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如何实现

以下是 C++ OpenCV 实现 find_center 函数的代码：

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int find_center(Mat img) {
    int h = img.rows;
    int w = img.cols;
    int roi_h = 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); // 高斯模糊
    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);
    Mat x_sum = Mat::zeros(1, w, CV_32F);
    reduce(open_img, x_sum, 0, REDUCE_SUM, CV_32F);
    int nonzero_count = countNonZero(x_sum);
    if (nonzero_count == 0) {
        return -1;
    }
    int nonzero_start = 0;
    int nonzero_end = w - 1;
    for (int i = 0; i < w; i++) {
        if (x_sum.at<float>(0, i) > 0) {
            nonzero_start = i;
            break;
        }
    }
    for (int i = w - 1; i >= 0; i--) {
        if (x_sum.at<float>(0, i) > 0) {
            nonzero_end = i;
            break;
        }
    }
    int point_x = (nonzero_start + nonzero_end) / 2;
    return point_x;
}

int main() {
    Mat img = imread("test.jpg", IMREAD_GRAYSCALE);
    int center = find_center(img);
    cout << "Center point x-coordinate: " << center << endl;
    return 0;
}

需要注意的是，在 C++ 中，OpenCV 中的函数名和参数可能会略有不同，需要根据情况进行调整。