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main_WJ.py
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main_WJ.py
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import sys
import os
import numpy as np
import open3d as o3d
import time
import math
import operator
from matplotlib import pyplot as plt
import matplotlib.style as mplstyle
import loadData
import sortCar_yimju as socar
from TrackingModule_WJ import track
# from TrackingModule_for_clusterclass2 import track
#from clusterClass import clusterClass
import pandas as pd
#import cv2
def save_to_csv(index, start, duration, state, framenum, path_csv):
datalist = np.full((start,5),np.nan)
datalist = np.append(datalist, state, axis = 0)
datalist = np.append(datalist, np.full((framenum-start-duration+1, 5), np.nan), axis = 0)
pd.DataFrame(datalist).to_csv(path_csv + '{}.csv'.format(index))
# Set Track list
Track_list = []
Track_list_valid = []
frame_num = 0
mplstyle.use('fast')
plt.ion()
plt.figure()
path = "/media/jinwj1996/Samsung_T5/Lyft_test/2/"
path_lidar = path + "lidar/"
path_csv = path + "csvdata/"
#path_image = path + "images/"
file_list = loadData.load_data(path_lidar)
#image_list = loadData.load_data(path_image)
#cv2.namedWindow('Show Image')
for files in file_list:
starttime = time.time()
#clusterClass_list = []
measured_centroid = np.empty([0,3])
measured_box = np.empty([0,3])
cluster_id = []
processed = []
dt = 0.2
# img = cv2.imread(path_image + image_list[frame_num], cv2.IMREAD_COLOR)
# cv2.imshow("Show Image", img)
# cv2.waitKey(1)
data = np.fromfile(path_lidar+files,dtype = np.float32)
data = data.reshape(-1,5)
data = data[:,0:3]
data = (np.array([[math.cos(177*math.pi/180),-math.sin(177*math.pi/180),0], [math.sin(177*math.pi/180),math.cos(177*math.pi/180),0], [0,0,1]]) @ data.T).T
data = data[(data[:,0] <= 50)]
data = data[(data[:,0] >= -10)]
data = data[(data[:,1] <= 15)]
data = data[(data[:,1] >= -15)]
data_plot = (np.array([ [0,-1,0], [1,0,0], [0,0,1]]) @ data.T).T
plt.plot(data_plot[:,0], data_plot[:,1],'ko', markersize = 0.3)
plt.xlim(-30,30)
plt.ylim(-10,50)
plt.text(-30, 20, '{}-th frame'.format(frame_num))
data = data[((data[:, 2] >= -1.3))] # -1.56
data = data[((data[:, 2] <= 1.5))] # -1.56
cloud = o3d.geometry.PointCloud()
cloud.points = o3d.utility.Vector3dVector(data)
cloud_downsample = cloud.voxel_down_sample(voxel_size=0.05)
# cloud_downsample_plot = np.asarray(cloud_downsample.points)
# cloud_downsample_plot = (np.array([ [0,-1,0], [1,0,0], [0,0,1]]) @ cloud_downsample_plot.T).T
# plt.plot(cloud_downsample_plot[:,0], cloud_downsample_plot[:,1],'ko', markersize = 0.4)
# plt.xlim(-40,40)
# plt.ylim(-20,60)
# plt.text(-40, 20, '{}-th frame'.format(frame_num))
clustertime = time.time()
labels = np.asanyarray(cloud_downsample.cluster_dbscan(0.7,3))
print("Clustering time: ", time.time() - clustertime)
for i in range(np.max(labels)+1):
DBSCAN_Result = cloud_downsample.select_by_index(np.where(labels == i)[0])
clusterCloud = np.asarray(DBSCAN_Result.points)
if len(clusterCloud) <= 10:
continue
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])
center = DBSCAN_Result.get_center()
clusterCloud_plot = (np.array([[0,-1,0], [1,0,0], [0,0,1]]) @ clusterCloud.T).T
plt.plot(clusterCloud_plot[:,0], clusterCloud_plot[:,1],'bo', markersize = 0.4)
# center_plot = (np.array([[0,-1,0], [1,0,0], [0,0,1]]) @ center.T).T
# plt.plot(center_plot[0], center_plot[1],'ro', markersize = 0.8)
# Get 4 Box Points
box = DBSCAN_Result.get_oriented_bounding_box()
box_center = box.center
box_center_plot = (np.array([[0,-1,0], [1,0,0], [0,0,1]]) @ box_center.T).T
#plt.plot(box_center_plot[0], box_center_plot[1],'go', markersize = 1.5)
box_center = box_center[:2]
box_points = box.get_box_points()
box_points_numpy = np.asarray(box_points)
#print(box_points_numpy)
