OpenCV: Blurring Images

since: 2013/12/30
update: 2013/12/30
reference:
1. Amazon.com: Practical OpenCV eBook
2. Welcome to opencv documentation!
3. I touchs: Using OpenCV on Mac OS X
4. 高斯模糊 - 維基百科
5. 標準差 - 維基百科

A. 前置作業:
     1. 先依照 I touchs: Using OpenCV on Mac OS X 的說明, 建置好開發環境.

     2. 需要加入到專案的 OpenCV 函式庫爲: (Add Files to Project...)
          libopencv_core.dylib
          libopencv_highgui.dylib
          libopencv_imgproc.dylib

     3. 將 main.cpp 的 main function 更名.(不作為程式執行的進入點)
          //int main(int argc, const char * argv[])  
          int main_main(int argc, const char * argv[])

     4. 爲專案新增 C++ 檔案:      
         點選專案 > New File... > OS X > C and C++ > C++ Class > Next >
         Save as: GaussianKernelBlur.cpp > Create

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B. 撰寫程式:
    1. 開啓 GaussianKernelBlur.h 檔案, 修改如下:
#ifndef __HelloOpenCV__GaussianKernelBlur__
#define __HelloOpenCV__GaussianKernelBlur__

#include <iostream>
//@add
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#endif /* defined(__HelloOpenCV__GaussianKernelBlur__) */

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    2. 開啓 GaussianKernelBlur.cpp 檔案, 修改如下:
#include "GaussianKernelBlur.h"

using namespace std;
using namespace cv;

Mat image, image_blurred;
const int sliderMax = 21; // slider 最大的數值
int slider = 5;
float sigma; // 常態分佈的標準差

void on_trackbar(int, void*)
{
    // kernel size(k_size): 限制為大於零的奇數
    int k_size = max(1, slider);
    k_size = k_size % 2 == 0 ? k_size + 1 : k_size;
   
    setTrackbarPos("Kernel Size", "Blurred image", k_size);
    sigma = 0.3 * ((k_size - 1) * 0.5 - 1) + 0.8;
   
    printf("Gausscian k_size: %d \n", k_size); // kernel size
    printf("Gausscian sigma: %f \n", sigma); // 常態分佈的標準差
   
    GaussianBlur(image, image_blurred, Size(k_size, k_size), sigma);
    imshow("Blurred image", image_blurred);
}

int main()
{
    image = imread("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/pipi2.png");
    namedWindow("Original image");
    namedWindow("Blurred image");
   
    imshow("Original image", image);
    sigma = 0.3 * ((slider - 1) * 0.5 - 1) + 0.8;
   
    printf("Gausscian k_size: %d \n", slider); // kernel size
    printf("Gausscian sigma: %f \n", sigma); // 常態分佈的標準差
   
    GaussianBlur(image, image_blurred, Size(slider, slider), sigma);
    imshow("Blurred image", image_blurred);
   
    createTrackbar("Kernel Size", "Blurred image", &slider, sliderMax, on_trackbar);
    while (char(waitKey(1) != 'q')) {}
   
    return 0;
}

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C. 執行結果:
     原始圖片:

     Gaussian kernel size: 5
     sigma(標準差): 1.10

     Gaussian kernel size: 13
     sigma(標準差): 2.30

     Gaussian kernel size: 21
     sigma(標準差): 3.50