Caffe-SegNet安装笔记（基于Python2.7）（没有基于Anaconda2和Anaconda3，因为caffe-segnet基于Python2编写与运行）

骑猪看日落 2022-04-23 11:12 240阅读 0赞

# 首先安装caffe，安装过程请参考： #

[https://blog.csdn.net/zhuiqiuzhuoyue583/article/details/88756053][https_blog.csdn.net_zhuiqiuzhuoyue583_article_details_88756053]

我已经安装cuda8.0，cudnn5.1，opencv3.1，caffe1.0.0,

# 安装Caffe-SegNet： #

SegNet官网：[http://mi.eng.cam.ac.uk/projects/segnet/][http_mi.eng.cam.ac.uk_projects_segnet]

1.下载**[SegNet-Tutorial：][SegNet-Tutorial]**

[            https://github.com/alexgkendall/SegNet-Tutorial][SegNet-Tutorial]

2.解压后，得到“SegNet-Tutorial-master”，将其重命名为“SegNet”。

3. 下载caffe-segnet：

**  支持** **cudnn v2** ：[https://github.com/alexgkendall/caffe-segnet][https_github.com_alexgkendall_caffe-segnet]

**支持 cudnn v5：[https://github.com/TimoSaemann/caffe-segnet-cudnn5   （我用的cudnn5）][https_github.com_TimoSaemann_caffe-segnet-cudnn5_ _cudnn5]**

4. 解压后，名称为“caffe-segnet-cudnn5-master”，将其重命名为“caffe-segnet”

5.将“caffe-segnet”，放在“SegNet”目录下，因为后面运行python文件，需要这种模式

如图：

![2019032315220581.png][]

6. 因为caffe-segnet是基于caffe实现的，所以先编译过程和要求与caffe相同

进入主目录,caffe-segnet

cp Makefile.config.example Makefile.config

修改Makefile.config

a.使用 cudnn 
    USE_CUDNN := 1 
     
    b.使用的 opencv 版本是 3 
    OPENCV_VERSION := 3 
     
    c.使用 python 接口
    WITH_PYTHON_LAYER := 1 
     
    d.重要的一项 : 
    将# Whatever else you find you need goes here.下面的 
    INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 
    修改为： 
    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       
     
    这是因为ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，所以需要更改这一路径

这里附上我修改后的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
    
    # 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 lines for compatibility.
    CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
    		-gencode arch=compute_35,code=sm_35 \
    		-gencode arch=compute_50,code=sm_50 \
    		-gencode arch=compute_50,code=compute_50
    
    # 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/zqzy/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 \
    #		/home/zqzy/anaconda3/include/python3.6m
    
    # We need to be able to find libpythonX.X.so or .dylib.
    PYTHON_LIB := /usr/lib
    #PYTHON_LIB := $(ANACONDA_HOME)/lib \
    #	$(ANACONDA_HOME)/pkgs/python-3.6.4-hc3d631a_1/lib
    
    LINKFLAGS := -Wl,-rpath,/home/zqzy/anaconda3/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 /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
    
    # 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 ?= @

7.修改 Makefile 文件把下面第一行代码改为第二行代码：

将：
    NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
    替换为：
    NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

将：
    LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
    改为：
    LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

8.在caffe-segnet目录下，进行编译

cd /home/zqzy/SegNet/caffe-segnet
    sudo make all -j8
    sudo make pycaffe
    sudo make test
    sudo make runtest -j8

9.测试

Segnet分割网络caffe教程（一）：A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation 视频语义分割

[https://blog.csdn.net/zhuiqiuzhuoyue583/article/details/88782230][https_blog.csdn.net_zhuiqiuzhuoyue583_article_details_88782230]

[https_blog.csdn.net_zhuiqiuzhuoyue583_article_details_88756053]: https://blog.csdn.net/zhuiqiuzhuoyue583/article/details/88756053
[http_mi.eng.cam.ac.uk_projects_segnet]: http://mi.eng.cam.ac.uk/projects/segnet/
[SegNet-Tutorial]: https://github.com/alexgkendall/SegNet-Tutorial
[https_github.com_alexgkendall_caffe-segnet]: https://github.com/alexgkendall/caffe-segnet
[https_github.com_TimoSaemann_caffe-segnet-cudnn5_ _cudnn5]: https://github.com/TimoSaemann/caffe-segnet-cudnn5
[2019032315220581.png]: /images/20220320/fdd517747f7f40e5b38ad640c00b7c76.png
[https_blog.csdn.net_zhuiqiuzhuoyue583_article_details_88782230]: https://blog.csdn.net/zhuiqiuzhuoyue583/article/details/88782230