Add support for TIFF and improve output

codeformer_wrapper.py

@@ -1,13 +1,7 @@
-# codeformer_wrapper.py
-# Copyright (c) 2022 Shangchen Zhou
-# Modifications and additions copyright (c) 2025 Felipe Daragon
-
-# License: CC BY-NC-SA 4.0 (https://github.com/felipedaragon/codeformer/blob/main/README.md)
-# Same as the original code by Shangchen Zhou.
-
 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
@@ -15,10 +9,8 @@ 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
 
-# Prepare device
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-# Load CodeFormer model once
 pretrain_model_url = {
     'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
 }
@@ -32,9 +24,8 @@ checkpoint = torch.load(ckpt_path)['params_ema']
 net.load_state_dict(checkpoint)
 net.eval()
 
-# Load helper
 face_helper = FaceRestoreHelper(
-    upscale_factor=1,  # No background upscaling
+    upscale_factor=1,
     face_size=512,
     crop_ratio=(1, 1),
     det_model='retinaface_resnet50',
@@ -43,36 +34,18 @@ face_helper = FaceRestoreHelper(
     device=device
 )
 
-def enhance_image(input_image_path: str, w: float = 0.5) -> str:
+def _enhance_img(img: np.ndarray, w: float = 0.5) -> np.ndarray:
     """
-    Enhances an input image using CodeFormer and saves it with a '.enhanced.jpg' suffix.
-
-    Args:
-        input_image_path (str): Path to the input image (JPG or PNG).
-        w (float): Balance quality and fidelity (default=0.5).
-
-    Returns:
-        str: Path to the enhanced image.
+    Internal helper to enhance a numpy image with CodeFormer.
     """
-    input_path = Path(input_image_path)
-    output_path = input_path.with_name(f"{input_path.stem}.enhanced.jpg")
-
-    # Clean previous state
     face_helper.clean_all()
-
-    # Load image
-    img = cv2.imread(str(input_path), cv2.IMREAD_COLOR)
-    if img is None:
-        raise ValueError(f"Cannot read image: {input_image_path}")
-
     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:
-        raise ValueError(f"No faces detected in: {input_image_path}")
+        return img  # Return original if no faces detected
 
     face_helper.align_warp_face()
 
-    # Enhance each 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)
@@ -85,13 +58,30 @@ def enhance_image(input_image_path: str, w: float = 0.5) -> str:
         restored_face = restored_face.astype('uint8')
         face_helper.add_restored_face(restored_face)
 
-    # Paste faces back
     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)
 
-    # Save output
     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)