modern python support with lapx

.gitignore

@@ -1,2 +1,6 @@
 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://motchallenge.net/movies/ETH-Linthescher-SORT.mp4).
+See an example [video here](https://alex.bewley.ai/misc/SORT-MOT17-06-FRCNN.webm).
 
 By Alex Bewley  
 
@@ -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 the package:
-
-To install required dependencies run:
 ```
-$ pip install -r requirements.txt
+pip install simple-online-realtime-tracking==0.3
 ```
 
-
-### 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() 

pyproject.toml

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

requirements.txt

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

setup.cfg

@@ -0,0 +1,22 @@
+[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

src/sort/__init__.py

@@ -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

src/sort/sort.py

@@ -1,6 +1,6 @@
 """
     SORT: A Simple, Online and Realtime Tracker
-    Copyright (C) 2016 Alex Bewley alex@dynamicdetection.com
+    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
@@ -15,16 +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
-
-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
@@ -44,23 +36,25 @@ def linear_assignment(cost_matrix):
     x, y = linear_sum_assignment(cost_matrix)
     return np.array(list(zip(x, y)))
 
-  
 
-@jit
+def iou_batch(bb_test, bb_gt):
-def iou(bb_test, bb_gt):
   """
-  Computes IUO between two bboxes in the form [x1,y1,x2,y2]
+  From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
   """
-  xx1 = np.maximum(bb_test[0], bb_gt[0])
+  bb_gt = np.expand_dims(bb_gt, 0)
-  yy1 = np.maximum(bb_test[1], bb_gt[1])
+  bb_test = np.expand_dims(bb_test, 1)
-  xx2 = np.minimum(bb_test[2], bb_gt[2])
+  
-  yy2 = np.minimum(bb_test[3], bb_gt[3])
+  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):
   """
@@ -76,6 +70,7 @@ 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
@@ -99,7 +94,7 @@ class KalmanBoxTracker(object):
     Initialises a tracker using initial bounding box.
     """
     #define constant velocity model
-    self.kf = KalmanFilter(dim_x=7, dim_z=4, compute_log_likelihood=False)
+    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]])
 
@@ -117,6 +112,7 @@ class KalmanBoxTracker(object):
     self.hits = 0
     self.hit_streak = 0
     self.age = 0
+    self.original_id = bbox[5]
 
   def update(self,bbox):
     """
@@ -126,6 +122,7 @@ 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):
@@ -148,6 +145,7 @@ 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)
@@ -156,11 +154,8 @@ 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)
 
-  for d,det in enumerate(detections):
+  iou_matrix = iou_batch(detections, trackers)
-    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)
@@ -197,12 +192,13 @@ def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
 
 
 class Sort(object):
-  def __init__(self, max_age=1, min_hits=3):
+  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
 
@@ -228,15 +224,11 @@ 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)
+    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], :])
-    #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:
@@ -246,7 +238,7 @@ 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):
@@ -254,72 +246,3 @@ 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')
-    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")