mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2026-02-24 04:19:41 +01:00
124 lines
4.7 KiB
Python
124 lines
4.7 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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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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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=None)
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parser.add_argument('--png', dest='png', action='store_true', help='whether to output png format outputs')
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parser.add_argument('--ext', dest='ext', type=str, default='mp4', help='output video extension')
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parser.add_argument('--exp', dest='exp', type=int, default=1, help='interpolation exponent (base 2)')
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args = parser.parse_args()
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assert (args.exp in [1, 2, 3])
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args.times = 2 ** args.exp
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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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if args.fps is None:
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args.fps = fps * args.times
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fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
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video_path_wo_ext, ext = os.path.splitext(args.video)
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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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vid_out = None
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else:
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vid_out = cv2.VideoWriter('{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.times, int(np.round(args.fps)), args.ext), fourcc, args.fps, (w, h))
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cnt = 0
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skip_frame = 1
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def write_frame(vid_out, i0, infs, i1, p, user_args):
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global skip_frame, cnt
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for i in range(i0.shape[0]):
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# Result was not good enough to write, use previous frames.
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if p[i] > 0.2:
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if user_args.exp > 1:
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infs = [i0[i] for _ in range(len(infs) - 1)]
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infs[-1] = i1[-1]
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else:
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infs = [i0[i] for _ in range(len(infs))]
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# Result was too similar to previous frame, skip if given.
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if p[i] < 5e-3 and user_args.skip:
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if skip_frame % 100 == 0:
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print("Warning: Your video has {} static frames, "
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"skipping them may change the duration of the generated video.".format(skip_frame))
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skip_frame += 1
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continue
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# Write results.
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if user_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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for inf in infs:
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cv2.imwrite('output/{:0>7d}.png'.format(cnt), inf[i])
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cnt += 1
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else:
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vid_out.write(i0[i])
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for inf in infs:
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vid_out.write(inf[i])
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def make_inference(model, I0, I1, exp):
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middle = model.inference(I0, I1)
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if exp == 1:
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return [middle]
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first_half = make_inference(model, I0, middle, exp=exp - 1)
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second_half = make_inference(model, middle, I1, exp=exp - 1)
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return [*first_half, middle, *second_half]
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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('{}.{}, {} frames in total, {}FPS to {}FPS'.format(video_path_wo_ext, args.ext, tot_frame, fps, args.fps))
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pbar = tqdm(total=tot_frame)
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img_list = [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 and len(img_list) > 1):
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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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inferences = make_inference(model, I0, I1, exp=args.exp)
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I0 = ((I0[:, :, :h, :w] * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1)).astype('uint8')
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I1 = ((I1[:, :, :h, :w] * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1)).astype('uint8')
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inferences = list(map(lambda x: ((x[:, :, :h, :w] * 255.).cpu().detach().numpy().transpose(0, 2, 3, 1)).astype('uint8'), inferences))
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write_frame(vid_out, I0, inferences, I1, p.mean(3).mean(2).mean(1), args)
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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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vid_out.release()
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