cv::Mat::zeros(img.size(),CV_8UC3);

This line of code creates a new `cv::Mat` object with dimensions equal to `img` and initializes all its values to zero. The `CV_8UC3` parameter specifies the data type of the matrix as an 8-bit unsigned integer with 3 channels (corresponding to the Red, Green, and Blue color channels). This can be useful for creating a blank image of the same size and color format as another image, which can then be used as a canvas for drawing or processing operations.

相关问题

Mat gray = img, gray2 = gray; Cv2.CvtColor(img, gray, ColorConversionCodes.BGR2GRAY); Mat gray3 = Cv2.ImRead("D:\\test.png", ImreadModes.Grayscale); hist = img; Mat hist3 = hist; int[] channels = { 0 }; Rangef[] inRanges = new Rangef[] { new Rangef(0, 256) }; int[] histSize = { 256 }; Mat[] grays = new Mat[] { gray }; Mat[] grays3 = new Mat[] { gray3 }; Cv2.CalcHist(grays, channels, new Mat(), hist, 1, histSize, inRanges); Cv2.CalcHist(grays3, channels, new Mat(), hist3, 1, histSize, inRanges); Mat histImage = Mat.Zeros(400, 512, MatType.CV_8UC3);

这段代码使用了OpenCV库对图像进行处理和直方图计算。首先将读入的彩色图像转换为灰度图像，使用Cv2.CvtColor函数实现。然后使用Cv2.ImRead函数读取另一张灰度图像。接下来使用Cv2.CalcHist函数计算两张灰度图像的直方图，其中hist和hist3是存储直方图的Mat变量。channel表示通道数，由于是灰度图像，所以只有一个通道。inRanges表示像素值范围，这里是0-255。histSize表示直方图的区间数，这里是256。grays和grays3是要计算直方图的灰度图像数组。最后使用Mat.Zeros函数创建一个大小为400x512的黑色Mat变量作为直方图图像的背景，它的类型为CV_8UC3。

import numpy as np import cv2 as cv # 加载图片 img = cv.imread('4.jpg') # 灰度化 img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) # 二值化 ret, thresh = cv.threshold(img_gray, 127, 255, cv.THRESH_BINARY) # 寻找连通域 num_labels, labels, stats, centroids = cv.connectedComponentsWithStats(thresh, connectivity=8) # 计算平均面积 areas = list() for i in range(num_labels): areas.append(stats[i][-1]) print("轮廓%d的面积:%d" % (i, stats[i][-1])) area_avg = np.average(areas[1:-1]) print("轮廓平均面积:", area_avg) # 筛选超过平均面积的连通域 image_filtered = np.zeros_like(img) for (i, label) in enumerate(np.unique(labels)): # 如果是背景，忽略 if label == 0: continue if stats[i][-1] < area_avg : image_filtered[labels == i] = 255 #cv.imshow("image_filtered", image_filtered) #cv.imshow("img", img) cv.imwrite('4.jpg',image_filtered ) cv.waitKey() cv.destroyAllWindows(),将上述代码转化为c++形式

以下是将上述Python代码转化为C++形式的示例：

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

using namespace std;
using namespace cv;

int main() {
    // 加载图片
    Mat img = imread("4.jpg");

    // 灰度化
    Mat img_gray;
    cvtColor(img, img_gray, COLOR_BGR2GRAY);

    // 二值化
    Mat thresh;
    threshold(img_gray, thresh, 127, 255, THRESH_BINARY);

    // 寻找连通域
    Mat labels, stats, centroids;
    int num_labels = connectedComponentsWithStats(thresh, labels, stats, centroids, 8);

    // 计算平均面积
    vector<int> areas;
    for (int i = 0; i < num_labels; i++) {
        areas.push_back(stats.at<int>(i, CC_STAT_AREA));
        cout << "轮廓" << i << "的面积：" << stats.at<int>(i, CC_STAT_AREA) << endl;
    }
    double area_avg = mean(Mat(areas))[0];
    cout << "轮廓平均面积：" << area_avg << endl;

    // 筛选超过平均面积的连通域
    Mat image_filtered = Mat::zeros(img.size(), CV_8UC1);
    for (int i = 1; i < num_labels; i++) {
        if (stats.at<int>(i, CC_STAT_AREA) < area_avg) {
            labels.setTo(0, labels == i);
        } else {
            image_filtered.setTo(255, labels == i);
        }
    }

    // 显示结果
    imshow("image_filtered", image_filtered);
    imshow("img", img);
    waitKey();
    destroyAllWindows();

    // 保存结果
    imwrite("4.jpg", image_filtered);

    return 0;
}

需要注意的是，C++中没有Python中的`list`类型，需要使用`vector`替代。同时，C++中没有Python中的`np.average()`方法，需要使用OpenCV的`mean()`方法计算平均值。此外，C++中的Mat类需要使用`.at()`方法访问元素，而不是Python中的下标索引。