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Copy pathTrackingModule_for_clusterclass.py
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TrackingModule_for_clusterclass.py
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########################### TrackingModule ###########################
import numpy as np
from numpy.linalg import inv, cholesky
import math as mt
from clusterClass import clusterClass
from matplotlib import pyplot as plt
class track:
# Initialization
def __init__(self, cluster, frame_num, id):
self.state = cluster.res
self.state = np.insert(self.state,2,0.5)
self.state = np.insert(self.state,4,0.1)
self.box = cluster.box
self.P = np.array([[0.3, 0, 0, 0, 0],
[0, 0.3, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0.3, 0],
[0, 0, 0, 0, 1]])
self.Q = np.array([[0.2, 0, 0, 0, 0],
[0, 0.2, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0.2, 0],
[0, 0, 0, 0, 1]])
self.R = np.array([[0.01, 0, 0, 0],
[0, 0.01, 0, 0],
[0, 0, 0.2, 0],
[0, 0, 0, 0.1]])
self.width_max = 2
self.length_max = 4
self.processed = 0
self.kappa = 0
self.alpha = 0.3
self.Age = 1
self.ClusterID = id
self.Start = frame_num
self.Activated = 0
self.DelCnt = 0
self.history_state = np.empty([0,5])
self.history_box = np.empty([0,3])
self.dead_flag = 0
def sigma_points(self,P):
# Should We take kappa len - 3 because of wid, len, hei term ??
# wid, len, hei term -> not in the kalman filter but in the LPF ??
n = len(self.state)
Xi = np.zeros((n, 2*n+1))
W = np.zeros(2*n+1)
self.kappa=20
Xi[:, 0] = self.state
W[0] = self.kappa / (n + self.kappa)
U = cholesky((n + self.kappa)*P)
for i in range(n):
Xi[:, i+1] = self.state + U[:, i]
Xi[:, n+i+1] = self.state - U[:, i]
W[i+1] = 1 / (2*(n+self.kappa))
W[n+i+1] = W[i+1]
return Xi, W
def UT(self,Xi, W, noiseCov):
mean = np.sum(W * Xi, axis=1)
cov = W * (Xi - mean.reshape(-1, 1)) @ (Xi - mean.reshape(-1, 1)).T
return mean, cov + noiseCov
def fx(self,Xi, dt):
'''
cosy=mt.cos(self.state[2])
siny=mt.sin(self.state[2])
A=np.array([[1,0,dt*cosy,0,0,0,0,0],
[0,1,dt*siny,0,0,0,0,0],
[0,0,1,0,0,0,0,0],
[0,0,0,1,dt,0,0,0],
[0,0,0,0,1,0,0,0],
[0,0,0,0,0,1,0,0],
[0,0,0,0,0,0,1,0],
[0,0,0,0,0,0,0,1]])
'''
# Revised CTRV Model
##################################
# Use state of sigma points
##################################
Xi_pred = np.zeros([5,11])
for i in range(0,len(Xi.T)):
coeff = Xi[2][i]/Xi[4][i]
add_part = np.array([coeff * (mt.sin(Xi[3][i] + Xi[4][i]*dt) - mt.sin(Xi[3][i])),
coeff * (-mt.cos(Xi[3][i] + Xi[4][i]*dt) + mt.cos(Xi[3][i])),
0,
Xi[4][i] * dt,
0])
Xi_pred[:,i] = Xi[:,i] + add_part
'''coeff = self.state[2]/self.state[4]
add_part = np.array([coeff * (mt.sin(self.state[3] + self.state[4]*dt) - mt.sin(self.state[3])),
coeff * (-mt.cos(self.state[3] + self.state[4]*dt) + mt.cos(self.state[3])),
0,
self.state[4] * dt,
0,
0,
0,
0])'''
#return Xi + add_part.reshape(-1,1)
return Xi_pred
def hx(self,Xi):
# B = np.array([[1,0,0,0,0],
# [0,1,0,0,0],
# [0,0,1,0,0],
# [0,0,0,1,0],
# [0,0,0,0,1]])
B = np.array([[1,0,0,0,0],
[0,1,0,0,0],
[0,0,1,0,0],
[0,0,0,1,0]])
return B @ Xi
#def unscented_kalman_filter(self, z_meas, box_meas, car_list, z_processed, dt):
def unscented_kalman_filter(self, clusters, dt):
temp = -1
"""Unscented Kalman Filter Algorithm."""
# (1) Sample Sigma Points and Weights.
Xi, W = self.sigma_points(self.P)
