update emotion and new model

app.py

@@ -13,7 +13,7 @@ from tensorflow.keras import layers
 MODEL_DIR = 'models/siamese_model-final'  
 VERIF_IMGS_DIR = 'registered_images'
 INPUT_IMG_DIR = 'input_images/cropped_face.png'
-THRESHOLD = 0.1
+THRESHOLD = 0.2
 VERIFICATION_THRESHOLD = 0.3
 GREEN_COLOR = (0, 255, 0)
 RED_COLOR = (0, 0, 255)
@@ -23,14 +23,9 @@ siamese_model = None
 # face detection model
 FACE_CASCADE_MODEL = cv2.CascadeClassifier("models/face_detection.xml")
 
-# anti-spoofing model
-# json_file = open('models/anti_spoofing.json','r')
-# loaded_model_json = json_file.read()
-# json_file.close()
-# ANTI_SPOOFING_MODEL = model_from_json(loaded_model_json)
-# ANTI_SPOOFING_MODEL.load_weights('models/anti_spoofing.h5')
+ANTI_SPOOFING_MODEL = tf.keras.models.load_model('models/antispoofing.h5')
 
-ANTI_SPOOFING_MODEL = tf.keras.models.load_model('models/test.h5')
+EMOTION_MODEL = tf.keras.models.load_model('models/facialemotionmodel.h5')
 
 def get_encoder(input_shape):
     """ Returns the image encoding model """
@@ -125,33 +120,40 @@ def verify_user(encoder):
         img_pairs = read_image_pairs(load_images(full_path))
 
         similarities = compute_cosine_similarity(encoder, img_pairs)
-        
         print(similarities)
-        predictions = [1 if sim > THRESHOLD else 0 for sim in similarities]
-
-        similar_pairs_count = sum(predictions)
 
-        similarity = similar_pairs_count / len(img_pairs) if img_pairs else 0
+        avg_similarity = np.mean(similarities)
+        std_similarity = np.std(similarities)
+        min_similarity = min(similarities)
 
         user = {
             'username': current_user,
-            'similarity': similarity,
-            'total_match': f"{similar_pairs_count}/{len(img_pairs)}",
+            'avg_similarity': avg_similarity,
+            'std_similarity': std_similarity,
+            'min_similarity': min_similarity,
+            'total_match': f"{sum(sim > THRESHOLD for sim in similarities)}/{len(img_pairs)} \n",
         }
 
         users.append(user)
 
     print(users)
-    possible_user = max(users, key=lambda user: user['similarity'])
 
-    username = possible_user['username']
-    similarity = possible_user['similarity']
-    total_match = possible_user['total_match']
+    verified_users = [user for user in users if user['min_similarity'] > THRESHOLD]
+    verified_users.sort(key=lambda u: (u['avg_similarity'], -u['std_similarity']), reverse=True)
 
-    if (similarity > VERIFICATION_THRESHOLD):
-        return username, similarity
+    if verified_users:
+        top_user = verified_users[0]
+        if top_user['avg_similarity'] > VERIFICATION_THRESHOLD:
+            print('verified')
+            # detect emotion, and return emotion
+            emotion = detect_emotion()
+            return top_user['username'], top_user['avg_similarity'], emotion
+        else:
+            print('not verified')
     else:
-        return None, None
+        print('not verified')
+        return None, None, None
+    
 
 def detect_faces(image):
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -190,6 +192,24 @@ def is_real_face(image):
     else:
         return True
 
+def detect_emotion():
+    emotion_labels = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
+    captured_image_path = INPUT_IMG_DIR
+
+    # Load and preprocess the captured image
+    captured_image = cv2.imread(captured_image_path)
+    captured_image = cv2.cvtColor(captured_image, cv2.COLOR_BGR2GRAY)
+    captured_image = cv2.resize(captured_image, (48, 48))
+    captured_image = np.reshape(captured_image, (1, 48, 48, 1))
+
+    # Predict emotion
+    emotion_prediction = EMOTION_MODEL(captured_image)
+    detected_emotion_index = np.argmax(emotion_prediction)
+    detected_emotion = emotion_labels[detected_emotion_index]
+
+    return detected_emotion
+
+
 st.title("Face attendance recognition")
 
