基於OpenCV實現圖像分割

本文實例為大傢分享瞭基於OpenCV實現圖像分割的具體代碼,供大傢參考,具體內容如下

1、圖像閾值化

源代碼:

#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("原圖",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("閾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;
}

實現結果:

2、閾值處理

//閾值處理
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
    
    using namespace cv;
    using namespace std;
    
    int main()
    {
     printf("鍵盤按鍵ESC--退出程序");
     Mat g_srcImage = imread("1.tif",0);
     if(!g_srcImage.data)
     {
      printf("讀取圖片失敗");
     }
     imshow("原始圖",g_srcImage);
    
     //大津法閾值分割顯示
     /*大津法,簡稱OTSU.它是按圖像的灰度特性,將圖像分成背景
     和目標2部分。背景和目標之間的類間方差越大,說明構成圖像
     的2部分的差別越大,當部分目標錯分為背景或部分背景錯分為
     目標都會導致2部分差別變小。*/
     Mat OtsuImage;
     threshold(g_srcImage,OtsuImage,0,255,THRESH_OTSU);//0不起作用,可為任意閾值
     imshow("OtsuImage",OtsuImage);
    
     //自適應分割並顯示
     Mat AdaptImage;
     //THRESH_BINARY_INV:參數二值化取反
     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; }
     }
    }

效果圖:

3、拉普拉斯檢測

//Laplacian檢測
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
using namespace std;

/*,在隻關心邊緣的位置而不考慮其周圍的象素灰度差值時比較合適。
Laplace 算子對孤立象素的響應要比對邊緣或線的響應要更強烈,因此
隻適用於無噪聲圖象。存在噪聲情況下,使用 Laplacian 算子檢測邊
緣之前需要先進行低通濾波。*/
int main()
{
 Mat src,src_gray,dst,abs_dst;
 src = imread("1.jpg");
 imshow("原始圖像",src);

 //高斯濾波
 GaussianBlur(src,src,Size(3,3),0,0,BORDER_DEFAULT);
 //轉化為灰度圖,輸入隻能為單通道
 cvtColor(src,src_gray,CV_BGR2GRAY);

 Laplacian(src_gray,dst,CV_16S,3,1,0,BORDER_DEFAULT);
 convertScaleAbs(dst,abs_dst);
 imshow("效果圖Laplace變換",abs_dst);
 waitKey();
 return 0;

}

效果圖:

4、canny算法的邊緣檢測

源代碼

#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過高閾值,該像素被保留為邊緣像素。如果某
一像素位置的幅值小於低閾值,該像素被排除。如果某一像素位置的幅值在
兩個閾值之間,該像素僅僅在連接到一個高於高閾值的像素時被保留。 */
int main()
{
 Mat picture2=imread("1.jpg");
 Mat new_picture2;
 Mat picture2_1=picture2.clone();
 Mat gray_picture2 , edge , new_edge;
 imshow("【原始圖】Canny邊緣檢測" , picture2);
 Canny(picture2_1 , new_picture2 ,150 , 100 ,3  );
 imshow("【效果圖】Canny邊緣檢測", new_picture2 );
 Mat dstImage,grayImage;
 //dstImage與srcImage同大小類型
 dstImage.create(picture2_1.size() , picture2_1.type());
 cvtColor(picture2_1,gray_picture2,CV_BGR2GRAY);//轉化為灰度圖
 blur(gray_picture2 , edge , Size(3,3));//用3x3的內核降噪
 Canny(edge,edge,3,9,3);
 dstImage = Scalar::all(0);//將dst內所有元素設置為0
 //使用canny算子的邊緣圖edge作為掩碼,將原圖拷貝到dst中
 picture2_1.copyTo(dstImage,edge);
 imshow("效果圖Canny邊緣檢測2",dstImage);
 waitKey();
}

效果圖:

5、圖像的分水嶺算法

源代碼:

#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 "顯示/操作窗口"
#define WINDOW_NAME2 "分水嶺算法效果圖"

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*);

//輸出一些幫助信息
static void ShowHelpText()
{
 printf("當前使用的版本為:"CV_VERSION);
 printf("\n");
 printf("分水嶺算法---點中圖片進行鼠標或按鍵操作\n");
 printf("請先用鼠標在圖片窗口中標記出大致的區域,\n然後再按鍵【1】或者【space】啟動算法");
 printf("\n按鍵操作說明:\n"
  "鍵盤按鍵【1】或者【space】--運行的分水嶺分割算法\n"
  "鍵盤按鍵【2】--回復原始圖片\n"
  "鍵盤按鍵【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);//灰度圖轉BGR3通道,但每通道的值都是原先單通道的值,所以也是顯示灰色的
 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;//定義輪廓
   vector<Vec4i> hierarchy;//定義輪廓的層次

   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));
   }
    //計算處理時間並輸出到窗口中
   double dTime = (double)getTickCount();
   watershed(srcImage,maskImage);
   dTime = (double)getTickCount()-dTime;
   printf("\t處理時間=%gms\n",dTime*1000./getTickFrequency());
   //雙層循環,將分水嶺圖像遍歷存入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]; 
    }
    //混合灰度圖和分水嶺效果圖並顯示最終的窗口
    watershedImage = watershedImage*0.5+grayImage*0.5;
    imshow(WINDOW_NAME2,watershedImage);        
  } 
 }
 waitKey();
 return 0;
}

效果圖:

以上就是本文的全部內容,希望對大傢的學習有所幫助,也希望大傢多多支持WalkonNet。

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