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main_test.py
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main_test.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 loadData
import sortCar_yimju as socar
from TrackingModule_final 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
plt.ion()
plt.figure()
path = "/media/jinyoung/Samsung_T5/Lyft_test/38/"
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:
#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))
labels = np.asanyarray(cloud_downsample.cluster_dbscan(0.7,3))
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))
########### 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")
# 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] >= -1:
# u, v = math.cos(Track_list[i].state[3]), math.sin(Track_list[i].state[3])
# elif Track_list[i].state[2] < -1:
# 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')
# 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
plt.draw()
plt.pause(0.001)
plt.clf()
# 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()'''