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import os | |
import sys | |
import gradio as gr | |
from PIL import Image | |
## environment settup | |
os.system("git clone https://github.com/codeslake/RefVSR.git") | |
os.chdir("RefVSR") | |
os.system("./install/install_cudnn113.sh") | |
os.mkdir("ckpt") | |
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/SPyNet.pytorch -O ckpt/SPyNet.pytorch") | |
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/RefVSR_MFID_8K.pytorch -O ckpt/RefVSR_MFID_8K.pytorch") | |
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/RefVSR_small_MFID_8K.pytorch -O ckpt/RefVSR_small_MFID_8K.pytorch") | |
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/RefVSR_MFID.pytorch -O ckpt/RefVSR_MFID.pytorch") | |
os.system("wget https://huggingface.co/spaces/codeslake/RefVSR/resolve/main/RefVSR_small_MFID_8K.pytorch -O ckpt/RefVSR_small_MFID.pytorch") | |
sys.path.append("RefVSR") | |
## I/O setup (creates folders and places inputs corresponding to the original RefVSR code) | |
# HD input | |
HR_LR_path = "test/RealMCVSR/test/HR/UW/0000" | |
HR_Ref_path = "test/RealMCVSR/test/HR/W/0000" | |
HR_Ref_path_T = "test/RealMCVSR/test/HR/T/0000" | |
os.makedirs(HR_LR_path) | |
os.makedirs(HR_Ref_path) | |
os.makedirs(HR_Ref_path_T) | |
os.system("wget https://www.dropbox.com/s/x33ka2jlzwsde7r/LR.png -O HR_LR1.png") | |
os.system("wget https://www.dropbox.com/s/pp903wlz3syf68w/Ref.png -O HR_Ref1.png") | |
os.system("wget https://www.dropbox.com/s/zl0h83x0le6ejfw/LR.png -O HR_LR2.png") | |
os.system("wget https://www.dropbox.com/s/9hzupmc3clt0f0e/Ref.png -O HR_Ref2.png") | |
os.system("wget https://www.dropbox.com/s/2u6lcfdhvcylklg/LR.png -O HR_LR3.png") | |
os.system("wget https://www.dropbox.com/s/a7bwfy3gl26tvbq/Ref.png -O HR_Ref3.png") | |
# 4x downsampled input | |
LR_path = "test/RealMCVSR/test/LRx4/UW/0000" | |
Ref_path = "test/RealMCVSR/test/LRx4/W/0000" | |
Ref_path_T = "test/RealMCVSR/test/LRx4/T/0000" | |
os.makedirs(LR_path) | |
os.makedirs(Ref_path) | |
os.makedirs(Ref_path_T) | |
os.system("wget https://www.dropbox.com/s/hkvdwm3grshjt0k/LR.png -O LR.png") | |
os.system("wget https://www.dropbox.com/s/4sv34su3kg1ifkp/Ref.png -O Ref.png") | |
# output directory | |
os.makedirs('result') | |
## resize if necessary (not used) | |
def resize(img): | |
"""max_side = 480 | |
w = img.size[0] | |
h = img.size[1] | |
if max(h, w) > max_side: | |
scale_ratio = max_side / max(h, w) | |
wsize=int(w*scale_ratio) | |
hsize=int(h*scale_ratio) | |
img = img.resize((wsize,hsize), Image.ANTIALIAS) | |
w = img.size[0] | |
h = img.size[1]""" | |
img = img.crop((0, 0, img.size[0] - (img.size[0] % 8), img.size[1] - (img.size[1] % 8))) | |
return img | |
#################### 8K ################## | |
## inference | |
def inference_8K(LR, Ref): | |
## resize for user selected input (not used) | |
LR = resize(LR) | |
Ref = resize(Ref) | |
## Input setup (creates folders and places inputs corresponding to the original RefVSR code) | |
LR.save(os.path.join(LR_path, '0000.png')) | |
Ref.save(os.path.join(Ref_path, '0000.png')) | |
Ref.save(os.path.join(Ref_path_T, '0000.png')) | |
LR.save(os.path.join(HR_LR_path, '0000.png')) | |
Ref.save(os.path.join(HR_Ref_path, '0000.png')) | |
Ref.save(os.path.join(HR_Ref_path_T, '0000.png')) | |
## Run RefVSR model | |
os.system("python -B run.py \ | |
--mode RefVSR_MFID_8K \ | |
