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import os |
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import torch |
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import cv2 |
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import numpy as np |
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from pathlib import Path |
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from torchvision.transforms.functional import normalize |
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from basicsr.utils import img2tensor, tensor2img |
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from basicsr.utils.download_util import load_file_from_url |
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from facelib.utils.face_restoration_helper import FaceRestoreHelper |
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from basicsr.utils.registry import ARCH_REGISTRY |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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elif torch.backends.mps.is_available(): |
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device = torch.device("mps") |
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else: |
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device = torch.device("cpu") |
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pretrain_model_url = { |
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'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth', |
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} |
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net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, |
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connect_list=['32', '64', '128', '256']).to(device) |
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ckpt_path = load_file_from_url(url=pretrain_model_url['restoration'], |
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model_dir='weights/CodeFormer', progress=True, file_name=None) |
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checkpoint = torch.load(ckpt_path, map_location=device)['params_ema'] |
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net.load_state_dict(checkpoint) |
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net.eval() |
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face_helper = FaceRestoreHelper( |
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upscale_factor=1, |
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face_size=512, |
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crop_ratio=(1, 1), |
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det_model='retinaface_resnet50', |
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save_ext='jpg', |
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use_parse=True, |
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device=device |
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) |
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def _enhance_img(img: np.ndarray, w: float = 0.5) -> np.ndarray: |
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""" |
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Internal helper to enhance a numpy image with CodeFormer. |
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""" |
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face_helper.clean_all() |
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face_helper.read_image(img) |
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num_faces = face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) |
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if num_faces == 0: |
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return img |
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face_helper.align_warp_face() |
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for cropped_face in face_helper.cropped_faces: |
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True).to(device) |
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) |
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cropped_face_t = cropped_face_t.unsqueeze(0) |
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with torch.no_grad(): |
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output = net(cropped_face_t, w=w, adain=True)[0] |
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restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) |
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restored_face = restored_face.astype('uint8') |
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face_helper.add_restored_face(restored_face) |
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face_helper.get_inverse_affine(None) |
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restored_img = face_helper.paste_faces_to_input_image() |
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return restored_img |
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def enhance_image(input_image_path: str, w: float = 0.5) -> str: |
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""" |
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Enhances an input image using CodeFormer and saves it with a '.enhanced.jpg' suffix. |
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""" |
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input_path = Path(input_image_path) |
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output_path = input_path.with_name(f"{input_path.stem}.enhanced.jpg") |
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img = cv2.imread(str(input_path), cv2.IMREAD_COLOR) |
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if img is None: |
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raise ValueError(f"Cannot read image: {input_image_path}") |
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restored_img = _enhance_img(img, w=w) |
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os.makedirs(output_path.parent, exist_ok=True) |
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cv2.imwrite(str(output_path), restored_img) |
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print(f"Enhanced image saved to: {output_path}") |
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return str(output_path) |
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def enhance_image_memory(img: np.ndarray, w: float = 0.5) -> np.ndarray: |
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""" |
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Enhances an input image entirely in memory and returns the enhanced image. |
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""" |
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return _enhance_img(img, w=w) |
