目标检测算法安装：Ubuntu 64bit下手动安装深度学习包Caffe记录(CPU)

阳光穿透心脏的1/2处 2022-04-12 07:15 173阅读 0赞

[https://blog.csdn.net/m\_buddy/article/details/78409326][https_blog.csdn.net_m_buddy_article_details_78409326]

前言  
之前在电脑上安装Caffe都是使用的简单安装（sudo…sudo..do），这几天闲下来折腾起了手动安装。这样做主要是为了方便进行移植，这里将所有的依赖库安装到了/home/pc\_name(你自己的电脑账户名，下同)/local\_install/目录下。博主在Ubuntu 64bit上安装成功，现在将整个的安装过程记录下来，希望对各位看官有所帮助。若各位看官需要本次安装所需的全部安装包可以QQ联系我（2414385027）。  
整篇文章基本讲干货，其他的什么介绍之类的就省略了-\_-||…

1. Protobuf安装

tar zxvf protobuf-cpp-3.4.1.tar.gz  
cd protobuf-cpp-3.4.1/  
./configure --prefix=/home/pc\_name/local\_install

make  
make install  
  
安装完成之后为了校验安装的正确性，查看对应目录下有没有protoc文件

ls ~/local\_install/bin/  
protoc  
为了能在命令行运行，将该目录加入到PATH中去 export PATH=~/local\_install/bin/:$PATH将其写入到

/home/pc\_name/.bashrc  
1  
2. BOOST安装

tar zxvf boost\_1\_56\_0.tar.gz  
cd boost\_1\_56\_0/

./bootstrap.sh --with-libraries=system,thread,python  
./b2  
./b2 install

cp -r boost/ /home/pc\_name/local\_install/include/  
cp stage/lib/\* /home/pc\_name/local\_install/lib/  
  
3. CFLAGS安装

tar zxvf gflags-2.2.1.tar.gz  
cd gflags-2.2.1/  
mkdir build && cd build  
cmake ..  
ccmake ..  
  
若未能识别ccmake需要执行下面命令进行安装

sudo apt-get install cmake-curses-gui  
  
按照上面的进行配置完成之后按C键，之后G键生成Makefile文件

make  
make install  
  
CFLAGS在Caffe中主要起到命令行参数解析的作用，这与protobuf功能类似，只是参数输入源不同。CFLAGS的使用方法可参考Caffe源码中的tools/caffe.cpp

4. GLOG安装

tar zxvf glog-0.3.5.tar.gz  
cd glog-0.3.5/  
./configure --prefix=/home/pc\_name/local\_install/  
make  
make install  
5. OpenBLAS安装

unzip OpenBLASv0.2.20.zip  
make –j  
make PREFIX=/home/pc\_name/local\_install/ install  
  
6. HDF5安装

tar xvf hdf5-1.10.1.tar  
cd hdf5-1.10.1/  
./configure --prefix=/home/pc\_name/local\_install/  
make -j && make install  
  
7. OpenCV安装

unzip opencv-3.0.0.zip  
cd opencv-3.0.0/  
mkdir build && cd build  
cmake ..  
ccmake ..  
  
make  
make install

8. LMDB安装

tar zxvf lmdb-LMDB\_0.9.21.tar.gz  
cd lmdb-LMDB\_0.9.21/  
make  
  
将生成的lmdb.h拷贝到/home/pc\_name/local\_install/include目录下，liblmdb.so拷贝到/home/pc\_name/local\_install/lib目录下。

9. LEVELDB安装

tar zxvf leveldb-1.20.tar.gz  
cd leveldb-1.20/  
make

cp -r include/leveldb ~/local\_install/include/  
cp libleveldb.so\* ~/local\_install/lib/  
  
10. Snappy安装

tar zxvf snappy-1.1.1.tar.gz  
cd snappy-1.1.1/  
./configure --prefix=/home/pc\_name/local\_install/  
make && make install  
  
在所有的附加库安装完成之后得到的2级目录结构是这样的

.  
├── bin  
├── include  
│ ├── boost  
│ ├── gflags  
│ ├── glog  
│ ├── google  
│ ├── leveldb  
│ ├── opencv  
│ └── opencv2  
├── lib  
│ ├── cmake  
│ └── pkgconfig  
└── share  
├── doc  
├── hdf5\_examples  
└── OpenCV  
16 directories  
  
11. Caffe安装

至于Caffe的安装需要的是配置好Makefile.config文件就好了，下面提供本次讲解使用的文件内容（只是用CPU）

\#\# Refer to http://caffe.berkeleyvision.org/installation.html  
\# Contributions simplifying and improving our build system are welcome!

\# cuDNN acceleration switch (uncomment to build with cuDNN).  
\# USE\_CUDNN := 1

\# CPU-only switch (uncomment to build without GPU support).  
CPU\_ONLY := 1

\# uncomment to disable IO dependencies and corresponding data layers  
\# USE\_OPENCV := 0  
\# USE\_LEVELDB := 0  
\# USE\_LMDB := 0

\# uncomment to allow MDB\_NOLOCK when reading LMDB files (only if necessary)  
\# You should not set this flag if you will be reading LMDBs with any  
\# possibility of simultaneous read and write  
\# ALLOW\_LMDB\_NOLOCK := 1

\# Uncomment if you're using OpenCV 3  
OPENCV\_VERSION := 3

\# To customize your choice of compiler, uncomment and set the following.  
\# N.B. the default for Linux is g++ and the default for OSX is clang++  
\# CUSTOM\_CXX := g++

