Update app.py
Browse files
app.py
CHANGED
@@ -24,8 +24,11 @@ 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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@@ -39,18 +42,18 @@ def resize(img):
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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
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--config
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--data RealMCVSR \
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--ckpt_abs_name ckpt/
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--data_offset ./test \
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--output_offset ./result \
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--qualitative_only \
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@@ -59,7 +62,7 @@ def inference(LR, Ref):
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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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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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os.system("wget https://www.dropbox.com/s/vqekqdz80d85gi4/UW.png -O LR.png")
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os.system("wget https://www.dropbox.com/s/lsopmquhpm87v83/W.png -O Ref.png")
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def resize(img):
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max_side = 512
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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 RefVSR_MFID_8K \
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--config config_RefVSR_MFID_8K \
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--data RealMCVSR \
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--ckpt_abs_name ckpt/RefVSR_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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return "result/0000.png"
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title="RefVSR (under construction)"
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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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