（双系统GPU版）DynaSLAM超详细安装配置运行ubantu20.0.4+opencv2.4.11+tensorflow-gpu1.14.0
下面链接是之前在虚拟机上安装的cpu版教程：
DynaSLAM超详细安装配置运行ubantu20.0.4+opencv2.4.11+tensorflow1.4.0
一、安装Anaconda
参考：安装Anaconda
二、安装boost库
sudo apt-get install libboost-all-dev
三. 下载DynaSLAM源码和mask_rcnn_coco.h5
3.1 下载DynaSLAM源码
git clone https://github.com/BertaBescos/DynaSLAM.git
3.2 下载mask_rcnn_coco.h5
- 从这个页面https://github.com/matterport/Mask_RCNN/releases下载
mask_rcnn_coco.h5文件 - 把文件
mask_rcnn_coco.h5复制到DynaSLAM/src/python/下
注：找不到的拉到页面的最下面。
四、用Anaconda配置Python相关的环境
4.1 配置Anaconda环境
这里先在Anaconda创建一个新的虚拟环境并激活，然后在虚拟环境中依次安装tensorflow和keras。
PS: 我已经安装了cuda11.4： ubuntu20.04,GeForce RTX 3060,CUDA Version: 11.4安装cuda
conda create -n DynaSLAM python=2.7
conda activate DynaSLAM
pip install tensorflow-gpu==1.14.0
pip install keras==2.0.9
pip install h5py==2.10.0
pip install numpy==1.16.6
pip install pillow==6.2.2
pip install pycocotools==2.0.3
pip install scikit-image==0.14.5
sudo apt-get install libcanberra-gtk-module
我后面又配置了一个conda环境 (MASKRCNN) 用的是 conda create -n MASKRCNN python=2.7.18
PS: 如果安装了scikit-image但是在运行 conda list后，找不到scikit-image，可以试试关闭这个conda环境，再重新激活环境。
conda deactivate
conda activate DynaSLAM
如果下载慢，可以试试添加清华镜像
- 第一步：添加Anaconda的清华镜像
anaconda默认的各种包的下载源，全部在国外，下载速度慢，而且经常中断，所以需要配置国内安装的镜像，这样下载速度就很快了。
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
- 第二步：设置搜索时显示通道地址
conda config --set show_channel_urls yes
注意：首次运行conda config会产生一个anacnoda的配置文件，这个配置文件和jupyter的配置文件一样，默认是不存在的。Windows为的默认位置为C://Users/username/.condarc，Linux/Mac为~/.condarc。
- 第三步：更新pip的源
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
- 查看当前下载源
(yolact) cgm:~$ conda config --show channels
channels:
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
- http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- defaults
添加上了上述镜像后，原先源仍然存在，文件中的-defaults就是原来的源。
- 清除添加的所有下载源
当我们想换回Anaconda的默认下载源时，把之前设置的移除就行了：
conda config --remove-key channels
4.2 以下是安装的截图
四、测试Mask R-CNN环境
4.1 测试Mask R-CNN环境
前提：激活conda环境，cd到DynaSLAM，再运行以下命令
python src/python/Check.py
如果输出为Mask R-CNN is correctly working，就可以下一步了。
PS: 如果安装了scikit-image，在测试的时候报错ImportError: No module named skimage.io，并且在运行 conda list后，找不到scikit-image，可以试试关闭这个conda环境，再重新激活环境。
conda deactivate DynaSLAM
conda activate DynaSLAM
4.2、相应的依赖的版本
(DynaSLAM) cgm@cgm:~$ conda list
# packages in environment at /home/cgm/anaconda3/envs/DynaSLAM:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
_openmp_mutex 4.5 2_gnu http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
absl-py 0.15.0 pypi_0 pypi
astor 0.8.1 pypi_0 pypi
backports-functools-lru-cache 1.6.4 pypi_0 pypi
backports-weakref 1.0.post1 pypi_0 pypi
ca-certificates 2022.12.7 ha878542_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
certifi 2016.9.26 py27_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cloudpickle 1.3.0 pypi_0 pypi
cycler 0.10.0 pypi_0 pypi
cython 0.29.33 pypi_0 pypi
decorator 4.4.2 pypi_0 pypi
enum34 1.1.10 pypi_0 pypi
funcsigs 1.0.2 pypi_0 pypi
futures 3.4.0 pypi_0 pypi
gast 0.5.3 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.41.1 pypi_0 pypi
h5py 2.10.0 pypi_0 pypi
keras 2.0.9 pypi_0 pypi
keras-applications 1.0.8 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.1.0 pypi_0 pypi
