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Make it into installable package

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Tim 2021-05-01 02:27:41 +02:00
parent bce9f0d1fc
commit cb848c066c
7 changed files with 60 additions and 116 deletions
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4
.gitignore vendored
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@ -1,2 +1,6 @@
output/
mot_benchmark
venv
dist
build
**/*egg-info

35
README.md
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@ -18,6 +18,8 @@ For your convenience, this repo also contains *Faster* RCNN detections for the M
**Also see:**
A new and improved version of SORT with a Deep Association Metric implemented in tensorflow is available at [https://github.com/nwojke/deep_sort](https://github.com/nwojke/deep_sort) .
This fork packages the SORT algorithm as a pip package (simple-online-realtime-tracking).
### License
SORT is released under the GPL License (refer to the LICENSE file for details) to promote the open use of the tracker and future improvements. If you require a permissive license contact Alex (alex@bewley.ai).
@ -36,37 +38,13 @@ If you find this repo useful in your research, please consider citing:
doi={10.1109/ICIP.2016.7533003}
}
### Installing:
### Dependencies:
To install required dependencies run:
To install the package:
```
$ pip install -r requirements.txt
pip install simple-online-realtime-tracking==0.2
```
### Demo:
To run the tracker with the provided detections:
```
$ cd path/to/sort
$ python sort.py
```
To display the results you need to:
1. Download the [2D MOT 2015 benchmark dataset](https://motchallenge.net/data/2D_MOT_2015/#download)
0. Create a symbolic link to the dataset
```
$ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 mot_benchmark
```
0. Run the demo with the ```--display``` flag
```
$ python sort.py --display
```
### Main Results
Using the [MOT challenge devkit](https://motchallenge.net/devkit/) the method produces the following results (as described in the paper).
@ -81,12 +59,11 @@ Using the [MOT challenge devkit](https://motchallenge.net/devkit/) the method pr
KITTI-17 | 67.1 | 92.3 | 0.26 | 9 1 8 0| 38 225 9 16| 60.2 72.3 61.3
*Overall* | 49.5 | 77.5 | 1.24 | 234 48 111 75| 3311 11660 274 499| 34.0 73.3 35.1
### Using SORT in your own project
Below is the gist of how to instantiate and update SORT. See the ['__main__'](https://github.com/abewley/sort/blob/master/sort.py#L239) section of [sort.py](https://github.com/abewley/sort/blob/master/sort.py#L239) for a complete example.
from sort import *
from sort import Sort
#create instance of SORT
mot_tracker = Sort()

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pyproject.toml Normal file
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@ -0,0 +1,6 @@
[build-system]
requires = [
"setuptools>=42",
"wheel"
]
build-backend = "setuptools.build_meta"

3
requirements.txt
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@ -1,3 +0,0 @@
filterpy==1.4.5
scikit-image==0.17.2
lap==0.4.0

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setup.cfg Normal file
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@ -0,0 +1,23 @@
[metadata]
name = simple-online-realtime-tracking
version = 0.2
author = Alex Bewley
description = A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/timstokman/sort
classifiers =
Programming Language :: Python :: 3
Operating System :: OS Independent
License :: OSI Approved :: GNU General Public License v3 (GPLv3)
[options]
package_dir =
= src
packages = sort
python_requires = >=3.8
install_requires =
filterpy==1.4.5
scikit-image==0.17.2
lap==0.4.0
numpy>=1.18.5

18
src/sort/__init__.py Normal file
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@ -0,0 +1,18 @@
"""
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/>.
"""
from .sort import Sort

87
sort.py → src/sort/sort.py
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@ -15,14 +15,8 @@
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from __future__ import print_function
import os
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io
import glob
@ -119,6 +113,7 @@ class KalmanBoxTracker(object):
self.hits = 0
self.hit_streak = 0
self.age = 0
self.original_id = bbox[5]
def update(self,bbox):
"""
@ -128,6 +123,7 @@ class KalmanBoxTracker(object):
self.history = []
self.hits += 1
self.hit_streak += 1
self.original_id = bbox[5]
self.kf.update(convert_bbox_to_z(bbox))
def predict(self):
@ -243,7 +239,7 @@ class Sort(object):
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
ret.append(np.concatenate((d,[trk.id+1],[trk.original_id])).reshape(1,-1)) # +1 as MOT benchmark requires positive
i -= 1
# remove dead tracklet
if(trk.time_since_update > self.max_age):
@ -251,80 +247,3 @@ class Sort(object):
if(len(ret)>0):
return np.concatenate(ret)
return np.empty((0,5))
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")