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import os
import cv2
import torch
import argparse
from torch.nn import functional as F
import warnings
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)
parser.add_argument('--ratio', default=0, type=float, help='inference ratio between two images with 0 - 1 range')
parser.add_argument('--rthreshold', default=0.02, type=float, help='returns image when actual ratio falls in given range threshold')
parser.add_argument('--rmaxcycles', default=8, type=int, help='limit max number of bisectional cycles')
parser.add_argument('--model', dest='modelDir', type=str, default='train_log', help='directory with trained model files')
args = parser.parse_args()
try:
try:
try:
from model.RIFE_HDv2 import Model
model = Model()
model.load_model(args.modelDir, -1)
print("Loaded v2.x HD model.")
except:
from train_log.RIFE_HDv3 import Model
model = Model()
model.load_model(args.modelDir, -1)
print("Loaded v3.x HD model.")
except:
from model.RIFE_HD import Model
model = Model()
model.load_model(args.modelDir, -1)
print("Loaded v1.x HD model")
except:
from model.RIFE import Model
model = Model()
model.load_model(args.modelDir, -1)
print("Loaded ArXiv-RIFE model")
model.eval()
model.device()
if args.img[0].endswith('.exr') and args.img[1].endswith('.exr'):
img0 = cv2.imread(args.img[0], cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH)
img1 = cv2.imread(args.img[1], cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH)
img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device)).unsqueeze(0)
img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device)).unsqueeze(0)
else:
img0 = cv2.imread(args.img[0], cv2.IMREAD_UNCHANGED)
img1 = cv2.imread(args.img[1], cv2.IMREAD_UNCHANGED)
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)
if args.ratio:
img_list = [img0]
img0_ratio = 0.0
img1_ratio = 1.0
if args.ratio <= img0_ratio + args.rthreshold / 2:
middle = img0
elif args.ratio >= img1_ratio - args.rthreshold / 2:
middle = img1
else:
tmp_img0 = img0
tmp_img1 = img1
for inference_cycle in range(args.rmaxcycles):
middle = model.inference(tmp_img0, tmp_img1)
middle_ratio = ( img0_ratio + img1_ratio ) / 2
if args.ratio - (args.rthreshold / 2) <= middle_ratio <= args.ratio + (args.rthreshold / 2):
break
if args.ratio > middle_ratio:
tmp_img0 = middle
img0_ratio = middle_ratio
else:
tmp_img1 = middle
img1_ratio = middle_ratio
img_list.append(middle)
img_list.append(img1)
else:
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)):
if args.img[0].endswith('.exr') and args.img[1].endswith('.exr'):
cv2.imwrite('output/img{}.exr'.format(i), (img_list[i][0]).cpu().numpy().transpose(1, 2, 0)[:h, :w], [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_HALF])
else:
cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
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