Function visualize_sample

tools/render.py:27–107

Visualizes a sample from BEV with annotations and detection results. :param nusc: NuScenes object. :param sample_token: The nuScenes sample token. :param gt_boxes: Ground truth boxes grouped by sample. :param pred_boxes: Prediction grouped by sample. :param nsweeps: Number o

(nusc: NuScenes,
                     sample_token: str,
                     gt_boxes: EvalBoxes,
                     pred_boxes: EvalBoxes,
                     nsweeps: int = 1,
                     conf_th: float = 0.15,
                     eval_range: float = 50,
                     verbose: bool = True,
                     savepath: str = None)

def visualize_sample(nusc: NuScenes,
                     sample_token: str,
                     gt_boxes: EvalBoxes,
                     pred_boxes: EvalBoxes,
                     nsweeps: int = 1,
                     conf_th: float = 0.15,
                     eval_range: float = 50,
                     verbose: bool = True,
                     savepath: str = None) -> None:
    """
    Visualizes a sample from BEV with annotations and detection results.
    :param nusc: NuScenes object.
    :param sample_token: The nuScenes sample token.
    :param gt_boxes: Ground truth boxes grouped by sample.
    :param pred_boxes: Prediction grouped by sample.
    :param nsweeps: Number of sweeps used for lidar visualization.
    :param conf_th: The confidence threshold used to filter negatives.
    :param eval_range: Range in meters beyond which boxes are ignored.
    :param verbose: Whether to print to stdout.
    :param savepath: If given, saves the the rendering here instead of displaying.
    """
    # Retrieve sensor & pose records.
    sample_rec = nusc.get('sample', sample_token)
    sd_record = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
    cs_record = nusc.get('calibrated_sensor', sd_record['calibrated_sensor_token'])
    pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])

    # Get boxes.
    boxes_gt_global = gt_boxes[sample_token]
    boxes_est_global = pred_boxes[sample_token]

    # Map GT boxes to lidar.
    boxes_gt = boxes_to_sensor(boxes_gt_global, pose_record, cs_record)

    # Map EST boxes to lidar.
    boxes_est = boxes_to_sensor(boxes_est_global, pose_record, cs_record)

    # Add scores to EST boxes.
    for box_est, box_est_global in zip(boxes_est, boxes_est_global):
        box_est.score = box_est_global.detection_score

    # Get point cloud in lidar frame.
    pc, _ = LidarPointCloud.from_file_multisweep(nusc, sample_rec, 'LIDAR_TOP', 'LIDAR_TOP', nsweeps=nsweeps)

    # Init axes.
    _, ax = plt.subplots(1, 1, figsize=(9, 9))

    # Show point cloud.
    points = view_points(pc.points[:3, :], np.eye(4), normalize=False)
    dists = np.sqrt(np.sum(pc.points[:2, :] ** 2, axis=0))
    colors = np.minimum(1, dists / eval_range)
    ax.scatter(points[0, :], points[1, :], c=colors, s=0.2)

    # Show ego vehicle.
    ax.plot(0, 0, 'x', color='black')

    # Show GT boxes.
    for box in boxes_gt: