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plot_time.py
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import numpy as np
import matplotlib.pyplot as plt
import sys, os
from scipy.spatial.transform import Rotation
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib import ticker
from matplotlib.patches import ConnectionPatch
import open3d as o3d
import matplotlib
import cv2
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
def zone_and_linked(ax,axins,zone_left,zone_right,x,y,linked='bottom', x_ratio=0.05,y_ratio=0.05):
"""缩放内嵌图形，并且进行连线
ax: 调用plt.subplots返回的画布。例如： fig,ax = plt.subplots(1,1)
axins: 内嵌图的画布。 例如 axins = ax.inset_axes((0.4,0.1,0.4,0.3))
zone_left: 要放大区域的横坐标左端点
zone_right: 要放大区域的横坐标右端点
x: X轴标签
y: 列表，所有y值
linked: 进行连线的位置，{'bottom','top','left','right'}
x_ratio: X轴缩放比例
y_ratio: Y轴缩放比例
"""
xlim_left = x[zone_left]-(x[zone_right]-x[zone_left])*x_ratio
xlim_right = x[zone_right]+(x[zone_right]-x[zone_left])*x_ratio
y_data = np.hstack([yi[zone_left:zone_right] for yi in y])
# ylim_bottom = np.min(y_data)-(np.max(y_data)-np.min(y_data))*y_ratio
ylim_bottom = 0.3
ylim_top = np.max(y_data)+(np.max(y_data)-np.min(y_data))*y_ratio
axins.set_xlim(xlim_left, xlim_right)
axins.set_ylim(ylim_bottom, ylim_top)
ax.plot([xlim_left,xlim_right,xlim_right,xlim_left,xlim_left],
[ylim_bottom,ylim_bottom,ylim_top,ylim_top,ylim_bottom],"black")
if linked == 'bottom':
xyA_1, xyB_1 = (xlim_left,ylim_top), (xlim_left,ylim_bottom)
xyA_2, xyB_2 = (xlim_right,ylim_top), (xlim_right,ylim_bottom)
elif linked == 'top':
xyA_1, xyB_1 = (xlim_left,ylim_bottom), (xlim_left,ylim_top)
xyA_2, xyB_2 = (xlim_right,ylim_bottom), (xlim_right,ylim_top)
elif linked == 'left':
xyA_1, xyB_1 = (xlim_right,ylim_top), (xlim_left,ylim_top)
xyA_2, xyB_2 = (xlim_right,ylim_bottom), (xlim_left,ylim_bottom)
elif linked == 'right':
xyA_1, xyB_1 = (xlim_left,ylim_top), (xlim_right,ylim_top)
xyA_2, xyB_2 = (xlim_left,ylim_bottom), (xlim_right,ylim_bottom)
con = ConnectionPatch(xyA=xyA_1,xyB=xyB_1,coordsA="data", coordsB="data",axesA=axins,axesB=ax)
axins.add_artist(con)
con = ConnectionPatch(xyA=xyA_2,xyB=xyB_2,coordsA="data", coordsB="data",axesA=axins,axesB=ax)
axins.add_artist(con)
def zone_and_linked2(ax,axins,zone_left,zone_right,x,y,linked='bottom', ylim_bottom_ = None, ylim_top_=None, x_ratio=0.05,y_ratio=0.05):
"""缩放内嵌图形，并且进行连线
ax: 调用plt.subplots返回的画布。例如： fig,ax = plt.subplots(1,1)
axins: 内嵌图的画布。 例如 axins = ax.inset_axes((0.4,0.1,0.4,0.3))
zone_left: 要放大区域的横坐标左端点
zone_right: 要放大区域的横坐标右端点
x: X轴标签
y: 列表，所有y值
linked: 进行连线的位置，{'bottom','top','left','right'}
x_ratio: X轴缩放比例
y_ratio: Y轴缩放比例
"""
xlim_left = x[zone_left]-(x[zone_right]-x[zone_left])*x_ratio
xlim_right = x[zone_right]+(x[zone_right]-x[zone_left])*x_ratio
y_data = np.hstack([yi[zone_left:zone_right] for yi in y])
# ylim_bottom = np.min(y_data)-(np.max(y_data)-np.min(y_data))*y_ratio
ylim_bottom = 0.3 if ylim_bottom_==None else ylim_bottom_
ylim_top = np.max(y_data)+(np.max(y_data)-np.min(y_data))*y_ratio if ylim_top_==None else ylim_top_
axins.set_xlim(xlim_left, xlim_right)
axins.set_ylim(ylim_bottom, ylim_top)
