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| import os | |
| import torch | |
| import cv2 | |
| import numpy as np | |
| from pathlib import Path | |
| from torchvision.transforms.functional import normalize | |
| from basicsr.utils import img2tensor, tensor2img | |
| from basicsr.utils.download_util import load_file_from_url | |
| from facelib.utils.face_restoration_helper import FaceRestoreHelper | |
| from basicsr.utils.registry import ARCH_REGISTRY | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| pretrain_model_url = { | |
| 'restoration': 'https://huggingface.co/VanNguyen1214/models_swap_face/resolve/main/codeformer.pth', | |
| } | |
| 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 = 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() | |
| face_helper = FaceRestoreHelper( | |
| upscale_factor=1, | |
| face_size=512, | |
| crop_ratio=(1, 1), | |
| det_model='retinaface_resnet50', | |
| save_ext='jpg', | |
| use_parse=True, | |
| device=device | |
| ) | |
| def _enhance_img(img: np.ndarray, w: float = 0.5) -> np.ndarray: | |
| """ | |
| Internal helper to enhance a numpy image with CodeFormer. | |
| """ | |
| face_helper.clean_all() | |
| face_helper.read_image(img) | |
| num_faces = face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) | |
| if num_faces == 0: | |
| return img # Return original if no faces detected | |
| face_helper.align_warp_face() | |
| for cropped_face in face_helper.cropped_faces: | |
| 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) | |
| with torch.no_grad(): | |
| output = net(cropped_face_t, w=w, adain=True)[0] | |
| restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) | |
| restored_face = restored_face.astype('uint8') | |
| face_helper.add_restored_face(restored_face) | |
| face_helper.get_inverse_affine(None) | |
| restored_img = face_helper.paste_faces_to_input_image() | |
| return restored_img | |
| def enhance_image(input_image_path: str, w: float = 0.5) -> str: | |
| """ | |
| Enhances an input image using CodeFormer and saves it with a '.enhanced.jpg' suffix. | |
| """ | |
| input_path = Path(input_image_path) | |
| output_path = input_path.with_name(f"{input_path.stem}.enhanced.jpg") | |
| img = cv2.imread(str(input_path), cv2.IMREAD_COLOR) | |
| if img is None: | |
| raise ValueError(f"Cannot read image: {input_image_path}") | |
| restored_img = _enhance_img(img, w=w) | |
| os.makedirs(output_path.parent, exist_ok=True) | |
| cv2.imwrite(str(output_path), restored_img) | |
| print(f"Enhanced image saved to: {output_path}") | |
| return str(output_path) | |
| def enhance_image_memory(img: np.ndarray, w: float = 0.5) -> np.ndarray: | |
| """ | |
| Enhances an input image entirely in memory and returns the enhanced image. | |
| """ | |
| return _enhance_img(img, w=w) | |