"""SORT: A Simple, Online and Realtime Tracker.
Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import argparse
import glob
import os
import time
import matplotlib.pyplot as plt
import numpy as np
from filterpy.kalman import KalmanFilter
from matplotlib import patches
from skimage import io
np.random.seed(0)
[docs]
def linear_assignment(cost_matrix):
try:
import lap
_, x, y = lap.lapjv(cost_matrix, extend_cost=True)
return np.array([[y[i], i] for i in x if i >= 0]) #
except ImportError:
from scipy.optimize import linear_sum_assignment
x, y = linear_sum_assignment(cost_matrix)
return np.array(list(zip(x, y)))
[docs]
def iou_batch(bb_test, bb_gt):
"""From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]."""
bb_gt = np.expand_dims(bb_gt, 0)
bb_test = np.expand_dims(bb_test, 1)
xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
wh = w * h
o = wh / (
(bb_test[..., 2] - bb_test[..., 0])
* (bb_test[..., 3] - bb_test[..., 1])
+ (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1])
- wh
)
return o
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def convert_bbox_to_z(bbox):
"""
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
"""
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2.0
y = bbox[1] + h / 2.0
s = w * h # scale is just area
r = w / float(h)
return np.array([x, y, s, r]).reshape((4, 1))
[docs]
def convert_x_to_bbox(x, score=None):
"""
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
"""
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if score == None:
return np.array(
[x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0]
).reshape((1, 4))
else:
return np.array(
[
x[0] - w / 2.0,
x[1] - h / 2.0,
x[0] + w / 2.0,
x[1] + h / 2.0,
score,
]
).reshape((1, 5))
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class KalmanBoxTracker(object):
"""
This class represents the internal state of individual tracked objects observed as bbox.
"""
count = 0
def __init__(self, bbox):
"""
Initialises a tracker using initial bounding box.
"""
# define constant velocity model
self.kf = KalmanFilter(dim_x=7, dim_z=4)
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],
]
)
self.kf.R[2:, 2:] *= 10.0
self.kf.P[4:, 4:] *= (
1000.0 # give high uncertainty to the unobservable initial velocities
)
self.kf.P *= 10.0
self.kf.Q[-1, -1] *= 0.01
self.kf.Q[4:, 4:] *= 0.01
self.kf.x[:4] = convert_bbox_to_z(bbox)
self.time_since_update = 0
self.id = KalmanBoxTracker.count
KalmanBoxTracker.count += 1
self.history = []
self.hits = 0
self.hit_streak = 0
self.age = 0
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def update(self, bbox):
"""
Updates the state vector with observed bbox.
"""
self.time_since_update = 0
self.history = []
self.hits += 1
self.hit_streak += 1
self.kf.update(convert_bbox_to_z(bbox))
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def predict(self):
"""
Advances the state vector and returns the predicted bounding box estimate.
"""
if (self.kf.x[6] + self.kf.x[2]) <= 0:
self.kf.x[6] *= 0.0
self.kf.predict()
self.age += 1
if self.time_since_update > 0:
self.hit_streak = 0
self.time_since_update += 1
self.history.append(convert_x_to_bbox(self.kf.x))
return self.history[-1]
[docs]
def get_state(self):
"""
Returns the current bounding box estimate.
"""
return convert_x_to_bbox(self.kf.x)
[docs]
def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
"""Assign detections to tracked object (both represented as bounding boxes).
