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ocsort.py
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ocsort.py
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"""
This script is adopted from the SORT script by Alex Bewley alex@bewley.ai
"""
from __future__ import print_function
import numpy as np
from .association import *
def k_previous_obs(observations, cur_age, k):
if len(observations) == 0:
return [-1, -1, -1, -1, -1]
for i in range(k):
dt = k - i
if cur_age - dt in observations:
return observations[cur_age-dt]
max_age = max(observations.keys())
return observations[max_age]
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+1e-6)
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))
def speed_direction(bbox1, bbox2):
cx1, cy1 = (bbox1[0]+bbox1[2]) / 2.0, (bbox1[1]+bbox1[3])/2.0
cx2, cy2 = (bbox2[0]+bbox2[2]) / 2.0, (bbox2[1]+bbox2[3])/2.0
speed = np.array([cy2-cy1, cx2-cx1])
norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6
return speed / norm
class KalmanBoxTracker(object):
"""
This class represents the internal state of individual tracked objects observed as bbox.
"""
count = 0
def __init__(self, bbox, delta_t=3, orig=False):
"""
Initialises a tracker using initial bounding box.
"""
# define constant velocity model
if not orig:
from .kalmanfilter import KalmanFilterNew as KalmanFilter
self.kf = KalmanFilter(dim_x=7, dim_z=4)
else:
from filterpy.kalman import KalmanFilter
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
"""
NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of
function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a
fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.
"""
self.last_observation = np.array([-1, -1, -1, -1, -1]) # placeholder
self.observations = dict()
self.history_observations = []
self.velocity = None
self.delta_t = delta_t
def update(self, bbox):
"""
Updates the state vector with observed bbox.
"""
if bbox is not None:
if self.last_observation.sum() >= 0: # no previous observation
previous_box = None
for i in range(self.delta_t):
dt = self.delta_t - i
if self.age - dt in self.observations:
previous_box = self.observations[self.age-dt]
break
if previous_box is None:
previous_box = self.last_observation
"""
Estimate the track speed direction with observations \Delta t steps away
"""
self.velocity = speed_direction(previous_box, bbox)
"""
Insert new observations. This is a ugly way to maintain both self.observations
and self.history_observations. Bear it for the moment.
"""
self.last_observation = bbox
self.observations[self.age] = bbox
self.history_observations.append(bbox)
self.time_since_update = 0
self.history = []
self.hits += 1
self.hit_streak += 1
self.kf.update(convert_bbox_to_z(bbox))
else:
self.kf.update(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)
"""
We support multiple ways for association cost calculation, by default
we use IoU. GIoU may have better performance in some situations. We note
that we hardly normalize the cost by all methods to (0,1) which may not be
the best practice.
"""
ASSO_FUNCS = { "iou": iou_batch,
"giou": giou_batch,
"ciou": ciou_batch,
"diou": diou_batch,
"ct_dist": ct_dist}
class OCSort(object):
def __init__(self, det_thresh, max_age=30, min_hits=3,
iou_threshold=0.3, delta_t=3, asso_func="iou", inertia=0.2, use_byte=False):
"""
Sets key parameters for SORT
"""
self.max_age = max_age
self.min_hits = min_hits
self.iou_threshold = iou_threshold
self.trackers = []
self.frame_count = 0
self.det_thresh = det_thresh
self.delta_t = delta_t
self.asso_func = ASSO_FUNCS[asso_func]
self.inertia = inertia
self.use_byte = use_byte
KalmanBoxTracker.count = 0
def update(self, output_results, img_info, img_size):
"""
Params:
dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
if output_results is None:
return np.empty((0, 5))
self.frame_count += 1
# post_process detections
if output_results.shape[1] == 5:
scores = output_results[:, 4]
bboxes = output_results[:, :4]
else:
output_results = output_results.cpu().numpy()
scores = output_results[:, 4] * output_results[:, 5]
bboxes = output_results[:, :4] # x1y1x2y2
img_h, img_w = img_info[0], img_info[1]
scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))
bboxes /= scale
dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)
inds_low = scores > 0.1
inds_high = scores < self.det_thresh
inds_second = np.logical_and(inds_low, inds_high) # self.det_thresh > score > 0.1, for second matching
dets_second = dets[inds_second] # detections for second matching
remain_inds = scores > self.det_thresh
dets = dets[remain_inds]
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
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]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
velocities = np.array(
[trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])
last_boxes = np.array([trk.last_observation for trk in self.trackers])
k_observations = np.array(
[k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])
"""
First round of association
"""
matched, unmatched_dets, unmatched_trks = associate(
dets, trks, self.iou_threshold, velocities, k_observations, self.inertia)
for m in matched:
self.trackers[m[1]].update(dets[m[0], :])
"""
Second round of associaton by OCR
"""
# BYTE association
if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[0] > 0:
u_trks = trks[unmatched_trks]
iou_left = self.asso_func(dets_second, u_trks) # iou between low score detections and unmatched tracks
iou_left = np.array(iou_left)
if iou_left.max() > self.iou_threshold:
"""
NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
get a higher performance especially on MOT17/MOT20 datasets. But we keep it
uniform here for simplicity
"""
matched_indices = linear_assignment(-iou_left)
to_remove_trk_indices = []
for m in matched_indices:
det_ind, trk_ind = m[0], unmatched_trks[m[1]]
if iou_left[m[0], m[1]] < self.iou_threshold:
continue
self.trackers[trk_ind].update(dets_second[det_ind, :])
to_remove_trk_indices.append(trk_ind)
unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
left_dets = dets[unmatched_dets]
left_trks = last_boxes[unmatched_trks]
iou_left = self.asso_func(left_dets, left_trks)
iou_left = np.array(iou_left)
if iou_left.max() > self.iou_threshold:
"""
NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
get a higher performance especially on MOT17/MOT20 datasets. But we keep it
uniform here for simplicity
"""
rematched_indices = linear_assignment(-iou_left)
to_remove_det_indices = []
to_remove_trk_indices = []
for m in rematched_indices:
det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
if iou_left[m[0], m[1]] < self.iou_threshold:
continue
self.trackers[trk_ind].update(dets[det_ind, :])
to_remove_det_indices.append(det_ind)
to_remove_trk_indices.append(trk_ind)
unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
for m in unmatched_trks:
self.trackers[m].update(None)
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i, :], delta_t=self.delta_t)
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if trk.last_observation.sum() < 0:
d = trk.get_state()[0]
else:
"""
this is optional to use the recent observation or the kalman filter prediction,
we didn't notice significant difference here
"""
d = trk.last_observation[:4]
if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
# +1 as MOT benchmark requires positive
ret.append(np.concatenate((d, [trk.id+1])).reshape(1, -1))
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 update_public(self, dets, cates, scores):
self.frame_count += 1
det_scores = np.ones((dets.shape[0], 1))
dets = np.concatenate((dets, det_scores), axis=1)
remain_inds = scores > self.det_thresh
cates = cates[remain_inds]
dets = dets[remain_inds]
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
cat = self.trackers[t].cate
trk[:] = [pos[0], pos[1], pos[2], pos[3], cat]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0,0)) for trk in self.trackers])
last_boxes = np.array([trk.last_observation for trk in self.trackers])
k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])
matched, unmatched_dets, unmatched_trks = associate_kitti\
(dets, trks, cates, self.iou_threshold, velocities, k_observations, self.inertia)
for m in matched:
self.trackers[m[1]].update(dets[m[0], :])
if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
"""
The re-association stage by OCR.
NOTE: at this stage, adding other strategy might be able to continue improve
the performance, such as BYTE association by ByteTrack.
"""
left_dets = dets[unmatched_dets]
left_trks = last_boxes[unmatched_trks]
left_dets_c = left_dets.copy()
left_trks_c = left_trks.copy()
iou_left = self.asso_func(left_dets_c, left_trks_c)
iou_left = np.array(iou_left)
det_cates_left = cates[unmatched_dets]
trk_cates_left = trks[unmatched_trks][:,4]
num_dets = unmatched_dets.shape[0]
num_trks = unmatched_trks.shape[0]
cate_matrix = np.zeros((num_dets, num_trks))
for i in range(num_dets):
for j in range(num_trks):
if det_cates_left[i] != trk_cates_left[j]:
"""
For some datasets, such as KITTI, there are different categories,
we have to avoid associate them together.
"""
cate_matrix[i][j] = -1e6
iou_left = iou_left + cate_matrix
if iou_left.max() > self.iou_threshold - 0.1:
rematched_indices = linear_assignment(-iou_left)
to_remove_det_indices = []
to_remove_trk_indices = []
for m in rematched_indices:
det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
if iou_left[m[0], m[1]] < self.iou_threshold - 0.1:
continue
self.trackers[trk_ind].update(dets[det_ind, :])
to_remove_det_indices.append(det_ind)
to_remove_trk_indices.append(trk_ind)
unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i,:])
trk.cate = cates[i]
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if trk.last_observation.sum() > 0:
d = trk.last_observation[:4]
else:
d = trk.get_state()[0]
if (trk.time_since_update < 1):
if (self.frame_count <= self.min_hits) or (trk.hit_streak >= self.min_hits):
# id+1 as MOT benchmark requires positive
ret.append(np.concatenate((d, [trk.id+1], [trk.cate], [0])).reshape(1,-1))
if trk.hit_streak == self.min_hits:
# Head Padding (HP): recover the lost steps during initializing the track
for prev_i in range(self.min_hits - 1):
prev_observation = trk.history_observations[-(prev_i+2)]
ret.append((np.concatenate((prev_observation[:4], [trk.id+1], [trk.cate],
[-(prev_i+1)]))).reshape(1,-1))
i -= 1
if (trk.time_since_update > self.max_age):
self.trackers.pop(i)
if(len(ret)>0):
return np.concatenate(ret)
return np.empty((0, 7))