Real-ESRGAN/inference_realesrgan_video.py

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 -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()