基于OpenCV实现图像分割基于OpenCV实现图像分割

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 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：

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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; }}}

【基于OpenCV实现图像分割】效果图：

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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 = https://www.it610.com/article/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：

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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：

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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"#includeusing 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 contours; //定义轮廓vector 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 colorTab; for(i=0; i(i,j); if(index == -1)watershedImage.at(i,j) = Vec3b(255,255,255); else if(index<=0||index>compCount)watershedImage.at(i,j) = Vec3b(0,0,0); elsewatershedImage.at(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：