Update visualization.py

visualization.py

@@ -9,6 +9,7 @@ import cv2
 from matplotlib.patches import Rectangle
 from utils import seconds_to_timecode
 from anomaly_detection import determine_anomalies
+from scipy import interpolate
 import gradio as gr
 
 def plot_mse(df, mse_values, title, color='navy', time_threshold=3, anomaly_threshold=4):
@@ -209,7 +210,18 @@ def plot_posture(df, posture_scores, color='blue', anomaly_threshold=3):
 
 def create_video_with_heatmap(video_path, df, mse_embeddings, mse_posture, mse_voice, output_path, desired_fps, largest_cluster):
     # Filter the DataFrame to only include frames from the largest cluster
-    df_largest_cluster = df[df['Cluster'] == largest_cluster]
+        df_largest_cluster = df[df['Cluster'] == largest_cluster]
+    
+    # Interpolate mse_voice to match the length of df
+    x_voice = np.linspace(0, len(mse_voice)-1, len(mse_voice))
+    x_new = np.linspace(0, len(mse_voice)-1, len(df))
+    f = interpolate.interp1d(x_voice, mse_voice)
+    mse_voice_interpolated = f(x_new)
+    
+    mse_embeddings = mse_embeddings[df['Cluster'] == largest_cluster]
+    mse_posture = mse_posture[df['Cluster'] == largest_cluster]
+    mse_voice = mse_voice_interpolated[df['Cluster'] == largest_cluster]
+
     mse_embeddings = mse_embeddings[df['Cluster'] == largest_cluster]
     mse_posture = mse_posture[df['Cluster'] == largest_cluster]
     mse_voice = mse_voice[df['Cluster'] == largest_cluster]