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()