Update app.py

app.py

@@ -10,11 +10,18 @@ checkpoint = torch.load('part_B_pre.pth.tar',map_location=torch.device('cpu'))
 model.load_state_dict(checkpoint['state_dict'])
 model.eval()
 
+## Defining the transform function
 transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])
 
+
 def crowd(img):
+    ## Transforming the image
     img = transform(img)
+    
+    ## Adding batch dimension
     img = rearrange(img, "c h w -> 1 c h w")
+    
+    ## Slicing the image into four parts
     h = img.shape[2]
     w = img.shape[3]
     h_d = int(h/2)
@@ -23,11 +30,15 @@ def crowd(img):
     img_2 = img[:,:,:h_d,w_d:]
     img_3 = img[:,:,h_d:,:w_d]
     img_4 = img[:,:,h_d:,w_d:]
+    
+    ## Inputting the 4 images into the model, converting it to numpy array, and summing to get the density
     with torch.no_grad():
         density_1 = model(img_1).numpy().sum()
         density_2 = model(img_2).numpy().sum()
         density_3 = model(img_3).numpy().sum()
         density_4 = model(img_4).numpy().sum()
+        
+    ## Summing up the estimated density and rounding the result to get an integer
     pred = density_1 + density_2 + density_3 + density_4
     pred = int(pred.round())
     return pred