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# --------------------------------------------------------
# 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')
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