Add original SORT implementation
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Ruben van de Ven
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sort_abewley.py
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sort_abewley.py
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"""
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from https://raw.githubusercontent.com/abewley/sort/master/sort.py
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SORT: A Simple, Online and Realtime Tracker
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Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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from __future__ import print_function
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import os
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import numpy as np
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import matplotlib
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matplotlib.use('TkAgg')
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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from skimage import io
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import glob
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import time
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import argparse
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from filterpy.kalman import KalmanFilter
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np.random.seed(0)
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def linear_assignment(cost_matrix):
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try:
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import lap
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_, x, y = lap.lapjv(cost_matrix, extend_cost=True)
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return np.array([[y[i],i] for i in x if i >= 0]) #
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except ImportError:
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from scipy.optimize import linear_sum_assignment
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x, y = linear_sum_assignment(cost_matrix)
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return np.array(list(zip(x, y)))
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def iou_batch(bb_test, bb_gt):
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"""
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From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
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"""
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bb_gt = np.expand_dims(bb_gt, 0)
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bb_test = np.expand_dims(bb_test, 1)
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xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
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yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
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xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
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yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
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w = np.maximum(0., xx2 - xx1)
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h = np.maximum(0., yy2 - yy1)
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wh = w * h
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o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
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+ (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
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return(o)
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def convert_bbox_to_z(bbox):
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"""
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Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
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[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
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the aspect ratio
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"""
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w = bbox[2] - bbox[0]
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h = bbox[3] - bbox[1]
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x = bbox[0] + w/2.
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y = bbox[1] + h/2.
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s = w * h #scale is just area
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r = w / float(h)
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return np.array([x, y, s, r]).reshape((4, 1))
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def convert_x_to_bbox(x,score=None):
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"""
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Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
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[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
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"""
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w = np.sqrt(x[2] * x[3])
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h = x[2] / w
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if(score==None):
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return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))
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else:
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return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))
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class KalmanBoxTracker(object):
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"""
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This class represents the internal state of individual tracked objects observed as bbox.
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"""
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count = 0
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def __init__(self,bbox):
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"""
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Initialises a tracker using initial bounding box.
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"""
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#define constant velocity model
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self.kf = KalmanFilter(dim_x=7, dim_z=4)
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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]])
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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]])
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self.kf.R[2:,2:] *= 10.
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self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
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self.kf.P *= 10.
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self.kf.Q[-1,-1] *= 0.01
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self.kf.Q[4:,4:] *= 0.01
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self.kf.x[:4] = convert_bbox_to_z(bbox)
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self.time_since_update = 0
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self.id = KalmanBoxTracker.count
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KalmanBoxTracker.count += 1
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self.history = []
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self.hits = 0
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self.hit_streak = 0
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self.age = 0
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def update(self,bbox):
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"""
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Updates the state vector with observed bbox.
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"""
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self.time_since_update = 0
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self.history = []
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self.hits += 1
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self.hit_streak += 1
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self.kf.update(convert_bbox_to_z(bbox))
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def predict(self):
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"""
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Advances the state vector and returns the predicted bounding box estimate.
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"""
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if((self.kf.x[6]+self.kf.x[2])<=0):
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self.kf.x[6] *= 0.0
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self.kf.predict()
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self.age += 1
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if(self.time_since_update>0):
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self.hit_streak = 0
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self.time_since_update += 1
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self.history.append(convert_x_to_bbox(self.kf.x))
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return self.history[-1]
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def get_state(self):
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"""
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Returns the current bounding box estimate.
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"""
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return convert_x_to_bbox(self.kf.x)
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def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
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"""
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Assigns detections to tracked object (both represented as bounding boxes)
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Returns 3 lists of matches, unmatched_detections and unmatched_trackers
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"""
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if(len(trackers)==0):
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return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
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iou_matrix = iou_batch(detections, trackers)
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if min(iou_matrix.shape) > 0:
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a = (iou_matrix > iou_threshold).astype(np.int32)
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if a.sum(1).max() == 1 and a.sum(0).max() == 1:
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matched_indices = np.stack(np.where(a), axis=1)
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else:
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matched_indices = linear_assignment(-iou_matrix)
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else:
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matched_indices = np.empty(shape=(0,2))
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unmatched_detections = []
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for d, det in enumerate(detections):
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if(d not in matched_indices[:,0]):
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unmatched_detections.append(d)
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unmatched_trackers = []
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for t, trk in enumerate(trackers):
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if(t not in matched_indices[:,1]):
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unmatched_trackers.append(t)
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#filter out matched with low IOU
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matches = []
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for m in matched_indices:
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if(iou_matrix[m[0], m[1]]<iou_threshold):
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unmatched_detections.append(m[0])
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unmatched_trackers.append(m[1])
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else:
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matches.append(m.reshape(1,2))
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if(len(matches)==0):
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matches = np.empty((0,2),dtype=int)
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else:
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matches = np.concatenate(matches,axis=0)
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return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
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class Sort(object):
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def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
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"""
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Sets key parameters for SORT
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"""
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self.max_age = max_age
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self.min_hits = min_hits
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self.iou_threshold = iou_threshold
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self.trackers = []
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self.frame_count = 0
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def update(self, dets=np.empty((0, 5))):
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"""
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Params:
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dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
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Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
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Returns the a similar array, where the last column is the object ID.
