1.准备
cuda 8.0 注意环境配置，动态库及bin启动文件
cudnn 解压匹配的tgz包，拷贝到系统配置路径，并授权
cmake 安装3.12.3版本，适应软件编译版本要求
java + ant 配置jvm环境，便于部署及后续opencv执行jar的生成
opencv 2.4.11 gpu版本
openblas caffe准备
caffe 修改makefile.config 文件，gpu版本
jsoncpp 后续水印识别依赖包
logo_detect 校验环境配置
2.cmake安装
# 安装gcc等必备程序包 yum install -y gcc gcc-c++ make automake # 获取安装包并解压 # 进入安装目录，此处为 cmake-3.12.3.tar.gz ./bootstrap gmake gmake install
3.cuda与cudnn的安装
两者的安装直接按照官网步骤即可，注意/etc/profile中的相关配置，如果指定cuda的bin路径与lib64在/usr/local下的软链接下，注意判断是否匹配
此处需特别注意，在安装时需指定版本 ， 实操如下（此处为本机ubuntu16.04 安装命令）：
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub sudo apt-get update sudo apt-get install cuda-9-0 cuda-libraries-9-0
cudnn安装：
# 下载 cudnn的tgz压缩包，解压并执行如下命令 sudo cp cuda/include/cudnn.h /usr/local/cuda/include sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
4.opencv安装
因此处的OpenCV不止是后续caffe的依赖项，也将用于Java web项目，故需要安装Java环境，以及ant构建OpenCV项目并生成执行jar。
java与ant的安装不再赘述，解压并配置环境变量即可，下为OpenCV安装：
# 解压opencv，创建并进入build目录 mkdir build && cd build # 构建makefile编译依赖环境 cmake .. make -j8 make install
注意，安装gpu版本有时会报以下错误：
nvcc fatal : Unsupported gpu architecture 'compute_11' CMake Error at cuda_compile_generated_matrix_operations.cu.o.cmake:206 (message): Error generating /home/smie/Documents/opencv2.4.11/build/modules/core/CMakeFiles/cuda_compile.dir/__/dynamicuda/src/cuda/./cuda_compile_gene rated_matrix_operations.cu.o make[2]: *** [modules/core/CMakeFiles/cuda_compile.dir/__/dynamicuda/src/cuda/./cuda_compile_generated_matrix_operations.cu.o] Error 1 make[1]: *** [modules/core/CMakeFiles/opencv_core.dir/all] Error 2 make[1]: *** Waiting for unfinished jobs....
解决方案如下：
# 使用cmake重新构建编译环境
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D CUDA_GENERATION=Kepler ..
因本机安装的OpenCV为2.4.11，cuda版本为8.0，编译报错，处理方案来源：https://www.cnblogs.com/jessezeng/p/7018267.html：
/data/opencv-2.4.11/modules/gpu/src/graphcuts.cpp:120:54: error: ‘NppiGraphcutState’ has not been declared typedef NppStatus (*init_func_t)(NppiSize oSize, NppiGraphcutState** ppStat ^ /data/opencv-2.4.11/modules/gpu/src/graphcuts.cpp:135:18: error: ‘NppiGraphcutState’ does not name a type operator NppiGraphcutState*() ^ /data/opencv-2.4.11/modules/gpu/src/graphcuts.cpp:141:9: error: ‘NppiGraphcutState’ does not name a type NppiGraphcutState* pState;
cuda8.0较新，opencv-2.4.11较早，要编译通过需要修改源码：
修改modules/gpu/src/graphcuts.cpp 将 #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) 改为 #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || (CUDART_VERSION >= 8000) 重新编译即可。
5.openblas安装
caffe依赖blas（BLAS(Basic Linear Algebra Subprograms)是一组线性代数计算中通用的基本运算操作函数集合），Linux本身已自带有atlas，但安装时会报错：
/usr/bin/ld: cannot find -lcblas /usr/bin/ld: cannot find -latlas collect2: error: ld returned 1 exit status make: * [.build_release/lib/libcaffe.so.1.0.0-rc3] Error 1
此处参考博客：https://blog.csdn.net/iotlpf/article/details/74669503，沿用其中的处理方式，安装openblas
git clone https://github.com/xianyi/OpenBLAS.git cd OpenBLAS make -j8 make install
6.jsoncpp安装
安装步骤如下：
git clone https://github.com/open-source-parsers/jsoncpp.git cd jsoncpp mkdir -p build/debug cd build/debug cmake -DCMAKE_BUILD_TYPE=debug -DJSONCPP_LIB_BUILD_SHARED=OFF -G "Unix Makefiles" ../../ make make install
7.caffe安装
依赖项安装：
yum install epel-release -y yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel -y yum install gflags-devel glog-devel lmdb-devel -y yum install atlas-devel -y
配置Makefile.config，因为使用了openblas以及cuda+cudnn，直接上本地配置：
## 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 := 1 # 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 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 := open # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. # Leave commented to accept the defaults for your choice of BLAS # (which should work)! BLAS_INCLUDE := /opt/OpenBLAS/include BLAS_LIB := /opt/OpenBLAS/lib # 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/serial 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 # 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 ?= @
直接编译，随后配置环境变量即可：
make distribute -j16
8.去水印项目
首先需注意Makefile文件中的相关配置，尤其是新机器各种环境变量可能的变量需要特别注意。
我这里报错：device_alternate.hpp:34:23: fatal error: cublas_v2.h: No such file or direct，因为device_alternate.cpp是caffe的一个文件，一直怀疑是caffe安装有问题，查看无误后，断定是Makefile定义的环境变量有错，因为其中重写了cuda的路径，且该路径在当前机器有变动，导致找不到cuda的cublas_v2.h文件，重新配置路径即可。