mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2026-02-24 04:19:41 +01:00
98 lines
3.8 KiB
Python
98 lines
3.8 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('--skip', dest='skip', action='store_true', help='whether to remove static frames before processing')
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parser.add_argument('--fps', dest='fps', type=int, default=60)
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parser.add_argument('--png', dest='png', action='store_true', help='whether to output png format outputs')
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args = parser.parse_args()
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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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if args.png:
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if not os.path.exists('output'):
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os.mkdir('output')
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else:
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output = cv2.VideoWriter('{}_4x.mp4'.format(args.video[:-4]), fourcc, args.fps, (w, h))
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cnt = 0
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skip_frame = 1
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def writeframe(I0, mid0, mid1, mid2, I1, p):
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global cnt, skip_frame
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for i in range(I0.shape[0]):
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if p[i] > 0.2:
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mid0[i] = I0[i]
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mid1[i] = I0[i]
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mid2[i] = I1[i]
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if p[i] < 1e-3 and args.skip:
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if skip_frame % 100 == 0:
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print("Warning: Your video has {} static frames, skipping them may change the duration of the generated video.".format(skip_frame))
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skip_frame += 1
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if args.png:
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cv2.imwrite('output/{:0>7d}.png'.format(cnt), I0[i])
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cnt += 1
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cv2.imwrite('output/{:0>7d}.png'.format(cnt), mid0[i])
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cnt += 1
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cv2.imwrite('output/{:0>7d}.png'.format(cnt), mid1[i])
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cnt += 1
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cv2.imwrite('output/{:0>7d}.png'.format(cnt), mid2[i])
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cnt += 1
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else:
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output.write(I0[i])
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output.write(mid0[i])
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output.write(mid1[i])
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output.write(mid2[i])
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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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img_list = []
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img_list.append(frame)
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while success:
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success, frame = videoCapture.read()
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if success:
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img_list.append(frame)
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if len(img_list) == 5 or not success:
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I0 = torch.from_numpy(np.transpose(img_list[:-1], (0, 3, 1, 2)).astype("float32") / 255.).to(device)
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I1 = torch.from_numpy(np.transpose(img_list[1:], (0, 3, 1, 2)).astype("float32") / 255.).to(device)
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p = (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()
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I0 = F.pad(I0, padding)
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I1 = F.pad(I1, padding)
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mid1 = model.inference(I0, I1)
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mid0 = model.inference(I0, mid1)
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mid2 = model.inference(mid1, I1)
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I0 = (((I0 * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1))).astype('uint8')
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I1 = (((I1 * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1))).astype('uint8')
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mid0 = (((mid0 * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1))).astype('uint8')
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mid1 = (((mid1 * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1))).astype('uint8')
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mid2 = (((mid2 * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1))).astype('uint8')
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writeframe(I0, mid0, mid1, mid2, I1, p.mean(3).mean(2).mean(1))
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pbar.update(4)
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img_list = img_list[-1:]
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pbar.close()
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output.release()
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