OpenCV与tensorflow静态库依赖的一些问题
1， find_package是在系统环境变量指定的目录中寻找 ***Config.cmake 和 ****Config-version.cmake文件，通常在乌班图下使用apt-get install 命令会自动在相应的目录下生成文件，如果手动编译的话，在执行make install也会在此输出。
2，对于OpenCV静态库的编译，有两种方法
2.1 命令行编译
mkdir build
cd build
cmake -D WITH_IPP=OFF -D CMAKE_BUILD_TYPE=RELEASE -D BUILD_SHARED_LIBS=OFF ..
make -j4g++ -std=c++11 -L/opt/openCV_2_4_5/lib -I/opt/openCV_2_4_5/include -o ex2 ex2.cpp -L. -lpthread -lz -lopencv_stitching -lopencv_superres -lopencv_videostab -lopencv_aruco -lopencv_bgsegm -lopencv_bioinspired -lopencv_ccalib -lopencv_dnn -lopencv_dpm -lopencv_fuzzy -lopencv_line_descriptor -lopencv_optflow -lopencv_plot -lopencv_reg -lopencv_saliency -lopencv_stereo -lopencv_structured_light -lopencv_rgbd -lopencv_surface_matching -lopencv_tracking -lopencv_datasets -lopencv_text -lopencv_face -lopencv_xfeatures2d -lopencv_shape -lopencv_video -lopencv_ximgproc -lopencv_calib3d -lopencv_features2d -lopencv_flann -lopencv_xobjdetect -lopencv_objdetect -lopencv_ml -lopencv_xphoto -lopencv_videoio -lopencv_imgcodecs -lopencv_photo -lopencv_imgproc -lopencv_core 这里参考： https://segmentfault.com/a/1190000011586084
2.2 gui界面编译
有些时候可能会报错，然后的话手动cmake编译。先安装cmake-gui，然后执行编译，手动勾选，将shared_libs 钩掉。还可以选择其他的库比如png,tiff等等。
3，加载OpenCV静态库
CMakeList.txt
set(OpenCV_DIR /home/chaofan/opt/opencv-3.4.4/release)
find_package( OpenCV 3 REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
add_executable( imageBasics imageBasics.cpp )
target_link_libraries( imageBasics ${OpenCV_LIBS} )
怎么找到opencv的安装路径
去找opencv的时候建立的release或者build的文件夹，主要依据是里面有OpenCVConfig.cmake文件
4，Tensorflow编译及应用C++静态库
clone tensorflow git 仓库
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow进入 tensorflow 工程下contrib/makefile路径
tensorflow/contrib/makefile运行编译脚本
# MacOS 以及Linux 使用此脚本
./build_all_linux.sh脚本执行完成后我们得到tensorflow静态库以及相应头文件。
下一步我们将Tensorflow静态库头文件及Tensorflow依赖的静态库头文件整理到统一路径下，其他c++ 工程就可以应用这些库文件及头文件。
# 将tensorflow 头文件以及库文件收集到此路径
mkdir -p ~/tensorflow_libs/tensorflow
# include 路径存放tensorflow主要头文件
mkdir -p ~/tensorflow_libs/tensorflow/include/tensorflow
# lib 路径存放编译好的静态库
mkdir -p ~/tensorflow_libs/tensorflow/lib
# 拷贝tensorflow 主要头文件
cp -r tensorflow/core ~/tensorflow_libs/tensorflow/include
# 拷贝tensoflow 静态库
cp tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a ~/tensorflow_libs/tensorflow/lib
# 拷贝tensorflow 第三方头文件
mkdir -p ~/tensorflow_libs/tensorflow/tensorflow_third_party
cp -r third_party ~/tensorflow_libs/tensorflow/tensorflow_third_party
# 拷贝gen目录下文件
cp -r tensorflow/contrib/makefile/gen/host_obj ~/tensorflow_libs/tensorflow/
cp -r tensorflow/contrib/makefile/gen/proto ~/tensorflow_libs/tensorflow/
cp -r tensorflow/contrib/makefile/gen/protobuf ~/tensorflow_libs/tensorflow/
cp -r tensorflow/contrib/makefile/gen/proto_text ~/tensorflow_libs/tensorflow/
# 拷贝downloads下文件
# eigen3
mkdir -p ~/tensorflow_libs/tensorflow/eigen3
cp -r tensorflow/contrib/makefile/downloads/eigen/Eigen ~/tensorflow_libs/tensorflow/eigen3
cp -r tensorflow/contrib/makefile/downloads/eigen/unsupported ~/tensorflow_libs/tensorflow/eigen3
# absl
cp -r tensorflow/contrib/makefile/downloads/absl ~/tensorflow_libs/tensorflow/
# nsyc
mkdir -p ~/tensorflow_libs/tensorflow/nsyc/include
mkdir -p ~/tensorflow_libs/tensorflow/nsyc/lib
# 拷贝nsyc 头文件
cp -r tensorflow/contrib/makefile/downloads/nsync/* ~/tensorflow_libs/tensorflow/nsyc/include
# 拷贝nsyc 库文件
cp tensorflow/contrib/makefile/downloads/nsync/builds/default.macos.c++11/libnsync.a ~/tensorflow_libs/tensorflow/nsyc/libCMakeLists.txt 如下
CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
SET(TENSORFLOW_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/include)
SET(TENSORFLOW_LIBARY ${CMAKE_SOURCE_DIR}/tensorflow/lib/libtensorflow-core.a)
MESSAGE(STATUS "TENSORFLOW_INCLUDE_PATH ${TENSORFLOW_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_LIBARY ${TENSORFLOW_LIBARY}")
SET(TENSORFLOW_PROTOBUF_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/protobuf/include)
SET(TENSORFLOW_PROTOBUF_LIBRARY_PATH ${CMAKE_SOURCE_DIR}/tensorflow/protobuf/lib)
SET(TENSORFLOW_PROTOBUF_LIBRARY ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}/libprotobuf.a)
SET(TENSORFLOW_PROTOBUF_LITE_LIBRARY ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}/libprotobuf-lite.a)
SET(TENSORFLOW_PROTOC_LIBRARY ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}/libprotoc.a)
MESSAGE(STATUS "TENSORFLOW_PROTOBUF_INCLUDE_PATH ${TENSORFLOW_PROTOBUF_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_PROTOBUF_LIBRARY_PATH ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}")
SET(TENSORFLOW_NSYNC_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/nsync/include)
SET(TENSORFLOW_NSYNC_LIBRARY_PATH ${CMAKE_SOURCE_DIR}/tensorflow/nsync/lib)
MESSAGE(STATUS "TENSORFLOW_NSYNC_INCLUDE_PATH ${TENSORFLOW_NSYNC_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_NSYNC_LIBRARY_PATH ${TENSORFLOW_NSYNC_LIBRARY_PATH}")
SET(TENSORFLOW_NSYNC_LIBRARY ${TENSORFLOW_NSYNC_LIBRARY_PATH}/libnsync.a)
SET(TENSORFLOW_PROTO_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/proto)
SET(TENSORFLOW_PROTO_TEXT_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/proto_text)
SET(TENSORFLOW_HOST_OBJ_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/host_obj)
SET(TENSORFLOW_EIGEN_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/eigen3)
SET(TENSORFLOW_ABSL_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/absl)
SET(TENSORFLOW_THIRD_PARTY_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/tensorflow_third_party)
MESSAGE(STATUS "TENSORFLOW_PROTO_INCLUDE_PATH ${TENSORFLOW_PROTO_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_PROTO_TEXT_INCLUDE_PATH ${TENSORFLOW_PROTO_TEXT_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_HOST_OBJ_INCLUDE_PATH ${TENSORFLOW_HOST_OBJ_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_EIGEN_INCLUDE_PATH ${TENSORFLOW_EIGEN_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_ABSL_INCLUDE_PATH ${TENSORFLOW_ABSL_INCLUDE_PATH}")
MESSAGE(STATUS "TENSORFLOW_THIRD_PARTY_INCLUDE_PATH ${TENSORFLOW_THIRD_PARTY_INCLUDE_PATH}")
INCLUDE_DIRECTORIES(${TENSORFLOW_PROTOBUF_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_PROTO_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_PROTO_TEXT_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_HOST_OBJ_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_EIGEN_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_ABSL_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_NSYNC_INCLUDE_PATH})
INCLUDE_DIRECTORIES(${TENSORFLOW_THIRD_PARTY_INCLUDE_PATH})
ADD_EXECUTABLE(load_model load_model.cpp)
SET(LOAD_MODEL_LIBRARIES
${TENSORFLOW_PROTOBUF_LIBRARY}
${TENSORFLOW_PROTOC_LIBRARY}
${TENSORFLOW_NSYNC_LIBRARY}
${TENSORFLOW_LIBARY})
SET(LDFLAGS "-std=c++11 -msse4.1 -fPIC -O3 -march=native -Wall -finline-functions -undefined dynamic_lookup -all_load")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}${LDFLAGS}")
MESSAGE(STATUS "CMAKE_CXX_COMPILER: ${CMAKE_CXX_COMPILER}")
MESSAGE(STATUS "CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
TARGET_LINK_LIBRARIES(load_model ${LOAD_MODEL_LIBRARIES} ${CMAKE_CXX_FLAGS})编译
mkdir -p build && cd build
cmake ..
make
# 将编译好的二进制文件拷贝到上级目录
cp load_model ..参考：
https://ce39906.github.io/2018/09/11/Tensorflow-%E7%BC%96%E8%AF%91%E5%8F%8A%E5%BA%94%E7%94%A8C-%E9%9D%99%E6%80%81%E5%BA%93/
2，ldd 查看程序依赖库
用来查看程序运行所需的共享库，常用来解决程序因缺少某个库文件而不能运行的一些问题。
/opt/app/todeav1/test$ldd test libstdc++.so.6 => /usr/lib64/libstdc++.so.6 (0x00000039a7e00000) libm.so.6 => /lib64/libm.so.6 (0x0000003996400000) libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00000039a5600000) libc.so.6 => /lib64/libc.so.6 (0x0000003995800000) /lib64/ld-linux-x86-64.so.2 (0x0000003995400000)
- 第一列：程序需要依赖什么库
- 第二列: 系统提供的与程序需要的库所对应的库
- 第三列：库加载的开始地址
如果依赖的某个库找不到，可以通过这个命令迅速定位问题所在。
https://linuxtools-rst.readthedocs.io/zh_CN/latest/tool/ldd.html