open3d.ml.tf.vis.Visualizer

class open3d.ml.tf.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)

Initialize self. See help(type(self)) for accurate signature.

set_on_changed(callback)

update(colormap, min_val, max_val)

class LabelLUTEdit

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

__init__()

Initialize self. See help(type(self)) for accurate signature.

clear()

get_colors()

is_empty()

set_labels(labellut)

set_on_changed(callback)

class ProgressDialog(title, window, n_items)

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

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)

Initialize self. See help(type(self)) for accurate signature.

post_update(text=None)

set_text(text)

update()

__init__()

Initialize self. See help(type(self)) for accurate signature.

set_lut(attr_name, lut)

setup_camera()

show_geometries_under(name, show)

visualize(data, lut=None, bounding_boxes=None, width=1024, 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)

Args:

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.

width: window width. height: window height.

visualize_dataset(dataset, split, indices=None, width=1024, 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))

Args:

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'