box_points_numpy_plot = (np.array([[0,-1,0], [1,0,0], [0,0,1]]) @ box_points_numpy.T).T
#plt.plot(box_points_numpy_plot[:,0], box_points_numpy_plot[:,1], 'go', markersize = 1.5)
# for i in range(0,8):
# plt.text(box_points_numpy_plot[i,0], box_points_numpy_plot[i,1], '{}'.format(i))
box = np.array([[(box_points_numpy[0,0]+box_points_numpy[3,0])/2, (box_points_numpy[0,1]+box_points_numpy[3,1])/2],
[(box_points_numpy[2,0]+box_points_numpy[5,0])/2, (box_points_numpy[2,1]+box_points_numpy[5,1])/2],
[(box_points_numpy[4,0]+box_points_numpy[7,0])/2, (box_points_numpy[4,1]+box_points_numpy[7,1])/2],
[(box_points_numpy[1,0]+box_points_numpy[6,0])/2, (box_points_numpy[1,1]+box_points_numpy[6,1])/2]])
box_plot = (np.array([[0,-1], [1,0,]]) @ box.T).T
#plt.plot(box_plot[:,0], box_plot[:,1], 'ro', markersize = 3)
# Sort box
box = socar.sortline_angle(box, box_center)
# Get length of 4 line
width = 100
length = 0
yaw = 0
yaw_norm = 0
# width : min / length : max
for i in range(0,len(box)-1):
tmp = math.sqrt((box[i,0] - box[i+1,0])**2 + (box[i,1] - box[i+1,1])**2)
if width > tmp:
width = tmp
yaw_norm = math.atan( (box[i,1] - box[i+1,1]) / (box[i,0] - box[i+1,0]) )
if length < tmp:
length = tmp
yaw = math.atan( (box[i,1] - box[i+1,1]) / (box[i,0] - box[i+1,0]) )
templist_res = [box_center[0], box_center[1], yaw]
templist_box = [width, length, z_max - z_min]
# Sort Car by length and angle conditions
# Save measured centroid, box, id, and processed
if (width >= 1 or length >= 1) and width<=4 and length <= 8:
if math.fabs(yaw - yaw_norm) >= math.pi/3:
# cluster = clusterClass(np.array(templist_res), np.array(templist_box), i, 1)
# clusterClass_list.append(cluster)
measured_centroid = np.append(measured_centroid, [templist_res], axis = 0)
measured_box = np.append(measured_box, [templist_box], axis = 0)
cluster_id.append(i)
#plt.plot(box_plot[:,0], box_plot[:,1], 'ro', markersize = 3)
# u, v = math.cos(yaw), math.sin(yaw)
# [u,v] = (np.array([ [0,-1], [1,0]]) @ np.asarray([u,v]).T).T
# plt.quiver(box_center_plot[0], box_center_plot[1], u, v, scale= 3, scale_units = 'inches', color = 'red')
# else:
# cluster = clusterClass(np.array(templist_res), np.array(templist_box), i, 0)
# clusterClass_list.append(cluster)
processed = np.zeros(len(cluster_id))
trackingtime = time.time()
########### Track Update ############
if Track_list:
for i in range(0,len(Track_list)):
if Track_list[i].dead_flag == 1:
continue
Track_list[i].unscented_kalman_filter(measured_centroid, measured_box, cluster_id, processed, dt)
########### Create Track ###########
for i in range(0, len(measured_centroid)):
if processed[i] == 1:
continue
# z_meas[i] that are not used : Create new track
#clusterClass_list[i].processed = 1
Track = track(measured_centroid[i], measured_box[i], frame_num, cluster_id[i])
Track_list.append(Track)
########## Track Management ########
if Track_list:
try:
for i in range(0, len(Track_list)):
# Dismiss DeadTrack
if Track_list[i].dead_flag == 1:
continue
# Activate Track
if Track_list[i].Activated == 0 and Track_list[i].Age >= 5:
Track_list[i].Activated = 1
# deActivate Track
if Track_list[i].DelCnt >= 7:
Track_list[i].dead_flag = 1
'''# Delete Track
if Track_list[i].DelCnt >= 20:
#del Track_list[i]
Track_list[i].dead_flag = 1'''
# Initialize Tracks' processed check
#Track_list[i].processed = 0
except:
print("Track was deleted")
print("Tracking time: ", time.time() - trackingtime)
# cloud_downsample_plot = (np.array([ [0,-1,0], [1,0,0], [0,0,1]]) @ cloud_downsample.T).T
# # Plot all points
# plt.xlim(-40,40)
# plt.ylim(-20,60)
# plt.plot(cloud_downsample_plot[:,0], cloud_downsample_plot[:,1],'ko', markersize = 0.4)
# plt.text(-40, 20, '{}-th frame'.format(frame_num))
for i in range(0, len(Track_list)):
#print(Track_list[i].Activated, Track_list[i].processed)
