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app/Hackathon_setup/face_recognition.py

@@ -112,14 +112,23 @@ 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.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
+    ])
+
+    # Apply the transformations to the image
+    det_img1_tensor = transform(image)
+
     feature_net = SiameseNetwork() #Example Network 
     feature_net = feature_net.to(device)
     current_path = 'app/Hackathon_setup'
     model = torch.load(current_path + '/siamese_model_recog.t7', map_location=device)
     feature_net.load_state_dict(model['net_dict'])                      
     
-    output1 = feature_net.forward_once(det_img1)
-    probabilities = torch.softmax(outputs, dim=1)
+    output1 = feature_net.forward_once(det_img1_tensor)
+    probabilities = torch.softmax(output1, dim=1)
     predicted_class = torch.argmax(probabilities, dim=1).item()
     
     return predicted_class