Python最短路径算法可视化——Dijkstra算法

import random
import matplotlib.pyplot as plt
import math
import matplotlib.animation as animation


class Dijkstra:
    def __init__(self, nums: int):
        self.fig, self.ax = plt.subplots()  # 初始画布
        self.nums = nums  # 随机点个数
        self.points, self.nearPs = self.genPoints((1, 1000), (1, 1000))  # 生成点集
        print(f"Points:{self.nearPs}")
        self.startP, self.endP = self.findStartEndPoints()  # 随机起终点
        print(f"Start Point:{self.startP}\tEnd Point:{self.endP}")
        self.wayList = self.dijkstra()  # 获取路径
        plt.gca().axes.get_xaxis().set_visible(False) # 隐藏x轴
        plt.gca().axes.get_yaxis().set_visible(False) # 隐藏y轴
    # 点与点距离
    def lengthP2P(self, p1, p2):
        return int(math.sqrt(abs(p2[0] - p1[0]) ** 2 + abs(p2[1] - p1[1]) ** 2))

    # 生成随机点与点关系
    def genPoints(self, x, y):
        points = []
        nearPs = {}
        x = [random.randint(x[0], x[1]) for i in range(self.nums)]
        y = [random.randint(y[0], y[1]) for i in range(self.nums)]
        for i in range(len(x)):
            points.append((x[i], y[i]))
        for p in points:
            m = random.randint(1, int(math.sqrt(self.nums)))
            nearPs[p] = {}
            for i in range(m):
                n = random.randint(0, len(points) - 1)
                if p != points[n]:
                    nearPs[p][points[n]] = self.lengthP2P(p, points[n])
        for p in nearPs.keys():
            for k, v in nearPs.items():
                if p == k:
                    pass
                if p in v:
                    nearPs[p][k] = self.lengthP2P(p, k)
        return points, nearPs

    # 生成随机起点和终点
    def findStartEndPoints(self):
        start = random.choice(self.points)
        end = random.choice(self.points)
        if start == end:
            start, end = self.findStartEndPoints()
        return start, end

    # 画初始图像点线图
    def drawMap(self):
        for p in self.points:
            plt.scatter(p[0], p[1])
            plt.annotate(text=(p[0], p[1]), xy=(p[0], p[1]))
        for p, ps in self.nearPs.items():
            for point in ps:
                plt.plot((p[0], point[0]), (p[1], point[1]))
        self.ax.scatter(self.startP[0], self.startP[1], s=250, c="green", marker="*")
        self.ax.annotate((self.startP[0], self.startP[1]), xy=(self.startP[0], self.startP[1]))
        self.ax.scatter(self.endP[0], self.endP[1], s=250, c="red", marker="*")
        self.ax.annotate((self.endP[0], self.endP[1]), xy=(self.endP[0], self.endP[1]))
        plt.pause(1)
        plt.ioff()
        plt.savefig("map", dpi=300)

    # 算法
    def dijkstra(self):
        shortest_paths = {self.startP: (None, 0)}
        current_node = self.startP
        visited = set()

        while current_node != self.endP:
            visited.add(current_node)
            destinations = self.nearPs[current_node]
            weight_to_current_node = shortest_paths[current_node][1]

            for next_node in destinations:
                weight = self.nearPs[current_node][next_node] + weight_to_current_node
                if next_node not in shortest_paths:
                    shortest_paths[next_node] = (current_node, weight)
                else:
                    current_shortest_weight = shortest_paths[next_node][1]
                    if current_shortest_weight > weight:
                        shortest_paths[next_node] = (current_node, weight)

            next_destinations = {node: shortest_paths[node] for node in shortest_paths if node not in visited}
            if not next_destinations:
                return "Route Not Possible"
            current_node = min(next_destinations, key=lambda k: next_destinations[k][1])

        path = []
        while current_node is not None:
            path.append(current_node)
            next_node = shortest_paths[current_node][0]
            current_node = next_node
        path = path[::-1]
        return path

    # 画路径图
    def drawFig(self):
        print("Shortest Path:", self.wayList)
        if type(self.wayList) == str:
            return
        for i in range(1, len(self.wayList)):
            self.ax.plot((self.wayList[i - 1][0], self.wayList[i][0]), (self.wayList[i - 1][1], self.wayList[i][1]),
                         "-.",
                         color="black",
                         linewidth=3)
            plt.pause(1)
            plt.ioff()
        plt.savefig("route.png", dpi=300)
        plt.show()

    # 动画更新函数
    def update_ani(self, num, x, y, line):
        line.set_data(x[:num], y[:num])
        return line,

    # 动画保存
    def animate(self):
        if type(self.wayList) == str:
            return
        xdata = [i[0] for i in self.wayList]
        ydata = [i[1] for i in self.wayList]
        line, = self.ax.plot(xdata, ydata, "-.",
                             color="black",
                             linewidth=3)
        # 创建动画对象
        ani = animation.FuncAnimation(self.fig, self.update_ani, frames=len(self.wayList) + 1,
                                      fargs=[xdata, ydata, line],
                                      interval=500, blit=True)
        # 保存为gif
        ani.save('output.gif', writer='imagemagick')


D = Dijkstra(8)
D.drawMap()  # 地图
D.animate()  # 动画
D.drawFig()  # 展示

nearPs的示例如下：

{(823, 736): {(310, 428): 598, (400, 262): 635, (736, 534): 219},

(786, 790): {(19, 446): 840, (625, 559): 281},

(19, 446): {(400, 262): 423, (786, 790): 840, (348, 610): 367},

(400, 262): {(823, 736): 635, (310, 428): 188, (19, 446): 423},

(736, 534): {(310, 428): 438, (823, 736): 219},

(348, 610): {(19, 446): 367, (625, 559): 281, (310, 428): 185},

(310, 428): {(348, 610): 185, (823, 736): 598, (400, 262): 188, (736, 534): 438},

(625, 559): {(348, 610): 281, (786, 790): 281}}

其个数是初始化的nums大小。其格式是：{坐标A:{坐标B:AB距离}，{坐标C:AC距离}}

点坐标由genPoints生成，其坐标范围可修改

代表某个点和其所有邻近点及其距离

参数可传入Dijkstra算法，获得最短路径

画图时定义起点和终点（绿?是起点，红?是终点）。

最后生成动画，其效果如下：