OpenCV 实现分水岭算法

简介: 种子点的标记没有太搞懂,这个算法的速度还是很快的     // watershed_test20140801.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.

种子点的标记没有太搞懂,这个算法的速度还是很快的

 

 

// watershed_test20140801.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"

//
// ch9_watershed image
//   This is an exact copy of the watershed.cpp demo in the OpenCV ../samples/c directory
//
// Think about using a morphologically eroded forground and background segmented image as the template
// for the watershed algorithm to segment objects by color and edges for collecting 
//
/* *************** License:**************************
   Oct. 3, 2008
   Right to use this code in any way you want without warrenty, support or any guarentee of it working.

   BOOK: It would be nice if you cited it:
   Learning OpenCV: Computer Vision with the OpenCV Library
     by Gary Bradski and Adrian Kaehler
     Published by O'Reilly Media, October 3, 2008
 
   AVAILABLE AT: 
     http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134
     Or: http://oreilly.com/catalog/9780596516130/
     ISBN-10: 0596516134 or: ISBN-13: 978-0596516130    

   OTHER OPENCV SITES:
   * The source code is on sourceforge at:
     http://sourceforge.net/projects/opencvlibrary/
   * The OpenCV wiki page (As of Oct 1, 2008 this is down for changing over servers, but should come back):
     http://opencvlibrary.sourceforge.net/
   * An active user group is at:
     http://tech.groups.yahoo.com/group/OpenCV/
   * The minutes of weekly OpenCV development meetings are at:
     http://pr.willowgarage.com/wiki/OpenCV
   ************************************************** */

#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
using namespace std;
using namespace cv;


#pragma comment(lib,"opencv_core2410d.lib")      
#pragma comment(lib,"opencv_highgui2410d.lib")      
#pragma comment(lib,"opencv_imgproc2410d.lib")  

IplImage* marker_mask = 0;
IplImage* markers = 0;
IplImage* img0 = 0, *img = 0, *img_gray = 0, *wshed = 0;
CvPoint prev_pt = {-1,-1};

void on_mouse( int event, int x, int y, int flags, void* param )
{
    if( !img )
        return;

    if( event == CV_EVENT_LBUTTONUP || !(flags & CV_EVENT_FLAG_LBUTTON) )
        prev_pt = cvPoint(-1,-1);
    else if( event == CV_EVENT_LBUTTONDOWN )
        prev_pt = cvPoint(x,y);
    else if( event == CV_EVENT_MOUSEMOVE && (flags & CV_EVENT_FLAG_LBUTTON) )
    {
        CvPoint pt = cvPoint(x,y);
        if( prev_pt.x < 0 )
            prev_pt = pt;
        cvLine( marker_mask, prev_pt, pt, cvScalarAll(255), 5, 8, 0 );
        cvLine( img, prev_pt, pt, cvScalarAll(255), 5, 8, 0 );
        prev_pt = pt;
        cvShowImage( "image", img );
    }
}


int main( int argc, char** argv )
{
    cout<<"input image name:  "<<endl; 
	string file;
	cin>>file;


	char* filename = (char *)file.c_str();

    CvRNG rng = cvRNG(-1);

    if( (img0 = cvLoadImage(filename,1)) == 0 )
        return 0;

    printf( "Hot keys: \n"
            "\tESC - quit the program\n"
            "\tr - restore the original image\n"
            "\tw or ENTER - run watershed algorithm\n"
            "\t\t(before running it, roughly mark the areas on the image)\n"
            "\t  (before that, roughly outline several markers on the image)\n" );
    
    cvNamedWindow( "image", 1 );
    cvNamedWindow( "watershed transform", 1 );

    img = cvCloneImage( img0 );
    img_gray = cvCloneImage( img0 );
    wshed = cvCloneImage( img0 );
    marker_mask = cvCreateImage( cvGetSize(img), 8, 1 );
    markers = cvCreateImage( cvGetSize(img), IPL_DEPTH_32S, 1 );
    cvCvtColor( img, marker_mask, CV_BGR2GRAY );
    cvCvtColor( marker_mask, img_gray, CV_GRAY2BGR );

    cvZero( marker_mask );
    cvZero( wshed );
    cvShowImage( "image", img );
    cvShowImage( "watershed transform", wshed );
    cvSetMouseCallback( "image", on_mouse, 0 );

    for(;;)
    {
        int c = cvWaitKey(0);

        if( (char)c == 27 )
            break;

        if( (char)c == 'r' )
        {
            cvZero( marker_mask );
            cvCopy( img0, img );
            cvShowImage( "image", img );
        }

