image-process.rar_opencv 亮度_opencv 统计个数

// image process.cpp : Defines the entry point for the console application. // #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; }