if Track_list[i].Activated == 1 and Track_list[i].processed == 1:
temp = cloud_downsample.select_by_index(np.where(labels == Track_list[i].ClusterID)[0])
temp = np.asarray(temp.points)
Track_list[i].processed = 0
if len(temp) == 0:
continue
plt.xlim(-30,30)
plt.ylim(-10,50)
temp = (np.array([ [0,-1,0], [1,0,0], [0,0,1]]) @ temp.T).T
center = np.array([Track_list[i].state[0], Track_list[i].state[1]])
center = (np.array([[0,-1], [1,0]]) @ center.T).T
# plt.plot(temp[:,0], temp[:,1], 'ro', markersize = 0.4)
# plt.plot(center[0], center[1], 'go')
plt.text(center[0], center[1], 'Track{}'.format(i+1))
#u, v = math.cos(Track_list[i].state[3]), math.sin(Track_list[i].state[3])
if Track_list[i].state[2] >= 0.5:
u, v = math.cos(Track_list[i].state[3]), math.sin(Track_list[i].state[3])
[u,v] = (np.array([ [0,-1], [1,0]]) @ np.asarray([u,v]).T).T
plt.quiver(center[0], center[1], u, v, scale= 3, scale_units = 'inches', color = 'red')
elif Track_list[i].state[2] < -0.5:
u, v = math.cos(Track_list[i].state[3] + math.pi), math.sin(Track_list[i].state[3] + math.pi)
[u,v] = (np.array([ [0,-1], [1,0]]) @ np.asarray([u,v]).T).T
plt.quiver(center[0], center[1], u, v, scale= 3, scale_units = 'inches', color = 'red')
#[u,v] = (np.array([ [0,-1], [1,0]]) @ np.asarray([u,v]).T).T
# Plot Track's trace
#for j in range(0, len(Track_list[i].trace)):
# trace_for_plot
#plt.plot(Track_list[i].trace_x[:], Track_list[i].trace_y[:], 'g')
# Initialize Tracks' processed check
print("enumerate time: ", time.time() - starttime)
plttime = time.time()
plt.draw()
plt.pause(0.001)
plt.clf()
print("plot time: ", time.time() - plttime)
# plot Ego Vehicle
'''plt.text(0, 0, 'EgoCar')
plt.plot(res[:,0], res[:,1], 'ro')
for i in range(0, len(Track_list)):
if Track_list[i].Activated == 1 and Track_list[i].dead_flag == 0:
plt.plot(Track_list[i].state[0], Track_list[i].state[1], 'b*')
plt.text(Track_list[i].state[0], Track_list[i].state[1], 'Track{}'.format(i+1))
# Plot Track's trace
#for j in range(0, len(Track_list[i].trace)):
# trace_for_plot
#plt.plot(Track_list[i].trace_x[:], Track_list[i].trace_y[:], 'g')
plt.show()'''
#for i in range(0, len(Track_list)):
# print("Track value: ".format(i), Track_list[i].state)
#pre_time_stamp = time_stamp
frame_num += 1
#input("Press Enter to continue...")
for i in range(0, len(Track_list)):
if Track_list[i].Activated == 1:
Track_list_valid.append((Track_list[i], i+1))
validtracklistnum =len(Track_list_valid)
print("# of all track_list : ", len(Track_list))
print("# of valid track_list : ", validtracklistnum)
for i in range(validtracklistnum):
index = Track_list_valid[i][1]
start = Track_list_valid[i][0].Start
duration = len(Track_list_valid[i][0].history_state)
state = Track_list_valid[i][0].history_state
framenum = frame_num
save_to_csv(index, start, duration, state, framenum, path_csv)
# reach to csv file
# csv_file = pd.read_csv('{}.csv'.format(i), index_col=0) # set i~
# '''for i in range(len(Track_list)):
# print("Track_list {}-th all state".format(i+1))
# print(Track_list[i].history_state)
# plt.figure()
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_state[:,0], label = 'x_point', color = 'b')
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_state[:,1], label = 'y_point', color = 'g')
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_state[:,2], label = 'velocity', color = 'r')
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_state[:,3], label = 'yaw-angle', color = 'c')
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_state[:,4], label = 'yaw_rate', color = 'm')
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_box[:,0], label = 'width', color = 'y')
# plt.plot(range(1,len(Track_list[i].history_state) + 1) , Track_list[i].history_box[:,1], label = 'length', color = 'k')
# plt.legend(loc='upper left', bbox_to_anchor=(1.0, 1.0))
# plt.show()'''