def stream(streamer):
session, inputs, logits = construct_model(
"../models/deeplab_v3_plus_53_19e_0095", (513, 513), 5)
parser = Parser()
cap = cv2.VideoCapture("rtsp://127.0.0.1:5000")
framerate = 12.0
out = cv2.VideoWriter(
'appsrc ! videoconvert ! '
'x264enc noise-reduction=10000 speed-preset=ultrafast tune=zerolatency ! '
'rtph264pay config-interval=1 pt=96 !'
'tcpserversink host=127.0.0.1 port=5000 sync=false', 0, framerate,
(1920, 1080))
counter = 0
while cap.isOpened():
frame = streamer.get_frame()
pred = inference(frame, session, logits, inputs)
result = parser.parse(pred, frame)
# result = cv2.imencode('.jpg', result)[1].tobytes()
out.write(result)
cap.release()
out.release()
def gen(streamer):
session, inputs, logits = construct_model(
"../models/deeplab_v3_plus_53_19e_0095", (513, 513), 5)
parser = Parser()
while True:
frame = streamer.get_frame()
pred = inference(frame, session, logits, inputs)
result = parser.parse(pred, frame)
result = cv2.imencode('.jpg', result)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + result + b'\r\n\r\n')
dest='global_model',
action='store_true',
help='Train the model with Global Average Pooling')
parser.add_argument('--glimpse-clouds',
dest='glimpse_clouds',
action='store_true',
help='Train the model with Glimpse Clouds')
parser.add_argument(
'--pose-predictions',
dest='pose_predictions',
action='store_true',
help='Regress the pose from the penultimate features maps')
# Args
args, _ = parser.parse_known_args()
# Transform to dict
options = vars(args)
options['global_model'] = True
# options['glimpse_clouds'] = True
# options['pose_predictions'] = True
# mini-syn test
# options['root'] = '/home/hochul/Desktop/mini_syn_data'
# AIR+SYN
options['root'] = '/media/hochul/my_book/data/'
options['workers'] = 0 if platform == "darwin" else 12
# Infer
inference.inference(options)