change-2

app/Hackathon_setup/face_recognition.py

@@ -112,15 +112,16 @@ def get_face_class(img1):
     ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
     ##Better Hint: Siamese experiment is covered in one of the labs
 
-    transform = transforms.Compose([
-        transforms.Grayscale(num_output_channels = 1),
-        transforms.Resize((100, 100)),  # Resize the image to the desired size
-        transforms.ToTensor(),          # Convert the image to a PyTorch tensor
-        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # Normalize if needed
-    ])
-
+    # transform = transforms.Compose([
+    #     transforms.Grayscale(num_output_channels = 1),
+    #     transforms.Resize((100, 100)),  # Resize the image to the desired size
+    #     transforms.ToTensor(),          # Convert the image to a PyTorch tensor
+    #     transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # Normalize if needed
+    # ])
+    trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])
+    det_img1_tensor = trnscm(det_img1).unsqueeze(0)
     # Apply the transformations to the image
-    det_img1_tensor = transform(det_img1)
+    # det_img1_tensor = transform(det_img1)
 
     feature_net = SiameseNetwork() #Example Network 
     feature_net = feature_net.to(device)