File size: 10,314 Bytes
d4b77ac |
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 |
# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import argparse
import logging
import numpy as np
import cv2
import torch
from os import makedirs
from os.path import isfile, isdir, join
from utils.log_helper import init_log, add_file_handler
from utils.bbox_helper import get_axis_aligned_bbox, cxy_wh_2_rect
from utils.load_helper import load_pretrain
from utils.benchmark_helper import load_dataset
from tools.test import siamese_init, siamese_track
from utils.config_helper import load_config
from utils.pyvotkit.region import vot_overlap, vot_float2str
def parse_range(arg):
param = map(float, arg.split(','))
return np.arange(*param)
def parse_range_int(arg):
param = map(int, arg.split(','))
return np.arange(*param)
parser = argparse.ArgumentParser(description='Finetune parameters for SiamMask tracker on VOT')
parser.add_argument('--arch', dest='arch', default='Custom', choices=['Custom', ],
help='architecture of pretrained model')
parser.add_argument('--resume', default='', type=str, required=True,
metavar='PATH',help='path to latest checkpoint (default: none)')
parser.add_argument('--config', dest='config',help='hyperparameter of SiamRPN in json format')
parser.add_argument('--mask', action='store_true', help='whether use mask output')
parser.add_argument('--refine', action='store_true', help='whether use mask refine output')
parser.add_argument('-v', '--visualization', dest='visualization', action='store_true',
help='whether visualize result')
parser.add_argument('--dataset', default='VOT2018', type=str,
metavar='DATASET', help='dataset')
parser.add_argument('-l', '--log', default="log_tune.txt", type=str,
help='log file')
parser.add_argument('--penalty-k', default='0.05,0.5,0.05', type=parse_range,
help='penalty_k range')
parser.add_argument('--lr', default='0.35,0.5,0.05', type=parse_range,
help='lr range')
parser.add_argument('--window-influence', default='0.1,0.8,0.05', type=parse_range,
help='window influence range')
parser.add_argument('--search-region', default='255,256,8', type=parse_range_int,
help='search region size')
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def tune(param):
regions = [] # result and states[1 init / 2 lost / 0 skip]
# save result
benchmark_result_path = join('result', param['dataset'])
tracker_path = join(benchmark_result_path, (param['network_name'] +
'_r{}'.format(param['hp']['instance_size']) +
'_penalty_k_{:.3f}'.format(param['hp']['penalty_k']) +
'_window_influence_{:.3f}'.format(param['hp']['window_influence']) +
'_lr_{:.3f}'.format(param['hp']['lr'])).replace('.', '_')) # no .
if param['dataset'].startswith('VOT'):
baseline_path = join(tracker_path, 'baseline')
video_path = join(baseline_path, param['video'])
result_path = join(video_path, param['video'] + '_001.txt')
elif param['dataset'].startswith('OTB') or param['dataset'].startswith('DAVIS'):
video_path = tracker_path
result_path = join(video_path, param['video']+'.txt')
if isfile(result_path):
return
try:
if not isdir(video_path):
makedirs(video_path)
except OSError as err:
print(err)
with open(result_path, 'w') as f: # Occupation
f.write('Occ')
global ims, gt, image_files
if ims is None:
print(param['video'] + ' Only load image once and if needed')
ims = [cv2.imread(x) for x in image_files]
start_frame, lost_times, toc = 0, 0, 0
for f, im in enumerate(ims):
tic = cv2.getTickCount()
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
state = siamese_init(im, target_pos, target_sz, param['network'], param['hp'], device=device) # init tracker
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
if param['dataset'].startswith('VOT'):
regions.append(1)
elif param['dataset'].startswith('OTB') or param['dataset'].startswith('DAVIS'):
regions.append(gt[f])
elif f > start_frame: # tracking
state = siamese_track(state, im, args.mask, args.refine, device=device)
