import cv2
import numpy as np
from detector import LBWDetector
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))
    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