安装caffe(opencv3+anaconda3)

目录

  • 仅安装CPU版本的caffe

    • 1.下载相关的依赖包:
    • 2.安装opencv3
    • 3.安装caffe
  • 参考文献:

仅安装CPU版本的caffe

1.下载相关的依赖包:

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler

sudo apt-get install --no-install-recommends libboost-all-dev

sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev

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

sudo apt-get install git cmake build-essential

2.安装opencv3

进入官网 : http://opencv.org/releases.html , 选择 3.4.1 版本的 source,并下载,解压到你要安装的位置.如/home/whb/Documents/PC/opencv/opencv-3.4.4,进入该目录。

#创建build文件
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release  -D CMAKE_INSTALL_PREFIX=/usr/local ..
make -j8 #编译
make install #安装

如以上步骤不出错,通过以下命令检查opencv是否安装成功

opencv_version

3.安装caffe

3.1 下载caffe

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

进入caffe目录

3.2 修改Makefile.config文件

cp Makefile.config.example Makefile.config
## 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 ##关键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 ##关键2

# 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/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)/anaconda3 ##关键3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ ##关键4
        $(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 ###关键5
# 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 := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(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 ##关键6

# 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 ?= @

共需要修改6个地方,仅安装cpu,配置anaconda3的相关路径,使用opencv3,取消注释USE_PKG_CONFIG=1这一行.

3.3 编译caffe

make all -j8
make test -j8
make runtest -j8

出现,PASSED表示大功告成

3.4 编译pycaffe

修改Makefile文件
PYTHON_LIBRARIES ?= boost_python3 python3.6
重新编译caffe
make clean
make caffe -j8
make test -j8
make runtest -j8
make pycaffe -j8

3.5 测试import caffe

为了使得import caffe成功,需要完成以下2个步骤:

1.将caffe的python路径加入到环境变量中

找到安装caffe的根目录,我这里是home/whb/Documents/PC/caffe,打开bashrc文件

vim /.bashrc 
#加入
export PYTHONPATH=/home/whb/Documents/PC/caffe/python:$PYTHONPATH
#生效
source  ~/.bashrc

2.安装protobuf

 pip install protobuf

大功告成0.0

参考文献:

  1. https://blog.csdn.net/yhaolpz/article/details/71375762
  2. https://blog.csdn.net/muzilinxi90/article/details/53673184
  3. https://blog.csdn.net/yhaolpz/article/details/71375762

原文地址:https://www.cnblogs.com/whb-20160329/p/10256766.html

时间: 01-11

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