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我有两个课程如下。

class NeuroShield
{
public:

    NeuroShield();
    uint16_t begin();

    void setNcr(uint16_t value);
    uint16_t getNcr();
    void setComp(uint8_t value);
    uint8_t getComp();
    void setLastComp(uint8_t value);
    void setIndexComp(uint16_t value);
    uint16_t getDist();
    void setCat(uint16_t value);
    uint16_t getCat();
    void setAif(uint16_t value);
    uint16_t getAif();
    void setMinif(uint16_t value);
    uint16_t getMinif();
    void setMaxif(uint16_t value);
    uint16_t getMaxif();
    uint16_t getNid();
    void setGcr(uint16_t value);
    uint16_t getGcr();
    void resetChain();
    void setNsr(uint16_t value);
    uint16_t getNsr();
    uint16_t getNcount();
    void setPowerSave();
    void forget();
    void forget(uint16_t maxif);

    void countTotalNeurons();
    void clearNeurons();

    void setContext(uint8_t context);
    void setContext(uint8_t context, uint16_t minif, uint16_t maxif);
    void getContext(uint8_t* context, uint16_t* minif, uint16_t* maxif);
    void setRbfClassifier();
    void setKnnClassifier();

    uint16_t broadcast(uint8_t vector[], uint16_t length);
    uint16_t learn(uint8_t vector[], uint16_t length, uint16_t category);
    uint16_t classify(uint8_t vector[], uint16_t length);
    uint16_t classify(uint8_t vector[], uint16_t length, uint16_t* distance, uint16_t* category, uint16_t* nid);
    uint16_t classify(uint8_t vector[], uint16_t length, uint16_t k, uint16_t distance[], uint16_t category[], uint16_t nid[]);

    void readNeuron(uint16_t nid, uint16_t model[], uint16_t* ncr, uint16_t* aif, uint16_t* cat);
    void readNeuron(uint16_t nid, uint16_t nuerons[]);
    uint16_t readNeurons(uint16_t neurons[]);
    void readCompVector(uint16_t* data, uint16_t size);
    void writeNeurons(uint16_t neurons[], uint16_t ncount);
    void writeCompVector(uint16_t* data, uint16_t size);

    uint16_t testCommand(uint8_t read_write, uint8_t reg, uint16_t data);

    uint16_t fpgaVersion();
    void nm500Reset();
    void ledSelect(uint8_t data);

    uint16_t total_neurons;

private:
    uint16_t support_burst_read = 0;
};

另一个类是来自 opencv 的 Parallel_process。

class Parallel_process : public cv::ParallelLoopBody
{

private:
    Mat gray_img;
    Mat orig_img;
    int size;
    int row;
    NeuroShield hnn;
    vector<uint16_t> dists;
public:
    uint16_t nm_cat, nm_nid;
    Parallel_process(Mat inputImgage, Mat orgImg, int row_, NeuroShield &hnn_) : gray_img(inputImgage), row(row_), hnn(hnn_){}

    virtual void operator()(const Range& range) const
    {
        for (int col = range.start; col < range.end; col = col +2)
        {
            uint8_t vector[NEURON_SIZE];
            Mat roi_img = gray_img(Rect(col, row, size, size));
            Mat res;
            resize(roi_img, res, Size(16, 16), 0, 0, INTER_LINEAR);
            uint8_t* data = (uint8_t*)res.data;
            for (int j = 0; j < VECTOR_SIZE; j++)
                vector[j] = *data++;
            uint16_t nm_dist;
            hnn.classify(vector, VECTOR_SIZE, &nm_dist, &nm_cat, &nm_nid);
            dists.push_back(nm_dist);

        }
    }
};

在 main 函数中,并行进程被称为

cv::parallel_for_(cv::Range(0, 8), Parallel_process(inputImgage, orgImg, row_, hnn, dists_))

但是我在以下两行有两个编译错误。

hnn.classify(vector, VECTOR_SIZE, &nm_dist, &nm_cat, &nm_nid);
dists.push_back(nm_dist);

错误是

Error   C2663   'NeuroShield::classify': 3 overloads have no legal conversion for 'this' pointer    
Error   C2663   'std::vector<uint16_t,std::allocator<_Ty>>::push_back': 2 overloads have no legal conversion for 'this' pointer 

有什么问题?

4

2 回答 2

3

您不能在const调用它的 -qualified 函数中修改实例。*)

从 中删除const-qualifier Parallel_process::operator()()

*) 缺少声明为可变的成员。

于 2018-11-08T06:38:55.043 回答
3

Parallel_process类继承自cv::ParallelLoopBody,所以你必须重写

virtual void operator()(const Range& range) const
                                            ^^^^^

const限定符意味着任何数据成员都不能在此方法内修改。

NeuroShield hnn;

被调用修改

hnn.classify(vector, VECTOR_SIZE, &nm_dist, &nm_cat, &nm_nid);

因为classify方法NeuroShield是非常量的。您可以制作classify方法,因为const编译器不会抱怨。第二个问题是vector<uint16_t> dists;. operator()()仅当您向其添加说明符时,才能修改此向量mutable

mutable vector<uint16_t> dists;

以上所有内容都解释了为什么您的代码无法编译。您的代码中的主要问题是您使用cv::ParallelLoopBody.

正确的方法是:

  1. 准备用于存储结果的容器

  2. 通过引用/指针将此容器传递给派生自的对象cv::ParallelLoopBody

  3. 现在operator()() const你可以修改引用/指针指向的数据[指针没有改变,但指向的数据可以——这是解决问题的关键]

所以

int size;
int row;
NeuroShield& hnn; // make reference 
vector<uint16_t>& dists; // make reference

导演:

Parallel_process(Mat inputImgage, Mat orgImg, int row_, NeuroShield &hnn_, vector<uint16_t>& vec) : 
gray_img(inputImgage), 
row(row_), 
hnn(hnn_),
dists(vec) {}

现在这些行

   hnn.classify(vector, VECTOR_SIZE, &nm_dist, &nm_cat, &nm_nid);
   dists.push_back(nm_dist);

应该管用。dists可能您在访问向量时应该使用一些同步方法,因为此代码是同时运行的。

于 2018-11-08T07:15:47.363 回答