ld_impl_linux-64 2.40 h41732ed_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libffi 3.2.1 he1b5a44_1007 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgcc-ng 12.2.0 h65d4601_19 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgomp 12.2.0 h65d4601_19 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libsqlite 3.40.0 h753d276_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libstdcxx-ng 12.2.0 h46fd767_19 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libzlib 1.2.13 h166bdaf_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
markdown 3.1.1 pypi_0 pypi
matplotlib 2.2.5 pypi_0 pypi
mock 3.0.5 pypi_0 pypi
ncurses 6.3 h27087fc_1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
networkx 2.2 pypi_0 pypi
numpy 1.16.6 pypi_0 pypi
openssl 1.1.1t h0b41bf4_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pillow 6.2.2 pypi_0 pypi
pip 20.0.2 py27_1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
protobuf 3.17.3 pypi_0 pypi
pycocotools 2.0.3 pypi_0 pypi
pyparsing 2.4.7 pypi_0 pypi
python 2.7.15 h5a48372_1011_cpython http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-dateutil 2.8.2 pypi_0 pypi
python_abi 2.7 1_cp27mu http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytz 2022.7.1 pypi_0 pypi
pywavelets 1.0.3 pypi_0 pypi
pyyaml 5.4.1 pypi_0 pypi
readline 8.2 h8228510_1 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
scikit-image 0.14.5 pypi_0 pypi
scipy 1.2.3 pypi_0 pypi
setuptools 44.1.1 pypi_0 pypi
six 1.16.0 pypi_0 pypi
sqlite 3.40.0 h4ff8645_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
subprocess32 3.5.4 pypi_0 pypi
tensorboard 1.14.0 pypi_0 pypi
tensorflow-estimator 1.14.0 pypi_0 pypi
tensorflow-gpu 1.14.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
tk 8.6.12 h27826a3_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
werkzeug 1.0.1 pypi_0 pypi
wheel 0.34.2 py27_0 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
wrapt 1.15.0 pypi_0 pypi
zlib 1.2.13 h166bdaf_4 http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
五、安装OpenCV2.4.11和OpenCV4.2.0双版本共存
5.1 OpenCV版本要求
ps：有人说：（不是我说的，我没测试OpenCV 3.X）
README.md中写的DynaSLAM 现在支持 OpenCV 2.X 和 OpenCV 3.X。在实测中发现3.4.3版本运行异常，具体原因不知道；根据网上的建议，我重新安装了老版本的OpenCV 2.4.11
查看DynaSLAM的CMakeLists.txt ，我电脑是OpenCV4.2.0，现在安装OpenCV2.4.11双版本共存。
- 查看自己opencv版本
pkg-config opencv --modversion
- 查看opencv4版本的命令：
pkg-config opencv4 --modversion
5.2下载安装包
官网opencv 2.4.11安装包，点击下载，下载好后
注：第三行和 cmake .. 一样，最后有两个点点
- 防止报错如下：
CMake Error at cmake/OpenCVDetectCXXCompiler.cmake:85 (list) - 解决：将
OpenCVDetectCXXCompiler.cmake的内容替换为如下：
# ----------------------------------------------------------------------------
# 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()
- 防止报错如下：
rgbdodometry.cpp:65:12: fatal error: unsupported/Eigen/MatrixFunctions: 没有那个文件或目录 - 解决方案：在
opencv-2.4.11/modules/contrib/src/rgbdodometry.cpp的65行加上eigen3
#include <eigen3/unsupported/Eigen/MatrixFunctions>
//或者
#include </usr/include/eigen3/unsupported/Eigen/MatrixFunctions>
5.2 开始编译并安装
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local/opencv2.4.11 -DENABLE_PRECOMPILED_HEADERS=OFF -D WITH_FFMPEG=OFF -D WITH_CUDA=OFF -D CUDA_nppicom_LIBRARY=stdc++ ..
make -j4
sudo make install
我这里没有用
cuda：-D WITH_CUDA=OFF
我试过：-D WITH_CUDA=ON或者不要这个命令，但是编译出来有个错误：CUDA_nppi_LIBRARY (ADVANCED)，折腾了一下没弄好就算了。
cgm@cgm:~/opencv-2.4.11/build$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local/opencv2.4.11 -DENABLE_PRECOMPILED_HEADERS=OFF -D WITH_FFMPEG=OFF -D WITH_CUDA=OFF ..
