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import cv2
import os
import torch
from basicsr.utils import img2tensor, tensor2img
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from torch.hub import download_url_to_file, get_dir
from torchvision.transforms.functional import normalize
from urllib.parse import urlparse
from gfpgan.archs.gfpganv1_arch import GFPGANv1
from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
class GFPGANer():
def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None):
self.upscale = upscale
self.bg_upsampler = bg_upsampler
# initialize model
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# initialize the GFP-GAN
if arch == 'clean':
self.gfpgan = GFPGANv1Clean(
out_size=512,
num_style_feat=512,
channel_multiplier=channel_multiplier,
decoder_load_path=None,
fix_decoder=False,
num_mlp=8,
input_is_latent=True,
different_w=True,
narrow=1,
sft_half=True)
else:
self.gfpgan = GFPGANv1(
out_size=512,
num_style_feat=512,
channel_multiplier=channel_multiplier,
decoder_load_path=None,
fix_decoder=True,
num_mlp=8,
input_is_latent=True,
different_w=True,
narrow=1,
sft_half=True)
# initialize face helper
self.face_helper = FaceRestoreHelper(
upscale,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
device=self.device)
if model_path.startswith('https://'):
model_path = load_file_from_url(url=model_path, model_dir='gfpgan/weights', progress=True, file_name=None)
loadnet = torch.load(model_path)
if 'params_ema' in loadnet:
keyname = 'params_ema'
else:
keyname = 'params'
self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
self.gfpgan.eval()
self.gfpgan = self.gfpgan.to(self.device)
@torch.no_grad()
def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
self.face_helper.clean_all()
if has_aligned:
img = cv2.resize(img, (512, 512))
self.face_helper.cropped_faces = [img]
else:
self.face_helper.read_image(img)
# get face landmarks for each face
self.face_helper.get_face_landmarks_5(only_center_face=only_center_face)
# align and warp each face
self.face_helper.align_warp_face()
# face restoration
for cropped_face in self.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(self.device)
try:
output = self.gfpgan(cropped_face_t, return_rgb=False)[0]
# convert to image
restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
except RuntimeError as error:
print(f'\tFailed inference for GFPGAN: {error}.')
restored_face = cropped_face
restored_face = restored_face.astype('uint8')
self.face_helper.add_restored_face(restored_face)
if not has_aligned and paste_back:
if self.bg_upsampler is not None:
# Now only support RealESRGAN
bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
else:
bg_img = None
self.face_helper.get_inverse_affine(None)
# paste each restored face to the input image
restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
else:
return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
"""Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
"""
if model_dir is None:
hub_dir = get_dir()
model_dir = os.path.join(hub_dir, 'checkpoints')
os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
parts = urlparse(url)
filename = os.path.basename(parts.path)
if file_name is not None:
filename = file_name
cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
if not os.path.exists(cached_file):
print(f'Downloading: "{url}" to {cached_file}\n')
download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
return cached_file
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