如何在c++ OpenCV中将图像(Mat)转换为输入数组

Question

我想对灰度图像的强度值应用k-means聚类。我真的很困惑如何将像素表示成矢量。如果图像是H x W个像素，那么向量应该是H*W维的。

我试过的是:

int myClass::myFunction(const cv::Mat& img)
{
    cv::Mat grayImg;    
    cvtColor(img, grayImg, CV_RGB2GRAY);    
    cv::Mat bestLabels, centers, clustered;
    cv::Mat p = cv::Mat::zeros(grayImg.cols*grayImg.rows, 1, CV_32F);
    int i = -1;
    for (int c = 0; c<img.cols; c++) {
        for (int r = 0; r < img.rows; r++) {
            i++;
            p.at<float>(i, 0) = grayImg.at<float>(r, c);
        }
    }
// I should have obtained the vector in p, so now I want to supply it to k-means: 
int K = 2;
    cv::kmeans(p, K, bestLabels,
        cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0),
        3, cv::KMEANS_PP_CENTERS, centers);
// Since K=2, I want to obtain a binary image with this, so the same operation needs to be reversed (grayImg -> p , then bestLabels -> binaryImage)
}

然而，我得到一个错误:Unhandled exception at 0x00007FFD76406C51 (ntdll.dll) in myapp.exe

我是OpenCV的新手，所以我不确定如何使用这些函数。我在这里找到了这个代码。例如，为什么我们使用.at<float>，其他一些帖子说灰度图像像素存储为<char> s ??我越来越困惑了，所以任何帮助都会很感激:)

谢谢!

编辑

多亏了Miki，我找到了正确的方法来做这件事。但最后一个问题，我如何看到cv::Mat1b result的内容?我试着像这样打印它们:

for (int r = 0; r < result.rows; ++r)
    {
        for (int c = 0; c < result.cols; ++c)
        {
            result(r, c) = static_cast<uchar>(centers(bestLabels(r*grayImg.cols + c)));
            if (result(r, c) != 0) {
                std::cout << "result = " << result(r, c) << " n";
            }               
        }
    }

但它一直打印result=0，即使我特别要求它不要:)我如何访问值?

Answer 1

1. 您不需要从Mat转换为InputArray，但您可以(并且应该)在请求InputArray时传递Mat对象。详细说明请见此处

2. kmeans接受一个InputArray，它应该是一个n维点的数组，需要浮点坐标

3. 对于Mat对象，您需要img.at<type>(row, col)来访问像素值。但是，您可以使用Mat_，它是Mat的模板版本，您可以在其中修复类型，因此您可以像img(r,c)一样访问值。

所以最后的代码是:

#include <opencv2opencv.hpp>
using namespace cv;

int main()
{
    Mat1b grayImg = imread("path_to_image", IMREAD_GRAYSCALE);
    Mat1f data(grayImg.rows*grayImg.cols, 1);
    for (int r = 0; r < grayImg.rows; r++)
    {
        for (int c = 0; c < grayImg.cols; c++)
        {
            data(r*grayImg.cols + c) = float(grayImg(r, c));
        }
    }
    // Or, equivalently
    //Mat1f data;
    //grayImg.convertTo(data, CV_32F);
    //data = data.reshape(1, 1).t();

    // I should have obtained the vector in p, so now I want to supply it to k-means: 
    int K = 8;
    Mat1i bestLabels(data.size(), 0); // integer matrix of labels
    Mat1f centers;                    // float matrix of centers
    cv::kmeans(data, K, bestLabels,
        cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0),
        3, cv::KMEANS_PP_CENTERS, centers);

    // Show results
    Mat1b result(grayImg.rows, grayImg.cols);
    for (int r = 0; r < result.rows; ++r)
    {
        for (int c = 0; c < result.cols; ++c)
        {
            result(r, c) = static_cast<uchar>(centers(bestLabels(r*grayImg.cols + c)));
        }
    }
    imshow("Image", grayImg);
    imshow("Result", result);
    waitKey();
    return 0;
}