帮我写一段从obj格式文件中提取线特征和角特征的python代码
时间: 2023-05-17 09:06:56 浏览: 66
以下是提取线特征和角特征的Python代码:
```python
import numpy as np
import trimesh
# 读取obj格式文件
mesh = trimesh.load('example.obj')
# 提取线特征
edges = mesh.edges_unique
edge_coords = mesh.vertices[edges]
edge_vectors = np.diff(edge_coords, axis=1).squeeze()
edge_lengths = np.linalg.norm(edge_vectors, axis=1)
# 提取角特征
angles = mesh.vertex_angles
print('线特征:')
print('边数:', len(edges))
print('边向量:', edge_vectors)
print('边长度:', edge_lengths)
print('角特征:')
print('角度数:', len(angles))
print('角度值:', angles)
```
这段代码使用了trimesh库来读取obj格式文件,并提取了线特征和角特征。其中,线特征包括边数、边向量和边长度;角特征包括角度数和角度值。
相关问题
帮我写一段从obj格式文件中提取线特征和角特征并可视化的python代码
当然可以,以下是一段可以提取线特征和角特征并可视化的Python代码:
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import open3d as o3d
def extract_line_and_corner_features_from_obj_file(obj_file_path):
# Load the OBJ file
mesh = o3d.io.read_triangle_mesh(obj_file_path)
# Convert the mesh to a point cloud
pcd = mesh.sample_points_uniformly(number_of_points=5000)
# Compute the normals of the point cloud
pcd.estimate_normals()
# Compute the curvature of the point cloud
pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
radius_normal = 0.1
pcd_tree = o3d.geometry.KDTreeFlann(pcd)
pcd_points = np.asarray(pcd.points)
pcd_normals = np.asarray(pcd.normals)
pcd_curvatures = np.asarray(pcd.curvature)
line_features = []
corner_features = []
# Extract line features
for i in range(len(pcd_points)):
p_i = pcd_points[i]
n_i = pcd_normals[i]
curvature_i = pcd_curvatures[i]
pcd_points_without_i = np.delete(pcd_points, i, axis=0)
pcd_normals_without_i = np.delete(pcd_normals, i, axis=0)
pcd_tree_without_i = o3d.geometry.KDTreeFlann(pcd)
[k, idx, _] = pcd_tree_without_i.search_radius_vector_3d(p_i, radius_normal)
pcd_points_knn = pcd_points_without_i[idx, :]
pcd_normals_knn = pcd_normals_without_i[idx, :]
pcd_points_knn_centered = pcd_points_knn - p_i
pcd_points_knn_centered_norm = np.linalg.norm(pcd_points_knn_centered, axis=1)
pcd_points_knn_centered_normalized = pcd_points_knn_centered / np.expand_dims(pcd_points_knn_centered_norm, axis=1)
dot_products = np.abs(np.dot(pcd_points_knn_centered_normalized, np.expand_dims(n_i, axis=0).T))
line_feature = np.mean(dot_products)
line_features.append(line_feature)
# Extract corner features
for i in range(len(pcd_points)):
p_i = pcd_points[i]
n_i = pcd_normals[i]
curvature_i = pcd_curvatures[i]
pcd_points_without_i = np.delete(pcd_points, i, axis=0)
pcd_normals_without_i = np.delete(pcd_normals, i, axis=0)
pcd_tree_without_i = o3d.geometry.KDTreeFlann(pcd)
[k, idx, _] = pcd_tree_without_i.search_radius_vector_3d(p_i, radius_normal)
pcd_points_knn = pcd_points_without_i[idx, :]
pcd_normals_knn = pcd_normals_without_i[idx, :]
pcd_points_knn_centered = pcd_points_knn - p_i
pcd_points_knn_centered_norm = np.linalg.norm(pcd_points_knn_centered, axis=1)
pcd_points_knn_centered_normalized = pcd_points_knn_centered / np.expand_dims(pcd_points_knn_centered_norm, axis=1)
dot_products = np.abs(np.dot(pcd_points_knn_centered_normalized, np.expand_dims(n_i, axis=0).T))
line_feature = np.mean(dot_products)
if curvature_i > 0.1:
corner_feature = line_feature * curvature_i
corner_features.append(corner_feature)
# Visualize the line and corner features
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pcd_points[:, 0], pcd_points[:, 1], pcd_points[:, 2], c=line_features, cmap='coolwarm')
ax.scatter(pcd_points[:, 0], pcd_points[:, 1], pcd_points[:, 2], c=corner_features, cmap='coolwarm')
plt.show()
# Example usage
extract_line_and_corner_features_from_obj_file('example.obj')
```
这段代码使用了Open3D库来加载OBJ文件并将其转换为点云。然后,它计算了点云的法线和曲率,并使用KD树搜索来查找每个点的最近邻。接下来,它提取了每个点的线特征和角特征,并将它们可视化为散点图。
帮我用python写一段从obj文件中提取角特征和线特征并可视化的代码
当然可以,以下是一段Python代码,可以从obj文件中提取角特征和线特征,并将其可视化:
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def read_obj_file(file_path):
vertices = []
faces = []
with open(file_path, 'r') as f:
for line in f:
if line.startswith('v '):
vertex = [float(x) for x in line[2:].split()]
vertices.append(vertex)
elif line.startswith('f '):
face = [int(x.split('/')[0]) for x in line[2:].split()]
faces.append(face)
return np.array(vertices), np.array(faces)
def compute_edge_features(vertices, faces):
edges = set()
for face in faces:
for i in range(len(face)):
edge = (face[i], face[(i+1)%len(face)])
if edge[0] > edge[1]:
edge = (edge[1], edge[0])
edges.add(edge)
edges = list(edges)
edge_features = np.zeros((len(edges), 3))
for i, edge in enumerate(edges):
v1 = vertices[edge[0]-1]
v2 = vertices[edge[1]-1]
edge_features[i] = v2 - v1
return edges, edge_features
def compute_vertex_features(vertices, faces):
vertex_features = np.zeros((len(vertices), 3))
for i, vertex in enumerate(vertices):
adjacent_faces = []
for face in faces:
if i+1 in face:
adjacent_faces.append(face)
normal = np.zeros(3)
for face in adjacent_faces:
v1 = vertices[face[0]-1]
v2 = vertices[face[1]-1]
v3 = vertices[face[2]-1]
normal += np.cross(v2-v1, v3-v1)
normal /= np.linalg.norm(normal)
vertex_features[i] = normal
return vertex_features
def visualize(vertices, edges, edge_features, vertex_features):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(vertices[:,0], vertices[:,1], vertices[:,2], c=vertex_features[:,2], cmap='coolwarm')
for i, edge in enumerate(edges):
v1 = vertices[edge[0]-1]
v2 = vertices[edge[1]-1]
ax.plot([v1[0], v2[0]], [v1[1], v2[1]], [v1[2], v2[2]], color=plt.cm.coolwarm(edge_features[i,2]))
plt.show()
vertices, faces = read_obj_file('example.obj')
edges, edge_features = compute_edge_features(vertices, faces)
vertex_features = compute_vertex_features(vertices, faces)
visualize(vertices, edges, edge_features, vertex_features)
```
这段代码首先读取obj文件中的顶点和面信息,然后计算出所有的边和每条边的向量特征,以及每个顶点的法向量特征。最后,使用matplotlib库将顶点和边可视化出来,其中顶点的颜色表示法向量在z轴上的分量,边的颜色表示边向量在z轴上的分量。
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