() counter = Counter() for _, label in view.map_samples(map_fcn): counter[label] += 1 print(dict(counter)) ``` * **Parameters:** * **map_fcn** – a function to apply to each sample in the collection * **save** (*False*) – whether to save any sample edits applied by `map_fcn` * **skip_failures** (*False*) – whether to gracefully continue without raising an error if the update function raises an exception for a sample * **parallelize_method** (*None*) – the parallelization method to use. Supported values are `{"process", "thread"}`. The default is `fiftyone.config.default_parallelization_method` * **num_workers** (*None*) – the number of workers to use. When using process parallelism, this defaults to `fiftyone.config.default_process_pool_workers` if the value is set, else [`fiftyone.core.utils.recommend_process_pool_workers()`](fiftyone.core.utils.md#fiftyone.core.utils.recommend_process_pool_workers) workers are used. If this value is <= 1, all work is done in the main process * **batch_method** (*None*) – whether to use IDs (`"id"`) or slices (`"slice"`) to assign samples to workers * **batch_size** (*None*) – an optional number of samples to distribute to each worker at a time. By default, samples are evenly distributed to workers with one batch per worker * **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, or “workers” to render per-worker progress bars * **Returns:** a generator that emits `(sample_id, map_output)` tuples #### map_values(field, map) Maps the values in the given field to new values for each sample in the collection. Examples: ```default import random import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F ANIMALS = [ "bear", "bird", "cat", "cow", "dog", "elephant", "giraffe", "horse", "sheep", "zebra" ] dataset = foz.load_zoo_dataset("quickstart") values = [random.choice(ANIMALS) for _ in range(len(dataset))] dataset.set_values("str_field", values) dataset.set_values("list_field", [[v] for v in values]) dataset.set_field("ground_truth.detections.tags", [F("label")]).save() # Map all animals to string "animal" mapping = {a: "animal" for a in ANIMALS} # # Map values in top-level fields # view = dataset.map_values("str_field", mapping) print(view.count_values("str_field")) # {"animal": 200} view = dataset.map_values("list_field", mapping) print(view.count_values("list_field")) # {"animal": 200} # # Map values in nested fields # view = dataset.map_values("ground_truth.detections.label", mapping) print(view.count_values("ground_truth.detections.label")) # {"animal": 183, ...} view = dataset.map_values("ground_truth.detections.tags", mapping) print(view.count_values("ground_truth.detections.tags")) # {"animal": 183, ...} ``` * **Parameters:** * **field** – the field or `embedded.field.name` to map * **map** – a dict mapping values to new values * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### match(filter) Filters the samples in the collection by the given filter. Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", weather=fo.Classification(label="sunny"), predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.5, 0.5], confidence=0.9, ), fo.Detection( label="dog", bounding_box=[0.2, 0.2, 0.3, 0.3], confidence=0.8, ), ] ), ), fo.Sample( filepath="/path/to/image2.jpg", weather=fo.Classification(label="cloudy"), predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.5, 0.5, 0.4, 0.4], confidence=0.95, ), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image3.png", weather=fo.Classification(label="partly cloudy"), predictions=fo.Detections( detections=[ fo.Detection( label="squirrel", bounding_box=[0.25, 0.25, 0.5, 0.5], confidence=0.5, ), ] ), ), fo.Sample( filepath="/path/to/image4.jpg", predictions=None, ), ] ) # # Only include samples whose `filepath` ends with ".jpg" # view = dataset.match(F("filepath").ends_with(".jpg")) # # Only include samples whose `weather` field is "sunny" # view = dataset.match(F("weather.label") == "sunny") # # Only include samples with at least 2 objects in their # `predictions` field # view = dataset.match(F("predictions.detections").length() >= 2) # # Only include samples whose `predictions` field contains at least # one object with area smaller than 0.2 # # Bboxes are in [top-left-x, top-left-y, width, height] format bbox = F("bounding_box") bbox_area = bbox[2] * bbox[3] small_boxes = F("predictions.detections").filter(bbox_area < 0.2) view = dataset.match(small_boxes.length() > 0) ``` * **Parameters:** **filter** – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that returns a boolean describing the filter to apply * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### match_frames(filter, omit_empty=True) Filters the frames in the video collection by the given filter. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart-video") # # Match frames with at least 10 detections # num_objects = F("detections.detections").length() view = dataset.match_frames(num_objects > 10) print(dataset.count()) print(view.count()) print(dataset.count("frames")) print(view.count("frames")) ``` * **Parameters:** * **filter** – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB aggregation expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that returns a boolean describing the filter to apply * **omit_empty** (*True*) – whether to omit samples with no frame labels after filtering * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### match_labels(labels=None, ids=None, instance_ids=None, tags=None, filter=None, fields=None, bool=None) Selects the samples from the collection that contain (or do not contain) at least one label that matches the specified criteria. Note that, unlike [`select_labels()`](#fiftyone.core.dataset.Dataset.select_labels) and [`filter_labels()`](#fiftyone.core.dataset.Dataset.filter_labels), this stage will not filter the labels themselves; it only selects the corresponding samples. You can perform a selection via one or more of the following methods: - Provide the `labels` argument, which should contain a list of dicts in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels), to match specific labels - Provide the `ids` argument to match labels with specific IDs - Provide the `instance_ids` argument to match labels with specific instance IDs - Provide the `tags` argument to match labels with specific tags - Provide the `filter` argument to match labels based on a boolean [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) that is applied to each individual [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) element - Pass `bool=False` to negate the operation and instead match samples that *do not* contain at least one label matching the specified criteria If multiple criteria are specified, labels must match all of them in order to trigger a sample match. By default, the selection is applied to all [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields, but you can provide the `fields` argument to explicitly define the field(s) in which to search. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Only show samples whose labels are currently selected in the App # session = fo.launch_app(dataset) # Select some labels in the App... view = dataset.match_labels(labels=session.selected_labels) # # Only include samples that contain labels with the specified IDs # # Grab some label IDs ids = [ dataset.first().ground_truth.detections[0].id, dataset.last().predictions.detections[0].id, ] view = dataset.match_labels(ids=ids) print(len(view)) print(view.count("ground_truth.detections")) print(view.count("predictions.detections")) # # Only include samples that contain labels with the specified tags # # Grab some label IDs ids = [ dataset.first().ground_truth.detections[0].id, dataset.last().predictions.detections[0].id, ] # Give the labels a "test" tag dataset = dataset.clone() # create copy since we're modifying data dataset.select_labels(ids=ids).tag_labels("test") print(dataset.count_values("ground_truth.detections.tags")) print(dataset.count_values("predictions.detections.tags")) # Retrieve the labels via their tag view = dataset.match_labels(tags="test") print(len(view)) print(view.count("ground_truth.detections")) print(view.count("predictions.detections")) # # Only include samples that contain labels matching a filter # filter = F("confidence") > 0.99 view = dataset.match_labels(filter=filter, fields="predictions") print(len(view)) print(view.count("ground_truth.detections")) print(view.count("predictions.detections")) ``` * **Parameters:** * **labels** (*None*) – a list of dicts specifying the labels to select in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels) * **ids** (*None*) – an ID or iterable of IDs of the labels to select * **instance_ids** (*None*) – an instance ID or iterable of instance IDs of the labels to select * **tags** (*None*) – a tag or iterable of tags of labels to select * **filter** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB aggregation expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that returns a boolean describing whether to select a given label. In the case of list fields like [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), the filter is applied to the list elements, not the root field * **fields** (*None*) – a field or iterable of fields from which to select * **bool** (*None*) – whether to match samples that have (None or True) or do not have (False) at least one label that matches the specified criteria * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### match_tags(tags, bool=None, all=False) Returns a view containing the samples in the collection that have or don’t have any/all of the given tag(s). Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample(filepath="image1.png", tags=["train"]), fo.Sample(filepath="image2.png", tags=["test"]), fo.Sample(filepath="image3.png", tags=["train", "test"]), fo.Sample(filepath="image4.png"), ] ) # # Only include samples that have the "test" tag # view = dataset.match_tags("test") # # Only include samples that do not have the "test" tag # view = dataset.match_tags("test", bool=False) # # Only include samples that have the "test" or "train" tags # view = dataset.match_tags(["test", "train"]) # # Only include samples that have the "test" and "train" tags # view = dataset.match_tags(["test", "train"], all=True) # # Only include samples that do not have the "test" or "train" tags # view = dataset.match_tags(["test", "train"], bool=False) # # Only include samples that do not have the "test" and "train" tags # view = dataset.match_tags(["test", "train"], bool=False, all=True) ``` * **Parameters:** * **tags** – the tag or iterable of tags to match * **bool** (*None*) – whether to match samples that have (None or True) or do not have (False) the given tags * **all** (*False*) – whether to match samples that have (or don’t have) all (True) or any (False) of the given tags * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### max(field_or_expr, expr=None, safe=False) Computes the maximum of a numeric field of the collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* or *date* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) - [`fiftyone.core.fields.DateField`](fiftyone.core.fields.md#fiftyone.core.fields.DateField) - [`fiftyone.core.fields.DateTimeField`](fiftyone.core.fields.md#fiftyone.core.fields.DateTimeField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the maximum of a numeric field # max = dataset.max("numeric_field") print(max) # the max # # Compute the maximum of a numeric list field # max = dataset.max("numeric_list_field") print(max) # the max # # Compute the maximum of a transformation of a numeric field # max = dataset.max(2 * (F("numeric_field") + 1)) print(max) # the max ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the maximum value #### mean(field_or_expr, expr=None, safe=False) Computes the arithmetic mean of the field values of the collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the mean of a numeric field # mean = dataset.mean("numeric_field") print(mean) # the mean # # Compute the mean of a numeric list field # mean = dataset.mean("numeric_list_field") print(mean) # the mean # # Compute the mean of a transformation of a numeric field # mean = dataset.mean(2 * (F("numeric_field") + 1)) print(mean) # the mean ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the mean #### merge_labels(in_field, out_field) Merges the labels from the given input field into the given output field of the collection. If this collection is a dataset, the input field is deleted after the merge. If this collection is a view, the input field will still exist on the underlying dataset but will only contain the labels not present in this view. * **Parameters:** * **in_field** – the name of the input label field * **out_field** – the name of the output label field, which will be created if necessary #### min(field_or_expr, expr=None, safe=False) Computes the minimum of a numeric field of the collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* or *date* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) - [`fiftyone.core.fields.DateField`](fiftyone.core.fields.md#fiftyone.core.fields.DateField) - [`fiftyone.core.fields.DateTimeField`](fiftyone.core.fields.md#fiftyone.core.fields.DateTimeField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the minimum of a numeric field # min = dataset.min("numeric_field") print(min) # the min # # Compute the minimum of a numeric list field # min = dataset.min("numeric_list_field") print(min) # the min # # Compute the minimum of a transformation of a numeric field # min = dataset.min(2 * (F("numeric_field") + 1)) print(min) # the min ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the minimum value #### mongo(pipeline, \_needs_frames=None, \_group_slices=None) Adds a view stage defined by a raw MongoDB aggregation pipeline. See [MongoDB aggregation pipelines](https://docs.mongodb.com/manual/core/aggregation-pipeline/) for more details. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.5, 0.5], confidence=0.9, ), fo.Detection( label="dog", bounding_box=[0.2, 0.2, 0.3, 0.3], confidence=0.8, ), ] ), ), fo.Sample( filepath="/path/to/image2.png", predictions=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.5, 0.5, 0.4, 0.4], confidence=0.95, ), fo.Detection(label="rabbit"), ] ), ), fo.Sample( filepath="/path/to/image3.png", predictions=fo.Detections( detections=[ fo.Detection( label="squirrel", bounding_box=[0.25, 0.25, 0.5, 0.5], confidence=0.5, ), ] ), ), fo.Sample( filepath="/path/to/image4.png", predictions=None, ), ] ) # # Extract a view containing the second and third samples in the # dataset # view = dataset.mongo([{"$skip": 1}, {"$limit": 2}]) # # Sort by the number of objects in the `precictions` field # view = dataset.mongo([ { "$addFields": { "_sort_field": { "$size": {"$ifNull": ["$predictions.detections", []]} } } }, {"$sort": {"_sort_field": -1}}, {"$project": {"_sort_field": False}}, ]) ``` * **Parameters:** **pipeline** – a MongoDB aggregation pipeline (list of dicts) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### quantiles(field_or_expr, quantiles, expr=None, safe=False) Computes the quantile(s) of the field values of a collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the quantiles of a numeric field # quantiles = dataset.quantiles("numeric_field", [0.1, 0.5, 0.9]) print(quantiles) # the quantiles # # Compute the quantiles of a numeric list field # quantiles = dataset.quantiles("numeric_list_field", [0.1, 0.5, 0.9]) print(quantiles) # the quantiles # # Compute the mean of a transformation of a numeric field # quantiles = dataset.quantiles(2 * (F("numeric_field") + 1), [0.1, 0.5, 0.9]) print(quantiles) # the quantiles ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate * **quantiles** – the quantile or iterable of quantiles to compute. Each quantile must be a numeric value in `[0, 1]` * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the quantile or list of quantiles #### register_run(run_key, config, results=None, overwrite=False, cleanup=True, cache=True) Registers a run under the given key on this collection. * **Parameters:** * **run_key** – a run key * **config** – a [`fiftyone.core.runs.RunConfig`](fiftyone.core.runs.md#fiftyone.core.runs.RunConfig) * **results** (*None*) – an optional [`fiftyone.core.runs.RunResults`](fiftyone.core.runs.md#fiftyone.core.runs.RunResults) * **overwrite** (*False*) – whether to allow overwriting an existing run of the same type * **cleanup** (*True*) – whether to execute an existing run’s [`fiftyone.core.runs.Run.cleanup()`](fiftyone.core.runs.md#fiftyone.core.runs.Run.cleanup) method when overwriting it * **cache** (*True*) – whether to cache the results on the collection #### rename_annotation_run(anno_key, new_anno_key) Replaces the key for the given annotation run with a new key. * **Parameters:** * **anno_key** – an annotation key * **new_anno_key** – a new annotation key #### rename_brain_run(brain_key, new_brain_key) Replaces the key for the given brain run with a new key. * **Parameters:** * **brain_key** – a brain key * **new_brain_key** – a new brain key #### rename_evaluation(eval_key, new_eval_key) Replaces the key for the given evaluation with a new key. * **Parameters:** * **eval_key** – an evaluation key * **new_anno_key** – a new evaluation key #### rename_run(run_key, new_run_key) Replaces the key for the given run with a new key. * **Parameters:** * **run_key** – a run key * **new_run_key** – a new run key #### save_context(batch_size=None, batching_strategy=None) Returns a context that can be used to save samples from this collection according to a configurable batching strategy. Examples: ```default import random as r import string as s import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("cifar10", split="test") def make_label(): return "".join(r.choice(s.ascii_letters) for i in range(10)) # No save context for sample in dataset.iter_samples(progress=True): sample.ground_truth.label = make_label() sample.save() # Save using default batching strategy with dataset.save_context() as context: for sample in dataset.iter_samples(progress=True): sample.ground_truth.label = make_label() context.save(sample) # Save in batches of 10 with dataset.save_context(batch_size=10) as context: for sample in dataset.iter_samples(progress=True): sample.ground_truth.label = make_label() context.save(sample) # Save every 0.5 seconds with dataset.save_context(batch_size=0.5) as context: for sample in dataset.iter_samples(progress=True): sample.ground_truth.label = make_label() context.save(sample) ``` * **Parameters:** * **batch_size** (*None*) – the batch size to use. If a `batching_strategy` is provided, this parameter configures the strategy as described below. If no `batching_strategy` is provided, this can either be an integer specifying the number of samples to save in a batch (in which case `batching_strategy` is implicitly set to `"static"`) or a float number of seconds between batched saves (in which case `batching_strategy` is implicitly set to `"latency"`) * **batching_strategy** (*None*) – the batching strategy to use for each save operation. Supported values are: - `"static"`: a fixed sample batch size for each save - `"size"`: a target batch size, in bytes, for each save - `"latency"`: a target latency, in seconds, between saves By default, `fo.config.default_batcher` is used * **Returns:** a `SaveContext` #### save_run_results(run_key, results, overwrite=True, cache=True) Saves run results for the run with the given key. * **Parameters:** * **run_key** – a run key * **results** – a [`fiftyone.core.runs.RunResults`](fiftyone.core.runs.md#fiftyone.core.runs.RunResults) * **overwrite** (*True*) – whether to overwrite an existing result with the same key * **cache** (*True*) – whether to cache the results on the collection #### schema(field_or_expr, expr=None, dynamic_only=False, \_doc_type=None, \_include_private=False) Extracts the names and types of the attributes of a specified embedded document field across all samples in the collection. Schema aggregations are useful for detecting the presence and types of dynamic attributes of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields across a collection. Examples: ```default import fiftyone as fo dataset = fo.Dataset() sample1 = fo.Sample( filepath="image1.png", ground_truth=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.4, 0.4], foo="bar", hello=True, ), fo.Detection( label="dog", bounding_box=[0.5, 0.5, 0.4, 0.4], hello=None, ) ] ) ) sample2 = fo.Sample( filepath="image2.png", ground_truth=fo.Detections( detections=[ fo.Detection( label="rabbit", bounding_box=[0.1, 0.1, 0.4, 0.4], foo=None, ), fo.Detection( label="squirrel", bounding_box=[0.5, 0.5, 0.4, 0.4], hello="there", ), ] ) ) dataset.add_samples([sample1, sample2]) # # Get schema of all dynamic attributes on the detections in a # `Detections` field # print(dataset.schema("ground_truth.detections", dynamic_only=True)) # {'foo': StringField, 'hello': [BooleanField, StringField]} ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **dynamic_only** (*False*) – whether to only include dynamically added attributes * **Returns:** a dict mapping field names to [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) instances. If a field’s values takes multiple non-None types, the list of observed types will be returned #### select(sample_ids, ordered=False) Selects the samples with the given IDs from the collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") # # Create a view containing the currently selected samples in the App # session = fo.launch_app(dataset) # Select samples in the App... view = dataset.select(session.selected) ``` * **Parameters:** **sample_ids** – the samples to select. Can be any of the following: - a sample ID - an iterable of sample IDs - an iterable of booleans of same length as the collection encoding which samples to select - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) ordered (False): whether to sort the samples in the returned view to : match the order of the provided IDs * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### select_by(field, values, ordered=False) Selects the samples with the given field values from the collection. This stage is typically used to work with categorical fields (strings, ints, and bools). If you want to select samples based on floating point fields, use [`match()`](#fiftyone.core.dataset.Dataset.match). Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample(filepath="image%d.jpg" % i, int=i, str=str(i)) for i in range(100) ] ) # # Create a view containing samples whose `int` field have the given # values # view = dataset.select_by("int", [1, 51, 11, 41, 21, 31]) print(view.head(6)) # # Create a view containing samples whose `str` field have the given # values, in order # view = dataset.select_by( "str", ["1", "51", "11", "41", "21", "31"], ordered=True ) print(view.head(6)) ``` * **Parameters:** * **field** – a field or `embedded.field.name` * **values** – a value or iterable of values to select by * **ordered** (*False*) – whether to sort the samples in the returned view to match the order of the provided values * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### select_fields(field_names=None, meta_filter=None, \_allow_missing=False) Selects only the fields with the given names from the samples in the collection. All other fields are excluded. Note that default sample fields are always selected. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", uniqueness=1.0, ground_truth=fo.Detections( detections=[ fo.Detection( label="cat", bounding_box=[0.1, 0.1, 0.5, 0.5], mood="surly", age=51, ), fo.Detection( label="dog", bounding_box=[0.2, 0.2, 0.3, 0.3], mood="happy", age=52, ), ] ) ), fo.Sample( filepath="/path/to/image2.png", uniqueness=0.0, ), fo.Sample( filepath="/path/to/image3.png", ), ] ) # # Include only the default fields on each sample # view = dataset.select_fields() # # Include only the `uniqueness` field (and the default fields) on # each sample # view = dataset.select_fields("uniqueness") # # Include only the `mood` attribute (and the default attributes) of # each `Detection` in the `ground_truth` field # view = dataset.select_fields("ground_truth.detections.mood") ``` * **Parameters:** * **field_names** (*None*) – a field name or iterable of field names to select. May contain `embedded.field.name` as well * **meta_filter** (*None*) – a filter that dynamically selects fields in the collection’s schema according to the specified rule, which can be matched against the field’s `name`, `type`, `description`, and/or `info`. For example: - Use `meta_filter="2023"` or `meta_filter={"any": "2023"}` to select fields that have the string “2023” anywhere in their name, type, description, or info - Use `meta_filter={"type": "StringField"}` or `meta_filter={"type": "Classification"}` to select all string or classification fields, respectively - Use `meta_filter={"description": "my description"}` to select fields whose description contains the string “my description” - Use `meta_filter={"info": "2023"}` to select fields that have the string “2023” anywhere in their info - Use `meta_filter={"info.key": "value"}}` to select fields that have a specific key/value pair in their info - Include `meta_filter={"include_nested_fields": True, ...}` in your meta filter to include all nested fields in the filter * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### select_frames(frame_ids, omit_empty=True) Selects the frames with the given IDs from the video collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-video") # # Select some specific frames # frame_ids = [ dataset.first().frames.first().id, dataset.last().frames.last().id, ] view = dataset.select_frames(frame_ids) print(dataset.count()) print(view.count()) print(dataset.count("frames")) print(view.count("frames")) ``` * **Parameters:** * **frame_ids** – the frames to select. Can be any of the following: - a frame ID - an iterable of frame IDs - a [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) or [`fiftyone.core.frame.FrameView`](fiftyone.core.frame.md#fiftyone.core.frame.FrameView) - an iterable of [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) or [`fiftyone.core.frame.FrameView`](fiftyone.core.frame.md#fiftyone.core.frame.FrameView) instances - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) whose frames to select * **omit_empty** (*True*) – whether to omit samples that have no frames after selecting the specified frames * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### select_group_slices(slices=None, media_type=None, flat=True, \_allow_mixed=False, \_force_mixed=False) Selects the specified group slice(s) from the grouped collection. When `flat==True`, the returned view is a flattened non-grouped view containing the samples from the slice(s) of interest. When `flat=False`, the returned view is a grouped collection containing only the slice(s) of interest. #### NOTE When `flat=True`, this stage performs a `$lookup` that pulls the requested slice(s) for each sample in the input collection from the source dataset. As a result, the stage emits *unfiltered samples*. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_group_field("group", default="ego") group1 = fo.Group() group2 = fo.Group() dataset.add_samples( [ fo.Sample( filepath="/path/to/left-image1.jpg", group=group1.element("left"), ), fo.Sample( filepath="/path/to/video1.mp4", group=group1.element("ego"), ), fo.Sample( filepath="/path/to/right-image1.jpg", group=group1.element("right"), ), fo.Sample( filepath="/path/to/left-image2.jpg", group=group2.element("left"), ), fo.Sample( filepath="/path/to/video2.mp4", group=group2.element("ego"), ), fo.Sample( filepath="/path/to/right-image2.jpg", group=group2.element("right"), ), ] ) # # Retrieve the samples from the "ego" group slice # view = dataset.select_group_slices("ego") # # Retrieve the samples from the "left" or "right" group slices # view = dataset.select_group_slices(["left", "right"]) # # Select only the "left" and "right" group slices # view = dataset.select_group_slices(["left", "right"], flat=False) # # Retrieve all image samples # view = dataset.select_group_slices(media_type="image") ``` * **Parameters:** * **slices** (*None*) – a group slice or iterable of group slices to select. If neither argument is provided, a flattened list of all samples is returned * **media_type** (*None*) – a media type or iterable of media types whose slice(s) to select * **flat** (*True*) – whether to return a flattened collection (True) or a grouped collection (False) * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### select_groups(group_ids, ordered=False) Selects the groups with the given IDs from the grouped collection. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart-groups") # # Select some specific groups by ID # group_ids = dataset.take(10).values("group.id") view = dataset.select_groups(group_ids) assert set(view.values("group.id")) == set(group_ids) view = dataset.select_groups(group_ids, ordered=True) assert view.values("group.id") == group_ids ``` * **Parameters:** * **groups_ids** – the groups to select. Can be any of the following: - a group ID - an iterable of group IDs - a [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) - a group dict returned by [`get_group()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_group) - an iterable of [`fiftyone.core.sample.Sample`](fiftyone.core.sample.md#fiftyone.core.sample.Sample) or [`fiftyone.core.sample.SampleView`](fiftyone.core.sample.md#fiftyone.core.sample.SampleView) instances - an iterable of group dicts returned by [`get_group()`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection.get_group) - a [`fiftyone.core.collections.SampleCollection`](fiftyone.core.collections.md#fiftyone.core.collections.SampleCollection) * **ordered** (*False*) – whether to sort the groups in the returned view to match the order of the provided IDs * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### select_labels(labels=None, ids=None, instance_ids=None, tags=None, fields=None, omit_empty=True) Selects only the specified labels from the collection. The returned view will omit samples, sample fields, and individual labels that do not match the specified selection criteria. You can perform a selection via one or more of the following methods: - Provide the `labels` argument, which should contain a list of dicts in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels), to select specific labels - Provide the `ids` argument to select labels with specific IDs - Provide the `instance_ids` argument to select labels with specific instance IDs - Provide the `tags` argument to select labels with specific tags If multiple criteria are specified, labels must match all of them in order to be selected. By default, the selection is applied to all [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields, but you can provide the `fields` argument to explicitly define the field(s) in which to select. