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pages/1_analisis_wajah.py
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584
pages/1_analisis_wajah.py
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#1_analisis_wajah.py
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import streamlit as st
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import cv2
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import numpy as np
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import time
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import matplotlib.pyplot as plt
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from collections import Counter, deque
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import tensorflow as tf
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from tensorflow.keras.applications.efficientnet import preprocess_input
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from contextlib import contextmanager
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import os
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import logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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CATEGORIES = ['Angry', 'Sad', 'Happy', 'Fearful', 'Disgust', 'Neutral', 'Surprised']
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POSITIVE_EMOTIONS = {'Happy', 'Surprised', 'Neutral'}
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NEGATIVE_EMOTIONS = {'Angry', 'Sad', 'Fearful', 'Disgust'}
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FRAME_INTERVAL = 0.5
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MODEL_PATH = "model/Model_EfficientNet.tflite"
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CAMERA_WIDTH = 320
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CAMERA_HEIGHT = 180
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MEDIA_WIDTH = 320
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MEDIA_HEIGHT = 180
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YOUTUBE_URL = "https://www.youtube.com/embed/3XA0bB79oGc?autoplay={}&mute=1"
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MAX_PREDICTIONS_BUFFER = 1000
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NEUTRAL_INDEX = CATEGORIES.index('Neutral')
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def load_custom_css():
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css = """
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<style>
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.main {
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background-color: #0b0f2e;
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color: white;
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}
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.stApp {
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background-color: #0b0f2e;
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}
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.metric-container {
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background-color: #1a2352;
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padding: 15px;
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border-radius: 8px;
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text-align: center;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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}
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.metric-label {
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font-size: 14px;
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opacity: 0.8;
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margin-bottom: 5px;
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}
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.metric-value {
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font-size: 20px;
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font-weight: bold;
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color: white;
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}
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.results-container {
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background-color: #111c4e;
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border-radius: 10px;
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padding: 20px;
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margin-top: 20px;
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}
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.emotion-score {
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font-size: 24px;
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font-weight: bold;
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text-align: center;
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margin: 10px 0;
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}
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.negative-score { color: #ff6b6b; }
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.positive-score { color: #69db7c; }
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.stButton > button {
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background-color: #007bff;
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color: white;
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border-radius: 8px;
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padding: 12px 24px;
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width: 100%;
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transition: background-color 0.3s ease;
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font-size: 16px;
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border: none;
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cursor: pointer;
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}
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.stButton > button:hover {
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background-color: #0056b3;
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}
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.status-indicator {
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padding: 8px 16px;
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border-radius: 20px;
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font-size: 14px;
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font-weight: 600;
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display: inline-block;
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margin: 15px 0;
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}
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.status-analyzing { background-color: #28a745; color: white; }
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.status-stopped { background-color: #dc3545; color: white; }
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.status-ready { background-color: #ffc107; color: black; }
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.youtube-container {
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background-color: #1a2352;
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border-radius: 8px;
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padding: 15px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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margin-bottom: 20px;
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}
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.camera-container {
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background-color: #1a2352;
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border-radius: 8px;
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padding: 15px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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margin-bottom: 20px;
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}
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.control-panel {
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background-color: #1a2352;
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border-radius: 8px;
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padding: 20px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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}
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.panel-title {
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font-size: 18px;
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font-weight: bold;
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margin-bottom: 15px;
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color: white;
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}
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.main-title {
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font-size: 32px;
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font-weight: bold;
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margin-bottom: 15px;
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color: white;
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}
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.hidden {
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display: none;
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}
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.stImage > img {
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border-radius: 8px;
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}
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</style>
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"""
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st.markdown(css, unsafe_allow_html=True)
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class EmotionAnalyzer:
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def __init__(self):
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self.interpreter = None
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self.input_details = None
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self.output_details = None
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self.is_loaded = False
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@st.cache_resource
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def load_model(_self):
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if not os.path.exists(MODEL_PATH):
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raise FileNotFoundError(f"Model file {MODEL_PATH} tidak ditemukan")
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try:
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with st.spinner('Memuat model TFLite...'):
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start_time = time.time()
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interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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output_details = interpreter.get_output_details()
