nuscenes 点云和框 画效果图
时间: 2023-10-09 11:05:03 浏览: 48
可以使用Python库matplotlib或者OpenCV来进行点云和框的可视化。其中,matplotlib适合绘制简单的二维图形,OpenCV则可绘制更加复杂的三维图形。你可以根据具体需求进行选择。不过,如果你希望绘制更加生动形象的效果图,推荐使用Unity或Unreal Engine等游戏引擎。
相关问题
nuscenes 点云 框 显示
您可以使用nuscenes库中的代码来显示nuscenes点云框。以下是一些示例代码:
1. 在nuscenes可视化工具中显示点云框
```python
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.data_classes import LidarPointCloud
from nuscenes.utils.geometry_utils import BoxVisibility, transform_matrix
nusc = NuScenes(version='v1.0-mini', dataroot='/data/nuscenes', verbose=True)
# Get a lidar point cloud and transform it to the ego vehicle frame.
sample_rec = nusc.sample[0]
lidar_token = sample_rec['data']['LIDAR_TOP']
lidar_data = nusc.get('sample_data', lidar_token)
pc = LidarPointCloud.from_file(nusc.get_sample_data_path(lidar_token))
ego_pose = nusc.get('ego_pose', lidar_data['ego_pose_token'])
calib = nusc.get('calibrated_sensor', lidar_data['calibrated_sensor_token'])
lidar_points = pc.points[:3, :] # Take the first 3 rows because the fourth row is reflectance.
# Transform the point cloud to the ego vehicle frame.
lidar_points = transform_matrix(ego_pose['translation'], ego_pose['rotation'], inverse=False) @ lidar_points
lidar_points = transform_matrix(calib['translation'], calib['rotation'], inverse=False) @ lidar_points
# Get the annotations for the first sample.
annotations = nusc.get('sample_annotation', sample_rec['anns'])
# Set up the visualization.
vis = nuscenes.utils.visualizations.NuscenesVisualizer(nusc=nusc)
# Loop over the annotations and add them to the visualization.
for i, ann in enumerate(annotations):
# Only show boxes that are visible from the lidar point cloud.
if not BoxVisibility.isVisible(ann['bbox'], lidar_points):
continue
# Get the box corners and transform them to the ego vehicle frame.
corners = nuscenes.utils.geometry_utils.get_3d_box(ann, nusc)
corners = transform_matrix(ego_pose['translation'], ego_pose['rotation'], inverse=False) @ corners
corners = transform_matrix(calib['translation'], calib['rotation'], inverse=False) @ corners
# Add the box to the visualization.
vis.add_box(corners, label=ann['category_name'], color='red', alpha=0.5)
# Render the visualization.
vis.render()
```
2. 使用matplotlib库显示点云框
```python
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
# Get the annotations for the first sample.
annotations = nusc.get('sample_annotation', sample_rec['anns'])
# Set up the plot.
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim3d(-50, 50)
ax.set_ylim3d(-50, 50)
ax.set_zlim3d(-5, 5)
# Loop over the annotations and add them to the plot.
for i, ann in enumerate(annotations):
# Only show boxes that are visible from the lidar point cloud.
if not BoxVisibility.isVisible(ann['bbox'], lidar_points):
continue
# Get the box corners and transform them to the ego vehicle frame.
corners = nuscenes.utils.geometry_utils.get_3d_box(ann, nusc)
corners = transform_matrix(ego_pose['translation'], ego_pose['rotation'], inverse=False) @ corners
corners = transform_matrix(calib['translation'], calib['rotation'], inverse=False) @ corners
# Add the box to the plot.
facecolor = 'red'
edgecolor = 'black'
alpha = 0.5
collection = Poly3DCollection([corners.T[:4], corners.T[4:8], [corners.T[0], corners.T[4], corners.T[5], corners.T[1]], [corners.T[1], corners.T[5], corners.T[6], corners.T[2]], [corners.T[2], corners.T[6], corners.T[7], corners.T[3]], [corners.T[3], corners.T[7], corners.T[4], corners.T[0]]], alpha=alpha, facecolor=facecolor, edgecolor=edgecolor)
ax.add_collection(collection)