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") # # Only include the labels currently selected in the App # session = fo.launch_app(dataset) # Select some labels in the App... view = dataset.select_labels(labels=session.selected_labels) # # Only include labels with the specified IDs # # Grab some label IDs ids = [ dataset.first().ground_truth.detections[0].id, dataset.last().predictions.detections[0].id, ] view = dataset.select_labels(ids=ids) print(view.count("ground_truth.detections")) print(view.count("predictions.detections")) # # Only include labels with the specified tags # # Grab some label IDs ids = [ dataset.first().ground_truth.detections[0].id, dataset.last().predictions.detections[0].id, ] # Give the labels a "test" tag dataset = dataset.clone() # create copy since we're modifying data dataset.select_labels(ids=ids).tag_labels("test") print(dataset.count_label_tags()) # Retrieve the labels via their tag view = dataset.select_labels(tags="test") print(view.count("ground_truth.detections")) print(view.count("predictions.detections")) ``` * **Parameters:** * **labels** (*None*) – a list of dicts specifying the labels to select in the format returned by [`fiftyone.core.session.Session.selected_labels`](fiftyone.core.session.md#fiftyone.core.session.Session.selected_labels) * **ids** (*None*) – an ID or iterable of IDs of the labels to select * **instance_ids** (*None*) – an instance ID or iterable of instance IDs of the labels to select * **tags** (*None*) – a tag or iterable of tags of labels to select * **fields** (*None*) – a field or iterable of fields from which to select * **omit_empty** (*True*) – whether to omit samples that have no labels after filtering * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### set_field(field, expr, \_allow_missing=False) Sets a field or embedded field on each sample in a collection by evaluating the given expression. This method can process embedded list fields. To do so, simply append `[]` to any list component(s) of the field path. #### NOTE There are two cases where FiftyOne will automatically unwind array fields without requiring you to explicitly specify this via the `[]` syntax: **Top-level lists:** when you specify a `field` path that refers to a top-level list field of a dataset; i.e., `list_field` is automatically coerced to `list_field[]`, if necessary. **List fields:** When you specify a `field` path that refers to the list field of a [`Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) class, such as the [`Detections.detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections.detections) attribute; i.e., `ground_truth.detections.label` is automatically coerced to `ground_truth.detections[].label`, if necessary. See the examples below for demonstrations of this behavior. The provided `expr` is interpreted relative to the document on which the embedded field is being set. For example, if you are setting a nested field `field="embedded.document.field"`, then the expression `expr` you provide will be applied to the `embedded.document` document. Note that you can override this behavior by defining an expression that is bound to the root document by prepending `"$"` to any field name(s) in the expression. See the examples below for more information. #### NOTE Note that you cannot set a non-existing top-level field using this stage, since doing so would violate the dataset’s schema. You can, however, first declare a new field via [`fiftyone.core.dataset.Dataset.add_sample_field()`](#fiftyone.core.dataset.Dataset.add_sample_field) and then populate it in a view via this stage. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Replace all values of the `uniqueness` field that are less than # 0.5 with `None` # view = dataset.set_field( "uniqueness", (F("uniqueness") >= 0.5).if_else(F("uniqueness"), None) ) print(view.bounds("uniqueness")) # # Lower bound all object confidences in the `predictions` field at # 0.5 # view = dataset.set_field( "predictions.detections.confidence", F("confidence").max(0.5) ) print(view.bounds("predictions.detections.confidence")) # # Add a `num_predictions` property to the `predictions` field that # contains the number of objects in the field # view = dataset.set_field( "predictions.num_predictions", F("$predictions.detections").length(), ) print(view.bounds("predictions.num_predictions")) # # Set an `is_animal` field on each object in the `predictions` field # that indicates whether the object is an animal # ANIMALS = [ "bear", "bird", "cat", "cow", "dog", "elephant", "giraffe", "horse", "sheep", "zebra" ] view = dataset.set_field( "predictions.detections.is_animal", F("label").is_in(ANIMALS) ) print(view.count_values("predictions.detections.is_animal")) ``` * **Parameters:** * **field** – the field or `embedded.field.name` to set * **expr** – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that defines the field value to set * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### set_label_values(field_name, values, dynamic=False, skip_none=False, validate=True, progress=False) Sets the fields of the specified labels in the collection to the given values. You can use this method in two ways: - **List syntax (recommended):** provide a list of dicts of the form `{"sample_id": sample_id, "label_id": label_id, "value": value}` specifying the sample IDs and label IDs of each label you want to edit - **Dict syntax:** provide a dict mapping label IDs to values #### NOTE This method is most efficient when you use the list syntax, which includes the sample/frame ID of each label that you are modifying. #### NOTE This method is appropriate when you have the IDs of the labels you wish to modify. See [`set_values()`](#fiftyone.core.dataset.Dataset.set_values) and [`set_field()`](#fiftyone.core.dataset.Dataset.set_field) if your updates are not keyed by label ID. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Populate a new boolean attribute on all high confidence labels # view = dataset.filter_labels("predictions", F("confidence") > 0.99) # Option 1 (recommended): provide label IDs and sample IDs values = [] sample_ids, label_ids = view.values(["id", "predictions.detections.id"]) for sid, lids in zip(sample_ids, label_ids): for lid in lids: values.append({"sample_id": sid, "label_id": lid, "value": True}) dataset.set_label_values("predictions.detections.high_conf", values) print(dataset.count("predictions.detections")) print(len(values)) print(dataset.count_values("predictions.detections.high_conf")) # Option 2: provide only label IDs label_ids = view.values("predictions.detections.id", unwind=True) values = {_id: True for _id in label_ids} dataset.set_label_values("predictions.detections.high_conf", values) print(dataset.count("predictions.detections")) print(len(label_ids)) print(dataset.count_values("predictions.detections.high_conf")) ``` * **Parameters:** * **field_name** – a field or `embedded.field.name` * **values** – the label values to set, in one of the following formats: - a list of dicts of the form `{"sample_id": sample_id, "label_id": label_id, "value": value}` when setting sample-level labels - a list of dicts of the form `{"frame_id": frame_id, "label_id": label_id, "value": value}` when setting frame-level labels - a dict mapping label IDs to values * **skip_none** (*False*) – whether to treat None data in `values` as missing data that should not be set * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **validate** (*True*) – whether to validate that the values are compliant with the dataset schema before adding them * **progress** (*False*) – 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 #### set_values(field_name, values, key_field=None, skip_none=False, expand_schema=True, dynamic=False, validate=True, progress=False, \_allow_missing=False, \_sample_ids=None, \_frame_ids=None) Sets the field or embedded field on each sample or frame in the collection to the given values. You can use this method in two ways: - **Dict syntax (recommended):** provide `values` as a dict whose keys specify the `key_field` values of the samples whose `field_name` you want to set to the corresponding values - **List syntax:** provide `values` as a list, one for each sample in the collection on which you are invoking this method #### NOTE The most performant strategy for setting large numbers of field values is to use the dict syntax with `key_field="id"` when setting sample fields and `key_field="frames.id"` when setting frame fields. All other syntaxes internally convert to these IDs before ultimately performing the updates. When setting a sample field `embedded.field.name` via the list `values` syntax, this function is an efficient implementation of the following loop: ```default for sample, value in zip(sample_collection, values): sample.embedded.field.name = value sample.save() ``` When setting an embedded field that contains an array, say `embedded.array.field.name`, via the list `values` syntax, this function is an efficient implementation of the following loop: ```default for sample, array_values in zip(sample_collection, values): for doc, value in zip(sample.embedded.array, array_values): doc.field.name = value sample.save() ``` When setting a frame field `frames.embedded.field.name` via the list `values` syntax, this function is an efficient implementation of the following loop: ```default for sample, frame_values in zip(sample_collection, values): for frame, value in zip(sample.frames.values(), frame_values): frame.embedded.field.name = value sample.save() ``` When setting an embedded frame field that contains an array, say `frames.embedded.array.field.name`, via the list `values` syntax, this function is an efficient implementation of the following loop: ```default for sample, frame_values in zip(sample_collection, values): for frame, array_values in zip(sample.frames.values(), frame_values): for doc, value in zip(frame.embedded.array, array_values): doc.field.name = value sample.save() ``` When setting a sample field `embedded.field.name` via the dict `values` syntax, this function is an efficient implementation of the following loop: ```default for key, value in values.items(): sample = sample_collection.one(F(key_field) == key) sample.embedded.field.name = value sample.save() ``` When setting frame fields using the dict `values` syntax with a frame-level `key_field`, this function is an efficient implementation of the following loop: ```default frames = sample_collection.to_frames(...) for key, value in values.items(): frame = frames.one(F(key_field) == key) frame.embedded.field.name = value frame.save() ``` When setting frame fields using the dict `values` syntax with a sample-level `key_field`, each value in `values` may either be a list corresponding to the frames of the sample matching the given key, or each value may itself be a dict mapping frame numbers to values. In the latter case, this function is an efficient implementation of the following loop: ```default for key, frame_values in values.items(): sample = sample_collection.one(F(key_field) == key) for frame_number, value in frame_values.items(): frame = sample[frame_number] frame.embedded.field.name = value sample.save() ``` You can also update list fields using the dict `values` syntaxes, in which case this method is an efficient implementation of the natural nested list modifications of the above sample/frame loops. The dual function of [`set_values()`](#fiftyone.core.dataset.Dataset.set_values) is [`values()`](#fiftyone.core.dataset.Dataset.values), which can be used to efficiently extract the values of a field or embedded field of all samples in a collection as lists of values. #### NOTE If you are setting attributes of a nested list of labels, such as attributes of the objects in a [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) field, then