search

BertaBescos / DynaSLAM

DynaSLAM is a SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups
https://bertabescos.github.io/DynaSLAM/
Other
831 stars 202 forks source link

Bus error (core dumped) #16

Open kxhit opened 5 years ago

kxhit commented 5 years ago

Hi! I met an error when running the code. I have tried this both with cpu and gpu.

Initialated Mask RCNN network...
Creating net instance...
Loading net parameters...
Bus error (core dumped)

I run the Check.py successfully and can run the code without [PATH TO MASK] [PATH TO OUTPUT] parameters. Could anyone help me? Thanks!!!

fanguohao commented 5 years ago

Hi! I met an error when running the code. I have tried this both with cpu and gpu.

Initialated Mask RCNN network...
Creating net instance...
Loading net parameters...
Bus error (core dumped)

I run the Check.py successfully and can run the code without [PATH TO MASK] [PATH TO OUTPUT] parameters. Could anyone help me? Thanks!!!

Hello there! Is your problem solved? I also encountered the same problem.

leah0206 commented 5 years ago

@fanguohao @kxhit Hello! I also encountered the same problem. My configuration is GTX 1080. The version of my opencv is 3.4.1

leah0206 commented 5 years ago

@BertaBescos I am sorry to bother you. Do you have any suggestions on this problem?

jsdd25 commented 5 years ago

I have fixed this problem. Just change your opencv version to 2.4.11

kxhit commented 5 years ago

@BertaBescos I can run the code with openCV2 but not OpenCV3. I find the program not fluent either with MaskRCNN or not. Waiting for your reply. Thanks!

jmfacil commented 5 years ago

I could not reproduce the problem. I would suggest to do as @jsdd25 proposes and change OpenCV version to 2.4.11. We will write you back if we find out what the problem is.

fangzhangyun commented 5 years ago

I also have this problem. I find the C++ can't call the MaskRCNN.py. DynaSLAM::SegmentDynObject::SegmentDynObject(): Assertion `this->py_module != NULL' failed. I don't know how to solve it. Can you help me?

BertaBescos commented 5 years ago

Are you using OpenCV 2.4.11? If not, I would recommend you to use it. We will try to fix this issue for newer versions within the next weeks.

fangzhangyun commented 5 years ago

Are you using OpenCV 2.4.11? If not, I would recommend you to use it. We will try to fix this issue for newer versions within the next weeks.

Thanks for your answer,I had changed my OpenCV from 2.4.13 to 2.4.11 ,but it is useless, I am looking forward to your newer versions

BertaBescos commented 5 years ago

Then the problem must be something else. It should work without problems on OpenCV 2.4.11. Are you running it from the DynaSLAM directory path?

fangzhangyun commented 5 years ago

Then the problem must be something else. It should work without problems on OpenCV 2.4.11. Are you running it from the DynaSLAM directory path?

I run it from DynaSLAM directory path like the ORB_SLAM2,it succeeded. when I added the parameter [PATH_TO_MASK] [PATH_TO_OUTPUT] to call the MaskRCNN, the problem has happened.

fangzhangyun commented 5 years ago

Then the problem must be something else. It should work without problems on OpenCV 2.4.11. Are you running it from the DynaSLAM directory path?

I found the python version is the key problem. Mask_RCNN requires the Python3.4 + TensorFlow1.3 + Keras2.0.8,but your DynaSLAM is python2.7. I will try this way to build the DynaSLAM.

PushyamiKaveti commented 5 years ago

Any luck with this error? I have the same problem

qxdaaaaa commented 5 years ago

I have meet the trouble for several days.But ,i can't solver it .my system is Ubuntu16.04+cuda8.0+cudnn6+opencv 2.4.11+orbslam2 environment.Can anyone help me ?

PushyamiKaveti commented 5 years ago

I got it working finally. The issue on my side was opencv 3.3. It works with opencv 2.4.10. When I debugged I found that the problem is in Conversion.cc inside the method toNDArray(const cv::Mat& m). The method has two sections for two opencv versions (2 and 3) in case of opencv 3 the PyObject returned from Mat seems to be the problem when it gets passed to GetDynSeg() of MaskRCNN.py. I am trying to fix it now.

qxdaaaaa commented 5 years ago

You are too good！Can you tell me about your configuration environment in detail ?and have you run check.py？I have trouble again yesterday, maybe I have to configure the environment for this code again.

qxdaaaaa commented 5 years ago

@PushyamiKaveti Can you help me ?

qxdaaaaa commented 5 years ago

@BertaBescos Can you help me? When I give [PATH TO MASK] [PATH TO OUTPUT] ,It is "core dumped".