# Plot the lidar points.
ax.scatter(lidar_points[0], lidar_points[1], lidar_points[2], s=0.1)
# Show the plot.
plt.show()
```
这些示例代码可以帮助您显示nuscenes点云框。
nuscenes 点云 显示
Nuscenes是一个用于自动驾驶研究的数据集,其中包含了大量的点云数据。在显示nuscenes点云时,需要使用相应的点云可视化工具,如PCL(Point Cloud Library)或者ROS(Robot Operating System)的rviz。
使用PCL显示nuscenes点云的步骤如下:
1. 安装PCL库和nuscenes-devkit
```
sudo apt-get install libpcl-dev
pip install nuscenes-devkit
```
2. 使用nuscenes-devkit中的方法读取nuscenes数据集中的点云数据,并转换为PCL点云数据类型
```python
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.data_classes import LidarPointCloud
nusc = NuScenes(version='v1.0-mini', dataroot='/path/to/nuscenes', verbose=True)
sample_data_token = 'xxxxxx' # sample_data的token
sample_data = nusc.get('sample_data', sample_data_token)
pcd_path = sample_data['filename']
pcd = LidarPointCloud.from_file(pcd_path)
pcl_pcd = pcd.points.T.astype(np.float32)
```
3. 创建PCL点云对象,并将转换后的点云数据添加到该对象中
```python
import pcl
pcl_pcd = pcl.PointCloud(pcl_pcd)
```
4. 可以使用pcl_visualization库中的可视化工具,如PCLVisualizer来显示点云
```python
from pclpy import pcl_visualization
viewer = pcl_visualization.PCLVisualizering("viewer")
viewer.AddPointCloud(pcl_pcd, b"cloud")
viewer.SpinOnce()
```
在ROS中显示nuscenes点云的步骤如下:
1. 安装ros-kinetic-perception-pcl和nuscenes-devkit
```
sudo apt-get install ros-kinetic-perception-pcl
pip install nuscenes-devkit
```
2. 使用nuscenes-devkit中的方法读取nuscenes数据集中的点云数据,并转换为ROS点云数据类型
```python
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.data_classes import LidarPointCloud
from sensor_msgs.msg import PointCloud2, PointField
from std_msgs.msg import Header
import numpy as np
nusc = NuScenes(version='v1.0-mini', dataroot='/path/to/nuscenes', verbose=True)
sample_data_token = 'xxxxxx' # sample_data的token
sample_data = nusc.get('sample_data', sample_data_token)
pcd_path = sample_data['filename']
pcd = LidarPointCloud.from_file(pcd_path)
ros_pcd = PointCloud2()
header = Header()
header.frame_id = 'lidar'
header.stamp = rospy.Time.now()
ros_pcd.header = header
ros_pcd.height = 1
ros_pcd.width = pcd.points.shape[1]
ros_pcd.is_dense = True
ros_pcd.is_bigendian = False
fields = []
fields.append(PointField('x', 0, PointField.FLOAT32, 1))
fields.append(PointField('y', 4, PointField.FLOAT32, 1))
fields.append(PointField('z', 8, PointField.FLOAT32, 1))
ros_pcd.fields = fields
ros_pcd.point_step = 12
ros_pcd.row_step = ros_pcd.point_step * ros_pcd.width
ros_pcd.data = np.array(pcd.points.T, dtype=np.float32).tostring()
```
3. 发布ROS点云消息
```python
import rospy
from sensor_msgs.msg import PointCloud2
from std_msgs.msg import Header
rospy.init_node('nuscenes_pointcloud_publisher', anonymous=True)
pcd_pub = rospy.Publisher('nuscenes_pointcloud', PointCloud2, queue_size=1)
while not rospy.is_shutdown():
header = Header()
header.frame_id = 'lidar'
header.stamp = rospy.Time.now()
ros_pcd.header = header
pcd_pub.publish(ros_pcd)
rospy.sleep(0.1)
```
4. 可以使用rviz来显示点云消息。在rviz中添加PointCloud2类型的显示器,并将topic设置为发布的topic即可显示点云。
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