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LinearRegression2.py
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LinearRegression2.py
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# FreeRiderHMC Team
# main_Ver.3 0708
# Segment the road just by cutting the z values below threshold instead of RANSAC segmentation
# Visualize Each Clusters
# load binary data
# Add sequent visualization
# Add z slicing
# Add Sorting convexhull
import sys
import os
import numpy as np
import open3d as o3d
import time
import math
import operator
from sklearn.cluster import MeanShift
from sklearn.neighbors import KDTree
import clusteringModule as clu
import planeSegmentation as seg
import loadData
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
############################## Macro ###############################
pi = 3.141592653589793238
######################### Define Function ##########################
def get_angle(input_list):
angle = math.atan2(input_list[1], input_list[0])
if input_list[1]<0:
angle = angle+2*pi
return angle*180/pi
def diff_angle(ang1, ang2):
if abs(ang1-ang2) >180:
diffang = 360-abs(ang1-ang2)
else:
diffang = abs(ang1-ang2)
return diffang
def get_distance(xy1,xy2):
distance = ((xy1[0]-xy2[0])**2 + (xy1[1]-xy2[1])**2)**0.5
return distance
########################### Variable init ##########################
# Set mod
mod = sys.modules[__name__]
# Expand iteration limit
sys.setrecursionlimit(5000)
# Set Car Standard
carz_min, carz_max = 0, 2
carx_min, carx_max = 1.5, 5
cary_min, cary_max = 1.5, 5
car_count = 0
car = False
Axis_Points = [[0,0,0], [20,0,0],[0,20,0]]
Axis_Lines = [[0,1],[0,2]]
colors = [[0,0,0] for i in range(len(Axis_Lines))]
line_set = o3d.geometry.LineSet(points = o3d.utility.Vector3dVector(Axis_Points), lines = o3d.utility.Vector2iVector(Axis_Lines))
line_set.colors = o3d.utility.Vector3dVector(colors)
# Load binary data
path = './2011_09_26/2011_09_26_drive_0005_sync/velodyne_points/data/'
res = np.empty([0,6])
file_list = loadData.load_data(path)
num = 0
############################## Main Loop ############################
# get points from all lists
for files in file_list:
#files = file_list[3]
data = np.fromfile(path+files, dtype = np.float32)
data = data.reshape(-1,4)
data = data[:,0:3]
# Convert numpy into pointcloud
cloud = o3d.geometry.PointCloud()
cloud.points = o3d.utility.Vector3dVector(data)
# Downsampling pointcloud
cloud_downsample = cloud.voxel_down_sample(voxel_size=0.1)
#print(cloud_downsample.segment_plane(0.4,300,300)[1])
#outerBox = [[20,-10,-1.8],[20,-10,-1.8]]
#cloud_downsample.crop()
# Convert pcd to numpy array
cloud_downsample = np.asarray(cloud_downsample.points)
# Crop Pointcloud -20m < x < 20m && -20m < y < 20m && z > -1.80m
cloud_downsample = cloud_downsample[((cloud_downsample[:, 0] <= 15))]
cloud_downsample = cloud_downsample[((cloud_downsample[:, 0] >= -15))]
cloud_downsample = cloud_downsample[((cloud_downsample[:, 1] <= 10))]
cloud_downsample = cloud_downsample[((cloud_downsample[:, 1] >= -10))]
# threshold z value cut the road
cloudoutliers = cloud_downsample[((cloud_downsample[:, 2] >= -1.3))] # -1.56
# Clustering Pointcloud
# adjust the threshold into Clustering
start = time.time()
tree = KDTree(cloudoutliers, leaf_size = 100)
clusters = clu.euclideanCluster(cloudoutliers, tree, 0.5)
print("number of estimated clusters : ", len(clusters))
print("How much time for Clustering")
print(time.time() - start)
cluster = np.empty([0,3])
clustersCloud = np.empty(shape = [0,3])
# Visualize Clusters
for i in range(len(clusters)):
car_count = 0
# Find the Cars
# 1) Extract each cluster
clusterCloud = cloudoutliers[clusters[i][:],:]
#clustersCloud_pcd.points = np.append(clustersCloud_pcd.points,clusterCloud_pcd.points)
# 2) Find Cars with weak condition
z_max=z_min=x_max=x_min=y_max=y_min=0
z_max = np.max(clusterCloud[:,2])
