在opencv中使用c++对二值图筛选大面积的轮廓并提取较大轮廓

可以使用以下步骤来实现：

1. 读取二值图像并转换为灰度图像

Mat binaryImage = imread("binaryImage.png", IMREAD_GRAYSCALE);

2. 对图像进行膨胀操作，以填充轮廓内部的空白区域

Mat dilatedImage;
Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(5, 5));
dilate(binaryImage, dilatedImage, kernel);

3. 查找轮廓并筛选出较大的轮廓

vector<vector<Point>> contours;
findContours(dilatedImage, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

vector<vector<Point>> largeContours;
for (int i = 0; i < contours.size(); i++) {
    double area = contourArea(contours[i]);
    if (area > 1000) { // 根据面积大小筛选轮廓
        largeContours.push_back(contours[i]);
    }
}

4. 绘制筛选出的轮廓

Mat contourImage = Mat::zeros(dilatedImage.size(), CV_8UC3);
Scalar color(0, 255, 0);
drawContours(contourImage, largeContours, -1, color, 2);

完整代码示例：

#include <opencv2/opencv.hpp>
using namespace cv;

int main() {
    Mat binaryImage = imread("binaryImage.png", IMREAD_GRAYSCALE);
    Mat dilatedImage;
    Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(5, 5));
    dilate(binaryImage, dilatedImage, kernel);

    vector<vector<Point>> contours;
    findContours(dilatedImage, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

    vector<vector<Point>> largeContours;
    for (int i = 0; i < contours.size(); i++) {
        double area = contourArea(contours[i]);
        if (area > 1000) {
            largeContours.push_back(contours[i]);
        }
    }

    Mat contourImage = Mat::zeros(dilatedImage.size(), CV_8UC3);
    Scalar color(0, 255, 0);
    drawContours(contourImage, largeContours, -1, color, 2);

    imshow("Contour Image", contourImage);
    waitKey(0);
    return 0;
}