--config config_RefVSR_MFID_8K \ | |
--data RealMCVSR \ | |
--ckpt_abs_name ckpt/RefVSR_MFID_8K.pytorch \ | |
--data_offset ./test \ | |
--output_offset ./result \ | |
--qualitative_only \ | |
--cpu \ | |
--is_gradio") | |
return "result/0000.png" | |
title="RefVSR" | |
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. The demo runs on CPUs and takes about 30s." | |
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 frames due to computational complexity.<br>Hence, the model <b>will not take advantage</b> of temporal LR and Ref frames.</p><p style='text-align: center'>Moreover, the model is trained <b>with the proposed 2-stage training strategy</b>, but due to the memory and computational complexity, we downsampled sample frames to have the 480x270 resolution.</p><p style='text-align: center'>For user given frames, the size will be adjusted for the longer side of the frames to have 480 pixels.</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>" | |
## resize for sample (not used) | |
#LR = resize(Image.open('LR.png')).save('LR.png') | |
#Ref = resize(Image.open('Ref.png')).save('Ref.png') | |
## input | |
examples=[['HR_LR1.png', 'HR_Ref1.png'], ['HR_LR2.png', 'HR_Ref2.png'], ['HR_LR3.png', 'HR_Ref3.png']] | |
## interface | |
# gr.Interface(inference_8K,[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) | |
#################### low res ################## | |
## inference | |
def inference(LR, Ref): | |
## resize for user selected input | |
LR = resize(LR) | |
Ref = resize(Ref) | |
## Input setup (creates folders and places inputs corresponding to the original RefVSR code) | |
LR.save(os.path.join(LR_path, '0000.png')) | |
Ref.save(os.path.join(Ref_path, '0000.png')) | |
Ref.save(os.path.join(Ref_path_T, '0000.png')) | |
LR.save(os.path.join(HR_LR_path, '0000.png')) | |
Ref.save(os.path.join(HR_Ref_path, '0000.png')) | |
Ref.save(os.path.join(HR_Ref_path_T, '0000.png')) | |
## Run RefVSR model | |
os.system("python -B run.py \ | |
--mode RefVSR_MFID \ | |
--config config_RefVSR_MFID \ | |
--data RealMCVSR \ | |
--ckpt_abs_name ckpt/RefVSR_MFID.pytorch \ | |
--data_offset ./test \ | |
--output_offset ./result \ | |
--qualitative_only \ | |
--cpu \ | |
--is_gradio") | |
return "result/0000.png" | |
title="Demo for RefVSR (CVPR 2022)" | |
description="The demo applies 4xVSR on a video frame. It runs on CPUs and takes about 150s. For the demo, upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively. It is recommended for the reference frame to have a 2x larger zoom factor than that of the low-resolution frame." | |
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 frames due to computational complexity.<br>Hence, the model <b>will not take advantage</b> of temporal LR and Ref frames.</p><p style='text-align: center'>Moreover, the model is trained <b>only with the proposed pre-training strategy</b> to cope with downsampled sample frames, which are in the 480x270 resolution.</p><p style='text-align: center'>For user given frames, the size will be adjusted for the longer side of the frames to have 480 pixels.</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>" | |
## resize for sample | |
LR = resize(Image.open('LR.png')).save('LR.png') | |
Ref = resize(Image.open('Ref.png')).save('Ref.png') | |
## input | |
examples=[['LR.png','Ref.png']] | |
## interface | |
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) | |