qxdaaaaa commented 5 years ago

@PushyamiKaveti @BertaBescos My environment is :Ubuntu16.04 +cuda8.0+cudnn6+opencv2.4.11+python2.7+orbslam2.

qxdaaaaa commented 5 years ago

I have solved this problem, thank you for your help.@PushyamiKaveti @BertaBescos

jzpwhu1996 commented 5 years ago

I have solved this problem, thank you for your help.@PushyamiKaveti @BertaBescos

I meet the same problem, can you help me? My error is Bus error too, my python is 2.7, opencv version is 2.4.11,and Ican run Check.py successfully. @qxdaaaaa

zxGH86 commented 4 years ago

I meet the same problem, can you help me? My error is Bus error too, my python is 2.7, opencv version is 2.4.11 and 3.4.1,tensorflow-gpu1.4.0 and I can run Check.py successfully. @qxdaaaaa @kxhit @fanguohao

qxdaaaaa commented 4 years ago

you can test the tensflow .the problem ,may be the tensflow.you can use tf==cpu------------------ 原始邮件 ------------------ 发件人: "zxGH86"notifications@github.com 发送时间: 2019年12月8日(星期天) 晚上8:51 收件人: "BertaBescos/DynaSLAM"DynaSLAM@noreply.github.com; 抄送: "qxd"2096172183@qq.com;"Mention"mention@noreply.github.com; 主题: Re: [BertaBescos/DynaSLAM] Bus error (core dumped) (#16)

I meet the same problem, can you help me? My error is Bus error too, my python is 2.7, opencv version is 2.4.11 and 3.4.1,tensorflow-gpu1.4.0 and I can run Check.py successfully. @qxdaaaaa @kxhit @fanguohao

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.

zxGH86 commented 4 years ago

Thanks,@qxdaaaaa. I just test the masknet .cc find that the functioncv::Mat SegmentDynObject::GetSegmentation(cv::Mat &image,std::string dir, std::string name){ .... PyObject* py_mask_image = PyObject_CallMethod(this->net, const_cast<char*>(this->get_dyn_seg.c_str()),"(O)",py_image); ......} doesn't working, how to solve this problem?

SuperBigBrother commented 4 years ago

I meet the same problem, can you help me? My error is Bus error too, my python is 2.7, opencv version is 2.4.11 and 3.4.1,tensorflow-gpu1.4.0 and I can run Check.py successfully.

I‘d like to ask if cuda10.0 + tensorflow1.13.1 is ok? I think I have correctly installed with python2 but I can't pass check when I use terminal to run 'python check.py'. I think I need you help.....

ghost commented 4 years ago

I have solved this problem, thank you for your help.@PushyamiKaveti @BertaBescos

How did you solve the problem? @PushyamiKaveti @qxdaaaaa My system is Python 2.7, OpenCV 3.3.0 , tensorflow-gpu=1.13.1, CUDA 10.0 and I can run Check.py successfully.

KaninchenM commented 4 years ago

I have solved this problem, thank you for your help.@PushyamiKaveti @BertaBescos

How did you solve the problem? @PushyamiKaveti @qxdaaaaa My system is Python 2.7, OpenCV 3.3.0 , tensorflow-gpu=1.13.1, CUDA 10.0 and I can run Check.py successfully.

Have you run successfully in cuda10?

hanxiumeng commented 3 years ago

I got it working finally. The issue on my side was opencv 3.3. It works with opencv 2.4.10. When I debugged I found that the problem is in Conversion.cc inside the method toNDArray(const cv::Mat& m). The method has two sections for two opencv versions (2 and 3) in case of opencv 3 the PyObject returned from Mat seems to be the problem when it gets passed to GetDynSeg() of MaskRCNN.py. I am trying to fix it now.

Yes,it is the problem,the Conversion.cc failed with opencv3.And I have fixed it, here is my Conversion.cc:

include "Conversion.h"

include

namespace DynaSLAM {

static void init() { import_array(); }

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;

}

using namespace cv; //=================== ERROR HANDLING =========================================================

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;

}

//=================== THREADING ============================================================== class PyAllowThreads { public: PyAllowThreads() : _state(PyEval_SaveThread()) { } ~PyAllowThreads() { PyEval_RestoreThread(_state); } private: PyThreadState* _state; };

class PyEnsureGIL { public: PyEnsureGIL() : _state(PyGILState_Ensure()) { } ~PyEnsureGIL() { PyGILState_Release(_state); } private: PyGILState_STATE _state; };

enum { ARG_NONE = 0, ARG_MAT = 1, ARG_SCALAR = 2 };

class NumpyAllocator: public MatAllocator { public: 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;

};

NumpyAllocator g_numpyAllocator;

NDArrayConverter::NDArrayConverter() { init(); }

void NDArrayConverter::init() { import_array(); }

cv::Mat NDArrayConverter::toMat(const PyObject o) { cv::Mat m; bool allowND = true; if (!PyArray_Check(o)) { failmsg("argument is not a numpy array"); if (!m.data) m.allocator = &g_numpyAllocator; } else { PyArrayObject oarr = (PyArrayObject*) o;

    bool needcopy = false, needcast = false;
    int typenum = PyArray_TYPE(oarr), new_typenum = typenum;
    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 ? CV_32S :
                typenum == NPY_INT32 ? CV_32S :
                typenum == NPY_FLOAT ? CV_32F :
                typenum == NPY_DOUBLE ? CV_64F : -1;

    if (type < 0) {
        if (typenum == NPY_INT64 || typenum == NPY_UINT64
                || type == NPY_LONG) {
            needcopy = needcast = true;
            new_typenum = NPY_INT;
            type = CV_32S;
        } else {
            failmsg("Argument data type is not supported");
            m.allocator = &g_numpyAllocator;
            return m;
        }
    }

ifndef CV_MAX_DIM

    const int CV_MAX_DIM = 32;

endif

    int ndims = PyArray_NDIM(oarr);
    if (ndims >= CV_MAX_DIM) {
        failmsg("Dimensionality of argument is too high");
        if (!m.data)
            m.allocator = &g_numpyAllocator;
        return m;
    }

    int size[CV_MAX_DIM + 1];
    size_t step[CV_MAX_DIM + 1];
    size_t elemsize = CV_ELEM_SIZE1(type);
    const npy_intp* _sizes = PyArray_DIMS(oarr);
    const npy_intp* _strides = PyArray_STRIDES(oarr);
    bool ismultichannel = ndims == 3 && _sizes[2] <= CV_CN_MAX;

    for (int i = ndims - 1; i >= 0 && !needcopy; i--) {
        // these checks handle cases of
        //  a) multi-dimensional (ndims > 2) arrays, as well as simpler 1- and 2-dimensional cases
        //  b) transposed arrays, where _strides[] elements go in non-descending order
        //  c) flipped arrays, where some of _strides[] elements are negative
        if ((i == ndims - 1 && (size_t) _strides[i] != elemsize)
                || (i < ndims - 1 && _strides[i] < _strides[i + 1]))
            needcopy = true;
    }

    if (ismultichannel && _strides[1] != (npy_intp) elemsize * _sizes[2])
        needcopy = true;

    if (needcopy) {

        if (needcast) {
            o = PyArray_Cast(oarr, new_typenum);
            oarr = (PyArrayObject*) o;
        } else {
            oarr = PyArray_GETCONTIGUOUS(oarr);
            o = (PyObject*) oarr;
        }

        _strides = PyArray_STRIDES(oarr);
    }

    for (int i = 0; i < ndims; i++) {
        size[i] = (int) _sizes[i];
        step[i] = (size_t) _strides[i];
    }

    // handle degenerate case
    if (ndims == 0) {
        size[ndims] = 1;
        step[ndims] = elemsize;
        ndims++;
    }

    if (ismultichannel) {
        ndims--;
        type |= CV_MAKETYPE(0, size[2]);
    }

    if (ndims > 2 && !allowND) {
        failmsg("%s has more than 2 dimensions");
    } else {

        m = Mat(ndims, size, type, PyArray_DATA(oarr), step);
        m.u = g_numpyAllocator.allocate((PyObject*)o, ndims, size, type, step);
        m.addref();

        if (!needcopy) {
            Py_INCREF(o);
        }
    }
    m.allocator = &g_numpyAllocator;
}
return m;

}

PyObject NDArrayConverter::toNDArray(const cv::Mat& m) { if (!m.data) Py_RETURN_NONE; Mat temp, p = (Mat) &m; if (!p->u || p->allocator != &g_numpyAllocator) { temp.allocator = &g_numpyAllocator; ERRWRAP2(m.copyTo(temp)); p = &temp; } std::cout<u->refcount<<std::endl; std::cout<u->userdata<<std::endl; PyObject o = (PyObject*) p->u->userdata; Py_INCREF(o); return o;

}

}

mobiiin commented 3 years ago

@hanxiumeng your code doesn't work either, leads to the same error could you share the modified conversion.cc file? did you get it to work with opencv 3?