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# Copyright (c) 2021 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/LCFractal/AIC21-MTMC/tree/main/reid/reid-matching/tools
Note: The following codes are strongly related to zone of the AIC21 test-set S06,
so they can only be used in S06, and can not be used for other MTMCT datasets.
"""
import os
import cv2
import numpy as np
try:
from sklearn.cluster import AgglomerativeClustering
except:
print(
'Warning: Unable to use MTMCT in PP-Tracking, please install sklearn, for example: `pip install sklearn`'
)
pass
BBOX_B = 10 / 15
class Zone(object):
def __init__(self, zone_path='datasets/zone'):
# 0: b 1: g 3: r 123:w
# w r not high speed
# b g high speed
assert zone_path != '', "Error: zone_path is not empty!"
zones = {}
for img_name in os.listdir(zone_path):
camnum = int(img_name.split('.')[0][-3:])
zone_img = cv2.imread(os.path.join(zone_path, img_name))
zones[camnum] = zone_img
self.zones = zones
self.current_cam = 0
def set_cam(self, cam):
self.current_cam = cam
def get_zone(self, bbox):
cx = int((bbox[0] + bbox[2]) / 2)
cy = int((bbox[1] + bbox[3]) / 2)
pix = self.zones[self.current_cam][max(cy - 1, 0), max(cx - 1, 0), :]
zone_num = 0
if pix[0] > 50 and pix[1] > 50 and pix[2] > 50: # w
zone_num = 1
if pix[0] < 50 and pix[1] < 50 and pix[2] > 50: # r
zone_num = 2
if pix[0] < 50 and pix[1] > 50 and pix[2] < 50: # g
zone_num = 3
if pix[0] > 50 and pix[1] < 50 and pix[2] < 50: # b
zone_num = 4
return zone_num
def is_ignore(self, zone_list, frame_list, cid):
# 0 not in any corssroad, 1 white 2 red 3 green 4 bule
zs, ze = zone_list[0], zone_list[-1]
fs, fe = frame_list[0], frame_list[-1]
if zs == ze:
# if always on one section, excluding
if ze in [1, 2]:
return 2
if zs != 0 and 0 in zone_list:
return 0
if fe - fs > 1500:
return 2
if fs < 2:
if cid in [45]:
if ze in [3, 4]:
return 1
else:
return 2
if fe > 1999:
if cid in [41]:
if ze not in [3]:
return 2
else:
return 0
if fs < 2 or fe > 1999:
if ze in [3, 4]:
return 0
if ze in [3, 4]:
return 1
return 2
else:
# if camera section change
if cid in [41, 42, 43, 44, 45, 46]:
# come from road extension, exclusing
if zs == 1 and ze == 2:
return 2
if zs == 2 and ze == 1:
return 2
if cid in [41]:
# On 41 camera, no vehicle come into 42 camera
if (zs in [1, 2]) and ze == 4:
return 2
if zs == 4 and (ze in [1, 2]):
return 2
if cid in [46]:
# On 46 camera，no vehicle come into 45
if (zs in [1, 2]) and ze == 3:
return 2
if zs == 3 and (ze in [1, 2]):
return 2
return 0
def filter_mot(self, mot_list, cid):
new_mot_list = dict()
sub_mot_list = dict()
for tracklet in mot_list:
tracklet_dict = mot_list[tracklet]
frame_list = list(tracklet_dict.keys())
frame_list.sort()
zone_list = []
for f in frame_list:
zone_list.append(tracklet_dict[f]['zone'])
if self.is_ignore(zone_list, frame_list, cid) == 0:
new_mot_list[tracklet] = tracklet_dict
if self.is_ignore(zone_list, frame_list, cid) == 1:
sub_mot_list[tracklet] = tracklet_dict
return new_mot_list
def filter_bbox(self, mot_list, cid):
new_mot_list = dict()
yh = self.zones[cid].shape[0]
for tracklet in mot_list:
tracklet_dict = mot_list[tracklet]
frame_list = list(tracklet_dict.keys())
frame_list.sort()
bbox_list = []
for f in frame_list:
bbox_list.append(tracklet_dict[f]['bbox'])
bbox_x = [b[0] for b in bbox_list]
bbox_y = [b[1] for b in bbox_list]
bbox_w = [b[2] - b[0] for b in bbox_list]
bbox_h = [b[3] - b[1] for b in bbox_list]
new_frame_list = list()