Returns 3 lists of matches, unmatched_detections and unmatched_trackers
"""
if len(detections) == 0:
return (
np.empty((0, 2), dtype=int),
np.empty((0, 5), dtype=int),
np.arange(len(trackers)),
)
if len(trackers) == 0:
return (
np.empty((0, 2), dtype=int),
np.arange(len(detections)),
np.empty((0, 5), dtype=int),
)
iou_matrix = iou_batch(detections, trackers)
if min(iou_matrix.shape) > 0:
a = (iou_matrix > iou_threshold).astype(np.int32)
if a.sum(1).max() == 1 and a.sum(0).max() == 1:
matched_indices = np.stack(np.where(a), axis=1)
else:
matched_indices = linear_assignment(-iou_matrix)
else:
matched_indices = np.empty(shape=(0, 2))
unmatched_detections = []
for d, det in enumerate(detections):
if d not in matched_indices[:, 0]:
unmatched_detections.append(d)
unmatched_trackers = []
for t, trk in enumerate(trackers):
if t not in matched_indices[:, 1]:
unmatched_trackers.append(t)
# filter out matched with low IOU
matches = []
for m in matched_indices:
if iou_matrix[m[0], m[1]] < iou_threshold:
unmatched_detections.append(m[0])
unmatched_trackers.append(m[1])
else:
matches.append(m.reshape(1, 2))
if len(matches) == 0:
matches = np.empty((0, 2), dtype=int)
else:
matches = np.concatenate(matches, axis=0)
return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
[docs]
class Sort(object):
def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
"""
Sets key parameters for SORT
"""
self.max_age = max_age
self.min_hits = min_hits
self.iou_threshold = iou_threshold
self.trackers = []
self.frame_count = 0
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def update(self, dets=np.empty((0, 5))):
"""
Params:
dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
self.frame_count += 1
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
matched, unmatched_dets, unmatched_trks = (
associate_detections_to_trackers(dets, trks, self.iou_threshold)
)
# update matched trackers with assigned detections
for m in matched:
self.trackers[m[1]].update(dets[m[0], :])
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i, :])
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
d = trk.get_state()[0]
if (trk.time_since_update < 1) and (
trk.hit_streak >= self.min_hits
or self.frame_count <= self.min_hits
):
ret.append(
np.concatenate((d, [trk.id + 1])).reshape(1, -1)
) # +1 as MOT benchmark requires positive
i -= 1
# remove dead tracklet
if trk.time_since_update > self.max_age:
self.trackers.pop(i)
if len(ret) > 0:
return np.concatenate(ret)
return np.empty((0, 5))
[docs]
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description="SORT demo")
parser.add_argument(
"--display",
dest="display",
help="Display online tracker output (slow) [False]",
action="store_true",
)
parser.add_argument(
"--seq_path", help="Path to detections.", type=str, default="data"
)
parser.add_argument(
"--phase", help="Subdirectory in seq_path.", type=str, default="train"
)
parser.add_argument(
"--max_age",
help="Maximum number of frames to keep alive a track without associated detections.",
type=int,
default=1,
)
parser.add_argument(
"--min_hits",
help="Minimum number of associated detections before track is initialised.",
type=int,
default=3,
)
parser.add_argument(
"--iou_threshold",
help="Minimum IOU for match.",
type=float,
default=0.3,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
# all train
args = parse_args()
display = args.display
phase = args.phase
total_time = 0.0
total_frames = 0
colours = np.random.rand(32, 3) # used only for display
if display:
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()
ax1 = fig.add_subplot(111, aspect="equal")
if not os.path.exists("output"):
os.makedirs("output")
pattern = os.path.join(args.seq_path, phase, "*", "det", "det.txt")
for seq_dets_fn in glob.glob(pattern):
mot_tracker = Sort(
max_age=args.max_age,
min_hits=args.min_hits,
iou_threshold=args.iou_threshold,
) # create instance of the SORT tracker
seq_dets = np.loadtxt(seq_dets_fn, delimiter=",")
seq = seq_dets_fn[pattern.find("*") :].split(os.path.sep)[0]
with open(os.path.join("output", "%s.txt" % (seq)), "w") as out_file:
print("Processing %s." % (seq))
for frame in range(int(seq_dets[:, 0].max())):
frame += 1 # detection and frame numbers begin at 1
dets = seq_dets[seq_dets[:, 0] == frame, 2:7]
dets[:, 2:4] += dets[
:, 0:2
] # convert to [x1,y1,w,h] to [x1,y1,x2,y2]
total_frames += 1
if display:
fn = os.path.join(
"mot_benchmark",
phase,
seq,
"img1",
"%06d.jpg" % (frame),
)
im = io.imread(fn)
ax1.imshow(im)
plt.title(seq + " Tracked Targets")
start_time = time.time()
trackers = mot_tracker.update(dets)
cycle_time = time.time() - start_time
total_time += cycle_time
for d in trackers:
print(
"%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1"
% (frame, d[4], d[0], d[1], d[2] - d[0], d[3] - d[1]),
file=out_file,
)
if display:
d = d.astype(np.int32)
ax1.add_patch(
patches.Rectangle(
(d[0], d[1]),
d[2] - d[0],
d[3] - d[1],
fill=False,
lw=3,
ec=colours[d[4] % 32, :],
)
)
if display:
fig.canvas.flush_events()
plt.draw()
ax1.cla()
print(
"Total Tracking took: %.3f seconds for %d frames or %.1f FPS"
% (total_time, total_frames, total_frames / total_time)
)
if display:
print(
"Note: to get real runtime results run without the option: --display"
)