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app.py

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+# app.py
+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import supervision as sv
+from scipy.spatial import distance as dist
+import tempfile
+
+# Initialize components
+model = YOLO('yolov8s.pt')
+tracker = sv.ByteTrack()
+
+# Streamlit UI
+st.title("⚽️ Player Tracking System")
+uploaded_video = st.file_uploader("Upload match video", type=["mp4", "mov"])
+calibration_dist = st.number_input("Field width in meters (for speed calibration):", value=68.0)
+
+# Initialize session state
+if 'player_data' not in st.session_state:
+    st.session_state.player_data = {}
+
+if uploaded_video:
+    tfile = tempfile.NamedTemporaryFile(delete=False)
+    tfile.write(uploaded_video.read())
+    
+    cap = cv2.VideoCapture(tfile.name)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    pixels_per_meter = frame_width / calibration_dist if calibration_dist > 0 else 1.0
+    
+    st_frame = st.empty()
+    frame_count = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Detection + tracking
+        results = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections = tracker.update_with_detections(detections)
+
+        for detection in detections:
+            track_id = int(detection[4])
+            bbox = detection[0]
+            centroid = (int((bbox[0]+bbox[2])/2), int((bbox[1]+bbox[3])/2))
+            speed = 0.0  # Initialize speed for all cases
+
+            # Initialize new player
+            if track_id not in st.session_state.player_data:
+                st.session_state.player_data[track_id] = {
+                    'positions': [centroid],
+                    'timestamps': [frame_count/fps],
+                    'distance': 0.0,
+                    'speeds': []
+                }
+            else:
+                # Calculate movement metrics
+                prev_pos = st.session_state.player_data[track_id]['positions'][-1]
+                time_diff = (frame_count/fps) - st.session_state.player_data[track_id]['timestamps'][-1]
+                
+                pixel_dist = dist.euclidean(prev_pos, centroid)
+                speed_px = pixel_dist / time_diff if time_diff > 0 else 0.0
+                speed = speed_px * pixels_per_meter  # Convert to m/s
+                
+                # Update player record
+                st.session_state.player_data[track_id]['positions'].append(centroid)
+                st.session_state.player_data[track_id]['timestamps'].append(frame_count/fps)
+                st.session_state.player_data[track_id]['distance'] += pixel_dist
+                st.session_state.player_data[track_id]['speeds'].append(speed)
+
+            # Annotation with failsafe
+            label = f"ID:{track_id} Speed:{speed:.1f}m/s"
+            cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), 
+                         (int(bbox[2]), int(bbox[3])), (0,255,0), 2)
+            cv2.putText(frame, label, (int(bbox[0]), int(bbox[1]-10)),
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1)
+
+        st_frame.image(frame, channels="BGR")
+        frame_count += 1
+
+    cap.release()
+    
+    # Display final analytics
+    st.subheader("Player Statistics")
+    for player_id, data in st.session_state.player_data.items():
+        avg_speed = np.mean(data['speeds']) if data['speeds'] else 0
+        st.write(f"""
+        **Player {player_id}**  
+        - Total distance: {data['distance'] * pixels_per_meter:.2f}m  
+        - Avg speed: {avg_speed:.1f}m/s  
+        - Max speed: {np.max(data['speeds']):.1f}m/s  
+        - Active duration: {data['timestamps'][-1]-data['timestamps'][0]:.1f}s
+        """)

yolov8s.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f47a78bf100391c2a140b7ac73a1caae18c32779be7d310658112f7ac9aa78a
+size 22588772