Update app.py

app.py

@@ -77,67 +77,77 @@ def get_face(img):
         return img[y1:y2, x1:x2]
     return None
 
-def verify(image, model, person):
+def verify(image, model, person, validation_image=None):
     
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_image:
         temp_image.write(image.read())
         temp_image_path = temp_image.name
 
-        image = cv2.imread(temp_image_path)
-            
-        face = get_face(image)
-
-        temp_face_path = tempfile.mktemp(suffix=".jpg")
-        cv2.imwrite(temp_face_path, face)
+    image = cv2.imread(temp_image_path)
         
-        if face is not None:
-            if model == "Siamese":
-                siamese = SiameseNetwork()
-                siamese.load_state_dict(torch.load(f'siamese_{person.lower()}.pth'))
-                siamese.eval()
+    face = get_face(image)
+
+    temp_face_path = tempfile.mktemp(suffix=".jpg")
+    cv2.imwrite(temp_face_path, face)
     
-                face = preprocess_image_siamese(temp_face_path)
-            
-                with torch.no_grad():
-                    output = siamese(face)
-                    probability = output.item()
-                    pred = 1.0 if probability > 0.7 else 0.0
+    if face is not None:
+        if model == "Siamese":
+            siamese = SiameseNetwork()
+            siamese.load_state_dict(torch.load(f'siamese_{person.lower()}.pth'))
+            siamese.eval()
+
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as validation_temp_image:
+                validation_temp_image.write(validation_image.read())
+                validation_temp_image_path = validation_temp_image.name
+
+
+            face = preprocess_image_siamese(temp_face_path)
+            validation_face = preprocess_image_siamese(validation_temp_image_path)
+        
+            with torch.no_grad():
+                output = siamese(face, validation_face)
+                probability = output.item()
+                pred = 1.0 if probability > 0.7 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:
+                pca = joblib.load(f)
+
+            face = cv2.imread(temp_face_path)
             
-                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:
-                    pca = joblib.load(f)
-
-                face = cv2.imread(temp_face_path)
-                
-                face = preprocess_image_svm(face)
-    
-                hog = extract_hog_features(face)
-    
-                hog_pca = pca.transform([hog])
-    
-                pred = svm.predict(hog_pca)
-    
-                if pred == 1:
-                    st.write("Match")
-                else:
-                    st.write("Not Match")
+            face = preprocess_image_svm(face)
+
+            hog = extract_hog_features(face)
+
+            hog_pca = pca.transform([hog])
+
+            pred = svm.predict(hog_pca)
+
+            if pred == 1:
+                st.write("Match")
+            else:
+                st.write("Not Match")
 
 def main():
     st.title("Face Verification")
     
     model = st.selectbox("Select Model", ["Siamese", "HOG-SVM"])
     person = st.selectbox("Select Person", ["Theo", "Deverel", "Justin"])
+    if model == "Siamese":
+        uploaded_image = st.file_uploader("Upload Validation Image (Siamese)", type=["jpg", "png"])
     enable = st.checkbox("Enable camera")
     captured_image = st.camera_input("Take a picture", disabled=not enable)
 
-    if captured_image:
+    if captured_image and model == "Siamese":
+        verify(captured_image, model, person, uploaded_image)
+    elif captured_image and model == "HOG-SVM":
         verify(captured_image, model, person)
 
 if __name__ == "__main__":