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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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