def run_camera(args, ctx):
assert args.batch_size == 1, "only batch size of 1 is supported"
logging.info("Detection threshold is {}".format(args.thresh))
iter = CameraIterator(frame_resize=parse_frame_resize(args.frame_resize))
class_names = parse_class_names(args.class_names)
mean_pixels = (args.mean_r, args.mean_g, args.mean_b)
data_shape = int(args.data_shape)
batch_size = int(args.batch_size)
detector = Detector(
get_symbol(args.network, data_shape, num_classes=len(class_names)),
network_path(args.prefix, args.network, data_shape), args.epoch,
data_shape, mean_pixels, batch_size, ctx)
for frame in iter:
logging.info("Frame info: shape %s type %s", frame.shape, frame.dtype)
logging.info("Generating batch")
data_batch = detector.create_batch(frame)
logging.info("Detecting objects")
detections_batch = detector.detect_batch(data_batch)
#detections = [mx.nd.array((1,1,0.2,0.2,0.4,0.4))]
detections = detections_batch[0]
logging.info("%d detections", len(detections))
for det in detections:
obj = det.asnumpy()
(klass, score, x0, y0, x1, y1) = obj
if score > args.thresh:
draw_detection(frame, obj, class_names)
cv2.imshow('frame', frame)
def run_camera(args,ctx):
assert args.batch_size == 1, "only batch size of 1 is supported"
logging.info("Detection threshold is {}".format(args.thresh))
iter = CameraIterator()
class_names = parse_class_names(args.class_names)
mean_pixels = (args.mean_r, args.mean_g, args.mean_b)
data_shape = int(args.data_shape)
batch_size = int(args.batch_size)
detector = Detector(
get_symbol(args.network, data_shape, num_classes=len(class_names)),
network_path(args.prefix, args.network, data_shape),
args.epoch,
data_shape,
mean_pixels,
batch_size,
ctx
)
for frame in iter:
logging.info("Frame info: shape %s type %s", frame.shape, frame.dtype)
logging.info("Generating batch")
data_batch = detector.create_batch(frame)
logging.info("Detecting objects")
detections_batch = detector.detect_batch(data_batch)
#detections = [mx.nd.array((1,1,0.2,0.2,0.4,0.4))]
detections = detections_batch[0]
logging.info("%d detections", len(detections))
for det in detections:
obj = det.asnumpy()
(klass, score, x0, y0, x1, y1) = obj
if score > args.thresh:
draw_detection(frame, obj, class_names)
cv2.imshow('frame', frame)
