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

----------------------------------------------------------------------------------------

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__) */

---------------------------------------------------------------------------------

    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;
}

----------------------------------------------------------------------------------------

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

OpenCV: Detecting Horizontal Edges

since: 2013/12/27
update: 2013/12/27
reference:
1. Amazon.com: Practical OpenCV eBook
2. Welcome to opencv documentation!
3. I touchs: Using OpenCV on Mac OS X


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

     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: detectHorizontalEdges.cpp > Create

     5. 說明:    
          a. 使用 OpenCV 的 filter2D() 函式, 來作 kernel-based filtering.

          b. 在 filter matrix(or kernel) 與被 kernel 涵蓋住的像素間, 會作
              Element-wise multiplication(矩陣元素對應相乘)之和的運算.

          c. filter2D() 函式使用的演算法:
              DFT(Discrete Fourier transform)-based algorithm for large kernels;
              direct algorithm for small kernels.

----------------------------------------------------------------------------------------

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

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

#endif /* defined(__HelloOpenCV__detectHorizontalEdges__) */

---------------------------------------------------------------------------------

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

using namespace std;
using namespace cv;

int main()
{
    Mat img = imread("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/officeRoom.png", CV_LOAD_IMAGE_GRAYSCALE);
    Mat img_filtered;
   
    // Filter kernel for detecting vertical edges
    //float vertical_fk[5][5] = {{0,0,0,0,0}, {0,0,0,0,0}, {-1,-2,6,-2,-1}, {0,0,0,0,0}, {0,0,0,0,0}};
    /*
     0  0   -1  0   0
     0  0   -2  0   0
     0  0    6  0   0
     0  0   -2  0   0
     0  0   -1  0   0
     */
    //Mat filter_kernel = Mat(5, 5, CV_32FC1, vertical_fk);
   
   
    // Filter kernel for detecting horizontal edges
    float horizontal_fk[5][5] = {{0,0,-1,0,0}, {0,0,-2,0,0}, {0,0,6,0,0}, {0,0,-2,0,0}, {0,0,-1,0,0}};
    /*
      0   0   0   0    0
      0   0   0   0    0
     -1  -2   6  -2   -1
      0   0   0   0    0
      0   0   0   0    0
     */
    Mat filter_kernel = Mat(5, 5, CV_32FC1, horizontal_fk); // for float 32 bits,  1 channel
   
    // Apply filter
    // -1: the output image will have the same depth as the source.
    filter2D(img, img_filtered, -1, filter_kernel);
   
    namedWindow("Image");
    imshow("Image", img);
   
    namedWindow("Filtered image");
    imshow("Filtered image", img_filtered);
   
    imwrite("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/officeRoom_filtered.png", img_filtered);
    while (char(waitKey(1)) != 'q') {
    }
   
    return 0;
}

----------------------------------------------------------------------------------------

C. 執行結果:
     原始圖片:

     水平濾波:

OpenCV: Displaying the video feed from camera device

since: 2013/12/26
update: 2013/12/26
reference:
1. Amazon.com: Practical OpenCV eBook
2. Welcome to opencv documentation!
3. I touchs: Using OpenCV on Mac OS X

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: displayVideo.cpp > Create

     5. 使用設備:  
          Logitech HD Webcam C310
          參考: 支援 Mac OS 10.4.9 與更新版本的 UVC 網路攝影機

----------------------------------------------------------------------------------------
  
B. 撰寫程式:
    1. 開啓 displayVideo.h 檔案, 修改如下:
#ifndef __HelloOpenCV__displayVideo__
#define __HelloOpenCV__displayVideo__

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

#endif /* defined(__HelloOpenCV__displayVideo__) */

---------------------------------------------------------------------------------

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

//@add
using namespace cv;
using namespace std;

#define CAMERA_OUTPUT_WINDOW_NAME "camera-output"

int main(int argc, char **argv)
{
    // C structure: for video capturing from video files or cameras.
    CvCapture *camCapture;
    int ret = 0;
   
    if (!(camCapture = cvCaptureFromCAM(CV_CAP_ANY))) {
        cout << "Failed to capture from camera" << endl;
       
        ret = 1;
       
        goto exitCameraOpenFailed;
    }
   
    cout << "Camera opened successfully" << endl;
   