CMake Deprecation Warning at CMakeLists.txt:47 (cmake_policy):
The OLD behavior for policy CMP0022 will be removed from a future version
of CMake.
The cmake-policies(7) manual explains that the OLD behaviors of all
policies are deprecated and that a policy should be set to OLD only under
specific short-term circumstances. Projects should be ported to the NEW
behavior and not rely on setting a policy to OLD.
CMake Deprecation Warning at CMakeLists.txt:52 (cmake_policy):
The OLD behavior for policy CMP0026 will be removed from a future version
of CMake.
The cmake-policies(7) manual explains that the OLD behaviors of all
policies are deprecated and that a policy should be set to OLD only under
specific short-term circumstances. Projects should be ported to the NEW
behavior and not rely on setting a policy to OLD.
-- Detected version of GNU GCC: 90 (900)
-- Could NOT find Jasper (missing: JASPER_LIBRARIES JASPER_INCLUDE_DIR)
-- Found OpenEXR: /usr/lib/x86_64-linux-gnu/libIlmImf.so
-- Looking for linux/videodev.h
-- Looking for linux/videodev.h - not found
-- Looking for linux/videodev2.h
-- Looking for linux/videodev2.h - found
-- Looking for sys/videoio.h
-- Looking for sys/videoio.h - not found
--
-- General configuration for OpenCV 2.4.11 =====================================
-- Version control: unknown
--
-- Platform:
-- Host: Linux 5.15.0-67-generic x86_64
-- CMake: 3.16.3
-- CMake generator: Unix Makefiles
-- CMake build tool: /usr/bin/make
-- Configuration: RELEASE
--
-- C/C++:
-- Built as dynamic libs?: YES
-- C++ Compiler: /usr/bin/c++ (ver 9.4.0)
-- C++ flags (Release): -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -msse3 -ffunction-sections -O3 -DNDEBUG -DNDEBUG
-- C++ flags (Debug): -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -msse3 -ffunction-sections -g -O0 -DDEBUG -D_DEBUG
-- C Compiler: /usr/bin/cc
-- C flags (Release): -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -msse3 -ffunction-sections -O3 -DNDEBUG -DNDEBUG
-- C flags (Debug): -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -msse3 -ffunction-sections -g -O0 -DDEBUG -D_DEBUG
-- Linker flags (Release):
-- Linker flags (Debug):
-- Precompiled headers: NO
--
-- OpenCV modules:
-- To be built: core flann imgproc highgui features2d calib3d ml video legacy objdetect photo gpu ocl nonfree contrib python stitching superres ts videostab
-- Disabled: world
-- Disabled by dependency: -
-- Unavailable: androidcamera dynamicuda java viz
--
-- GUI:
-- QT: NO
-- GTK+ 2.x: YES (ver 2.24.32)
-- GThread : YES (ver 2.64.6)
-- GtkGlExt: NO
-- OpenGL support: NO
-- VTK support: NO
--
-- Media I/O:
-- ZLib: /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
-- JPEG: /usr/lib/x86_64-linux-gnu/libjpeg.so (ver )
