File size: 4,842 Bytes
c7122d8 |
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 |
import argparse
import cv2
import glob
import numpy as np
import os
import torch
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from torchvision.transforms.functional import normalize
from archs.gfpganv1_arch import GFPGANv1
from basicsr.utils import img2tensor, imwrite, tensor2img
def restoration(gfpgan,
face_helper,
img_path,
save_root,
has_aligned=False,
only_center_face=True,
suffix=None,
paste_back=False):
# read image
img_name = os.path.basename(img_path)
print(f'Processing {img_name} ...')
basename, _ = os.path.splitext(img_name)
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
face_helper.clean_all()
if has_aligned:
input_img = cv2.resize(input_img, (512, 512))
face_helper.cropped_faces = [input_img]
else:
face_helper.read_image(input_img)
# get face landmarks for each face
face_helper.get_face_landmarks_5(only_center_face=only_center_face, pad_blur=False)
# align and warp each face
save_crop_path = os.path.join(save_root, 'cropped_faces', img_name)
face_helper.align_warp_face(save_crop_path)
# face restoration
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('cuda')
try:
with torch.no_grad():
output = 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')
face_helper.add_restored_face(restored_face)
if suffix is not None:
save_face_name = f'{basename}_{idx:02d}_{suffix}.png'
else:
save_face_name = f'{basename}_{idx:02d}.png'
save_restore_path = os.path.join(save_root, 'restored_faces', save_face_name)
imwrite(restored_face, save_restore_path)
# save cmp image
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
imwrite(cmp_img, os.path.join(save_root, 'cmp', f'{basename}_{idx:02d}.png'))
if not has_aligned and paste_back:
face_helper.get_inverse_affine(None)
save_restore_path = os.path.join(save_root, 'restored_imgs', img_name)
# paste each restored face to the input image
face_helper.paste_faces_to_input_image(save_restore_path)
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser()
parser.add_argument('--upscale_factor', type=int, default=1)
parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.pth')
parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
parser.add_argument('--only_center_face', action='store_true')
parser.add_argument('--aligned', action='store_true')
parser.add_argument('--paste_back', action='store_true')
args = parser.parse_args()
if args.test_path.endswith('/'):
args.test_path = args.test_path[:-1]
save_root = 'results/'
os.makedirs(save_root, exist_ok=True)
# initialize the GFP-GAN
gfpgan = GFPGANv1(
out_size=512,
num_style_feat=512,
channel_multiplier=1,
decoder_load_path=None,
fix_decoder=True,
# for stylegan decoder
num_mlp=8,
input_is_latent=True,
different_w=True,
narrow=1,
sft_half=True)
gfpgan.to(device)
checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
gfpgan.load_state_dict(checkpoint['params_ema'])
gfpgan.eval()
# initialize face helper
face_helper = FaceRestoreHelper(
args.upscale_factor, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png')
img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
for img_path in img_list:
restoration(
gfpgan,
face_helper,
img_path,
save_root,
has_aligned=args.aligned,
only_center_face=args.only_center_face,
suffix=args.suffix,
paste_back=args.paste_back)
print('Results are in the <results> folder.')
|