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inference_gfpgan.py
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inference_gfpgan.py
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import argparse
import cv2
import glob
import numpy as np
import os
import torch
from basicsr.utils import imwrite
from gfpgan import GFPGANer
def main():
"""Inference demo for GFPGAN (for users).
"""
parser = argparse.ArgumentParser()
parser.add_argument(
'-i',
'--input',
type=str,
default='inputs/whole_imgs',
help='Input image or folder. Default: inputs/whole_imgs')
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder. Default: results')
# we use version to select models, which is more user-friendly
parser.add_argument(
'-v', '--version', type=str, default='1.3', help='GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3')
parser.add_argument(
'-s', '--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2')
parser.add_argument(
'--bg_upsampler', type=str, default='realesrgan', help='background upsampler. Default: realesrgan')
parser.add_argument(
'--bg_tile',
type=int,
default=400,
help='Tile size for background sampler, 0 for no tile during testing. Default: 400')
parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
parser.add_argument('--aligned', action='store_true', help='Input are aligned faces')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto')
parser.add_argument('-w', '--weight', type=float, default=0.5, help='Adjustable weights.')
args = parser.parse_args()
args = parser.parse_args()
# ------------------------ input & output ------------------------
if args.input.endswith('/'):
args.input = args.input[:-1]
if os.path.isfile(args.input):
img_list = [args.input]
else:
img_list = sorted(glob.glob(os.path.join(args.input, '*')))
os.makedirs(args.output, exist_ok=True)
# ------------------------ set up background upsampler ------------------------
if args.bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
import warnings
warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
'If you really want to use it, please modify the corresponding codes.')
bg_upsampler = None
else:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
bg_upsampler = RealESRGANer(
scale=2,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
model=model,
tile=args.bg_tile,
tile_pad=10,
pre_pad=0,
half=True) # need to set False in CPU mode
else:
bg_upsampler = None
# ------------------------ set up GFPGAN restorer ------------------------
if args.version == '1':
arch = 'original'
channel_multiplier = 1
model_name = 'GFPGANv1'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth'
elif args.version == '1.2':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANCleanv1-NoCE-C2'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth'
elif args.version == '1.3':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANv1.3'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'
elif args.version == '1.4':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANv1.4'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'
elif args.version == 'RestoreFormer':
arch = 'RestoreFormer'
channel_multiplier = 2
model_name = 'RestoreFormer'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth'
else:
raise ValueError(f'Wrong model version {args.version}.')
# determine model paths
model_path = os.path.join('experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
model_path = os.path.join('gfpgan/weights', model_name + '.pth')
if not os.path.isfile(model_path):
# download pre-trained models from url
model_path = url
restorer = GFPGANer(
model_path=model_path,
upscale=args.upscale,
arch=arch,
channel_multiplier=channel_multiplier,
bg_upsampler=bg_upsampler)
# ------------------------ restore ------------------------
for img_path in img_list:
# read image
img_name = os.path.basename(img_path)
print(f'Processing {img_name} ...')
basename, ext = os.path.splitext(img_name)
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
# restore faces and background if necessary
cropped_faces, restored_faces, restored_img = restorer.enhance(
input_img,
has_aligned=args.aligned,
only_center_face=args.only_center_face,
paste_back=True,
weight=args.weight)
# save faces
for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
# save cropped face
save_crop_path = os.path.join(args.output, 'cropped_faces', f'{basename}_{idx:02d}.png')
imwrite(cropped_face, save_crop_path)
# save restored face
if args.suffix is not None:
save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
else:
save_face_name = f'{basename}_{idx:02d}.png'
save_restore_path = os.path.join(args.output, 'restored_faces', save_face_name)
imwrite(restored_face, save_restore_path)
# save comparison image
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
imwrite(cmp_img, os.path.join(args.output, 'cmp', f'{basename}_{idx:02d}.png'))
# save restored img
if restored_img is not None:
if args.ext == 'auto':
extension = ext[1:]
else:
extension = args.ext
if args.suffix is not None:
save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}_{args.suffix}.{extension}')
else:
save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}.{extension}')
imwrite(restored_img, save_restore_path)
print(f'Results are in the [{args.output}] folder.')
if __name__ == '__main__':
main()