好的，又来到了一篇源码运行记录← ←

一. 基础环境

根据作者源码Readme文件，必须需要有Python2.7，后续我装tensorflow什么都是在Anaconda建立的一个虚拟环境下弄的。这篇记录是对于OpenCV 3.x的版本的！对于3.x的版本的修改在第四大点，前面都是通用的部分。

二. 满足能编译ORB-SLAM2的条件

因为这个源码是基于ORB-SLAM2写的，所以当然要满足一些预备条件，需要预先安装一些库比如C++11 or C++0x Compiler, Pangolin, OpenCV and Eigen3，如果不知道怎么装，可以再手动搜索一下。DynaSLAM刚出时只能支持OpenCV2.4.11，不过19年有热心人提交了可以支持Opencv3.x的代码，后面我们再详细说。我自己装的是OpenCV 3.4.5

三. 编译DynaSLAM前需要安装的其他库

按照开源代码的Readme文件：

1. 安装boost库

sudo apt-get install libboost-all-dev

2. 下载DynaSLAM源码并放入h5文件

git clone https://github.com/BertaBescos/DynaSLAM.git
然后从这个页面https://github.com/matterport/Mask_RCNN/releases下载h5文件，把文件存到DynaSLAM/src/python/下

3.Python相关的环境

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。

conda create -n MaskRCNN python=2.7
conda activate MaskRCNN
pip install tensorflow==1.14.0  #或者 pip install tensorflow-gpu==1.14.0
pip install keras==2.0.9

完成上面的步骤后，python的环境差不多就弄好了，下面可以测试一下

cd DynaSLAM
python src/python/Check.py

如果输出为Mask R-CNN is correctly working，就可以下一步了。然而，事情很难这么顺利哈哈哈，那么就一一解决。我这里碰到了两个问题：

3.1 没有安装scikit-image

sudo pip install scikit-image

3.2 关于pycocotools的报错

注意！这里一定要在Python2.7(cocoapi只支持Python2)的时候进行安装！否则运行Check.py的时候会报错找不到_mask, 因为Python3运行的话就不会生成_mask.so这个文件。

git clone https://github.com/waleedka/coco
python PythonAPI/setup.py build_ext install

运行完上面指令之后就把pycocotools文件夹整个复制到src/python/下，像这样：

四. 修改部分DynaSLAM源码

这里非常感谢这个小姐姐（？），原地址在这里：Pushyami_dev，如果想看看代码具体增删了哪些可以点进去看看。
提交的代码主要是针对Opencv3的使用做出了一些修改，然后在这个代码的基础上，去掉CMakeLists.txt中的-march=native（会出现Segment Default报错），/Thirdparty/DBoW2中的记得也要去一下。代码主要在以下部分做了修改，直接整个复制到文件夹里就行，注意修改一下自己OpenCV3.x的版本。

CMakeLists.txt
Thirdparty/DBoW2/CMakeLists.txt
include/Conversion.h
src/Conversion.cc

