netC = MonoPortNet(cfg.netC)
netC.load_legacy_pifu(cfg.netC.ckpt_path)
netC.image_filter = netC.image_filter.to(cuda_backbone_C)
netC.surface_classifier = netC.surface_classifier.to(cuda_color)
netC.eval()
else:
netC = None
print("we are not loading netC ...")
########################################
## initialize data streamer
########################################
print("initialize data streamer ...")
if args.camera:
data_stream = streamer.CaptureStreamer(pad=False)
elif len(args.videos) > 0:
data_stream = streamer.VideoListStreamer(args.videos)
elif len(args.images) > 0:
data_stream = streamer.ImageListStreamer(args.images)
elif args.image_folder is not None:
images = sorted(glob.glob(args.image_folder + "/*.jpg"))
images += sorted(glob.glob(args.image_folder + "/*.png"))
data_stream = streamer.ImageListStreamer(images)
########################################
## human segmentation model
########################################
seg_engine = human_inst_seg.Segmentation(device=cuda_backbone_G, verbose=False)
seg_engine.eval()
bbox = bboxes[0, 0, 0].cpu().numpy()
window = cv2.rectangle(window, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)
window = cv2.cvtColor(window, cv2.COLOR_BGR2RGB)
window = cv2.resize(window, (0, 0), fx=2, fy=2)
cv2.imshow('window', window)
cv2.waitKey(30)
seg_engine = human_inst_seg.Segmentation()
seg_engine.eval()
if args.camera:
data_stream = streamer.CaptureStreamer()
elif len(args.videos) > 0:
data_stream = streamer.VideoListStreamer(args.videos *
(10000 if args.loop else 1))
elif len(args.images) > 0:
data_stream = streamer.ImageListStreamer(args.images *
(10000 if args.loop else 1))
loader = torch.utils.data.DataLoader(
data_stream,
batch_size=1,
num_workers=1,
pin_memory=False,
)
try: