基本环境：ubuntu18.04+python2.7+opencv2.4.11
ubuntu18.04需要更新一下软件源，具体操作参考：https://blog.youkuaiyun.com/hymanjack/article/details/80285400 中的第一部分。
由于ubuntu18.04自带的是python3，所以需要安装python2.7和pip2以及顺便安装pip3：
sudo apt install python2.7
sudo apt install python-pip
sudo apt install python3-pip安装之后可以执行python看看默认进入的是哪个版本，若默认不是python2.7，则自己去搜一下如何配置，也可参考：https://cloud.tencent.com/developer/article/1570927
配置完python之后，就进入到了DynaSLAM的配置过程了，主要参考的是https://blog.youkuaiyun.com/qq_43525260/article/details/105746452 和 https://zhuanlan.zhihu.com/p/119422186 ，网上找了很多相关教程，感觉这两篇文章中排的坑比较实用。
- 安装tensorflow-1.12.3：pip install tensorflow==1.12.3 -i https://pypi.tuna.tsinghua.edu.cn/simple ，过程中若出现缺少依赖，则安装相关依赖包之后继续执行该命令安装TF；
- 安装keras-2.0.9：pip install keras==2.0.9 -i https://pypi.tuna.tsinghua.edu.cn/simple ；
- 下载相关文件：
mask_rcnn_coco.h5 ： https://github.com/matterport/Mask_RCNN/releases/tag/v2.0 opencv2.4.11 ： https://opencv.org/releases/ coco数据集 ： https://github.com/waleedka/coco pangolin ： https://github.com/stevenlovegrove/Pangolin DynaSLAM： https://github.com/BertaBescos/DynaSLAM - 编译COCO数据集：
解压下载好的COCO数据集 cd coco-master/PythonAPI sudo make install该过程应该是比较顺利的，编译也很快。编译完成之后，无需把编译好的PythonAPI下的pycocotools文件夹放置到Dynaslam的src/python目录下。我按照上文提到的其中一个博客中的操作，把pycocotools放过去之后，测试Check.py的时候会报No module named _mask的错误，产生的原因应该是文件重复了，去掉之后测试正常。
- 测试Mask-RCNN
将下载好的mask_rcnn_coco.h5文件放入Dynaslam的src/python目录下 打开Check.py文件，将ROOT_DIR = "./src/python" 改为 ROOT_DIR = "./" ， 不改似乎也能成功，参考博客中提到的需要修改路径问题好像在最新版本的DynaSLAM源码中已经进行修改 执行 python Check.py #以此检验python部分是否有错误运行成功是输出：Mask R-CNN is correctly working。可能会报一些缺少依赖包的错误，可参考第一篇博客中的第五步，这边就不赘述了。
- 安装pangolin，比较简单，可能会出现缺少xkbcommon包的错误，执行命令安装即可
sudo apt-get install libxkbcommon-x11-dev- 第一步先安装依赖
sudo apt install libgl1-mesa-dev sudo apt install libglew-dev sudo apt install cmake - 解压下载好的pangolin源码包进行编译
cd Pangolin-master mkdir build cd build cmake .. make -j4
- 第一步先安装依赖
- 安装eigen3，一句命令就能安装成功
sudo apt-get install libeigen3-dev - 安装boost libraries，中间可能会提示缺少依赖包，按提示安装相应的依赖包后继续执行命令安装即可
sudo apt-get install libboost-all-dev - 安装opencv-2.4.11
- 安装相关依赖
sudo apt-get install build-essential sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libopenexr-dev libdc1394-22-dev - 执行第三个依赖安装时，会报错-------出现错误errorE: unable to locate libjasper-dev，解决方法如下：
sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main" sudo apt update sudo apt install libjasper1 libjasper-dev 最后继续执行上一步第三批依赖安装的命令，以防有依赖丢失 - 开始编译
cd opencv-2.4.11 mkdir build cd build- 由于在cmake时会出现CMake Error at cmake/OpenCVDetectCXXCompiler.cmake:85 (list)错误，因此先在文件夹中搜索OpenCVDetectCXXCompiler.cmake文件，并将里面的内容改为：
# ---------------------------------------------------------------------------- # Detect Microsoft compiler: # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- if(CMAKE_CL_64) set(MSVC64 1) endif() if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang") set(CMAKE_COMPILER_IS_GNUCXX 1) set(CMAKE_COMPILER_IS_CLANGCXX 1) endif() if(CMAKE_C_COMPILER_ID STREQUAL "Clang") set(CMAKE_COMPILER_IS_GNUCC 1) set(CMAKE_COMPILER_IS_CLANGCC 1) endif() if("${CMAKE_CXX_COMPILER};${CMAKE_C_COMPILER}" MATCHES "ccache") set(CMAKE_COMPILER_IS_CCACHE 1) endif() # ---------------------------------------------------------------------------- # Detect Intel ICC compiler -- for -fPIC in 3rdparty ( UNIX ONLY ): # see include/opencv/cxtypes.h file for related ICC & CV_ICC defines. # NOTE: The system needs to determine if the '-fPIC' option needs to be added # for the 3rdparty static libs being compiled. The CMakeLists.txt files # in 3rdparty use the CV_ICC definition being set here to determine if # the -fPIC flag should be used. # ---------------------------------------------------------------------------- if(UNIX) if (__ICL) set(CV_ICC __ICL) elseif(__ICC) set(CV_ICC __ICC) elseif(__ECL) set(CV_ICC __ECL) elseif(__ECC) set(CV_ICC __ECC) elseif(__INTEL_COMPILER) set(CV_ICC __INTEL_COMPILER) elseif(CMAKE_C_COMPILER MATCHES "icc") set(CV_ICC icc_matches_c_compiler) endif() endif() if(MSVC AND CMAKE_C_COMPILER MATCHES "icc|icl") set(CV_ICC __INTEL_COMPILER_FOR_WINDOWS) endif() # ---------------------------------------------------------------------------- # Detect GNU version: # ---------------------------------------------------------------------------- if(CMAKE_COMPILER_IS_CLANGCXX) set(CMAKE_GCC_REGEX_VERSION "4.2.1") set(CMAKE_OPENCV_GCC_VERSION_MAJOR 4) set(CMAKE_OPENCV_GCC_VERSION_MINOR 2) set(CMAKE_OPENCV_GCC_VERSION 42) set(CMAKE_OPENCV_GCC_VERSION_NUM 402) execute_process(COMMAND ${CMAKE_CXX_COMPILER} ${CMAKE_CXX_COMPILER_ARG1} -v ERROR_VARIABLE CMAKE_OPENCV_CLANG_VERSION_FULL ERROR_STRIP_TRAILING_WHITESPACE) string(REGEX MATCH "version.*$" CMAKE_OPENCV_CLANG_VERSION_FULL "${CMAKE_OPENCV_CLANG_VERSION_FULL}") string(REGEX MATCH "[0-9]+\\.[0-9]+" CMAKE_CLANG_REGEX_VERSION "${CMAKE_OPENCV_CLANG_VERSION_FULL}") elseif(CMAKE_COMPILER_IS_GNUCXX) execute_process(COMMAND ${CMAKE_CXX_COMPILER} ${CMAKE_CXX_COMPILER_ARG1} -dumpversion OUTPUT_VARIABLE CMAKE_OPENCV_GCC_VERSION_FULL OUTPUT_STRIP_TRAILING_WHITESPACE) execute_process(COMMAND ${CMAKE_CXX_COMPILER} ${CMAKE_CXX_COMPILER_ARG1} -v ERROR_VARIABLE CMAKE_OPENCV_GCC_INFO_FULL OUTPUT_STRIP_TRAILING_WHITESPACE) # Typical output in CMAKE_OPENCV_GCC_VERSION_FULL: "c+//0 (whatever) 4.2.3 (...)" # Look for the version number, major.minor.build string(REGEX MATCH "[0-9]+\\.[0-9]+\\.[0-9]+" CMAKE_GCC_REGEX_VERSION "${CMAKE_OPENCV_GCC_VERSION_FULL}") if(NOT CMAKE_GCC_REGEX_VERSION)#major.minor string(REGEX MATCH "[0-9]+\\.[0-9]+" CMAKE_GCC_REGEX_VERSION "${CMAKE_OPENCV_GCC_VERSION_FULL}") endif() if(CMAKE_GCC_REGEX_VERSION) # Split the parts: string(REGEX MATCHALL "[0-9]+" CMAKE_OPENCV_GCC_VERSIONS "${CMAKE_GCC_REGEX_VERSION}") list(GET CMAKE_OPENCV_GCC_VERSIONS 0 CMAKE_OPENCV_GCC_VERSION_MAJOR) list(GET CMAKE_OPENCV_GCC_VERSIONS 1 CMAKE_OPENCV_GCC_VERSION_MINOR) else()#compiler returned just the major version number string(REGEX MATCH "[0-9]+" CMAKE_GCC_REGEX_VERSION "${CMAKE_OPENCV_GCC_VERSION_FULL}") if(NOT CMAKE_GCC_REGEX_VERSION)#compiler did not return anything reasonable set(CMAKE_GCC_REGEX_VERSION "0") message(WARNING "GCC version not detected!") endif() set(CMAKE_OPENCV_GCC_VERSION_MAJOR ${CMAKE_GCC_REGEX_VERSION}) set(CMAKE_OPENCV_GCC_VERSION_MINOR 0) endif() set(CMAKE_OPENCV_GCC_VERSION ${CMAKE_OPENCV_GCC_VERSION_MAJOR}${CMAKE_OPENCV_GCC_VERSION_MINOR}) math(EXPR CMAKE_OPENCV_GCC_VERSION_NUM "${CMAKE_OPENCV_GCC_VERSION_MAJOR}*100 + ${CMAKE_OPENCV_GCC_VERSION_MINOR}") message(STATUS "Detected version of GNU GCC: ${CMAKE_OPENCV_GCC_VERSION} (${CMAKE_OPENCV_GCC_VERSION_NUM})") if(WIN32) execute_process(COMMAND ${CMAKE_CXX_COMPILER} -dumpmachine OUTPUT_VARIABLE OPENCV_GCC_TARGET_MACHINE OUTPUT_STRIP_TRAILING_WHITESPACE) if(OPENCV_GCC_TARGET_MACHINE MATCHES "amd64|x86_64|AMD64") set(MINGW64 1) endif() endif() endif() if(MSVC64 OR MINGW64) set(X86_64 1) elseif(MINGW OR (MSVC AND NOT CMAKE_CROSSCOMPILING)) set(X86 1) elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "amd64.*|x86_64.*|AMD64.*") set(X86_64 1) elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "i686.*|i386.*|x86.*|amd64.*|AMD64.*") set(X86 1) elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(arm.*|ARM.*)") set(ARM 1) elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64.*|AARCH64.*)") set(AARCH64 1) endif() # Workaround for 32-bit operating systems on 64-bit x86_64 processor if(X86_64 AND CMAKE_SIZEOF_VOID_P EQUAL 4 AND NOT FORCE_X86_64) message(STATUS "sizeof(void) = 4 on x86 / x86_64 processor. Assume 32-bit compilation mode (X86=1)") unset(X86_64) set(X86 1) endif() # Similar code exists in OpenCVConfig.cmake if(NOT DEFINED OpenCV_STATIC) # look for global setting if(NOT DEFINED BUILD_SHARED_LIBS OR BUILD_SHARED_LIBS) set(OpenCV_STATIC OFF) else() set(OpenCV_STATIC ON) endif() endif() if(MSVC) if(CMAKE_CL_64) set(OpenCV_ARCH x64) elseif((CMAKE_GENERATOR MATCHES "ARM") OR ("${arch_hint}" STREQUAL "ARM") OR (CMAKE_VS_EFFECTIVE_PLATFORMS MATCHES "ARM|arm")) # see Modules/CmakeGenericSystem.cmake set(OpenCV_ARCH ARM) else() set(OpenCV_ARCH x86) endif() if(MSVC_VERSION EQUAL 1400) set(OpenCV_RUNTIME vc8) elseif(MSVC_VERSION EQUAL 1500) set(OpenCV_RUNTIME vc9) elseif(MSVC_VERSION EQUAL 1600) set(OpenCV_RUNTIME vc10) elseif(MSVC_VERSION EQUAL 1700) set(OpenCV_RUNTIME vc11) elseif(MSVC_VERSION EQUAL 1800) set(OpenCV_RUNTIME vc12) elseif(MSVC_VERSION EQUAL 1900) set(OpenCV_RUNTIME vc14) elseif(MSVC_VERSION EQUAL 1910) set(OpenCV_RUNTIME vc15) endif() elseif(MINGW) set(OpenCV_RUNTIME mingw) if(MINGW64) set(OpenCV_ARCH x64) else() set(OpenCV_ARCH x86) endif() endif() - 若出现<linux/videodev.h> not found错误，则执行
sudo apt-get install libv4l-dev sudo ln -s /usr/include/libv4l1-videodev.h /usr/include/linux/videodev.h - 若出现<sys/videoio.h> not found，可以忽略该错误，具体参考https://blog.youkuaiyun.com/qq_17783559/article/details/105078293 ， 也可以直接在usr/include中新建sys文件夹，然后执行 touch videoio.h，直接建立一个空的文件，不影响最终使用
- cmake -D WITH_FFMPEG=OFF -D ENABLE_PRECOMPILED_HEADERS=OFF ..
- 若不按前两步修改导致cmake失败，则删除build文件夹中的内容，重新执行cmake命令
- 成功后，执行 make
- 最后，执行 sudo make install
- 由于在cmake时会出现CMake Error at cmake/OpenCVDetectCXXCompiler.cmake:85 (list)错误，因此先在文件夹中搜索OpenCVDetectCXXCompiler.cmake文件，并将里面的内容改为：
- 安装相关依赖
- 编译DynaSLAM
- cd DynaSLAM-master
- 修改Dynaslam的src中viewer.cc的内容：进入代码文件中，搜索imshow，可找到一处，进行如下修改：
cv::imshow("DynaSLAM: Current Frame",im); 修改为 if(!im.empty()) { cv::imshow("DynaSLAM: Current Frame",im); } - 将Dynaslam根目录中的CMakeLists.txt 以及 Thirdparty中DBoW2和g2o中的CMakeLists.txt文件中的 -match native去掉 (否则会报核心转储的错误)
- 将Dynaslam根目录中的CMakeLists.txt中最后关于carla的三行代码注释掉
- bash build.sh
- 运行实例，下载一个数据集，这边下载的是单目数据集 rgbd_dataset_freiburg1_rpy文件，按照https://github.com/BertaBescos/DynaSLAM 的提示进行命令编辑，我这边的执行命令为：
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml /home/lmx/rgbd_dataset_freiburg1_rpy data/mask
至此，全部完结。。。。。。。。。（如遇不明白的欢迎留言交流）
重点推荐：参考两篇博文
https://blog.youkuaiyun.com/qq_43525260/article/details/105746452
https://zhuanlan.zhihu.com/p/119422186
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