对轨迹数据集csv中某两属性值用Douglas-Peucker算法降噪的代码实例
时间: 2023-05-19 20:01:35 浏览: 52
以下是一个使用Douglas-Peucker算法对轨迹数据集csv中某两属性值进行降噪的代码实例:
```python
import numpy as np
def douglas_peucker(points, epsilon):
"""
Douglas-Peucker算法实现
:param points: 待降噪的轨迹点集
:param epsilon: 控制降噪的精度参数
:return: 降噪后的轨迹点集
"""
if len(points) <= 2:
return points
# 计算所有点到起点和终点的距离
dmax = 0
index = 0
for i in range(1, len(points) - 1):
d = perpendicular_distance(points[i], points[0], points[-1])
if d > dmax:
index = i
dmax = d
# 如果最大距离小于等于epsilon,则直接返回起点和终点
if dmax <= epsilon:
return [points[0], points[-1]]
# 否则将轨迹点集分为两部分,对每一部分递归调用Douglas-Peucker算法
left_points = points[:index+1]
right_points = points[index:]
left_simplified = douglas_peucker(left_points, epsilon)
right_simplified = douglas_peucker(right_points, epsilon)
# 将两部分的结果合并
return left_simplified[:-1] + right_simplified
def perpendicular_distance(point, start, end):
"""
计算点到线段的垂直距离
:param point: 待计算的点
:param start: 线段起点
:param end: 线段终点
:return: 点到线段的垂直距离
"""
x0, y0 = point
x1, y1 = start
x2, y2 = end
return np.abs((y2 - y1) * x0 - (x2 - x1) * y0 + x2 * y1 - y2 * x1) / np.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2)
# 示例
points = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9)]
epsilon = 1
simplified_points = douglas_peucker(points, epsilon)
print(simplified_points)
```
这个代码实例演示了如何使用Douglas-Peucker算法对轨迹数据集csv中某两属性值进行降噪。