Bin-ze
commented
7 months ago 您好，我想关于BEV图请教一下您：
1、关于在最终生成BEV图中如何给不一样的类别附上不一样的颜色box呢，例如，在BEV图上，给行人用蓝色box表示，给car用红色的box表示，给bus用绿色的box表示呢？是在下面的visual_det_seg.py代码中这个位置进行修改吗？应该如何修改呢？
def visualize_sample(nusc: NuScenes, sample_token: str, gt_boxes: EvalBoxes, pred_boxes: EvalBoxes, conf_th: float = 0.30,) -> 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. """
# seg map seg_map = padding_seg_to_det(os.path.join(pred_seg_path, sample_token + '.png')) seg_map = np.ascontiguousarray(seg_map, dtype=np.uint8) # 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) #c = get_color(box.name) # 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 # Show GT boxes. for box in boxes_gt: #c = get_color(box.name) view = np.array([[seg_map.shape[0] // (det_grid_conf['xbound'][1] * 2), 0, 0, seg_map.shape[0] / 2], [0, -seg_map.shape[0] // (det_grid_conf['xbound'][1] * 2), 0, seg_map.shape[0] / 2], [0, 0, 1, 0], [0, 0, 0, 1]]) box.render_cv2(seg_map, view=view, colors=((0, 0, 255), (0, 0, 255), (0, 0, 255)), linewidth=2) # Show EST boxes. for box in boxes_est: # Show only predictions with a high score. assert not np.isnan(box.score), 'Error: Box score cannot be NaN!' if box.score >= conf_th: view = np.array([[seg_map.shape[0] // (det_grid_conf['xbound'][1] * 2), 0, 0, seg_map.shape[0] / 2], [0, -seg_map.shape[0] // (det_grid_conf['xbound'][1] * 2), 0, seg_map.shape[0] / 2], [0, 0, 1, 0], [0, 0, 0, 1]]) box.render_cv2(seg_map, view=view, normalize=False, colors=((255, 0, 0), (255, 0, 0), (255, 0, 0)), linewidth=1) return seg_map3、为什么最终得到的BEV图不是连续帧呢？使用MINI数据集才有几十张BEV的图
非常的感谢您们给予的回复，万分感谢您的指导！
分隔图颜色在test.py中修改https://github.com/Bin-ze/BEVFormer_segmentation_detection/blob/master/projects/mmdet3d_plugin/apis/test.py#L22
可能与读取方式有关，我使用完整的nuscene数据集生成的帧是连续的，就像主页展示的那样
MQC1111
您好，我想关于BEV图请教一下您：
1、关于在最终生成BEV图中如何给不一样的类别附上不一样的颜色box呢，例如，在BEV图上，给行人用蓝色box表示，给car用红色的box表示，给bus用绿色的box表示呢？是在下面的visual_det_seg.py代码中这个位置进行修改吗？应该如何修改呢？
def visualize_sample(nusc: NuScenes, sample_token: str, gt_boxes: EvalBoxes, pred_boxes: EvalBoxes, conf_th: float = 0.30,) -> 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. """
3、为什么最终得到的BEV图不是连续帧呢？使用MINI数据集才有几十张BEV的图
非常的感谢您们给予的回复，万分感谢您的指导！