从表面数据到图像数据#

在这个笔记本中，我们将把表面转换为二值图像和标签图像。

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 size31.277
number of vertices19040
number of faces38076

二值体积#

一个常见的任务是填充体积以创建二值图像堆栈。

binary_image = nppas.surface_to_binary_volume(surface)
binary_image
shape(49, 86, 81)
dtypeint32
size1.3 MB
min0
max1

标记的表面体素#

我们还可以从表面采样点，并将这些点转换为标签图像。

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)
dtypeint32
size2.2 MB
min0
max5167
 
point_labels[50]
shape(90, 85)
dtypeint32
size29.9 kB
min0
max2678

这些标签也可以再次转换为点。

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)]