-- PNG: /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
-- TIFF: /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 - 4.1.0)
-- JPEG 2000: build (ver 1.900.1)
-- OpenEXR: /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so (ver 2.3.0)
--
-- Video I/O:
-- DC1394 1.x: NO
-- DC1394 2.x: YES (ver 2.2.5)
-- FFMPEG: NO
-- codec: NO
-- format: NO
-- util: NO
-- swscale: NO
-- gentoo-style: NO
-- GStreamer:
-- base: YES (ver 1.16.3)
-- video: YES (ver 1.16.3)
-- app: YES (ver 1.16.3)
-- riff: YES (ver 1.16.3)
-- pbutils: YES (ver 1.16.3)
-- OpenNI: NO
-- OpenNI PrimeSensor Modules: NO
-- PvAPI: NO
-- GigEVisionSDK: NO
-- UniCap: NO
-- UniCap ucil: NO
-- V4L/V4L2: Using libv4l1 (ver 1.18.0) / libv4l2 (ver 1.18.0)
-- XIMEA: NO
-- Xine: NO
--
-- Other third-party libraries:
-- Use IPP: NO
-- Use Eigen: YES (ver 3.3.7)
-- Use TBB: NO
-- Use OpenMP: NO
-- Use GCD NO
-- Use Concurrency NO
-- Use C=: NO
-- Use Cuda: NO
-- Use OpenCL: YES
--
-- OpenCL:
-- Version: dynamic
-- Include path: /home/cgm/opencv-2.4.11/3rdparty/include/opencl/1.2
-- Use AMD FFT: NO
-- Use AMD BLAS: NO
--
-- Python:
-- Interpreter: /usr/bin/python2 (ver 2.7.18)
-- Libraries: /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.18)
-- numpy: /usr/lib/python2.7/dist-packages/numpy/core/include (ver 1.16.5)
-- packages path: lib/python2.7/dist-packages
--
-- Java:
-- ant: NO
-- JNI: /usr/lib/jvm/java-8-openjdk-amd64/include /usr/lib/jvm/java-8-openjdk-amd64/include/linux /usr/lib/jvm/java-8-openjdk-amd64/include
-- Java tests: NO
--
-- Documentation:
-- Build Documentation: NO
-- Sphinx: NO
-- PdfLaTeX compiler: NO
-- Doxygen: YES (/usr/bin/doxygen)
--
-- Tests and samples:
-- Tests: YES
-- Performance tests: YES
-- C/C++ Examples: NO
--
-- Install path: /usr/local/opencv2.4.11
--
-- cvconfig.h is in: /home/cgm/opencv-2.4.11/build
-- -----------------------------------------------------------------
--
-- Configuring done
CMake Warning (dev) at apps/haartraining/CMakeLists.txt:37 (add_library):
Policy CMP0038 is not set: Targets may not link directly to themselves.
Run "cmake --help-policy CMP0038" for policy details. Use the cmake_policy
command to set the policy and suppress this warning.
Target "opencv_haartraining_engine" links to itself.
This warning is for project developers. Use -Wno-dev to suppress it.
CMake Warning (dev) at apps/haartraining/CMakeLists.txt:37 (add_library):
Policy CMP0038 is not set: Targets may not link directly to themselves.
Run "cmake --help-policy CMP0038" for policy details. Use the cmake_policy
command to set the policy and suppress this warning.
Target "opencv_haartraining_engine" links to itself.
This warning is for project developers. Use -Wno-dev to suppress it.
-- Generating done