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load_time = time.time() - start_time
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logger.info(f"Model loaded successfully in {load_time:.2f} seconds")
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return interpreter, input_details, output_details
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except Exception as e:
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logger.error(f"Error loading model: {e}")
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raise
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def initialize(self):
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if not self.is_loaded:
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self.interpreter, self.input_details, self.output_details = self.load_model()
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self.is_loaded = True
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def preprocess_image(self, image):
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try:
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image_resized = cv2.resize(image, (224, 224))
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image_array = np.expand_dims(image_resized, axis=0).astype(np.float32)
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image_array = preprocess_input(image_array)
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return image_array
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except Exception as e:
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logger.error(f"Error in preprocessing: {e}")
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return None
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def predict(self, image):
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if not self.is_loaded:
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raise RuntimeError("Model belum dimuat")
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try:
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processed_image = self.preprocess_image(image)
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if processed_image is None:
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return NEUTRAL_INDEX, 1.0
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self.interpreter.set_tensor(self.input_details[0]['index'], processed_image)
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self.interpreter.invoke()
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predictions = self.interpreter.get_tensor(self.output_details[0]['index'])[0]
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pred_idx = np.argmax(predictions)
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confidence = predictions[pred_idx]
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return pred_idx, confidence
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except Exception as e:
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logger.error(f"Error in prediction: {e}")
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return NEUTRAL_INDEX, 1.0
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class EmotionDataManager:
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def __init__(self):
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self.predictions = deque(maxlen=MAX_PREDICTIONS_BUFFER)
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self.timestamps = deque(maxlen=MAX_PREDICTIONS_BUFFER)
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self.start_time = None
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def reset(self):
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self.predictions.clear()
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self.timestamps.clear()
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self.start_time = None
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def add_prediction(self, pred_idx, timestamp=None):
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if self.start_time is None:
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self.start_time = time.time()
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if timestamp is None:
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timestamp = time.time() - self.start_time
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self.predictions.append(pred_idx)
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self.timestamps.append(timestamp)
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def get_emotion_scores(self):
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if not self.predictions:
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return 0, 0
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emotion_names = [CATEGORIES[idx] for idx in self.predictions]
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total = len(emotion_names)
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negative_count = sum(1 for emotion in emotion_names if emotion in NEGATIVE_EMOTIONS)
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positive_count = sum(1 for emotion in emotion_names if emotion in POSITIVE_EMOTIONS)
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negative_score = (negative_count / total) * 100
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positive_score = (positive_count / total) * 100
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return negative_score, positive_score
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def get_dominant_emotions(self, top_n=3):
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if not self.predictions:
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return []
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emotion_counts = Counter(self.predictions)
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most_common = emotion_counts.most_common(top_n)
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total = sum(emotion_counts.values())
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return [(CATEGORIES[emotion], count, (count/total)*100)
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for emotion, count in most_common]
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def get_emotion_changes(self, top_n=3):
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if len(self.predictions) < 2:
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return []
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changes = []
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for i in range(1, len(self.predictions)):
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if self.predictions[i] != self.predictions[i-1]:
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from_emotion = CATEGORIES[self.predictions[i-1]]
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to_emotion = CATEGORIES[self.predictions[i]]
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change_time = self.timestamps[i]
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changes.append((from_emotion, to_emotion, change_time))
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return sorted(changes, key=lambda x: x[2])[:top_n]
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@contextmanager
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def camera_context():
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cap = cv2.VideoCapture(0)
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, CAMERA_WIDTH)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, CAMERA_HEIGHT)
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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try:
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yield cap
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finally:
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cap.release()
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def create_metric_display(label, value, container_class="metric-container"):
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return f"""
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<div class="{container_class}">
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<div class="metric-label">{label}</div>
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<div class="metric-value">{value}</div>
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</div>
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"""
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def create_status_indicator(status):
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status_map = {
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'analyzing': ('Sedang Menganalisis', 'status-analyzing'),
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'stopped': ('Analisis Dihentikan', 'status-stopped'),
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'ready': ('Siap Memulai', 'status-ready')
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}
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text, css_class = status_map.get(status, ('Unknown', 'status-ready'))
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return f'<span class="status-indicator {css_class}">{text}</span>'
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def initialize_session_state():
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defaults = {
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'is_analyzing': False,
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'results_ready': False,
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'current_expression': "-",
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'current_accuracy': "-",
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'last_capture_time': 0,
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'analyzer': None,
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'data_manager': None,
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'video_started': False,
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'analysis_start_time': None
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}
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for key, value in defaults.items():
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if key not in st.session_state:
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st.session_state[key] = value
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if st.session_state.analyzer is None:
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st.session_state.analyzer = EmotionAnalyzer()
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if st.session_state.data_manager is None:
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st.session_state.data_manager = EmotionDataManager()
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def render_results():
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data_manager = st.session_state.data_manager
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if not data_manager.predictions:
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st.warning("Tidak ada data yang dikumpulkan selama analisis.")
