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| with gr.Blocks() as iface: | |
| gr.Markdown(""" | |
| # Multimodal Behavioral Anomalies Detection | |
| This tool detects anomalies in facial expressions, body language, and voice over the timeline of a video. | |
| It extracts faces, postures, and voice from video frames, and analyzes them to identify anomalies using time series analysis and a variational autoencoder (VAE) approach. | |
| """) | |
| with gr.Row(): | |
| video_input = gr.Video() | |
| anomaly_threshold = gr.Slider(minimum=1, maximum=5, step=0.1, value=3, label="Anomaly Detection Threshold (Standard deviation)") | |
| fps_slider = gr.Slider(minimum=5, maximum=20, step=1, value=10, label="Frames Per Second (FPS)") | |
| process_btn = gr.Button("Detect Anomalies") | |
| progress_bar = gr.Progress() | |
| execution_time = gr.Number(label="Execution Time (seconds)") | |
| with gr.Group(visible=False) as results_group: | |
| results_text = gr.TextArea(label="Anomaly Detection Results", lines=4) | |
| with gr.Tabs(): | |
| with gr.TabItem("Facial Features"): | |
| mse_features_plot = gr.Plot(label="MSE: Facial Features") | |
| mse_features_hist = gr.Plot(label="MSE Distribution: Facial Features") | |
| mse_features_heatmap = gr.Plot(label="MSE Heatmap: Facial Features") | |
| anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2, height="auto") | |
| face_samples_most_frequent = gr.Gallery(label="Most Frequent Person Samples", columns=10, rows=2, height="auto") | |
| with gr.TabItem("Body Posture"): | |
| mse_posture_plot = gr.Plot(label="MSE: Body Posture") | |
| mse_posture_hist = gr.Plot(label="MSE Distribution: Body Posture") | |
| mse_posture_heatmap = gr.Plot(label="MSE Heatmap: Body Posture") | |
| anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2, height="auto") | |
| with gr.TabItem("Voice"): | |
| mse_voice_plot = gr.Plot(label="MSE: Voice") | |
| mse_voice_hist = gr.Plot(label="MSE Distribution: Voice") | |
| mse_voice_heatmap = gr.Plot(label="MSE Heatmap: Voice") | |
| with gr.TabItem("Video with Heatmap"): | |
| heatmap_video = gr.Video(label="Video with Anomaly Heatmap") | |
| df_store = gr.State() | |
| mse_features_store = gr.State() | |
| mse_posture_store = gr.State() | |
| mse_voice_store = gr.State() | |
| aligned_faces_folder_store = gr.State() | |
| frames_folder_store = gr.State() | |
| mse_heatmap_embeddings_store = gr.State() | |
| mse_heatmap_posture_store = gr.State() | |
| mse_heatmap_voice_store = gr.State() | |
| def show_results(outputs): | |
| return gr.Group(visible=True) | |
| process_btn.click( | |
| process_and_show_completion, | |
| inputs=[video_input, anomaly_threshold, fps_slider], | |
| outputs=[ | |
| execution_time, results_text, df_store, | |
| mse_features_store, mse_posture_store, mse_voice_store, | |
| mse_features_plot, mse_posture_plot, mse_voice_plot, | |
| mse_features_hist, mse_posture_hist, mse_voice_hist, | |
| mse_features_heatmap, mse_posture_heatmap, mse_voice_heatmap, | |
| anomaly_frames_features, anomaly_frames_posture, | |
| face_samples_most_frequent, | |
| aligned_faces_folder_store, frames_folder_store, | |
| mse_heatmap_embeddings_store, mse_heatmap_posture_store, mse_heatmap_voice_store, | |
| heatmap_video | |
| ] | |
| ).then( | |
| show_results, | |
| inputs=None, | |
| outputs=results_group | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |