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| import os | |
| import random | |
| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| from random import randint | |
| import sys | |
| from subprocess import call | |
| import psutil | |
| 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") | |
| os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .") | |
| def inference(img,mode): | |
| _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") | |
| if mode == "base": | |
| run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i "+ INPUT_DIR + " -o " + OUTPUT_DIR) | |
| else: | |
| os.system("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i "+ INPUT_DIR + " -o " + OUTPUT_DIR) | |
| 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 = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>" | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="base", label="model type")], | |
| gr.outputs.Image(type="file", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ['bear.jpg'] | |
| ], | |
| enable_queue=True | |
| ).launch(debug=True) |