g2=sum(xn.*p*RA*);什么意思

这段代码看起来是在使用矩阵运算，其中`g2 = sum(xn .* p * RA;)`是一个向量化操作。在这个表达式中：

- `xn`: 可能是一个向量或数组，表示一些数据系列的元素。
- `p`: 可能是另一个与`xn`相乘的系数向量或矩阵。
- `RA`: 这个可能是"Responsivity Area"或者其他含义的矩阵，它通常用于对`xn`进行某种线性变换或权重应用。
- `.*`: 是点乘（element-wise multiplication）操作，它对应于数学中的逐元素乘法，将两个向量对应位置的元素相乘。
- `sum`: 表示对所有元素求和，也就是对结果数组计算总和。

整体来说，这行代码的作用是计算`xn`与`p`按元素相乘后再乘以`RA`得到的结果的累加和。

相关问题

Rgg = diag(g.*conj(g))

As an AI language model, I cannot assume the value of g since it is not given in the question. However, assuming that g is a vector, the expression Rgg = diag(g.*conj(g)) would be the diagonal matrix whose diagonal entries are the products of each element of g with its complex conjugate.

In other words, if g = [g1, g2, ..., gn], then

Rgg = [ |g1|^2 0 ... 0 ]
[ 0 |g2|^2 ... 0 ]
[ ... ... ... ... ]
[ 0 ... 0 |gn|^2 ]

where |gk| is the magnitude of the complex number gk.

def find_center(img): h, w = img.shape roi_h = int(h * 2 / 3) roi_img = img[roi_h:, :] img_blur = cv2.GaussianBlur(roi_img, (15, 15), 0) # 高斯模糊 ret, th2 = cv2.threshold(img_blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) g2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) open_img = cv2.morphologyEx(th2, cv2.MORPH_OPEN, g2, iterations=3) x_sum = np.sum(open_img, axis=0) x_point = np.where(x_sum > 0) point_x = int((x_point[0][0] + x_point[0][-1]) / 2) # print(roi_h, w) # np.savetxt('reshape_data.txt', x_point, delimiter=' ', fmt='%i') return point_x c++ opencv Eigen::Tensor如何实现

首先需要在项目中引入Eigen库，然后可以通过以下代码实现：

#include <opencv2/opencv.hpp>
#include <unsupported/Eigen/CXX11/Tensor>

using namespace cv;
using namespace Eigen;

int find_center(Mat img) {
    int h = img.rows;
    int w = img.cols;
    int roi_h = int(h * 2 / 3);
    Mat roi_img = img(Rect(0, roi_h, w, h - roi_h));
    Mat img_blur;
    GaussianBlur(roi_img, img_blur, Size(15, 15), 0);
    Mat th2;
    threshold(img_blur, th2, 0, 255, THRESH_BINARY + THRESH_OTSU);
    Mat g2 = getStructuringElement(MORPH_RECT, Size(3, 3));
    Mat open_img;
    morphologyEx(th2, open_img, MORPH_OPEN, g2, Point(-1, -1), 3);
    Tensor<int, 1> x_sum(w);
    for (int j = 0; j < w; j++) {
        int sum = 0;
        uchar* data = open_img.data + j;
        for (int i = 0; i < open_img.rows; i++) {
            sum += *data;
            data += open_img.step;
        }
        x_sum(j) = sum;
    }
    std::vector<int> x_point;
    for (int j = 0; j < x_sum.size(); j++) {
        if (x_sum(j) > 0) {
            x_point.push_back(j);
        }
    }
    int point_x = (x_point.front() + x_point.back()) / 2;
    return point_x;
}

这里使用了Eigen的Tensor来存储x_sum数组，避免了使用矩阵乘法的复杂度。同时，对于OpenCV中的二维数组，可以使用指针来进行遍历，避免了使用at()函数的性能损失。