Update visualization.py

visualization.py

@@ -127,6 +127,33 @@ def plot_mse(df, mse_values, title, color='navy', time_threshold=3, anomaly_thre
     plt.close()
     return fig, anomaly_frames
 
+def plot_combined_mse(df, mse_embeddings, mse_posture, mse_voice, title):
+    plt.figure(figsize=(16, 8), dpi=300)
+    fig, ax = plt.subplots(figsize=(16, 8))
+
+    if 'Seconds' not in df.columns:
+        df['Seconds'] = df['Timecode'].apply(
+            lambda x: sum(float(t) * 60 ** i for i, t in enumerate(reversed(x.split(':')))))
+
+    # Normalize MSE values to the same scale
+    mse_embeddings = (mse_embeddings - np.min(mse_embeddings)) / (np.max(mse_embeddings) - np.min(mse_embeddings))
+    mse_posture = (mse_posture - np.min(mse_posture)) / (np.max(mse_posture) - np.min(mse_posture))
+    mse_voice = (mse_voice - np.min(mse_voice)) / (np.max(mse_voice) - np.min(mse_voice))
+
+    ax.plot(df['Seconds'], mse_embeddings, color='navy', label='Facial Features')
+    ax.plot(df['Seconds'], mse_posture, color='purple', label='Body Posture')
+    ax.plot(df['Seconds'], mse_voice, color='green', label='Voice')
+
+    ax.set_xlabel('Timecode')
+    ax.set_ylabel('Normalized Mean Squared Error')
+    ax.set_title(title)
+
+    ax.grid(True, linestyle='--', alpha=0.7)
+    ax.legend()
+    plt.tight_layout()
+    plt.close()
+    return fig
+
 def plot_mse_histogram(mse_values, title, anomaly_threshold, color='blue'):
     plt.figure(figsize=(16, 3), dpi=300)
     fig, ax = plt.subplots(figsize=(16, 3))