Ubuntu18.04安装caffe python3.6 opencv3.2 CPU
2024-10-19 22:42:17
设置ubuntu的softwares&updates的源为国内源,这样会提高下载速度。
如果是安装python相关库,为提高速度使用:
pip3 install 要下载的库 -i https://国内源
安装caffe依赖库:
# python3 modules (numpy, protobuf, skimage)
sudo pip3 install numpy
sudo apt-get install python3-skimage
sudo apt-get install python3-protobuf # build essential
sudo apt-get install build-essential cmake git pkg-config # gflags, glog, lmdb
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev # boost
sudo apt-get install libboost-all-dev # hdf5
sudo apt-get install libhdf5-dev # protobuf
sudo apt-get install protobuf-compiler libprotobuf-dev # blas
sudo apt-get install libblas-dev libcblas-dev libatlas-base-dev libopenblas-dev # leveldb
sudo apt-get install libleveldb-dev # snappy
sudo apt-get install libsnappy-dev
安装opencv3.2
下载源代码 opencv-3.2.0.zip
unzip opencv-3.2..zip
cd opencv-3.2.
mkdir release
cd release
cmake ..
make -j4
sudo make install
安装git:
sudo apt-get install git
安装caffe:
git clone https://github.com/BVLC/caffe
cd caffe
cp Makefile.config.example Makefile.config
Makefile.config文件:
## 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 := # CPU-only switch (uncomment to build without GPU support).
CPU_ONLY := # uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV :=
# USE_LEVELDB :=
# USE_LMDB :=
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := # 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 := # Uncomment if you're using OpenCV 3
OPENCV_VERSION := # 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. \
# /usr/lib/python2./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. \
# $(ANACONDA_HOME)/lib/python2./site-packages/numpy/core/include # Uncomment to use Python (default is Python )
PYTHON_LIBRARIES := boost_python3 python3.6m
PYTHON_INCLUDE := /usr/include/python3.6m \
# /usr/lib/python3./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 := # Whatever else you find you need goes here.
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/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 := # 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 := # 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 := # The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := # enable pretty build (comment to see full commands)
Q ?= @
编译并测试:
make all -j4
make test
make runtest
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