/
recognition_protocol_main.py
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/
recognition_protocol_main.py
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# -*- coding: utf8 -*-
# ! /usr/bin/python
import sys,signal
from reconition_protocol.face_subscriber_protocol import FaceSubscriber
from reconition_protocol.face_publisher_protocol import FacePublisher
from reconition_protocol.face_recognition_protocol import FaceRecognition
from multiprocessing import Queue,Lock
import time
from src import classifier
def sigint_handler(signum,frame):
print("main-thread exit")
global subs, pubs, face_proc
pubs.stop()
subs.stop()
for i in range(len(face_proc)):
face_proc[i].stop()
sys.exit()
def get_classes(path):
path_exp = os.path.expanduser(path)
classes = [path for path in os.listdir(path_exp) \
if os.path.isdir(os.path.join(path_exp, path))]
classes.sort()
return classes
def KNN_train(arg):
classifier.main(arg)
if __name__ == '__main__':
signal.signal(signal.SIGINT, sigint_handler)
thed = 0.6
procnum = 1
load_mode_finish_q = Queue()
face_msg_queue = Queue()
yolo_msg_queue = Queue()
subs = FaceSubscriber("tcp://127.0.0.1:812341","tcp://127.0.0.1:812302",yolo_msg_queue)
subs.setDaemon(True)
pubs = FacePublisher("tcp://127.0.0.1:812354", face_msg_queue)
pubs.setDaemon(True)
yololock = Lock()
facelock = Lock()
face_proc = []
for i in range(procnum):
face_proc.append(FaceRecognition(load_mode_finish_q,yolo_msg_queue,yololock,face_msg_queue,facelock))
for i in range(procnum):
face_proc[i].start()
while load_mode_finish_q.qsize()<procnum:
#print(load_mode_finish_q.get())
time.sleep(1)
pubs.start()
subs.start()
for i in range(len(face_proc)):
load_mode_finish_q.get()
config = edict()
config.mode = 16
config.data_dir = 1e-4
config.classifier = 'KNN'
config.use_split_dataset = 0
config.test_data_dir = ''
config.batch_size = 100
config.image_size = 160
config.seed = 666
config.min_nrof_images_per_class = 5
config.nrof_train_images_per_class = 5
knn_classes = get_classes('/opt/yanhong.jia/classes')
while True:
time.sleep(60)
classes = get_classes('/opt/yanhong.jia/classes')
if(len(classes) != len(knn_classes)):
#start classifier train
knn_train = Process(target=KNN_train, args=(config,))
knn_train.start()
knn_train.join()
for i in range(len(face_proc)):
face_proc[i].stop()
face_proc[i].start()
print(load_mode_finish_q.get())
for i in range(classes):
if(classes[i] != knn_classes[i]):
# start classifier train
break
knn_train = Process(target=KNN_train, args=(config,))
knn_train.start()
knn_train.join()
for i in range(len(face_proc)):
face_proc[i].stop()
face_proc[i].start()
print(load_mode_finish_q.get())
for i in range(len(face_proc)):
face_proc[i].join()
subs.join()
pubs.join()