-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathopts.py
285 lines (270 loc) · 13.6 KB
/
opts.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
import json
import argparse
from os.path import join
data = {
'MOT17': {
'train': [
'MOT17-02-FRCNN',
'MOT17-04-FRCNN',
'MOT17-05-FRCNN',
'MOT17-09-FRCNN',
'MOT17-10-FRCNN',
'MOT17-11-FRCNN',
'MOT17-13-FRCNN'
],
'test': [
'MOT17-01-FRCNN',
'MOT17-03-FRCNN',
'MOT17-06-FRCNN',
'MOT17-07-FRCNN',
'MOT17-08-FRCNN',
'MOT17-12-FRCNN',
'MOT17-14-FRCNN'
]
},
'MOT20': {
'test': [
'MOT20-04',
'MOT20-06',
'MOT20-07',
'MOT20-08'
],
'train': [
'MOT20-01',
'MOT20-02',
'MOT20-03',
'MOT20-05'
]
},
'KITTI': {
'train': [
"0000", "0002", "0004", "0006", "0008", "0010", "0012", "0014", "0016", "0018", "0020",
"0001", "0003", "0005", "0007", "0009", "0011", "0013", "0015", "0017", "0019"
]
},
'PersonPath22': {
'test': [
"uid_vid_00008.mp4", "uid_vid_00009.mp4", "uid_vid_00011.mp4", "uid_vid_00013.mp4", "uid_vid_00018.mp4", "uid_vid_00019.mp4",
"uid_vid_00020.mp4", "uid_vid_00024.mp4", "uid_vid_00028.mp4", "uid_vid_00030.mp4", "uid_vid_00031.mp4", "uid_vid_00035.mp4",
"uid_vid_00036.mp4", "uid_vid_00038.mp4", "uid_vid_00043.mp4", "uid_vid_00045.mp4", "uid_vid_00046.mp4", "uid_vid_00048.mp4",
"uid_vid_00051.mp4", "uid_vid_00056.mp4", "uid_vid_00057.mp4", "uid_vid_00063.mp4", "uid_vid_00064.mp4", "uid_vid_00066.mp4",
"uid_vid_00067.mp4", "uid_vid_00068.mp4", "uid_vid_00069.mp4", "uid_vid_00071.mp4", "uid_vid_00076.mp4", "uid_vid_00078.mp4",
"uid_vid_00079.mp4", "uid_vid_00080.mp4", "uid_vid_00082.mp4", "uid_vid_00085.mp4", "uid_vid_00086.mp4", "uid_vid_00087.mp4",
"uid_vid_00090.mp4", "uid_vid_00092.mp4", "uid_vid_00096.mp4", "uid_vid_00098.mp4", "uid_vid_00099.mp4", "uid_vid_00100.mp4",
"uid_vid_00102.mp4", "uid_vid_00105.mp4", "uid_vid_00107.mp4", "uid_vid_00109.mp4", "uid_vid_00113.mp4", "uid_vid_00114.mp4",
"uid_vid_00117.mp4", "uid_vid_00144.mp4", "uid_vid_00147.mp4", "uid_vid_00149.mp4", "uid_vid_00150.mp4", "uid_vid_00118.mp4",
"uid_vid_00121.mp4", "uid_vid_00122.mp4", "uid_vid_00124.mp4", "uid_vid_00125.mp4", "uid_vid_00126.mp4", "uid_vid_00127.mp4",
"uid_vid_00130.mp4", "uid_vid_00133.mp4", "uid_vid_00141.mp4", "uid_vid_00153.mp4", "uid_vid_00158.mp4", "uid_vid_00161.mp4",
"uid_vid_00163.mp4", "uid_vid_00166.mp4", "uid_vid_00167.mp4", "uid_vid_00169.mp4", "uid_vid_00170.mp4", "uid_vid_00172.mp4",
"uid_vid_00173.mp4", "uid_vid_00174.mp4", "uid_vid_00175.mp4", "uid_vid_00178.mp4", "uid_vid_00179.mp4", "uid_vid_00183.mp4",
"uid_vid_00189.mp4", "uid_vid_00190.mp4", "uid_vid_00191.mp4", "uid_vid_00193.mp4", "uid_vid_00198.mp4", "uid_vid_00200.mp4", "uid_vid_00201.mp4",
"uid_vid_00205.mp4", "uid_vid_00207.mp4", "uid_vid_00212.mp4", "uid_vid_00218.mp4", "uid_vid_00219.mp4", "uid_vid_00221.mp4",
"uid_vid_00222.mp4", "uid_vid_00226.mp4", "uid_vid_00228.mp4", "uid_vid_00230.mp4"
