Remove numba dependency and expose hparams flags.

requirements.txt

@@ -1,4 +1,3 @@
 filterpy==1.4.5
-numba==0.49.0
 scikit-image==0.14.0
 lap==0.4.0

sort.py

@@ -30,12 +30,6 @@ import time
 import argparse
 from filterpy.kalman import KalmanFilter
 
-try:
-  from numba import jit
-except:
-  def jit(func):
-    return func
-
 np.random.seed(0)
 
 
@@ -50,20 +44,22 @@ def linear_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 IUO between two bboxes in the form [l,t,w,h]                                                         
   """                                                                                                                      
-  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)  
 
 
@@ -163,11 +159,8 @@ 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)
@@ -204,12 +197,13 @@ 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
 
@@ -235,7 +229,7 @@ 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:
@@ -264,6 +258,13 @@ def parse_args():
     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
 
@@ -287,7 +288,9 @@ if __name__ == '__main__':
     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
+    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('/')[0]