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269adbb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | import numpy as np
from scipy.ndimage import distance_transform_edt, gaussian_filter, zoom, median_filter
import matplotlib.pyplot as plt
from skimage.measure import marching_cubes
from scipy.ndimage import binary_fill_holes, label
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from scipy.spatial import Delaunay
import open3d as o3d
import pyvista as pv
from scipy.interpolate import griddata
# import trimesh
def compute_sdf(voxel_matrix):
inside_dist = distance_transform_edt(voxel_matrix)
outside_dist = distance_transform_edt(1 - voxel_matrix)
sdf = outside_dist - inside_dist
# sdf = gaussian_filter(sdf, sigma=3.0)
sdf = sdf / 32.0
sdf = sdf.clip(-0.2, 0.2)
return sdf
def visualize_sdf(voxel_matrix, sdf, slice_index, prefix='slice'):
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.title("Voxel Matrix Slice")
plt.imshow(voxel_matrix[slice_index], cmap='gray')
plt.subplot(1, 2, 2)
plt.title("SDF Slice")
plt.imshow(sdf[slice_index], cmap='jet')
plt.colorbar()
plt.show()
plt.savefig(f'{prefix}_{slice_index}.png')
plt.close()
def extract_surface(sdf):
vertices, faces, normals, values = marching_cubes(sdf, level=0)
return vertices, faces
def visualize_surface(vertices, faces):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
mesh = Poly3DCollection(vertices[faces])
mesh.set_edgecolor('k')
ax.add_collection3d(mesh)
ax.set_xlim(0, 32)
ax.set_ylim(0, 32)
ax.set_zlim(0, 32)
plt.show()
plt.savefig('surface.png')
plt.close()
def create_3d_mask(vertices, faces, shape):
mask = np.zeros(shape, dtype=np.uint8)
# Create a Delaunay triangulation for the vertices
delaunay = Delaunay(vertices)
# Generate grid points
x = np.arange(0, shape[0])
y = np.arange(0, shape[1])
z = np.arange(0, shape[2])
grid = np.array(np.meshgrid(x, y, z)).T.reshape(-1, 3)
# Find grid points that are inside the convex hull
inside = delaunay.find_simplex(grid) >= 0
# Convert the grid points back to 3D index
grid_points = grid[inside]
# Set the mask values to 1 where points are inside the convex hull
mask[grid_points[:, 0], grid_points[:, 1], grid_points[:, 2]] = 1
return mask
def fill_holes_along_axis(volume, axis):
filled_volume = np.copy(volume)
for i in range(volume.shape[axis]):
if axis == 0:
slice_2d = volume[i, :, :]
elif axis == 1:
slice_2d = volume[:, i, :]
else:
slice_2d = volume[:, :, i]
filled_slice = binary_fill_holes(slice_2d)
# 确保单连通性
labeled_array, num_features = label(filled_slice)
if num_features > 1:
max_label = max(range(1, num_features + 1), key=lambda x: np.sum(labeled_array == x))
filled_slice = labeled_array == max_label
if axis == 0:
filled_volume[i, :, :] = filled_slice
elif axis == 1:
filled_volume[:, i, :] = filled_slice
else:
filled_volume[:, :, i] = filled_slice
return filled_volume
def mesh_to_voxel(vertices, faces):
shape = [64, 64, 64]
faces = np.hstack([[3] + list(face) for face in faces])
mesh = pv.PolyData(vertices, faces)
grid = pv.voxelize(mesh, density=1.0)
voxel_points = np.array(grid.points)
filled_voxel = np.zeros(shape)
for voxel in voxel_points:
x, y, z = voxel
x, y, z = int(np.round(x)), int(np.round(y)), int(np.round(z)) # 将顶点坐标转换为整数索引
if 0 <= x < shape[0] and 0 <= y < shape[1] and 0 <= z < shape[2]:
filled_voxel[x, y, z] = 1 # 设置为1表示该位置有网格顶点
return filled_voxel
def sdf_to_filled_mesh(sdf, level=0.02):
# vertices, faces, _, _ = marching_cubes(sdf, level=level)
# filled_voxel = mesh_to_voxel(vertices, faces)
# for _ in range(1):
# filled_voxel = fill_holes_along_axis(filled_voxel, 0)
# filled_voxel = fill_holes_along_axis(filled_voxel, 1)
# filled_voxel = fill_holes_along_axis(filled_voxel, 2)
# while True:
# prev_filled_voxel = filled_voxel.copy()
# filled_voxel = fill_holes_along_axis(filled_voxel, 0)
# filled_voxel = fill_holes_along_axis(filled_voxel, 1)
# filled_voxel = fill_holes_along_axis(filled_voxel, 2)
# if np.array_equal(filled_voxel, prev_filled_voxel):
# break
vertices_filled, faces_filled, normals_filled, values_filled = marching_cubes(sdf, level=level)
# 将网格转换为 Open3D 的三角网格对象
mesh = o3d.geometry.TriangleMesh()
mesh.vertices = o3d.utility.Vector3dVector(vertices_filled)
mesh.triangles = o3d.utility.Vector3iVector(faces_filled)
mesh.compute_vertex_normals()
# 拉普拉斯平滑
mesh = mesh.filter_smooth_simple(number_of_iterations=20)
mesh.compute_vertex_normals()
return np.asarray(mesh.vertices), np.asarray(mesh.triangles)
def sdf_to_voxel(sdf, level=0.02):
sdf_smoothed = gaussian_filter(sdf, sigma=0.5)
binary_mask = binary_fill_holes(sdf_smoothed <= level).astype(np.uint8)
# structuring_element = ball(radius=1)
# binary_mask_opened = binary_opening(binary_mask, footprint=structuring_element)
# binary_mask_closed = binary_closing(binary_mask_opened, footprint=structuring_element)
# binary_mask_eroded = binary_erosion(binary_mask_closed, structure=structuring_element)
# binary_mask_dilated = binary_dilation(binary_mask_eroded, structure=structuring_element)
# filled_voxel = gaussian_filter(binary_mask_dilated.astype(np.float32), sigma=1.0)
# filled_voxel = filled_voxel > 0.5
filled_voxel = binary_mask
return filled_voxel |