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Zero
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import cv2
import numpy as np
from PIL import Image
import glob
import os
import sys
import types
# Compatibility shim for older basicsr expecting torchvision.transforms.functional_tensor
# Newer torchvision versions removed the functional_tensor module. If it's missing,
# create a minimal shim exposing rgb_to_grayscale that basicsr imports.
try:
# If this import works, no action is needed
from torchvision.transforms.functional_tensor import rgb_to_grayscale as _tv_rgb_to_grayscale # noqa: F401
except Exception:
try:
from torchvision.transforms import functional as _tv_functional
_shim = types.ModuleType('torchvision.transforms.functional_tensor')
_shim.rgb_to_grayscale = _tv_functional.rgb_to_grayscale
sys.modules['torchvision.transforms.functional_tensor'] = _shim
except Exception:
# If even this fails, let downstream imports raise as before
pass
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 realEsrgan(
model_name="RealESRGAN_x4plus_anime_6B",
model_path=None,
input_dir="inputs",
output_dir="results",
denoise_strength=0.5,
outscale=4,
suffix="out",
tile=200,
tile_pad=10,
pre_pad=0,
face_enhance=True,
alpha_upsampler="realsrgan",
out_ext="auto",
fp32=True,
gpu_id=None,
):
# determine models according to model names
model_name = model_name.split(".")[0]
if 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 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 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 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 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 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 model_path is None:
model_path = os.path.join("weights", 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 model_name == "realesr-general-x4v3" and 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 = [denoise_strength, 1 - denoise_strength]
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=tile,
tile_pad=tile_pad,
pre_pad=pre_pad,
half=not fp32,
gpu_id=gpu_id,
)
if 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=outscale,
arch="clean",
channel_multiplier=2,
bg_upsampler=upsampler,
)
os.makedirs(output_dir, exist_ok=True)
if not isinstance(input_dir, list):
paths = [input_dir]
else:
paths = sorted(glob.glob(os.path.join(input_dir, "*")))
Imgs = []
for idx, path in enumerate(paths):
print(f"Scaling x{outscale}:", path)
if isinstance(path, Image.Image):
img = path
img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
imgname = f"img_{idx}"
else:
imgname, extension = os.path.splitext(os.path.basename(path))
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 face_enhance:
_, _, output = face_enhancer.enhance(
img, has_aligned=False, only_center_face=False, paste_back=True
)
else:
output, _ = upsampler.enhance(img, outscale=outscale)
except RuntimeError as error:
print("Error", error)
print(
"If you encounter CUDA or RAM out of memory, try to set --tile with a smaller number."
)
else:
# if out_ext == "auto":
# extension = extension[1:]
# else:
# extension = out_ext
# if img_mode == "RGBA": # RGBA images should be saved in png format
# extension = "png"
# if suffix == "":
# save_path = os.path.join(output_dir, f"{imgname}.{extension}")
# else:
# save_path = os.path.join(output_dir, f"{imgname}_{suffix}.{extension}")
#
# cv2.imwrite(save_path, output)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img)
Imgs.append(img)
return Imgs
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