| import os | |
| import cv2 | |
| import torch | |
| import gfpgan | |
| from PIL import Image | |
| from upscaler.RealESRGAN import RealESRGAN | |
| 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 | |
| supported_enhancers = { | |
| "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 == '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) | |