Delete analyze_video

analyze_video

@@ -1,50 +0,0 @@
-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