# (2) Predict Mean and Error Covariance of States.
fXi = self.fx(Xi, dt)
x_pred, P_x = self.UT(fXi, W, self.Q)
# x_pred_plot = (np.array([ [0,-1], [1,0]]) @ x_pred[:2].T).T
# plt.plot(x_pred_plot[0], x_pred_plot[1], 'mo')
# (3) Data Association
##### 1) Activated == 0 : Use only (car_flag == 0) cluster
if self.Activated == 0:
# First Gate
for i in range(0, len(clusters)):
if clusters[i].processed == 1 or clusters[i].car_flag == 0:
continue
z_meas_trans = np.array([0,0])
z_meas_trans[0] = clusters[i].res[0] - x_pred[0]
z_meas_trans[1] = clusters[i].res[1] - x_pred[1]
# z_meas_trans[0] = clusters[i].res[0] - self.state[0]
# z_meas_trans[1] = clusters[i].res[1] - self.state[1]
Rot_inverse = np.array([[mt.cos(self.state[3]), mt.sin(self.state[3])],
[-mt.sin(self.state[3]), mt.cos(self.state[3])]])
z_meas_rot = Rot_inverse @ z_meas_trans
if -self.width_max * 0.5 <= z_meas_rot[0] <= self.width_max * 0.5 and -self.length_max * 0.5 <= z_meas_rot[1] <= self.length_max * 0.5:
self.processed = 1
clusters[i].processed = 1
temp = i
break
# Second Gate (When Self Track is Not updated by the First gate)
if self.processed == 0:
for i in range(0, len(clusters)):
if clusters[i].processed == 1 or clusters[i].car_flag == 0:
continue
z_meas_trans = np.array([0,0])
z_meas_trans[0] = clusters[i].res[0] - x_pred[0]
z_meas_trans[1] = clusters[i].res[1] - x_pred[1]
# z_meas_trans[0] = clusters[i].res[0] - self.state[0]
# z_meas_trans[1] = clusters[i].res[1] - self.state[1]
Rot_inverse = np.array([[mt.cos(self.state[3]), mt.sin(self.state[3])],
[-mt.sin(self.state[3]), mt.cos(self.state[3])]])
z_meas_rot = Rot_inverse @ z_meas_trans
if -self.width_max * 0.8 <= z_meas_rot[0] <= self.width_max * 0.8 and -self.length_max * 1.0 <= z_meas_rot[1] <= self.length_max * 1.0:
self.processed = 1
clusters[i].processed = 1
temp = i
break
##### 2) Activated == 1 : Use All Clusters
else:
# First Gate
for i in range(0, len(clusters)):
if clusters[i].processed == 1:
continue
z_meas_trans = np.array([0,0])
z_meas_trans[0] = clusters[i].res[0] - x_pred[0]
z_meas_trans[1] = clusters[i].res[1] - x_pred[1]
# z_meas_trans[0] = clusters[i].res[0] - self.state[0]
# z_meas_trans[1] = clusters[i].res[1] - self.state[1]
Rot_inverse = np.array([[mt.cos(self.state[3]), mt.sin(self.state[3])],
[-mt.sin(self.state[3]), mt.cos(self.state[3])]])
z_meas_rot = Rot_inverse @ z_meas_trans
if -self.width_max * 0.5 <= z_meas_rot[0] <= self.width_max * 0.5 and -self.length_max * 0.5 <= z_meas_rot[1] <= self.length_max * 0.5:
self.processed = 1
clusters[i].processed = 1
temp = i
break
# Second Gate (When Self Track is Not updated by the First gate)
if self.processed == 0:
for i in range(0, len(clusters)):
if clusters[i].processed == 1:
continue
z_meas_trans = np.array([0,0])
z_meas_trans[0] = clusters[i].res[0] - x_pred[0]
z_meas_trans[1] = clusters[i].res[1] - x_pred[1]
# z_meas_trans[0] = clusters[i].res[0] - self.state[0]
# z_meas_trans[1] = clusters[i].res[1] - self.state[1]
Rot_inverse = np.array([[mt.cos(self.state[3]), mt.sin(self.state[3])],
[-mt.sin(self.state[3]), mt.cos(self.state[3])]])
z_meas_rot = Rot_inverse @ z_meas_trans
if -self.width_max * 0.8 <= z_meas_rot[0] <= self.width_max * 0.8 and -self.length_max * 1.0 <= z_meas_rot[1] <= self.length_max * 1.0:
self.processed = 1
clusters[i].processed = 1
temp = i
break
# (4) Measurement Update
if temp == -1:
self.state = x_pred
self.P = P_x
self.DelCnt += 1
else:
hXi = self.hx(fXi)
z_pred, P_z = self.UT(hXi, W, self.R)
# Calculate Off Diagonal Elements of Error Covariance and Kalman Gain.
Pxz = W * (fXi - x_pred.reshape(-1, 1)) @ (hXi - z_pred.reshape(-1, 1)).T
K = Pxz @ inv(P_z)
# Validation Check : Yaw angle
#heading_angle =
measured_state = clusters[temp].res
print("measured angle:", measured_state[2])
if mt.pi/3 <= mt.fabs(measured_state[2] - self.state[3]) < 3*mt.pi/4:
if self.state[3] >= 0:
measured_state[2] += mt.pi/2
elif self.state[3] < 0:
measured_state[2] -= mt.pi/2
if mt.fabs(measured_state[2] - self.state[3]) >= 3*mt.pi/4:
if self.state[3] >= 0:
measured_state[2] -= mt.pi
elif self.state[3] < 0:
measured_state[2] += mt.pi
'''if measured_state[2] > mt.pi/2:
measured_state[2] -= mt.pi
elif measured_state[2] < -mt.pi/2:
measured_state[2] += mt.pi'''
if measured_state[2] > mt.pi/2 or measured_state[2] < -mt.pi/2:
print("check")
measured_state[2] = self.state[3]
measured_state = np.insert(measured_state, 2, (self.state[0]-measured_state[0])/mt.fabs(self.state[0]-measured_state[0])*mt.sqrt((self.state[0] - measured_state[0])**2 + (self.state[1] - measured_state[1])**2)/dt)
#measured_state = np.insert(measured_state, 4, (measured_state[2] - self.state[3])/dt)
self.state = x_pred + K @ (measured_state - z_pred)
self.P = P_x - K @ P_z @ K.T
self.update_box(clusters[temp].box)
self.Age += 1
self.DelCnt = 0
self.ClusterID = clusters[temp].id
# (5) Get max width and length box
if self.width_max < self.box[0]:
self.width_max = self.box[0]
if self.length_max < self.box[1]:
self.length_max = self.box[1]
# (6) Store History
self.history_state = np.append(self.history_state, [self.state], axis = 0)
self.history_box = np.append(self.history_box, [self.box], axis = 0)
def update_box(self, box_meas):
self.box = (1 - self.alpha) * self.box + self.alpha * box_meas