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import os |
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os.system("pip install gradio==2.9b23") |
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import random |
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import gradio as gr |
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from PIL import Image |
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import torch |
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from random import randint |
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import sys |
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from subprocess import call |
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import psutil |
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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') |
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def run_cmd(command): |
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try: |
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print(command) |
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call(command, shell=True) |
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except KeyboardInterrupt: |
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print("Process interrupted") |
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sys.exit(1) |
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run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .") |
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run_cmd("pip install basicsr") |
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run_cmd("pip freeze") |
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .") |
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def inference(img,mode): |
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_id = randint(1, 10000) |
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INPUT_DIR = "/tmp/input_image" + str(_id) + "/" |
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OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/" |
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run_cmd("rm -rf " + INPUT_DIR) |
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run_cmd("rm -rf " + OUTPUT_DIR) |
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run_cmd("mkdir " + INPUT_DIR) |
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run_cmd("mkdir " + OUTPUT_DIR) |
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basewidth = 256 |
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wpercent = (basewidth/float(img.size[0])) |
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hsize = int((float(img.size[1])*float(wpercent))) |
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img = img.resize((basewidth,hsize), Image.ANTIALIAS) |
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img.save(INPUT_DIR + "1.jpg", "JPEG") |
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if mode == "base": |
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run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i "+ INPUT_DIR + " -o " + OUTPUT_DIR) |
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else: |
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os.system("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i "+ INPUT_DIR + " -o " + OUTPUT_DIR) |
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return os.path.join(OUTPUT_DIR, "1_out.jpg") |
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title = "Real-ESRGAN" |
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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" |
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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>" |
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gr.Interface( |
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inference, |
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[gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="base", label="model type")], |
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gr.outputs.Image(type="file", label="Output"), |
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title=title, |
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description=description, |
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article=article, |
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examples=[ |
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['bear.jpg','base'], |
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['anime.png','anime'] |
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]).launch() |