z_min = np.min(clusterCloud[:,2])
z_for_slicing = 4/5*z_min + 1/5*z_max
'''
#clusterCloud = clusterCloud[:,0:2]
#clusterCloud[:,2] = z_for_slicing
tempcloud = o3d.geometry.PointCloud()
tempcloud.points = o3d.utility.Vector3dVector(clusterCloud)
tempcloud.compute_convex_hull()
print(type(clusterCloud))
print(clusterCloud.shape)
'''
# slicing by z values
clusterCloud = clusterCloud[(clusterCloud[:,2] >= z_for_slicing - 0.07)]
clusterCloud = clusterCloud[(clusterCloud[:,2] <= z_for_slicing + 0.07)]
x_max = np.max(clusterCloud[:,0])
x_min = np.min(clusterCloud[:,0])
y_max = np.max(clusterCloud[:,1])
y_min = np.min(clusterCloud[:,1])
x_len = abs(x_min - x_max)
y_len = abs(y_min - y_max)
z_len = abs(z_min - z_max)
if carx_min < x_len < carx_max and cary_min < y_len < cary_max and carz_min < z_len < carz_max:
car_count += 1
car = True
end = False
while(car):
# Get Centroid
x_sum = clusterCloud[:,0].sum()
y_sum = clusterCloud[:,1].sum()
z_sum = clusterCloud[:,2].sum()
num_points = len(clusterCloud)
x_inner = x_sum / num_points
y_inner = y_sum / num_points
#z_inner = z_sum / num_points
inner_point = [x_inner, y_inner]
# Should get Centroid in other way
# clusterCloud = np.append(clusterCloud, mean, axis=0)
# print(clusterCloud)
# Convert Numpy to Pointcloud
clusterCloud_pcd = o3d.geometry.PointCloud()
clusterCloud_pcd.points = o3d.utility.Vector3dVector(clusterCloud)
################ Convex hull and Sorting ################
convexhull = clusterCloud[(clusterCloud_pcd.compute_convex_hull()[1])[:],:]
# Ordering the convex hull result
convexhull_2D = convexhull[:,0:2]
convexhull_2D = convexhull_2D - inner_point
length = len(convexhull)
convexhull_angle = list(map(get_angle, convexhull_2D))
#print(convexhull_angle)
convexhull_dict = {}
for j in range(0,length):
convexhull_dict[convexhull_angle[j]] = convexhull_2D[j]
convexhull_dict_sorted = sorted(convexhull_dict.items())
convexhull_sorted_numpy = np.empty([0,2])
for j in range(0,length):
convexhull_sorted_numpy = np.append(convexhull_sorted_numpy, [convexhull_dict_sorted[j][1]],axis = 0)
#convexhull_dict = list(convexhull_dict.values())
#convexhull_dict_sorted = convexhull_dict_sorted[:][1]
#print(convexhull_sorted_numpy[0:5])
print('*'*8)
'''arrange angle to start with first point of cluster'''
for h in range(0,length-2,1):
dist1 = get_distance(convexhull_sorted_numpy[h],convexhull_sorted_numpy[h+1])
dist2 = get_distance(convexhull_sorted_numpy[h+1],convexhull_sorted_numpy[h+2])
if dist1>1.5:
if dist2 < 0.5:
move = convexhull_sorted_numpy[0:h+1][:]
convexhull_sorted_numpy = convexhull_sorted_numpy[h+1:][:]
convexhull_sorted_numpy = np.append(convexhull_sorted_numpy, move, axis = 0)
print('*')
elif dist2 > 0.8:
convexhull_sorted_numpy = np.delete(convexhull_sorted_numpy,h,axis = 0)
print(convexhull_sorted_numpy)
break
length = len(convexhull_sorted_numpy)
if length<5:
car = False
break
################### Linear Regression ###################
''' Initialization '''
line1_clust = np.empty([0,2])
line2_clust = np.empty([0,2])
line3_clust = np.empty([0,2])
flag1 = False
flag2 = False
#flag3 = False
getend2 = False
getend3 = False
angle_clust = np.empty([0,1]).reshape(-1,1)
longline = np.empty([0,2])
shortline = np.empty([0,2])
cnt1 = 0
cnt2 = 0
line1_clust = np.append(line1_clust, convexhull_sorted_numpy[0:5][:], axis = 0)
for j in range(length-1):
#x_0~x_len-1
x1=convexhull_sorted_numpy[j][0]
y1=convexhull_sorted_numpy[j][1]
x2=convexhull_sorted_numpy[j+1][0]
y2=convexhull_sorted_numpy[j+1][1]
if j-4 >0 and j+4 < length-1:
xback1 = convexhull_sorted_numpy[j-4][0]
yback1 = convexhull_sorted_numpy[j-4][1]
xback2 = convexhull_sorted_numpy[j-5][0]
yback2 = convexhull_sorted_numpy[j-5][1]
xfront1 = convexhull_sorted_numpy[j+4][0]
yfront1 = convexhull_sorted_numpy[j+4][1]
xfront2 = convexhull_sorted_numpy[j+5][0]
yfront2 = convexhull_sorted_numpy[j+5][1]
aback1 = get_angle([xback1-x1, yback1-y1])
