import cv2 import numpy as np original_img1 = cv2.imread("F:\\Image_2024_0528_1446_34_945-000030.bmp") # 利用切片操作裁剪出ROI区域 roi_img1 = original_img1[3001:10235, 408:7485] cv2.imwrite("F:\\30.bmp",roi_img1) original_img2 = cv2.imread("F:\\Image

将Python代码转换为C++代码可能需要一些适应性和语法调整。以下是给出的Python代码的C++版本：

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

using namespace cv;

int main() {
    Mat original_img1 = imread("F:\\Image_2024_0528_1446_34_945-000030.bmp");
    Mat roi_img1 = original_img1(Range(3001, 10235), Range(408, 7485));
    imwrite("F:\\30.bmp", roi_img1);

    Mat original_img2 = imread("F:\\Image_2024_0528_1446_37_867-000031.bmp");
    Mat roi_img2 = original_img2(Range(1632, 8920), Range(411, 7485));
    imwrite("F:\\31.bmp", roi_img2);

    // Canny边缘检测
    Mat img1;
    GaussianBlur(original_img1, img1, Size(3, 3), 0);
    Mat canny;
    Canny(img1, canny, 50, 150);

    // 形态学梯度运算
    Mat thr_img;
    threshold(original_img1, thr_img, 210, 255, THRESH_BINARY);
    Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5));
    Mat gradient;
    morphologyEx(thr_img, gradient, MORPH_GRADIENT , kernel);

	//imwrite("F:\\gradient.bmp", gradient);
	//imwrite("F:\\canny.bmp", canny);

	int height = canny.rows;
	int width = canny.cols;

	int threshold_val = 300;  
	int count_threshold_val = 20; 

	int top_boundary = -1;
	int bottom_boundary = -1;

	for (int row = 0; row < height; row++) {
		int white_pixels_count = countNonZero(canny.row(row));

		if (row != 0) {
			int white_pixels_count_1 = countNonZero(canny.row(row - 1));
		}

		if (row != height - 1) {
			int white_pixels_count_2 = countNonZero(canny.row(row + 1));
		}

		if (white_pixels_count > threshold_val || white_pixels_count_1 > threshold_val || white_pixels_count_2 > threshold_val) {
			if (top_boundary == -1) {
				top_boundary = row;
			}

			bottom_boundary = row;
		}
    }

	std::cout << "上边界行数：" << top_boundary << std::endl;
	std::cout << "下边界行数：" << bottom_boundary << std::endl;

    return 0;
}

请确保安装并链接OpenCV库，并将图像文件的路径替换为您实际的文件路径。另外，请根据需要进行适当的调整和错误处理。

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