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import cv2
from PIL import Image
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 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 os.path.isfile(input_dir):
paths = [input_dir]
else:
paths = sorted(glob.glob(os.path.join(input_dir, '*')))
Imgs = []
for idx, path in enumerate(paths):
imgname, extension = os.path.splitext(os.path.basename(path))
print(f'Scaling x{outscale}:', 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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