opencv-基于c++实现的opencv图像处理算法之区域生长分割算法.zip

#include <iostream> #include <opencv2/core.hpp> #include <opencv2/highgui.hpp> #include <opencv2/imgproc.hpp> /*************************************************************************************** Function: 区域生长算法 Input: src 待处理原图像 pt 初始生长点 th 生长的阈值条件 Output: 肺实质的所在的区域 实质区是白色，其他区域是黑色 Description: 生长结果区域标记为白色(255),背景色为黑色(0) Return: NULL Others: NULL ***************************************************************************************/ void RegionGrow(cv::Mat& src, cv::Mat& matDst, cv::Point2i pt, int th) { cv::Point2i ptGrowing; //待生长点位置 int nGrowLable = 0; //标记是否生长过 int nSrcValue = 0; //生长起点灰度值 int nCurValue = 0; //当前生长点灰度值 matDst = cv::Mat::zeros(src.size(), CV_8UC1); //创建一个空白区域，填充为黑色 //生长方向顺序数据 int DIR[8][2] = { { -1, -1 }, { 0, -1 }, { 1, -1 }, { 1, 0 }, { 1, 1 }, { 0, 1 }, { -1, 1 }, { -1, 0 } }; std::vector<cv::Point2i> vcGrowPt; //生长点栈 vcGrowPt.push_back(pt); //将生长点压入栈中 matDst.at<uchar>(pt.y, pt.x) = 255; //标记生长点 nSrcValue = src.at<uchar>(pt.y, pt.x); //记录生长点的灰度值 while (!vcGrowPt.empty()) //生长栈不为空则生长 { pt = vcGrowPt.back(); //取出一个生长点 vcGrowPt.pop_back(); //分别对八个方向上的点进行生长 for (int i = 0; i<8; ++i) { ptGrowing.x = pt.x + DIR[i][0]; ptGrowing.y = pt.y + DIR[i][1]; //检查是否是边缘点 if (ptGrowing.x < 0 || ptGrowing.y < 0 || ptGrowing.x >(src.cols - 1) || (ptGrowing.y > src.rows - 1)) continue; nGrowLable = matDst.at<uchar>(ptGrowing.y, ptGrowing.x); //当前待生长点的灰度值 if (nGrowLable == 0) //如果标记点还没有被生长 { nCurValue = src.at<uchar>(ptGrowing.y, ptGrowing.x); if (abs(nSrcValue - nCurValue) < th) //在阈值范围内则生长 { matDst.at<uchar>(ptGrowing.y, ptGrowing.x) = 255; //标记为白色 vcGrowPt.push_back(ptGrowing); //将下一个生长点压入栈中 } } } } } void on_MouseHandle(int event, int x, int y, int flags, void* param){ cv::Mat& src = *(cv::Mat*) param; cv::Mat src_gray, dst; if (src.channels() > 1) cv::cvtColor(src, src_gray, CV_RGB2GRAY); cv::Point2i pt; switch (event) { //左键按下 case cv::EVENT_LBUTTONDOWN: { //x:列 y:行 pt=cv::Point2i(x, y); std::cout <<"种子点位置："<< "(x,y)=" << "(" << x << "," << y << ")" << std::endl; } break; //左键放开 char str[16]; case cv::EVENT_LBUTTONUP: { cv::circle(src, cv::Point2i(x, y),2, cv::Scalar(0, 0, 255), -1,CV_AA); sprintf_s(str, "(%d,%d)", x, y); //cv::putText(src, str, cv::Point2i(x, y), 3, 1, cv::Scalar(150, 200,0), 2, 8); cv::namedWindow("dst", CV_WINDOW_NORMAL);//定义一个dst窗口 pt = cv::Point2i(x, y); RegionGrow(src_gray,dst,pt,40); //区域生长 cv::bitwise_and(src_gray, dst, dst); //与运算 imshow("src", src); imshow("dst", dst); } break; } } int main(){ cv::Mat src = cv::imread("I:\\Learning-and-Practice\\2019Change\\Image process algorithm\\Img\\lung2.jpeg"); if (src.empty()){ return -1; } cv::namedWindow("src", CV_WINDOW_NORMAL);//定义一个img窗口 cv::setMouseCallback("src", on_MouseHandle, (void*)&src);//调用回调函数 imshow("src", src); cv::waitKey(0); }