        if( (char)c == 'w' || (char)c == '\n' )
        {
            CvMemStorage* storage = cvCreateMemStorage(0);
            CvSeq* contours = 0;
            CvMat* color_tab;
            int i, j, comp_count = 0;
            //cvSaveImage( "wshed_mask.png", marker_mask );
            //marker_mask = cvLoadImage( "wshed_mask.png", 0 );
            cvFindContours( marker_mask, storage, &contours, sizeof(CvContour),
                            CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
            cvZero( markers );
            for( ; contours != 0; contours = contours->h_next, comp_count++ )
            {
                cvDrawContours( markers, contours, cvScalarAll(comp_count+1),
                                cvScalarAll(comp_count+1), -1, -1, 8, cvPoint(0,0) );
            }

            color_tab = cvCreateMat( 1, comp_count, CV_8UC3 );
            for( i = 0; i < comp_count; i++ )
            {
                uchar* ptr = color_tab->data.ptr + i*3;
                ptr[0] = (uchar)(cvRandInt(&rng)%180 + 50);
                ptr[1] = (uchar)(cvRandInt(&rng)%180 + 50);
                ptr[2] = (uchar)(cvRandInt(&rng)%180 + 50);
            }

            {
            double t = (double)cvGetTickCount();
            cvWatershed( img0, markers );
            t = (double)cvGetTickCount() - t;
            printf( "exec time = %gms\n", t/(cvGetTickFrequency()*1000.) );
            }

            // paint the watershed image
            for( i = 0; i < markers->height; i++ )
                for( j = 0; j < markers->width; j++ )
                {
                    int idx = CV_IMAGE_ELEM( markers, int, i, j );
                    uchar* dst = &CV_IMAGE_ELEM( wshed, uchar, i, j*3 );
                    if( idx == -1 )
                        dst[0] = dst[1] = dst[2] = (uchar)255;
                    else if( idx <= 0 || idx > comp_count )
                        dst[0] = dst[1] = dst[2] = (uchar)0; // should not get here
                    else
                    {
                        uchar* ptr = color_tab->data.ptr + (idx-1)*3;
                        dst[0] = ptr[0]; dst[1] = ptr[1]; dst[2] = ptr[2];
                    }
                }

            cvAddWeighted( wshed, 0.5, img_gray, 0.5, 0, wshed );
            cvShowImage( "watershed transform", wshed );
            cvReleaseMemStorage( &storage );
            cvReleaseMat( &color_tab );
        }
    }

    return 1;
}


 

 

 

实现效果:

 

 

 

相关文章
|
1月前
|
算法 计算机视觉
OpenCV(四十一):图像分割-分水岭法
OpenCV(四十一):图像分割-分水岭法
21 0
|
6月前
|
算法 C++
OpenCV-白平衡(完美反射算法)
OpenCV-白平衡(完美反射算法)
163 0
|
6月前
|
算法 C++
OpenCV-白平衡(灰度世界算法)
OpenCV-白平衡(灰度世界算法)
135 0
|
1月前
|
算法 C++ 计算机视觉
Opencv(C++)学习系列---Laplacian拉普拉斯边缘检测算法
Opencv(C++)学习系列---Laplacian拉普拉斯边缘检测算法
|
1月前
|
算法 C++ 计算机视觉
Opencv(C++)学习系列---Canny边缘检测算法
Opencv(C++)学习系列---Canny边缘检测算法
|
4月前
|
算法 数据挖掘 计算机视觉
OpenCV中应用尺度不变特征变换SIFT算法讲解及实战(附源码)
OpenCV中应用尺度不变特征变换SIFT算法讲解及实战(附源码)
31 0
|
9月前
|
算法 计算机视觉 Python
转:Python的分水岭算法如何分割图像?
分水岭算法是一种图像分割算法。它将图像分割为两个或多个连通区域。算法使用图像的梯度信息来确定图像中的“分水岭”。分水岭是指图像中的边界或轮廓。算法通过找到图像中的分水岭来将图像分割成不同的区域。
91 0
|
4月前
|
算法 计算机视觉
OpenCV4-图像分割-watershed(分水岭算法)
1.分水岭概念 分水岭法是根据像素灰度值之间的差值寻找相同区域以实现分割的算法。我们可以将灰度值理解成像素的高度,这样一张图像可以看成崎岖不平的地面或者山区。向地面低洼的地方倾倒一定量的水,水将会掩盖低于某个高度的区域。
73 0
|
8月前
|
算法 安全 机器人
Baumer工业相机堡盟工业相机如何联合BGAPISDK和OpenCV实现图像的伽马变换校正算法增强(C++)
Baumer工业相机堡盟工业相机如何联合BGAPISDK和OpenCV实现图像的伽马变换校正算法增强(C++)
79 0
|
8月前
|
算法 安全 机器人
Baumer工业相机堡盟工业相机如何联合BGAPISDK和OpenCV实现图像的直方图算法增强(C++)
Baumer工业相机堡盟工业相机如何联合BGAPISDK和OpenCV实现图像的直方图算法增强(C++)
42 0