add inference_realesrgan_video
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Xintao
198
inference_realesrgan_video.py
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198
inference_realesrgan_video.py
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import argparse
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import glob
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import mimetypes
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import os
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import queue
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import shutil
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from basicsr.utils.logger import AvgTimer
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from tqdm import tqdm
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from realesrgan import IOConsumer, PrefetchReader, RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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def main():
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"""Inference demo for Real-ESRGAN.
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It mainly for restoring anime videos.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
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parser.add_argument(
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'-n',
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'--model_name',
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type=str,
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default='RealESRGAN_x4plus',
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help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
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'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
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'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
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parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
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parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
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parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
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parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
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parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
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parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
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parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
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parser.add_argument('--half', action='store_true', help='Use half precision during inference')
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parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg')
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parser.add_argument('-a', '--audio', action='store_true', help='Keep audio')
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parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
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parser.add_argument(
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'--alpha_upsampler',
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type=str,
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default='realesrgan',
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help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
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parser.add_argument(
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'--ext',
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type=str,
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default='auto',
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help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
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args = parser.parse_args()
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# ---------------------- determine models according to model names ---------------------- #
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args.model_name = args.model_name.split('.')[0]
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if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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netscale = 4
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elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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netscale = 2
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elif args.model_name in [
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'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
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]: # x2 VGG-style model (XS size)
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
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netscale = 2
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elif args.model_name in [
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'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
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]: # x4 VGG-style model (XS size)
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
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netscale = 4
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# ---------------------- determine model paths ---------------------- #
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model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
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if not os.path.isfile(model_path):
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model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
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if not os.path.isfile(model_path):
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raise ValueError(f'Model {args.model_name} does not exist.')
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# restorer
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=model_path,
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model=model,
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tile=args.tile,
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tile_pad=args.tile_pad,
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pre_pad=args.pre_pad,
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half=args.half)
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if args.face_enhance: # Use GFPGAN for face enhancement
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from gfpgan import GFPGANer
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face_enhancer = GFPGANer(
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model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
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upscale=args.outscale,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=upsampler)
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os.makedirs(args.output, exist_ok=True)
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# for saving restored frames
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save_frame_folder = os.path.join(args.output, 'frames_tmpout')
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os.makedirs(save_frame_folder, exist_ok=True)
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if mimetypes.guess_type(args.input)[0].startswith('video'): # is a video file
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video_name = os.path.splitext(os.path.basename(args.input))[0]
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frame_folder = os.path.join('tmp_frames', video_name)
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os.makedirs(frame_folder, exist_ok=True)
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# use ffmpeg to extract frames
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os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {frame_folder}/frame%08d.png')
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# get image path list
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paths = sorted(glob.glob(os.path.join(frame_folder, '*')))
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if args.video:
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if args.fps is None:
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# get input video fps
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import ffmpeg
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probe = ffmpeg.probe(args.input)
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video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
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args.fps = eval(video_streams[0]['avg_frame_rate'])
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elif mimetypes.guess_type(args.input)[0].startswith('image'): # is an image file
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paths = [args.input]
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video_name = 'video'
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else:
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paths = sorted(glob.glob(os.path.join(args.input, '*')))
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video_name = 'video'
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timer = AvgTimer()
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timer.start()
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pbar = tqdm(total=len(paths), unit='frame', desc='inference')
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# set up prefetch reader
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reader = PrefetchReader(paths, num_prefetch_queue=4)
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reader.start()
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que = queue.Queue()
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num_consumer = 4
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consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(num_consumer)]
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for consumer in consumers:
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consumer.start()
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for idx, (path, img) in enumerate(zip(paths, reader)):
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imgname, extension = os.path.splitext(os.path.basename(path))
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if len(img.shape) == 3 and img.shape[2] == 4:
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img_mode = 'RGBA'
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else:
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img_mode = None
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try:
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if args.face_enhance:
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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else:
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output, _ = upsampler.enhance(img, outscale=args.outscale)
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except RuntimeError as error:
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print('Error', error)
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print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
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else:
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if args.ext == 'auto':
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extension = extension[1:]
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else:
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extension = args.ext
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if img_mode == 'RGBA': # RGBA images should be saved in png format
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extension = 'png'
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save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}')
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que.put({'output': output, 'save_path': save_path})
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pbar.update(1)
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torch.cuda.synchronize()
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timer.record()
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avg_fps = 1. / (timer.get_avg_time() + 1e-7)
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pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
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for _ in range(num_consumer):
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que.put('quit')
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for consumer in consumers:
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consumer.join()
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pbar.close()
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# merge frames to video
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if args.video:
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video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
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if args.audio:
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os.system(
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f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}'
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f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
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else:
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os.system(f'ffmpeg -i {save_frame_folder}/frame%08d_out.{extension} '
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f'-c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}')
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# delete tmp file
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shutil.rmtree(save_frame_folder)
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if os.path.isdir(frame_folder):
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shutil.rmtree(frame_folder)
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if __name__ == '__main__':
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main()
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@@ -3,4 +3,4 @@ from .archs import *
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from .data import *
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from .models import *
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from .utils import *
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from .version import __version__
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from .version import *
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@@ -2,6 +2,8 @@ import cv2
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import math
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import numpy as np
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import os
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import queue
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import threading
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import torch
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from basicsr.utils.download_util import load_file_from_url
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from torch.nn import functional as F
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@@ -38,7 +40,7 @@ class RealESRGANer():
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if model_path.startswith('https://'):
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model_path = load_file_from_url(
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url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
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loadnet = torch.load(model_path)
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loadnet = torch.load(model_path, map_location=torch.device('cpu'))
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# prefer to use params_ema
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if 'params_ema' in loadnet:
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keyname = 'params_ema'
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@@ -226,3 +228,53 @@ class RealESRGANer():
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), interpolation=cv2.INTER_LANCZOS4)
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return output, img_mode
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class PrefetchReader(threading.Thread):
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"""Prefetch images.
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Args:
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img_list (list[str]): A image list of image paths to be read.
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num_prefetch_queue (int): Number of prefetch queue.
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"""
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def __init__(self, img_list, num_prefetch_queue):
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super().__init__()
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self.que = queue.Queue(num_prefetch_queue)
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self.img_list = img_list
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def run(self):
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for img_path in self.img_list:
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img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
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self.que.put(img)
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self.que.put(None)
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def __next__(self):
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next_item = self.que.get()
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if next_item is None:
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raise StopIteration
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return next_item
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def __iter__(self):
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return self
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class IOConsumer(threading.Thread):
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def __init__(self, opt, que, qid):
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super().__init__()
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self._queue = que
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self.qid = qid
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self.opt = opt
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def run(self):
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while True:
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msg = self._queue.get()
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if isinstance(msg, str) and msg == 'quit':
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break
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output = msg['output']
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save_path = msg['save_path']
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cv2.imwrite(save_path, output)
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print(f'IO worker {self.qid} is done.')
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