Update visualization.py

visualization.py

@@ -72,8 +72,8 @@ def plot_mse(df, mse_values, title, color='navy', time_threshold=3, anomaly_thre
     ax.axhline(y=median, color='black', linestyle='--', label='Median Baseline')
 
     threshold = np.mean(mse_values) + anomaly_threshold * np.std(mse_values)
-    ax.axhline(y=threshold, color='red', linestyle='--', label=f'Threshold: {anomaly_threshold:.1f}')
-    ax.text(ax.get_xlim()[1], threshold, f'Threshold: {anomaly_threshold:.1f}', verticalalignment='center', horizontalalignment='left', color='red')
+    ax.axhline(y=threshold, color='red', linestyle='--', label=f'Anomaly Threshold')
+    ax.text(ax.get_xlim()[1], threshold, f'Anomaly Threshold', verticalalignment='center', horizontalalignment='left', color='red')
 
     anomalies = determine_anomalies(mse_values, anomaly_threshold)
     anomaly_frames = df['Frame'].iloc[anomalies].tolist()
@@ -214,7 +214,7 @@ def plot_mse_histogram(mse_values, title, anomaly_threshold, color='blue'):
 
     ax.hist(mse_values, bins=100, edgecolor='black', color=color, alpha=0.7)
     ax.set_xlabel('Mean Squared Error')
-    ax.set_ylabel('N')
+    ax.set_ylabel('Number of Frames')
     ax.set_title(title)
 
     mean = np.mean(mse_values)