def imgint(img1, img2):
warnings.filterwarnings("ignore")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
if torch.cuda.is_available():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
# parser.add_argument('--img', dest='img', nargs=2, required=True)
# parser.add_argument('--exp', default=4, type=int)
# args = parser.parse_args()
model = Model()
model.load_model('./train_log', -1)
model.eval()
model.device()
open_cv_image1 = np.array(img1)
# Convert RGB to BGR
open_cv_image1 = open_cv_image1[:, :, ::-1].copy()
open_cv_image2 = np.array(img2)
# Convert RGB to BGR
open_cv_image2 = open_cv_image2[:, :, ::-1].copy()
img0 = open_cv_image1
img1 = open_cv_image2
img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
n, c, h, w = img0.shape
ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
img0 = F.pad(img0, padding)
img1 = F.pad(img1, padding)
img_list = [img0, img1]
for i in range(4):
tmp = []
for j in range(len(img_list) - 1):
mid = model.inference(img_list[j], img_list[j + 1])
tmp.append(img_list[j])
tmp.append(mid)
tmp.append(img1)
img_list = tmp
# if not os.path.exists('output'):
# os.mkdir('output')
return img_list, h, w
outgoing_read_buffer = Queue(maxsize=500)
_thread.start_new_thread(
build_read_buffer,
(args.incoming, incoming_read_buffer, incoming_files_list))
_thread.start_new_thread(
build_read_buffer,
(args.outgoing, outgoing_read_buffer, outgoing_files_list))
if 'v1.8.model' in args.model:
from model.RIFE_HD import Model # type: ignore
else:
from model.RIFE_HDv2 import Model # type: ignore
model = Model()
model.load_model(args.model, -1)
model.eval()
model.device()
print('Trained model loaded: %s' % args.model)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
torch.set_grad_enabled(False)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
_thread.start_new_thread(clear_write_buffer,
(args.output, write_buffer, input_duration))
rstep = 1 / (input_duration + 1)
ratio = rstep
