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import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
import copy
from sklearn.metrics.pairwise import cosine_similarity as cosine
class Tracker(object):
def __init__(self, opt):
self.opt = opt
self.reset()
self.nID = 10000
self.alpha = 0.1
def init_track(self, results):
for item in results:
if item['score'] > self.opt.new_thresh:
self.id_count += 1
# active and age are never used in the paper
item['active'] = 1
item['age'] = 1
item['tracking_id'] = self.id_count
if not ('ct' in item):
bbox = item['bbox']
item['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
self.tracks.append(item)
self.nID = 10000
self.embedding_bank = np.zeros((self.nID, 128))
self.cat_bank = np.zeros((self.nID), dtype=np.int)
def reset(self):
self.id_count = 0
self.nID = 10000
self.tracks = []
self.embedding_bank = np.zeros((self.nID, 128))
self.cat_bank = np.zeros((self.nID), dtype=np.int)
self.tracklet_ages = np.zeros((self.nID), dtype=np.int)
self.alive = []
def step(self, results_with_low, public_det=None):
results = [item for item in results_with_low if item['score'] >= self.opt.track_thresh]
# first association
N = len(results)
M = len(self.tracks)
self.alive = []
track_boxes = np.array([[track['bbox'][0], track['bbox'][1],
track['bbox'][2], track['bbox'][3]] for track in self.tracks], np.float32) # M x 4
det_boxes = np.array([[item['bbox'][0], item['bbox'][1],
item['bbox'][2], item['bbox'][3]] for item in results], np.float32) # N x 4
box_ious = self.bbox_overlaps_py(det_boxes, track_boxes)
dets = np.array(
[det['ct'] + det['tracking'] for det in results], np.float32) # N x 2
track_size = np.array([((track['bbox'][2] - track['bbox'][0]) * \
(track['bbox'][3] - track['bbox'][1])) \
for track in self.tracks], np.float32) # M
track_cat = np.array([track['class'] for track in self.tracks], np.int32) # M
item_size = np.array([((item['bbox'][2] - item['bbox'][0]) * \
(item['bbox'][3] - item['bbox'][1])) \
for item in results], np.float32) # N
item_cat = np.array([item['class'] for item in results], np.int32) # N
tracks = np.array(
[pre_det['ct'] for pre_det in self.tracks], np.float32) # M x 2
dist = (((tracks.reshape(1, -1, 2) - \
dets.reshape(-1, 1, 2)) ** 2).sum(axis=2)) # N x M
if self.opt.dataset == 'youtube_vis':
invalid = ((dist > track_size.reshape(1, M)) + \
(dist > item_size.reshape(N, 1)) + (box_ious < self.opt.overlap_thresh)) > 0
else:
invalid = ((dist > track_size.reshape(1, M)) + \
(dist > item_size.reshape(N, 1)) + \
(item_cat.reshape(N, 1) != track_cat.reshape(1, M)) + (box_ious < self.opt.overlap_thresh)) > 0
dist = dist + invalid * 1e18
if self.opt.hungarian:
item_score = np.array([item['score'] for item in results], np.float32) # N
dist[dist > 1e18] = 1e18
matched_indices = linear_assignment(dist)
else:
matched_indices = greedy_assignment(copy.deepcopy(dist))
unmatched_dets = [d for d in range(dets.shape[0]) \
if not (d in matched_indices[:, 0])]
unmatched_tracks = [d for d in range(tracks.shape[0]) \
if not (d in matched_indices[:, 1])]
if self.opt.hungarian:
matches = []
for m in matched_indices:
if dist[m[0], m[1]] > 1e16:
unmatched_dets.append(m[0])
unmatched_tracks.append(m[1])
else:
matches.append(m)
matches = np.array(matches).reshape(-1, 2)
else:
matches = matched_indices
ret = []
for m in matches:
track = results[m[0]]
