Waymo Open Dataset (WOD) 可视化程序

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Waymo Open Dataset (WOD) 可视化程序

Waymo Open Dataset（WOD）可视化程序

• 1 运行环境
• • 1.1 安装环境
• 2 代码
• 3 注意事项

1 运行环境

1.1 安装环境

其他环境在此不再赘述，之将两个库的安装

1. 安装Mayavi

pip3 install mayavi -i 

2. 安装pyqt5

pip3 install pyqt5 -i 

不安装会报错，详情请参考这里

ImportError: Could not import backend for traitsui.  Make sure youhave a suitable UI toolkit like PyQt/PySide or wxPythoninstalled.

2 代码

代码来源酱油与清洛 ，笔者做一些补充，使用时直接复制进python脚本程序，然后将FILENAME后面的路径改为你自己的文件存放路径，python3 {程序名}.py运行即可。

# 作者：酱油与清洛  出处：bilibili
# 修改：Leoimport os
import tensorflowpat.v1 as tf
import math
import numpy as np
import itertools
import timetf.enable_eager_execution()from waymo_open_dataset.utils import range_image_utils
from waymo_open_dataset.utils import transform_utils
from waymo_open_dataset.utils import frame_utils
from waymo_open_dataset import dataset_pb2 as open_datasetfrom mayavi import mlab# 配置GPU显存自适应分配，防止占用所有显存
# set GPU memory to adaptively be allocated avoid all the memory being occupied
gpus = tf.config.list_physical_devices('GPU')
if gpus:try:for gpu in gpus:tf.config.experimental.set_memory_growth(gpu, True)print('info: following GPU is setted as memory_growth:')print(gpu)except RuntimeError as e:print(e)
else:print('info: there have no GPUs in this computer')@mlab.animate(delay=100)
def updateAnimation():framenumber = 0FILENAME = '../waymo_open_dataset_v_1_3_2/validation/segment-272435602399417322_2884_130_2904_130_with_camera_labels.tfrecord'dataset = tf.data.TFRecordDataset(FILENAME, compression_type='')MAXPOINT = 200000for data in dataset:framenumber += 1print('Frame Number : {}'.format(framenumber))frame = open_dataset.Frame()frame.ParseFromString(bytearray(data.numpy()))(range_images, camera_projections, range_image_top_pose) = frame_utils.parse_range_image_and_camera_projection(frame)points, cp_points = frame_utils.convert_range_image_to_point_cloud(frame,range_images,camera_projections,range_image_top_pose)x = [0]*MAXPOINTy = [0]*MAXPOINTz = [0]*MAXPOINTfor point in points:# print('len(point)=',len(point))for i in range(len(point)):x[i] = point[i][0]y[i] = point[i][1]z[i] = point[i][2]fig.mlab_source.set(x=x, y=y, z=z, mode="point")yieldMAXPOINT = 200000
fig = mlab.points3d(np.zeros(MAXPOINT), np.zeros(MAXPOINT), np.zeros(MAXPOINT),mode="point")
updateAnimation()
mlab.show() 

  上面的程序中添加了GPU显存设置，不添加会报错如下所示。当然，如果没有安装CUDA的话应该也不用添加，程序会直接使用CPU进行计算。

F tensorflow/core/util/cuda_solvers:115] Check failed: cusolverDnCreate(&cusolver_dn_handle) == CUSOLVER_STATUS_SUCCESS Failed to create cuSolverDN instance.

视频效果展示看这里

3 注意事项

注意，如下图所示，一定要单击一下这里面的任意一个视图，才可以清楚的看到点云数据。