if 0 in bbox_x or 0 in bbox_y:
b0 = [
i for i, f in enumerate(frame_list)
if bbox_x[i] < 5 or bbox_y[i] + bbox_h[i] > yh - 5
]
if len(b0) == len(frame_list):
if cid in [41, 42, 44, 45, 46]:
continue
max_w = max(bbox_w)
max_h = max(bbox_h)
for i, f in enumerate(frame_list):
if bbox_w[i] > max_w * BBOX_B and bbox_h[
i] > max_h * BBOX_B:
new_frame_list.append(f)
else:
l_i, r_i = 0, len(frame_list) - 1
if len(b0) == 0:
continue
if b0[0] == 0:
for i in range(len(b0) - 1):
if b0[i] + 1 == b0[i + 1]:
l_i = b0[i + 1]
else:
break
if b0[-1] == len(frame_list) - 1:
for i in range(len(b0) - 1):
i = len(b0) - 1 - i
if b0[i] - 1 == b0[i - 1]:
r_i = b0[i - 1]
else:
break
max_lw, max_lh = bbox_w[l_i], bbox_h[l_i]
max_rw, max_rh = bbox_w[r_i], bbox_h[r_i]
for i, f in enumerate(frame_list):
if i < l_i:
if bbox_w[i] > max_lw * BBOX_B and bbox_h[
i] > max_lh * BBOX_B:
new_frame_list.append(f)
elif i > r_i:
if bbox_w[i] > max_rw * BBOX_B and bbox_h[
i] > max_rh * BBOX_B:
new_frame_list.append(f)
else:
new_frame_list.append(f)
new_tracklet_dict = dict()
for f in new_frame_list:
new_tracklet_dict[f] = tracklet_dict[f]
new_mot_list[tracklet] = new_tracklet_dict
else:
new_mot_list[tracklet] = tracklet_dict
return new_mot_list
def break_mot(self, mot_list, cid):
new_mot_list = dict()
new_num_tracklets = max(mot_list) + 1
for tracklet in mot_list:
tracklet_dict = mot_list[tracklet]
frame_list = list(tracklet_dict.keys())
frame_list.sort()
zone_list = []
back_tracklet = False
new_zone_f = 0
pre_frame = frame_list[0]
time_break = False
for f in frame_list:
if f - pre_frame > 100:
if cid in [44, 45]:
time_break = True
break
if not cid in [41, 44, 45, 46]:
break
pre_frame = f
new_zone = tracklet_dict[f]['zone']
if len(zone_list) > 0 and zone_list[-1] == new_zone:
continue
if new_zone_f > 1:
if len(zone_list) > 1 and new_zone in zone_list:
back_tracklet = True
zone_list.append(new_zone)
new_zone_f = 0
else:
new_zone_f += 1
if back_tracklet:
new_tracklet_dict = dict()
pre_bbox = -1
pre_arrow = 0
have_break = False
for f in frame_list:
now_bbox = tracklet_dict[f]['bbox']
if type(pre_bbox) == int:
if pre_bbox == -1:
pre_bbox = now_bbox
now_arrow = now_bbox[0] - pre_bbox[0]
if pre_arrow * now_arrow < 0 and len(
new_tracklet_dict) > 15 and not have_break:
new_mot_list[tracklet] = new_tracklet_dict
new_tracklet_dict = dict()
have_break = True
if have_break:
tracklet_dict[f]['id'] = new_num_tracklets
new_tracklet_dict[f] = tracklet_dict[f]
pre_bbox, pre_arrow = now_bbox, now_arrow
if have_break:
new_mot_list[new_num_tracklets] = new_tracklet_dict
new_num_tracklets += 1
else:
new_mot_list[tracklet] = new_tracklet_dict
elif time_break:
new_tracklet_dict = dict()
have_break = False
pre_frame = frame_list[0]
for f in frame_list:
if f - pre_frame > 100:
new_mot_list[tracklet] = new_tracklet_dict
new_tracklet_dict = dict()
have_break = True
new_tracklet_dict[f] = tracklet_dict[f]
pre_frame = f
if have_break:
new_mot_list[new_num_tracklets] = new_tracklet_dict
new_num_tracklets += 1
else:
new_mot_list[tracklet] = new_tracklet_dict
else:
new_mot_list[tracklet] = tracklet_dict
return new_mot_list
def intra_matching(self, mot_list, sub_mot_list):
sub_zone_dict = dict()
new_mot_list = dict()
new_mot_list, new_sub_mot_list = self.do_intra_matching2(mot_list,
sub_mot_list)
return new_mot_list
def do_intra_matching2(self, mot_list, sub_list):
new_zone_dict = dict()
def get_trac_info(tracklet1):
t1_f = list(tracklet1)
t1_f.sort()
t1_fs = t1_f[0]
t1_fe = t1_f[-1]