track['tracking_id'] = self.tracks[m[1]]['tracking_id']
track['age'] = 1
track['active'] = self.tracks[m[1]]['active'] + 1
if 'embedding' in track:
self.alive.append(track['tracking_id'])
self.embedding_bank[self.tracks[m[1]]['tracking_id'] - 1, :] = self.alpha * track['embedding'] \
+ (1 - self.alpha) * self.embedding_bank[
self.tracks[m[1]][
'tracking_id'] - 1,
:]
self.cat_bank[self.tracks[m[1]]['tracking_id'] - 1] = track['class']
ret.append(track)
if self.opt.public_det and len(unmatched_dets) > 0:
# Public detection: only create tracks from provided detections
pub_dets = np.array([d['ct'] for d in public_det], np.float32)
dist3 = ((dets.reshape(-1, 1, 2) - pub_dets.reshape(1, -1, 2)) ** 2).sum(
axis=2)
matched_dets = [d for d in range(dets.shape[0]) \
if not (d in unmatched_dets)]
dist3[matched_dets] = 1e18
for j in range(len(pub_dets)):
i = dist3[:, j].argmin()
if dist3[i, j] < item_size[i]:
dist3[i, :] = 1e18
track = results[i]
if track['score'] > self.opt.new_thresh:
self.id_count += 1
track['tracking_id'] = self.id_count
track['age'] = 1
track['active'] = 1
ret.append(track)
else:
# Private detection: create tracks for all un-matched detections
for i in unmatched_dets:
track = results[i]
if track['score'] > self.opt.new_thresh:
if 'embedding' in track:
max_id, max_cos = self.get_similarity(track['embedding'], False, track['class'])
if max_cos >= 0.3 and self.tracklet_ages[max_id - 1] < self.opt.window_size:
track['tracking_id'] = max_id
track['age'] = 1
track['active'] = 1
self.embedding_bank[track['tracking_id'] - 1, :] = self.alpha * track['embedding'] \
+ (1 - self.alpha) * self.embedding_bank[track['tracking_id'] - 1,:]
else:
self.id_count += 1
track['tracking_id'] = self.id_count
track['age'] = 1
track['active'] = 1
self.embedding_bank[self.id_count - 1, :] = track['embedding']
self.cat_bank[self.id_count - 1] = track['class']
self.alive.append(track['tracking_id'])
ret.append(track)
else:
self.id_count += 1
track['tracking_id'] = self.id_count
track['age'] = 1
track['active'] = 1
ret.append(track)
self.tracklet_ages[:self.id_count] = self.tracklet_ages[:self.id_count] + 1
for track in ret:
self.tracklet_ages[track['tracking_id'] - 1] = 1
# second association
results_second = [item for item in results_with_low if item['score'] < self.opt.track_thresh]
self_tracks_second = [self.tracks[i] for i in unmatched_tracks if self.tracks[i]['active'] > 0]
second2original = [i for i in unmatched_tracks if self.tracks[i]['active'] > 0]
N = len(results_second)
M = len(self_tracks_second)
if N > 0 and M > 0:
track_boxes_second = np.array([[track['bbox'][0], track['bbox'][1],
track['bbox'][2], track['bbox'][3]] for track in self_tracks_second], np.float32) # M x 4
det_boxes_second = np.array([[item['bbox'][0], item['bbox'][1],
item['bbox'][2], item['bbox'][3]] for item in results_second], np.float32) # N x 4
box_ious_second = self.bbox_overlaps_py(det_boxes_second, track_boxes_second)
dets = np.array(
[det['ct'] + det['tracking'] for det in results_second], np.float32) # N x 2
track_size = np.array([((track['bbox'][2] - track['bbox'][0]) * \
(track['bbox'][3] - track['bbox'][1])) \
for track in self_tracks_second], np.float32) # M
track_cat = np.array([track['class'] for track in self_tracks_second], np.int32) # M
item_size = np.array([((item['bbox'][2] - item['bbox'][0]) * \
(item['bbox'][3] - item['bbox'][1])) \
