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ndx-pose Extension for NWB

PyPI version

ndx-pose is a standardized format for storing pose estimation data in NWB, such as from DeepLabCut and SLEAP. Please post an issue or PR to suggest or add support for another pose estimation tool.

This extension consists of several new neurodata types:

  • Skeleton which stores the relationship between the body parts (nodes and edges).
  • Skeletons which is a container that stores multiple Skeleton objects.
  • PoseEstimationSeries which stores the estimated positions (x, y) or (x, y, z) of a body part over time as well as the confidence/likelihood of the estimated positions.
  • PoseEstimation which stores the estimated position data (PoseEstimationSeries) for multiple body parts, computed from the same video(s) with the same tool/algorithm.
  • SkeletonInstance which stores the estimated positions and visibility of the body parts for a single frame.
  • TrainingFrame which stores the ground truth data for a single frame. It contains SkeletonInstance objects and references a frame of a source video (ImageSeries). The source videos can be stored internally as data arrays or externally as files referenced by relative file path.
  • TrainingFrames which is a container that stores multiple TrainingFrame objects.
  • SourceVideos which is a container that stores multiple ImageSeries objects representing source videos used in training.
  • PoseTraining which is a container thatstores the ground truth data (TrainingFrames) and source videos (SourceVideos) used to train the pose estimation model.

It is recommended to place the Skeletons, PoseEstimation, and PoseTraining objects in an NWB processing module named "behavior", as shown below.

Installation

pip install ndx-pose

Usage examples

  1. Example writing pose estimates (keypoints) to an NWB file.

  2. Example writing training data to an NWB file.

Handling pose estimates for multiple subjects

NWB files are designed to store data from a single subject and have only one root-level Subject object. As a result, ndx-pose was designed to store pose estimates from a single subject. Pose estimates data from different subjects should be stored in separate NWB files.

Training images can involve multiple skeletons, however. These training images may be the same across subjects, and therefore the same across NWB files. These training images should be duplicated between files, until multi-subject support is added to NWB and ndx-pose. See #3

Resources

Utilities to convert DLC output to/from NWB: https://github.com/DeepLabCut/DLC2NWB

  • For multi-animal projects, one NWB file is created per animal. The NWB file contains only a PoseEstimation object under /processing/behavior. That PoseEstimation object contains PoseEstimationSeries objects, one for each body part, and general metadata about the pose estimation process, skeleton, and videos. The PoseEstimationSeries objects contain the estimated positions for that body part for a particular animal.

Utilities to convert SLEAP pose tracking data to/from NWB: https://github.com/talmolab/sleap-io

Keypoint MoSeq: https://github.com/dattalab/keypoint-moseq

  • Supports read of PoseEstimation objects from NWB files.

NeuroConv: https://neuroconv.readthedocs.io/en/main/conversion_examples_gallery/conversion_example_gallery.html#behavior

  • NeuroConv supports converting data from DeepLabCut (using dlc2nwb described above), SLEAP (using sleap_io described above), FicTrac, and LightningPose to NWB. It supports appending pose estimation data to an existing NWB file.

Ethome: Tools for machine learning of animal behavior: https://github.com/benlansdell/ethome

  • Supports read of PoseEstimation objects from NWB files.

Related work:

Several NWB datasets use ndx-pose 0.1.1:

Several open-source conversion scripts on GitHub also use ndx-pose.

Diagram of non-training-related types

%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#ffffff', "primaryBorderColor': '#144E73', 'lineColor': '#D96F32'}}}%%

classDiagram
    direction LR
    namespace ndx-pose {
        class PoseEstimationSeries{
            <<SpatialSeries>>
            name : str
            description : str
            timestamps : array[float; dims [frame]]
            data : array[float; dims [frame, [x, y]] or [frame, [x, y, z]]]
            confidence : array[float; dims [frame]]
            reference_frame: str
        }

        class PoseEstimation {
            <<NWBDataInterface>>
            name : str
            description : str, optional
            original_videos : array[str; dims [file]], optional
            labeled_videos : array[str; dims [file]], optional
            dimensions : array[uint, dims [file, [width, height]]], optional
            scorer : str, optional
            scorer_software : str, optional
            scorer_software__version : str, optional
            PoseEstimationSeries
            Skeleton, link
            Device, link
        }

        class Skeleton {
            <<NWBDataInterface>>
            name : str
            nodes : array[str; dims [body part]]
            edges : array[uint; dims [edge, [node, node]]]
        }

    }

    class Device

    PoseEstimation --o PoseEstimationSeries : contains 0 or more
    PoseEstimation --> Skeleton : links to
    PoseEstimation --> Device : links to
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Diagram of all types

%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#ffffff', "primaryBorderColor': '#144E73', 'lineColor': '#D96F32'}}}%%

classDiagram
    direction LR
    namespace ndx-pose {
        class PoseEstimationSeries{
            <<SpatialSeries>>
            name : str
            description : str
            timestamps : array[float; dims [frame]]
            data : array[float; dims [frame, [x, y]] or [frame, [x, y, z]]]
            confidence : array[float; dims [frame]]
            reference_frame: str
        }

        class PoseEstimation {
            <<NWBDataInterface>>
            name : str
            description : str, optional
            original_videos : array[str; dims [file]], optional
            labeled_videos : array[str; dims [file]], optional
            dimensions : array[uint, dims [file, [width, height]]], optional
            scorer : str, optional
            scorer_software : str, optional
            scorer_software__version : str, optional
            PoseEstimationSeries
            Skeleton, link
            Device, link
        }

        class Skeleton {
            <<NWBDataInterface>>
            name : str
            nodes : array[str; dims [body part]]
            edges : array[uint; dims [edge, [node, node]]]
        }

        class TrainingFrame {
            <<NWBDataInterface>>
            name : str
            annotator : str, optional
            source_video_frame_index : uint, optional
            skeleton_instances : SkeletonInstances
            source_video : ImageSeries, link, optional
            source_frame : Image, link, optional
        }

        class SkeletonInstance {
            <<NWBDataInterface>>
            id: uint, optional
            node_locations : array[float; dims [body part, [x, y]] or [body part, [x, y, z]]]
            node_visibility : array[bool; dims [body part]], optional
            Skeleton, link
        }

        class TrainingFrames {
            <<NWBDataInterface>>
            TrainingFrame
        }

        class SkeletonInstances {
            <<NWBDataInterface>>
            SkeletonInstance
        }

        class SourceVideos {
            <<NWBDataInterface>>
            ImageSeries
        }

        class Skeletons {
            <<NWBDataInterface>>
            Skeleton
        }

        class PoseTraining {
            <<NWBDataInterface>>>
            training_frames : TrainingFrames, optional
            source_videos : SourceVideos, optional
        }

    }

    class Device
    class ImageSeries
    class Image

    PoseEstimation --o PoseEstimationSeries : contains 0 or more
    PoseEstimation --> Skeleton : links to
    PoseEstimation --> Device : links to

    PoseTraining --o TrainingFrames : contains
    PoseTraining --o SourceVideos : contains

    TrainingFrames --o TrainingFrame : contains 0 or more

    TrainingFrame --o SkeletonInstances : contains
    TrainingFrame --> ImageSeries : links to
    TrainingFrame --> Image : links to

    SkeletonInstances --o SkeletonInstance : contains 0 or more
    SkeletonInstance --o Skeleton : links to

    SourceVideos --o ImageSeries : contains 0 or more

    Skeletons --o Skeleton : contains 0 or more
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Contributors

  • @rly
  • @bendichter
  • @AlexEMG
  • @roomrys
  • @CBroz1
  • @h-mayorquin
  • @talmo
  • @eberrigan

This extension was created using ndx-template.