mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2026-02-24 04:19:41 +01:00
86 lines
3.3 KiB
Python
86 lines
3.3 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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import numpy as np
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from tqdm import tqdm
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from torch.nn import functional as F
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():
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torch.set_grad_enabled(False)
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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('--video', dest='video', required=True)
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parser.add_argument('--montage', dest='montage', action='store_true', help='montage origin video')
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parser.add_argument('--fps', dest='fps', type=int, default=60)
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parser.add_argument('--model', dest='model', type=str, default='RIFE')
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args = parser.parse_args()
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if args.model == '2F':
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from model.RIFE2F import Model
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else:
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from model.RIFE import Model
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model = Model()
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model.load_model('./train_log')
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model.eval()
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model.device()
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videoCapture = cv2.VideoCapture(args.video)
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fps = np.round(videoCapture.get(cv2.CAP_PROP_FPS))
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success, frame = videoCapture.read()
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h, w, _ = frame.shape
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fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
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output = cv2.VideoWriter('{}_4x.mp4'.format(args.video[:-4]), fourcc, args.fps, (w, h))
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if args.montage:
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left = w // 4
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w = w // 2
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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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tot_frame = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
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print('{}.mp4, {} frames in total, {}FPS to {}FPS'.format(args.video[:-4], tot_frame, fps, args.fps))
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pbar = tqdm(total=tot_frame)
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if args.montage:
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frame = frame[:, left: left + w]
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while success:
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lastframe = frame
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success, frame = videoCapture.read()
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if success:
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if args.montage:
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frame = frame[:, left: left + w]
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I0 = torch.from_numpy(np.transpose(lastframe, (2,0,1)).astype("float32") / 255.).to(device).unsqueeze(0)
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I1 = torch.from_numpy(np.transpose(frame, (2,0,1)).astype("float32") / 255.).to(device).unsqueeze(0)
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I0 = F.pad(I0, padding)
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I1 = F.pad(I1, padding)
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if (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)
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- F.interpolate(I1, (16, 16), mode='bilinear', align_corners=False)).abs().mean() > 0.2:
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mid0 = lastframe
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mid1 = lastframe
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mid2 = frame
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else:
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mid1 = model.inference(I0, I1)
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mid = model.inference(torch.cat((I0, mid1), 0), torch.cat((mid1, I1), 0))
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mid0 = (((mid[0] * 255.).cpu().detach().numpy().transpose(1, 2, 0))).astype('uint8')
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mid1 = (((mid1[0] * 255.).cpu().detach().numpy().transpose(1, 2, 0))).astype('uint8')
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mid2 = (((mid[1]* 255.).cpu().detach().numpy().transpose(1, 2, 0))).astype('uint8')
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if args.montage:
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output.write(np.concatenate((lastframe, lastframe), 1))
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output.write(np.concatenate((lastframe, mid0[:h, :w]), 1))
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output.write(np.concatenate((lastframe, mid1[:h, :w]), 1))
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output.write(np.concatenate((lastframe, mid2[:h, :w]), 1))
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else:
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output.write(lastframe)
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output.write(mid0[:h, :w])
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output.write(mid1[:h, :w])
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output.write(mid2[:h, :w])
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pbar.update(1)
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if args.montage:
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output.write(np.concatenate((lastframe, lastframe), 1))
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else:
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output.write(lastframe)
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pbar.close()
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output.release()
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