File size: 6,688 Bytes
8cee56f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
# flake8: noqa
# This file is used for deploying replicate models
# running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0
# push: cog push r8.im/xinntao/realesrgan
import os
os.system('pip install gfpgan')
os.system('python setup.py develop')
import cv2
import shutil
import tempfile
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from realesrgan.utils import RealESRGANer
try:
from cog import BasePredictor, Input, Path
from gfpgan import GFPGANer
except Exception:
print('please install cog and realesrgan package')
class Predictor(BasePredictor):
def setup(self):
os.makedirs('output', exist_ok=True)
# download weights
if not os.path.exists('weights/realesr-general-x4v3.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./weights'
)
if not os.path.exists('weights/GFPGANv1.4.pth'):
os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./weights')
if not os.path.exists('weights/RealESRGAN_x4plus.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./weights'
)
if not os.path.exists('weights/RealESRGAN_x4plus_anime_6B.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P ./weights'
)
if not os.path.exists('weights/realesr-animevideov3.pth'):
os.system(
'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P ./weights'
)
def choose_model(self, scale, version, tile=0):
half = True if torch.cuda.is_available() else False
if version == 'General - RealESRGANplus':
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
model_path = 'weights/RealESRGAN_x4plus.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
elif version == 'General - v3':
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'weights/realesr-general-x4v3.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
elif version == 'Anime - anime6B':
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
model_path = 'weights/RealESRGAN_x4plus_anime_6B.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
elif version == 'AnimeVideo - v3':
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
model_path = 'weights/realesr-animevideov3.pth'
self.upsampler = RealESRGANer(
scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
self.face_enhancer = GFPGANer(
model_path='weights/GFPGANv1.4.pth',
upscale=scale,
arch='clean',
channel_multiplier=2,
bg_upsampler=self.upsampler)
def predict(
self,
img: Path = Input(description='Input'),
version: str = Input(
description='RealESRGAN version. Please see [Readme] below for more descriptions',
choices=['General - RealESRGANplus', 'General - v3', 'Anime - anime6B', 'AnimeVideo - v3'],
default='General - v3'),
scale: float = Input(description='Rescaling factor', default=2),
face_enhance: bool = Input(
description='Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes', default=False),
tile: int = Input(
description=
'Tile size. Default is 0, that is no tile. When encountering the out-of-GPU-memory issue, please specify it, e.g., 400 or 200',
default=0)
) -> Path:
if tile <= 100 or tile is None:
tile = 0
print(f'img: {img}. version: {version}. scale: {scale}. face_enhance: {face_enhance}. tile: {tile}.')
try:
extension = os.path.splitext(os.path.basename(str(img)))[1]
img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
elif len(img.shape) == 2:
img_mode = None
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
else:
img_mode = None
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
self.choose_model(scale, version, tile)
try:
if face_enhance:
_, _, output = self.face_enhancer.enhance(
img, has_aligned=False, only_center_face=False, paste_back=True)
else:
output, _ = self.upsampler.enhance(img, outscale=scale)
except RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set "tile" to a smaller size, e.g., 400.')
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
# save_path = f'output/out.{extension}'
# cv2.imwrite(save_path, output)
out_path = Path(tempfile.mkdtemp()) / f'out.{extension}'
cv2.imwrite(str(out_path), output)
except Exception as error:
print('global exception: ', error)
finally:
clean_folder('output')
return out_path
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(f'Failed to delete {file_path}. Reason: {e}')
|