基于弗雷歇距离的轨迹相似度画图（plot）

基于弗雷歇距离的轨迹相似度画图（plot）

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.collections import LineCollection
from matplotlib.ticker import MaxNLocator
from shapesimilarity import shape_similarity

import seaborn as sns


def get_data(path):
    df = pd.read_excel(path, header=None)
    df2 = df.iloc[:, [i % step == 0 for i in range(len(df.columns))]]
    data = df.values
    data = data.reshape(2, -1)

    return data.T, df2.values.reshape(2, -1).T


def getpath(scen, tra):
    return "../track/Scen" + str(tra) + "Tra" + str(scen) + ".xlsx"


def draw(arr, arr2, color, title, xlabel, ylabel, sc1, x1, sc2, x2):
    drawplot(arr, Dividing_Line_Segments(arr, color), Dividing_Line_Segments(arr2, color), title,
             xlabel,
             ylabel,  sc1, x1, sc2, x2)


def Frechet(CarA, CarB):
    return shape_similarity(CarA, CarB)


def Dividing_Line_Segments(arr, color):
    segments = np.stack((arr[:-1], arr[1:]), axis=1)
    cmap = color  # jet, hsv等也是常用的颜色映射方案
    colors = color_map(arr[1:, :-1], cmap)
    # colors = colors[::-1] #可以进行颜色的反转，但是有必要吗？
    line_segments = LineCollection(segments, colors=colors, linewidths=linewidth, linestyles='solid', cmap=cmap)
    return line_segments


def color_map(data, cmap):
    """数值映射为颜色"""
    dmin, dmax = np.nanmin(data), np.nanmax(data)
    cmo = plt.cm.get_cmap(cmap)
    cs, k = list(), 256 / cmo.N
    for i in range(cmo.N):

        c = cmo(i)
        # print(c)
        for j in range(int(i * k), int((i + 1) * k)):
            cs.append(c)
    cs = np.array(cs)
    data = np.uint8(255 * (data - dmin) / (dmax - dmin))

    return cs[data]


def drawplot(arr, seg1, seg2, title, xlabel, ylabel, sc1, x1, sc2, x2):
    # sns.set(style='darkgrid')

    ax = plt.axes([0.12, 0.35, 0.82, 0.28])
    ax.set_ylim(ly)
    ax.set_xlim(lx)
    if xlabel:
        plt.xlabel(xlabel)
    if ylabel:
        plt.ylabel(ylabel)
    plt.title(title)

    ax.add_collection(seg1)
    ax.add_collection(seg2)
    # cb = fig.colorbar(seg1, extend='both', orientation='horizontal')
    # cb.set_label(cbarlabel)
    # plt.savefig('./IoU3.png', format='png', transparent=True)
    # plt.savefig('./IoU2.png', format='png', transparent=True)
    plt.savefig("C:/Users/asus/Desktop/Sim/SimGNN-master/scen2/" + "S" + str(sc1) + "T" + str(x1) + "-" + "S" + str(
        sc2) + "T" + str(x2))
    plt.show()


def getgraph(scx, x, scy, y):
    path1 = getpath(scx, x)
    path2 = getpath(scy, y)

    da, data = get_data(path1)
    da2, data2 = get_data(path2)
    similarity = Frechet(data, data2)
    print("S" + str(scx) + "T" + str(x) + "-S" + str(scy) + "T" + str(y) + "'s similarity=" + str(similarity))
    draw(da, da2, color,
         "S" + str(scx) + "T" + str(x) + "-S" + str(scy) + "T" + str(y) + "'s similarity=" + str(similarity),
         "Longitudinal", "Lateral", scx, x, scy, y)

    # plt.title(f'scen{1} Car{x} and scen{2} Car{y}\'s similarity is: {similarity}', fontsize=14, fontweight='bold')
    # plt.title(f'Shape similarity is: {similarity}', fontsize=14, fontweight='bold')
    # plt.savefig("C:/Users/asus/Desktop/Sim/SimGNN-master/scen/0419scen/" + str(x) + " " + str(y))
    # plt.show()


color = "viridis_r"
step = 50
cblabel = "asd"
lx = (0, 150)
ly = (-5, 5)
linewidth = 2
cnt = 0
for i in range(1, 4):
    for j in range(1, 4):
        if j <= i:
            continue
        cnt += 4
        getgraph(i, 1, j, 1)
        getgraph(i, 1, j, 2)
        getgraph(i, 2, j, 1)
        getgraph(i, 2, j, 2)
# getgraph(2, 2, 3, 2)

print(cnt)