Update app.py

app.py

@@ -1,49 +1,48 @@
-
-import gradio as gr
 from fastai.vision.all import *
-
+import gradio as gr
+import torchvision.transforms as transforms
 from pathlib import Path
 import PIL
-import torchvision.transforms as transforms
-
-
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
 model = torch.jit.load("unet.pth")
 model = model.cpu()
 model.eval()
 
-def transform_image(image):
-    my_transforms = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize(
-                                            [0.485, 0.456, 0.406],
-                                            [0.229, 0.224, 0.225])])
-    image_aux = image
-    
-image = transforms.Resize((480,640))(Image.fromarray(image))
-tensor = my_transforms(image_aux).unsqueeze(0).to(device)
 
+def transform_image(image):
 
-model.to(device)
-with torch.no_grad():
+  #mask = PILMask.create(Path(str(image).replace("Images","Labels").replace("color","gt").replace(".jpg",".png")))
+  #image = PIL.Image.open(image)
+  my_transforms = transforms.Compose([transforms.ToTensor(),
+                                      transforms.Normalize(
+                                          [0.485, 0.456, 0.406],
+                                          [0.229, 0.224, 0.225])])
+  image_aux = image
+  #my_transforms(image_aux).unsqueeze(0).to(device)
+  image = transforms.Resize((480,640))(Image.fromarray(image))
+  tensor = my_transforms(image_aux).unsqueeze(0).to(device)
+  #tensor = transform_image(image=image)
+     
+  
+  
+  model.to(device)
+  with torch.no_grad():
     outputs = model(tensor)
-
-outputs = torch.argmax(outputs,1)
-
-
-mask = np.array(outputs.cpu())
-mask[mask==0]=255
-mask[mask==1]=150
-mask[mask==2]=76
-mask[mask==3]=25
-mask[mask==4]=0
-
-mask=np.reshape(mask,(480,640))
-return Image.fromarray(mask.astype('uint8'))
-
+  
+  outputs = torch.argmax(outputs,1)
+  
+  mask = np.array(outputs.cpu())
+  mask[mask==0]=255
+  mask[mask==1]=150
+  mask[mask==2]=76
+  mask[mask==3]=25
+  mask[mask==4]=0
+  
+  mask=np.reshape(mask,(480,640))
+  return Image.fromarray(mask.astype('uint8'))
 
     
 # Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_188.jpg','color_189.jpg']).launch(share=False)
- 
+gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_156.jpg','color_179.jpg']).launch(share=False)