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NOTE: The number of objects returned may differ from the number of detections provided.
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"""
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self.frame_count += 1
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# get predicted locations from existing trackers.
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trks = np.zeros((len(self.trackers), 5))
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to_del = []
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ret = []
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for t, trk in enumerate(trks):
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pos = self.trackers[t].predict()[0]
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trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
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if np.any(np.isnan(pos)):
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to_del.append(t)
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trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
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for t in reversed(to_del):
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self.trackers.pop(t)
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matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks, self.iou_threshold)
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# update matched trackers with assigned detections
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for m in matched:
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self.trackers[m[1]].update(dets[m[0], :])
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# create and initialise new trackers for unmatched detections
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for i in unmatched_dets:
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trk = KalmanBoxTracker(dets[i,:])
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self.trackers.append(trk)
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i = len(self.trackers)
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for trk in reversed(self.trackers):
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d = trk.get_state()[0]
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if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
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ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive
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i -= 1
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# remove dead tracklet
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if(trk.time_since_update > self.max_age):
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self.trackers.pop(i)
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if(len(ret)>0):
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return np.concatenate(ret)
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return np.empty((0,5))
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def parse_args():
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"""Parse input arguments."""
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parser = argparse.ArgumentParser(description='SORT demo')
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parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',action='store_true')
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parser.add_argument("--seq_path", help="Path to detections.", type=str, default='data')
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parser.add_argument("--phase", help="Subdirectory in seq_path.", type=str, default='train')
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parser.add_argument("--max_age",
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help="Maximum number of frames to keep alive a track without associated detections.",
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type=int, default=1)
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parser.add_argument("--min_hits",
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help="Minimum number of associated detections before track is initialised.",
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type=int, default=3)
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parser.add_argument("--iou_threshold", help="Minimum IOU for match.", type=float, default=0.3)
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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# all train
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args = parse_args()
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display = args.display
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phase = args.phase
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total_time = 0.0
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total_frames = 0
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colours = np.random.rand(32, 3) #used only for display
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if(display):
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if not os.path.exists('mot_benchmark'):
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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')
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exit()
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plt.ion()
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fig = plt.figure()
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ax1 = fig.add_subplot(111, aspect='equal')
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if not os.path.exists('output'):
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os.makedirs('output')
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pattern = os.path.join(args.seq_path, phase, '*', 'det', 'det.txt')
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for seq_dets_fn in glob.glob(pattern):
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mot_tracker = Sort(max_age=args.max_age,
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min_hits=args.min_hits,
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iou_threshold=args.iou_threshold) #create instance of the SORT tracker
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seq_dets = np.loadtxt(seq_dets_fn, delimiter=',')
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seq = seq_dets_fn[pattern.find('*'):].split(os.path.sep)[0]
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with open(os.path.join('output', '%s.txt'%(seq)),'w') as out_file:
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print("Processing %s."%(seq))
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for frame in range(int(seq_dets[:,0].max())):
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frame += 1 #detection and frame numbers begin at 1
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dets = seq_dets[seq_dets[:, 0]==frame, 2:7]
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dets[:, 2:4] += dets[:, 0:2] #convert to [x1,y1,w,h] to [x1,y1,x2,y2]
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total_frames += 1
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if(display):
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fn = os.path.join('mot_benchmark', phase, seq, 'img1', '%06d.jpg'%(frame))
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im =io.imread(fn)
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ax1.imshow(im)
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plt.title(seq + ' Tracked Targets')
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start_time = time.time()
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trackers = mot_tracker.update(dets)
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cycle_time = time.time() - start_time
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total_time += cycle_time
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for d in trackers:
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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)
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if(display):
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d = d.astype(np.int32)
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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,:]))
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if(display):
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fig.canvas.flush_events()
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plt.draw()
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ax1.cla()
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print("Total Tracking took: %.3f seconds for %d frames or %.1f FPS" % (total_time, total_frames, total_frames / total_time))
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if(display):
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print("Note: to get real runtime results run without the option: --display")
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