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inference_realesrgan_video.py
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inference_realesrgan_video.py
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import argparse
import glob
import mimetypes
import os
import queue
import shutil
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.logger import AvgTimer
from tqdm import tqdm
from realesrgan import IOConsumer, PrefetchReader, RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
def main():
"""Inference demo for Real-ESRGAN.
It mainly for restoring anime videos.
"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
parser.add_argument(
'-n',
'--model_name',
type=str,
default='RealESRGAN_x4plus',
help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
parser.add_argument('--half', action='store_true', help='Use half precision during inference')
parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg')
parser.add_argument('-a', '--audio', action='store_true', help='Keep audio')
parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
parser.add_argument('--consumer', type=int, default=4, help='Number of IO consumers')
parser.add_argument(
'--alpha_upsampler',
type=str,
default='realesrgan',
help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
args = parser.parse_args()
# ---------------------- determine models according to model names ---------------------- #
args.model_name = args.model_name.split('.')[0]
if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
netscale = 2
elif args.model_name in [
'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
]: # x2 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
netscale = 2
elif args.model_name in [
'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
]: # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
# ---------------------- determine model paths ---------------------- #
model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
if not os.path.isfile(model_path):
model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
if not os.path.isfile(model_path):
raise ValueError(f'Model {args.model_name} does not exist.')
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=args.half)
if args.face_enhance: # Use GFPGAN for face enhancement
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler)
os.makedirs(args.output, exist_ok=True)
# for saving restored frames
save_frame_folder = os.path.join(args.output, 'frames_tmpout')
os.makedirs(save_frame_folder, exist_ok=True)
if mimetypes.guess_type(args.input)[0].startswith('video'): # is a video file
video_name = os.path.splitext(os.path.basename(args.input))[0]
frame_folder = os.path.join('tmp_frames', video_name)
os.makedirs(frame_folder, exist_ok=True)
# use ffmpeg to extract frames
os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {frame_folder}/frame%08d.png')
# get image path list
paths = sorted(glob.glob(os.path.join(frame_folder, '*')))
if args.video:
if args.fps is None:
# get input video fps
import ffmpeg
probe = ffmpeg.probe(args.input)
video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
args.fps = eval(video_streams[0]['avg_frame_rate'])
elif mimetypes.guess_type(args.input)[0].startswith('image'): # is an image file
paths = [args.input]
video_name = 'video'
else:
paths = sorted(glob.glob(os.path.join(args.input, '*')))
video_name = 'video'
timer = AvgTimer()
timer.start()
pbar = tqdm(total=len(paths), unit='frame', desc='inference')
# set up prefetch reader
reader = PrefetchReader(paths, num_prefetch_queue=4)
reader.start()
que = queue.Queue()
consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)]
for consumer in consumers:
consumer.start()
for idx, (path, img) in enumerate(zip(paths, reader)):
imgname, extension = os.path.splitext(os.path.basename(path))
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
else:
img_mode = None
try:
if args.face_enhance:
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
else:
output, _ = upsampler.enhance(img, outscale=args.outscale)
except RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
else:
if args.ext == 'auto':
extension = extension[1:]
else:
extension = args.ext
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}')
que.put({'output': output, 'save_path': save_path})
pbar.update(1)
torch.cuda.synchronize()
timer.record()
avg_fps = 1. / (timer.get_avg_time() + 1e-7)
pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
for _ in range(args.consumer):
que.put('quit')
for consumer in consumers:
consumer.join()
pbar.close()
# merge frames to video
if args.video:
video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
if args.audio:
os.system(
f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}'
f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
else:
os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} '
f'-c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
# delete tmp file
shutil.rmtree(save_frame_folder)
if os.path.isdir(frame_folder):
shutil.rmtree(frame_folder)
if __name__ == '__main__':
main()