cloudViewer.ml.torch.vis.Visualizer

class cloudViewer.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 cloudViewer.ml.torch as ml3d
# or import cloudViewer.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 cloudViewer.ml.torch as ml3d # or cloudViewer.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'