98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
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# codeformer_wrapper.py
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# Copyright (c) 2022 Shangchen Zhou
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# Modifications and additions copyright (c) 2025 Felipe Daragon
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# License: CC BY-NC-SA 4.0 (https://github.com/felipedaragon/codeformer/blob/main/README.md)
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# Same as the original code by Shangchen Zhou.
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import os
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import torch
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import cv2
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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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# Prepare device
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# Load CodeFormer model once
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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)['params_ema']
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net.load_state_dict(checkpoint)
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net.eval()
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# Load helper
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face_helper = FaceRestoreHelper(
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upscale_factor=1, # No background upscaling
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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_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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Args:
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input_image_path (str): Path to the input image (JPG or PNG).
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w (float): Balance quality and fidelity (default=0.5).
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Returns:
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str: Path to the enhanced image.
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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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# Clean previous state
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face_helper.clean_all()
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# Load image
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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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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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raise ValueError(f"No faces detected in: {input_image_path}")
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face_helper.align_warp_face()
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# Enhance each 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)
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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).to(device)
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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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# Paste faces back
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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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# Save output
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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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