# , "uid_vid_00162.mp4", "uid_vid_00234.mp4", "uid_vid_00235.mp4" # ignored as its videos is not the same as others
]
},
'VIRAT-S': {'train': [
'VIRAT_S_050000_10_001462_001491', 'VIRAT_S_010206_03_000546_000580', 'VIRAT_S_010005_04_000299_000323', 'VIRAT_S_010109_07_000876_000910', 'VIRAT_S_010200_10_000923_000959', 'VIRAT_S_010200_09_000886_000915', 'VIRAT_S_010004_02_000191_000237', 'VIRAT_S_010003_01_000111_000137', 'VIRAT_S_010200_05_000658_000700', 'VIRAT_S_010201_05_000499_000527', 'VIRAT_S_010208_08_000807_000831', 'VIRAT_S_010001_05_000649_000684', 'VIRAT_S_010110_04_000777_000812', 'VIRAT_S_010106_05_000954_000996', 'VIRAT_S_010110_05_000899_000935', 'VIRAT_S_010208_07_000768_000791', 'VIRAT_S_010111_05_000762_000799', 'VIRAT_S_010201_01_000125_000152', 'VIRAT_S_010207_01_000712_000752', 'VIRAT_S_010206_04_000720_000767', 'VIRAT_S_010204_05_000856_000890', 'VIRAT_S_010205_03_000370_000395', 'VIRAT_S_010004_01_000163_000188', 'VIRAT_S_010002_04_000307_000350', 'VIRAT_S_050000_12_001591_001619', 'VIRAT_S_010114_02_000765_000802', 'VIRAT_S_010202_02_000161_000189', 'VIRAT_S_010003_11_000956_000982', 'VIRAT_S_010202_00_000001_000033', 'VIRAT_S_010207_06_001064_001097', 'VIRAT_S_010207_02_000790_000816', 'VIRAT_S_010107_00_000019_000057', 'VIRAT_S_010202_03_000313_000355', 'VIRAT_S_010111_00_000000_000032', 'VIRAT_S_010200_08_000838_000867', 'VIRAT_S_010002_05_000397_000420', 'VIRAT_S_010203_10_001092_001121', 'VIRAT_S_010111_09_000981_001014', 'VIRAT_S_010111_07_000872_000909', 'VIRAT_S_010002_01_000123_000148', 'VIRAT_S_010002_02_000174_000204', 'VIRAT_S_010003_03_000219_000259', 'VIRAT_S_010002_06_000441_000467', 'VIRAT_S_010001_06_000685_000722', 'VIRAT_S_010207_09_001484_001510', 'VIRAT_S_010206_00_000007_000035', 'VIRAT_S_010204_07_000942_000989', 'VIRAT_S_010208_09_000857_000886', 'VIRAT_S_010001_03_000537_000563', 'VIRAT_S_010207_08_001308_001332', 'VIRAT_S_010205_04_000545_000576', 'VIRAT_S_010106_01_000493_000526', 'VIRAT_S_010005_06_000475_000499', 'VIRAT_S_010204_06_000913_000939', 'VIRAT_S_010201_09_000770_000801', 'VIRAT_S_010003_05_000499_000523', 'VIRAT_S_010002_07_000522_000547', 'VIRAT_S_010208_02_000150_000180', 'VIRAT_S_010005_02_000177_000203', 'VIRAT_S_010113_07_000965_001013', 'VIRAT_S_010207_04_000929_000954', 'VIRAT_S_010005_00_000048_000075', 'VIRAT_S_010115_02_000485_000516', 'VIRAT_S_010201_02_000167_000197', 'VIRAT_S_050203_06_001202_001264', 'VIRAT_S_010111_06_000820_000860', 'VIRAT_S_010203_03_000400_000435', 'VIRAT_S_010001_09_000921_000952', 'VIRAT_S_010113_02_000434_000479', 'VIRAT_S_010000_06_000728_000762', 'VIRAT_S_010207_05_001013_001038', 'VIRAT_S_010204_00_000030_000059', 'VIRAT_S_010005_05_000397_000430', 'VIRAT_S_010002_03_000236_000261', 'VIRAT_S_010204_03_000606_000632', 'VIRAT_S_010000_07_000827_000860', 