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# Modified by Shangchen Zhou from: https://github.com/TencentARC/GFPGAN/blob/master/inference_gfpgan.py
import os
import cv2
import argparse
import glob
import torch
from torchvision.transforms.functional import normalize
from basicsr.utils import imwrite, img2tensor, tensor2img
from basicsr.utils.download_util import load_file_from_url
from facelib.utils.face_restoration_helper import FaceRestoreHelper
import torch.nn.functional as F
from basicsr.utils.registry import ARCH_REGISTRY
pretrain_model_url = {
'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
}
def set_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.',
category=RuntimeWarning)
bg_upsampler = None
else:
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.realesrgan_utils 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=40,
pre_pad=0,
half=True) # need to set False in CPU mode
return bg_upsampler
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser()
parser.add_argument('--w', type=float, default=0.5, help='Balance the quality and fidelity')
parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2')
parser.add_argument('--test_path', type=str, default='./inputs/cropped_faces')
parser.add_argument('--has_aligned', action='store_true', help='Input are cropped and aligned faces')
parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
# large det_model: 'YOLOv5l', 'retinaface_resnet50'
# small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
parser.add_argument('--detection_model', type=str, default='retinaface_resnet50')
parser.add_argument('--draw_box', action='store_true')
parser.add_argument('--bg_upsampler', type=str, default='None', help='background upsampler. Optional: realesrgan')
parser.add_argument('--face_upsample', action='store_true', help='face upsampler after enhancement.')
parser.add_argument('--bg_tile', type=int, default=400, help='Tile size for background sampler. Default: 400')
args = parser.parse_args()
# ------------------------ input & output ------------------------
if args.test_path.endswith('/'): # solve when path ends with /
args.test_path = args.test_path[:-1]
w = args.w
result_root = f'results/{os.path.basename(args.test_path)}_{w}'
# ------------------ set up background upsampler ------------------
if args.bg_upsampler == 'realesrgan':
bg_upsampler = set_realesrgan()
else:
bg_upsampler = None
# ------------------ set up face upsampler ------------------
if args.face_upsample:
if bg_upsampler is not None:
face_upsampler = bg_upsampler
else:
face_upsampler = set_realesrgan()
else:
face_upsampler = None
# ------------------ set up CodeFormer restorer -------------------
net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9,
connect_list=['32', '64', '128', '256']).to(device)
# ckpt_path = 'weights/CodeFormer/codeformer.pth'
ckpt_path = load_file_from_url(url=pretrain_model_url['restoration'],
model_dir='weights/CodeFormer', progress=True, file_name=None)
checkpoint = torch.load(ckpt_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
# ------------------ set up FaceRestoreHelper -------------------
# large det_model: 'YOLOv5l', 'retinaface_resnet50'
# small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
if not args.has_aligned:
print(f'Face detection model: {args.detection_model}')
if bg_upsampler is not None:
print(f'Background upsampling: True, Face upsampling: {args.face_upsample}')
else:
print(f'Background upsampling: False, Face upsampling: {args.face_upsample}')
face_helper = FaceRestoreHelper(
args.upscale,
face_size=512,
crop_ratio=(1, 1),
det_model = args.detection_model,
save_ext='png',
use_parse=True,
device=device)
# -------------------- start to processing ---------------------
# scan all the jpg and png images
for img_path in sorted(glob.glob(os.path.join(args.test_path, '*.[jp][pn]g'))):
# clean all the intermediate results to process the next image
face_helper.clean_all()
img_name = os.path.basename(img_path)
print(f'Processing: {img_name}')
basename, ext = os.path.splitext(img_name)
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
if args.has_aligned:
# the input faces are already cropped and aligned
img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
face_helper.cropped_faces = [img]
else:
face_helper.read_image(img)
# get face landmarks for each face
num_det_faces = face_helper.get_face_landmarks_5(
only_center_face=args.only_center_face, resize=640, eye_dist_threshold=5)
print(f'\tdetect {num_det_faces} faces')
# align and warp each face
face_helper.align_warp_face()
# face restoration for each cropped face
for idx, cropped_face in enumerate(face_helper.cropped_faces):
# prepare data
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
try:
with torch.no_grad():
output = net(cropped_face_t, w=w, adain=True)[0]
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
except Exception as error:
print(f'\tFailed inference for CodeFormer: {error}')
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
face_helper.add_restored_face(restored_face)
# paste_back
if not args.has_aligned:
# upsample the background
if bg_upsampler is not None:
# Now only support RealESRGAN for upsampling background
bg_img = bg_upsampler.enhance(img, outscale=args.upscale)[0]
else:
bg_img = None
face_helper.get_inverse_affine(None)
# paste each restored face to the input image
if args.face_upsample and face_upsampler is not None:
restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box, face_upsampler=face_upsampler)
else:
restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box)
# save faces
for idx, (cropped_face, restored_face) in enumerate(zip(face_helper.cropped_faces, face_helper.restored_faces)):
# save cropped face
if not args.has_aligned:
save_crop_path = os.path.join(result_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
imwrite(cropped_face, save_crop_path)
# save restored face
if args.has_aligned:
save_face_name = f'{basename}.png'
else:
save_face_name = f'{basename}_{idx:02d}.png'
save_restore_path = os.path.join(result_root, 'restored_faces', save_face_name)
imwrite(restored_face, save_restore_path)
# save restored img
if not args.has_aligned and restored_img is not None:
save_restore_path = os.path.join(result_root, 'final_results', f'{basename}.png')
imwrite(restored_img, save_restore_path)
print(f'\nAll results are saved in {result_root}')
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