# 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}')