import os import sys import gradio as gr from PIL import Image ## environment settup if not os.path.exists("RefVSR"): os.system("git clone https://github.com/codeslake/RefVSR.git") # Mudando para o diretório clonado (seja na primeira vez ou após reinício do container) os.chdir("RefVSR") if not os.path.exists("ckpt"): os.system("./install/install_cudnn113.sh") os.makedirs("ckpt", exist_ok=True) 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") if os.getcwd() not in sys.path: sys.path.append(os.getcwd()) ## 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, exist_ok=True) os.makedirs(HR_Ref_path, exist_ok=True) os.makedirs(HR_Ref_path_T, exist_ok=True) if not os.path.exists("HR_LR1.png"): 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, exist_ok=True) os.makedirs(Ref_path, exist_ok=True) os.makedirs(Ref_path_T, exist_ok=True) if not os.path.exists("LR.png"): 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', exist_ok=True) ## resize if necessary 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) # Compatibilidade com Pillow 10+ resample_filter = getattr(Image, 'Resampling', Image).LANCZOS img = img.resize((wsize, hsize), resample_filter) w = img.size[0] h = img.size[1] img = img.crop((0, 0, w - w % 8, h - h % 8)) return img #################### 8K ################## ## inference def inference_8K(LR, Ref): LR = resize(LR) Ref = resize(Ref) 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')) 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_8K = "RefVSR (8K Model)" description_8K = "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_8K = "

To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.

This demo runs on CPUs and only supports RefVSR for a single LR and Ref frames due to computational complexity.

Project | arXiv | Github

" examples_8K = [['HR_LR1.png', 'HR_Ref1.png'], ['HR_LR2.png', 'HR_Ref2.png'], ['HR_LR3.png', 'HR_Ref3.png']] # Interface 1 demo_8k = gr.Interface( fn=inference_8K, inputs=[gr.Image(type="pil", label="LR Input"), gr.Image(type="pil", label="Ref Input")], outputs=gr.Image(type="filepath", label="Output"), title=title_8K, description=description_8K, article=article_8K, examples=examples_8K ) #################### low res ################## ## inference def inference(LR, Ref): LR = resize(LR) Ref = resize(Ref) 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')) 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_low = "Demo for RefVSR (CVPR 2022) - Low Res" description_low = "The demo applies 4xVSR on a video frame. It runs on CPUs and takes about 150s. It is recommended for the reference frame to have a 2x larger zoom factor than that of the low-resolution frame." article_low = article_8K ## Resize sample for Low Res if os.path.exists('LR.png') and os.path.exists('Ref.png'): resize(Image.open('LR.png')).save('LR.png') resize(Image.open('Ref.png')).save('Ref.png') examples_low = [['LR.png','Ref.png']] # Interface 2 demo_low = gr.Interface( fn=inference, inputs=[gr.Image(type="pil", label="LR Input"), gr.Image(type="pil", label="Ref Input")], outputs=gr.Image(type="filepath", label="Output"), title=title_low, description=description_low, article=article_low, examples=examples_low ) ## Combina as duas interfaces em abas e inicia app = gr.TabbedInterface([demo_8k, demo_low], ["8K Model", "Low Res Model"]) app.launch()