open3d.ml.torch.vis.Visualizer#

class open3d.ml.torch.vis.Visualizer#

The visualizer class for dataset objects and custom point clouds.

class ColormapEdit(window, em)#

This class is used to create a color map for visualization of points.

__init__(window, em)#

set_on_changed(callback)#

update(colormap, min_val, max_val)#: Updates the colormap based on the minimum and maximum values passed.

class LabelLUTEdit#

This class includes functionality for managing a labellut (label look-up-table).

__init__()#

clear()#: Clears the look-up table.

get_colors()#: Returns a list of label keys.

is_empty()#: Checks if the look-up table is empty.

set_labels(labellut)#: Updates the labels based on look-up table passsed.

set_on_changed(callback)#

class ProgressDialog(title, window, n_items)#

This class is used to manage the progress dialog displayed during visualization.

Parameters:

title – The title of the dialog box.
window – The window where the progress dialog box should be displayed.
n_items – The maximum number of items.

__init__(title, window, n_items)#

post_update(text=None)#: Post updates to the main thread.

set_text(text)#: Set the label text on the dialog box.

update()#: Enumerate the progress in the dialog box.

__init__()#

set_lut(attr_name, lut)#

Set the LUT for a specific attribute.

Args: attr_name: The attribute name as string. lut: The LabelLUT object that should be updated.

setup_camera()#: Set up camera for visualization.

show_geometries_under(name, show)#: Show geometry for a given node.

visualize(data, lut=None, bounding_boxes=None, width=1280, height=768)#

Visualize a custom point cloud data.

Example

Minimal example for visualizing a single point cloud with an attribute:

import numpy as np
import open3d.ml.torch as ml3d
# or import open3d.ml.tf as ml3d

data = [ {
    'name': 'my_point_cloud',
    'points': np.random.rand(100,3).astype(np.float32),
    'point_attr1': np.random.rand(100).astype(np.float32),
    } ]

vis = ml3d.vis.Visualizer()
vis.visualize(data)

Parameters:

data – A list of dictionaries. Each dictionary is a point cloud with attributes. Each dictionary must have the entries ‘name’ and ‘points’. Points and point attributes can be passed as numpy arrays, PyTorch tensors or TensorFlow tensors.
lut – Optional lookup table for colors.
bounding_boxes – Optional bounding boxes.
width – window width.
height – window height.

visualize_dataset(dataset, split, indices=None, width=1280, height=768)#

Visualize a dataset.

Example

Minimal example for visualizing a dataset::

import open3d.ml.torch as ml3d # or open3d.ml.tf as ml3d

dataset = ml3d.datasets.SemanticKITTI(dataset_path=’/path/to/SemanticKITTI/’) vis = ml3d.vis.Visualizer() vis.visualize_dataset(dataset, ‘all’, indices=range(100))

Parameters:

dataset – The dataset to use for visualization.
split – The dataset split to be used, such as ‘training’
indices – An iterable with a subset of the data points to visualize, such as [0,2,3,4].
width – The width of the visualization window.
height – The height of the visualization window.

COLOR_NAME = 'RGB'#

GREYSCALE_NAME = 'Colormap (Greyscale)'#

LABELS_NAME = 'Label Colormap'#

RAINBOW_NAME = 'Colormap (Rainbow)'#

SOLID_NAME = 'Solid Color'#

X_ATTR_NAME = 'x position'#

Y_ATTR_NAME = 'y position'#

Z_ATTR_NAME = 'z position'#