diff --git a/sort_abewley.py b/sort_abewley.py
new file mode 100644
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--- /dev/null
+++ b/sort_abewley.py
@@ -0,0 +1,332 @@
+"""
+ from https://raw.githubusercontent.com/abewley/sort/master/sort.py
+
+ 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 .
+"""
+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
+import time
+import argparse
+from filterpy.kalman import KalmanFilter
+
+np.random.seed(0)
+
+
+def linear_assignment(cost_matrix):
+ try:
+ import lap
+ _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
+ return np.array([[y[i],i] for i in x if i >= 0]) #
+ except ImportError:
+ from scipy.optimize import linear_sum_assignment
+ 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])
+ 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])
+ + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
+ return(o)
+
+
+def convert_bbox_to_z(bbox):
+ """
+ Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+ [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
+ the aspect ratio
+ """
+ w = bbox[2] - bbox[0]
+ h = bbox[3] - bbox[1]
+ x = bbox[0] + w/2.
+ y = bbox[1] + h/2.
+ s = w * h #scale is just area
+ 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
+ [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
+ """
+ w = np.sqrt(x[2] * x[3])
+ h = x[2] / w
+ if(score==None):
+ return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))
+ else:
+ return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))
+
+
+class KalmanBoxTracker(object):
+ """
+ This class represents the internal state of individual tracked objects observed as bbox.
+ """
+ count = 0
+ def __init__(self,bbox):
+ """
+ Initialises a tracker using initial bounding box.
+ """
+ #define constant velocity model
+ 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]])
+
+ self.kf.R[2:,2:] *= 10.
+ self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
+ self.kf.P *= 10.
+ self.kf.Q[-1,-1] *= 0.01
+ self.kf.Q[4:,4:] *= 0.01
+
+ self.kf.x[:4] = convert_bbox_to_z(bbox)
+ self.time_since_update = 0
+ self.id = KalmanBoxTracker.count
+ KalmanBoxTracker.count += 1
+ self.history = []
+ self.hits = 0
+ self.hit_streak = 0
+ self.age = 0
+
+ def update(self,bbox):
+ """
+ Updates the state vector with observed bbox.
+ """
+ self.time_since_update = 0
+ self.history = []
+ self.hits += 1
+ self.hit_streak += 1
+ self.kf.update(convert_bbox_to_z(bbox))
+
+ def predict(self):
+ """
+ Advances the state vector and returns the predicted bounding box estimate.
+ """
+ if((self.kf.x[6]+self.kf.x[2])<=0):
+ self.kf.x[6] *= 0.0
+ self.kf.predict()
+ self.age += 1
+ if(self.time_since_update>0):
+ self.hit_streak = 0
+ self.time_since_update += 1
+ self.history.append(convert_x_to_bbox(self.kf.x))
+ return self.history[-1]
+
+ def get_state(self):
+ """
+ Returns the current bounding box estimate.
+ """
+ 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)
+
+ Returns 3 lists of matches, unmatched_detections and unmatched_trackers
+ """
+ if(len(trackers)==0):
+ return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
+
+ iou_matrix = iou_batch(detections, trackers)
+
+ if min(iou_matrix.shape) > 0:
+ a = (iou_matrix > iou_threshold).astype(np.int32)
+ if a.sum(1).max() == 1 and a.sum(0).max() == 1:
+ matched_indices = np.stack(np.where(a), axis=1)
+ else:
+ matched_indices = linear_assignment(-iou_matrix)
+ else:
+ matched_indices = np.empty(shape=(0,2))
+
+ unmatched_detections = []
+ for d, det in enumerate(detections):
+ if(d not in matched_indices[:,0]):
+ unmatched_detections.append(d)
+ unmatched_trackers = []
+ for t, trk in enumerate(trackers):
+ if(t not in matched_indices[:,1]):
+ unmatched_trackers.append(t)
+
+ #filter out matched with low IOU
+ matches = []
+ for m in matched_indices:
+ if(iou_matrix[m[0], m[1]]= 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
+ 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))
+
+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")