本文实例为大家分享了基于OpenCV实现图像分割的具体代码，供大家参考，具体内容如下
1、图像阈值化
源代码：
#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>using namespace std;using namespace cv;int thresholds=50;int model=2;Mat image,srcimage;void track(int ,void *){ Mat result; threshold(srcimage,result,thresholds,255,CV_THRESH_BINARY); //imshow("原图",result); if(model==0) { threshold(srcimage,result,thresholds,255,CV_THRESH_BINARY); imshow("分割",result); } if(model==1) { threshold(srcimage,result,thresholds,255,THRESH_BINARY_INV); imshow("分割",result); } if(model==2) { threshold(srcimage,result,thresholds,255,THRESH_TRUNC); imshow("分割",result); } if(model==3) { threshold(srcimage,result,thresholds,255,THRESH_TOZERO); imshow("分割",result); } if(model==4) { threshold(srcimage,result,thresholds,255,THRESH_TOZERO_INV); imshow("分割",result); }}int main(){ image=imread("2.2.tif"); if(!image.data) { return 0; } cvtColor(image,srcimage,CV_BGR2GRAY); namedWindow("分割",WINDOW_AUTOSIZE); cv::createTrackbar("阈a值：","分割",&thresholds,255,track); cv::createTrackbar("模式：","分割",&model,4,track); track(thresholds,0); track(model,0); waitKey(0); return 0;}
实现结果：
2、阈值处理
//阈值处理#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp" using namespace cv; using namespace std; int main() { printf("键盘按键ESC--退出程序"); Mat g_srcImage = imread("1.tif",0); if(!g_srcImage.data) { printf("读取图片失败"); } imshow("原始图",g_srcImage); //大津法阈值分割显示 /*大津法,简称OTSU.它是按图像的灰度特性,将图像分成背景 和目标2部分。背景和目标之间的类间方差越大,说明构成图像 的2部分的差别越大,当部分目标错分为背景或部分背景错分为 目标都会导致2部分差别变小。*/ Mat OtsuImage; threshold(g_srcImage,OtsuImage,0,255,THRESH_OTSU);//0不起作用，可为任意阈值 imshow("OtsuImage",OtsuImage); //自适应分割并显示 Mat AdaptImage; //THRESH_BINARY_INV：参数二值化取反 adaptiveThreshold(g_srcImage,AdaptImage,255,0,THRESH_BINARY_INV,7,8); imshow("AdaptImage",AdaptImage); while(1) { int key; key = waitKey(20); if((char)key == 27) { break; } } }
效果图：
3、拉普拉斯检测
//Laplacian检测#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"using namespace cv;using namespace std;/*，在只关心边缘的位置而不考虑其周围的象素灰度差值时比较合适。Laplace 算子对孤立象素的响应要比对边缘或线的响应要更强烈，因此只适用于无噪声图象。存在噪声情况下，使用 Laplacian 算子检测边缘之前需要先进行低通滤波。*/int main(){ Mat src,src_gray,dst,abs_dst; src = imread("1.jpg"); imshow("原始图像",src); //高斯滤波 GaussianBlur(src,src,Size(3,3),0,0,BORDER_DEFAULT); //转化为灰度图，输入只能为单通道 cvtColor(src,src_gray,CV_BGR2GRAY); Laplacian(src_gray,dst,CV_16S,3,1,0,BORDER_DEFAULT); convertScaleAbs(dst,abs_dst); imshow("效果图Laplace变换",abs_dst); waitKey(); return 0;}
效果图：
4、canny算法的边缘检测
源代码
#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"using namespace cv;using namespace std;/*如果某一像素位置的幅值超过高阈值，该像素被保留为边缘像素。如果某一像素位置的幅值小于低阈值，该像素被排除。如果某一像素位置的幅值在两个阈值之间，该像素仅仅在连接到一个高于高阈值的像素时被保留。 */int main(){ Mat picture2=imread("1.jpg"); Mat new_picture2; Mat picture2_1=picture2.clone(); Mat gray_picture2 , edge , new_edge; imshow("【原始图】Canny边缘检测" , picture2); Canny(picture2_1 , new_picture2 ,150 , 100 ,3 ); imshow("【效果图】Canny边缘检测", new_picture2 ); Mat dstImage,grayImage; //dstImage与srcImage同大小类型 dstImage.create(picture2_1.size() , picture2_1.type()); cvtColor(picture2_1,gray_picture2,CV_BGR2GRAY);//转化为灰度图 blur(gray_picture2 , edge , Size(3,3));//用3x3的内核降噪 Canny(edge,edge,3,9,3); dstImage = Scalar::all(0);//将dst内所有元素设置为0 //使用canny算子的边缘图edge作为掩码，将原图拷贝到dst中 picture2_1.copyTo(dstImage,edge); imshow("效果图Canny边缘检测2",dstImage); waitKey();}
