废话不多说 直接开干
需要用到模块
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple math #对浮点数的数学运算函数
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple shapely #提供几何形状的操作和分析，如交集、并集、差集等
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple matplotlib #可视化模块
项目需要优化运动轨迹路线 用到道格拉斯算法 相对来说很实用 建议 用到GPS定位同行可以试试看
运行代码
# -*- coding:utf-8 -*-
"""
道格拉斯算法的实现
程序需要安装shapely模块
"""
import math
from shapely import wkt, geometry
import matplotlib.pyplot as pltclass Point:"""点类"""x = 0.0y = 0.0index = 0 # 点在线上的索引def __init__(self, x, y, index):self.x = xself.y = yself.index = indexclass Douglas:"""道格拉斯算法类"""points = []D = 1 # 容差def readPoint(self):"""生成点要素"""g = wkt.loads("LINESTRING(1 4,2 3,4 2,6 6,7 7,8 6,9 5,10 10)")coords = g.coordsfor i in range(len(coords)):self.points.append(Point(coords[i][0], coords[i][1], i))def compress(self, p1, p2):"""具体的抽稀算法"""swichvalue = False# 一般式直线方程系数 A*x+B*y+C=0,利用点斜式,分母可以省略约区# A=(p1.y-p2.y)/math.sqrt(math.pow(p1.y-p2.y,2)+math.pow(p1.x-p2.x,2))A = (p1.y - p2.y)# B=(p2.x-p1.x)/math.sqrt(math.pow(p1.y-p2.y,2)+math.pow(p1.x-p2.x,2))B = (p2.x - p1.x)# C=(p1.x*p2.y-p2.x*p1.y)/math.sqrt(math.pow(p1.y-p2.y,2)+math.pow(p1.x-p2.x,2))C = (p1.x * p2.y - p2.x * p1.y)m = self.points.index(p1)n = self.points.index(p2)distance = []middle = Noneif (n == m + 1):return# 计算中间点到直线的距离for i in range(m + 1, n):d = abs(A * self.points[i].x + B * self.points[i].y + C) / math.sqrt(math.pow(A, 2) + math.pow(B, 2))distance.append(d)dmax = max(distance)if dmax > self.D:swichvalue = Trueelse:swichvalue = Falseif (not swichvalue):for i in range(m + 1, n):del self.points[i]else:for i in range(m + 1, n):if (abs(A * self.points[i].x + B * self.points[i].y + C) / math.sqrt(math.pow(A, 2) + math.pow(B, 2)) == dmax):middle = self.points[i]self.compress(p1, middle)self.compress(middle, p2)def printPoint(self):"""打印数据点"""for p in self.points:print( "%d,%f,%f" % (p.index, p.x, p.y))def main():"""测试"""d = Douglas()d.readPoint()# d.printPoint()# 结果图形的绘制，抽稀之前绘制fig = plt.figure()a1 = fig.add_subplot(121)dx = []dy = []for i in range(len(d.points)):dx.append(d.points[i].x)dy.append(d.points[i].y)a1.plot(dx, dy, color='g', linestyle='-', marker='+')d.compress(d.points[0], d.points[len(d.points) - 1]) #稀释后轨迹# 抽稀之后绘制dx1 = []dy1 = []a2 = fig.add_subplot(122)for p in d.points:print(p.x,p.y)dx1.append(p.x)dy1.append(p.y)a2.plot(dx1, dy1, color='r', linestyle='-', marker='+')plt.show()if __name__ == '__main__':main()
看下效果 优化轨迹路线
