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track.py
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track.py
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import cv2
import numpy as np
from scipy.linalg import block_diag
class LaneTracker:
def __init__(self, n_lanes, proc_noise_scale, meas_noise_scale,
process_cov_parallel=0, proc_noise_type='white'):
self.n_lanes = n_lanes
self.meas_size = 4 * self.n_lanes
self.state_size = self.meas_size * 2
self.contr_size = 0
self.kf = cv2.KalmanFilter(self.state_size, self.meas_size,
self.contr_size)
self.kf.transitionMatrix = np.eye(self.state_size, dtype=np.float32)
self.kf.measurementMatrix = np.zeros((self.meas_size, self.state_size),
np.float32)
for i in range(self.meas_size):
self.kf.measurementMatrix[i, i*2] = 1
if proc_noise_type == 'white':
block = np.matrix([[0.25, 0.5],
[0.5, 1.]], dtype=np.float32)
proc_noise = block_diag(*([block] * self.meas_size))
if proc_noise_type == 'identity':
proc_noise = np.eye(self.state_size, dtype=np.float32)
self.kf.processNoiseCov = proc_noise * proc_noise_scale
for i in range(0, self.meas_size, 2):
for j in range(1, self.n_lanes):
self.kf.processNoiseCov[i, i+(j*8)] = process_cov_parallel
self.kf.processNoiseCov[i+(j*8), i] = process_cov_parallel
self.kf.measurementNoiseCov = np.eye(self.meas_size, dtype=np.float32)\
* meas_noise_scale
self.kf.errorCovPre = np.eye(self.state_size)
self.meas = np.zeros((self.meas_size, 1), np.float32)
self.state = np.zeros((self.state_size, 1), np.float32)
self.first_detected = False
def _update_dt(self, dt):
for i in range(0, self.state_size, 2):
self.kf.transitionMatrix[i, i+1] = dt
def _first_detect(self, lanes):
for l, i in zip(lanes, range(0, self.state_size, 8)):
self.state[i:i+8:2, 0] = l
self.kf.statePost = self.state
self.first_detected = True
def update(self, lanes):
if self.first_detected:
for l, i in zip(lanes, range(0, self.meas_size, 4)):
if l is not None:
self.meas[i:i+4, 0] = l
self.kf.correct(self.meas)
else:
if lanes.count(None) == 0:
self._first_detect(lanes)
def predict(self, dt):
if self.first_detected:
self._update_dt(dt)
state = self.kf.predict()
lanes = []
for i in range(0, len(state), 8):
lanes.append((state[i], state[i+2], state[i+4], state[i+6]))
return lanes
else:
return None