import argparse import cv2 import glob import os from basicsr.archs.rrdbnet_arch import RRDBNet from basicsr.utils.download_util import load_file_from_url from realesrgan import RealESRGANer from realesrgan.archs.srvgg_arch import SRVGGNetCompact def main(): """Inference demo for Real-ESRGAN.""" 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 | " "realesr-animevideov3 | realesr-general-x4v3" ), ) parser.add_argument( "-o", "--output", type=str, default="results", help="Output folder" ) parser.add_argument( "-dn", "--denoise_strength", type=float, default=0.5, help=( "Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. " "Only used for the realesr-general-x4v3 model" ), ) parser.add_argument( "-s", "--outscale", type=float, default=4, help="The final upsampling scale of the image", ) parser.add_argument( "--model_path", type=str, default=None, help="[Option] Model path. Usually, you do not need to specify it", ) parser.add_argument( "--suffix", type=str, default="out", help="Suffix of the restored image" ) 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( "--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", ) parser.add_argument( "-g", "--gpu-id", type=int, default=None, help="gpu device to use (default=None) can be 0,1,2 for multi-gpu", ) args = parser.parse_args() # determine models according to model names args.model_name = args.model_name.split(".")[0] if args.model_name == "RealESRGAN_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 file_url = [ "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth" ] elif args.model_name == "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 file_url = [ "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth" ] elif ( args.model_name == "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 file_url = [ "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth" ] elif args.model_name == "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 file_url = [ "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth" ] elif args.model_name == "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 file_url = [ "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth" ] elif args.model_name == "realesr-general-x4v3": # x4 VGG-style model (S size) model = SRVGGNetCompact( num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type="prelu", ) netscale = 4 file_url = [ "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth", "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth", ] # determine model paths if args.model_path is not None: model_path = args.model_path else: model_path = os.path.join("weights", args.model_name + ".pth") if not os.path.isfile(model_path): ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) for url in file_url: # model_path will be updated model_path = load_file_from_url( url=url, model_dir=os.path.join(ROOT_DIR, "weights"), progress=True, file_name=None, ) # use dni to control the denoise strength dni_weight = None if args.model_name == "realesr-general-x4v3" and args.denoise_strength != 1: wdn_model_path = model_path.replace( "realesr-general-x4v3", "realesr-general-wdn-x4v3" ) model_path = [model_path, wdn_model_path] dni_weight = [args.denoise_strength, 1 - args.denoise_strength] # restorer upsampler = RealESRGANer( scale=netscale, model_path=model_path, dni_weight=dni_weight, model=model, tile=args.tile, tile_pad=args.tile_pad, pre_pad=args.pre_pad, half=not args.fp32, gpu_id=args.gpu_id, ) 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, ) os.makedirs(args.output, exist_ok=True) if os.path.isfile(args.input): paths = [args.input] else: paths = sorted(glob.glob(os.path.join(args.input, "*"))) for idx, path in enumerate(paths): imgname, extension = os.path.splitext(os.path.basename(path)) print("Testing", idx, imgname) img = cv2.imread(path, cv2.IMREAD_UNCHANGED) 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" if args.suffix == "": save_path = os.path.join(args.output, f"{imgname}.{extension}") else: save_path = os.path.join( args.output, f"{imgname}_{args.suffix}.{extension}" ) cv2.imwrite(save_path, output) if __name__ == "__main__": main()