RealESRGANv030/inference_realesrgan.py

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()