aback2 = get_angle([xback2-x1, yback2-y1])
afront1 = get_angle([xfront1-x1, yfront1-y1])
afront2 = get_angle([xfront2-x1, yfront2-y1])
#print(diff_angle(aback1,afront1))
if flag1 == False:
if get_distance([x1,y1],[x2,y2])<0.6:
line1_clust = np.append(line1_clust, [convexhull_sorted_numpy[j][:]], axis = 0)
if 120>diff_angle(aback1,afront1)>85 and 120>diff_angle(aback2,afront2)>85:
flag1 = True
else:
flag1= False
car = False
break
elif flag1 == True and flag2 == False:
line2_clust = np.append(line2_clust, [convexhull_sorted_numpy[j][:]], axis = 0)
cnt1 +=1
if(cnt1 >= 3):
if 120>diff_angle(aback1,afront1)>85 or 120>diff_angle(aback2,afront2)>85:
flag2 = True
getend2 = False
else: getend2 = True
else: getend2 = True
elif flag1 == True and flag2 == True:
line3_clust = np.append(line3_clust, [convexhull_sorted_numpy[j][:]], axis = 0)
cnt2 +=1
if(cnt2 >= 3):
if 120>diff_angle(aback1,afront1)>85 or 120>diff_angle(aback2,afront2)>85:
car = False
else: getend3 = True
else: getend3 = True
if getend2: line2_clust = np.append(line2_clust, convexhull_sorted_numpy[length-5:length][:], axis = 0)
if getend3: line3_clust = np.append(line3_clust, convexhull_sorted_numpy[length-5:length][:], axis = 0)
# if 100>diff_angle(aback1,afront1)>80 or 100>diff_angle(aback2,afront2)>80:
# flag2 = True
# elif flag1 == True and flag2 == True:
# line3_clust = np.append(line3_clust, [convexhull_sorted_numpy[j][:]], axis = 0)
# if 100>diff_angle(aback1,afront1)>80 or 100>diff_angle(aback2,afront2)>80:
# list1_clust = []
# list2_clust = []
# #print(line1_clust)
#make fitter
line_fitter = LinearRegression()
if len(line1_clust[:][:,0])>3:
len1 = len(line1_clust[:][:,0])
dis1 = get_distance(line1_clust[0],line1_clust[len1-1])
xline1 = line1_clust[:][:,0].reshape([len1,1])
yline1 = line1_clust[:][:,1].reshape([len1,1])
line1_fit = line_fitter.fit(xline1,yline1)
line1dy = line1_fit.coef_
line1bias = line1_fit.intercept_
line1pred = line1_fit.predict(xline1).reshape([len1,1])
# print('*'*8)
# print(line1pred[:])
# print('*'*20)
if line2_clust != np.empty([0,2]):
#print(line2_clust[:][:,1])
len2 = len(line2_clust[:][:,0])
dis2 = get_distance(line2_clust[0],line2_clust[len2-1])
xline2 = line2_clust[:][:,0].reshape([len2,1])
yline2 = line2_clust[:][:,1].reshape([len2,1])
line2_fit = line_fitter.fit(xline2,yline2)
line2dy = line2_fit.coef_
line2bias = line2_fit.intercept_
else:
car = False
break
if line3_clust != np.empty([0,2]):
len3 = len(line3_clust[:][0])
#plot for check
# plt.figure()
# plt.scatter(line1_clust[:][:,0],line1_clust[:][:,1],color ='black')
# plt.scatter(line2_clust[:][:,0],line2_clust[:][:,1],color ='blue')
# plt.plot(xline1,line1pred[:],color = 'red')
# plt.show()
####################### Get Centroid #######################
x1, y1 = line1_clust[0][0], line1_clust[0][1]
x2, y2 = line2_clust[0][0], line2_clust[0][1]
x3, y3 = line2_clust[len2-1][0], line2_clust[len2-1][1]
if line3_clust != np.empty([0,2]):
x3, y3 = line3_clust[len3-1][0], line3_clust[len3-1][1]
delx, dely = x1-x2, y1-y2
x4 = x3+delx
y4 = y3+dely
xlist = np.array([x1,x2,x3,x4])
ylist = np.array([y1,y2,y3,y4])
center = [(x1+x2+x3+x4)/4,(y1+y2+y3+y4)/4]
#yaw = get_angle(linedy,1)
x = center[0]
y = center[1]
yaw = get_angle([1,line2dy])*pi/180
l = (abs(x1-x4)**2+abs(y1-y4)**2)**0.5
w = (abs(x4-x3)**2+abs(y4-y3)**2)**0.5
h = z_max - z_min + 0.5
if(l<w):
temp = w
w = l
l = temp
yaw = get_angle([1,line1dy])*pi/180
if(0.8<w<2.8 and 1.2<l<7.1 and 1.2<h<2.9):
res = np.append(res, [[x,y, yaw, w, l, h]], axis = 0)
#flagflag = True
#plot for check
plt.figure()
plt.plot(xline1,line1pred[:],color = 'red')
plt.scatter(line1_clust[:][:,0],line1_clust[:][:,1],color ='black')
plt.scatter(line2_clust[:][:,0],line2_clust[:][:,1],color ='blue')
plt.scatter(xlist,ylist,color ='red')
plt.scatter(center[0],center[1],color ='green')
plt.show()
print(res)
print(res.shape)
break