《学习OpenCV》第五章课后题2

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题目说明：显示滤波器的效果。建立一个100*100单通道图像。将图像全部像素置零，然后设置中心像素值等于255.
a.利用5*5高斯滤波器平滑此图像并显示结果。你发现了什么？
b.改用9*9高斯滤波器重复a操作。
c.如果你重新对原始图像用*5*5过滤器平滑两次，会出现什么结果？与用9*9过滤器的结果对比，它们相似吗？为什么？

#include <stdio.h>#include <opencv/highgui.h>#include <opencv/cv.h> /* *《学习OpenCV》第五章第二题   * 完成时间：21:43 10/13 星期日 2013   * 作者：qdsclove@163.com *//* Image Size */#define   IMG_SIZE   20/* * Window Title */#define   WNDTITLE_IMAGE   "source image"#define   WNDTITLE_FIVE    "5*5 Gaussian"#define   WNDTITLE_NINE    "9*9 Gaussian"#define   WNDTITLE_FIVE_TEICE   "5*5 Gaussian Twice"/* * function: calculate MSE & PSNR of two GrayScale(8-bit depth & one channel) images. * param: img1 -- the first image. * param: img2 -- the second image. * param: dMSE -- the MSE of two images(output) * param: dPSNR -- the PSNR of two images(output) * return: 0 -- success;  others -- failed. */int calculateGrayImgsPSNR(IplImage* img1, IplImage* img2, double& dMSE, double& dPSNR){    if( !img1 || !img2 ||         img1->nChannels != 1 ||        img2->nChannels != 1 ||         img1->depth != img2->depth ||        img1->width != img2->width ||         img1->height != img2->height )    {        return -1;    }    int width = img1->width;    int height = img1->height;    // calculate MSE of the two images    double dSumOfSquares = 0;    for(int i = 0; i < height; i++)    {        char* pdata1 = img1->imageData + i * img1->widthStep;        char* pdata2 = img2->imageData + i *img2->widthStep;        for(int j = 0; j < width; j++ )        {            uchar value1 = *(pdata1 + j);            uchar value2 = *(pdata2 + j);            double square = pow( (double)(value1 - value2), 2 );            dSumOfSquares += square;        }    }    dMSE = dSumOfSquares / (width * height);    // this is means the two images are strictly same.     if(dMSE == 0)    {        dPSNR = -1;        return 0;    }    int iDepth = img1->depth;    int iMAX = pow( 2., iDepth) - 1;    dPSNR = 20 * log10(iMAX / (sqrt(dMSE)));    return 0;}int main(){    IplImage* image = cvCreateImage( cvSize(IMG_SIZE, IMG_SIZE), IPL_DEPTH_8U, 1 );    IplImage* dst_five_gaussian = cvCreateImage( cvGetSize(image), image->depth, image->nChannels );    IplImage* dst_nine_gaussian = cvCreateImage( cvGetSize(image), image->depth, image->nChannels );    IplImage* dst_twice_five_gaussian = cvCreateImage(  cvGetSize(image), image->depth, image->nChannels );    // 全部像素置零    cvZero(image);    // 设置中心像素为255    cvSet2D(image, IMG_SIZE/2, IMG_SIZE/2, cvScalarAll(255));    // 5*5 高斯滤波    cvSmooth(image, dst_five_gaussian, CV_GAUSSIAN, 5, 5);    // 9*9 高斯滤波    cvSmooth(image, dst_nine_gaussian, CV_GAUSSIAN, 9, 9);    // 5*5高斯滤波 第二次    cvSmooth(dst_five_gaussian, dst_twice_five_gaussian, 5, 5);    cvNamedWindow(WNDTITLE_IMAGE, CV_WINDOW_NORMAL);    cvNamedWindow(WNDTITLE_FIVE, CV_WINDOW_NORMAL);    cvNamedWindow(WNDTITLE_NINE, CV_WINDOW_NORMAL);    cvNamedWindow(WNDTITLE_FIVE_TEICE, CV_WINDOW_NORMAL);    cvShowImage(WNDTITLE_IMAGE, image);    cvShowImage(WNDTITLE_FIVE, dst_five_gaussian);    cvShowImage(WNDTITLE_NINE, dst_nine_gaussian);    cvShowImage(WNDTITLE_FIVE_TEICE, dst_twice_five_gaussian);    cvSaveImage("source.bmp", image);    cvSaveImage("5_5_gaussian.bmp", dst_five_gaussian);    cvSaveImage("9_9_gaussian.bmp", dst_nine_gaussian);    cvSaveImage("5_5_gaussian_twice.bmp", dst_twice_five_gaussian);    // c part    double dMSE = 0, dPSNR = 0;    calculateGrayImgsPSNR(dst_nine_gaussian, dst_twice_five_gaussian, dMSE, dPSNR);    printf("9*9 GAUSSIAN & 5*5 GAUSSIAN Twice: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    cvWaitKey(0);    cvReleaseImage(&image);    cvReleaseImage(&dst_five_gaussian);    cvReleaseImage(&dst_nine_gaussian);    cvDestroyAllWindows();    return 0;}

不知道出这道题的用意，其实感觉c的对比结果并不相似。

引用qdsclove的专栏
http://blog.csdn.net/stk_overflow/article/details/12686931

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