| import streamlit as st
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| import cv2
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| import tempfile
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| import os
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| import pandas as pd
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| import numpy as np
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| import plotly.express as px
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| from main import process_video
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| from src.visualization import generate_density_plot, generate_direction_chart, overlay_heatmap
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|
|
|
|
| st.set_page_config(
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| page_title="AI Pedestrian Analytics",
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| page_icon="🚶♀️",
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| layout="wide",
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| initial_sidebar_state="expanded"
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| )
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|
|
|
|
| st.markdown("""
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| <style>
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| .main {
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| background-color: #0e1117;
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| }
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| .stMetric {
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| background-color: #1a1c24;
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| padding: 15px;
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| border-radius: 10px;
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| border: 1px solid #30363d;
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| }
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| .metric-container {
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| display: flex;
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| justify-content: space-between;
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| }
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| </style>
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| """, unsafe_allow_html=True)
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|
|
|
|
| st.sidebar.title("🚶♀️ Crowd Analysis Engine")
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| st.sidebar.markdown("---")
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| uploaded_file = st.sidebar.file_uploader("Upload Video (MP4)", type=["mp4", "mov", "avi"])
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| conf_threshold = st.sidebar.slider("Confidence Threshold", 0.1, 1.0, 0.25)
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| model_type = st.sidebar.selectbox("Model Size", ["yolov8n.pt", "yolov8m.pt", "yolov8l.pt"], index=1)
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|
|
|
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| st.title("🏙️ Shibuya Scramble Analytics")
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| st.markdown("Automated pedestrian tracking, dwell-time analysis, and crowd density visualization.")
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|
|
| if uploaded_file is not None:
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|
|
| tfile = tempfile.NamedTemporaryFile(delete=False)
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| tfile.write(uploaded_file.read())
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| video_path = tfile.name
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|
|
| col1, col2 = st.columns([2, 1])
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|
|
| with col1:
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| st.subheader("Source Video")
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| st.video(uploaded_file)
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|
|
| with col2:
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| st.subheader("Process Settings")
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| run_btn = st.button("🚀 Run Analysis", use_container_width=True)
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|
|
| if run_btn:
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| with st.status("Analyzing footage...", expanded=True) as status:
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| progress_bar = st.progress(0)
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|
|
| output_video_path = os.path.join(tempfile.gettempdir(), "annotated_output.mp4")
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|
|
| results = process_video(
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| video_path,
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| output_path=output_video_path,
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| model_path=model_type,
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| progress_callback=lambda p: progress_bar.progress(p)
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| )
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| status.update(label="Analysis Complete!", state="complete")
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|
|
|
|
| analytics = results['analytics']
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|
|
| st.markdown("---")
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| st.subheader("📊 Key Performance Metrics")
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|
|
| m_col1, m_col2, m_col3, m_col4 = st.columns(4)
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|
|
| dwell_times = analytics.calculate_dwell_times()
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| avg_dwell = np.mean(list(dwell_times.values())) if dwell_times else 0
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|
|
| m_col1.metric("Unique Pedestrians", analytics.get_unique_count())
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| m_col2.metric("Avg Dwell Time", f"{avg_dwell:.2f}s")
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| m_col3.metric("System Efficiency", f"{results['fps']:.1f} FPS")
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| m_col4.metric("Processing Time", f"{results['processing_time']:.1f}s")
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|
|
| tab1, tab2, tab3 = st.tabs(["🎥 Annotated Result", "🔥 Density Heatmap", "📈 Behavioral Insights"])
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|
|
| with tab1:
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| st.subheader("Object Tracking & IDs")
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|
|
|
|
| st.video(output_video_path)
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| st.info("Output video shows consistent track IDs and movement trails.")
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|
|
| with tab2:
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| st.subheader("Crowd Density Heatmap")
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|
|
| cap = cv2.VideoCapture(video_path)
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| ret, first_frame = cap.read()
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| cap.release()
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|
|
| if ret:
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| heatmap = analytics.get_heatmap()
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| overlay = overlay_heatmap(first_frame, heatmap)
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| st.image(cv2.cvtColor(overlay, cv2.COLOR_BGR2RGB), use_column_width=True)
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| else:
|
| st.warning("Could not generate heatmap overlay.")
|
|
|
| with tab3:
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| col_a, col_b = st.columns(2)
|
|
|
| with col_a:
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| st.plotly_chart(generate_density_plot(analytics.get_density_df()), use_container_width=True)
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|
|
| with col_b:
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| directions = analytics.get_direction_distribution()
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| st.plotly_chart(generate_direction_chart(directions), use_container_width=True)
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|
|
|
|
| st.markdown("---")
|
| st.subheader("💾 Export Data")
|
| json_data = {
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| "total_count": analytics.get_unique_count(),
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| "avg_dwell_time": avg_dwell,
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| "directions": directions,
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| "processing_metadata": {
|
| "fps": results['fps'],
|
| "frames": len(analytics.density_over_time)
|
| }
|
| }
|
| st.download_button(
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| label="Download JSON Report",
|
| data=str(json_data),
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| file_name="analytics_report.json",
|
| mime="application/json"
|
| )
|
|
|
| else:
|
|
|
| st.info("Please upload a video file to begin analysis.")
|
| st.image("https://images.unsplash.com/photo-1542011681-9988549d2c5d?q=80&w=2000&auto=format&fit=crop",
|
| caption="Crowded Crossing Analysis Ready")
|
|
|
