deeplabcut.pose_estimation_pytorch.modelzoo.fmpose_3d.fmpose3d

DeepLabCut2.0-3.0 Toolbox (deeplabcut.org) © A. & M. Mathis Labs https://github.com/DeepLabCut/DeepLabCut Please see AUTHORS for contributors. https://github.com/DeepLabCut/DeepLabCut/blob/main/AUTHORS Licensed under GNU Lesser General Public License v3.0

Classes:

Name	Description
FMPose3DModelMetadata	Metadata for an FMPose3D model variant.

Functions:

Name	Description
get_fmpose3d_inference_api	Get a FMPose3DInference API for a given model type and snapshot path.

FMPose3DModelMetadata dataclass

Metadata for an FMPose3D model variant.

Methods:

Name	Description
build_model_cfg	Build a DLC-compatible model_cfg dict for create_df_from_prediction.

Source code in deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/fmpose3d.py

@dataclass(frozen=True)
class FMPose3DModelMetadata:
    """Metadata for an FMPose3D model variant."""

    superanimal_name: str
    bodyparts: tuple[str, ...]

    @property
    def num_bodyparts(self) -> int:
        return len(self.bodyparts)

    def build_model_cfg(self, max_individuals: int) -> dict:
        """Build a DLC-compatible model_cfg dict for create_df_from_prediction."""
        return {
            "metadata": {
                "bodyparts": list(self.bodyparts),
                "unique_bodyparts": [],
                "individuals": [f"individual{i + 1}" for i in range(max_individuals)],
            },
        }

build_model_cfg

build_model_cfg(max_individuals: int) -> dict

Build a DLC-compatible model_cfg dict for create_df_from_prediction.

Source code in deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/fmpose3d.py

def build_model_cfg(self, max_individuals: int) -> dict:
    """Build a DLC-compatible model_cfg dict for create_df_from_prediction."""
    return {
        "metadata": {
            "bodyparts": list(self.bodyparts),
            "unique_bodyparts": [],
            "individuals": [f"individual{i + 1}" for i in range(max_individuals)],
        },
    }

get_fmpose3d_inference_api

get_fmpose3d_inference_api(
    model_type: SupportedModel = "fmpose3d_humans",
    snapshot_path: str | None = None,
    device: str | None = None,
    config_kwargs: dict = None,
) -> FMPose3DInference

Get a FMPose3DInference API for a given model type and snapshot path.

Parameters:

Name	Type	Description	Default
model_type	SupportedModel	one of the supported model types: "fmpose3d_humans", "fmpose3d_animals",	'fmpose3d_humans'
snapshot_path	str | None	The path to the snapshot file. If None, FMPose3D will download the default snapshot.	None
device	str | None	The device to use. If None, the device will be inferred from the environment.	None
config_kwargs	dict	Additional keyword arguments to pass to the FMPose3DConfig.	None

Returns: FMPose3DInference: An FMPose3DInference API runner.

Example Usages

# Initialize the API (downloads the default weights automatically from huggingface)
fmpose = get_fmpose3d_inference_api(
    model_type="fmpose3d_animals",
    device="cuda:0",
)

# Run inference on an image
predictions_3d = fmpose.predict(source="path/to/image.jpg") # or (H, W, 3) numpy array

# Lift 2d predictions to 3d
keypoints_2d = np.random.rand(num_frames, num_joints, 2)
predictions_3d = fmpose.pose_3d(keypoints_2d=keypoints_2d)

Source code in deeplabcut/pose_estimation_pytorch/modelzoo/fmpose_3d/fmpose3d.py

def get_fmpose3d_inference_api(
    model_type: SupportedModel = "fmpose3d_humans",
    snapshot_path: str | None = None,
    device: str | None = None,
    config_kwargs: dict = None,
) -> FMPose3DInference:
    """
    Get a FMPose3DInference API for a given model type and snapshot path.

    Args:
        model_type: one of the supported model types: "fmpose3d_humans", "fmpose3d_animals",
        snapshot_path: The path to the snapshot file. If None, FMPose3D will download the default snapshot.
        device: The device to use. If None, the device will be inferred from the environment.
        config_kwargs: Additional keyword arguments to pass to the FMPose3DConfig.
    Returns:
        FMPose3DInference: An FMPose3DInference API runner.

    Example Usages
    ```python
    # Initialize the API (downloads the default weights automatically from huggingface)
    fmpose = get_fmpose3d_inference_api(
        model_type="fmpose3d_animals",
        device="cuda:0",
    )

    # Run inference on an image
    predictions_3d = fmpose.predict(source="path/to/image.jpg") # or (H, W, 3) numpy array

    # Lift 2d predictions to 3d
    keypoints_2d = np.random.rand(num_frames, num_joints, 2)
    predictions_3d = fmpose.pose_3d(keypoints_2d=keypoints_2d)
    ```
    """
    if config_kwargs is None:
        config_kwargs = {}
    model_config = FMPose3DConfig(model_type=model_type, **config_kwargs)
    fmpose3d_api = FMPose3DInference(
        model_config,
        model_weights_path=snapshot_path,
        device=device,
    )
    return fmpose3d_api