reab5555 commited on
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9ad3ed2
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1 Parent(s): cd6c09b

Update video_processing.py

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  1. video_processing.py +17 -31
video_processing.py CHANGED
@@ -167,41 +167,27 @@ def process_video(video_path, anomaly_threshold, desired_fps, progress=None):
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  video.export(audio_path, format="wav")
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  # Process audio
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- most_frequent_voice, voice_features, voice_clusters = process_audio(audio_path)
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  if len(voice_features) < 2:
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  print("Not enough voice segments for processing. Skipping voice analysis.")
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- raise ValueError("Insufficient voice data")
 
 
 
 
 
 
 
 
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- # Perform anomaly detection on voice
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- X_voice = np.array(most_frequent_voice)
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- mse_voice = anomaly_detection(X_voice, X_voice)
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-
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- progress(0.95, "Generating plots")
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-
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- # Generate plots for facial features
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- mse_plot_embeddings, anomaly_frames_embeddings = plot_mse(df, mse_embeddings, "Facial Features",
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- color=GRAPH_COLORS['facial_embeddings'],
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- anomaly_threshold=anomaly_threshold)
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- mse_histogram_embeddings = plot_mse_histogram(mse_embeddings, "MSE Distribution: Facial Features",
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- anomaly_threshold, color=GRAPH_COLORS['facial_embeddings'])
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- mse_heatmap_embeddings = plot_mse_heatmap(mse_embeddings, "Facial Features MSE Heatmap", df)
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-
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- # Generate plots for body posture
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- mse_plot_posture, anomaly_frames_posture = plot_mse(df, mse_posture, "Body Posture",
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- color=GRAPH_COLORS['body_posture'],
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- anomaly_threshold=anomaly_threshold)
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- mse_histogram_posture = plot_mse_histogram(mse_posture, "MSE Distribution: Body Posture",
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- anomaly_threshold, color=GRAPH_COLORS['body_posture'])
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- mse_heatmap_posture = plot_mse_heatmap(mse_posture, "Body Posture MSE Heatmap", df)
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-
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- # Generate plots for voice
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- mse_plot_voice, anomaly_segments_voice = plot_mse(df, mse_voice, "Voice",
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- color='green',
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- anomaly_threshold=anomaly_threshold)
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- mse_histogram_voice = plot_mse_histogram(mse_voice, "MSE Distribution: Voice",
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- anomaly_threshold, color='green')
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- mse_heatmap_voice = plot_mse_heatmap(mse_voice, "Voice MSE Heatmap", df)
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  except Exception as e:
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  print(f"Error details: {str(e)}")
 
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  video.export(audio_path, format="wav")
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  # Process audio
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+ most_frequent_voice, voice_features, voice_clusters = process_audio(audio_path, desired_fps, video_duration)
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  if len(voice_features) < 2:
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  print("Not enough voice segments for processing. Skipping voice analysis.")
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+ mse_voice = None
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+ mse_plot_voice = None
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+ mse_histogram_voice = None
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+ mse_heatmap_voice = None
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+ anomaly_segments_voice = None
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+ else:
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+ # Perform anomaly detection on voice
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+ X_voice = np.array(most_frequent_voice)
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+ mse_voice = anomaly_detection(X_voice, X_voice)
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+ # Generate plots for voice
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+ mse_plot_voice, anomaly_segments_voice = plot_mse(df, mse_voice, "Voice",
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+ color='green',
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+ anomaly_threshold=anomaly_threshold)
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+ mse_histogram_voice = plot_mse_histogram(mse_voice, "MSE Distribution: Voice",
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+ anomaly_threshold, color='green')
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+ mse_heatmap_voice = plot_mse_heatmap(mse_voice, "Voice MSE Heatmap", df)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  except Exception as e:
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  print(f"Error details: {str(e)}")