1. CMakeLists.txt

cmake_minimum_required(VERSION 2.8)
project(DynaSLAM)IF(NOT CMAKE_BUILD_TYPE)SET(CMAKE_BUILD_TYPE Release)# SET(CMAKE_BUILD_TYPE Debug)
ENDIF()MESSAGE("Build type: " ${CMAKE_BUILD_TYPE})#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 -march=native ")
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall   -O3 -march=native")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3  ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall   -O3 ")
# This is required if opencv is built from source locally
#SET(OpenCV_DIR "~/opencv/build")# set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O0 -march=native ")
# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall   -O0 -march=native")# Check C++11 or C++0x support
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")add_definitions(-DCOMPILEDWITHC11)message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")add_definitions(-DCOMPILEDWITHC0X)message(STATUS "Using flag -std=c++0x.")
else()message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules)set(Python_ADDITIONAL_VERSIONS "2.7")
#This is to avoid detecting python 3
find_package(PythonLibs 2.7 EXACT REQUIRED)
if (NOT PythonLibs_FOUND)message(FATAL_ERROR "PYTHON LIBS not found.")
else()message("PYTHON LIBS were found!")message("PYTHON LIBS DIRECTORY: " ${PYTHON_LIBRARY} ${PYTHON_INCLUDE_DIRS})
endif()message("PROJECT_SOURCE_DIR: " ${OpenCV_DIR})
find_package(OpenCV 3.4 QUIET)
if(NOT OpenCV_FOUND)find_package(OpenCV 2.4 QUIET)if(NOT OpenCV_FOUND)message(FATAL_ERROR "OpenCV > 2.4.x not found.")endif()
endif()find_package(Qt5Widgets REQUIRED)
find_package(Qt5Concurrent REQUIRED)
find_package(Qt5OpenGL REQUIRED)
find_package(Qt5Test REQUIRED)find_package(Boost REQUIRED COMPONENTS thread)
if(Boost_FOUND)message("Boost was found!")message("Boost Headers DIRECTORY: " ${Boost_INCLUDE_DIRS})message("Boost LIBS DIRECTORY: " ${Boost_LIBRARY_DIRS})message("Found Libraries: " ${Boost_LIBRARIES})
endif()find_package(Eigen3 3.1.0 REQUIRED)
find_package(Pangolin REQUIRED)set(PYTHON_INCLUDE_DIRS ${PYTHON_INCLUDE_DIRS} /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy)include_directories(
${PROJECT_SOURCE_DIR}
${PROJECT_SOURCE_DIR}/include
${EIGEN3_INCLUDE_DIR}
${Pangolin_INCLUDE_DIRS}
${PYTHON_INCLUDE_DIRS}
/usr/include/python2.7/
#/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/
${Boost_INCLUDE_DIRS}
)
message("PROJECT_SOURCE_DIR: " ${PROJECT_SOURCE_DIR})
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/lib)add_library(${PROJECT_NAME} SHARED
src/System.cc
src/Tracking.cc
src/LocalMapping.cc
src/LoopClosing.cc
src/ORBextractor.cc
src/ORBmatcher.cc
src/FrameDrawer.cc
src/Converter.cc
src/MapPoint.cc
src/KeyFrame.cc
src/Map.cc
src/MapDrawer.cc
src/Optimizer.cc
src/PnPsolver.cc
src/Frame.cc
src/KeyFrameDatabase.cc
src/Sim3Solver.cc
src/Initializer.cc
src/Viewer.cc
src/Conversion.cc
src/MaskNet.cc
src/Geometry.cc
)target_link_libraries(${PROJECT_NAME}
${OpenCV_LIBS}
${EIGEN3_LIBS}
${Pangolin_LIBRARIES}
${PROJECT_SOURCE_DIR}/Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/Thirdparty/g2o/lib/libg2o.so
/usr/lib/x86_64-linux-gnu/libpython2.7.so
${Boost_LIBRARIES}
)# Build examplesset(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/RGB-D)add_executable(rgbd_tum
Examples/RGB-D/rgbd_tum.cc)
target_link_libraries(rgbd_tum ${PROJECT_NAME})set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/Stereo)add_executable(stereo_kitti
Examples/Stereo/stereo_kitti.cc)
target_link_libraries(stereo_kitti ${PROJECT_NAME})set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/Monocular)add_executable(mono_tum
Examples/Monocular/mono_tum.cc)
target_link_libraries(mono_tum ${PROJECT_NAME})