consider using [`set_label_values()`](#fiftyone.core.dataset.Dataset.set_label_values) instead for greater efficiency. #### NOTE If the values you are setting can be described by a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) applied to the existing dataset contents, then consider using [`set_field()`](#fiftyone.core.dataset.Dataset.set_field) + [`save()`](fiftyone.core.view.md#fiftyone.core.view.DatasetView.save) for an even more efficient alternative to explicitly iterating over the dataset or calling [`values()`](#fiftyone.core.dataset.Dataset.values) + [`set_values()`](#fiftyone.core.dataset.Dataset.set_values) to perform the update in-memory. Examples: ```default import random import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Create a new sample field # # list syntax values = [random.random() for _ in range(len(dataset))] dataset.set_values("random", values) print(dataset.bounds("random")) # # Edit a frame field # # dict syntax sample_ids = dataset.values("id") values = {id: random.random() for id in sample_ids} dataset.set_values("random", values, key_field="id") print(dataset.bounds("random")) # # Add a tag to all low confidence labels # view = dataset.filter_labels("predictions", F("confidence") < 0.06) # list syntax on a filtered view tags = view.values("predictions.detections.tags") for sample_tags in tags: for detection_tags in sample_tags: detection_tags.append("low_confidence") view.set_values("predictions.detections.tags", tags) print(view.count("predictions.detections")) # 447 print(dataset.count_label_tags()) # 447 # # Create a new frame field # dataset = foz.load_zoo_dataset("quickstart-video") # list syntax values = [] for sample in dataset: values.append([random.random() for _ in sample.frames]) dataset.set_values("frames.random", values) print(dataset.bounds("frames.random")) # # Edit a frame field # # dict syntax frame_ids = dataset.values("frames.id", unwind=True) values = {id: random.random() for id in frame_ids} dataset.set_values("frames.random", values, key_field="frames.id") print(dataset.bounds("frames.random")) ``` * **Parameters:** * **field_name** – a field or `embedded.field.name` * **values** – the field values to set, provided in either of the following formats: - **list syntax**: an iterable of values, one for each sample in the collection. If `field_name` contains array fields, the corresponding elements of `values` must be arrays of the same lengths. When setting frame fields, each element can either be an iterable of values (one for each existing frame of the sample) or a dict mapping frame numbers to values - **dict syntax**: a dict whose keys specify the `key_field` values of the samples for which to set `field_name` to the corresponding values. When setting frame fields, you can either provide a sample-level `key_field`, in which case each corresponding value in `values` must be a list or dict of per-frame field values to set as described in the previous bullet, or you can provide a frame-level `key_field`, in which case each key-value pair in `values` represents a per-frame update * **key_field** (*None*) – a key field to use when choosing which samples to update when `values` is a dict * **skip_none** (*False*) – whether to treat None data in `values` as missing data that should not be set * **expand_schema** (*True*) – whether to dynamically add new sample/frame fields encountered to the dataset schema. If False, an error is raised if the root `field_name` does not exist * **dynamic** (*False*) – whether to declare dynamic attributes of embedded document fields that are encountered * **validate** (*True*) – whether to validate that the values are compliant with the dataset schema before adding them * **progress** (*False*) – 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 #### shuffle(seed=None) Randomly shuffles the samples in the collection. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), ), fo.Sample( filepath="/path/to/image3.png", ground_truth=None, ), ] ) # # Return a view that contains a randomly shuffled version of the # samples in the dataset # view = dataset.shuffle() # # Shuffle the samples with a fixed random seed # view = dataset.shuffle(seed=51) ``` * **Parameters:** **seed** (*None*) – an optional random seed to use when shuffling the samples * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### skip(skip) Omits the given number of samples from the head of the collection. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), ), fo.Sample( filepath="/path/to/image3.png", ground_truth=fo.Classification(label="rabbit"), ), fo.Sample( filepath="/path/to/image4.png", ground_truth=None, ), ] ) # # Omit the first two samples from the dataset # view = dataset.skip(2) ``` * **Parameters:** **skip** – the number of samples to skip. If a non-positive number is provided, no samples are omitted * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### sort_by(field_or_expr, reverse=False, create_index=False) Sorts the samples in the collection by the given field(s) or expression(s). Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart") # # Sort the samples by their `uniqueness` field in ascending order # view = dataset.sort_by("uniqueness", reverse=False) # # Sorts the samples in descending order by the number of detections # in their `predictions` field whose bounding box area is less than # 0.2 # # Bboxes are in [top-left-x, top-left-y, width, height] format bbox = F("bounding_box") bbox_area = bbox[2] * bbox[3] small_boxes = F("predictions.detections").filter(bbox_area < 0.2) view = dataset.sort_by(small_boxes.length(), reverse=True) # # Performs a compound sort where samples are first sorted in # descending or by number of detections and then in ascending order # of uniqueness for samples with the same number of predictions # view = dataset.sort_by( [ (F("predictions.detections").length(), -1), ("uniqueness", 1), ] ) num_objects, uniqueness = view[:5].values( [F("predictions.detections").length(), "uniqueness"] ) print(list(zip(num_objects, uniqueness))) ``` * **Parameters:** * **field_or_expr** – the field(s) or expression(s) to sort by. This can be any of the following: - a field to sort by - an `embedded.field.name` to sort by - a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or a [MongoDB aggregation expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) that defines the quantity to sort by - a list of `(field_or_expr, order)` tuples defining a compound sort criteria, where `field_or_expr` is a field or expression as defined above, and `order` can be 1 or any string starting with “a” for ascending order, or -1 or any string starting with “d” for descending order * **reverse** (*False*) – whether to return the results in descending order * **create_index** (*False*) – whether to create an index, if necessary, to optimize the sort. Only applicable when sorting by field(s), not expressions * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### sort_by_similarity(query, k=None, reverse=False, dist_field=None, brain_key=None) Sorts the collection by similarity to a specified query. In order to use this stage, you must first use [`fiftyone.brain.compute_similarity()`](fiftyone.brain.md#fiftyone.brain.compute_similarity) to index your dataset by similarity. Examples: ```default import fiftyone as fo import fiftyone.brain as fob import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") fob.compute_similarity( dataset, model="clip-vit-base32-torch", brain_key="clip" ) # # Sort samples by their similarity to a sample by its ID # query_id = dataset.first().id view = dataset.sort_by_similarity(query_id, k=5) # # Sort samples by their similarity to a manually computed vector # model = foz.load_zoo_model("clip-vit-base32-torch") embeddings = dataset.take(2, seed=51).compute_embeddings(model) query = embeddings.mean(axis=0) view = dataset.sort_by_similarity(query, k=5) # # Sort samples by their similarity to a text prompt # query = "kites high in the air" view = dataset.sort_by_similarity(query, k=5) ``` * **Parameters:** * **query** – the query, which can be any of the following: - an ID or iterable of IDs - a `num_dims` vector or `num_queries x num_dims` array of vectors - a prompt or iterable of prompts (if supported by the index) * **k** (*None*) – the number of matches to return. By default, the entire collection is sorted * **reverse** (*False*) – whether to sort by least similarity (True) or greatest similarity (False). Some backends may not support least similarity * **dist_field** (*None*) – the name of a float field in which to store the distance of each example to the specified query. The field is created if necessary * **brain_key** (*None*) – the brain key of an existing [`fiftyone.brain.compute_similarity()`](fiftyone.brain.md#fiftyone.brain.compute_similarity) run on the dataset. If not specified, the dataset must have an applicable run, which will be used by default * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### split_labels(in_field, out_field, filter=None) Splits the labels from the given input field into the given output field of the collection. This method is typically invoked on a view that has filtered the contents of the specified input field, so that the labels in the view are moved to the output field and the remaining labels are left in-place. Alternatively, you can provide a `filter` expression that selects the labels of interest to move in this collection. * **Parameters:** * **in_field** – the name of the input label field * **out_field** – the name of the output label field, which will be created if necessary * **filter** (*None*) – a boolean [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) to apply to each label in the input field to determine whether to move it (True) or leave it (False) #### std(field_or_expr, expr=None, safe=False, sample=False) Computes the standard deviation of the field values of the collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the standard deviation of a numeric field # std = dataset.std("numeric_field") print(std) # the standard deviation # # Compute the standard deviation of a numeric list field # std = dataset.std("numeric_list_field") print(std) # the standard deviation # # Compute the standard deviation of a transformation of a numeric field # std = dataset.std(2 * (F("numeric_field") + 1)) print(std) # the standard deviation ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **sample** (*False*) – whether to compute the sample standard deviation rather than the population standard deviation * **Returns:** the standard deviation #### sum(field_or_expr, expr=None, safe=False) Computes the sum of the field values of the