.gitignore

@@ -1,6 +1,2 @@
 output/
 mot_benchmark
-venv
-dist
-build
-**/*egg-info

README.md

@@ -2,7 +2,7 @@ SORT
 =====
 
 A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences.
-See an example [video here](https://alex.bewley.ai/misc/SORT-MOT17-06-FRCNN.webm).
+See an example [video here](https://motchallenge.net/movies/ETH-Linthescher-SORT.mp4).
 
 By Alex Bewley  
 
@@ -18,8 +18,6 @@ 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).
@@ -38,13 +36,37 @@ If you find this repo useful in your research, please consider citing:
       doi={10.1109/ICIP.2016.7533003}
     }
 
-### Installing:
 
-To install the package:
+### Dependencies:
+
+To install required dependencies run:
 ```
-pip install simple-online-realtime-tracking==0.3
+$ pip install -r requirements.txt
 ```
 
+
+### 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).
@@ -59,11 +81,12 @@ 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 Sort
+    from sort import *
     
     #create instance of SORT
     mot_tracker = Sort() 

pyproject.toml

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

requirements.txt

@@ -0,0 +1,4 @@
+filterpy==1.4.1
+numba==0.38.1
+scikit-image==0.14.0
+lap==0.4.0

setup.cfg

@@ -1,22 +0,0 @@
-[metadata]
-name = simple-online-realtime-tracking
-version = 0.3
-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
-    lapx>=0.5.0
-    numpy>=1.18.5

sort.py

@@ -1,6 +1,6 @@
 """
     SORT: A Simple, Online and Realtime Tracker
-    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
+    Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com
 
     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
@@ -15,8 +15,16 @@
     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
+
+from numba import jit
 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
 import time
@@ -36,25 +44,23 @@ def linear_assignment(cost_matrix):
     x, y = linear_sum_assignment(cost_matrix)
     return np.array(list(zip(x, y)))
 
-
-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])
+@jit
-  xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
+def iou(bb_test, bb_gt):
-  yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
+  """
+  Computes IUO between two bboxes in the form [x1,y1,x2,y2]
+  """
+  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., xx2 - xx1)
   h = np.maximum(0., yy2 - yy1)
   wh = w * h
-  o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])                                      
+  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)                                              
+    + (bb_gt[2] - bb_gt[0]) * (bb_gt[3] - bb_gt[1]) - wh)
-  return(o)  
+  return(o)
-
 
 def convert_bbox_to_z(bbox):
   """
@@ -70,7 +76,6 @@ def convert_bbox_to_z(bbox):
   r = w / float(h)
   return np.array([x, y, s, r]).reshape((4, 1))
 
-
 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
@@ -94,7 +99,7 @@ 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]])
 
@@ -112,7 +117,6 @@ class KalmanBoxTracker(object):
     self.hits = 0
     self.hit_streak = 0
     self.age = 0
-    self.original_id = bbox[5]
 
   def update(self,bbox):
     """
@@ -122,7 +126,6 @@ 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):
@@ -145,7 +148,6 @@ class KalmanBoxTracker(object):
     """
     return convert_x_to_bbox(self.kf.x)
 
-
 def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
   """
   Assigns detections to tracked object (both represented as bounding boxes)
@@ -154,8 +156,11 @@ def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
   """
   if(len(trackers)==0):
     return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
+  iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32)
 
-  iou_matrix = iou_batch(detections, trackers)
+  for d,det in enumerate(detections):
+    for t,trk in enumerate(trackers):
+      iou_matrix[d,t] = iou(det,trk)
 
   if min(iou_matrix.shape) > 0:
     a = (iou_matrix > iou_threshold).astype(np.int32)
@@ -192,13 +197,12 @@ def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
 
 
 class Sort(object):
-  def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
+  def __init__(self, max_age=1, min_hits=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
 
@@ -224,11 +228,15 @@ class Sort(object):
     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)
+    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks)
 
     # update matched trackers with assigned detections
     for m in matched:
       self.trackers[m[1]].update(dets[m[0], :])
+    #for t, trk in enumerate(self.trackers):
+    #  if(t not in unmatched_trks):
+    #    d = matched[np.where(matched[:,1]==t)[0],0]
+    #    trk.update(dets[d,:][0])
 
     # create and initialise new trackers for unmatched detections
     for i in unmatched_dets:
@@ -238,7 +246,7 @@ 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],[trk.original_id])).reshape(1,-1)) # +1 as MOT benchmark requires positive
+          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):
@@ -246,3 +254,72 @@ 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')
+    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() #create instance of the SORT tracker
+    seq_dets = np.loadtxt(seq_dets_fn, delimiter=',')
+    seq = seq_dets_fn[pattern.find('*'):].split('/')[0]
+    
+    with open('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 = 'mot_benchmark/%s/%s/img1/%06d.jpg'%(phase, seq, 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,:]))
+            #ax1.set_adjustable('box-forced')
+
+        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")

src/sort/__init__.py

@@ -1,18 +0,0 @@
-"""
-    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