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Set compute_log_likelihood=False = 1.5X speed gain as pointed out by laclouis5@

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Alex Bewley 2020-01-06 23:15:36 +01:00
parent 2193212957
commit 528738a692
1 changed files with 5 additions and 5 deletions
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10
sort.py
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@ -18,7 +18,7 @@
from __future__ import print_function
from numba import jit
import os.path as osp
import os
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
@ -87,7 +87,7 @@ class KalmanBoxTracker(object):
Initialises a tracker using initial bounding box.
"""
#define constant velocity model
self.kf = KalmanFilter(dim_x=7, dim_z=4)
self.kf = KalmanFilter(dim_x=7, dim_z=4, compute_log_likelihood=False)
self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0], [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])
self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])
@ -266,15 +266,15 @@ if __name__ == '__main__':
total_frames = 0
colours = np.random.rand(32, 3) #used only for display
if(display):
if not osp.exists('mot_benchmark'):
if not os.path.exists('mot_benchmark'):
print('\n\tERROR: mot_benchmark link not found!\n\n Create a symbolic link to the MOT benchmark\n (https://motchallenge.net/data/2D_MOT_2015/#download). E.g.:\n\n $ ln -s /path/to/MOT2015_challenge/2DMOT2015 mot_benchmark\n\n')
exit()
plt.ion()
fig = plt.figure()
if not osp.exists('output'):
if not os.path.exists('output'):
os.makedirs('output')
pattern = osp.join(args.seq_path, phase, '*', 'det', 'det.txt')
pattern = os.path.join(args.seq_path, phase, '*', 'det', 'det.txt')
for seq_dets_fn in glob.glob(pattern):
mot_tracker = Sort() #create instance of the SORT tracker
print(seq_dets_fn)