From 192f672f91a62f501c8d98aa4753cebd53cf31ef Mon Sep 17 00:00:00 2001 From: Xintao Date: Sun, 12 Dec 2021 16:49:35 +0800 Subject: [PATCH] add inference_realesrgan_video --- inference_realesrgan_video.py | 198 ++++++++++++++++++++++++++++++++++ realesrgan/__init__.py | 2 +- realesrgan/utils.py | 54 +++++++++- 3 files changed, 252 insertions(+), 2 deletions(-) create mode 100644 inference_realesrgan_video.py diff --git a/inference_realesrgan_video.py b/inference_realesrgan_video.py new file mode 100644 index 0000000..353f143 --- /dev/null +++ b/inference_realesrgan_video.py @@ -0,0 +1,198 @@ +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( + '--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() + num_consumer = 4 + consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(num_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(num_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() diff --git a/realesrgan/__init__.py b/realesrgan/__init__.py index f3c0535..2276f1e 100644 --- a/realesrgan/__init__.py +++ b/realesrgan/__init__.py @@ -3,4 +3,4 @@ from .archs import * from .data import * from .models import * from .utils import * -from .version import __version__ +from .version import * diff --git a/realesrgan/utils.py b/realesrgan/utils.py index ddb6ab2..10e7c23 100644 --- a/realesrgan/utils.py +++ b/realesrgan/utils.py @@ -2,6 +2,8 @@ import cv2 import math import numpy as np import os +import queue +import threading import torch from basicsr.utils.download_util import load_file_from_url from torch.nn import functional as F @@ -38,7 +40,7 @@ class RealESRGANer(): if model_path.startswith('https://'): model_path = load_file_from_url( url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None) - loadnet = torch.load(model_path) + loadnet = torch.load(model_path, map_location=torch.device('cpu')) # prefer to use params_ema if 'params_ema' in loadnet: keyname = 'params_ema' @@ -226,3 +228,53 @@ class RealESRGANer(): ), interpolation=cv2.INTER_LANCZOS4) return output, img_mode + + +class PrefetchReader(threading.Thread): + """Prefetch images. + + Args: + img_list (list[str]): A image list of image paths to be read. + num_prefetch_queue (int): Number of prefetch queue. + """ + + def __init__(self, img_list, num_prefetch_queue): + super().__init__() + self.que = queue.Queue(num_prefetch_queue) + self.img_list = img_list + + def run(self): + for img_path in self.img_list: + img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) + self.que.put(img) + + self.que.put(None) + + def __next__(self): + next_item = self.que.get() + if next_item is None: + raise StopIteration + return next_item + + def __iter__(self): + return self + + +class IOConsumer(threading.Thread): + + def __init__(self, opt, que, qid): + super().__init__() + self._queue = que + self.qid = qid + self.opt = opt + + def run(self): + while True: + msg = self._queue.get() + if isinstance(msg, str) and msg == 'quit': + break + + output = msg['output'] + save_path = msg['save_path'] + cv2.imwrite(save_path, output) + print(f'IO worker {self.qid} is done.')