import argparse import cv2 import glob import mimetypes import numpy as np 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 get_frames(args, extract_frames=False): # input can be a video file / a folder of frames / an image is_video = False if mimetypes.guess_type(args.input)[0].startswith('video'): # is a video file is_video = True video_name = os.path.splitext(os.path.basename(args.input))[0] if extract_frames: 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, '*'))) else: paths = [] # get input video fps if args.fps is None: 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] else: paths = sorted(glob.glob(os.path.join(args.input, '*'))) assert len(paths) > 0, 'the input folder is empty' if args.fps is None: args.fps = 24 return is_video, paths def inference_stream(args, upsampler, face_enhancer): try: import ffmpeg except ImportError: import pip pip.main(['install', '--user', 'ffmpeg-python']) import ffmpeg is_video, paths = get_frames(args, extract_frames=False) video_name = os.path.splitext(os.path.basename(args.input))[0] video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4') # decoder if is_video: # get height and width probe = ffmpeg.probe(args.input) video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video'] width = video_streams[0]['width'] height = video_streams[0]['height'] # set up frame decoder decoder = ( ffmpeg.input(args.input).output('pipe:', format='rawvideo', pix_fmt='rgb24', loglevel='warning').run_async( pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)) else: from PIL import Image tmp_img = Image.open(paths[0]) width, height = tmp_img.size idx = 0 out_width, out_height = int(width * args.outscale), int(height * args.outscale) if out_height > 2160: print('You are generating video that is larger than 4K, which will be very slow due to IO speed.', 'We highly recommend to decrease the outscale(aka, -s).') # encoder if is_video: audio = ffmpeg.input(args.input).audio encoder = ( ffmpeg.input( 'pipe:', format='rawvideo', pix_fmt='rgb24', s=f'{out_width}x{out_height}', framerate=args.fps).output( audio, video_save_path, pix_fmt='yuv420p', vcodec='libx264', loglevel='info', acodec='copy').overwrite_output().run_async(pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)) else: encoder = ( ffmpeg.input( 'pipe:', format='rawvideo', pix_fmt='rgb24', s=f'{out_width}x{out_height}', framerate=args.fps).output(video_save_path, pix_fmt='yuv420p', vcodec='libx264', loglevel='info').overwrite_output().run_async( pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)) while True: if is_video: img_bytes = decoder.stdout.read(width * height * 3) # 3 bytes for one pixel if not img_bytes: break img = np.frombuffer(img_bytes, np.uint8).reshape([height, width, 3]) else: if idx >= len(paths): break img = cv2.imread(paths[idx]) idx += 1 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: output = output.astype(np.uint8).tobytes() encoder.stdin.write(output) torch.cuda.synchronize() if is_video: decoder.stdin.close() decoder.wait() encoder.stdin.close() encoder.wait() def inference_frames(args, upsampler, face_enhancer): is_video, paths = get_frames(args, extract_frames=True) video_name = os.path.splitext(os.path.basename(args.input))[0] # for saving restored frames save_frame_folder = os.path.join(args.output, video_name, 'frames_tmpout') os.makedirs(save_frame_folder, exist_ok=True) 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 video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4') 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}') # delete tmp file shutil.rmtree(save_frame_folder) frame_folder = os.path.join('tmp_frames', video_name) if os.path.isdir(frame_folder): shutil.rmtree(frame_folder) 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 video, image or folder') parser.add_argument( '-n', '--model_name', type=str, default='realesr-animevideov3', help=('Model names: realesr-animevideov3 | RealESRGAN_x4plus_anime_6B | RealESRGAN_x4plus | RealESRNet_x4plus |' ' RealESRGAN_x2plus | ' 'Default:realesr-animevideov3')) 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( '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).') 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('--stream', action='store_true') parser.add_argument('--ffmpeg_bin', type=str, default='ffmpeg', help='The path to ffmpeg') 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() args.input = args.input.rstrip('/').rstrip('\\') # ---------------------- determine models according to model names ---------------------- # args.model_name = args.model_name.split('.pth')[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 ['realesr-animevideov3']: # 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=not args.fp32) if 'anime' in args.model_name and args.face_enhance: print('face_enhance is not supported in anime models, we turned this option off for you. ' 'if you insist on turning it on, please manually comment the relevant lines of code.') args.face_enhance = False 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/v1.3.0/GFPGANv1.3.pth', upscale=args.outscale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) else: face_enhancer = None os.makedirs(args.output, exist_ok=True) if args.stream: inference_stream(args, upsampler, face_enhancer) else: inference_frames(args, upsampler, face_enhancer) if __name__ == '__main__': main()