diff --git a/app.py b/app.py index 1baca6a3988cd89d403b49176888b1d1992448c7..7d3f53def873d5b9d0bc5f65914d172176042914 100644 --- a/app.py +++ b/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!") diff --git a/models/anti_spoofing.h5 b/models/anti_spoofing.h5 deleted file mode 100644 index f3b01fc26ed8b2ad7cf31660fe6225565971c045..0000000000000000000000000000000000000000 --- a/models/anti_spoofing.h5 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9fcc2e8074fa48bb5692f8e67174f09e610747484294b71f57c7f8dd8bcb2fdc -size 10444552 diff --git a/models/anti_spoofing.json b/models/anti_spoofing.json deleted file mode 100644 index 0ce1082381428b7de5a45c3084cbdc5f5dc35728..0000000000000000000000000000000000000000 --- a/models/anti_spoofing.json +++ /dev/null @@ -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", 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