Update video_processor.py

video_processor.py

@@ -1,27 +1,55 @@
 import cv2
-import os
-import tempfile
+import numpy as np
 from detector import LBWDetector
-from utils import draw_boxes
+from utils import draw_boxes, overlay_decision_text
 
 def process_video(video_path, output_path="output.mp4"):
     detector = LBWDetector()
     cap = cv2.VideoCapture(video_path)
 
-    width  = int(cap.get(3))
+    width = int(cap.get(3))
     height = int(cap.get(4))
     fps = cap.get(cv2.CAP_PROP_FPS)
 
     out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
 
+    impact_frame = None
+    impact_point = None
+    hit_stumps = False
+
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
+
         detections, class_names = detector.detect_objects(frame)
+        labels = [class_names[int(cls_id)] for *_, cls_id in detections]
+
+        # Draw overlays
         frame = draw_boxes(frame, detections, class_names)
+
+        # Detect impact frame
+        if 'pad' in labels and 'ball' in labels:
+            impact_frame = frame.copy()
+            # Assume impact point is ball's center in this frame
+            for x1, y1, x2, y2, conf, cls_id in detections:
+                if class_names[int(cls_id)] == 'ball':
+                    impact_point = ((x1 + x2) / 2, (y1 + y2) / 2)
+                    break
+
+        # Check if ball is later detected near stumps
+        if 'stumps' in labels and 'ball' in labels:
+            hit_stumps = True
+
         out.write(frame)
 
     cap.release()
+
+    # Append decision screen frame
+    decision_frame = np.zeros((height, width, 3), dtype=np.uint8)
+    decision_frame = overlay_decision_text(decision_frame, impact_point, hit_stumps, impact_frame is not None)
+    for _ in range(int(fps * 2)):  # show for 2 seconds
+        out.write(decision_frame)
+
     out.release()
     return output_path