From d2b0b9735bedbd73dbd4c020e87f8a9f75561589 Mon Sep 17 00:00:00 2001 From: Ruben van de Ven Date: Mon, 8 May 2023 21:32:55 +0200 Subject: [PATCH] Add original SORT implementation --- sort_abewley.py | 332 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 332 insertions(+) create mode 100644 sort_abewley.py diff --git a/sort_abewley.py b/sort_abewley.py new file mode 100644 index 0000000..d07dedf --- /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")