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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on https://github.com/xingyizhou/CenterTrack/blob/master/src/lib/utils/tracker.py
"""
import copy
import numpy as np
import sklearn
__all__ = ['CenterTracker']
class CenterTracker(object):
__shared__ = ['num_classes']
def __init__(self,
num_classes=1,
min_box_area=0,
vertical_ratio=-1,
track_thresh=0.4,
pre_thresh=0.5,
new_thresh=0.4,
out_thresh=0.4,
hungarian=False):
self.num_classes = num_classes
self.min_box_area = min_box_area
self.vertical_ratio = vertical_ratio
self.track_thresh = track_thresh
self.pre_thresh = max(track_thresh, pre_thresh)
self.new_thresh = max(track_thresh, new_thresh)
self.out_thresh = max(track_thresh, out_thresh)
self.hungarian = hungarian
self.reset()
def init_track(self, results):
print('Initialize tracking!')
for item in results:
if item['score'] > self.new_thresh:
self.id_count += 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)
def reset(self):
self.id_count = 0
self.tracks = []
def update(self, results, public_det=None):
N = len(results)
M = len(self.tracks)
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
invalid = ((dist > track_size.reshape(1, M)) + \
(dist > item_size.reshape(N, 1)) + \
(item_cat.reshape(N, 1) != track_cat.reshape(1, M))) > 0
dist = dist + invalid * 1e18
if self.hungarian:
item_score = np.array([item['score'] for item in results],
np.float32)
dist[dist > 1e18] = 1e18
from sklearn.utils.linear_assignment_ import linear_assignment
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.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']
ret.append(track)
# Private detection: create tracks for all un-matched detections
for i in unmatched_dets:
track = results[i]
if track['score'] > self.new_thresh:
self.id_count += 1
track['tracking_id'] = self.id_count
ret.append(track)
self.tracks = ret
return ret
def greedy_assignment(dist):
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] < 1e16:
dist[:, j] = 1e18
matched_indices.append([i, j])
return np.array(matched_indices, np.int32).reshape(-1, 2)
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