collection. `None`-valued fields are ignored. This aggregation is typically applied to *numeric* field types (or lists of such types): - [`fiftyone.core.fields.IntField`](fiftyone.core.fields.md#fiftyone.core.fields.IntField) - [`fiftyone.core.fields.FloatField`](fiftyone.core.fields.md#fiftyone.core.fields.FloatField) Examples: ```default import fiftyone as fo from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Compute the sum of a numeric field # total = dataset.sum("numeric_field") print(total) # the sum # # Compute the sum of a numeric list field # total = dataset.sum("numeric_list_field") print(total) # the sum # # Compute the sum of a transformation of a numeric field # total = dataset.sum(2 * (F("numeric_field") + 1)) print(total) # the sum ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **safe** (*False*) – whether to ignore nan/inf values when dealing with floating point values * **Returns:** the sum #### sync_last_modified_at(include_frames=True) Syncs the `last_modified_at` property(s) of the dataset. Updates the [`last_modified_at`](#fiftyone.core.dataset.Dataset.last_modified_at) property of the dataset if necessary to incorporate any modification/deletion timestamps to its samples. If `include_frames==True`, the `last_modified_at` property of each video sample is first updated if necessary to incorporate any modification timestamps to its frames. * **Parameters:** **include_frames** (*True*) – whether to update the `last_modified_at` property of video samples. Only applicable to datasets that contain videos #### tag_labels(tags, label_fields=None) Adds the tag(s) to all labels in the specified label field(s) of this collection, if necessary. * **Parameters:** * **tags** – a tag or iterable of tags * **label_fields** (*None*) – an optional name or iterable of names of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields. By default, all label fields are used #### tag_samples(tags) Adds the tag(s) to all samples in this collection, if necessary. * **Parameters:** **tags** – a tag or iterable of tags #### take(size, seed=None) Randomly samples the given number of samples from the collection. Examples: ```default import fiftyone as fo dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", ground_truth=fo.Classification(label="cat"), ), fo.Sample( filepath="/path/to/image2.png", ground_truth=fo.Classification(label="dog"), ), fo.Sample( filepath="/path/to/image3.png", ground_truth=fo.Classification(label="rabbit"), ), fo.Sample( filepath="/path/to/image4.png", ground_truth=None, ), ] ) # # Take two random samples from the dataset # view = dataset.take(2) # # Take two random samples from the dataset with a fixed seed # view = dataset.take(2, seed=51) ``` * **Parameters:** * **size** – the number of samples to return. If a non-positive number is provided, an empty view is returned * **seed** (*None*) – an optional random seed to use when selecting the samples * **Returns:** a [`fiftyone.core.view.DatasetView`](fiftyone.core.view.md#fiftyone.core.view.DatasetView) #### to_clips(field_or_expr, \*\*kwargs) Creates a view that contains one sample per clip defined by the given field or expression in the video collection. The returned view will contain: - A `sample_id` field that records the sample ID from which each clip was taken - A `support` field that records the `[first, last]` frame support of each clip - All frame-level information from the underlying dataset of the input collection Refer to [`fiftyone.core.clips.make_clips_dataset()`](fiftyone.core.clips.md#fiftyone.core.clips.make_clips_dataset) to see the available configuration options for generating clips. #### NOTE The clip generation logic will respect any frame-level modifications defined in the input collection, but the output clips will always contain all frame-level labels. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart-video") # # Create a clips view that contains one clip for each contiguous # segment that contains at least one road sign in every frame # clips = ( dataset .filter_labels("frames.detections", F("label") == "road sign") .to_clips("frames.detections") ) print(clips) # # Create a clips view that contains one clip for each contiguous # segment that contains at least two road signs in every frame # signs = F("detections.detections").filter(F("label") == "road sign") clips = dataset.to_clips(signs.length() >= 2) print(clips) ``` * **Parameters:** * **field_or_expr** – can be any of the following: - a [`fiftyone.core.labels.TemporalDetection`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetection), [`fiftyone.core.labels.TemporalDetections`](fiftyone.core.labels.md#fiftyone.core.labels.TemporalDetections), [`fiftyone.core.fields.FrameSupportField`](fiftyone.core.fields.md#fiftyone.core.fields.FrameSupportField), or list of [`fiftyone.core.fields.FrameSupportField`](fiftyone.core.fields.md#fiftyone.core.fields.FrameSupportField) field - a frame-level label list field of any of the following types: - [`fiftyone.core.labels.Classifications`](fiftyone.core.labels.md#fiftyone.core.labels.Classifications) - [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) - [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) - [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) - a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) that returns a boolean to apply to each frame of the input collection to determine if the frame should be clipped - a list of `[(first1, last1), (first2, last2), ...]` lists defining the frame numbers of the clips to extract from each sample * **other_fields** (*None*) – controls whether sample fields other than the default sample fields are included. Can be any of the following: - a field or list of fields to include - `True` to include all other fields - `None`/`False` to include no other fields * **include_indexes** (*False*) – whether to recreate any custom indexes on `field_or_expr` and `other_fields` on the clips view (True) or a list of specific indexes or index prefixes to recreate. By default, no custom indexes are recreated * **tol** (*0*) – the maximum number of false frames that can be overlooked when generating clips. Only applicable when `field_or_expr` is a frame-level list field or expression * **min_len** (*0*) – the minimum allowable length of a clip, in frames. Only applicable when `field_or_expr` is a frame-level list field or an expression * **trajectories** (*False*) – whether to create clips for each unique object trajectory defined by their `(label, index)`. Only applicable when `field_or_expr` is a frame-level field * **Returns:** a [`fiftyone.core.clips.ClipsView`](fiftyone.core.clips.md#fiftyone.core.clips.ClipsView) #### to_dict(rel_dir=None, include_private=False, include_frames=False, frame_labels_dir=None, pretty_print=False, progress=None) Returns a JSON dictionary representation of the collection. * **Parameters:** * **rel_dir** (*None*) – a relative directory to remove from the `filepath` of each sample, if possible. The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path). The typical use case for this argument is that your source data lives in a single directory and you wish to serialize relative, rather than absolute, paths to the data within that directory * **include_private** (*False*) – whether to include private fields * **include_frames** (*False*) – whether to include the frame labels for video samples * **frame_labels_dir** (*None*) – a directory in which to write per-sample JSON files containing the frame labels for video samples. If omitted, frame labels will be included directly in the returned JSON dict (which can be quite quite large for video datasets containing many frames). Only applicable to datasets that contain videos when `include_frames` is True * **pretty_print** (*False*) – whether to render frame labels JSON in human readable format with newlines and indentations. Only applicable to datasets that contain videos when a `frame_labels_dir` is provided * **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 * **Returns:** a JSON dict #### to_evaluation_patches(eval_key, \*\*kwargs) Creates a view based on the results of the evaluation with the given key that contains one sample for each true positive, false positive, and false negative example in the collection, respectively. True positive examples will result in samples with both their ground truth and predicted fields populated, while false positive/negative examples will only have one of their corresponding predicted/ground truth fields populated, respectively. If multiple predictions are matched to a ground truth object (e.g., if the evaluation protocol includes a crowd attribute), then all matched predictions will be stored in the single sample along with the ground truth object. The returned dataset will also have top-level `type` and `iou` fields populated based on the evaluation results for that example, as well as a `sample_id` field recording the sample ID of the example, and a `crowd` field if the evaluation protocol defines a crowd attribute. #### NOTE The returned view will contain patches for the contents of this collection, which may differ from the view on which the `eval_key` evaluation was performed. This may exclude some labels that were evaluated and/or include labels that were not evaluated. If you would like to see patches for the exact view on which an evaluation was performed, first call [`load_evaluation_view()`](#fiftyone.core.dataset.Dataset.load_evaluation_view) to load the view and then convert to patches. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") dataset.evaluate_detections("predictions", eval_key="eval") session = fo.launch_app(dataset) # # Create a patches view for the evaluation results # view = dataset.to_evaluation_patches("eval") print(view) session.view = view ``` * **Parameters:** * **eval_key** – an evaluation key that corresponds to the evaluation of ground truth/predicted fields that are of type [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines), or [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) * **other_fields** (*None*) – controls whether fields other than the ground truth/predicted fields and the default sample fields are included. Can be any of the following: - a field or list of fields to include - `True` to include all other fields - `None`/`False` to include no other fields * **include_indexes** (*False*) – whether to recreate any custom indexes on the ground truth/predicted fields and `other_fields` on the patches view (True) or a list of specific indexes or index prefixes to recreate. By default, no custom indexes are recreated * **Returns:** a [`fiftyone.core.patches.EvaluationPatchesView`](fiftyone.core.patches.md#fiftyone.core.patches.EvaluationPatchesView) #### to_frames(\*\*kwargs) Creates a view that contains one sample per frame in the video collection. The returned view will contain all frame-level fields and the `tags` of each video as sample-level fields, as well as a `sample_id` field that records the IDs of the parent sample for each frame. By default, `sample_frames` is False and this method assumes that the frames of the input collection have `filepath` fields populated pointing to each frame image. Any frames without a `filepath` populated will be omitted from the returned view. When `sample_frames` is True, this method samples each video in the collection into a directory of per-frame images and stores the filepaths in the `filepath` frame field of the source dataset. By default, each folder of images is written using the same basename as the input video. For example, if `frames_patt = "%%06d.jpg"`, then videos with the following paths: ```default /path/to/video1.mp4 /path/to/video2.mp4 ... ``` would be sampled as follows: ```default /path/to/video1/ 000001.jpg 000002.jpg ... /path/to/video2/ 000001.jpg 000002.jpg ... ``` However, you can use the optional `output_dir` and `rel_dir` parameters to customize the location and shape of the sampled frame folders. For example, if `output_dir = "/tmp"` and `rel_dir = "/path/to"`, then videos with the following paths: ```default /path/to/folderA/video1.mp4 /path/to/folderA/video2.mp4 /path/to/folderB/video3.mp4 ... ``` would be sampled as follows: ```default /tmp/folderA/ video1/ 000001.jpg 000002.jpg ... video2/ 000001.jpg 000002.jpg ... /tmp/folderB/ video3/ 000001.jpg 000002.jpg ... ``` By default, samples will be generated for every video frame at full resolution, but this method provides a variety of parameters that can be used to customize the sampling behavior. #### NOTE If this method is run multiple times with `sample_frames` set to True, existing frames will not be resampled unless you set `force_sample` to True. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart-video") session = fo.launch_app(dataset) # # Create a frames view for an entire video dataset # frames = dataset.to_frames(sample_frames=True) print(frames) session.view = frames # # Create a frames view that only contains frames with at least 10 # objects, sampled at a maximum frame rate of 1fps # num_objects = F("detections.detections").length() view = dataset.match_frames(num_objects > 10) frames = view.to_frames(max_fps=1) print(frames) session.view = frames ``` * **Parameters:** * **sample_frames** (*False*) – whether to assume that the frame images have already been sampled at locations stored in the `filepath` field of each frame (False), or whether to sample the video frames now according to the specified parameters (True) * **fps** (*None*) – an optional frame rate at which to sample each video’s frames * **max_fps** (*None*) – an optional maximum frame rate at which to sample. Videos with frame rate exceeding this value are downsampled * **size** (*None*) – an optional `(width, height)` at which to sample frames. A dimension can be -1, in which case the aspect ratio is preserved. Only applicable when `sample_frames=True` * **min_size** (*None*) – an optional minimum `(width, height)` for each frame. A dimension can be -1 if no constraint should be applied. The frames are resized (aspect-preserving) if necessary to meet this constraint. Only applicable when `sample_frames=True` * **max_size** (*None*) – an optional maximum `(width, height)` for each frame. A dimension can be -1 if no constraint should be applied. The frames are resized (aspect-preserving) if necessary to meet this constraint. Only applicable when `sample_frames=True` * **sparse** (*False*) – whether to only sample frame images for frame numbers for which [`fiftyone.core.frame.Frame`](fiftyone.core.frame.md#fiftyone.core.frame.Frame) instances exist in the input collection. This parameter has no effect when `sample_frames==False` since frames must always exist in order to have `filepath` information use * **output_dir** (*None*) – an optional output directory in which to write the sampled frames. By default, the frames are written in folders with the same basename of each video * **rel_dir** (*None*) – a relative directory to remove from the filepath of each video, if possible. The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path). This argument can be used in conjunction with `output_dir` to cause the sampled frames to be written in a nested directory structure within `output_dir` matching the shape of the input video’s folder structure * **frames_patt** (*None*) – a pattern specifying the filename/format to use to write or check or existing sampled frames, e.g., `"%%06d.jpg"`. The default value is `fiftyone.config.default_sequence_idx + fiftyone.config.default_image_ext` * **force_sample** (*False*) – whether to resample videos whose sampled frames already exist. Only applicable when `sample_frames=True` * **skip_failures** (*True*) – whether to gracefully continue without raising an error if a video cannot be sampled * **verbose** (*False*) – whether to log information about the frames that will be sampled, if any * **include_indexes** (*False*) – whether to recreate any custom frame indexes on the frames view (True) or a list of specific indexes or index prefixes to recreate. By default, no custom indexes are recreated * **Returns:** a [`fiftyone.core.video.FramesView`](fiftyone.core.video.md#fiftyone.core.video.FramesView) #### to_json(rel_dir=None, include_private=False, include_frames=False, frame_labels_dir=None, pretty_print=False) Returns a JSON string representation of the collection. The samples will be written as a list in a top-level `samples` field of the returned dictionary. * **Parameters:** * **rel_dir** (*None*) – a relative directory to remove from the `filepath` of each sample, if possible. The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path). The typical use case for this argument is that your source data lives in a single directory and you wish to serialize relative, rather than absolute, paths to the data within that directory * **include_private** (*False*) – whether to include private fields * **include_frames** (*False*) – whether to include the frame labels for video samples * **frame_labels_dir** (*None*) – a directory in which to write per-sample JSON files containing the frame labels for video samples. If omitted, frame labels will be included directly in the returned JSON dict (which can be quite quite large for video datasets containing many frames). Only applicable to datasets that contain videos when `include_frames` is True * **pretty_print** (*False*) – whether to render the JSON in human readable format with newlines and indentations * **Returns:** a JSON string #### to_patches(field, \*\*kwargs) Creates a view that contains one sample per object patch in the specified field of the collection. Fields other than `field` and the default sample fields will not be included in the returned view. A `sample_id` field will be added that records the sample ID from which each patch was taken. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("quickstart") session = fo.launch_app(dataset) # # Create a view containing the ground truth patches # view = dataset.to_patches("ground_truth") print(view) session.view = view ``` * **Parameters:** * **field** – the patches field, which must be of type [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections), [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines), or [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) * **other_fields** (*None*) – controls whether fields other than `field` and the default sample fields are included. Can be any of the following: - a field or list of fields to include - `True` to include all other fields - `None`/`False` to include no other fields * **keep_label_lists** (*False*) – whether to store the patches in label list fields of the same type as the input collection rather than using their single label variants * **include_indexes** (*False*) – whether to recreate any custom indexes on `field` and `other_fields` on the patches view (True) or a list of specific indexes or index prefixes to recreate. By default, no custom indexes are recreated * **Returns:** a [`fiftyone.core.patches.PatchesView`](fiftyone.core.patches.md#fiftyone.core.patches.PatchesView) #### to_torch(get_item, , index_field='id', vectorize=False, skip_failures=False, local_process_group=None) Constructs a [`torch.utils.data.Dataset`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) that loads data from this collection via the provided [`fiftyone.utils.torch.GetItem`](fiftyone.utils.torch.md#fiftyone.utils.torch.GetItem) instance. * **Parameters:** * **get_item** – a [`fiftyone.utils.torch.GetItem`](fiftyone.utils.torch.md#fiftyone.utils.torch.GetItem) * **index_field** ( *"id"*) – the dotted field path that defines the dataset’s rows. The default `"id"` yields one row per sample. Use a list-valued path such as `"frames.id"` or `"ground_truth.detections.id"` to fan out into one row per frame, detection, etc. * **vectorize** (*False*) – whether to load and cache the required fields from the sample collection upfront (True) or lazily load the values from each sample when items are retrieved (False). Vectorizing gives faster data loading times, but you must have enough memory to store the required field values for the entire sample collection. When `vectorize=True`, all field values must be serializable; ie `pickle.dumps(field_value)` must not raise an error * **skip_failures** (*False*) – whether to skip failures that occur when calling `get_item`. If True, the exception will be returned rather than the intended field values * **local_process_group** (*None*) – the local process group. Only used during distributed training * **Returns:** a [`torch.utils.data.Dataset`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) #### to_trajectories(field, \*\*kwargs) Creates a view that contains one clip for each unique object trajectory defined by their `(label, index)` in a frame-level field of a video collection. The returned view will contain: - A `sample_id` field that records the sample ID from which each clip was taken - A `support` field that records the `[first, last]` frame support of each clip - A sample-level label field that records the `label` and `index` of each trajectory Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = foz.load_zoo_dataset("quickstart-video") # # Create a trajectories view for the vehicles in the dataset # trajectories = ( dataset .filter_labels("frames.detections", F("label") == "vehicle") .to_trajectories("frames.detections") ) print(trajectories) ``` * **Parameters:** * **field** – a