-- Build files have been written to: /home/cgm/opencv-2.4.11/build
- 编译到96%时报错：
error: the compiler can assume that the address of ‘annotate_img’ will never be NULL [-Werror=address] - 找到
opencv-2.4.11/build/modules/contrib/CMakeFiles/opencv_contrib.dir/flags.make文件，删掉-Werror=address
然后再就成功安装了
make -j4
sudo make install
安装在这里的/usr/local/opencv2.4.11
5.3 添加环境变量
sudo gedit ~/.bashrc
//在文件末尾加上下面两行：
export PKG_CONFIG_PATH="/usr/local/opencv2.4.11/lib/pkgconfig:$PKG_CONFIG_PATH"
export LD_LIBRARY_PATH="/usr/local/opencv2.4.11/lib:$LD_LIBRARY_PATH"
//保存之后
//更新环境
source ~/.bashrc
//查看opencv的版本
pkg-config --modversion opencv
使用另一个版本（我的是4.2.0）的时候，同样终端输入 gedit ~/.bashrc 就将前两行的#号去掉即可（取消注释）。
记得要 source ~/.bashrc
六 安装编译DynaSLAM
6.1下载bbescos/feature/carla分支的DynaSLAM源码
//通过以下命令可以克隆带有carla文件的源码
git clone -b bbescos/feature/carla https://github.com/BertaBescos/DynaSLAM
PS：如果克隆的master分支，master分支里没有mono_carla.cc这个文件，需要注释掉
git clone https://github.com/BertaBescos/DynaSLAM
//注释master分支的CMakeLists.txt的末尾部分
# add_executable(mono_carla
# Examples/Monocular/mono_carla.cc)
# target_link_libraries(mono_carla ${
PROJECT_NAME})
注:master里没有mono_carla.cc这个文件
6.2 运行DynaSLAM报错解决
cgm@ubuntu:~$ conda activate DynaSLAM
(DynaSLAM) cgm@ubuntu:~$ cd DynaSLAM/
(DynaSLAM) cgm@ubuntu:~/DynaSLAM$ chmod +x build.sh
(DynaSLAM) cgm@ubuntu:~/DynaSLAM$ ./build.sh
（1）将Dynaslam根目录中的CMakeLists.txt 以及 Thirdparty中DBoW2和g2o中的CMakeLists.txt文件中的 -march=native去掉 (否则会报核心转储的错误)
//快速去掉的操作是：vscode里 ctrl+shift+F 打开搜索框，输入`-march=native` 进行全部替换
注：我最后的测试发现不删掉也可以运行。
（2）修改有错误的文件：
- 进入
Dynaslam的src中viewer.cc中，按Ctrl+F查找imshow，我们可以看到2个imshow，而且调用之前没有判断，会在某些情况导致程序终止。我们将两句话依次对应替换即可（其实就是先if(!image.empty())判断一下）
pangolin::FinishFrame();
cv::Mat im = mpFrameDrawer->DrawFrame();
//cv::imshow("DynaSLAM: Current Frame",im); //替换为如下if语句
if(!im.empty())
{
cv::imshow("DynaSLAM: Current Frame",im);
}
cv::Mat im_dyn = mpFrameDrawer->GetDynamicFrame();
//cv::imshow("DynaSLAM: Dynamic Frame", im_dyn); //替换为如下if语句
if(!im_dyn.empty())
{
cv::imshow("DynaSLAM: Dynamic Frame", im_dyn);
}
我测试的是不修改这个会闪退
注意：master分支viewer.cc是这样的
（3）报错：/usr/include/c++/9/bits/stl_map.h:122:71: error: static assertion failed: std::map must have the same value_type as its allocator
- 解决办法：（这个不修改100%报错）
//打开LoopClosing.h，将
typedef map<KeyFrame*,g2o::Sim3,std::less<KeyFrame*>,
Eigen::aligned_allocator<std::pair<const KeyFrame*, g2o::Sim3> > > KeyFrameAndPose;
//改为
typedef map<KeyFrame*,g2o::Sim3,std::less<KeyFrame*>,
Eigen::aligned_allocator<std::pair<KeyFrame *const, g2o::Sim3> > > KeyFrameAndPose;
（4）如果报错：
In file included from /home/cgm/DynaSLAM/src/Conversion.cc:9:/home/cgm/DynaSLAN/include/Conversion.h:17:10: fatal error: ndarrayobject.h:没有那个文件或目录
17|#include "ndarrayobject.h"
compilation terminated .
