ubutu16.04 python3.6 opencv3.2 caffe(CPU) 填坑指南

最近由於需要，安裝一下caffe,由於手邊暫時沒有gpu，所以在自己筆記本先安裝一下cpu版本的caffe。由於caffe是屬於比較老的框架，然後對python 2.7支持比較好，或者python3.5以下也是可以的，但是由於我們的python是3.6,以及tensorflow == 1.6，因此我們嘗試在python 3.6的情況下，安裝caffe CPU版本。

系統版本：

ubutu 16.04
anoconda 4.3.30
python 3.6
opencv 3.2.0
caffe 1.0.0

1. 首先利用anaconda創建虛擬環境

爲了和tensorflow、pytorch、theano等環境區分，我們首先創建一個caffe環境，使用anaconda創建。conda創建、查看、刪除虛擬環境

1.首先查看當前已有環境：

conda-env list

2.創建caffe虛擬環境

conda create -n CAFFE  python=3.6

3.進入環境

source activate CAFFE

小提示，退出環境命令爲source deactivate

2. 安裝opencv3以及其他各種依賴項

2.1 安裝opencv3

爲了安裝caffe，我們首先先將opencv3安裝好

參考我的博客:
ubutu16.04卸載opencv2安裝opencv3

2.2 安裝其他各種依賴項

1.切換到root權限，su, 依次安裝：

apt-get install libprotobuf-dev 
apt-get install libleveldb-dev 
apt-get install libsnappy-dev 
apt-get install libopencv-dev 
apt-get install libhdf5-serial-dev 
apt-get install protobuf-compiler
apt-get install --no-install-recommends libboost-all-dev

CPU Only的情況下，跳過了CUDA相關的安裝；
接下來是BLAS：

apt-get install libatlas-base-dev

使用默認Python來建立pycaffe接口，需要安裝：

apt-get install python-dev

一些兼容性依賴庫：

apt-get install libgflags-dev
apt-get install libgoogle-glog-dev 
apt-get install liblmdb-dev

3 安裝caffe

參考：

3.1 下載caffe相關包

沒有安裝git的話需要先裝一下git，同樣也是需要root權限的。

apt-get install git

下載Caffe源碼：

git clone https://github.com/BVLC/caffe.git

如果需要Caffe的Python接口，切換到caffe下的python目錄下，輸入以下命令下載python依賴庫（先安裝pip）：

apt-get install python-pip
for req in $(cat requirements.txt); do pip install $req; done

小技巧，從這裏可以看出這裏的requirements.txt需要安裝的東西還是比較多的，由於下載速度可能會比較慢，所以有些包可能需要下載，然後離線安裝，安裝包的格式就是.whl和.zip格式等。

3.2 開始編譯caffe

前面的各種包並不是太難，caffe難裝的原因就是編譯可能會出現各種各樣的錯誤，這裏我們也是一步一步來。

3.2.1 到Caffe文件夾中

cd caffe

拷貝一份Makefile.config.example並重命名成Makefile.config，修改該配置文件：

cp Makefile.config.example Makefile.config

3.2.2 修改`Makefile.config`

這一步非常麻煩，但是也十分重要。。。

這是我的Makefile.config，裏面做了一些修改。主要參考的是ubuntu16.04 python3.6 caffe(CPU) 配置記錄，同時也在這個基礎上，做了一些修改，這是依照後面的錯誤來的。

添加HOME := /home/xuchao，原來沒有寫。
添加代碼

LIBRARIES += glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m  opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs

我的Makefile.config如下：

## 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
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 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.
# For CUDA >= 9.0, comment the *_20 and *_21 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 := atlas
# 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/local/lib/python2.7/dist-packages/numpy/core/include

# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.

