Files
ECCV2022-RIFE/inference_video.py
Logan King e654de6f41 videos keep sound
fixed #12
2020-11-25 02:10:31 -04:00

182 lines
6.3 KiB
Python

import os
import cv2
import torch
import argparse
import numpy as np
from tqdm import tqdm
from torch.nn import functional as F
import warnings
import _thread
import skvideo.io
from queue import Queue, Empty
import moviepy.editor
import shutil
def transferAudio(sourceVideo, targetVideo):
tempAudioFileName = "./temp/audio.mp3"
try:
# split audio from original video file and store in "temp" directory
if True:
# extract audio from video
if True:
video = moviepy.editor.VideoFileClip(sourceVideo)
audio = video.audio
# clear old "temp" directory if it exits
if os.path.isdir("temp"):
# remove temp directory
shutil.rmtree("temp")
# create new "temp" directory
os.makedirs("temp")
# write audio file to "temp" directory
audio.write_audiofile(tempAudioFileName)
os.rename(targetVideo, "noAudio_"+targetVideo)
# combine audio file and new video file
os.system("ffmpeg -y -i " + "noAudio_"+targetVideo + " -i " + tempAudioFileName + " -c copy " + targetVideo)
# remove audio-less video
os.remove("noAudio_"+targetVideo)
except:
pass
# remove temp directory
shutil.rmtree("temp")
warnings.filterwarnings("ignore")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
torch.set_grad_enabled(False)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
parser.add_argument('--video', dest='video', required=True)
parser.add_argument('--montage', dest='montage', action='store_true', help='montage origin video')
parser.add_argument('--skip', dest='skip', action='store_true', help='whether to remove static frames before processing')
parser.add_argument('--fps', dest='fps', type=int, default=None)
parser.add_argument('--png', dest='png', action='store_true', help='whether to vid_out png format vid_outs')
parser.add_argument('--ext', dest='ext', type=str, default='mp4', help='vid_out video extension')
parser.add_argument('--exp', dest='exp', type=int, default=1)
args = parser.parse_args()
assert (args.exp == 1 or args.exp == 2)
args.exp = 2 ** args.exp
from model.RIFE import Model
model = Model()
model.load_model('./train_log')
model.eval()
model.device()
videoCapture = cv2.VideoCapture(args.video)
fps = videoCapture.get(cv2.CAP_PROP_FPS)
tot_frame = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
videoCapture.release()
if args.fps is None:
fpsNotAssigned = True
args.fps = fps * args.exp
else:
fpsNotAssigned = False
videogen = skvideo.io.vreader(args.video)
lastframe = next(videogen)
h, w, _ = lastframe.shape
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
if args.png:
if not os.path.exists('vid_out'):
os.mkdir('vid_out')
else:
video_path_wo_ext, ext = os.path.splitext(args.video)
vid_out = cv2.VideoWriter('{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.exp, int(np.round(args.fps)), args.ext), fourcc, args.fps, (w, h))
def clear_buffer(user_args, buffer):
cnt = 0
while True:
item = buffer.get()
if item is None:
break
if user_args.png:
cv2.imwrite('vid_out/{:0>7d}.png'.format(cnt), item[:, :, ::-1])
cnt += 1
else:
vid_out.write(item[:, :, ::-1])
if args.montage:
left = w // 4
w = w // 2
ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
print('{}.{}, {} frames in total, {}FPS to {}FPS'.format(video_path_wo_ext, args.ext, tot_frame, fps, args.fps))
pbar = tqdm(total=tot_frame)
skip_frame = 1
if args.montage:
lastframe = lastframe[:, left: left + w]
buffer = Queue()
_thread.start_new_thread(clear_buffer, (args, buffer))
for frame in videogen:
if args.montage:
frame = frame[:, left: left + w]
I0 = torch.from_numpy(np.transpose(lastframe, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
I1 = torch.from_numpy(np.transpose(frame, (2,0,1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
I0 = F.pad(I0, padding)
I1 = F.pad(I1, padding)
p = (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)
- F.interpolate(I1, (16, 16), mode='bilinear', align_corners=False)).abs().mean()
if p < 5e-3 and args.skip:
if skip_frame % 100 == 0:
print("Warning: Your video has {} static frames, skipping them may change the duration of the generated video.".format(skip_frame))
skip_frame += 1
pbar.update(1)
continue
if p > 0.2:
mid1 = lastframe
mid0 = lastframe
mid2 = frame
else:
mid1 = model.inference(I0, I1)
if args.exp == 4:
mid = model.inference(torch.cat((I0, mid1), 0), torch.cat((mid1, I1), 0))
mid1 = (((mid1[0] * 255.).byte().cpu().detach().numpy().transpose(1, 2, 0)))
if args.exp == 4:
mid0 = (((mid[0] * 255.).byte().cpu().detach().numpy().transpose(1, 2, 0)))
mid2 = (((mid[1] * 255.).byte().cpu().detach().numpy().transpose(1, 2, 0)))
if args.montage:
buffer.put(np.concatenate((lastframe, lastframe), 1))
if args.exp == 4:
buffer.put(np.concatenate((lastframe, mid0[:h, :w]), 1))
buffer.put(np.concatenate((lastframe, mid1[:h, :w]), 1))
if args.exp == 4:
buffer.put(np.concatenate((lastframe, mid2[:h, :w]), 1))
else:
buffer.put(lastframe)
if args.exp == 4:
buffer.put(mid0[:h, :w])
buffer.put(mid1[:h, :w])
if args.exp == 4:
buffer.put(mid2[:h, :w])
pbar.update(1)
lastframe = frame
if args.montage:
buffer.put(np.concatenate((lastframe, lastframe), 1))
else:
buffer.put(lastframe)
import time
while(not buffer.empty()):
time.sleep(0.1)
pbar.close()
if not vid_out is None:
vid_out.release()
# move audio to new video file if appropriate
if args.png == False and fpsNotAssigned == True:
outputVideoFileName = '{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.exp, int(np.round(args.fps)), args.ext)
transferAudio(video_path_wo_ext + "." + args.ext, outputVideoFileName)