opencv图像处理

本代码主要是对一幅灰度图像rice.jpg进行一些处理，消除rice.jpg图像中的亮度不一致的背景，并使用阈值分割将修改后的图像转换为二值图像，使用轮廓检测返回图像中目标对象的个数以及统计属性。

转载自：opencv中文网站

#include <cv.h>#include <highgui.h>#include <math.h>//#include <stdlib.h>//#include <stdio.h>  int main(int argc, char* argv[]){    IplImage *src = 0; //定义源图像指针     IplImage *tmp = 0; //定义临时图像指针     IplImage *src_back = 0; //定义源图像背景指针     IplImage *dst_gray = 0; //定义源文件去掉背景后的目标灰度图像指针     IplImage *dst_bw = 0; //定义源文件去掉背景后的目标二值图像指针     IplImage *dst_contours = 0; //定义轮廓图像指针     IplConvKernel *element = 0; //定义形态学结构指针     int Number_Object =0; //定义目标对象数量     int contour_area_tmp = 0; //定义目标对象面积临时寄存器     int contour_area_sum = 0; //定义目标所有对象面积的和     int contour_area_ave = 0; //定义目标对象面积平均值     int contour_area_max = 0; //定义目标对象面积最大值     CvMemStorage *stor = 0;    CvSeq * cont = 0;    CvContourScanner contour_scanner;     CvSeq * a_contour= 0;     //1.读取和显示图像     /* the first command line parameter must be image file name */    if ( argc == 2 && (src = cvLoadImage(argv[1], 0))!=0 )    {        ;    }    else    {        src = cvLoadImage("rice.jpg", 0);    }    cvNamedWindow( "src", CV_WINDOW_AUTOSIZE );    cvShowImage( "src", src );    //cvSmooth(src, src, CV_MEDIAN, 3, 0, 0, 0); //中值滤波，消除小的噪声；     //2.估计图像背景     tmp = cvCreateImage( cvGetSize(src), src->depth, src->nChannels);    src_back = cvCreateImage( cvGetSize(src), src->depth, src->nChannels);    //创建结构元素     element = cvCreateStructuringElementEx( 4, 4, 1, 1, CV_SHAPE_ELLIPSE, 0);    //用该结构对源图象进行数学形态学的开操作后，估计背景亮度     cvErode( src, tmp, element, 10);    cvDilate( tmp, src_back, element, 10);    cvNamedWindow( "src_back", CV_WINDOW_AUTOSIZE );    cvShowImage( "src_back", src_back );     //3.从源图象中减区背景图像     dst_gray = cvCreateImage( cvGetSize(src), src->depth, src->nChannels);    cvSub( src, src_back, dst_gray, 0);    cvNamedWindow( "dst_gray", CV_WINDOW_AUTOSIZE );    cvShowImage( "dst_gray", dst_gray );     //4.使用阈值操作将图像转换为二值图像     dst_bw = cvCreateImage( cvGetSize(src), src->depth, src->nChannels);    cvThreshold( dst_gray, dst_bw ,50, 255, CV_THRESH_BINARY ); //取阈值为50把图像转为二值图像     //cvAdaptiveThreshold( dst_gray, dst_bw, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 3, 5 );     cvNamedWindow( "dst_bw", CV_WINDOW_AUTOSIZE );    cvShowImage( "dst_bw", dst_bw );       //5.检查图像中的目标对象数量     stor = cvCreateMemStorage(0);      cont = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint), stor);      Number_Object = cvFindContours(dst_bw, stor, &cont, sizeof(CvContour),       CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) ); //找到所有轮廓     printf("Number_Object: %d\n", Number_Object);     //6.计算图像中对象的统计属性     dst_contours = cvCreateImage( cvGetSize(src), src->depth, src->nChannels);    cvThreshold( dst_contours, dst_contours ,0, 255, CV_THRESH_BINARY_INV); //在画轮廓前先把图像变成白色     for(;cont;cont = cont->h_next)    {        cvDrawContours( dst_contours, cont, CV_RGB(255, 0, 0), CV_RGB(255, 0, 0), 0, 1, 8, cvPoint(0, 0) ); //绘制当前轮廓         contour_area_tmp = fabs(cvContourArea( cont, CV_WHOLE_SEQ )); //获取当前轮廓面积         if( contour_area_tmp > contour_area_max )        {            contour_area_max = contour_area_tmp; //找到面积最大的轮廓         }        contour_area_sum += contour_area_tmp; //求所有轮廓的面积和     }    contour_area_ave = contour_area_sum/ Number_Object; //求出所有轮廓的平均值     printf("contour_area_ave: %d\n", contour_area_ave );    printf("contour_area_max: %d\n", contour_area_max );    cvNamedWindow( "dst_contours", CV_WINDOW_AUTOSIZE );    cvShowImage( "dst_contours", dst_contours );     cvWaitKey(-1); //等待退出     cvReleaseImage(&src);    cvReleaseImage(&tmp);    cvReleaseImage(&src_back);    cvReleaseImage(&dst_gray);    cvReleaseImage(&dst_bw);    cvReleaseImage(&dst_contours);    cvReleaseMemStorage(&stor);    cvDestroyWindow( "src" );    cvDestroyWindow( "src_back" );    cvDestroyWindow( "dst_gray" );    cvDestroyWindow( "dst_bw" );    cvDestroyWindow( "dst_contours" );    //void cvDestroyAllWindows(void);     return 0;}