def main(args, model_path):
print('args:', args.seq, args.json, args.savefig, args.display)
np.random.seed(0)
torch.manual_seed(0)
# Generate sequence config
img_list, init_bbox, gt, savefig_dir, display, result_path = gen_config(
args)
# Run tracker
result, result_bb, fps, overlap = run_mdnet(img_list,
init_bbox,
gt=gt,
savefig_dir=savefig_dir,
display=display,
model_path=model_path)
# Save result
res = {}
res['res'] = result_bb.round().tolist()
res['type'] = 'rect'
res['fps'] = fps
json.dump(res, open(result_path, 'w'), indent=2)
return overlap, 'tracker002'
def confirmation_handler(update, context):
data = update.callback_query.data
user_id = data[1:]
if data[0] == 'n':
context.bot.send_message(chat_id=user_id,
text='Sorry payment is invalid.')
elif data[0] == 'y':
if check_user_exist(user_id):
if check_expired(user_id):
context.bot.send_message(
chat_id=user_id,
text='Your account updated for next month.')
update_user(user_id)
else:
user = get_tmp_user(user_id)
# Generate file config
file_name = gen_config()
with open(file_name, 'r') as fd:
context.bot.sendDocument(user_id, document=fd)
add_user(user_id, user[2])
context.bot.send_message(chat_id=user_id,
text=f'Hi! You are now registered.')
context.job_queue.run_daily(alarm,
datetime.time(13, 45, 00),
context=user_id,
name=str(user_id))
def init_info_and_rpaths(self, ext):
self.ssl_config = gen_config.gen_config(self.build_temp, couchbase_core=couchbase_core)
self.info.setbase(self.build_temp)
self.info.cfg = self.cfg_type()
self.compiler.add_include_dir(os.path.join(*self.info.base+["install","include"]))
self.compiler.add_library_dir(os.path.join(*self.info.base+["install","lib",self.cfg_type()]))
if sys.platform == 'darwin':
warnings.warn('Adding /usr/local to lib search path for OS X')
self.compiler.add_library_dir('/usr/local/lib')
self.compiler.add_include_dir('/usr/local/include')
self.add_rpaths(ext)
from gen_config import gen_config
CONFIG = {
"groups": {
"wireplane": range(0, 10),
},
"streams": ["example"],
"metrics": {
"rms": {},
"hit_occupancy": {}
}
}
gen_config(CONFIG)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--seq', default='', help='input seq')
parser.add_argument('-j', '--json', default='', help='input json')
parser.add_argument('-f', '--savefig', action='store_true')
parser.add_argument('-d', '--display', action='store_true')
args = parser.parse_args()
assert args.seq != '' or args.json != ''
np.random.seed(0)
torch.manual_seed(0)
# Generate sequence config
img_list, init_bbox, gt, savefig_dir, display, result_path = gen_config(
args)
# Run tracker
result, result_bb, fps = run_mdnet(img_list,
init_bbox,
gt=gt,
savefig_dir=savefig_dir,
display=display)
# Save result
res = {}
res['res'] = result_bb.round().tolist()
res['type'] = 'rect'
res['fps'] = fps
json.dump(res, open(result_path, 'w'), indent=2)