《学习OpenCV》练习题第五章第二题abc

代码：

  1 #include <stdio.h>
  2 #include <opencv/highgui.h>
  3 #include <opencv/cv.h> 
  4 #include <opencv_libs.h>
  5 
  6 
  7 /*
  8  *《学习OpenCV》第五章第二题  
  9  * 完成时间：21:43 10/13 星期日 2013  
 10  * 作者：qdsclove@163.com
 11  */
 12 
 13 /* Image Size */
 14 #define   IMG_SIZE   100
 15 
 16 /*
 17  * Window Title
 18  */
 19 #define   WNDTITLE_IMAGE   "source image"
 20 #define   WNDTITLE_FIVE    "5*5 Gaussian"
 21 #define   WNDTITLE_NINE    "9*9 Gaussian"
 22 #define   WNDTITLE_FIVE_TEICE   "5*5 Gaussian Twice"
 23 
 24 /*
 25  * function: calculate MSE & PSNR of two GrayScale(8-bit depth & one channel) images.
 26  * param: img1 -- the first image.
 27  * param: img2 -- the second image.
 28  * param: dMSE -- the MSE of two images(output)
 29  * param: dPSNR -- the PSNR of two images(output)
 30  * return: 0 -- success;  others -- failed.
 31  */
 32 int calculateGrayImgsPSNR(IplImage* img1, IplImage* img2, double& dMSE, double& dPSNR)
 33 {
 34     if( !img1 || !img2 || 
 35         img1->nChannels != 1 ||
 36         img2->nChannels != 1 || 
 37         img1->depth != img2->depth ||
 38         img1->width != img2->width || 
 39         img1->height != img2->height )
 40     {
 41         return -1;
 42     }
 43     int width = img1->width;
 44     int height = img1->height;
 45 
 46     // calculate MSE of the two images
 47     double dSumOfSquares = 0;
 48     for(int i = 0; i < height; i++)
 49     {
 50         char* pdata1 = img1->imageData + i * img1->widthStep;
 51         char* pdata2 = img2->imageData + i *img2->widthStep;
 52         for(int j = 0; j < width; j++ )
 53         {
 54             uchar value1 = *(pdata1 + j);
 55             uchar value2 = *(pdata2 + j);
 56 
 57             double square = pow( (double)(value1 - value2), 2 );
 58             dSumOfSquares += square;
 59         }
 60     }
 61 
 62     dMSE = dSumOfSquares / (width * height);
 63 
 64     // this is means the two images are strictly same. 
 65     if(dMSE == 0)
 66     {
 67         dPSNR = -1;
 68         return 0;
 69     }
 70     int iDepth = img1->depth;
 71     int iMAX = pow( 2., iDepth) - 1;
 72 
 73     dPSNR = 20 * log10(iMAX / (sqrt(dMSE)));
 74 
 75     return 0;
 76 }
 77 
 78 
 79 int main()
 80 {
 81     IplImage* image = cvCreateImage( cvSize(IMG_SIZE, IMG_SIZE), IPL_DEPTH_8U, 1 );
 82     IplImage* dst_five_gaussian = cvCreateImage( cvGetSize(image), image->depth, image->nChannels );
 83     IplImage* dst_nine_gaussian = cvCreateImage( cvGetSize(image), image->depth, image->nChannels );
 84     IplImage* dst_twice_five_gaussian = cvCreateImage(  cvGetSize(image), image->depth, image->nChannels );
 85     
 86     // 全部像素置零
 87     cvZero(image);
 88     // 设置中心像素为255
 89     cvSet2D(image, IMG_SIZE/2, IMG_SIZE/2, cvScalarAll(255));
 90 
 91     // 5*5 高斯滤波
 92     cvSmooth(image, dst_five_gaussian, CV_GAUSSIAN, 5, 5);
 93     // 9*9 高斯滤波
 94     cvSmooth(image, dst_nine_gaussian, CV_GAUSSIAN, 9, 9);
 95     // 5*5高斯滤波 第二次
 96     cvSmooth(dst_five_gaussian, dst_twice_five_gaussian, 5, 5);
 97 
 98     cvNamedWindow(WNDTITLE_IMAGE, CV_WINDOW_NORMAL);
 99     cvNamedWindow(WNDTITLE_FIVE, CV_WINDOW_NORMAL);
100     cvNamedWindow(WNDTITLE_NINE, CV_WINDOW_NORMAL);
101     cvNamedWindow(WNDTITLE_FIVE_TEICE, CV_WINDOW_NORMAL);
102 
103     cvShowImage(WNDTITLE_IMAGE, image);
104     cvShowImage(WNDTITLE_FIVE, dst_five_gaussian);
105     cvShowImage(WNDTITLE_NINE, dst_nine_gaussian);
106     cvShowImage(WNDTITLE_FIVE_TEICE, dst_twice_five_gaussian);
107 
108     cvSaveImage("source.bmp", image);
109     cvSaveImage("5_5_gaussian.bmp", dst_five_gaussian);
110     cvSaveImage("9_9_gaussian.bmp", dst_nine_gaussian);
111     cvSaveImage("5_5_gaussian_twice.bmp", dst_twice_five_gaussian);
112 
113     // c part
114     double dMSE = 0, dPSNR = 0;
115     calculateGrayImgsPSNR(dst_nine_gaussian, dst_twice_five_gaussian, dMSE, dPSNR);
116     printf("9*9 GAUSSIAN & 5*5 GAUSSIAN Twice: MSE: %f	PSNR: %f
", dMSE, dPSNR);
117 
118     cvWaitKey(0);
119 
120     cvReleaseImage(&image);
121     cvReleaseImage(&dst_five_gaussian);
122     cvReleaseImage(&dst_nine_gaussian);
123     cvDestroyAllWindows();
124 
125     return 0;
126 }

结果分析，这里的截图都是结果图像放大之后的结果：

原图 & 5*5高斯滤波后的图像：

原图 & 5*5高斯滤波后的图像 & 9*9高斯滤波后的图像：

c部分：

原图 & 5*5高斯滤波后的图像 & 9*9高斯滤波后的图像 & 两次5*5高斯滤波后的图像：

9*9平滑一次与5*5平滑两次的MSE与PSNR：

从上一篇博文（http://www.cnblogs.com/qdsclove/p/3366907.html）可知这两幅图像的相似度很高。