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return
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st.success("✅ Analisis emosi wajah selesai!")
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negative_score, positive_score = data_manager.get_emotion_scores()
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(create_metric_display(
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"Emosi Negatif",
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f"{negative_score:.1f}%",
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"metric-container negative-score"
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), unsafe_allow_html=True)
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with col2:
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st.markdown(create_metric_display(
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"Emosi Positif",
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f"{positive_score:.1f}%",
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"metric-container positive-score"
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), unsafe_allow_html=True)
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st.markdown("<div class='results-container'>", unsafe_allow_html=True)
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if len(data_manager.predictions) > 1:
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fig, ax = plt.subplots(figsize=(12, 6))
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fig.patch.set_facecolor('#f4f4f7')
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ax.set_facecolor('#f4f4f7')
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timestamps = list(data_manager.timestamps)
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expressions = [CATEGORIES[idx] for idx in data_manager.predictions]
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ax.scatter(timestamps, expressions, c='#3a7aff', alpha=0.7, s=30)
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ax.plot(timestamps, expressions, color='#3a7aff', alpha=0.5, linewidth=1)
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ax.set_xlabel("Waktu (detik)", color='#111c4e', fontsize=12)
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ax.set_ylabel("Ekspresi", color='#111c4e', fontsize=12)
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ax.set_title("Timeline Perubahan Ekspresi", color='#111c4e', fontsize=14, fontweight='bold')
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ax.tick_params(axis='both', colors='#111c4e', labelsize=10)
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for spine in ax.spines.values():
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spine.set_color('#111c4e')
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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st.pyplot(fig)
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plt.close(fig)
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("Emosi Dominan")
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dominant_emotions = data_manager.get_dominant_emotions()
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for emotion, count, percentage in dominant_emotions:
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st.write(f"**{emotion}**: {percentage:.1f}% ({count} deteksi)")
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with col2:
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st.subheader("Perubahan Tercepat")
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emotion_changes = data_manager.get_emotion_changes()
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if emotion_changes:
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for from_emotion, to_emotion, change_time in emotion_changes:
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st.write(f"**{from_emotion}** → **{to_emotion}** ({change_time:.1f}s)")
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else:
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st.info("Tidak ada perubahan emosi terdeteksi")
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st.markdown("</div>", unsafe_allow_html=True)
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def main():
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st.set_page_config(
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page_title="Analisis Emosi Wajah",
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layout="wide"
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)
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initialize_session_state()
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load_custom_css()
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st.markdown("<h1 class='main-title'>Analisis Emosi Wajah Real-time</h1>", unsafe_allow_html=True)
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status = 'analyzing' if st.session_state.is_analyzing else ('ready' if not st.session_state.results_ready else 'stopped')
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st.markdown(create_status_indicator(status), unsafe_allow_html=True)
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col1, col2 = st.columns([2, 1])
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with col1:
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youtube_placeholder = st.empty()
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youtube_placeholder.markdown(
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f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(1 if st.session_state.video_started else 0)}" '
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f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
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unsafe_allow_html=True
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)
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st.markdown('</div>', unsafe_allow_html=True)
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with col2:
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video_placeholder = st.empty()
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st.markdown('</div>', unsafe_allow_html=True)
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st.subheader("📊 Metrik Real-time")
