repo_description: ECCV2022 - Real-Time Intermediate Flow Estimation for Video Frame Interpolation
repo_topics: aigc computer-vision deep-learning slomo-filter video-interpolation
code: 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])
  