mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2026-02-24 12:29:43 +01:00
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
import os
|
|
import cv2
|
|
import torch
|
|
import argparse
|
|
from torch.nn import functional as F
|
|
from model.RIFE_HD import Model
|
|
import warnings
|
|
warnings.filterwarnings("ignore")
|
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
|
|
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()
|
|
|
|
img0 = cv2.imread(args.img[0])
|
|
img1 = cv2.imread(args.img[1])
|
|
|
|
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(args.exp):
|
|
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')
|
|
for i in range(len(img_list)):
|
|
cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
|