if args.mask:
location = state['ploygon'].flatten()
else:
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
if param['dataset'].startswith('VOT'):
if 'VOT' in args.dataset:
gt_polygon = ((gt[f][0], gt[f][1]),
(gt[f][2], gt[f][3]),
(gt[f][4], gt[f][5]),
(gt[f][6], gt[f][7]))
if args.mask:
pred_polygon = ((location[0], location[1]), (location[2], location[3]),
(location[4], location[5]), (location[6], location[7]))
else:
pred_polygon = ((location[0], location[1]),
(location[0] + location[2], location[1]),
(location[0] + location[2], location[1] + location[3]),
(location[0], location[1] + location[3]))
b_overlap = vot_overlap(gt_polygon, pred_polygon, (im.shape[1], im.shape[0]))
else:
b_overlap = 1
if b_overlap: # continue to track
regions.append(location)
else: # lost
regions.append(2)
lost_times += 1
start_frame = f + 5 # skip 5 frames
else:
regions.append(location)
else: # skip
regions.append(0)
toc += cv2.getTickCount() - tic
if args.visualization and f >= start_frame: # visualization (skip lost frame)
if f == 0: cv2.destroyAllWindows()
if len(gt[f]) == 8:
cv2.polylines(im, [np.array(gt[f], np.int).reshape((-1, 1, 2))], True, (0, 255, 0), 3)
else:
cv2.rectangle(im, (gt[f, 0], gt[f, 1]), (gt[f, 0] + gt[f, 2], gt[f, 1] + gt[f, 3]), (0, 255, 0), 3)
if len(location) == 8:
location = np.int0(location)
cv2.polylines(im, [location.reshape((-1, 1, 2))], True, (0, 255, 255), 3)
else:
location = [int(l) for l in location] # bad support for OPENCV
cv2.rectangle(im, (location[0], location[1]),
(location[0] + location[2], location[1] + location[3]), (0, 255, 255), 3)
cv2.putText(im, str(f), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2) # frame id
cv2.putText(im, str(lost_times), (40, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) # lost time
cv2.imshow(param['video'], im)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
print('Video: {:12s} Time: {:2.1f}s Speed: {:3.1f}fps Lost: {:d}'.format(param['video'], toc, f / toc, lost_times))
with open(result_path, 'w') as f:
for x in regions:
f.write('{:d}\n'.format(x)) if isinstance(x, int) else \
f.write(','.join([vot_float2str("%.4f", i) for i in x]) + '\n')
def main():
init_log('global', logging.INFO)
if args.log != "":
add_file_handler('global', args.log, logging.INFO)
params = {'penalty_k': args.penalty_k,
'window_influence': args.window_influence,
'lr': args.lr,
'instance_size': args.search_region}
num_search = len(params['penalty_k']) * len(params['window_influence']) * \
len(params['lr']) * len(params['instance_size'])
print(params)
print(num_search)
cfg = load_config(args)
if args.arch == 'Custom':
from custom import Custom
model = Custom(anchors=cfg['anchors'])
else:
model = models.__dict__[args.arch](anchors=cfg['anchors'])
if args.resume:
assert isfile(args.resume), '{} is not a valid file'.format(args.resume)
model = load_pretrain(model, args.resume)
model.eval()
model = model.to(device)
default_hp = cfg.get('hp', {})
p = dict()
p['network'] = model
p['network_name'] = args.arch+'_'+args.resume.split('/')[-1].split('.')[0]
p['dataset'] = args.dataset
global ims, gt, image_files
dataset_info = load_dataset(args.dataset)
videos = list(dataset_info.keys())
np.random.shuffle(videos)
for video in videos:
print(video)
if isfile('finish.flag'):
return
p['video'] = video
ims = None
image_files = dataset_info[video]['image_files']
gt = dataset_info[video]['gt']
np.random.shuffle(params['penalty_k'])
np.random.shuffle(params['window_influence'])
np.random.shuffle(params['lr'])
for penalty_k in params['penalty_k']:
for window_influence in params['window_influence']:
for lr in params['lr']:
for instance_size in params['instance_size']:
p['hp'] = default_hp.copy()
p['hp'].update({'penalty_k':penalty_k,
'window_influence':window_influence,
'lr':lr,
'instance_size': instance_size,
})
tune(p)
if __name__ == '__main__':
main()
with open('finish.flag', 'w') as f: # Occupation
f.write('finish')
|