Update app.py

app.py

@@ -26,8 +26,8 @@ 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/198390mgcba5fgj/LR.png -O HR_LR.png")
-os.system("wget https://www.dropbox.com/s/vc2jvuwj17p1fuy/Ref.png -O HR_Ref.png")
+os.system("wget https://www.dropbox.com/s/x33ka2jlzwsde7r/LR.png -O HR_LR.png")
+os.system("wget https://www.dropbox.com/s/pp903wlz3syf68w/Ref.png -O HR_Ref.png")
 
 # 4x downsampled input
 LR_path = "test/RealMCVSR/test/LRx4/UW/0000"
@@ -54,12 +54,13 @@ def resize(img):
         img = img.resize((wsize,hsize), Image.ANTIALIAS)
     return img
     
-#################### low res ##################
+
+#################### 8K ##################
 ## inference  
-def inference(LR, Ref):
-    ## resize for user selected input
-    LR = resize(LR)
-    Ref = resize(Ref)
+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'))
@@ -71,38 +72,38 @@ def inference(LR, Ref):
     
     ## Run RefVSR model
     os.system("python -B run.py \
-                --mode RefVSR_MFID \
-                --config config_RefVSR_MFID \
+                --mode RefVSR_MFID_8K \
+                --config config_RefVSR_MFID_8K \
                 --data RealMCVSR \
-                --ckpt_abs_name ckpt/RefVSR_MFID.pytorch \
+                --ckpt_abs_name ckpt/RefVSR_MFID_8K.pytorch \
                 --data_offset ./test \
                 --output_offset ./result \
                 --qualitative_only \
                 --cpu \
-                --is_gradio")             
+                --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 120s."
 
-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>"
+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'><div>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity.</div><div>Hence, the model will not take advantage of temporal LR and Ref frames.</div></p><p style='text-align: center'>The model is trained by the proposed two-stage training strategy. The sample frames are in HD resolution (1920x1080) and saved 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>"
 
-## resize for sample
-LR = resize(Image.open('LR.png')).save('LR.png')
-Ref = resize(Image.open('Ref.png')).save('Ref.png')
+## 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=[['LR.png','Ref.png']]
+examples=[['HR_LR.png', 'HR_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)
+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)    
 
-#################### 8K ##################
+#################### low res ##################
 ## inference  
-def inference_8K(LR, Ref):
-    ## resize for user selected input (not used)
-    #LR = resize(LR)
-    #Ref = resize(Ref)
+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'))
@@ -114,28 +115,28 @@ def inference_8K(LR, Ref):
     
     ## Run RefVSR model
     os.system("python -B run.py \
-                --mode RefVSR_MFID_8K \
-                --config config_RefVSR_MFID_8K \
+                --mode RefVSR_MFID \
+                --config config_RefVSR_MFID \
                 --data RealMCVSR \
-                --ckpt_abs_name ckpt/RefVSR_MFID_8K.pytorch \
+                --ckpt_abs_name ckpt/RefVSR_MFID.pytorch \
                 --data_offset ./test \
                 --output_offset ./result \
                 --qualitative_only \
                 --cpu \
-                --is_gradio")               
+                --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 120s."
 
-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'><div>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity.</div><div>Hence, the model will not take advantage of temporal LR and Ref frames.</div></p><p style='text-align: center'>The model is trained by the proposed two-stage training strategy. The sample frames are in HD resolution (1920x1080) and saved 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>"
+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."
 
-## resize for sample (not used)
-#LR = resize(Image.open('LR.png')).save('LR.png')
-#Ref = resize(Image.open('Ref.png')).save('Ref.png')
+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=[['HR_LR.png', 'HR_Ref.png']]
+examples=[['LR.png','Ref.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)    
+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)