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import os |
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import sys |
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import gradio as gr |
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from PIL import Image |
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os.system("git clone https://github.com/codeslake/RefVSR.git") |
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os.chdir("RefVSR") |
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os.system("./install/install_cudnn113.sh") |
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os.mkdir("ckpt") |
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os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/RefVSR_small_MFID_8K.pytorch -O ckpt/RefVSR_small_MFID_8K.pytorch") |
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os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/SPyNet.pytorch -O ckpt/SPyNet.pytorch") |
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sys.path.append("RefVSR") |
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LR_path = "test/RealMCVSR/test/HR/UW/0000" |
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Ref_path = "test/RealMCVSR/test/HR/W/0000" |
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Ref_path_T = "test/RealMCVSR/test/HR/T/0000" |
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os.makedirs(LR_path) |
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os.makedirs(Ref_path) |
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os.makedirs(Ref_path_T) |
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os.makedirs('result') |
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os.system("wget https://www.dropbox.com/s/xv6inxwy0so4ni0/LR.png -O LR.png") |
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os.system("wget https://www.dropbox.com/s/abydd1oczs1163l/Ref.png -O Ref.png") |
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def resize(img): |
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max_side = 512 |
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w = img.size[0] |
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h = img.size[1] |
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if max(h, w) > max_side: |
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scale_ratio = max_side / max(h, w) |
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wsize=int(w*scale_ratio) |
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hsize=int(h*scale_ratio) |
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img = img.resize((wsize,hsize), Image.ANTIALIAS) |
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return img |
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def inference(LR, Ref): |
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LR = resize(LR) |
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Ref = resize(Ref) |
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LR.save(os.path.join(LR_path, '0000.png')) |
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Ref.save(os.path.join(Ref_path, '0000.png')) |
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Ref.save(os.path.join(Ref_path_T, '0000.png')) |
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os.system("python -B run.py \ |
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--mode amp_RefVSR_small_MFID_8K \ |
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--config config_RefVSR_small_MFID_8K \ |
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--data RealMCVSR \ |
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--ckpt_abs_name ckpt/RefVSR_small_MFID_8K.pytorch \ |
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--data_offset ./test \ |
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--output_offset ./result \ |
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--qualitative_only \ |
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--cpu \ |
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--is_gradio") |
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return "result/0000.png" |
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title="RefVSR" |
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description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively." |
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article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model will not take advantage of temporal LR and Ref frames.</p><p style='text-align: center'>The model is the small-sized model trained with the proposed two-stage training strategy.</p><p style='text-align: center'>The sample frames are in HD resolution (1920x1080) and in the PNG format. </p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>" |
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examples=[['LR.png', 'Ref.png']] |
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gr.Interface(inference,[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],gr.outputs.Image(type="file"),title=title,description=description,article=article,theme ="peach",examples=examples).launch(enable_queue=True) |
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