'VIRAT_S_010208_03_000201_000232', 'VIRAT_S_010004_03_000239_000277', 'VIRAT_S_010003_07_000608_000636', 'VIRAT_S_010111_04_000718_000760', 'VIRAT_S_010111_08_000920_000954', 'VIRAT_S_010110_00_000000_000021', 'VIRAT_S_010003_02_000165_000202', 'VIRAT_S_010208_05_000591_000631', 'VIRAT_S_010206_02_000414_000439', 'VIRAT_S_010106_02_000656_000692', 'VIRAT_S_010206_05_000797_000823', 'VIRAT_S_010201_08_000705_000739', 'VIRAT_S_010003_10_000901_000934', 'VIRAT_S_010004_00_000064_000100', 'VIRAT_S_010003_06_000526_000560', 'VIRAT_S_010003_08_000739_000778', 'VIRAT_S_010204_10_001372_001395', 'VIRAT_S_010203_09_001010_001036', 'VIRAT_S_010005_07_000535_000584', 'VIRAT_S_010107_02_000282_000312', 'VIRAT_S_010113_05_000776_000805', 'VIRAT_S_010200_06_000702_000744', 'VIRAT_S_010005_08_000647_000693', 'VIRAT_S_010110_03_000712_000739'
]
},
'DanceTrack': {
'train': [
'dancetrack0001', 'dancetrack0052', 'dancetrack0068', 'dancetrack0039', 'dancetrack0086', 'dancetrack0008', 'dancetrack0053', 'dancetrack0045', 'dancetrack0055', 'dancetrack0066', 'dancetrack0062', 'dancetrack0083', 'dancetrack0037', 'dancetrack0023', 'dancetrack0033', 'dancetrack0096', 'dancetrack0029', 'dancetrack0049', 'dancetrack0016', 'dancetrack0044', 'dancetrack0098', 'dancetrack0082', 'dancetrack0015', 'dancetrack0051', 'dancetrack0061', 'dancetrack0087', 'dancetrack0069', 'dancetrack0075', 'dancetrack0020', 'dancetrack0006', 'dancetrack0032', 'dancetrack0012', 'dancetrack0057', 'dancetrack0074', 'dancetrack0024', 'dancetrack0027', 'dancetrack0080', 'dancetrack0002', 'dancetrack0072', 'dancetrack0099'
]
}
}
class opts:
def __init__(self):
self.parser = argparse.ArgumentParser()
self.parser.add_argument(
'--yolo_model',
type=str,
default='yolov8m',
help='YOLO model to use [n, s, m, l, x] and path to .weights file',
)
self.parser.add_argument(
'--visualize',
default=True,
action='store_true',
help='If set, visualizes the video',
)
self.parser.add_argument(
'--eval_mot',
type=bool,
default=False,
help='Uses FasterRCNN detections given by MOT Challenge',
)
self.parser.add_argument(
'--dataset',
type=str,
default='MOT17',
help='MOT17 or MOT20 or KITTI or PersonPath22 or VIRAT-S or DanceTrack',
)
self.parser.add_argument(
'--source',
default='demo/VIRAT_S_010204_07_000942_000989.mp4',
type=str,
help='The path to the video file to be processed'
)
self.parser.add_argument(
'--split',
type=str,
default='train',
help='train or val/test',
)
self.parser.add_argument(
'--tracker_name',
type=str,
default='LITEDeepSORT',
help='LITEDeepSORT or StrongSORT or DeepSORT or SORT',
)
self.parser.add_argument(
'--input_resolution',
type=int,
# required=True,
default=1280,
help='Resolution for input images (e.g., 1280 for 736x1280)',
)
self.parser.add_argument(
'--min_confidence',
type=float,
default=0.25,
# required=True,
help='Minimum confidence threshold for detections: default .25',