解决办法：在anaconda3/envs下搜索ndarrayobject.h，复制其路径，添加到ndarrayobject.h上
//Conversion.h
//将
#include "ndarrayobject.h"
//改为正确的python2.7下的路径
#include "/home/cgm/anaconda3/envs/DynaSLAM/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h"
6.3 编译成功DynaSLAM
七 运行DynaSLAM
7.1 TUM 数据集上的 RGB-D 示例
- 从http://vision.in.tum.de/data/datasets/rgbd-dataset/download下载序列并解压缩。
- 执行 python 脚本associate.py 关联RGB 图像和深度图像：
对于TUM 动态序列，这些关联文件在./Examples/RGB-D/associations/的文件夹中给出。
7.2 上面这些关联文件的由来
例如我们使用 数据集TUM的DYNAMIC OBJECTS下的（rgbd_dataset_freiburg2_desk_with_person）
使用associate.py将数据集中的RGB图片和深度图建立关联
原因：
Kinect 以非同步方式提供颜色和深度图像。这意味着来自彩色图像的时间戳集不会与深度图像的时间戳集相交。因此，我们需要某种方式将彩色图像与深度图像相关联。
为此，您可以使用“associate.py”脚本。rgb.txt文件和从文件中读取时间戳depth.txt，并通过查找最佳匹配来连接它们。
python associate.py rgb.txt depth.txt > rgbd_dataset_freiburg2_desk_with_person.txt
7.3 TUM 数据集上的 RGB-D 示例程序运行
说明：
- 运行
./Examples/RGB-D/rgbd_tum，传入参数分别为：ORB字典、配置信息、数据集路径、Mask目录、OUTPUT目录。其中mask目录和output目录是我们新建的。 - 如果提供了
PATH_TO_MASKS，Mask R-CNN用于分割每一帧的潜在动态内容。这些mask保存在提供的文件夹PATH_TO_MASKS中。如果此参数为no_save，则使用mask但不保存。如果它在PATH_TO_MASKS中找到Mask R-CNN计算的动态掩码，它会使用它们但不会再次计算它们。 - 如果提供了
PATH_TO_OUTPUT，则计算修复的帧并将其保存在PATH_TO_OUTPUT中。（背景修复）
（1）如果未提供 PATH_TO_MASKS 和 PATH_TO_OUTPUT，则仅使用几何方法检测动态对象。
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM3.yaml ../DataSet/TUM_Dataset/rgbd_dataset_freiburg3_walking_xyz/ Examples/RGB-D/associations/fr3_walking_xyz.txt
报错：
Light Tracking not working because Tracking is not initialized...
Geometry not working.
New map created with 717 points
Geometry not working.
Geometry not working.
Geometry not working.
- 解决：如果运行起来发现Light Track一直不成功，无法初始化，那么就把ORB参数设置中特征点的数目增多，github上大家一般改成3000就好了。
//TUM3.yaml文件
//ORB Extractor: Number of features per image
ORBextractor.nFeatures: 3000
我这里报了个错误：
Failed to load module "canberra-gtk-module"
解决：sudo apt-get install libcanberra-gtk-module
evo_ape tum groundtruth.txt KeyFrameTrajectory.txt --plot -va --plot_mode xy
（2）有PATH_TO_MASKS，Mask R-CNN用于分割每一帧的潜在动态内容
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM3.yaml ../DataSet/TUM_Dataset/rgbd_dataset_freiburg3_walking_xyz/ Examples/RGB-D/associations/fr3_walking_xyz.txt /data/mask
- 报错：
Geometry not working.
Light Tracking not working because Tracking is not initialized...
- 解决：如果运行起来发现Light Track一直不成功，无法初始化，那么就把ORB参数设置中特征点的数目增多，github上大家一般改成3000就好了。
//TUM3.yaml文件
//ORB Extractor: Number of features per image
ORBextractor.nFeatures: 3000
运行着运行着（卡着卡着）大概在kps:25，就崩了…
Depth Threshold (Close/Far Points): 2.98842
-------
Start processing sequence ...
Images in the sequence: 827
Light Tracking not working because Tracking is not initialized...
Geometry not working.
New map created with 769 points
OpenCV Error: Assertion failed (a_size.width == len) in gemm, file /home/cgm/opencv-2.4.11/modules/core/src/matmul.cpp, line 728
terminate called after throwing an instance of 'cv::Exception'
what(): /home/cgm/opencv-2.4.11/modules/core/src/matmul.cpp:728: error: (-215) a_size.width == len in function gemm
已放弃 (核心已转储)
哪的问题？？ OpenCV Error: Assertion failed (a_size.width == len) in gemm
（3）有PATH_TO_MASKS和PATH_TO_OUTPUT路径，Mask R-CNN用于分割每一帧的潜在动态内容
./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM3.yaml ../DataSet/TUM_Dataset/rgbd_dataset_freiburg3_walking_xyz/ Examples/RGB-D/associations/fr3_walking_xyz.txt /data/mask /data/f3_wxyz_imOut
这个不会报错，不知道为什么提供一个路径就要报错...
卡的话多跑几遍，后面再运行程序就直接读取你保存的掩码了。
