Update app.py

app.py

@@ -7,21 +7,14 @@ from torchvision import models,transforms
 core = Core()
 
 # Read model to OpenVINO Runtime
-model_onnx = core.read_model(model="Davinci_eye.onnx")
-compiled_model_onnx = core.compile_model(model=model_onnx, device_name='CPU')
+model_ir = core.read_model(model="Davinci_eye.xml")
+compiled_model_ir = core.compile_model(model=model_ir, device_name='CPU')
 
 tfms = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # imagenet
 ])
-def normalize(img):
-  img=img.astype(np.float32)
-  mean=(0.485, 0.456, 0.406)
-  std=(0.229, 0.224, 0.225)
-  img/=255.0
-  img-=mean
-  img/std
-  return img
+
 def segment_image(filepath):
   image=cv2.imread(filepath)
   image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
@@ -29,7 +22,7 @@ def segment_image(filepath):
   x=tfms(image.copy()/255.)
   #ort_input={ort_session.get_inputs()[0].name:x.cpu().unsqueeze(0).float().numpy()}
   #out=ort_session.run(None,ort_input)
-  out = compiled_model_onnx([x.unsqueeze(0).float().cpu().numpy()])
+  out = compiled_model_ir([x.unsqueeze(0).float().cpu().numpy()])
   pred_mask=np.squeeze(np.argmax(out[0],1)).astype(np.uint8)
   color_mask=cv2.applyColorMap(pred_mask,cv2.COLORMAP_MAGMA)*10
   masked_image=cv2.addWeighted(image,0.6,color_mask,0.4,0.1)