# HOME path !!!!

HOME := /home/xuchao

ANACONDA_HOME := $(HOME)/anaconda3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		  $(ANACONDA_HOME)/include/python3.6m \
		  $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)

PYTHON_LIBRARIES := boost_python3 python3.6m

# PYTHON_INCLUDE := /usr/include/python3.6m \
# /usr/lib/python3.6/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 := $(PYTHON_INCLUDE) /usr/local/include
# LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial



# 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

LIBRARIES += glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m  opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs

# 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 ?= @

LINKFLAGS := -Wl,-rpath,$(HOME)/anaconda3/lib#

3.2.3 安裝libboost（基於python3.6）的庫

1.首先輸入命令：

sudo ln -s /home/xuchao/anaconda3/bin/python3  /usr/lib/python3.6m(這裏是建立一個軟鏈接，把我本地目錄下的python3.6加到usr/lib/中去。

2.下載boost_1_67_0

wget -O boost_1_67_0.tar.gz http://sourceforge.net/projects/boost/files/boost/1.67.0/boost_1_67_0.tar.gz/download  

tar xzvf boost_1_67_0.tar.gz

3.安裝附加依賴庫

sudo apt-get update  

sudo apt-get install build-essential g++ python-dev autotools-dev libicu-dev build-essential libbz2-dev libboost-all-dev

4.編譯boost_1_67_0

cd boost_1_67_0/

./bootstrap.sh --with-libraries=python --with-toolset=gcc  

./b2 --with-python include="/home/xuchao/anaconda3/include/python3.6m/"  

sudo ./b2 install

/home/xuchao/可能要換成你自己的目錄名字。

編譯安裝成功後，/usr/local/lib下會有libboost_python36.so和libboost_python36.a，有些應用link時需要的是libboost_python3.so或者libboost_python3.a，我們建個軟鏈：

cd /usr/local/lib  

sudo ln -s libboost_python-py36.so libboost_python3.so  

sudo ln -s libboost_python-py36.a libboost_python3.a

這時，我們要把/usr/local/lib 中，相關文件，建立相對於名稱的軟鏈接到/usr/lib/x86_64-linux-gnu中。

sudo cp /usr/local/lib/libboost_python36.a  /usr/lib/x86_64-linux-gnu/libboost_python_python36.a

sudo cp /usr/local/lib/libboost_python36.so.1.67.0  /usr/lib/x86_64-linux-gnu/libboost_python3.so

3.2.4 編譯caffe

依次輸入以下命令：

sudo make all -j4
sudo make test -j4
sudo make runtest -j4
sudo make pycaffe -j4

make默認單核運算，如果想加快速度，我這裏是4核，可以在每條命令後面加上-j4，如果有報錯，建議最好make clean重新開始。
如果所有測試都通過，則說明安裝好了。

4 測試caffe

測試Caffe的Python接口，切換到caffe/python文件目錄下，記錄下來當前路徑，輸入以下命令：

export PYTHONPATH=/home/xuchao/caffe/python:$PYTHONPATH

/home/xuchao換成你自己的。

進入python環境，輸入：

import caffe

果沒有報錯，證明安裝成功。

上面的方法，一旦關閉終端或者打開新終端則失效，如果放到配置文件中，可以永久有效果，命令操作如下：

#A.把環境變量路徑放到 ~/.bashrc文件中
sudo echo export PYTHONPATH="~/caffe/python" >> ~/.bashrc
#B.使環境變量生效
source ~/.bashrc

其他參考：

花了一天的時間，終於把caffe安裝好了。後面安裝gpu版本的caffe再說了。

ubutu16.04 python3.6 opencv3.2 caffe(CPU) 填坑指南

1. 首先利用anaconda創建虛擬環境

2. 安裝opencv3以及其他各種依賴項

2.1 安裝opencv3

2.2 安裝其他各種依賴項

3 安裝caffe

3.1 下載caffe相關包

3.2 開始編譯caffe

3.2.1 到Caffe文件夾中

3.2.2 修改`Makefile.config`

3.2.3 安裝libboost（基於python3.6）的庫

3.2.4 編譯caffe

4 測試caffe

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ubutu16.04 python3.6 opencv3.2 caffe(CPU) 填坑指南

1. 首先利用anaconda創建虛擬環境

2. 安裝opencv3以及其他各種依賴項

2.1 安裝opencv3

2.2 安裝其他各種依賴項

3 安裝caffe

3.1 下載caffe相關包

3.2 開始編譯caffe

3.2.1 到Caffe文件夾中

3.2.2 修改Makefile.config

3.2.3 安裝libboost（基於python3.6）的庫

3.2.4 編譯caffe

4 測試caffe

3.2.2 修改`Makefile.config`