import os os.system("pip install gfpgan") #os.system("pip freeze") #os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") import random import gradio as gr from PIL import Image import torch # torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg') # torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') # torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') # torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') # torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') import cv2 import glob import numpy as np from basicsr.utils import imwrite from gfpgan import GFPGANer bg_upsampler = None # set up GFPGAN restorer restorer = GFPGANer( model_path='experiments/pretrained_models/GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=bg_upsampler) def inference(img): input_img = cv2.imread(img, cv2.IMREAD_COLOR) cropped_faces, restored_faces, restored_img = restorer.enhance( input_img, has_aligned=False, only_center_face=False, paste_back=True) #return Image.fromarray(restored_faces[0][:,:,::-1]) return Image.fromarray(restored_img[:, :, ::-1]) title = "让美好回忆更清晰" description = "上传老照片,点击Submit,稍等片刻,右侧Output将照片另存为即可。" article = "

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" gr.Interface( inference, [gr.inputs.Image(type="filepath", label="Input")], gr.outputs.Image(type="pil", label="Output"), title=title, description=description, article=article, examples=[ ['lincoln.jpg'], ['einstein.png'], ['edison.jpg'], ['Henry.jpg'], ['Frida.jpg'] ] ).launch(enable_queue=True,cache_examples=True,share=True)