WIP: update linear assignment.
- Fix for Scipy has a DeprecationWarning for linear_assignment_ module. - LAP scales better than Scipy for frames with 100s-1000s of objects.
This commit is contained in:
Alex Bewley
parent
54e63a7e43
commit
b69f2fc279
1 changed files with 77 additions and 56 deletions
133
sort.py
133
sort.py
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@ -18,19 +18,24 @@
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from __future__ import print_function
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from numba import jit
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import os.path
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import os.path as osp
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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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from sklearn.utils.linear_assignment_ import linear_assignment
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#from sklearn.utils.linear_assignment_ import linear_assignment
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import lap
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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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@jit
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def iou(bb_test,bb_gt):
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def iou(bb_test, bb_gt):
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"""
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Computes IUO between two bboxes in the form [x1,y1,x2,y2]
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"""
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@ -41,8 +46,8 @@ def iou(bb_test,bb_gt):
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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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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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@ -51,21 +56,21 @@ def convert_bbox_to_z(bbox):
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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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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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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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@ -74,7 +79,7 @@ def convert_x_to_bbox(x,score=None):
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class KalmanBoxTracker(object):
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"""
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This class represents the internel state of individual tracked objects observed as bbox.
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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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@ -144,21 +149,34 @@ def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
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for d,det in enumerate(detections):
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for t,trk in enumerate(trackers):
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iou_matrix[d,t] = iou(det,trk)
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matched_indices = linear_assignment(-iou_matrix)
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# TODO (bewley): remove rows and cols iou.max() < threshold
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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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#matrix is #TODO(this doesnt provide much gains)
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matched_indices = np.stack(np.where(a), axis=1)
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else:
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_, x, y = lap.lapjv(-iou_matrix, extend_cost=True)
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matched_indices = np.array([[y[i],i] for i in x if i >= 0]) #
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else:
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matched_indices = np.empty(shape=(0,2))
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#matched_indices = linear_assignment(-iou_matrix)
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unmatched_detections = []
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for d,det in enumerate(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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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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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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@ -171,9 +189,8 @@ def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
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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):
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def __init__(self, max_age=1, min_hits=3):
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"""
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Sets key parameters for SORT
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"""
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@ -182,96 +199,102 @@ class Sort(object):
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self.trackers = []
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self.frame_count = 0
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def update(self,dets):
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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.
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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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# 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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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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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)
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#update matched trackers with assigned detections
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for t,trk in enumerate(self.trackers):
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if(t not in unmatched_trks):
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d = matched[np.where(matched[:,1]==t)[0],0]
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trk.update(dets[d,:][0])
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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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#for t, trk in enumerate(self.trackers):
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# if(t not in unmatched_trks):
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# d = matched[np.where(matched[:,1]==t)[0],0]
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# trk.update(dets[d,:][0])
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#create and initialise new trackers for unmatched detections
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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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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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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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# 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='mot_benchmark')
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parser.add_argument("--phase", help="Subdirectory in seq_path.", type=str, default='train')
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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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sequences = ['PETS09-S2L1','TUD-Campus','TUD-Stadtmitte','ETH-Bahnhof','ETH-Sunnyday','ETH-Pedcross2','KITTI-13','KITTI-17','ADL-Rundle-6','ADL-Rundle-8','Venice-2']
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args = parse_args()
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display = args.display
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phase = 'train'
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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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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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if not osp.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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if not os.path.exists('output'):
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fig = plt.figure()
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if not osp.exists('output'):
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os.makedirs('output')
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for seq in sequences:
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pattern = osp.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() #create instance of the SORT tracker
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seq_dets = np.loadtxt('data/%s/det.txt'%(seq),delimiter=',') #load detections
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print(seq_dets_fn)
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seq_dets = np.loadtxt(seq_dets_fn, delimiter=',')
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seq = seq_dets_fn[pattern.find('*'):].split('/')[0]
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with open('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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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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ax1 = fig.add_subplot(111, aspect='equal')
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fn = 'mot_benchmark/%s/%s/img1/%06d.jpg'%(phase,seq,frame)
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fn = 'mot_benchmark/%s/%s/img1/%06d.jpg'%(phase, seq, 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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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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@ -283,16 +306,14 @@ if __name__ == '__main__':
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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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ax1.set_adjustable('box-forced')
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#ax1.set_adjustable('box-forced')
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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 for %d frames or %.1f FPS"%(total_time,total_frames,total_frames/total_time))
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print("Total Tracking took: %.3f 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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