)
self.parser.add_argument(
'--classes',
nargs='+', # '+' means "at least one", '*' for zero or more
type=int,
help='For Detection',
default=[0] # default list if nothing is provided
)
self.parser.add_argument(
'--appearance_only_matching', # Corrected typo here
action='store_true',
help='If set, skips IOU matching'
)
self.parser.add_argument(
'--BoT',
action='store_true',
help='Replacing the original feature extractor with BoT'
)
self.parser.add_argument(
'--ECC',
action='store_true',
help='CMC model'
)
self.parser.add_argument(
'--NSA',
action='store_true',
help='NSA Kalman filter'
)
self.parser.add_argument(
'--EMA',
action='store_true',
help='EMA feature updating mechanism'
)
self.parser.add_argument(
'--MC',
action='store_true',
help='Matching with both appearance and motion cost'
)
self.parser.add_argument(
'--woC',
action='store_true',
help='Replace the matching cascade with vanilla matching'
)
self.parser.add_argument(
'--root_dataset',
default='datasets/'
)
self.parser.add_argument(
'--dir_save',
default='output/',
help='e.g, results/MOT17/'
)
self.parser.add_argument(
'--EMA_alpha',
default=0.9
)
self.parser.add_argument(
'--MC_lambda',
default=0.98
)
self.parser.add_argument(
'--max_age',
type=int,
default=30
)
# add argument for --max_cosine_distance
self.parser.add_argument(
'--max_cosine_distance',
type=float,
default=0.7
)
self.parser.add_argument(
'--appearance_feature_layer',
type=str,
default=None
)
# add arg for gpu device
self.parser.add_argument(
'--device',
type=str,
default='cuda:0'
)
self.parser.add_argument(
'--sequence',
type=str,
)
self.parser.add_argument(
'--solution',
type=str,
default='object_counter',
help='object_counter, heatmap, etc.'
)
self.parser.add_argument(
'--fps_save',
default=True,
action='store_true',
help='If set, saves FPS results along with the .txt results'
)
def parse(self, args=''):
if args == '':
opt = self.parser.parse_args()
else:
opt = self.parser.parse_args(args)
opt.nms_max_overlap = 1.0
opt.min_detection_height = 0
if opt.tracker_name == 'StrongSORT':
# ECC is a complex scenario which offline processing and knowledge of the dataset
opt.ECC = False
opt.BoT = True
opt.NSA = True
opt.EMA = True
opt.MC = True
opt.woC = True
opt.max_cosine_distance = 0.4
elif opt.tracker_name.startswith('LITE'):
opt.woC = True
opt.ECC = False
# if opt.max_cosine_distance is none then set it to 0.3
if opt.max_cosine_distance is None:
opt.max_cosine_distance = 0.3
if opt.MC:
opt.max_cosine_distance += 0.05
if opt.EMA:
opt.nn_budget = 1
else:
opt.nn_budget = 100
if opt.ECC:
path_ECC = f'results/StrongSORT_Git/{opt.dataset}_ECC_{opt.split}.json'
opt.ecc = json.load(open(path_ECC))
opt.sequences = data[opt.dataset][opt.split]
opt.dir_dataset = join(
opt.root_dataset,
opt.dataset,
opt.split
)
return opt
opt = opts().parse()