表面测量

在这个笔记本中，我们演示如何在表格中检索表面/顶点测量结果，以及如何在表面上可视化它们。所使用的示例数据是从AV Luque和JV Veenvliet (2023)派生的，采用CC-BY许可。有关如何从原始成像数据创建表面，请参阅creating_surfaces笔记本。

另请参阅

• vedo库

• vedo添加曲率文档

import napari
import matplotlib.pyplot as plt
from napari.utils import nbscreenshot
import numpy as np
import vedo
from napari_process_points_and_surfaces import add_curvature, Curvature, spherefitted_curvature
import napari_process_points_and_surfaces as nppas

import vedo

viewer = napari.Viewer(ndisplay=3)
viewer.camera.angles = (40, -30, 55)

surface = nppas.gastruloid()

The nppas gastruloid example is derived from AV Luque and JV Veenvliet (2023) which is licensed CC-BY (https://creativecommons.org/licenses/by/4.0/legalcode) and can be downloaded from here: https://zenodo.org/record/7603081

表面可视化

表面本身并不包含任何量化信息。它看起来像这样：

surface

nppas.SurfaceTuple origin (z/y/x) [0. 0. 0.] center of mass(z/y/x) 57.561,308.175,440.144 scale(z/y/x) 1.000,1.000,1.000 bounds (z/y/x) 13.983...112.865 110.951...460.844 168.949...807.707 average size 171.204 number of vertices 3324 number of faces 6643

量化

我们可以像这样创建一个表格（pandas DataFrame）。

requested_measurements = [nppas.Quality.AREA, 
                          nppas.Quality.ASPECT_RATIO,
                          nppas.Quality.GAUSS_CURVATURE, 
                          nppas.Quality.MEAN_CURVATURE,
                          nppas.Quality.SPHERE_FITTED_CURVATURE_DECA_VOXEL,
                          nppas.Quality.SPHERE_FITTED_CURVATURE_HECTA_VOXEL,
                          nppas.Quality.SPHERE_FITTED_CURVATURE_KILO_VOXEL,
                         ]

df = nppas.surface_quality_table(surface, requested_measurements)
df

	vertex_index	Quality.AREA	Quality.ASPECT_RATIO	Quality.GAUSS_CURVATURE	Quality.MEAN_CURVATURE	Quality.SPHERE_FITTED_CURVATURE_DECA_VOXEL	Quality.SPHERE_FITTED_CURVATURE_HECTA_VOXEL	Quality.SPHERE_FITTED_CURVATURE_KILO_VOXEL
0	0	29.997389	1.600400	0.030287	0.000490	0.000691	0.000257	0.000019
1	1	46.087046	1.602183	0.011136	0.000018	NaN	0.000250	0.000019
2	2	35.886338	1.400599	0.012633	0.000142	0.000542	0.000253	0.000019
3	3	22.887296	1.751932	0.036979	0.000548	0.000339	0.000379	0.000019
4	4	29.952347	1.220882	0.010277	0.000047	0.000366	0.000391	0.000019
...	...	...	...	...	...	...	...	...
3319	3319	25.079661	1.340802	0.031878	0.000606	0.001081	0.000168	0.000019
3320	3320	47.213916	1.254924	0.004615	0.000003	NaN	0.000169	0.000019
3321	3321	35.964707	1.140267	0.015661	0.000198	0.000547	0.000163	0.000019
3322	3322	45.673529	1.189562	0.011380	0.000100	0.000026	0.000152	0.000019
3323	3323	30.105530	1.151230	0.014396	0.000190	0.000158	0.000163	0.000019

3324 rows × 8 columns

为了对测量结果有一个概览，我们可以对其进行汇总：

df.describe().T

	count	mean	std	min	25%	50%	75%	max
vertex_index	3324.0	1661.500000	959.700474	0.000000e+00	830.750000	1661.500000	2492.250000	3323.000000
Quality.AREA	3324.0	33.753233	10.790780	5.677486e+00	26.694735	32.956835	39.255080	125.564101
Quality.ASPECT_RATIO	3324.0	7.126810	89.909602	1.038034e+00	1.292444	1.437911	1.648299	3421.965459
Quality.GAUSS_CURVATURE	3324.0	0.016958	0.035275	-1.031106e+00	0.005509	0.013645	0.024739	0.348243
Quality.MEAN_CURVATURE	3324.0	0.000383	0.007653	-2.803460e-02	-0.000135	0.000010	0.000270	0.426018
Quality.SPHERE_FITTED_CURVATURE_DECA_VOXEL	2750.0	0.002875	0.004761	1.791201e-09	0.000341	0.001004	0.003054	0.043508
Quality.SPHERE_FITTED_CURVATURE_HECTA_VOXEL	3324.0	0.000258	0.000069	1.516446e-04	0.000214	0.000241	0.000275	0.000545
Quality.SPHERE_FITTED_CURVATURE_KILO_VOXEL	3324.0	0.000019	0.000000	1.853513e-05	0.000019	0.000019	0.000019	0.000019

我们可以从表格中提取单个列作为列表。

curvature = list(df['Quality.SPHERE_FITTED_CURVATURE_HECTA_VOXEL'])
curvature[:5]

[0.0002572409502622459,
 0.0002504286604301336,
 0.00025319922419934937,
 0.00037887302369609083,
 0.00039058361737075804]

可视化测量结果

为了可视化测量结果，我们需要将它们附加到表面上：

quantified_surface = nppas.set_vertex_values(surface, curvature)
quantified_surface

nppas.SurfaceTuple origin (z/y/x) [0. 0. 0.] center of mass(z/y/x) 57.561,308.175,440.144 scale(z/y/x) 1.000,1.000,1.000 bounds (z/y/x) 13.983...112.865 110.951...460.844 168.949...807.707 average size 171.204 number of vertices 3324 number of faces 6643 min 0.00015164456647196744 max 0.0005448766287854695

可视化也可以进行自定义，例如通过改变视角和颜色映射。

nppas.show(quantified_surface, azimuth=-90, cmap='jet')

nppas.show(quantified_surface, azimuth=-90, cmap='hsv')