高级图像处理初步——背景获取、轮廓检测、数量统计

#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); //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;
}

参考文献