frame-level label list field of any of the following types: - [`fiftyone.core.labels.Detections`](fiftyone.core.labels.md#fiftyone.core.labels.Detections) - [`fiftyone.core.labels.Polylines`](fiftyone.core.labels.md#fiftyone.core.labels.Polylines) - [`fiftyone.core.labels.Keypoints`](fiftyone.core.labels.md#fiftyone.core.labels.Keypoints) * **other_fields** (*None*) – controls whether sample fields other than the default sample fields are included. Can be any of the following: - a field or list of fields to include - `True` to include all other fields - `None`/`False` to include no other fields * **include_indexes** (*False*) – whether to recreate any custom indexes on `other_fields` on the clips view (True) or a list of specific indexes or index prefixes to recreate. By default, no custom indexes are recreated * **tol** (*0*) – the maximum number of false frames that can be overlooked when generating clips * **min_len** (*0*) – the minimum allowable length of a clip, in frames * **Returns:** a [`fiftyone.core.clips.TrajectoriesView`](fiftyone.core.clips.md#fiftyone.core.clips.TrajectoriesView) #### untag_labels(tags, label_fields=None) Removes the tag from all labels in the specified label field(s) of this collection, if necessary. * **Parameters:** * **tags** – a tag or iterable of tags * **label_fields** (*None*) – an optional name or iterable of names of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) fields. By default, all label fields are used #### untag_samples(tags) Removes the tag(s) from all samples in this collection, if necessary. * **Parameters:** **tags** – a tag or iterable of tags #### update_run_config(run_key, config) Updates the run config for the run with the given key. * **Parameters:** * **run_key** – a run key * **config** – a [`fiftyone.core.runs.RunConfig`](fiftyone.core.runs.md#fiftyone.core.runs.RunConfig) #### update_samples(update_fcn, skip_failures=True, parallelize_method=None, num_workers=None, batch_method=None, batch_size=None, progress=None) Applies the given function to each sample in the collection and saves the resulting sample edits. By default, a multiprocessing pool is used to parallelize the work, unless this method is called in a daemon process (subprocess), in which case no workers are used. This function effectively performs the following map operation with the outer loop in parallel: ```default for batch_view in fou.iter_slices(sample_collection, batch_size): for sample in batch_view.iter_samples(autosave=True): map_fcn(sample) ``` Example: ```default import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("cifar10", split="train") view = dataset.select_fields("ground_truth") def update_fcn(sample): sample.ground_truth.label = sample.ground_truth.label.upper() view.update_samples(update_fcn) print(dataset.count_values("ground_truth.label")) ``` * **Parameters:** * **update_fcn** – a function to apply to each sample in the collection * **skip_failures** (*True*) – whether to gracefully continue without raising an error if the update function raises an exception for a sample * **parallelize_method** (*None*) – the parallelization method to use. Supported values are `{"process", "thread"}`. The default is `fiftyone.config.default_parallelization_method` * **num_workers** (*None*) – the number of workers to use. When using process parallelism, this defaults to `fiftyone.config.default_process_pool_workers` if the value is set, else [`fiftyone.core.utils.recommend_process_pool_workers()`](fiftyone.core.utils.md#fiftyone.core.utils.recommend_process_pool_workers) workers are used. If this value is <= 1, all work is done in the main process * **batch_method** (*None*) – whether to use IDs (`"id"`) or slices (`"slice"`) to assign samples to workers * **batch_size** (*None*) – an optional number of samples to distribute to each worker at a time. By default, samples are evenly distributed to workers with one batch per worker * **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, or “workers” to render per-worker progress bars #### validate_field_type(path, ftype=None, embedded_doc_type=None, subfield=None) Validates that the collection has a field of the given type. * **Parameters:** * **path** – a field name or `embedded.field.name` * **ftype** (*None*) – an optional field type or iterable of types to enforce. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **embedded_doc_type** (*None*) – an optional embedded document type or iterable of types to enforce. Must be subclass(es) of [`fiftyone.core.odm.BaseEmbeddedDocument`](fiftyone.core.odm.md#fiftyone.core.odm.BaseEmbeddedDocument) * **subfield** (*None*) – an optional subfield type or iterable of subfield types to enforce. Must be subclass(es) of [`fiftyone.core.fields.Field`](fiftyone.core.fields.md#fiftyone.core.fields.Field) * **Raises:** **ValueError** – if the field does not exist or does not have the expected type #### validate_fields_exist(fields, include_private=False) Validates that the collection has field(s) with the given name(s). If embedded field names are provided, only the root field is checked. * **Parameters:** * **fields** – a field name or iterable of field names * **include_private** (*False*) – whether to include private fields when checking for existence * **Raises:** **ValueError** – if one or more of the fields do not exist #### values(field_or_expr, expr=None, missing_value=None, unwind=False, \_allow_missing=False, \_big_result=True, \_raw=False, \_field=None, \_enforce_natural_order=True) Extracts the values of a field from all samples in the collection. Values aggregations are useful for efficiently extracting a slice of field or embedded field values across all samples in a collection. See the examples below for more details. The dual function of [`values()`](#fiftyone.core.dataset.Dataset.values) is [`set_values()`](#fiftyone.core.dataset.Dataset.set_values), which can be used to efficiently set a field or embedded field of all samples in a collection by providing lists of values of same structure returned by this aggregation. #### NOTE Unlike other aggregations, [`values()`](#fiftyone.core.dataset.Dataset.values) does not automatically unwind list fields, which ensures that the returned values match the potentially-nested structure of the documents. You can opt-in to unwinding specific list fields using the `[]` syntax, or you can pass the optional `unwind=True` parameter to unwind all supported list fields. See [Aggregating list fields](../user_guide/using_aggregations.md#aggregations-list-fields) for more information. Examples: ```default import fiftyone as fo import fiftyone.zoo as foz from fiftyone import ViewField as F dataset = fo.Dataset() dataset.add_samples( [ fo.Sample( filepath="/path/to/image1.png", numeric_field=1.0, numeric_list_field=[1, 2, 3], ), fo.Sample( filepath="/path/to/image2.png", numeric_field=4.0, numeric_list_field=[1, 2], ), fo.Sample( filepath="/path/to/image3.png", numeric_field=None, numeric_list_field=None, ), ] ) # # Get all values of a field # values = dataset.values("numeric_field") print(values) # [1.0, 4.0, None] # # Get all values of a list field # values = dataset.values("numeric_list_field") print(values) # [[1, 2, 3], [1, 2], None] # # Get all values of transformed field # values = dataset.values(2 * (F("numeric_field") + 1)) print(values) # [4.0, 10.0, None] # # Get values from a label list field # dataset = foz.load_zoo_dataset("quickstart") # list of `Detections` detections = dataset.values("ground_truth") # list of lists of `Detection` instances detections = dataset.values("ground_truth.detections") # list of lists of detection labels labels = dataset.values("ground_truth.detections.label") ``` * **Parameters:** * **field_or_expr** – a field name, `embedded.field.name`, [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression), or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) defining the field or expression to aggregate. This can also be a list or tuple of such arguments, in which case a tuple of corresponding aggregation results (each receiving the same additional keyword arguments, if any) will be returned * **expr** (*None*) – a [`fiftyone.core.expressions.ViewExpression`](fiftyone.core.expressions.md#fiftyone.core.expressions.ViewExpression) or [MongoDB expression](https://docs.mongodb.com/manual/meta/aggregation-quick-reference/#aggregation-expressions) to apply to `field_or_expr` (which must be a field) before aggregating * **missing_value** (*None*) – a value to insert for missing or `None`-valued fields * **unwind** (*False*) – whether to automatically unwind all recognized list fields (True) or unwind all list fields except the top-level sample field (-1) * **Returns:** the list of values #### write_json(json_path, rel_dir=None, include_private=False, include_frames=False, frame_labels_dir=None, pretty_print=False) Writes the collection to disk in JSON format. * **Parameters:** * **json_path** – the path to write the JSON * **rel_dir** (*None*) – a relative directory to remove from the `filepath` of each sample, if possible. The path is converted to an absolute path (if necessary) via [`fiftyone.core.storage.normalize_path()`](fiftyone.core.storage.md#fiftyone.core.storage.normalize_path). The typical use case for this argument is that your source data lives in a single directory and you wish to serialize relative, rather than absolute, paths to the data within that directory * **include_private** (*False*) – whether to include private fields * **include_frames** (*False*) – whether to include the frame labels for video samples * **frame_labels_dir** (*None*) – a directory in which to write per-sample JSON files containing the frame labels for video samples. If omitted, frame labels will be included directly in the returned JSON dict (which can be quite quite large for video datasets containing many frames). Only applicable to datasets that contain videos when `include_frames` is True * **pretty_print** (*False*) – whether to render the JSON in human readable format with newlines and indentations ### fiftyone.core.dataset.register_extras_cloner(cloner) Registers a callable to be invoked during `_clone_extras()`. Each registered cloner is called as `cloner(src_dataset, dst_dataset, now, id_map)` whenever a full dataset (not a view) is cloned, where `id_map` is a `{str(old_id): str(new_id)}` dict mapping the source dataset doc id and every cloned run doc id to their newly-minted clone counterparts. Failures in a cloner are logged but do not abort the clone. * **Parameters:** **cloner** – a callable with signature `cloner(src_dataset, dst_dataset, now, id_map)`