Create app.py

app.py

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+import streamlit as st
+import cv2
+import tempfile
+import numpy as np
+from collections import defaultdict
+
+# Import your existing functions here
+# from quadball_tracker import detect_balls, update_trackers, check_events, get_quadrant, COLOR_RANGES
+
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    
+    frame_count = 0
+    ball_trackers = defaultdict(lambda: defaultdict(dict))
+    events = []
+
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        frame_count += 1
+        
+        progress_bar.progress(frame_count / total_frames)
+        status_text.text(f"Processing frame {frame_count}/{total_frames}")
+        
+        try:
+            balls = detect_balls(frame)
+            update_trackers(ball_trackers, balls, frame_count)
+            new_events = check_events(ball_trackers, frame_count)
+            events.extend(new_events)
+        except Exception as e:
+            st.error(f"Error processing frame {frame_count}: {str(e)}")
+        
+    cap.release()
+    return events
+
+st.title('QuadBall Tracker 🔴🟢🔵🟡')
+
+st.write("""
+Welcome to QuadBall Tracker! This application uses computer vision to track colored balls 
+across different quadrants in a video. Upload your video to get started!
+""")
+
+uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "avi", "mov"])
+
+if uploaded_file is not None:
+    tfile = tempfile.NamedTemporaryFile(delete=False) 
+    tfile.write(uploaded_file.read())
+    
+    st.video(tfile.name)
+    
+    if st.button('Process Video'):
+        events = process_video(tfile.name)
+        
+        st.success('Video processing complete!')
+        
+        st.subheader('Event Log')
+        for event in events:
+            st.write(f"Time: {event['time']}, Quadrant: {event['quadrant']}, "
+                     f"Color: {event['color']}, Type: {event['type']}")
+        
+        # Create a DataFrame for easy filtering
+        import pandas as pd
+        df = pd.DataFrame(events)
+        
+        st.subheader('Filter Events')
+        color_filter = st.multiselect('Select colors', df['color'].unique())
+        quadrant_filter = st.multiselect('Select quadrants', df['quadrant'].unique())
+        
+        filtered_df = df
+        if color_filter:
+            filtered_df = filtered_df[filtered_df['color'].isin(color_filter)]
+        if quadrant_filter:
+            filtered_df = filtered_df[filtered_df['quadrant'].isin(quadrant_filter)]
+        
+        st.dataframe(filtered_df)
+
+st.sidebar.title('About')
+st.sidebar.info('This application uses computer vision to track colored balls across video quadrants. '
+                'Upload a video to see it in action!')