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血泪教训，openpose安装了一周，才安装成功，接下来说一下如何成功地安装编译openpose。
由于我是在服务器上这个应用，系统的CUDA和Cudnn都已经事先安装好了，所以我接下来的教程都是在cuda和cudnn已经安装成功的基础上进行的。
根据openpose的官方guthub上的安装指南 https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/installation.md 需要安装opencv和caffe，这里就有一个问题了，我就是在这块被困住了。大部分人会先分别安装opencv和caffe然后再编译安装openpose，问题就在安装caffe的时候，我们可能会到caffe的官方github地址https://github.com/BVLC/caffe 下载源码安装，但是如果到这里下载编译安装caffe，在后期运行Openpose提供的例./build/examples/openpose/openpose.bin --video examples/media/video.avi 时，会报错，因此在编译安装caffe的时候，一定要下载指定的caffe版本，地址为 https://github.com/CMU-Perceptual-Computing-Lab/caffe/tree/b5ede488952e40861e84e51a9f9fd8fe2395cc8a
如果下载这个版本的caffe编译安装就不会出现问题了。
家下来分三个部分写安装过程：
（1）安装opencv
（2）编译caffe
（3）编译openpose
一.安装opencv:
最好是安装3.几的版本，不要安装4.0及以后的版本。 https://github.com/opencv/opencv/tree/master 注意选择
版本下载后进入安装过程：
（1）安装一些依赖文件：
sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
（2）解压压缩包，进入opencv解压后的文件夹：
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
make -j7
sudo make install
至此opencv已经安装完成，写一个小代码检测是否成功：
#include<iostream>
#include<opencv2/opencv.hpp>using namespace std;
using namespace cv;int main()
{Mat img = imread("me.png");imshow("ME",img);waitKey(0);return 0;
}
这是一个显示图片的小图片，如果能够显示图片，则说明Opencv安装成功。这个小程序文件名称为test.cpp 。编译语法为
g++ -std=c++11 test.cpp -o test `pkg-config --cflags --libs opencv`
编译后生成 test 文件，运行这个可执行文件 ./test
显示结果如图所示：
二.编译CAFFE
下载完成openpose指定的caffe源码后，解压这个压缩包，然后进入源码文件夹
新建一个文件 makefile.config 打开这个文件，将下面的代码复制粘贴进这个新建的文件，然后保存
## 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 3OPENCV_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-9.0
# 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_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)/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 := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5 /usr/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 /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# 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 ?= @
这和文件里面涉及到CUDA和CUDNN是否使用的选择，因为我要使用这两个东西，所以都设置为使用，注意这里面的
CUDA_DIR := /usr/local/cuda-9.0
路径要设置为自己的CUDA路径，前缀/usr/local 应该都一样，就看看自己的CUDA版本是否一样，根据自己的进行改进就行。
然后运行
make all
make test
make runtset
在编译的过程中，如果出现 fatal error: ****.h: No such file or directory 的错误指令，那么就说明编译指令没找到****.h这个头文件，那么我们打开一个终端，运行 locate ****.h 查看这个文件在什么位置，然后到makefile.config 中找到INCLUDE_DIRS := 这个字段，将对应的目录加入到后面。
三.编译openpose
先进入openpose源码的文件夹，然后建立build文件夹：
make build
cd build
cmake -D CUDA_TOOLKIT_ROOT=/usr/local/cuda-9.0 -D BUILD_CAFFE=OFF -D Caffe_INCLUDE_DIRS=/home/tcr/test_caffe/caffe/include -D Caffe_LIBS=/home/tcr/test_caffe/caffe/build/lib/libcaffe.so ..
make -j `nproc`
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