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 examples
set(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_DBOW2
DBoW2/BowVector.h
DBoW2/FORB.h
DBoW2/FClass.h
DBoW2/FeatureVector.h
DBoW2/ScoringObject.h
DBoW2/TemplatedVocabulary.h)
set(SRCS_DBOW2
DBoW2/BowVector.cpp
DBoW2/FORB.cpp
DBoW2/FeatureVector.cpp
DBoW2/ScoringObject.cpp)
set(HDRS_DUTILS
DUtils/Random.h
DUtils/Timestamp.h)
set(SRCS_DUTILS
DUtils/Random.cpp
DUtils/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_RETVAL
namespace 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 bug
cv::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);
}
#else
NumpyAllocator() {
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 case
return 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)
#else
m.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);
#else
if(!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~