t1_zs = tracklet1[t1_fs]['zone']
t1_ze = tracklet1[t1_fe]['zone']
t1_boxs = tracklet1[t1_fs]['bbox']
t1_boxe = tracklet1[t1_fe]['bbox']
t1_boxs = [(t1_boxs[2] + t1_boxs[0]) / 2,
(t1_boxs[3] + t1_boxs[1]) / 2]
t1_boxe = [(t1_boxe[2] + t1_boxe[0]) / 2,
(t1_boxe[3] + t1_boxe[1]) / 2]
return t1_fs, t1_fe, t1_zs, t1_ze, t1_boxs, t1_boxe
for t1id in sub_list:
tracklet1 = sub_list[t1id]
if tracklet1 == -1:
continue
t1_fs, t1_fe, t1_zs, t1_ze, t1_boxs, t1_boxe = get_trac_info(
tracklet1)
sim_dict = dict()
for t2id in mot_list:
tracklet2 = mot_list[t2id]
t2_fs, t2_fe, t2_zs, t2_ze, t2_boxs, t2_boxe = get_trac_info(
tracklet2)
if t1_ze == t2_zs:
if abs(t2_fs - t1_fe) < 5 and abs(t2_boxe[0] - t1_boxs[
0]) < 50 and abs(t2_boxe[1] - t1_boxs[1]) < 50:
t1_feat = tracklet1[t1_fe]['feat']
t2_feat = tracklet2[t2_fs]['feat']
sim_dict[t2id] = np.matmul(t1_feat, t2_feat)
if t1_zs == t2_ze:
if abs(t2_fe - t1_fs) < 5 and abs(t2_boxs[0] - t1_boxe[
0]) < 50 and abs(t2_boxs[1] - t1_boxe[1]) < 50:
t1_feat = tracklet1[t1_fs]['feat']
t2_feat = tracklet2[t2_fe]['feat']
sim_dict[t2id] = np.matmul(t1_feat, t2_feat)
if len(sim_dict) > 0:
max_sim = 0
max_id = 0
for t2id in sim_dict:
if sim_dict[t2id] > max_sim:
sim_dict[t2id] = max_sim
max_id = t2id
if max_sim > 0.5:
t2 = mot_list[max_id]
for t1f in tracklet1:
if t1f not in t2:
tracklet1[t1f]['id'] = max_id
t2[t1f] = tracklet1[t1f]
mot_list[max_id] = t2
sub_list[t1id] = -1
return mot_list, sub_list
def do_intra_matching(self, sub_zone_dict, sub_zone):
new_zone_dict = dict()
id_list = list(sub_zone_dict)
id2index = dict()
for index, id in enumerate(id_list):
id2index[id] = index
def get_trac_info(tracklet1):
t1_f = list(tracklet1)
t1_f.sort()
t1_fs = t1_f[0]
t1_fe = t1_f[-1]
t1_zs = tracklet1[t1_fs]['zone']
t1_ze = tracklet1[t1_fe]['zone']
t1_boxs = tracklet1[t1_fs]['bbox']
t1_boxe = tracklet1[t1_fe]['bbox']
t1_boxs = [(t1_boxs[2] + t1_boxs[0]) / 2,
(t1_boxs[3] + t1_boxs[1]) / 2]
t1_boxe = [(t1_boxe[2] + t1_boxe[0]) / 2,
(t1_boxe[3] + t1_boxe[1]) / 2]
return t1_fs, t1_fe, t1_zs, t1_ze, t1_boxs, t1_boxe
sim_matrix = np.zeros([len(id_list), len(id_list)])
for t1id in sub_zone_dict:
tracklet1 = sub_zone_dict[t1id]
t1_fs, t1_fe, t1_zs, t1_ze, t1_boxs, t1_boxe = get_trac_info(
tracklet1)
t1_feat = tracklet1[t1_fe]['feat']
for t2id in sub_zone_dict:
if t1id == t2id:
continue
tracklet2 = sub_zone_dict[t2id]
t2_fs, t2_fe, t2_zs, t2_ze, t2_boxs, t2_boxe = get_trac_info(
tracklet2)
if t1_zs != t1_ze and t2_ze != t2_zs or t1_fe > t2_fs:
continue
if abs(t1_boxe[0] - t2_boxs[0]) > 50 or abs(t1_boxe[1] -
t2_boxs[1]) > 50:
continue
if t2_fs - t1_fe > 5:
continue
t2_feat = tracklet2[t2_fs]['feat']
sim_matrix[id2index[t1id], id2index[t2id]] = np.matmul(t1_feat,
t2_feat)
sim_matrix[id2index[t2id], id2index[t1id]] = np.matmul(t1_feat,
t2_feat)
sim_matrix = 1 - sim_matrix
cluster_labels = AgglomerativeClustering(
n_clusters=None,
distance_threshold=0.7,
affinity='precomputed',
linkage='complete').fit_predict(sim_matrix)
new_zone_dict = dict()
label2id = dict()
for index, label in enumerate(cluster_labels):
tracklet = sub_zone_dict[id_list[index]]
if label not in label2id:
new_id = tracklet[list(tracklet)[0]]
new_tracklet = dict()
else:
new_id = label2id[label]
new_tracklet = new_zone_dict[label2id[label]]
for tf in tracklet:
tracklet[tf]['id'] = new_id
new_tracklet[tf] = tracklet[tf]
new_zone_dict[label] = new_tracklet
return new_zone_dict
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