def run(self):
while not self.stop_event.is_set():
try:
image = self.cap.get()
with chainer.using_config('autotune', True), \
chainer.using_config('use_ideep', 'auto'):
feature_map = get_feature(self.model, image.transpose(2, 0, 1).astype(np.float32))
self.queue.put((image, feature_map), timeout=1)
except queue.Full:
pass
except queue.Empty:
pass
def run(self):
while not self.stop_event.is_set():
try:
image, count = self.cap.get(2)
print('pred2 getting from cap:' + str(count) + '\n')
image = cv2.resize(image, self.insize)
with chainer.using_config('autotune', True), \
chainer.using_config('use_ideep', 'auto'):
feature_map = get_feature(
self.model,
image.transpose(2, 0, 1).astype(np.float32))
self.queue.put((image, feature_map), timeout=100)
print("pred2 queue: ", self.queue.qsize())
except queue.Full:
pass
except queue.Empty:
pass
def run(self):
model = create_model(self.modelargs, self.config)
logger.info('{} started at PID {} - 224x224 model loaded'.format(self.name, self.pid))
if chainer.backends.cuda.available:
model = model.to_gpu(1)
self.model = model
self.insize = (224, 224)
self.pipe_end.send(True) # model loaded sign
count = 0
run = False
if self.pipe_end.recv():
logger.info("start running 224x224")
run = True
# run the first forward path to get model auto tune right
# than wait for bigger model to complete first forward path
image, count = self.queue_in.get(timeout=1)
image = cv2.resize(image, self.insize)
with chainer.using_config('autotune', True), \
chainer.using_config('use_ideep', 'auto'):
feature_map = get_feature(self.model, image.transpose(2, 0, 1).astype(np.float32))
# self.queue.put((image, feature_map), timeout=1)
self.queue.put((feature_map), timeout=1)
if self.pipe_end.recv() == 2:
pass
while not self.stop_event.is_set():
try:
if run:
t_start = time.time()
# image, count = self.cap.get(2)
image, count = self.queue_in.get(timeout=1)
self.queue_get_time += time.time()-t_start
try:
humans = self.queue_comm.get(timeout=1)
# if humans: print(humans)
cropped_image_set = self.cut_human(image, humans)
except queue.Empty:
logger.info('humans queue empty')
cropped_image_set = self.random_crop(image, 20)
feature_map = self.model.predict_video(cropped_image_set)
# logger.info('get img from queue took {} sec'.format(time.time()-t_start))
# print('pred2 getting from cap:'+str(count)+'\n')
image = cv2.resize(image, self.insize)
# print(cropped_image_set)
t_start = time.time()
with chainer.using_config('autotune', True), \
chainer.using_config('use_ideep', 'auto'):
feature_map = get_feature(self.model, image.transpose(2, 0, 1).astype(np.float32))
if not self.queue.empty():
self.inf_time = time.time() - t_start
# self.queue.put((image, feature_map), timeout=1)
self.queue.put((feature_map), timeout=1) # maybe not needed to be a queue, just internal storage of process
#logger.debug("pred2 queue: {}".format(self.queue.qsize()))
else:
logger.info("waiting for other model to load....")
if self.pipe_end.recv() == 'stop':
print("STOP received via pipe")
if self.queue_in.qsize() == 0 and self.queue.qsize() > 0:
self.pipe_end.send('stop')
self.stop()
except queue.Full:
logger.info("queue full")
pass
except queue.Empty:
logger.info("queue empty")
if self.queue.qsize() > 0: # self.pipe_end.recv() == 'stop':
self.pipe_end.send('stop')
self.stop()
else:
pass
except cv2.error:
logger.info("CV2 error")
logger.info('{} exiting'.format(self.name))
self.pipe_end.send('stop')
time.sleep(1)
self.stop()
except KeyboardInterrupt:
self.pipe_end.send('stop')
self.stop()
except:
print("Unexpected error:", sys.exc_info()[0])
self.pipe_end.send('stop')
time.sleep(1)
self.stop()
raise
def run(self):
model = create_model(self.modelargs, self.config)
logger.info('{} started at PID {} - 1920x1080 model loaded'.format(self.name, self.pid))
if chainer.backends.cuda.available:
model = model.to_gpu(0)
self.model = model
self.insize = (1920, 1080)
self.pipe_end.send(True) # model loaded sign
count = 0
run = False
if self.pipe_end.recv():
logger.info("start running 1920x1080")
run = True
while not self.stop_event.is_set():
try:
if run and not self.pipe_end.poll():
t_start = time.time()
# image, count = self.cap.get(1)
image, count = self.queue_in.get(timeout=1)
self.queue_get_time += time.time() - t_start
# logger.info('get img from queue took {} sec'.format(time.time()-t_start))
# print('pred1 getting from cap:'+str(count)+'\n')
image = cv2.resize(image, self.insize)
t_start = time.time()
with chainer.using_config('autotune', True), \
chainer.using_config('use_ideep', 'auto'):
feature_map = get_feature(self.model, image.transpose(2, 0, 1).astype(np.float32))
if not self.queue.empty():
self.inf_time += time.time() - t_start
# self.queue.put((image, feature_map), timeout=1)
self.queue.put((feature_map), timeout=1)
humans = get_humans_by_feature(model, feature_map, self.detection_threshold, self.min_num_keypoints)
if len(humans) > 0:
self.queue_comm.put(humans, timeout=1)
# cropped_image_set = self.cut_human(image, humans)
# logger.debug("pred1 queue {}: ".format(self.queue.qsize()))
if self.queue.qsize() == 1: self.pipe_end.send(2) # sign that big model passed first forward path
## BLOCK ON PORPUSE ##
block = False
while block:
time.sleep(2)
pass
######
else:
# logger.info("waiting for other model to load....")
if self.pipe_end.recv() == 'stop':
logger.info("STOP received via pipe")
self.stop()
if self.queue_in.qsize()==0 and self.queue.qsize()>0:
self.pipe_end.send('stop')
self.stop()
except queue.Full:
logger.info("queue full")
pass
except queue.Empty:
logger.info("queue empty")
if self.queue.qsize() > 0:
self.stop()
else:
pass
except cv2.error:
logger.info("CV2 error")
self.pipe_end.send('stop')
time.sleep(1)
self.stop()
except KeyboardInterrupt:
self.pipe_end.send('stop')
self.stop()
except:
print("Unexpected error:", sys.exc_info()[0])
self.pipe_end.send('stop')
time.sleep(1)
self.stop()
raise