Function visualize_paf

deeplabcut/core/visualization.py:97–140

Plots the PAF on top of the image. Args: image: Shape (height, width, channels). The image on which the model was run. paf: Shape (height, width, 2 * len(paf_graph)). The PAF output by the model. step: The step with which to plot the scoremaps. colors: The colorm

(
    image: np.ndarray,
    paf: np.ndarray,
    step: int = 5,
    colors: list | None = None,
)

def visualize_paf(
    image: np.ndarray,
    paf: np.ndarray,
    step: int = 5,
    colors: list | None = None,
) -> tuple[plt.Figure, plt.Axes]:
    """Plots the PAF on top of the image.

    Args:
        image: Shape (height, width, channels). The image on which the model was run.
        paf: Shape (height, width, 2 * len(paf_graph)). The PAF output by the model.
        step: The step with which to plot the scoremaps.
        colors: The colormap to use.

    Returns:
        The figure and axis on which the image PAF was plot.
    """
    ny, nx = np.shape(image)[:2]
    fig, ax = form_figure(nx, ny)
    ax.imshow(image)
    n_fields = paf.shape[2]
    if colors is None:
        colors = ["r"] * n_fields
    for n in range(n_fields):
        U = paf[:, :, n, 0]
        V = paf[:, :, n, 1]
        X, Y = np.meshgrid(np.arange(U.shape[1]), np.arange(U.shape[0]))
        M = np.zeros(U.shape, dtype=bool)
        M[U**2 + V**2 < 0.5 * 0.5**2] = True
        U = np.ma.masked_array(U, mask=M)
        V = np.ma.masked_array(V, mask=M)
        ax.quiver(
            X[::step, ::step],
            Y[::step, ::step],
            U[::step, ::step],
            V[::step, ::step],
            scale=50,
            headaxislength=4,
            alpha=1,
            width=0.002,
            color=colors[n],
            angles="xy",
        )
    return fig, ax


def generate_model_output_plots(