OpenCV|opencv （三十七）图像形态学——腐蚀 opencv|计算机视觉|人工智能|C

小白学视觉，笔记，扩展

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#include #include #include using namespace cv; using namespace std; //绘制包含区域函数 void drawState(Mat& img, int number, Mat centroids, Mat stats, String str) { RNG rng(10086); vector colors; for (int i = 0; i < number; i++) { //使用均匀分布的随机数确定颜色 Vec3b vec3 = Vec3b(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)); colors.push_back(vec3); } for (int i = 1; i < number; i++) { // 中心位置 int center_x = centroids.at(i, 0); int center_y = centroids.at(i, 1); //矩形边框 int x = stats.at(i, CC_STAT_LEFT); int y = stats.at(i, CC_STAT_TOP); int w = stats.at(i, CC_STAT_WIDTH); int h = stats.at(i, CC_STAT_HEIGHT); // 中心位置绘制 circle(img, Point(center_x, center_y), 2, Scalar(0, 255, 0), 2, 8, 0); // 外接矩形 Rect rect(x, y, w, h); rectangle(img, rect, colors[i], 1, 8, 0); putText(img, format("%d", i), Point(center_x, center_y), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255), 1); } imshow(str, img); }int main() { //生成用于腐蚀的原图像,和腐蚀原理图的模型一样 Mat src = https://www.it610.com/article/(Mat_(6, 6) << 0, 0, 0, 0, 255, 0, 0, 255, 255, 255, 255, 255, 0, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 0, 0, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0); Mat struct1, struct2; struct1 = getStructuringElement(0, Size(3, 3)); //矩形结构元素 struct2 = getStructuringElement(1, Size(3, 3)); //十字结构元素 Mat erodeSrc; //存放腐蚀后的图像 erode(src, erodeSrc, struct2); namedWindow("src", WINDOW_GUI_NORMAL); namedWindow("erodeSrc", WINDOW_GUI_NORMAL); imshow("src", src); imshow("erodeSrc", erodeSrc); cout << "文字腐蚀验证" << endl; waitKey(0); Mat LearnCV_black = imread("LearnCV_black.png", IMREAD_ANYCOLOR); Mat erode_black1, erode_black2; //黑背景图像腐蚀 erode(LearnCV_black, erode_black1, struct1); erode(LearnCV_black, erode_black2, struct2); imshow("LearnCV_black", LearnCV_black); imshow("erode_black1", erode_black1); imshow("erode_black2", erode_black2); cout << "验证腐蚀对小连通域的去除" << endl; waitKey(0); Mat img = imread("rice.png"); if (img.empty()) { cout << "请确认图像文件名称是否正确" << endl; return -1; } Mat img2; img.copyTo(img2); //克隆一个单独的图像，用于后期图像绘制 Mat rice, riceBW; //将图像转成二值图像，用于统计连通域 cvtColor(img, rice, COLOR_BGR2GRAY); threshold(rice, riceBW, 50, 255, THRESH_BINARY); Mat out, stats, centroids; //统计图像中连通域的个数 int number = connectedComponentsWithStats(riceBW, out, stats, centroids, 8, CV_16U); drawState(img, number, centroids, stats, "未腐蚀时统计连通域"); //绘制图像 erode(riceBW, riceBW, struct1); //对图像进行腐蚀 number = connectedComponentsWithStats(riceBW, out, stats, centroids, 8, CV_16U); drawState(img2, number, centroids, stats, "腐蚀后统计连通域"); //绘制图像 waitKey(0); return 0; }

//生成用于腐蚀的原图像,6*6验证原理