2.Thirdparty/DBoW2/CMakeLists.txt

cmake_minimum_required(VERSION 2.8)
project(DBoW2)#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 -march=native ")
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall  -O3 -march=native")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall  -O3 ")set(HDRS_DBOW2DBoW2/BowVector.hDBoW2/FORB.h DBoW2/FClass.h       DBoW2/FeatureVector.hDBoW2/ScoringObject.h   DBoW2/TemplatedVocabulary.h)
set(SRCS_DBOW2DBoW2/BowVector.cppDBoW2/FORB.cpp      DBoW2/FeatureVector.cppDBoW2/ScoringObject.cpp)set(HDRS_DUTILSDUtils/Random.hDUtils/Timestamp.h)
set(SRCS_DUTILSDUtils/Random.cppDUtils/Timestamp.cpp)find_package(OpenCV 3.4 QUIET)
if(NOT OpenCV_FOUND)find_package(OpenCV 2.4.3 QUIET)if(NOT OpenCV_FOUND)message(FATAL_ERROR "OpenCV > 2.4.3 not found.")endif()
endif()set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib)include_directories(${OpenCV_INCLUDE_DIRS})
add_library(DBoW2 SHARED ${SRCS_DBOW2} ${SRCS_DUTILS})
target_link_libraries(DBoW2 ${OpenCV_LIBS})

3.include/Conversion.h

/**
* This file is part of DynaSLAM.
* Copyright (C) 2018 Berta Bescos <bbescos at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/bertabescos/DynaSLAM>.
*
*/#ifndef CONVERSION_H_#define CONVERSION_H_#include <Python.h>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include "numpy/ndarrayobject.h"
// #include "__multiarray_api.h"#define NUMPY_IMPORT_ARRAY_RETVALnamespace DynaSLAM
{static PyObject* opencv_error = 0;static int failmsg(const char *fmt, ...);class PyAllowThreads;class PyEnsureGIL;#define ERRWRAP2(expr) \
try \
{ \PyAllowThreads allowThreads; \expr; \
} \
catch (const cv::Exception &e) \
{ \PyErr_SetString(opencv_error, e.what()); \return 0; \
}static PyObject* failmsgp(const char *fmt, ...);static size_t REFCOUNT_OFFSET = (size_t)&(((PyObject*)0)->ob_refcnt) +(0x12345678 != *(const size_t*)"\x78\x56\x34\x12\0\0\0\0\0")*sizeof(int);static inline PyObject* pyObjectFromRefcount(const int* refcount)
{return (PyObject*)((size_t)refcount - REFCOUNT_OFFSET);
}static inline int* refcountFromPyObject(const PyObject* obj)
{return (int*)((size_t)obj + REFCOUNT_OFFSET);
}class NumpyAllocator;enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };class NDArrayConverter
{private:void init();
public:NDArrayConverter();//cv::Mat toMat(const PyObject* o);   //issue bugcv::Mat toMat(PyObject* o);PyObject* toNDArray(const cv::Mat& mat);
};}#endif /* CONVERSION_H_ */

