Reading Meshes, Lines, and Points

This example demonstrates how to fetch 3D data assets from the polyxios-data repository, read them into NumPy arrays using FURY’s dedicated I/O methods, and render them as distinct colored actors side by side: a surface mesh, a collection of lines, and a point cloud.

Polyxios is the lightweight, dependency-free I/O backend used by FURY. It is designed to auto-detect file formats by extension and map complex scientific surface structures (.vtk, .vtp, .ply, .obj, .mesh) seamlessly into memory-contiguous NumPy arrays, and its fetch helper downloads and caches sample assets so tutorials and tests can run without bundling large files.

This example highlights three fundamental data archetypes:

• Surface Meshes (via ``read_mesh``): Returns a triple of (vertices, faces, colors) where vertices is an (N, 3) float32 array, faces is an (M, 3) int32 array of triangulated indices, and colors is an optional (N, 3) float32 color map.

• Line Streams (via ``read_lines``): Returns a tuple of (lines, colors). Here, lines is a list of independent (P, 3) float32 arrays (where each array represents an individual continuous stroke or fiber pathway), and colors contains a corresponding list of per-vertex color maps.

• Point Clouds (via ``read_points``): Returns a tuple of (points, colors) where points is an (N, 3) float32 coordinate block representing un-connected point data, and colors is an optional per-point attribute array.

Import the required libraries.

import numpy as np
import polyxios as px

from fury import actor, ui, window
from fury.io import read_lines, read_mesh, read_points

Define helper to process geometry, normalize space, and apply fallback gradients.

def points_to_actor(file_path):
    """Read a point cloud file and build a colored point actor."""
    points, colors = read_points(file_path)

    if points.size == 0:
        raise ValueError(f"No points found in file: {file_path}")

    # Center on the origin and normalize the scale to roughly unit size
    points = points - points.mean(axis=0)
    points = (points / np.max(np.abs(points))).astype(np.float32)

    # Use the file's colors when present, otherwise apply a height-based gradient
    if colors is None:
        height = points[:, 1]
        height_range = height.max() - height.min()
        y_range = height_range if height_range > 0 else 1.0

        t = ((height - height.min()) / y_range)[:, None]
        low_color = np.array([0.10, 0.20, 0.70], dtype=np.float32)
        high_color = np.array([0.95, 0.75, 0.20], dtype=np.float32)
        colors = (low_color * (1.0 - t) + high_color * t).astype(np.float32)

    # ``actor.point`` turns an (N, 3) array of point positions and their
    # matching per-point colors into an optimized graphic object.
    point_actor = actor.point(points, colors=colors)
    return point_actor, len(points)


def lines_to_actor(file_path):
    """Read a lines file and build a colored stream/line actor."""
    lines, colors = read_lines(file_path)

    if not lines:
        raise ValueError(f"No line elements found in file: {file_path}")

    # Stack all points temporarily to calculate overall centering and scaling metrics
    all_points = np.vstack(lines)
    center = all_points.mean(axis=0)
    max_scale = np.max(np.abs(all_points - center))

    # Center on the origin and normalize the scale to roughly unit size
    normalized_lines = [
        ((line - center) / max_scale).astype(np.float32) for line in lines
    ]

    # Use the file's colors when present, otherwise apply a height-based gradient
    if colors is None:
        # Re-stack lines to compute a global bounding box for the height gradient
        all_norm_points = np.vstack(normalized_lines)
        y_min = all_norm_points[:, 1].min()
        y_max = all_norm_points[:, 1].max()
        y_range = y_max - y_min if (y_max - y_min) > 0 else 1.0

        low_color = np.array([0.10, 0.20, 0.70], dtype=np.float32)
        high_color = np.array([0.95, 0.75, 0.20], dtype=np.float32)

        colors = []
        for line in normalized_lines:
            heights = line[:, 1]
            t = ((heights - y_min) / y_range)[:, None]
            line_colors = (low_color * (1.0 - t) + high_color * t).astype(np.float32)
            colors.append(line_colors)

    # ``actor.streamlines`` accepts a list of coordinate arrays and color arrays
    line_actor = actor.streamlines(normalized_lines, colors=colors)

    # Calculate totals for reporting
    total_vertices = sum(len(line) for line in lines)
    total_lines = len(lines)

    return line_actor, total_vertices, total_lines


def mesh_to_actor(file_path):
    """Read a mesh file and build a colored surface actor."""
    vertices, faces, colors = read_mesh(file_path)