for item in results_second], np.float32) # N
item_cat = np.array([item['class'] for item in results_second], np.int32) # N
tracks_second = np.array(
[pre_det['ct'] for pre_det in self_tracks_second], np.float32) # M x 2
dist = (((tracks_second.reshape(1, -1, 2) - \
dets.reshape(-1, 1, 2)) ** 2).sum(axis=2)) # N x M
invalid = ((dist > track_size.reshape(1, M)) + \
(dist > item_size.reshape(N, 1)) + \
(item_cat.reshape(N, 1) != track_cat.reshape(1, M)) + (box_ious_second < 0.3)) > 0
dist = dist + invalid * 1e18
matched_indices_second = greedy_assignment(copy.deepcopy(dist), 1e8)
unmatched_tracks_second = [d for d in range(tracks_second.shape[0]) \
if not (d in matched_indices_second[:, 1])]
matches_second = matched_indices_second
for m in matches_second:
track = results_second[m[0]]
track['tracking_id'] = self_tracks_second[m[1]]['tracking_id']
track['age'] = 1
track['active'] = self_tracks_second[m[1]]['active'] + 1
if 'embedding' in track:
self.alive.append(track['tracking_id'])
self.embedding_bank[self_tracks_second[m[1]]['tracking_id'] - 1, :] = self.alpha * track['embedding'] \
+ (1 - self.alpha) * self.embedding_bank[self_tracks_second[m[1]]['tracking_id'] - 1,:]
self.cat_bank[self_tracks_second[m[1]]['tracking_id'] - 1] = track['class']
ret.append(track)
unmatched_tracks = [second2original[i] for i in unmatched_tracks_second] + \
[i for i in unmatched_tracks if self.tracks[i]['active'] == 0]
# Never used
for i in unmatched_tracks:
track = self.tracks[i]
if track['age'] < self.opt.max_age:
track['age'] += 1
track['active'] = 1 # 0
bbox = track['bbox']
ct = track['ct']
v = [0, 0]
track['bbox'] = [
bbox[0] + v[0], bbox[1] + v[1],
bbox[2] + v[0], bbox[3] + v[1]]
track['ct'] = [ct[0] + v[0], ct[1] + v[1]]
ret.append(track)
for r_ in ret:
del r_['embedding']
self.tracks = ret
return ret
def get_similarity(self, feat, stat, cls):
max_id = -1
max_cos = -1
if stat:
nID = self.id_count
else:
nID = self.id_count
a = feat[None, :]
b = self.embedding_bank[:nID, :]
if len(b) > 0:
alive = np.array(self.alive, dtype=np.int) - 1
cosim = cosine(a, b)
cosim = np.reshape(cosim, newshape=(-1))
cosim[alive] = -2
cosim[nID - 1] = -2
cosim[np.where(self.cat_bank[:nID] != cls)[0]] = -2
max_id = int(np.argmax(cosim) + 1)
max_cos = np.max(cosim)
return max_id, max_cos
def bbox_overlaps_py(self, boxes, query_boxes):
"""
determine overlaps between boxes and query_boxes
:param boxes: n * 4 bounding boxes
:param query_boxes: k * 4 bounding boxes
:return: overlaps: n * k overlaps
"""
n_ = boxes.shape[0]
k_ = query_boxes.shape[0]
overlaps = np.zeros((n_, k_), dtype=np.float)
for k in range(k_):
query_box_area = (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1)
for n in range(n_):
iw = min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1
if iw > 0:
ih = min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1
if ih > 0:
box_area = (boxes[n, 2] - boxes[n, 0] + 1) * (boxes[n, 3] - boxes[n, 1] + 1)
all_area = float(box_area + query_box_area - iw * ih)
overlaps[n, k] = iw * ih / all_area
return overlaps
def greedy_assignment(dist, thresh=1e16):
matched_indices = []
if dist.shape[1] == 0:
return np.array(matched_indices, np.int32).reshape(-1, 2)
for i in range(dist.shape[0]):
j = dist[i].argmin()
if dist[i][j] < thresh:
dist[:, j] = 1e18
matched_indices.append([i, j])
return np.array(matched_indices, np.int32).reshape(-1, 2)