mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2025-12-16 16:37:51 +01:00
25 lines
580 B
Python
25 lines
580 B
Python
|
import cv2
|
||
|
|
import torch
|
||
|
|
from model.RIFE import Model
|
||
|
|
|
||
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
|
|
|
||
|
|
model = Model()
|
||
|
|
model.load_model('./train_log')
|
||
|
|
model.eval()
|
||
|
|
model.device()
|
||
|
|
|
||
|
|
img0 = cv2.imread('0.png')
|
||
|
|
img1 = cv2.imread('1.png')
|
||
|
|
|
||
|
|
h, w, _ = img0.shape
|
||
|
|
|
||
|
|
img0 = torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.
|
||
|
|
img1 = torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.
|
||
|
|
|
||
|
|
imgs = torch.cat((img0, img1), 0).float()
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
res = model.inference(imgs.unsqueeze(0)) * 255
|
||
|
|
cv2.imwrite('out.png', res[0].numpy().transpose(1, 2, 0))
|