 # Capture an image from the camera
@@ -217,8 +237,6 @@ if image_file:
                 st.warning("Face is fake! Please try again.")
                 st.stop()
 
-            
-
             cropped_image = crop_img(image_np, detected_faces[0])
 
             temp_image_path = "cropped_face.jpg"
@@ -231,8 +249,8 @@ if image_file:
                     siamese_model = tf.keras.models.load_model(MODEL_DIR)
 
                 encoder = extract_encoder(siamese_model)
-                recognized_username, similarity = verify_user(encoder)
-                print(recognized_username, similarity)
+                recognized_username, similarity, emotion = verify_user(encoder)
+                print(recognized_username, similarity, emotion)
 
             os.remove(temp_image_path)
 
@@ -242,6 +260,7 @@ if image_file:
 
             st.write(f"Recognized User: {recognized_username}")
             st.write(f"Similarity Score: {similarity}")
+            st.write(f"You are: {emotion}!")
             
         else:
             st.warning("No face detected!")

models/anti_spoofing.json

@@ -1 +0,0 @@
-{"class_name": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 160, 160, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "Conv1", "trainable": false, "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [2, 2], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "Conv1", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "bn_Conv1", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "bn_Conv1", "inbound_nodes": [[["Conv1", 0, 0, {}]]]}, {"class_name": "ReLU", "config": {"name": "Conv1_relu", "trainable": false, "dtype": "float32", "max_value": 6.0, "negative_slope": 0.0, "threshold": 0.0}, "name": "Conv1_relu", "inbound_nodes": [[["bn_Conv1", 0, 0, {}]]]}, {"class_name": "DepthwiseConv2D", "config": {"name": "expanded_conv_depthwise", "trainable": false, "dtype": "float32", "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "depthwise_regularizer": null, "depthwise_constraint": null}, "name": "expanded_conv_depthwise", "inbound_nodes": [[["Conv1_relu", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "expanded_conv_depthwise_BN", "trainable": false, "dtype": "float32", "axis": [3], "momentum": 0.999, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "expanded_conv_depthwise_BN", "inbound_nodes": [[["expanded_conv_depthwise", 0, 0, {}]]]}, {"class_name": "ReLU", "config": {"name": "expanded_conv_depthwise_relu", "trainable": false, "dtype": "float32", "max_value": 6.0, "negative_slope": 0.0, "threshold": 0.0}, "name": "expanded_conv_depthwise_relu", "inbound_nodes": [[["expanded_conv_depthwise_BN", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "expanded_conv_project", "trainable": false, "dtype": "float32", "filters": 16, "kernel_size": [1, 1], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "expanded_conv_project", "inbound_nodes": 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{}]]]}, {"class_name": "ReLU", "config": {"name": "block_1_expand_relu", "trainable": false, "dtype": "float32", "max_value": 6.0, "negative_slope": 0.0, "threshold": 0.0}, "name": "block_1_expand_relu", "inbound_nodes": [[["block_1_expand_BN", 0, 0, {}]]]}, {"class_name": "ZeroPadding2D", "config": {"name": "block_1_pad", "trainable": false, "dtype": "float32", "padding": [[0, 1], [0, 1]], "data_format": "channels_last"}, "name": "block_1_pad", "inbound_nodes": [[["block_1_expand_relu", 0, 0, {}]]]}, {"class_name": "DepthwiseConv2D", "config": {"name": "block_1_depthwise", "trainable": false, "dtype": "float32", "kernel_size": [3, 3], "strides": [2, 2], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": 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"backend": "tensorflow"}

models/{anti_spoofing.h5 → facialemotionmodel.h5}

@@ -1,3 +1,3 @@
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