4. src/Conversion.cc

/**
* This file is part of DynaSLAM.
* Copyright (C) 2018 Berta Bescos <bbescos at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/bertabescos/DynaSLAM>.
*
*/#include "Conversion.h"
#include <iostream>namespace DynaSLAM
{static void init()
{import_array();
}static int failmsg(const char *fmt, ...)
{char str[1000];va_list ap;va_start(ap, fmt);vsnprintf(str, sizeof(str), fmt, ap);va_end(ap);PyErr_SetString(PyExc_TypeError, str);return 0;
}class PyAllowThreads
{public:PyAllowThreads() : _state(PyEval_SaveThread()) {}~PyAllowThreads(){PyEval_RestoreThread(_state);}
private:PyThreadState* _state;
};class PyEnsureGIL
{public:PyEnsureGIL() : _state(PyGILState_Ensure()) {}~PyEnsureGIL(){//std::cout << "releasing"<< std::endl;PyGILState_Release(_state);}
private:PyGILState_STATE _state;
};using namespace cv;static PyObject* failmsgp(const char *fmt, ...)
{char str[1000];va_list ap;va_start(ap, fmt);vsnprintf(str, sizeof(str), fmt, ap);va_end(ap);PyErr_SetString(PyExc_TypeError, str);return 0;
}class NumpyAllocator : public MatAllocator
{public:
#if ( CV_MAJOR_VERSION < 3)NumpyAllocator() {}~NumpyAllocator() {}void allocate(int dims, const int* sizes, int type, int*& refcount,uchar*& datastart, uchar*& data, size_t* step){//PyEnsureGIL gil;int depth = CV_MAT_DEPTH(type);int cn = CV_MAT_CN(type);const int f = (int)(sizeof(size_t)/8);int typenum = depth == CV_8U ? NPY_UBYTE : depth == CV_8S ? NPY_BYTE :depth == CV_16U ? NPY_USHORT : depth == CV_16S ? NPY_SHORT :depth == CV_32S ? NPY_INT : depth == CV_32F ? NPY_FLOAT :depth == CV_64F ? NPY_DOUBLE : f*NPY_ULONGLONG + (f^1)*NPY_UINT;int i;npy_intp _sizes[CV_MAX_DIM+1];for( i = 0; i < dims; i++ ){_sizes[i] = sizes[i];}if( cn > 1 ){_sizes[dims++] = cn;}PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);if(!o){CV_Error_(CV_StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));}refcount = refcountFromPyObject(o);npy_intp* _strides = PyArray_STRIDES(o);for( i = 0; i < dims - (cn > 1); i++ )step[i] = (size_t)_strides[i];datastart = data = (uchar*)PyArray_DATA(o);}void deallocate(int* refcount, uchar*, uchar*){//PyEnsureGIL gil;if( !refcount )return;PyObject* o = pyObjectFromRefcount(refcount);Py_INCREF(o);Py_DECREF(o);}
#elseNumpyAllocator() {stdAllocator = Mat::getStdAllocator();}~NumpyAllocator() {}UMatData* allocate(PyObject* o, int dims, const int* sizes, int type,size_t* step) const {UMatData* u = new UMatData(this);u->data = u->origdata = (uchar*) PyArray_DATA((PyArrayObject*) o);npy_intp* _strides = PyArray_STRIDES((PyArrayObject*) o);for (int i = 0; i < dims - 1; i++)step[i] = (size_t) _strides[i];step[dims - 1] = CV_ELEM_SIZE(type);u->size = sizes[0] * step[0];u->userdata = o;return u;}UMatData* allocate(int dims0, const int* sizes, int type, void* data,size_t* step, int flags, UMatUsageFlags usageFlags) const {if (data != 0) {CV_Error(Error::StsAssert, "The data should normally be NULL!");// probably this is safe to do in such extreme casereturn stdAllocator->allocate(dims0, sizes, type, data, step, flags,usageFlags);}PyEnsureGIL gil;int depth = CV_MAT_DEPTH(type);int cn = CV_MAT_CN(type);const int f = (int) (sizeof(size_t) / 8);int typenum =depth == CV_8U ? NPY_UBYTE :depth == CV_8S ? NPY_BYTE :depth == CV_16U ? NPY_USHORT :depth == CV_16S ? NPY_SHORT :depth == CV_32S ? NPY_INT :depth == CV_32F ? NPY_FLOAT :depth == CV_64F ?NPY_DOUBLE :f * NPY_ULONGLONG + (f ^ 1) * NPY_UINT;int i, dims = dims0;cv::AutoBuffer<npy_intp> _sizes(dims + 1);for (i = 0; i < dims; i++)_sizes[i] = sizes[i];if (cn > 1)_sizes[dims++] = cn;PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);if (!o)CV_Error_(Error::StsError,("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));return allocate(o, dims0, sizes, type, step);}bool allocate(UMatData* u, int accessFlags,UMatUsageFlags usageFlags) const {return stdAllocator->allocate(u, accessFlags, usageFlags);}void deallocate(UMatData* u) const {if (u) {PyEnsureGIL gil;PyObject* o = (PyObject*) u->userdata;Py_XDECREF(o);delete u;}}const MatAllocator* stdAllocator;
