| 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) |
|
|
