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import os |
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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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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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def inference(img): |
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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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run_cmd("python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input "+ INPUT_DIR + " --output " + OUTPUT_DIR + " --netscale 4 --outscale 3.5") |
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return os.path.join(OUTPUT_DIR, "1_out.jpg") |
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title = "Anime2Sketch" |
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description = "demo for Anime2Sketch. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.05703'>Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis</a> | <a href='https://github.com/Mukosame/Anime2Sketch'>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")], |
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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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).launch(debug=True) |