#endif
};NumpyAllocator g_numpyAllocator;NDArrayConverter::NDArrayConverter() { init(); }void NDArrayConverter::init()
{import_array();
}cv::Mat NDArrayConverter::toMat( PyObject *o)
{cv::Mat m;if(!o || o == Py_None){if( !m.data )m.allocator = &g_numpyAllocator;}if( !PyArray_Check(o) ){failmsg("toMat: Object is not a numpy array");}int typenum = PyArray_TYPE(o);int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S :typenum == NPY_USHORT ? CV_16U : typenum == NPY_SHORT ? CV_16S :typenum == NPY_INT || typenum == NPY_LONG ? CV_32S :typenum == NPY_FLOAT ? CV_32F :typenum == NPY_DOUBLE ? CV_64F : -1;if( type < 0 ){failmsg("toMat: Data type = %d is not supported", typenum);}int ndims = PyArray_NDIM(o);if(ndims >= CV_MAX_DIM){failmsg("toMat: Dimensionality (=%d) is too high", ndims);}int size[CV_MAX_DIM+1];size_t step[CV_MAX_DIM+1], elemsize = CV_ELEM_SIZE1(type);const npy_intp* _sizes = PyArray_DIMS(o);const npy_intp* _strides = PyArray_STRIDES(o);bool transposed = false;for(int i = 0; i < ndims; i++){size[i] = (int)_sizes[i];step[i] = (size_t)_strides[i];}if( ndims == 0 || step[ndims-1] > elemsize ) {size[ndims] = 1;step[ndims] = elemsize;ndims++;}if( ndims >= 2 && step[0] < step[1] ){std::swap(size[0], size[1]);std::swap(step[0], step[1]);transposed = true;}if( ndims == 3 && size[2] <= CV_CN_MAX && step[1] == elemsize*size[2] ){ndims--;type |= CV_MAKETYPE(0, size[2]);}if( ndims > 2){failmsg("toMat: Object has more than 2 dimensions");}m = Mat(ndims, size, type, PyArray_DATA(o), step);if( m.data ){#if ( CV_MAJOR_VERSION < 3)m.refcount = refcountFromPyObject(o);m.addref(); // protect the original numpy array from deallocation// (since Mat destructor will decrement the reference counter)
#elsem.u = g_numpyAllocator.allocate(o, ndims, size, type, step);m.addref();Py_INCREF(o);//m.u->refcount = *refcountFromPyObject(o);
#endif};m.allocator = &g_numpyAllocator;if( transposed ){Mat tmp;tmp.allocator = &g_numpyAllocator;transpose(m, tmp);m = tmp;}return m;
}PyObject* NDArrayConverter::toNDArray(const cv::Mat& m)
{if( !m.data )Py_RETURN_NONE;Mat temp;Mat *p = (Mat*)&m;
#if ( CV_MAJOR_VERSION < 3)if(!p->refcount || p->allocator != &g_numpyAllocator){temp.allocator = &g_numpyAllocator;m.copyTo(temp);p = &temp;}p->addref();return pyObjectFromRefcount(p->refcount);
#elseif(!p->u || p->allocator != &g_numpyAllocator){temp.allocator = &g_numpyAllocator;m.copyTo(temp);p = &temp;}//p->addref();//return pyObjectFromRefcount(&p->u->refcount);PyObject* o = (PyObject*) p->u->userdata;Py_INCREF(o);return o;
#endif}}

五.编译源码与运行

编译DynaSLAM源码

cd DynaSLAM
chmod +x build.sh
./build.sh

如果运行时不给后面两个参数，就相当于运行ORB-SLAM2
如果只想用MaskRCNN的功能但不想存mask，那么在PATH_MASK那里就写为no_save，否则就给一个存Mask的文件夹地址

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM3.yaml /XXX/tum_dataset/ /XXX/tum_dataset/associations.txt masks/ output/

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。

完结，撒花~

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大家还记得以前有一款趣味测嚒? 那款趣味测试可以说在当时是只要当时做小程序的基本是人手一款不过后来自从腾讯更新小程序登录接口以后,那款小程序也就和接口一起挂了那么呢现在小编就给大家发布修复过的,修 ...

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