    // C function, Creates a window.
    cvNamedWindow(CAMERA_OUTPUT_WINDOW_NAME, CV_WINDOW_AUTOSIZE);
   
    // C/C++ struct: The IplImage is taken from the Intel Image Processing Library, in which the format is native.
    IplImage *cameraFrame;
   
    while (true) {
        // cvQueryFrame(C function): It combine VideoCapture::grab() and VideoCapture::retrieve() in one call.
        if ((cameraFrame = cvQueryFrame(camCapture))) {
            cvShowImage(CAMERA_OUTPUT_WINDOW_NAME, cameraFrame);
        }
       
        if (cvWaitKey(60) != -1) {
            cout << "Input" << endl;
            break;
        }
    }
   
    cout << "Done" << endl;
   
    cvReleaseCapture(&camCapture);
    // C function, destroys the window with the given name.
    cvDestroyWindow(CAMERA_OUTPUT_WINDOW_NAME);
   
    exitCameraOpenFailed:
    return ret;
}

----------------------------------------------------------------------------------------

C. 執行結果:

OpenCV: Cropping a ROI(Regions of Interest) out of an Image

since: 2013/12/26
update: 2013/12/26
reference:
1. Amazon.com: Practical OpenCV eBook
2. Welcome to opencv documentation!
3. I touchs: Using OpenCV on Mac OS X

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: ROICropping.cpp > Create

----------------------------------------------------------------------------------------

B. 撰寫程式:
    1. 開啓 ROICropping.h 檔案, 修改如下:
#ifndef __HelloOpenCV__ROICropping__
#define __HelloOpenCV__ROICropping__
#include <iostream>
//@add
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#endif /* defined(__HelloOpenCV__ROICropping__) */

---------------------------------------------------------------------------------

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

//@add
using namespace std;
using namespace cv;

bool ldown = false; // left mouse button down flag
bool lup = false; // left mouse button up flag

Mat img; // original image
Mat croppedImage; // cropped image
Point corner1, corner2; // Starting and ending of the user's selection point
Rect box; // (ROI)Regions of Interest

// Callback function for mouse events
static void mouse_callback(int event, int x, int y, int, void*)
{
    // when left mouse button is pressed
    if (event == EVENT_LBUTTONDOWN) {
        ldown = true;
        // record its position and save it in corner1
        corner1.x = x;
        corner1.y = y;
        cout << "Corner 1 recorded at " << corner1 << endl;
    }
    // when left mouse button is released
    if (event == EVENT_LBUTTONUP) {
        // if user selection is bigger than 20 piexels
        if (abs(x - corner1.x) > 20 && abs(y - corner1.y) > 20) {
            lup = true;
            // record its position and save it in corner1
            corner2.x = x;
            corner2.y = y;
            cout << "Corner 2 recorded at " << corner2 <<  endl << endl;
        }
        else
        {
            cout << "Please select a bigger region" << endl;
            ldown = false;
        }
    }
    // update the box showing the selected region as the user drags the mouse
    if (ldown == true && lup == false) {
        Point pt;
        pt.x = x;
        pt.y = y;
        Mat local_img = img.clone();
        rectangle(local_img, corner1, pt, Scalar(0, 0, 255)); // b, g, r
        imshow("Cropping app", local_img);
    }
    // Define ROI and crop it out when both corners have been selected
    if (ldown == true && lup == true) {
        box.width = abs(corner1.x - corner2.x); // 2 個 corner X 軸座標距離的絕對值
        box.height = abs(corner1.y - corner2.y); // 2 個 corner Y 軸座標距離的絕對值
        box.x = min(corner1.x, corner2.x); // 2 個 corner X 軸座標的最小值
        box.y = min(corner1.y, corner2.y); // 2 個 corner Y 軸座標的最小值
       
        // Make a image out of just the selected ROI and display it in a new window
        Mat crop(img, box);
        namedWindow("Crop");
        imshow("Crop", crop);
       
        // clone the cropped image(ROI) and save it to a file
        croppedImage = img(box).clone();
        imwrite("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/pipi_cropped.png", croppedImage);
       
        ldown = false;
        lup = false;
    }
}

int main()
{
    // Read image
    img = imread("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/pipi.png");
    namedWindow("Cropping app");
    imshow("Cropping app", img);
   
    setMouseCallback("Cropping app", mouse_callback);
   
    while (char(waitKey(1) != 'q')) {
    }
   
    return 0;
}

----------------------------------------------------------------------------------------

