Delete face_enhancer.py

face_enhancer.py

@@ -1,72 +0,0 @@
-import os
-import cv2
-import torch
-import gfpgan
-from PIL import Image
-from upscaler.RealESRGAN import RealESRGAN
-from upscaler.codeformer import CodeFormerEnhancer
-
-def gfpgan_runner(img, model):
-    _, imgs, _ = model.enhance(img, paste_back=True, has_aligned=True)
-    return imgs[0]
-
-
-def realesrgan_runner(img, model):
-    img = model.predict(img)
-    return img
-
-
-def codeformer_runner(img, model):
-    img = model.enhance(img)
-    return img
-
-
-supported_enhancers = {
-    "CodeFormer": ("./assets/pretrained_models/codeformer.onnx", codeformer_runner),
-    "GFPGAN": ("./assets/pretrained_models/GFPGANv1.4.pth", gfpgan_runner),
-    "REAL-ESRGAN 2x": ("./assets/pretrained_models/RealESRGAN_x2.pth", realesrgan_runner),
-    "REAL-ESRGAN 4x": ("./assets/pretrained_models/RealESRGAN_x4.pth", realesrgan_runner),
-    "REAL-ESRGAN 8x": ("./assets/pretrained_models/RealESRGAN_x8.pth", realesrgan_runner)
-}
-
-cv2_interpolations = ["LANCZOS4", "CUBIC", "NEAREST"]
-
-def get_available_enhancer_names():
-    available = []
-    for name, data in supported_enhancers.items():
-        path = os.path.join(os.path.abspath(os.path.dirname(__file__)), data[0])
-        if os.path.exists(path):
-            available.append(name)
-    return available
-
-
-def load_face_enhancer_model(name='GFPGAN', device="cpu"):
-    assert name in get_available_enhancer_names() + cv2_interpolations, f"Face enhancer {name} unavailable."
-    if name in supported_enhancers.keys():
-        model_path, model_runner = supported_enhancers.get(name)
-        model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_path)
-    if name == 'CodeFormer':
-        model = CodeFormerEnhancer(model_path=model_path, device=device)
-    elif name == 'GFPGAN':
-        model = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=device)
-    elif name == 'REAL-ESRGAN 2x':
-        model = RealESRGAN(device, scale=2)
-        model.load_weights(model_path, download=False)
-    elif name == 'REAL-ESRGAN 4x':
-        model = RealESRGAN(device, scale=4)
-        model.load_weights(model_path, download=False)
-    elif name == 'REAL-ESRGAN 8x':
-        model = RealESRGAN(device, scale=8)
-        model.load_weights(model_path, download=False)
-    elif name == 'LANCZOS4':
-        model = None
-        model_runner = lambda img, _: cv2.resize(img, (512,512), interpolation=cv2.INTER_LANCZOS4)
-    elif name == 'CUBIC':
-        model = None
-        model_runner = lambda img, _: cv2.resize(img, (512,512), interpolation=cv2.INTER_CUBIC)
-    elif name == 'NEAREST':
-        model = None
-        model_runner = lambda img, _: cv2.resize(img, (512,512), interpolation=cv2.INTER_NEAREST)
-    else:
-        model = None
-    return (model, model_runner)