    # Center on the origin and normalize the scale to roughly unit size.
    vertices = vertices - vertices.mean(axis=0)
    vertices = (vertices / np.max(np.abs(vertices))).astype(np.float32)

    # Use the file's colors when present, otherwise a height-based gradient.
    if colors is None:
        height = vertices[:, 1]
        t = ((height - height.min()) / (height.max() - height.min()))[:, None]
        low_color = np.array([0.10, 0.20, 0.70], dtype=np.float32)
        high_color = np.array([0.95, 0.75, 0.20], dtype=np.float32)
        colors = (low_color * (1.0 - t) + high_color * t).astype(np.float32)

    # ``actor.surface`` wraps the geometry, material and mesh creation for us,
    # turning the vertices, faces and per-vertex colors into a ready actor.
    surf = actor.surface(vertices, faces, colors=colors)
    return surf, len(vertices), len(faces)

Fetch the sample data assets via polyxios.

• Human.vtp a surface mesh possessing native topological faces and color tracks.

• hello.vtk contains line elements forming spatial lettering out of poly-line.

• star.mesh handles plain point coordinates lacking explicitly structured links.

human_path = px.fetch("Human.vtp")
line_path = px.fetch("hello.vtk")
ball_path = px.fetch("star.mesh")

# Generate the actors and fetch metadata counts
mesh_actor, mesh_nv, mesh_nf = mesh_to_actor(human_path)
line_actor, line_nv, line_nl = lines_to_actor(line_path)
point_actor, point_nv = points_to_actor(ball_path)

print(f"Human.vtp (Mesh): {mesh_nv} vertices, {mesh_nf} faces")
print(f"hello.vtk (Lines): {line_nv} vertices across {line_nl} lines")
print(f"star.mesh (Points): {point_nv} points")

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Human.vtp (Mesh): 17665 vertices, 34660 faces
hello.vtk (Lines): 30 vertices across 15 lines
star.mesh (Points): 31 points

Coordinate alignment transforms. Human.vtp is modeled lying along its Z axis, so by default it faces the camera end-on. Every FURY actor exposes transform helpers (rotate, translate, scale), so we stand it upright with a -90 degrees rotation about the X axis, mapping its head-to-toe axis to the vertical and apply 180 degrees so it faces the camera.

mesh_actor.rotate((-90, 180, 0))

Place the three actors side by side (Left, Center, Right) to avoid overlapping.

mesh_actor.local.position = (-3.0, 0.0, 0.0)
line_actor.local.position = (0.0, 0.0, 0.0)
point_actor.local.position = (3.0, 0.0, 0.0)

Add matching 3D text labels directly beneath each of the three objects.

labels_actor = actor.text(
    ["Mesh (Human.vtp)", "Lines (hello.vtk)", "Points (star.mesh)"],
    position=[(-3.0, -1.25, 0.0), (0.0, -1.25, 0.0), (3.0, -1.25, 0.0)],
    colors=(0.9, 0.9, 0.95),
    font_size=0.16,
    anchor="top-center",
)

Set up the 3D scene and register all visual elements.

scene = window.Scene(background=(0.05, 0.05, 0.08))
scene.add(mesh_actor)
scene.add(line_actor)
scene.add(point_actor)
scene.add(labels_actor)

Add a 2D text HUD overlay detailing what each object represents.

info_text = (
    f"FURY x Polyxios Geometry Reader\n"
    f"Left:   Human.vtp (Mesh: {mesh_nv} verts, {mesh_nf} faces)\n"
    f"Center: hello.vtk (Lines: {line_nv} total vertices, {line_nl} lines)\n"
    f"Right:  star.mesh (Points: {point_nv} coordinates)\n"
    f"Decoupled data paths processed into native NumPy arrays."
)

hud_label = ui.TextBlock2D(
    text=info_text,
    position=(20, 20),
    font_size=16,
    color=(0.9, 0.9, 0.95),
    bold=False,
    dynamic_bbox=True,
)
scene.add(hud_label)

Initialize the ShowManager, frame the camera to fit the wider 3-element layout, and initialize the rendering window loop.

show_m = window.ShowManager(
    scene=scene, size=(1280, 728), title="FURY Multimodal Reader"
)

camera = show_m.screens[0].camera
camera.local.position = (0.0, -0.15, 6.0)
camera.look_at((0.0, -0.15, 0.0))

show_m.start()