C. 執行結果:
     Cropping:

     Saving:

OpenCV: Color-space conversion

since: 2013/12/23
update: 2013/12/24
reference:
1. Amazon.com: Practical OpenCV eBook
2. Welcome to opencv documentation!
3. I touchs: Using OpenCV on Mac OS X

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: colorSpaceConversion.cpp > Create

----------------------------------------------------------------------------------------

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

#include <iostream>
//@add
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#endif /* defined(__HelloOpenCV__colorSpaceConversion__) */

---------------------------------------------------------------------------------

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

//@add
using namespace std;
using namespace cv;

// Global variables
const int slider_max = 100; // slider 最大的數值
int slider; // slider 目前的數值
double alpha; // 加權數(0.0 ~ 1.0)
double beta;  // 加權數(0.0 ~ 1.0)

// Matrices to store images
Mat img_source; // 原始照片
Mat img_converted; // 存放色彩轉換後的照片
Mat img_destination; // 存放二張照片加權後所合成的照片

// Callback function for trackbar event
void on_trackbar(int pos, void *)
{
    // case 1 與 case 2: 擇一執行
    printf("slider position: %d \n", slider);
   
    // case 1: RGB to GRAY
    if (pos > 0) {
        // 將 RGB 的照片: img_source, 轉換成灰階並存入 img_converted 中
        cvtColor(img_source, img_converted, CV_RGB2GRAY);
    }
    else {
        img_converted = img_source;
    }
   
    // 顯示照片
    imshow("Trackbar app", img_converted);
   
    /*************************************************************/
   
    /*
    // case 2: RGB to BGR
    // 將 RGB 的照片: img_source, 轉換成 BGR 並存入 img_converted 中
    cvtColor(img_source, img_converted, CV_RGB2BGR);

    alpha = (double)slider/slider_max; // 加權數(0.0 ~ 1.0)
    beta = (1.0 - alpha); // 加權數(0.0 ~ 1.0)
   
    // 計算各別(照片)矩陣的加權值 (img_source x beta) + (img_converted) x alpha ,
    // 存入 img_destination 中, 並且各別的照片必須要有相同的大小(size)與類型(type)
    // p.s. 不可使用 RGB(3個 channel) 與 灰階(1個 channel) 來作加權計算.
    addWeighted(img_source, beta, img_converted, alpha, 0.0, img_destination);
    
    // 顯示照片
    imshow("Trackbar app", img_destination);
    */
}

int main()
{
    // Read image
    //img_source = imread("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/pipi.png", CV_LOAD_IMAGE_COLOR);
    //img_source = imread("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/pipi.png", CV_LOAD_IMAGE_GRAYSCALE);
    img_source = imread("/Lanli/RD/Projects/OpenCV_Mac/HelloOpenCV/pipi.png");
   
    // check if image was loaded
    if( !img_source.data ) {
        printf("Error loading img_source \n");
        return -1;
    }
   
    // Initialize values
    slider = 0;
   
    // Create Windows
    //namedWindow("Trackbar app", WINDOW_AUTOSIZE);
    //namedWindow("Trackbar app", WINDOW_NORMAL);
    namedWindow("Trackbar app");
   
    // Create Trackbars
    char TrackbarName[50];
    sprintf(TrackbarName, "TrackBar Max: %d", slider_max);
    createTrackbar(TrackbarName, "Trackbar app", &slider, slider_max, on_trackbar);
 
    // Show some stuff
    on_trackbar(slider, 0);
   
    // Wait until user press 'q'
    //
    // 回圈: 等待 1 微秒(以作業系統的最小時間爲下限)來偵測鍵盤事件,
    // 如果不是按下 'q', 就一直偵測下去.
    // p.s. 至少要有一個 HighGUI 的視窗存在且啟用, 才會有作用
    while(char(waitKey(1)) != 'q') {
    }
   
    // destroys all of the opened HighGUI windows.
    destroyAllWindows();
   
    return 0;
}

----------------------------------------------------------------------------------------

C. 執行結果:
     1. RGB to GRAY:
         slider position: 0


       
         slider position: 50

     2. RGB to BGR:
         slider position: 75

         slider position: 100