效果图：
5、图像的分水岭算法
源代码：
#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>using namespace cv;using namespace std;#define WINDOW_NAME1 "显示/操作窗口"#define WINDOW_NAME2 "分水岭算法效果图"Mat g_maskImage,g_srcImage;Point prevPt(-1,-1);static void ShowHelpText();static void on_Mouse(int event,int x,int y,int flags,void*);//输出一些帮助信息static void ShowHelpText(){ printf("当前使用的版本为:"CV_VERSION); printf("\n"); printf("分水岭算法---点中图片进行鼠标或按键操作\n"); printf("请先用鼠标在图片窗口中标记出大致的区域，\n然后再按键【1】或者【space】启动算法"); printf("\n按键操作说明：\n" "键盘按键【1】或者【space】--运行的分水岭分割算法\n" "键盘按键【2】--回复原始图片\n" "键盘按键【ESC】--退出程序\n");}static void on_Mouse(int event,int x,int y,int flags,void*){ if(x<0||x>=g_srcImage.cols||y<0||y>=g_srcImage.rows) return; if(event == CV_EVENT_LBUTTONUP||!(flags & CV_EVENT_FLAG_LBUTTON)) prevPt = Point(-1,-1); else if(event == CV_EVENT_LBUTTONDOWN) prevPt= Point(x,y); else if(event == CV_EVENT_MOUSEMOVE && (flags & CV_EVENT_FLAG_LBUTTON)) { Point pt(x,y); if(prevPt.x<0) prevPt = pt; line(g_maskImage,prevPt,pt,Scalar::all(255),5,8,0); line(g_srcImage,prevPt,pt,Scalar::all(255),5,8,0); prevPt = pt; imshow(WINDOW_NAME1,g_srcImage); }}int main(int argc,char** argv){ system("color A5"); ShowHelpText(); g_srcImage = imread("1.jpg",1); imshow(WINDOW_NAME1,g_srcImage); Mat srcImage,grayImage; g_srcImage.copyTo(srcImage); cvtColor(g_srcImage,g_maskImage,CV_BGR2GRAY); cvtColor(g_maskImage,grayImage,CV_GRAY2BGR);//灰度图转BGR3通道，但每通道的值都是原先单通道的值，所以也是显示灰色的 g_maskImage = Scalar::all(0);//黑 setMouseCallback(WINDOW_NAME1,on_Mouse,0); while(1) { int c = waitKey(0); if((char)c == 27) break; if((char)c == '2') { g_maskImage = Scalar::all(0);//黑 srcImage.copyTo(g_srcImage); imshow("image",g_srcImage); } if((char)c == '1'||(char)c == ' ') { int i,j,compCount = 0; vector<vector<Point>> contours;//定义轮廓 vector<Vec4i> hierarchy;//定义轮廓的层次 findContours(g_maskImage,contours,hierarchy,RETR_CCOMP,CHAIN_APPROX_SIMPLE); if(contours.empty()) continue; Mat maskImage(g_maskImage.size(),CV_32S); maskImage = Scalar::all(0); for(int index = 0;index >= 0;index = hierarchy[index][0],compCount++) drawContours(maskImage,contours,index,Scalar::all(compCount+1),-1,8,hierarchy,INT_MAX); if(compCount == 0) continue; vector<Vec3b> colorTab; for(i=0;i<compCount;i++) { int b = theRNG().uniform(0,255); int g = theRNG().uniform(0,255); int r = theRNG().uniform(0,255); colorTab.push_back(Vec3b((uchar)b,(uchar)g,(uchar)r)); } //计算处理时间并输出到窗口中 double dTime = (double)getTickCount(); watershed(srcImage,maskImage); dTime = (double)getTickCount()-dTime; printf("\t处理时间=%gms\n",dTime*1000./getTickFrequency()); //双层循环，将分水岭图像遍历存入watershedImage中 Mat watershedImage(maskImage.size(),CV_8UC3); for(i=0;i<maskImage.rows;i++) for(j=0;j<maskImage.cols;j++) { int index = maskImage.at<int>(i,j); if(index == -1) watershedImage.at<Vec3b>(i,j) = Vec3b(255,255,255); else if(index<=0||index>compCount) watershedImage.at<Vec3b>(i,j) = Vec3b(0,0,0); else watershedImage.at<Vec3b>(i,j) = colorTab[index-1]; } //混合灰度图和分水岭效果图并显示最终的窗口 watershedImage = watershedImage*0.5+grayImage*0.5; imshow(WINDOW_NAME2,watershedImage); } } waitKey(); return 0;}
效果图：
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。
可见内心底对旅行是多么的淡漠。
