视觉检测设计与实践(米粒计数)c++源程序

ID:209005 · 发表于 2017-6-7 22:51

这是视觉检测试验中的米粒计数：

c++源程序如下:

// 米粒计数.cpp : Defines the entry point for the console application.
//
#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include "cxcore.h"
#include "stdlib.h"
#include <iostream>
using namespace std;
//获取灰度图像
IplImage * LoadImage()
{
//IplImage（Cxcore基础结构）
//载入彩色图像（3信道）(HighGUI读取与保存图像)
IplImage * pImageSrc = cvLoadImage("rice.png",1);//测试图.jpg
if(!pImageSrc)
{
cout << "Could not load image file!" << endl;
exit(0);
}
//显示图像(HighGUI简单图形界面)
cvNamedWindow("原米粒图像");
// cvShowImage("原米粒图像",pImageSrc);
// cvWaitKey(0);
// cvDestroyWindow("原米粒图像");
//灰度图(Cxcore数组操作)
IplImage* pImageGray = cvCreateImage(cvGetSize(pImageSrc),IPL_DEPTH_8U,1);
//色彩空间转换(Cv图像处理)
cvCvtColor(pImageSrc,pImageGray,CV_BGR2GRAY);
cvShowImage("原米粒图像",pImageGray);
cvWaitKey(0);
//Cxcore数组操作
cvReleaseImage(&pImageSrc);
return pImageGray;
}
//对灰度图像进行预处理（均值滤波）
IplImage * PreProcess(IplImage * pImageGray)
{
//预处理结果图(Cxcore数组操作)
IplImage* pImagePrePro = cvCreateImage(cvGetSize(pImageGray),IPL_DEPTH_8U,1);
//均值滤波（Cv图像处理）
cvSmooth(pImageGray, pImagePrePro,CV_BLUR,3,3,0,0);
//显示图像(HighGUI简单图形界面)
cvNamedWindow("均值滤波图像");
cvShowImage("均值滤波图像",pImagePrePro);
cvWaitKey(0);
/* //opencv滤波结果
for(int xindex = 10; xindex < 20; xindex ++)
{
for(int yindex = 10; yindex < 20; yindex ++)
{
cout << cvRound(cvGet2D(pImagePrePro,yindex,xindex).val[0]) << '\t';
}
}
cout << endl;*/
//自己编程对比
//图像拷贝（Cxcore数组操作）
IplImage* pImagePrePro1 = cvCloneImage(pImageGray);
// cvSet(pImagePrePro1,cvScalarAll(0),0);
//均值滤波（Cxcore数组操作）
CvScalar sum;
//滑动窗口增量
int xincrement = -1,xincrement1 = 1;
int yincrement = -1,yincrement1 = 1;
for(int xindex = 1; xindex < pImageGray->width - 1; xindex ++)
{
for(int yindex = 1; yindex < pImageGray->height - 1; yindex ++)
{
sum.val[0] = 0;
//滑动窗口计算均值
for(int xtemp = xincrement; xtemp <= xincrement1 ;xtemp ++)
{
for(int ytemp = yincrement; ytemp <= yincrement1;ytemp ++)
{
//注意：数组和图像的xy坐标不一样
sum.val[0] += cvGet2D(pImageGray,yindex + ytemp,xindex + xtemp).val[0];
}
}
//求均值（Cxcore数组操作）
sum.val[0] = cvRound(sum.val[0] / ((yincrement1 - yincrement + 1) * (xincrement1 - xincrement + 1)));
cvSet2D(pImagePrePro1,yindex,xindex,sum);
}
}
//显示图像(HighGUI简单图形界面)
cvNamedWindow("均值滤波图像1");
cvShowImage("均值滤波图像1",pImagePrePro1);
cvWaitKey(0);
/* //测试滤波的正确性
//自己编程实现滤波结果
for(xindex = 10; xindex < 20; xindex ++)
{
for(int yindex = 10; yindex < 20; yindex ++)
{
cout << cvRound(cvGet2D(pImagePrePro1,yindex,xindex).val[0]) << '\t';
}
}
cout << endl;*/
cvReleaseImage(&pImagePrePro1);
return pImagePrePro;
}
//迭代阈值分割
IplImage * Segmentation(IplImage * pImagePrePro)
{
IplImage* pImageBw = cvCloneImage(pImagePrePro);
//二值分割图像（Cv图像处理）
cvThreshold(pImagePrePro, pImageBw, 119 ,255, CV_THRESH_BINARY);
//显示图像(HighGUI简单图形界面)
cvNamedWindow("二值图像");
cvShowImage("二值图像",pImageBw);
cvWaitKey(0);
//自己编程实现迭代阈值分割
IplImage* pImageBw1 = cvCloneImage(pImagePrePro);
//初始阈值为图像的平均灰度(Cxcore数组操作)
CvScalar threshold = cvAvg(pImageBw1);
// cout << threshold.val[0] << endl;
/*//求平均灰度，验证初始阈值的正确性(可以用cvSum)
CvScalar sum;
sum.val[0] = 0;
for(int xindex = 0; xindex < pImagePrePro->width; xindex ++)
{
for(int yindex = 0; yindex < pImagePrePro->height; yindex ++)
{
sum.val[0] += cvGet2D(pImagePrePro,yindex,xindex).val[0];
}
}
sum.val[0] = sum.val[0] / (pImagePrePro->width * pImagePrePro->height);
cout << sum.val[0] << endl;
*/
//迭代阈值分割
CvScalar threshold1;
threshold1.val[0] = 0;
CvScalar temp1,temp2;
temp1.val[0] = 0;
temp2.val[0] = 0;
