Function visualize_sample_radar

tools/render.py:109–205

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)

        plt.show()

def visualize_sample_radar(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)
    ref_sd_record = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
    cs_record = nusc.get('calibrated_sensor', ref_sd_record['calibrated_sensor_token'])
    pose_record = nusc.get('ego_pose', ref_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

    rad_types = [
        'RADAR_FRONT', 'RADAR_FRONT_LEFT', 'RADAR_FRONT_RIGHT',
        'RADAR_BACK_LEFT', 'RADAR_BACK_RIGHT'
    ]

    points = np.zeros((18,0))
    new_times = np.zeros((1,0))
       
    for token in rad_types: 

        sd_record = nusc.get('sample_data', sample_rec['data'][token])
        chan = sd_record['channel']

        pc, times = RadarPointCloud_v2.from_file_multisweep(nusc,
                        sample_rec, sample_rec, chan, 'LIDAR_TOP', nsweeps=nsweeps)

        points = np.concatenate([points, pc.points], axis=1)