\# CUDA directory contains bin/ and lib/ directories that we need.  
CUDA\_DIR := /usr/local/cuda  
\# On Ubuntu 14.04, if cuda tools are installed via  
\# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:  
\# CUDA\_DIR := /usr

\# CUDA architecture setting: going with all of them.  
\# For CUDA < 6.0, comment the \*\_50 through \*\_61 lines for compatibility.  
\# For CUDA < 8.0, comment the \*\_60 and \*\_61 lines for compatibility.  
CUDA\_ARCH := -gencode arch=compute\_20,code=sm\_20 \\  
\-gencode arch=compute\_20,code=sm\_21 \\  
\-gencode arch=compute\_30,code=sm\_30 \\  
\-gencode arch=compute\_35,code=sm\_35 \\  
\-gencode arch=compute\_50,code=sm\_50 \\  
\-gencode arch=compute\_52,code=sm\_52 \\  
\-gencode arch=compute\_60,code=sm\_60 \\  
\-gencode arch=compute\_61,code=sm\_61 \\  
\-gencode arch=compute\_61,code=compute\_61

\# BLAS choice:  
\# atlas for ATLAS (default)  
\# mkl for MKL  
\# open for OpenBlas  
BLAS := open  
\# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.  
\# Leave commented to accept the defaults for your choice of BLAS  
\# (which should work)!  
\# BLAS\_INCLUDE := \`/path/to/your/blas  
\# BLAS\_LIB := /path/to/your/blas

\# Homebrew puts openblas in a directory that is not on the standard search path  
\# BLAS\_INCLUDE := $(shell brew --prefix openblas)/include  
\# BLAS\_LIB := $(shell brew --prefix openblas)/lib

\# This is required only if you will compile the matlab interface  
\# MATLAB directory should contain the mex binary in /bin.  
\# MATLAB\_DIR := /usr/local  
\# MATLAB\_DIR := /Applications/MATLAB\_R2012b.app

\# NOTE: this is required only if you will compile the python interface.  
\# We need to be able to find Python.h and numpy/arrayobject.h.  
PYTHON\_INCLUDE := /usr/include/python2.7 \\  
/usr/lib/python2.7/dist-packages/numpy/core/include  
\# Anaconda Python distribution is quite popular. Include path:  
\# Verify anaconda location, sometimes it's in root.  
\# ANACONDA\_HOME := $(HOME)/anaconda  
\# PYTHON\_INCLUDE := $(ANACONDA\_HOME)/include \\  
\# $(ANACONDA\_HOME)/include/python2.7 \\  
\# $(ANACONDA\_HOME)/lib/python2.7/site-packages/numpy/core/include

\# Uncomment to use Python 3 (default is Python 2)  
\# PYTHON\_LIBRARIES := boost\_python3 python3.5m  
\# PYTHON\_INCLUDE := /usr/include/python3.5m \\  
\# /usr/lib/python3.5/dist-packages/numpy/core/include

\# We need to be able to find libpythonX.X.so or .dylib.  
PYTHON\_LIB := /usr/lib  
\# PYTHON\_LIB := $(ANACONDA\_HOME)/lib

\# Homebrew installs numpy in a non standard path (keg only)  
\# PYTHON\_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.\_\_file\_\_)'))/include  
\# PYTHON\_LIB += $(shell brew --prefix numpy)/lib

\# Uncomment to support layers written in Python (will link against Python libs)  
\# WITH\_PYTHON\_LAYER := 1

\# Whatever else you find you need goes here.  
INCLUDE\_DIRS := /home/sucker/local\_install/include $(PYTHON\_INCLUDE)\\  
/usr/local/include  
LIBRARY\_DIRS := /home/sucker/local\_install/lib $(PYTHON\_LIB)\\  
/usr/local/lib /usr/lib

\# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies  
\# INCLUDE\_DIRS += $(shell brew --prefix)/include  
\# LIBRARY\_DIRS += $(shell brew --prefix)/lib

\# NCCL acceleration switch (uncomment to build with NCCL)  
\# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)  
\# USE\_NCCL := 1

\# Uncomment to use \`pkg-config\` to specify OpenCV library paths.  
\# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY\_DIRS.)  
\# USE\_PKG\_CONFIG := 1

\# N.B. both build and distribute dirs are cleared on make clean  
BUILD\_DIR := build  
DISTRIBUTE\_DIR := distribute

\# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171  
\# DEBUG := 1

\# The ID of the GPU that 'make runtest' will use to run unit tests.  
TEST\_GPUID := 0

\# enable pretty build (comment to see full commands)  
Q ?= @  
  
编辑好config文件之后就是进行编译了，就是make命令。之后为了检验安装是否成功需要进行如下验证

make test  
make runtest  
  
本人在其中遇到这个错误

.build\_release/tools/caffe: error while loading shared libraries: libboost\_system.so.1.56.0: cannot open shared object file: No such file or directory make: \*\*\* \[runtest\] Error 127  
  
解决办法：  
在/home/pc\_name/下编辑.bashrc文件，添加

export LD\_LIBRARY\_PATH=$LD\_LIBRARY\_PATH:/home/pc\_name/local\_install/lib  
  
原文：https://blog.csdn.net/m\_buddy/article/details/78409326

[https_blog.csdn.net_m_buddy_article_details_78409326]: https://blog.csdn.net/m_buddy/article/details/78409326