import os import random import gradio as gr from PIL import Image import torch from random import randint import sys from subprocess import call torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg') def run_cmd(command): try: print(command) call(command, shell=True) except KeyboardInterrupt: print("Process interrupted") sys.exit(1) run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .") run_cmd("pip install basicsr") run_cmd("pip freeze") #run_cmd("python setup.py develop") def inference(img): _id = randint(1, 10000) INPUT_DIR = "/tmp/input_image" + str(_id) + "/" OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/" run_cmd("rm -rf " + INPUT_DIR) run_cmd("rm -rf " + OUTPUT_DIR) run_cmd("mkdir " + INPUT_DIR) run_cmd("mkdir " + OUTPUT_DIR) basewidth = 256 wpercent = (basewidth/float(img.size[0])) hsize = int((float(img.size[1])*float(wpercent))) img = img.resize((basewidth,hsize), Image.ANTIALIAS) img.save(INPUT_DIR + "1.jpg", "JPEG") run_cmd("python inference_realesrgan.py --model_path RealESRGAN_x4plus.pth --input "+ INPUT_DIR + " --output " + OUTPUT_DIR + " --netscale 4 --outscale 3.5") return os.path.join(OUTPUT_DIR, "1_out.jpg") title = "Real-ESRGAN" description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" article = "

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data | Github Repo

" gr.Interface( inference, [gr.inputs.Image(type="pil", label="Input")], gr.outputs.Image(type="file", label="Output"), title=title, description=description, article=article, examples=[ ['bear.jpg'] ] ).launch(debug=True)