while(abs(threshold.val[0] - threshold1.val[0]) > 0.1)
{
threshold1 = threshold;
IplImage* pImageTemp = cvCloneImage(pImageBw1);
//计算前景的平均灰度
cvThreshold(pImageBw1, pImageTemp,threshold1.val[0],255, CV_THRESH_TOZERO);
//注意：不能用cvAvg (Cxcore数组操作)
temp1.val[0] = cvSum(pImageTemp).val[0] / cvCountNonZero(pImageTemp);
//计算背景的平均灰度
cvThreshold(pImageBw1, pImageTemp,threshold1.val[0],255, CV_THRESH_TOZERO_INV);
temp2.val[0] = cvSum(pImageTemp).val[0] / cvCountNonZero(pImageTemp);
//获取新阈值
threshold.val[0] = (temp1.val[0] + temp2.val[0]) / 2;
cvReleaseImage(&pImageTemp);
}
//迭代后得到分割阈值
cout << "迭代阈值：" << threshold.val[0] << endl;
//二值分割图像（Cv图像处理）
cvThreshold(pImageBw1, pImageBw1, threshold.val[0] ,255, CV_THRESH_BINARY);
//显示图像(HighGUI简单图形界面)
cvNamedWindow("二值图像1");
cvShowImage("二值图像1",pImageBw1);
cvWaitKey(0);
cvReleaseImage(&pImageBw1);
return pImageBw;
}
void GetParameter(IplImage * pImageBw)
{
//轮廓的存储容器(Cxcore动态结构)
CvMemStorage* storage = cvCreateMemStorage(0);
//输出参数：包含第一个输出轮廓的指针
CvSeq* first_contour = 0;
IplImage* pImageBw1 = cvCloneImage(pImageBw);
//在二值图像中寻找定点序列轮廓（Cv图像处理）//注意pImgBw1会被改变
int contourNum = cvFindContours(pImageBw1,storage,&first_contour,
sizeof(CvContour), CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);
//freeman链码
//轮廓的存储容器1(Cxcore动态结构)
CvMemStorage* storage1 = cvCreateMemStorage(0);
//输出参数：包含第一个输出轮廓的指针
CvSeq* first_contour1 = 0;
IplImage* pImageBw2 = cvCloneImage(pImageBw);
//在二值图像中寻找freeman链码轮廓（Cv图像处理）//注意pImgBw1会被改变
int contourNum1 = cvFindContours(pImageBw2,storage1,&first_contour1,
sizeof(CvChain), CV_RETR_EXTERNAL,CV_CHAIN_CODE);
//轮廓图
IplImage* image = cvCloneImage(pImageBw);
cvSet(image,cvScalarAll(0),0);
//将所有轮廓画出（Cxcore绘图函数）
cvDrawContours(image, first_contour, CV_RGB(255,255,255), CV_RGB(255,255,255),2, 1, 0);
cvNamedWindow( "轮廓图");
cvShowImage( "轮廓图", image);
cvWaitKey(0);
//获取freeman链码
CvSeqReader reader;
//用于显示外接矩形
IplImage* image1 = cvCreateImage(cvGetSize(pImageBw),IPL_DEPTH_8U,3);
for(CvSeq* contour = first_contour,*contour1 = first_contour1;
contour != 0,contour1 != 0;contour=contour->h_next,contour1=contour1->h_next)
{
cvSet(image,cvScalarAll(0),0);
cvDrawContours(image,contour,CV_RGB(255, 255, 255),CV_RGB(255, 255, 255),0, CV_FILLED, 8);
// cvShowImage( "轮廓图", image);
cout << "轮廓点数：" << contour->total << '\t';
//(Cv结构分析)
cout << "周长：" << cvArcLength(contour) << '\t';
//自己计算周长
//（Cxcore动态结构）
cvStartReadSeq((CvSeq*)contour1, &reader, 0 );
double perimeter = 0;
for(int cNum = 0; cNum < contour->total; cNum ++ )
{
char code;
//获取链码
CV_READ_SEQ_ELEM(code, reader);
//链码为偶数则周长+1
if(int(code) % 2 == 0)
{
perimeter += 1;
}
//链码为奇数，则周长+sqrt(2)
if(int(code) % 2 != 0)
{
perimeter += sqrt(2);
}
}
cout << "计算的周长：" << perimeter << '\t';
cout << "面积：" << fabs(cvContourArea(contour)) << '\t';
cout << "轮廓内非0点数：" << cvCountNonZero(image) << '\t';
// cvWaitKey(0);
//最小外接矩形图
cvSet(image1,cvScalarAll(0),0);
cvDrawContours(image1,contour,CV_RGB(255, 255, 255),CV_RGB(255, 255, 255),0, CV_FILLED, 8);
//对给定2D点集，寻找最小面积的外围矩形（Cv结构分析）
CvBox2D box = cvMinAreaRect2(contour,NULL);
cout << "中心坐标：(" << box.center.x << "," << box.center.y << ")" << '\t';
cvSet2D(image1,box.center.y,box.center.x,CV_RGB(0, 0, 255));
cout << "长：" << (box.size.height > box.size.width?box.size.height:box.size.width) << '\t';
cout << "宽：" << (box.size.height > box.size.width?box.size.width:box.size.height) << '\t';
//绘制外接最小矩形
CvPoint2D32f pt[4];
//寻找盒子的顶点（Cv结构分析）
cvBoxPoints(box,pt);
for(int i = 0;i < 4; ++ i)
{
//(Cxcore绘图函数、Cxcore基础结构)
……………………
…………限于本文篇幅余下代码请从51黑下载附件…………