如何为Poly3DCollection着色和着色

Question

我正在尝试使3D等高线图变暗或具有阴影，以使其看起来“ 3D”。我正在使用matplotlib，主要是因为地块的质量很高，所以我希望继续使用它。

最终，我希望以matplotlib样式绘图在其上投射阴影的单色或平面颜色表面上。

我正在使用scipy进行一些插值和skimage以及行进立方体算法来生成轮廓。然后最后使用它来创建和隐藏多边形集合。

import numpy as np
from skimage import measure
from scipy.interpolate import griddata
import matplotlib as mpl
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from matplotlib.colors import LightSource

# Generate an grid to inerpolate to
X, Y, Z = np.meshgrid(0.0:1.0:50j, 0.0:1.0:50j, 0.0:1.0:50j)

# Interpolate (coor and phi are the numerical grid and scalar values)
F = griddata(coor, phi, (X, Y, Z), method='nearest')

# Make the contour, marching cubes
marchCubeSpace = 1.0 / 50.0
verts, faces, normals, values = measure.marching_cubes_lewiner(F, 0.5, spacing=(marchCubeSpace, marchCubeSpace, marchCubeSpace))

# Create Ploy3D
mesh = Poly3DCollection(verts[faces], alpha=1.0)

# An attempt to get some sort of height data.
facearray = np.array([np.array((np.sum(verts[face[:], 0]/3), np.sum(verts[face[:], 1]/3), np.sum(verts[face[:], 2]/3))) for face in faces])

# light source, ultimately I want to use not `reds` but just a red for all faces.
ls = LightSource(azdeg=45.0, altdeg=90.0)
rgb = ls.blend_hsv(rgb=ls.shade(facearray, plt.cm.Reds), intensity=ls.shade_normals(normals, fraction=0.25))
mesh.set_facecolor(rgb[:, 0])

# Plot
fig = plt.figure()
ax = fig.add_subplot(0, 0, 0, projection='3d')
ax.add_collection3d(mesh)

我正在寻找生成这样的东西：

Answer 1

好的，所以我有一个可以接受的解决方案。如果需要更多帮助，请给我发消息，我很乐意指导任何人。请注意，以下代码需要数据集中的coor和phi，因此，如果您不向其提供3D标量字段，则不会运行此代码。

import numpy as np
from skimage import measure
from scipy.interpolate import griddata
import matplotlib as mpl
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from matplotlib.colors import LightSource

# Generate an grid to inerpolate to
X, Y, Z = np.meshgrid(0.0:1.0:50j, 0.0:1.0:50j, 0.0:1.0:50j)

# Interpolate (coor and phi are the numerical grid and scalar values)
F = griddata(coor, phi, (X, Y, Z), method='nearest')

# Make the contour, marching cubes
marchCubeSpace = 1.0 / 50.0
verts, faces, normals, values = measure.marching_cubes_lewiner(F, 0.5, spacing=(marchCubeSpace, marchCubeSpace, marchCubeSpace))

# Create Ploy3D and set up a light source
mesh = Poly3DCollection(verts[faces], alpha=1.0)
ls = LightSource(azdeg=225.0, altdeg=45.0)

# First change - normals are per vertex, so I made it per face.
normalsarray = np.array([np.array((np.sum(normals[face[:], 0]/3), np.sum(normals[face[:], 1]/3), np.sum(normals[face[:], 2]/3))/np.sqrt(np.sum(normals[face[:], 0]/3)**2 + np.sum(normals[face[:], 1]/3)**2 + np.sum(normals[face[:], 2]/3)**2)) for face in faces])

# Next this is more asthetic, but it prevents the shadows of the image being too dark. (linear interpolation to correct)
min = np.min(ls.shade_normals(normalsarray, fraction=1.0)) # min shade value
max = np.max(ls.shade_normals(normalsarray, fraction=1.0)) # max shade value
diff = max-min
newMin = 0.3
newMax = 0.95
newdiff = newMax-newMin

# Using a constant color, put in desired RGB values here.
colourRGB = np.array((255.0/255.0, 54.0/255.0, 57/255.0, 1.0))

# The correct shading for shadows are now applied. Use the face normals and light orientation to generate a shading value and apply to the RGB colors for each face.
rgbNew = np.array([colourRGB*(newMin + newdiff*((shade-min)/diff)) for shade in ls = LightSource(azdeg=45.0, altdeg=90.0)

# Apply color to face
mesh.set_facecolor(rgb[:, 0])

# Plot
fig = plt.figure()
ax = fig.add_subplot(0, 0, 0, projection='3d')
ax.add_collection3d(mesh)

这就是我想要的。 （请注意，与上述图片并不完全相同）