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codeformer_wrapper_no_path.py

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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
+
+# Cross-platform device selection: CUDA > MPS > CPU
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif torch.backends.mps.is_available():
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+
+# Download and load model
+pretrain_model_url = {
+    'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/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, map_location=device)['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).to(device)
+        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)  # (1, 3, H, W), already on correct 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(img: 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")
+
+    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 restored_img
+
+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)