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expression_placeholder = st.empty()
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accuracy_placeholder = st.empty()
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st.subheader("Kontrol")
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button_text = 'Akhiri Analisis' if st.session_state.is_analyzing else 'Mulai Analisis'
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start_stop_button = st.button(button_text, key="main_control")
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st.markdown('</div>', unsafe_allow_html=True)
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expression_placeholder.markdown(
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create_metric_display("Ekspresi Terdeteksi", st.session_state.current_expression),
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unsafe_allow_html=True
|
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)
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accuracy_placeholder.markdown(
|
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create_metric_display("Tingkat Keyakinan", st.session_state.current_accuracy),
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unsafe_allow_html=True
|
||||
)
|
||||
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if start_stop_button:
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if not st.session_state.is_analyzing:
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try:
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st.session_state.analyzer.initialize()
|
||||
|
||||
st.session_state.data_manager.reset()
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st.session_state.is_analyzing = True
|
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st.session_state.results_ready = False
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st.session_state.last_capture_time = 0
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st.session_state.video_started = False
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st.session_state.analysis_start_time = time.time()
|
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|
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youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
st.rerun()
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error memulai analisis: {e}")
|
||||
st.session_state.is_analyzing = False
|
||||
else:
|
||||
st.session_state.is_analyzing = False
|
||||
st.session_state.results_ready = True
|
||||
st.session_state.current_expression = "-"
|
||||
st.session_state.current_accuracy = "-"
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||||
st.session_state.video_started = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
video_placeholder.image(np.zeros((MEDIA_HEIGHT, MEDIA_WIDTH, 3), dtype=np.uint8), channels="RGB", width=MEDIA_WIDTH)
|
||||
st.rerun()
|
||||
|
||||
if st.session_state.is_analyzing:
|
||||
try:
|
||||
with camera_context() as cap:
|
||||
if not cap.isOpened():
|
||||
st.error("❌ Tidak dapat mengakses kamera")
|
||||
st.session_state.is_analyzing = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
st.rerun()
|
||||
|
||||
while st.session_state.is_analyzing:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
st.error("❌ Error membaca frame dari kamera")
|
||||
break
|
||||
|
||||
current_time = time.time()
|
||||
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
video_placeholder.image(frame_rgb, channels="RGB", width=MEDIA_WIDTH)
|
||||
|
||||
if not st.session_state.video_started and (current_time - st.session_state.analysis_start_time) >= 10:
|
||||
st.session_state.video_started = True
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(1)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
if current_time - st.session_state.last_capture_time >= FRAME_INTERVAL:
|
||||
pred_idx, confidence = st.session_state.analyzer.predict(frame_rgb)
|
||||
|
||||
if pred_idx is not None:
|
||||
st.session_state.data_manager.add_prediction(pred_idx)
|
||||
|
||||
current_expression = CATEGORIES[pred_idx]
|
||||
current_accuracy = f"{confidence*100:.1f}%"
|
||||
|
||||
st.session_state.current_expression = current_expression
|
||||
st.session_state.current_accuracy = current_accuracy
|
||||
|
||||
expression_placeholder.markdown(
|
||||
create_metric_display("Ekspresi Terdeteksi", current_expression),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
accuracy_placeholder.markdown(
|
||||
create_metric_display("Tingkat Keyakinan", current_accuracy),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
st.session_state.last_capture_time = current_time
|
||||
|
||||
time.sleep(0.03)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error selama analisis: {e}")
|
||||
st.session_state.is_analyzing = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
video_placeholder.image(np.zeros((MEDIA_HEIGHT, MEDIA_WIDTH, 3), dtype=np.uint8), channels="RGB", width=MEDIA_WIDTH)
|
||||
|
||||
if not st.session_state.is_analyzing:
|
||||
expression_placeholder.markdown(
|
||||
create_metric_display("Ekspresi Terdeteksi", "-"),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
accuracy_placeholder.markdown(
|
||||
create_metric_display("Tingkat Keyakinan", "-"),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
if st.session_state.results_ready and not st.session_state.is_analyzing:
|
||||
render_results()
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
if st.button("🔄 Reset Analisis", key="reset"):
|
||||
st.session_state.data_manager.reset()
|
||||
st.session_state.is_analyzing = False
|
||||
st.session_state.results_ready = False
|
||||
st.session_state.current_expression = "-"
|
||||
st.session_state.current_accuracy = "-"
|
||||
st.session_state.video_started = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
video_placeholder.image(np.zeros((MEDIA_HEIGHT, MEDIA_WIDTH, 3), dtype=np.uint8), channels="RGB", width=MEDIA_WIDTH)
|
||||
st.rerun()
|
||||
|
||||
with col2:
|
||||
if st.button("📝 Lanjut ke Jurnal", key="journal"):
|
||||
try:
|
||||
st.switch_page("pages/2_jurnaling.py")
|
||||
except Exception as e:
|
||||
st.error(f"Error: Halaman journaling tidak tersedia - {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user