ax.plot([xlim_left,xlim_right,xlim_right,xlim_left,xlim_left],
[ylim_bottom,ylim_bottom,ylim_top,ylim_top,ylim_bottom],"black")
if linked == 'bottom':
xyA_1, xyB_1 = (xlim_left,ylim_top), (xlim_left,ylim_bottom)
xyA_2, xyB_2 = (xlim_right,ylim_top), (xlim_right,ylim_bottom)
elif linked == 'top':
xyA_1, xyB_1 = (xlim_left,ylim_bottom), (xlim_left,ylim_top)
xyA_2, xyB_2 = (xlim_right,ylim_bottom), (xlim_right,ylim_top)
elif linked == 'left':
xyA_1, xyB_1 = (xlim_right,ylim_top), (xlim_left,ylim_top)
xyA_2, xyB_2 = (xlim_right,ylim_bottom), (xlim_left,ylim_bottom)
elif linked == 'right':
xyA_1, xyB_1 = (xlim_left,ylim_top), (xlim_right,ylim_top)
xyA_2, xyB_2 = (xlim_left,ylim_bottom), (xlim_right,ylim_bottom)
con = ConnectionPatch(xyA=xyA_1,xyB=xyB_1,coordsA="data", coordsB="data",axesA=axins,axesB=ax)
axins.add_artist(con)
con = ConnectionPatch(xyA=xyA_2,xyB=xyB_2,coordsA="data", coordsB="data",axesA=axins,axesB=ax)
axins.add_artist(con)
#* 局部放大测试函数
def local_expand():
# x坐标
x = np.arange(1,1001)
# 生成y轴数据，并添加随机波动
y1 = np.log(x)
indexs = np.random.randint(0,1000,800)
for index in indexs:
y1[index] += np.random.rand() - 0.5
y2 = np.log(x)
indexs = np.random.randint(0,1000,800)
for index in indexs:
y2[index] += np.random.rand() - 0.5
y3 = np.log(x)
indexs = np.random.randint(0,1000,800)
for index in indexs:
y3[index] += np.random.rand() - 0.5
# 绘制主图
fig, ax = plt.subplots(1,1,figsize=(12,7))
ax.plot(x,y1,color='#f0bc94',label='trick-1',alpha=0.7)
ax.plot(x,y2,color='#7fe2b3',label='trick-2',alpha=0.7)
ax.plot(x,y3,color='#cba0e6',label='trick-3',alpha=0.7)
ax.legend(loc='right')
# plt.show()
# 绘制缩放图
axins = ax.inset_axes((0.4, 0.1, 0.4, 0.3))
# 在缩放图中也绘制主图所有内容，然后根据限制横纵坐标来达成局部显示的目的
axins.plot(x,y1,color='#f0bc94',label='trick-1',alpha=0.7)
axins.plot(x,y2,color='#7fe2b3',label='trick-2',alpha=0.7)
axins.plot(x,y3,color='#cba0e6',label='trick-3',alpha=0.7)
# 局部显示并且进行连线
zone_and_linked(ax, axins, 100, 150, x , [y1,y2,y3], 'right')
plt.show()
#* 随机数据集对比实验结果绘图
def plot_random_dataset():
font1 = {'family': 'Times New Roman',
'weight': 'normal',
'size': 26,
}
font2 = {'family': 'Times New Roman',
'weight': 'normal',
'size': 30,
}
data_dir = './examples/output/random_dataset'
incremental_time_files = []
knn_time_files = []
radius_time_files = []
files = os.listdir(data_dir)
for f in files:
if 'incremental_updates_time' in f:
incremental_time_files.append(os.path.join(data_dir,f))
if 'knn_time' in f:
knn_time_files.append(os.path.join(data_dir,f))
if 'radius_time' in f:
radius_time_files.append(os.path.join(data_dir,f))
incremental_time = [np.loadtxt(f) for f in incremental_time_files][0]
knn_time = [np.loadtxt(f) for f in knn_time_files][0]
radius_time = [np.loadtxt(f) for f in radius_time_files][0]
tree_size = knn_time[:,0]*2_000+200_000
print("Building: ",incremental_time[0,:][1:])
print("mean insertion: ",incremental_time[1:,:].mean(0)[1:])
print("mean knn: ",knn_time.mean(0)[1:])
print("mean radius: ",radius_time.mean(0)[1:])
# import pdb;pdb.set_trace()
fig, axes = plt.subplots(3, 1, sharex=False, sharey=False, figsize=(12, 8))
