mosaic.container.DataContainer#

class DataContainer(base_color=(0.7, 0.7, 0.7), highlight_color=(0.8, 0.2, 0.2))[source]#

Bases: object

Container for managing and manipulating point cloud data collections.

Parameters:
base_colortuple of float, optional

Default color for points in RGB format in range 0-1. Default is (0.7, 0.7, 0.7).

highlight_colortuple of float, optional

Highlight color for points in RGB format in range 0-1. Default is (0.8, 0.2, 0.2).

__init__(base_color=(0.7, 0.7, 0.7), highlight_color=(0.8, 0.2, 0.2))[source]#

Methods

__init__([base_color, highlight_color])

add(points[, color])

Add a new geometry object to the container.

add_selection(selected_point_ids)

Add new cloud from selected points.

birch_cluster(geometry, **kwargs)

Perform Birch clustering on the input points using skimage.

change_visibility(indices, visible, **kwargs)

Change visibility of specified geometries.

connected_components(geometry[, distance])

Identify connected components in a point cloud.

crop(geometry, distance, query[, keep_smaller])

Crop geometry based on distance to query points.

dbscan_cluster(geometry, **kwargs)

Perform DBSCAN clustering.

decimate(geometry[, method])

Decimate point cloud using specified method

downsample(geometry[, method])

Downsample point cloud using specified method

duplicate(indices)

Duplicate different geometries

get_actors()

Get VTK actors from all geometries.

get_cluster_count()

Get number of geometries in container.

get_cluster_size()

Get number of points in each cloud.

highlight(indices)

Highlight specified geometries.

highlight_points(index, point_ids, color)

Highlight specific points in a cloud.

merge(indices)

Merge multiple geometries into one.

new(data, *args, **kwargs)

Create new point cloud from existing data.

remove(indices)

Remove geometries at specified indices.

remove_outliers(geometry[, method])

Remove outliers from point cloud.

sample(geometry, sampling, method)

Sample points from cloud.

split(indices[, k])

Split point cloud into k using K-means.

trim(geometry, min_value, max_value[, axis])

Trim points based on axis-aligned bounds.

update(other)

Update current class instance with data from another container.

update_appearance(indices, parameters)