From surface data to image data

In this notebook we will turn surfaces into binary and label images.

import napari_process_points_and_surfaces as nppas
import vedo

mesh = vedo.load("../../data/branchoid.ply")
surface = nppas.to_napari_surface_data(mesh)

surface

nppas.SurfaceTuple origin (z/y/x) [0. 0. 0.] center of mass(z/y/x) 50.000,46.575,42.589 scale(z/y/x) 1.000,1.000,1.000 bounds (z/y/x) 25.500...74.500 2.500...88.500 2.500...83.500 average size 31.277 number of vertices 19040 number of faces 38076

Binary volumes

A common task is to fill a volume to create a binary image stack.

binary_image = nppas.surface_to_binary_volume(surface)
binary_image

shape (49, 86, 81) dtype int32 size 1.3 MB min 0 max 1

Labeled surface voxels

We can also sample points from the surface and turn these points into a label image.

point_cloud = nppas.sample_points_from_surface(surface, distance_fraction=0.01)
point_cloud

array([[26. , 44. , 46.5],
       [26. , 43.5, 48. ],
       [26. , 43.5, 50. ],
       ...,
       [74.5, 55. , 52. ],
       [74.5, 56. , 49. ],
       [74.5, 56. , 51. ]], dtype=float32)

point_labels = nppas.points_to_labels(point_cloud)
point_labels

shape (76, 90, 85) dtype int32 size 2.2 MB min 0 max 5167

point_labels[50]

shape (90, 85) dtype int32 size 29.9 kB min 0 max 2678

These labels can also be turned into points again.

centroids = nppas.labels_to_centroids(point_labels)
centroids[:10]

[(26.0, 44.0, 47.0),
 (26.0, 44.0, 48.0),
 (26.0, 44.0, 50.0),
 (26.0, 44.0, 52.0),
 (26.0, 44.0, 54.0),
 (26.0, 45.0, 46.0),
 (26.0, 45.0, 48.0),
 (26.0, 45.0, 50.0),
 (26.0, 45.0, 52.0),
 (26.0, 45.0, 55.0)]