Update app.py

app.py

@@ -8,6 +8,45 @@ from skimage.feature import hog
 import joblib
 import numpy as np
 
+class VGGFaceEmbedding(nn.Module):
+    def __init__(self):
+        super(VGGFaceEmbedding, self).__init__()
+        self.base_model = resnet50(pretrained=True)
+        self.base_model = nn.Sequential(*list(self.base_model.children())[:-2])
+        self.pooling = nn.AdaptiveAvgPool2d((1, 1))
+        self.flatten = nn.Flatten()
+
+    def forward(self, x):
+        x = self.base_model(x)
+        x = self.pooling(x)
+        x = self.flatten(x)
+        return x
+
+class L1Dist(nn.Module):
+    def __init__(self):
+        super(L1Dist, self).__init__()
+
+    def forward(self, input_embedding, validation_embedding):
+        return torch.abs(input_embedding - validation_embedding)
+
+class SiameseNetwork(nn.Module):
+    def __init__(self):
+        super(SiameseNetwork, self).__init__()
+        self.embedding = VGGFaceEmbedding()
+        self.distance = L1Dist()
+        self.fc1 = nn.Linear(2048, 512)
+        self.fc2 = nn.Linear(512, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, input_image, validation_image):
+        input_embedding = self.embedding(input_image)
+        validation_embedding = self.embedding(validation_image)
+        distances = self.distance(input_embedding, validation_embedding)
+        x = self.fc1(distances)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return x
+
 def preprocess_image_siamese(img):
     transform = transforms.Compose([
         transforms.Resize((224, 224)),
@@ -46,7 +85,28 @@ def verify(image, model, person):
     face = get_face(image)
 
     if face is not None:
-        if model == "HOG-SVM":
+        if model == "Siamese":
+            siamese = SiameseNetwork()
+            siamese.load_state_dict(torch.load(f'siamese_{person.lower()}.pth'))
+
+            face = preprocess_image_siamese(face)
+
+            # Move to device
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            model.to(device)
+            face = face.to(device)
+        
+            with torch.no_grad():
+                output = model(face)
+                probability = output.item()
+                pred = 1.0 if probability > 0.5 else 0.0
+        
+            if pred == 1:
+                st.write("Match")
+            else:
+                st.write("Not Match")
+        
+        elif model == "HOG-SVM":
             with open(f'./svm_{person.lower()}.pkl', 'rb') as f:
                 svm = joblib.load(f)
             with open(f'./pca_{person.lower()}.pkl', 'rb') as f: