Skip to main content

analysis.pose_segmentation

logger_config​

logger​

embedd_latent_vectors​

def embedd_latent_vectors(
        cfg: dict,
        sessions: List[str],
        model: RNN_VAE,
        fixed: bool,
        read_from_variable: str = "position_processed",
        tqdm_stream: Union[TqdmToLogger, None] = None) -> List[np.ndarray]

Embed latent vectors for the given files using the VAME model.

Parameters

  • cfg (dict): Configuration dictionary.
  • sessions (List[str]): List of session names.
  • model (RNN_VAE): VAME model.
  • fixed (bool): Whether the model is fixed.
  • tqdm_stream (TqdmToLogger, optional): TQDM Stream to redirect the tqdm output to logger.

Returns

  • List[np.ndarray]: List of latent vectors for each file.

get_latent_vectors​

def get_latent_vectors(project_path: str, sessions: list, model_name: str, seg,
                       n_clusters: int) -> List

Gets all the latent vectors from each session into one list

Parameters

  • project_path: str: Path to vame project folder
  • session: list: List of sessions
  • model_name: str: Name of model
  • seg: str: Type of segmentation algorithm
  • n_clusters (int): Number of clusters.

Returns

  • List: List of session latent vectors

get_motif_usage​

def get_motif_usage(session_labels: np.ndarray, n_clusters: int) -> np.ndarray

Count motif usage from session label array.

Parameters

  • session_labels (np.ndarray): Array of session labels.
  • n_clusters (int): Number of clusters.

Returns

  • np.ndarray: Array of motif usage counts.

save_session_data​

def save_session_data(project_path: str, session: int, model_name: str,
                      label: np.ndarray, cluster_center: np.ndarray,
                      latent_vector: np.ndarray, motif_usage: np.ndarray,
                      n_clusters: int, segmentation_algorithm: str)

Saves pose segmentation data for given session.

Parameters

  • project_path: str: Path to the vame project folder.
  • session: int: Session of interest to segment.
  • model_name: str: Name of model
  • label: np.ndarray: Array of the session's motif labels.
  • cluster_center: np.ndarray: Array of the session's kmeans cluster centers location in the latent space.
  • latent_vector: np.ndarray,: Array of the session's latent vectors.
  • motif_usage: np.ndarray: Array of the session's motif usage counts.
  • n_clusters (int): Number of clusters.
  • segmentation_algorithm: str: Type of segmentation method, either 'kmeans or 'hmm'.

Returns

  • None

same_segmentation​

def same_segmentation(
    cfg: dict, sessions: List[str], latent_vectors: List[np.ndarray],
    n_clusters: int, segmentation_algorithm: str
) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]

Apply the same segmentation to all animals.

Parameters

  • cfg (dict): Configuration dictionary.
  • sessions (List[str]): List of session names.
  • latent_vectors (List[np.ndarray]): List of latent vector arrays.
  • n_clusters (int): Number of clusters.
  • segmentation_algorithm (str): Segmentation algorithm.

Returns

  • Tuple: Tuple of labels, cluster centers, and motif usages.

individual_segmentation​

def individual_segmentation(cfg: dict, sessions: List[str],
                            latent_vectors: List[np.ndarray],
                            n_clusters: int) -> Tuple

Apply individual segmentation to each session.

Parameters

  • cfg (dict): Configuration dictionary.
  • sessions (List[str]): List of session names.
  • latent_vectors (List[np.ndarray]): List of latent vector arrays.
  • n_clusters (int): Number of clusters.

Returns

  • Tuple: Tuple of labels, cluster centers, and motif usages.

segment_session​

@save_state(model=SegmentSessionFunctionSchema)
def segment_session(config: dict, save_logs: bool = False) -> None

Perform pose segmentation using the VAME model. Fills in the values in the "segment_session" key of the states.json file. Creates files at:

  • project_name/
    • results/
      • hmm_trained.pkl
      • session/
        • model_name/
          • hmm-n_clusters/
            • latent_vector_session.npy
            • motif_usage_session.npy
            • n_cluster_label_session.npy
          • kmeans-n_clusters/
            • latent_vector_session.npy
            • motif_usage_session.npy
            • n_cluster_label_session.npy
            • cluster_center_session.npy

latent_vector_session.npy contains the projection of the data into the latent space, for each frame of the video. Dimmentions: (n_frames, n_latent_features)

motif_usage_session.npy contains the number of times each motif was used in the video. Dimmentions: (n_motifs,)

n_cluster_label_session.npy contains the label of the cluster assigned to each frame. Dimmentions: (n_frames,)

Parameters

  • config (dict): Configuration dictionary.
  • save_logs (bool, optional): Whether to save logs, by default False.

Returns

  • None