Updated app.py for Football Player Tracker

app.py

@@ -3,22 +3,31 @@ import gradio as gr
 import tempfile
 from ultralytics import YOLO
 from deep_sort_realtime.deepsort_tracker import DeepSort
+import os
 
-model = YOLO('yolov8n.pt')
+# Load model and tracker
+model = YOLO("yolov8n.pt")
 tracker = DeepSort(max_age=30)
 
 def track_players(video):
-    import os
-    import shutil
+    # Create temp output path
+    temp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    output_path = temp_out.name
 
-    cap = cv2.VideoCapture(video.name)
-    width  = int(cap.get(3))
-    height = int(cap.get(4))
-    fps = cap.get(cv2.CAP_PROP_FPS)
+    cap = cv2.VideoCapture(video)
+    if not cap.isOpened():
+        raise IOError("Video file could not be opened.")
 
-    os.makedirs("outputs", exist_ok=True)
-    output_path = os.path.join("outputs", "tracked_output.mp4")
-    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+
+    out = cv2.VideoWriter(
+        output_path,
+        cv2.VideoWriter_fourcc(*"avc1"),  # 'avc1' for Linux compatibility
+        fps,
+        (width, height)
+    )
 
     while True:
         ret, frame = cap.read()
@@ -48,15 +57,13 @@ def track_players(video):
 
     cap.release()
     out.release()
-
     return output_path
 
-
 demo = gr.Interface(
     fn=track_players,
     inputs=gr.Video(label="Upload Match Video"),
     outputs=gr.Video(label="Tracked Output"),
-    title="Football Player Tracker",
+    title="🏟️ Football Player Tracker",
     description="Upload a match video and get players tracked with consistent IDs."
 )
 

main.py

@@ -1,50 +1,42 @@
 import cv2
 from ultralytics import YOLO
-import numpy as np
 from deep_sort_realtime.deepsort_tracker import DeepSort
 import os
 
-# Load YOLOv8 model (download automatically if not present)
-model_path = 'yolov8n.pt'
-model = YOLO(model_path)
-
-# Initialize DeepSORT Tracker
+# Load YOLOv8 model
+model = YOLO('yolov8n.pt')
 tracker = DeepSort(max_age=30)
 
-# Check if video exists
+# Local video file
 video_path = r'C:\Users\alyhi\Desktop\Football Player Tracker\video\vid.mp4'
 if not os.path.exists(video_path):
-    raise FileNotFoundError(f"Video file not found at {video_path}")
+    raise FileNotFoundError(f"Video file not found: {video_path}")
 
-# Load video
 cap = cv2.VideoCapture(video_path)
 if not cap.isOpened():
-    raise IOError(f"Failed to open video file: {video_path}")
+    raise IOError(f"Could not open video: {video_path}")
 
 while True:
     ret, frame = cap.read()
     if not ret:
         break
 
-    # YOLOv8 detection
     results = model(frame)[0]
     detections = []
 
     for result in results.boxes.data.tolist():
         x1, y1, x2, y2, score, class_id = result
-        if int(class_id) == 0 and score > 0.4:  # Class 0 = person
-            bbox = [x1, y1, x2 - x1, y2 - y1]  # [x, y, w, h]
+        if int(class_id) == 0 and score > 0.4:
+            bbox = [x1, y1, x2 - x1, y2 - y1]
             detections.append((bbox, score, 'player'))
 
-    # DeepSORT tracking
     tracks = tracker.update_tracks(detections, frame=frame)
 
     for track in tracks:
         if not track.is_confirmed():
             continue
-
         track_id = track.track_id
-        l, t, r, b = track.to_ltrb()  # Use proper unpacking
+        l, t, r, b = track.to_ltrb()
         cv2.rectangle(frame, (int(l), int(t)), (int(r), int(b)), (0, 255, 0), 2)
         cv2.putText(frame, f'Player {track_id}', (int(l), int(t - 10)),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)