Pose Visualize

Auto-generated documentation for musicalgestures._pose_visualize module.

• Mgt-python / Modules / Musicalgestures / Pose Visualize
  • pose_center
  • pose_distance
  • render_average_pose
  • render_pose_center
  • render_pose_distance
  • render_pose_waterfall
  • render_segment_circular
  • render_trajectories

pose_center

[find in source code]

def pose_center(data, names):

Centre pose data on its global centroid (a 2D port of the MoCap Toolbox mccenter).

Computes a single offset per coordinate dimension — the mean of the per-marker temporal means (missing detections ignored) — and subtracts it from every marker so the overall spatiotemporal centroid sits at the origin (0, 0).

Arguments

• data list - Collected pose rows [time_ms, x0, y0, ...] (normalised coords).
• names list - Marker names (length n_points).

Returns

• tuple - (centered, offset, times) where centered is a (T, n, 2) array of centred normalised coordinates (NaN for missing), offset is the (x, y) centroid that was removed, and times is the per-frame time in seconds.

pose_distance

[find in source code]

def pose_distance(data, names, width, height):

Per-marker cumulative distance travelled (a 2D port of the MoCap Toolbox mccumdist).

Sums the frame-to-frame Euclidean displacement of each marker (in pixels) and accumulates it over time. Gaps from missing detections contribute no distance.

Arguments

• data list - Collected pose rows [time_ms, x0, y0, ...] (normalised coords).
• names list - Marker names (length n_points). width, height (int): Frame size in pixels (to scale the normalised coordinates).

Returns

• tuple - (cumulative, total, average, times) where cumulative is a (T-1, n) array of cumulative distance per marker (pixels), total is the per-marker total (n,), average is the mean total across markers, and times are the per-frame times (s).

render_average_pose

[find in source code]

def render_average_pose(
    data,
    names,
    connections,
    width,
    height,
    fps,
    avg_frame,
    target_name,
    overwrite=True,
    fmin=0.2,
    fmax=8.0,
    style='both',
):

Render the average pose of the whole video, with per-marker quantity of motion (colour + label) and dominant frequency (label) annotated.

style matches the video: 'both' draws markers + skeleton lines, 'markers' draws only the markers, 'skeleton' draws only the connecting joint lines. Per-marker labels are shown in all cases.

Returns an MgImage, or None if there are too few frames.

render_pose_center

[find in source code]

def render_pose_center(
    data,
    names,
    width,
    height,
    target_name,
    overwrite=True,
    cmap='hsv',
    dpi=200,
):

Centre the pose data (see :func:pose_center) and plot the centred marker trajectories.

Returns an MgFigure whose .data holds the centred coordinates and the removed offset, or None if there are too few frames.

render_pose_distance

[find in source code]

def render_pose_distance(
    data,
    names,
    width,
    height,
    fps,
    target_name,
    overwrite=True,
    cmap='hsv',
    dpi=200,
):

Plot per-marker cumulative distance travelled over time plus a ranked total per marker.

Returns an MgFigure (.data holds the totals, average, and cumulative curves) and saves a CSV of the per-marker totals; None if there are too few frames.

render_pose_waterfall

[find in source code]

def render_pose_waterfall(
    data,
    names,
    width,
    height,
    fps,
    target_name,
    overwrite=True,
    style='trajectories',
    connections=None,
    n_samples=40,
    markers=None,
    color_by=None,
    cmap='hsv',
    dpi=200,
    elev=20,
    azim=-60,
    lw=1.0,
    axes=True,
    crop=False,
):

Render a 3D spatio-temporal waterfall of the pose, cascading along the time (depth) axis — a pose-based counterpart to silhouette_waterfall().

style selects what is drawn:

• 'trajectories' (default): each marker's path is a continuous line through (x, time, y).
• 'markers': the markers themselves are scattered at n_samples time slices.
• 'skeleton': the skeleton joint lines are drawn at n_samples time slices.
• 'both': markers + skeleton at each time slice.

Arguments

• data list - Collected pose rows [time_ms, x0, y0, x1, y1, ...] (normalised coords).
• names list - Marker names (length n_points). width, height (int): Frame size in pixels (used to scale normalised coords).
• fps float - Frames per second (for the time axis).
• target_name str - Output PNG path.
• overwrite bool, optional - Overwrite or auto-increment the filename. Defaults to True.
• style str, optional - 'trajectories' (default), 'markers', 'skeleton', or 'both'.
• connections list, optional - Skeleton connection pairs (indices); required for the 'skeleton'/'both' styles.
• n_samples int, optional - Number of time slices for the marker/skeleton styles. Defaults to 40.
• markers list, optional - Subset of marker names or indices to draw. Defaults to all.
• color_by str, optional - 'marker' (one colour per marker) or 'time' (colour by time). Defaults to None ("auto"): 'marker' for trajectories, 'time' for the slice styles.
• cmap str, optional - Matplotlib colormap. Defaults to 'hsv'.
• dpi int, optional - Output DPI. Defaults to 200.
• elev float, optional - 3D elevation angle. Defaults to 20.
• azim float, optional - 3D azimuth angle. Defaults to -60.
• lw float, optional - Line width. Defaults to 1.0.
• axes bool, optional - Draw the axes and tick labels. Set to False for a clean render with all axes and text removed. Defaults to True.
• crop bool, optional - Tighten the spatial axis limits to the actual marker extent and trim the surrounding whitespace, so the figure shows mostly the data. Defaults to False.

Returns

• MgFigure - the 3D waterfall figure, or None if there are too few frames.

render_segment_circular

[find in source code]

def render_segment_circular(
    data,
    names,
    connections,
    width,
    height,
    fps,
    target_name,
    overwrite=True,
    segments=None,
    n_bins=36,
    cmap='viridis',
    dpi=200,
    ncols=6,
):

Circular (polar) motion plots and statistics for every body segment.

A segment is the bone between two connected joints. For each segment this computes the per-frame orientation angle and draws a polar rose histogram of the angle distribution with the mean-direction resultant vector overlaid, annotated with the segment's circular statistics. A CSV of the per-segment statistics is saved alongside the image.

Arguments

• data list - Collected pose rows [time_ms, x0, y0, ...] (normalised coords).
• names list - Marker names (length n_points).
• connections list - Segment connection pairs (joint-index tuples). width, height (int): Frame size in pixels (to scale coordinates).
• fps float - Frames per second (for angular speed).
• target_name str - Output PNG path.
• overwrite bool, optional - Overwrite or auto-increment the filename. Defaults to True.
• segments list, optional - Subset of connections (as (a, b) index tuples) to plot. Defaults to all connections.
• n_bins int, optional - Number of angular bins in each rose. Defaults to 36 (10° bins).
• cmap str, optional - Matplotlib colormap for the bars (by bin count). Defaults to 'viridis'.
• dpi int, optional - Output DPI. Defaults to 200.
• ncols int, optional - Number of columns in the subplot grid. Defaults to 6.

Returns

• MgFigure - the grid of circular plots (per-segment stats in .data), or None if there are too few frames.

render_trajectories

[find in source code]

def render_trajectories(
    data,
    names,
    width,
    height,
    fps,
    target_name,
    overwrite=True,
    background='black',
    labels=False,
):

Render every marker's spatial trajectory across the whole video.

background chooses the PNG background: 'black' (default), 'white', or 'transparent' (so the PNG can be overlaid on the original video later). Set labels=True to annotate each trajectory with its marker name (off by default). Returns an MgImage, or None if there are too few frames.