face_enhancer.py

import os
import cv2
import torch
import gfpgan
import gdown
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": ("./pretrained_models/GFPGANv1.4.pth", gfpgan_runner),
    "REAL-ESRGAN 2x": ("./pretrained_models/RealESRGAN_x2.pth", realesrgan_runner),
    "REAL-ESRGAN 4x": ("./pretrained_models/RealESRGAN_x4.pth", realesrgan_runner),
    "REAL-ESRGAN 8x": ("./pretrained_models/RealESRGAN_x8.pth", realesrgan_runner)
}

cv2_interpolations = ["LANCZOS4", "CUBIC", "NEAREST"]

def model_check(model_url, model_path):
    if not os.path.exists(model_path):
        gdown.download(model_url, model_path, quiet=False)


def load_face_enhancer_model(name='GFPGAN', device="cpu"):
    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_url = 'https://drive.google.com/uc?id=1QsJPgvZNwFsBktbeYENVsEq663UgBQRj'  
        model_check(model_url, model_path)
        model = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=device)
    elif name == 'REAL-ESRGAN 2x':
        model_url = 'https://drive.google.com/uc?id=1BYFc4ttYGHmA-GZMmgXW9NdgPkXkgjtv'  
        model_check(model_url, model_path)
        model = RealESRGAN(device, scale=2)
        model.load_weights(model_path, download=False)
    elif name == 'REAL-ESRGAN 4x':
        model_url = 'https://drive.google.com/uc?id=1N4MNjfGhrz-CHq99WCp6NEfgzMIGxAE0'  
        model_check(model_url, model_path)
        model = RealESRGAN(device, scale=4)
        model.load_weights(model_path, download=False)
    elif name == 'REAL-ESRGAN 8x':
        model_url = 'https://drive.google.com/uc?id=14FtSjtgtl8iySVrrvFDX-HxCCkdbsoPh'  
        model_check(model_url, model_path)
        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)