123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406 |
- # 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
- from sklearn.cluster import AgglomerativeClustering
- 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
|