112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
import os
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import cv2
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import torch
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import argparse
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from torch.nn import functional as F
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import warnings
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warnings.filterwarnings("ignore")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.set_grad_enabled(False)
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if torch.cuda.is_available():
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
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parser.add_argument('--img', dest='img', nargs=2, required=True)
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parser.add_argument('--exp', default=4, type=int)
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parser.add_argument('--ratio', default=0, type=float, help='inference ratio between two images with 0 - 1 range')
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parser.add_argument('--rthreshold', default=0.02, type=float, help='returns image when actual ratio falls in given range threshold')
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parser.add_argument('--rmaxcycles', default=8, type=int, help='limit max number of bisectional cycles')
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parser.add_argument('--model', dest='modelDir', type=str, default='train_log', help='directory with trained model files')
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args = parser.parse_args()
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try:
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try:
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try:
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from model.RIFE_HDv2 import Model
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model = Model()
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model.load_model(args.modelDir, -1)
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print("Loaded v2.x HD model.")
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except:
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from train_log.RIFE_HDv3 import Model
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model = Model()
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model.load_model(args.modelDir, -1)
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print("Loaded v3.x HD model.")
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except:
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from model.RIFE_HD import Model
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model = Model()
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model.load_model(args.modelDir, -1)
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print("Loaded v1.x HD model")
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except:
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from model.RIFE import Model
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model = Model()
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model.load_model(args.modelDir, -1)
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print("Loaded ArXiv-RIFE model")
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model.eval()
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model.device()
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if args.img[0].endswith('.exr') and args.img[1].endswith('.exr'):
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img0 = cv2.imread(args.img[0], cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH)
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img1 = cv2.imread(args.img[1], cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH)
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img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device)).unsqueeze(0)
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img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device)).unsqueeze(0)
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else:
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img0 = cv2.imread(args.img[0], cv2.IMREAD_UNCHANGED)
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img1 = cv2.imread(args.img[1], cv2.IMREAD_UNCHANGED)
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img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
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img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
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n, c, h, w = img0.shape
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ph = ((h - 1) // 32 + 1) * 32
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pw = ((w - 1) // 32 + 1) * 32
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padding = (0, pw - w, 0, ph - h)
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img0 = F.pad(img0, padding)
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img1 = F.pad(img1, padding)
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if args.ratio:
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img_list = [img0]
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img0_ratio = 0.0
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img1_ratio = 1.0
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if args.ratio <= img0_ratio + args.rthreshold / 2:
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middle = img0
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elif args.ratio >= img1_ratio - args.rthreshold / 2:
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middle = img1
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else:
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tmp_img0 = img0
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tmp_img1 = img1
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for inference_cycle in range(args.rmaxcycles):
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middle = model.inference(tmp_img0, tmp_img1)
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middle_ratio = ( img0_ratio + img1_ratio ) / 2
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if args.ratio - (args.rthreshold / 2) <= middle_ratio <= args.ratio + (args.rthreshold / 2):
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break
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if args.ratio > middle_ratio:
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tmp_img0 = middle
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img0_ratio = middle_ratio
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else:
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tmp_img1 = middle
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img1_ratio = middle_ratio
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img_list.append(middle)
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img_list.append(img1)
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else:
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img_list = [img0, img1]
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for i in range(args.exp):
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tmp = []
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for j in range(len(img_list) - 1):
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mid = model.inference(img_list[j], img_list[j + 1])
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tmp.append(img_list[j])
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tmp.append(mid)
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tmp.append(img1)
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img_list = tmp
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if not os.path.exists('output'):
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os.mkdir('output')
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for i in range(len(img_list)):
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if args.img[0].endswith('.exr') and args.img[1].endswith('.exr'):
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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])
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else:
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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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