点云测地线fastmarching代码
时间: 2023-07-02 16:17:46 浏览: 43
以下是使用Fast Marching算法实现点云测地线的Python代码示例:
```python
import numpy as np
from scipy.spatial.distance import cdist
from heapq import heappush, heappop
def find_neighbors(p, kd_tree, search_radius):
"""
找到点云中距离点p在search_radius范围内的所有点
"""
neighbors = kd_tree.query_ball_point(p, search_radius)
return neighbors
def compute_distance(p1, p2):
"""
计算两个点之间的欧几里得距离
"""
return np.linalg.norm(p1 - p2)
def fast_marching(start_point, end_point, point_cloud, search_radius):
"""
使用Fast Marching算法计算点云中start_point到end_point的测地线
"""
# 构建KD树
kd_tree = KDTree(point_cloud)
# 初始化距离和状态
distance = np.full(len(point_cloud), np.inf)
visited = np.zeros(len(point_cloud), dtype=bool)
# 设置起点的距离为0
start_idx = kd_tree.query(start_point)[1]
distance[start_idx] = 0
# 初始化堆
heap = []
heappush(heap, (0, start_idx))
# 迭代计算
while heap:
# 从堆中取出距离最小的点
d, idx = heappop(heap)
# 标记为已访问
visited[idx] = True
# 如果到达终点,结束迭代
if idx == end_idx:
break
# 找到距离当前点最近的所有邻居
neighbors = find_neighbors(point_cloud[idx], kd_tree, search_radius)
# 更新邻居的距离
for neighbor in neighbors:
if not visited[neighbor]:
new_distance = distance[idx] + compute_distance(point_cloud[idx], point_cloud[neighbor])
if new_distance < distance[neighbor]:
distance[neighbor] = new_distance
heappush(heap, (new_distance, neighbor))
# 反向追溯路径
path = []
current_idx = end_idx
while current_idx != start_idx:
path.append(current_idx)
current_idx = np.argmin(distance[find_neighbors(point_cloud[current_idx], kd_tree, search_radius)])
path.append(start_idx)
# 将路径转换为坐标
path_coords = [point_cloud[idx] for idx in reversed(path)]
return path_coords
```
需要注意的是,这段代码使用了Python的NumPy、SciPy和heapq库,需要先安装这些库。此外,代码中使用了KDTree来加速查找点云中距离某个点最近的点,需要先从scipy.spatial中导入KDTree。