Create analyze_video

analyze_video

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+def analyze_video(video_path):
+    frames = extract_frames(video_path)
+
+    ball_positions = []
+    impact_frame_idx = None
+    impact_zone = "unknown"
+
+    for i, frame in enumerate(frames):
+        mask = detect_objects_with_model(frame)
+
+        # Very simple segmentation logic
+        ball_mask = mask[0] > 0.5  # channel 0 for ball
+        pad_mask = mask[1] > 0.5 if mask.ndim > 2 else None  # channel 1 for pad
+
+        # Detect ball center
+        contours, _ = cv2.findContours(ball_mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if contours:
+            largest = max(contours, key=cv2.contourArea)
+            M = cv2.moments(largest)
+            if M['m00'] != 0:
+                cx = int(M['m10']/M['m00'])
+                cy = int(M['m01']/M['m00'])
+                ball_positions.append((i, cx, cy))
+
+        # Detect pad hit (optional logic: ball near pad area)
+        if pad_mask is not None and contours:
+            overlap = np.logical_and(ball_mask, pad_mask).sum()
+            if overlap > 10:  # simple overlap threshold
+                impact_frame_idx = i
+                impact_zone = "pad"
+                break
+
+    # Run trajectory prediction if ball was detected
+    trajectory = predict_trajectory(ball_positions)
+
+    # Predict outcome
+    decision = "OUT" if trajectory_hits_stumps(trajectory) and impact_zone == "pad" else "NOT OUT"
+
+    # Visualize
+    result_path = draw_visuals(frames, ball_positions, trajectory, impact_frame_idx, decision)
+
+    return result_path, decision
+
+
+def trajectory_hits_stumps(trajectory):
+    # Simple rule-based check (assuming stumps are around x=300 to 340 px for now)
+    for (x, y) in trajectory:
+        if 300 < x < 340 and y < 480:  # ball projected height intersects stump zone
+            return True
+    return False