opencv - 直方图归一化时的编译器错误

Question

我正在使用以下代码来规范视频文件中一系列帧的直方图，并且仅在compareHist方法返回的差值超过某个阈值时才更改帧:

bool
DMSUSBVideoDevicePlugin::_hasImageChanged()
{
    using namespace cv;
    Mat src = cv::Mat(_captureHeight, _captureWidth, CV_8UC3, (void*) _imageData);
    /// Separate the image in 3 places ( B, G and R )
    vector<Mat> bgr_planes;
    split( src, bgr_planes );

    /// Establish the number of bins
    int histSize = 256;

    /// Set the ranges ( for B,G,R) )
    float range[] = { 0, 256 } ;
    const float* histRange = { range };

    bool uniform = true; 
    bool accumulate = false;

    SparseMat b_hist, g_hist, r_hist;

    /// Compute the histograms:
    calcHist( &bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate);

    // Draw the histograms for B, G and R
    int hist_w = 512; int hist_h = 400;
    int bin_w = cvRound( (double) hist_w/histSize );
    Mat histImage( hist_h, hist_w, CV_8UC3, Scalar( 0,0,0) );

    normalize(g_hist, g_hist, 0.0, histImage.rows, 32, -1, SparseMat());

    static bool isFirstFrame = true;
    if(isFirstFrame)
    {
        _lastGreenHistogram = g_hist;
        isFirstFrame = false;
        return true;
    }

    double diff = compareHist(g_hist, _lastGreenHistogram, CV_COMP_BHATTACHARYYA );
    _lastGreenHistogram = g_hist;
    std::cout << "Diff val: " << diff << std::endl;
    if(diff > 1.f)
    {
        return true;
    }

    return false;
}

但是，我收到一个编译器错误消息:

1>.\DMSUSBVideoDevicePlugin.cpp(454) : error C2665: 'cv::normalize' : none of the 2 overloads could convert all the argument types 1>
C:\opencv\build\include\opencv2/core/core.hpp(2023): could be 'void cv::normalize(cv::InputArray,cv::OutputArray,double,double,int,int,cv::InputArray)'

我究竟做错了什么？

Answer 1

您正在尝试将SparseMat转换为InputArray。这是不合法的。 InputArray需要具有顺序内存的容器。
从文档:

where InputArray is a class that can be constructed from Mat, Mat, Matx, std::vector, std::vector > or std::vector. It can also be constructed from a matrix expression.

您可以阅读InputArray here。

您应该使用常规的 cv::Mat对象，因为normalize函数不会创建源的副本。
OutputArray也是如此。