I am trying to modify YOLO code for my lane detection project but it does not work as I expected.

I modified darknet_images.py to not draw the bounding box but rather use the coordinates to draw the center of each bounding boxes and line fit them by adding -lane_detection flag. But when I start the detection, it does not work and somehow just keep outputting the same results with the bounding boxes. I also have already tried to rebuilt it with CMAKE GUI and MSVC. Here is the modification that I made:

import utils

def parser():
    parser = argparse.ArgumentParser(description="YOLO Object Detection")
    parser.add_argument("--input", type=str, default="",
                        help="image source. It can be a single image, a"
                        "txt with paths to them, or a folder. Image valid"
                        " formats are jpg, jpeg or png."
                        "If no input is given, ")
    parser.add_argument("--batch_size", default=1, type=int,
                        help="number of images to be processed at the same time")
    parser.add_argument("--weights", default="yolov4.weights",
                        help="yolo weights path")
    parser.add_argument("--dont_show", action='store_true',
                        help="windown inference display. For headless systems")
    parser.add_argument("--ext_output", action='store_true',
                        help="display bbox coordinates of detected objects")
    parser.add_argument("--save_labels", action='store_true',
                        help="save detections bbox for each image in yolo format")
    parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
                        help="path to config file")
    parser.add_argument("--data_file", default="./cfg/coco.data",
                        help="path to data file")
    parser.add_argument("--thresh", type=float, default=.25,
                        help="remove detections with lower confidence")
    parser.add_argument("--lane_detection", help="activate lane detection utility")
    return parser.parse_args()

def image_detection(image_or_path, network, class_names, class_colors, thresh, is_lane_detection = False):
    # Darknet doesn't accept numpy images.
    # Create one with image we reuse for each detect
    width = darknet.network_width(network)
    height = darknet.network_height(network)
    darknet_image = darknet.make_image(width, height, 3)

    if isinstance(image_or_path, str):
        image = cv2.imread(image_or_path)
    else:
        image = image_or_path
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_resized = cv2.resize(image_rgb, (width, height),
                               interpolation=cv2.INTER_LINEAR)

    darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
    detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
    darknet.free_image(darknet_image)
    if not is_lane_detection:
        image = darknet.draw_boxes(detections, image_resized, class_colors)
        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
    return image_resized, detections

def main():
    args = parser()
    check_arguments_errors(args)

    random.seed(3)  # deterministic bbox colors
    network, class_names, class_colors = darknet.load_network(
        args.config_file,
        args.data_file,
        args.weights,
        batch_size=args.batch_size
    )

    images = load_images(args.input)

    index = 0
    while True:
        # loop asking for new image paths if no list is given
        if args.input:
            if index >= len(images):
                break
            image_name = images[index]
        else:
            image_name = input("Enter Image Path: ")
        prev_time = time.time()
        # Start lane detection if requested
        if args.lane_detection:
            image, detections = image_detection(
            image_name, network, class_names, class_colors, args.thresh, lane_detection=True
            )
            for label, confidence, bbox in detections:
                # Draw bounding box centers
                centers, img = utils.draw_centers(img, bbox, class_colors[label])
                # Run hough transform
                lines, img = utils.probabilistic_hough_transform(img, 10, 5, 100, 75, 105, 60)
        if not args.lane_detection:
            image, detections = image_detection(
            image_name, network, class_names, class_colors, args.thresh
            )
        if args.save_labels:
            save_annotations(image_name, image, detections, class_names)
        darknet.print_detections(detections, args.ext_output)
        fps = int(1/(time.time() - prev_time))
        print("FPS: {}".format(fps))
        if not args.dont_show:
            cv2.imshow('Inference', image)
            if cv2.waitKey() & 0xFF == ord('q'):
                break
        index += 1


if __name__ == "__main__":
    # unconmment next line for an example of batch processing
    # batch_detection_example()
    main()

Am I missing something or modifying the wrong file?