def worker(input_q, output_q):
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
fps = FPS().start()
while True:
fps.update()
frame = input_q.get()
output_q.put(detect_objects(frame, sess, detection_graph))
fps.stop()
sess.close()
def worker(input_q, output_q):
fps = FPS().start()
while True:
fps.update()
frame = input_q.get()
output_q.put(segment_img(frame, sess))
fps.stop()
sess.close()
def worker(input_q, output_q):
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
fps = FPS().start()
while True:
fps.update()
frame = input_q.get()
output_q.put(detect_objects(frame, sess, detection_graph))
fps.stop()
sess.close()
def worker(input_q, output_q):
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
config = tf.ConfigProto(device_count={'GPU': 0})
sess = tf.Session(graph=detection_graph, config=config)
fps = FPS().start()
while True:
fps.update()
frame = input_q.get()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
output_q.put(detect_objects(frame_rgb, sess, detection_graph))
fps.stop()
sess.close()
args = parser.parse_args()
logger = multiprocessing.log_to_stderr()
logger.setLevel(multiprocessing.SUBDEBUG)
input_q = Queue(maxsize=args.queue_size)
output_q = Queue(maxsize=args.queue_size)
process = Process(target=worker, args=((input_q, output_q)))
process.daemon = True
pool = Pool(args.num_workers, worker, (input_q, output_q))
video_capture = WebcamVideoStream(src=args.video_source,
width=args.width,
height=args.height).start()
fps = FPS().start()
while True: # fps._numFrames < 120
frame = video_capture.read()
input_q.put(frame)
t = time.time()
cv2.imshow('Video', output_q.get())
fps.update()
print('[INFO] elapsed time: {:.2f}'.format(time.time() - t))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
args = parser.parse_args()
logger = multiprocessing.log_to_stderr()
logger.setLevel(multiprocessing.SUBDEBUG)
input_q = Queue(maxsize=args.queue_size)
output_q = Queue(maxsize=args.queue_size)
process = Process(target=worker, args=((input_q, output_q)))
process.daemon = True
pool = Pool(args.num_workers, worker, (input_q, output_q))
video_capture = WebcamVideoStream(src=args.video_source,
width=args.width,
height=args.height).start()
fps = FPS().start()
while True: # fps._numFrames < 120
frame = video_capture.read()
input_q.put(frame)
t = time.time()
cv2.imshow('Video', output_q.get())
fps.update()
print('[INFO] elapsed time: {:.2f}'.format(time.time() - t))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
input_q = Queue(5) # fps is better if queue is higher but then more lags
output_q = Queue()
for i in range(1):
t = Thread(target=worker, args=(input_q, output_q))
t.daemon = True
t.start()
my_file = "/home/pcroot/Desktop/ABODA-master/video11.avi"
my_file_path = Path(my_file)
if not my_file_path.is_file():
print("Video does not exist")
exit()
stream = cv2.VideoCapture(my_file)
# stream = cv2.VideoCapture(0)
fps = FPS().start()
first_run = True
(ret, frame) = stream.read()
while not ret:
(ret, frame) = stream.read()
frame = imutils.resize(frame, width=450)
adjusted = adjust_gamma(frame, gamma=gamma) # gamma correction
frame = adjusted
(height, width, channel) = frame.shape
image_shape = (height, width)
rgb = IntensityProcessing(image_shape)
bbox_last_frame_proposals = []
static_objects = []
count = 0