add class number in the input and output of the update function.

.gitignore

@@ -1,2 +1,3 @@
 output/
 mot_benchmark
+__pycache__

requirements.txt

@@ -1,3 +1,5 @@
+wheel==0.34.2
+numpy
 filterpy==1.4.5
 scikit-image==0.17.2
 lap==0.4.0

sort.py

@@ -20,7 +20,6 @@
 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
@@ -96,6 +95,7 @@ class KalmanBoxTracker(object):
   This class represents the internal state of individual tracked objects observed as bbox.
   """
   count = 0
+  countReset = 10000
   def __init__(self,bbox):
     """
     Initialises a tracker using initial bounding box.
@@ -114,11 +114,14 @@ def __init__(self,bbox):
     self.kf.x[:4] = convert_bbox_to_z(bbox)
     self.time_since_update = 0
     self.id = KalmanBoxTracker.count
-    KalmanBoxTracker.count += 1
+    KalmanBoxTracker.count = (KalmanBoxTracker.count+1) % KalmanBoxTracker.countReset
     self.history = []
     self.hits = 0
     self.hit_streak = 0
     self.age = 0
+    self.original_id = bbox[5]
+    self.original_conf = bbox[4]
+
 
   def update(self,bbox):
     """
@@ -128,6 +131,8 @@ def update(self,bbox):
     self.history = []
     self.hits += 1
     self.hit_streak += 1
+    self.original_id = bbox[5]
+    self.original_conf = bbox[4]
     self.kf.update(convert_bbox_to_z(bbox))
 
   def predict(self):
@@ -207,7 +212,7 @@ def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
     self.trackers = []
     self.frame_count = 0
 
-  def update(self, dets=np.empty((0, 5))):
+  def update(self, dets=np.empty((0, 6))):
     """
     Params:
       dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
@@ -218,12 +223,12 @@ def update(self, dets=np.empty((0, 5))):
     """
     self.frame_count += 1
     # get predicted locations from existing trackers.
-    trks = np.zeros((len(self.trackers), 5))
+    trks = np.zeros((len(self.trackers), 7))
     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]
+      trk[:] = [pos[0], pos[1], pos[2], pos[3], 0, 0, 0]
       if np.any(np.isnan(pos)):
         to_del.append(t)
     trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
@@ -243,14 +248,14 @@ def update(self, dets=np.empty((0, 5))):
     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], [trk.original_conf])).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))
+    return np.empty((0,7))
 
 def parse_args():
     """Parse input arguments."""
@@ -309,7 +314,7 @@ def parse_args():
           plt.title(seq + ' Tracked Targets')
 
         start_time = time.time()
-        trackers = mot_tracker.update(dets)
+        trackers = mot_tracker.update(np.hstack((dets, np.zeros((dets.shape[0], 1)))))
         cycle_time = time.time() - start_time
         total_time += cycle_time