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import gradio as gr
import time
from video_processing import process_video
from PIL import Image
import matplotlib
matplotlib.rcParams['figure.dpi'] = 300
matplotlib.rcParams['savefig.dpi'] = 300
def process_and_show_completion(video_input_path, anomaly_threshold_input, fps, progress=gr.Progress()):
# ... (keep the existing function code)
def show_results(outputs):
return [gr.Tab.update(visible=True) for _ in range(4)] + [gr.Tab.update(visible=False)], gr.Group(visible=True)
def hide_description_show_results():
return [gr.Tab.update(visible=False)] + [gr.Tab.update(visible=True) for _ in range(4)]
with gr.Blocks() as iface:
with gr.Row():
video_input = gr.Video(label="Input Video", visible=False)
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_group = gr.Group(visible=False)
with execution_time_group:
execution_time = gr.Number(label="Execution Time (seconds)")
with gr.Tabs() as all_tabs:
with gr.Tab("Description", visible=True):
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.Tab("Facial Features", visible=False):
results_text = gr.TextArea(label="Faces Breakdown", lines=5)
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.Tab("Body Posture", visible=False):
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.Tab("Voice", visible=False):
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.Tab("Combined", visible=False):
heatmap_video = gr.Video(label="Video with Anomaly Heatmap")
combined_mse_plot = gr.Plot(label="Combined MSE Plot")
correlation_heatmap_plot = gr.Plot(label="Correlation 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()
process_btn.click(
hide_description_show_results,
inputs=None,
outputs=all_tabs.children
).then(
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, combined_mse_plot, correlation_heatmap_plot
]
).then(
show_results,
inputs=None,
outputs=[all_tabs.children, execution_time_group]
)
if __name__ == "__main__":
iface.launch() |