from skimage.morphology import skeletonize_3d from skimage import data # Load the 3D data points data = data.binary_blobs(length=100, blob_size_fraction=0.1, n_dim=3) # Convert the data points to a binary image binary_image = data.astype(bool) # Generate the distance map distance_map = ndimage.distance_transform_edt(binary_image) # Threshold the distance map thresholded_distance_map = (distance_map > 0) & (distance_map < 10) # Skeletonize the thresholded distance map skeleton = skeletonize_3d(thresholded_distance_map)对这段代码采用matplotlib进行可视化

好的，您可以尝试使用以下代码来可视化这段代码生成的三维骨架图像：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Plot the binary image
x, y, z = binary_image.nonzero()
ax.scatter(x, y, z, zdir='z', c='red')

# Plot the skeleton
verts, faces, _, _ = measure.marching_cubes_lewiner(skeleton, 0)
mesh = Poly3DCollection(verts[faces], alpha=0.1)
face_color = [0.5, 0.5, 1]
mesh.set_facecolor(face_color)
ax.add_collection3d(mesh)

ax.set_xlim(0, binary_image.shape[0])
ax.set_ylim(0, binary_image.shape[1])
ax.set_zlim(0, binary_image.shape[2])

plt.show()

这段代码使用了matplotlib库中的3D可视化功能，将原始的二进制图像和骨架图像在三维空间中显示出来。其中，红色的点表示二进制图像中的非零点，蓝色的线条表示骨架图像。