axes[0].plot(tree_size,incremental_time[1:,1], linewidth=2.5, label='i-Octree')
axes[0].plot(tree_size,incremental_time[1:,2], linewidth=2.5,label='ikd-Tree')
axes[0].plot(tree_size,incremental_time[1:,3], linewidth=2.5, label='PCL-octree')
axes[0].legend(prop=font1,edgecolor='black',loc='lower right')
# axes[0].set_xlabel(r'Bucket Size', font1)
axes[0].set_ylabel(r'Run Time/ms', font1)
axes[0].set_ylim([0,9])
axes[0].set_title('Point Insertion', font2)
labels = axes[0].get_xticklabels()+ axes[0].get_yticklabels()
[label.set_fontsize(30) for label in labels]
[label.set_fontname('Times New Roman') for label in labels]
axes[0].grid(True, which="both", ls="-")
axes[0].set_xticks([])
axes[1].plot(tree_size,knn_time[:,1], linewidth=2.5, label='i-Octree')
axes[1].plot(tree_size,knn_time[:,2], linewidth=2.5,label='ikd-Tree')
axes[1].plot(tree_size,knn_time[:,3], linewidth=2.5, label='PCL-octree')
axes[1].legend(prop=font1,edgecolor='black',loc='lower right')
# axes[0].set_xlabel(r'Bucket Size', font1)
axes[1].set_ylabel(r'Run Time/ms', font1)
axes[1].set_ylim([0,9])
axes[1].set_title('KNN search', font2)
labels = axes[1].get_xticklabels() + axes[1].get_yticklabels()
[label.set_fontsize(30) for label in labels]
[label.set_fontname('Times New Roman') for label in labels]
axes[1].grid(True, which="both", ls="-")
axes[1].set_xticks([])
axins = axes[1].inset_axes((0.2, 0.6, 0.4, 0.3))
# 在缩放图中也绘制主图所有内容，然后根据限制横纵坐标来达成局部显示的目的
axins.plot(tree_size,knn_time[:,1],linewidth=2.5, label='i-Octree')
axins.plot(tree_size,knn_time[:,2],linewidth=2.5,label='ikd-Tree')
# axins.plot(tree_size,knn_time[:,3])
labels = axins.get_yticklabels()#+ axins.get_xticklabels()
[label.set_fontsize(30) for label in labels]
[label.set_fontname('Times New Roman') for label in labels]
# 局部显示并且进行连线
zone_and_linked(axes[1], axins, 5, 10, tree_size , [knn_time[:,1],knn_time[:,2]], 'right')
axes[2].plot(tree_size,radius_time[:,1], linewidth=2.5, label='i-Octree')
axes[2].plot(tree_size,radius_time[:,2], linewidth=2.5,label='ikd-Tree')
axes[2].plot(tree_size,radius_time[:,3], linewidth=2.5, label='PCL-octree')
axes[2].legend(prop=font1,edgecolor='black',loc='lower right')
axes[2].set_xlabel(r'Tree size', font1)
axes[2].set_ylabel(r'Run Time/ms', font1)
axes[2].set_ylim([0,9])
axes[2].set_title('Radius Neighbors Search', font2)
axes[2].grid(True, which="both", ls="-")
formatter = ticker.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((0,0))
axes[2].xaxis.set_major_formatter(formatter)
axes[2].xaxis.offsetText.set_fontsize(24)
labels = axes[2].get_xticklabels()+ axes[2].get_yticklabels()
[label.set_fontsize(30) for label in labels]
[label.set_fontname('Times New Roman') for label in labels]
# axes[2].ticklabel_format(axis="x", style="sci", scilimits=(-1,2))
plt.subplots_adjust(wspace =0, hspace =0.3)
plt.subplots_adjust(left=0.07, right=0.99, top=0.95, bottom=0.12)
# fig.savefig(os.path.join('/media/zhujun/0DFD06D20DFD06D2/SLAM/octree_test/ikd-Tree-main-github/examples/output/figures','random.pdf'),dpi=600,format='pdf')
fig.savefig(os.path.join('./examples/output/figures','random.png'),dpi=600,format='png')
plt.show()
plot_random_dataset()