【Jetson Tx2 视觉slam开发】入门配置二
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Jetson Tx2安装ROS
https://github.com/jetsonhacks/installROSTX2
updateRepositories.sh This is an optional step. Adds the repositories universe, multiverse, and restricted and then apt-get update. These repositories are in the standard 27.1 install, so probably not needed.
installROS.sh Adds the ROS sources list, sets the keys and then loads ros-kinetic-ros-base. Edit this file to add other ROS packages for your installation. After loading, rosdep is initialized.
setupCatkinWorkspace.sh Usage:
$ ./setupCatkinWorkspace.sh [optionalWorkspaceName]
具体操作:
安装可以参照 http://wiki.ros.org/kinetic/Installation/Ubuntu,或者根据下面所示进行安装。
#!/bin/bash# Install Robot Operating System (ROS) on NVIDIA Jetson TX2# Maintainer of ARM builds for ROS is http://answers.ros.org/users/1034/ahendrix/# Information from:# http://wiki.ros.org/kinetic/Installation/UbuntuARM# Setup sources.lstsudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'# Setup keyssudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 0xB01FA116# Installationsudo apt-get updatesudo apt-get install ros-kinetic-ros-base -y or sudo apt-get install ros-kinetic-ros-desktop -y# Add Individual Packages here# You can install a specific ROS package (replace underscores with dashes of the package name):# sudo apt-get install ros-kinetic-PACKAGE# e.g.# sudo apt-get install ros-kinetic-navigation## To find available packages:# apt-cache search ros-kinetic# # Initialize rosdepsudo apt-get install python-rosdep -y# Certificates are messed upsudo c_rehash /etc/ssl/certs# Initialize rosdepsudo rosdep init# To find available packages, use:rosdep update# Environment Setupecho "source /opt/ros/kinetic/setup.bash" >> ~/.bashrcsource ~/.bashrc# Install rosinstallsudo apt-get install python-rosinstall -y
按照以上教程安装完成即可。
启动自带摄像头
打开Jetson TX2的终端
写入命令:nvgstcapture-1.0
,摄像头就会起来了
1. –prev_res 预览视屏的分辨率,高度和宽度,用的是CSI摄像头的话范围是 2 to 12 (5632x4224)
nvgstcapture-1.0 --prev-res=3
- –cus-prev-res 自定义预览分辨率,宽度和高度,仅支持CSI摄像头
nvgstcapture-1.0 --cus-prev-res=1920x1080
多个命令同时使用的话用!隔开,想关掉摄像头的额话,直接在终端输入q再按回车,想捕获图片的话,在终端输入j再按回车,图片将保存当前目录下。
Tensorflow安装
http://www.ncnynl.com/archives/201706/1754.html
git clone https://github.com/ncnynl/installTensorFlowTX2.gitcd installTensorFlowTX2
安装依赖:
chmod +x installPrerequisites.sh ./installPrerequisites.sh
下载tensorflow代码:
./cloneTensorFlow.sh
设置环境变量:
./setTensorFlowEV.sh
问题:提示junit.4.12.jar不能下载
解决:修改vim ~/tensorflow/tensorflow/workspace.bzl 更换地址,并去掉校验
native.http_jar( name = "junit_jar", #url = "https://github.com/junit-team/junit4/releases/download/r4.12/junit-4.12.jar", url = "https://github.com/orrsella/bazel-example/blob/master/third_party/junit/junit-4.12.jar", #sha256 = "59721f0805e223d84b90677887d9ff567dc534d7c502ca903c0c2b17f05c116a", #sha256 = "fe3d4c56388dc3d74049abae83f4520f6703062e174e16bb5551cdf439ca4f81", )
编译TensorFlow:
./buildTensorFlow.sh
打包成whl文件,放在$HOME目录下,如:tensorflow-1.0.1-cp27-cp27mu-linux_aarch64.whl
./packageTensorFlow.sh
安装whl文件
pip install $HOME/tensorflow-1.0.1-cp27-cp27mu-linux_aarch64.whl
提示权限错误的时候前边加上sudo即可
Caffee安装(安装成功)
参考:http://blog.csdn.net/jiongnima/article/details/70040262亲测可用
sudo gedit /etc/bash.bashrc
在文件的末尾加入如下
export PATH=/usr/local/cuda-8.0/bin:$PATHexport LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
然后cuda就配置完毕了,运行
nvcc -V
首先安装一般依赖项
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compilersudo apt-get install --no-install-recommends libboost-all-dev
再安装BLAS依赖项
sudo apt-get install libatlas-base-dev
然后是python和其余依赖项,安装python及其头文件
sudo apt-get install pythonsudo apt-get install python-dev
安装python的其他依赖
sudo apt-get install python-numpysudo apt-get install ipythonsudo apt-get install ipython-notebooksudo apt-get install python-sklearnsudo apt-get install python-skimagesudo apt-get install python-protobuf
然后是谷歌glog和gflags和lmdb依赖项
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
接着安装git,并且下载代码
sudo apt-get install gitgit clone https://github.com/BVLC/caffe.git
然后进入到源码目录
cd caffe
编译Caffe
cp Makefile.config.example Makefile.config
需要使用cuDNN的朋友可以将Makefile.config中USE_CUDNN := 1这一行之前的井号注释去掉,
同时,为了匹配cuda8.0的计算能力,请把Makefile.config中CUDA_ARCH中的前两行去掉,如下图所示(保留也行,编译的时候会弹出警告)
值得注意的是,笔者在第一次编译的时候遇到了如下问题:
./include/caffe/util/hdf5.hpp:6:18: fatal error: hdf5.h: No such file or directory
解决方法笔者参阅了http://blog.csdn.net/hongye000000/article/details/51043913点击打开链接,在链接博客中,解决方法如下:
在Makefile.config文件中,添加 /usr/include/hdf5/serial/ 到 INCLUDE_DIRS,也就是把下面第一行代码改为第二行代码。
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/includeINCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
在Makefile文件中,把 hdf5_hl 和hdf5修改为hdf5_serial_hl 和 hdf5_serial,也就是把下面第一行代码改为第二行代码。
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial
上述解决方法笔者亲测有效,感谢博主,然后
make cleanmake all -j8
即可编译成功。
处江湖之远,再见哦!bianxu7730@163.com
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