opencv學習歷程4

創建一個Trackbar

// 函數定義
int cv::createTrackbar 	( 	const String &  	trackbarname,
		const String &  	winname,
		int *  	value,
		int  	count,
		TrackbarCallback  	onChange = 0,
		void *  	userdata = 0 
// trackbarname trackbar的名字
// winname trackbar所在窗口的名字
// value 滑塊的值
// count 滑塊最大允許值
// onChange 指向一個函數，每次改變滑塊的值，自動調用這個函數，要求函數的原型爲 void function_name(int, void *);

// 一個例子, on_trackbar爲調用的函數
createTrackbar( "Trackbar", "Linear Blend", &alpha_slider, alpha_slider_max, on_trackbar );
	)

Reading Geospatial Raster files with GDAL

dem = cv::imread(argv[2], cv::IMREAD_LOAD_GDAL | cv::IMREAD_ANYDEPTH );

一些簡單處理參照Reading Geospatial Raster files with GDAL opencv官方例子

opencv讀取視頻或調用攝像頭

視頻流實質也就是連續若干幀的圖片，在opencv中讀取視頻的方法也是如此，先聲明一個視頻流對象，然後逐幀的取出圖片

    VideoCapture source(sourceVideo);
    VideoCapture test;
    test.open(testVideo);// 這兩種表達方式含義一致, 都是打開一個視頻文件，生成視頻流對象
    // VideoCapture本身除了讀取視頻時候也可以調用攝像頭，此時參數應該位設備的ID號
    // 在聲明瞭視頻流後可以通過>>操作取出來幀圖片
    Mat sourceImage, testImage;
    source >> sourceImage;
    test   >> testImage;
    // 可以通過get函數獲取視頻相關屬性
    // Size爲opencv默認數據類型， 構造函數爲Size(width, height);
    Size ssource = Size((int) source.get(CAP_PROP_FRAME_WIDTH),
                       (int) source.get(CAP_PROP_FRAME_HEIGHT));
    Size stest   = Size((int) test.get(CAP_PROP_FRAME_WIDTH),
                       (int) test.get(CAP_PROP_FRAME_HEIGHT));
    cout<<"width="<<ssource.width<<"Height="<<ssource.height<<"number="<<source.get(CAP_PROP_FRAME_COUNT);

一個輸入兩個視頻文件並計算ssim和psnr的例子

#include<opencv2/core.hpp>//opencv中基礎模塊
#include<opencv2/highgui.hpp>//opencv I/O
#include<opencv2/imgproc.hpp> //gaussian blur
#include<opencv2/video.hpp>

#include<iostream>
#include<string>
#include<iomanip> // 控制輸出精度
#include<sstream>// string to number 轉換

using namespace std;
using namespace cv;

double getPSNR(const Mat& I1, const Mat& I2);//計算PSNR值
Scalar getSSIM( const Mat& i1, const Mat& i2);// 計算SSIM值

int main(int agrc, char **argv)
{
    const string sourceVideo = argv[1], testVideo = argv[2];
    int frameNumber = -1; // 視頻幀數

    //聲明一個視頻流的對象
    VideoCapture source(sourceVideo);
    VideoCapture test;
    test.open(testVideo);// 這兩種表達方式含義一致
    if(!source.isOpened())
    {
        cout<<"can't open source video"<<endl;
        return -1;
    }
    if(!test.isOpened())
    {
        cout<<"can't open test video"<<endl;
        return -1;
    }
    // 獲取視頻屬性
    // Size爲opencv默認數據類型， 構造函數爲Size(width, height);
    Size ssource = Size((int) source.get(CAP_PROP_FRAME_WIDTH),
                       (int) source.get(CAP_PROP_FRAME_HEIGHT));
    Size stest   = Size((int) test.get(CAP_PROP_FRAME_WIDTH),
                       (int) test.get(CAP_PROP_FRAME_HEIGHT));
    cout<<"width="<<ssource.width<<"Height="<<ssource.height<<"number="<<source.get(CAP_PROP_FRAME_COUNT);
    if(ssource!=stest)
    {
        cout<<"source video and test video have different size"<<endl;
        return -1;
    }

    const char* WIN_SOURCE  = "source video";
    const char* WIN_TEST = "test video";

    namedWindow(WIN_SOURCE, WINDOW_AUTOSIZE);
    namedWindow(WIN_TEST, WINDOW_AUTOSIZE);
    moveWindow(WIN_SOURCE, 500, 0);
    moveWindow(WIN_TEST, 600+ssource.width, 0);
    

    Mat sourceImage, testImage;
    double psnrV;
    Scalar ssimV;//存儲三通道的ssim
    for(;;)
    {
        // 從視頻流中獲取每一幀圖像
        source >> sourceImage;
        test   >> testImage;

        if(sourceImage.empty()||testImage.empty())
        {
            cout<<"over"<<endl;
            break;
        }

        frameNumber++;
        cout<<"frame="<<frameNumber+1<<endl;
        psnrV = getPSNR(sourceImage, sourceImage);
        cout<<setiosflags(ios::fixed)<<setprecision(3)<<psnrV<<endl;

        ssimV = getSSIM(sourceImage, sourceImage);
        cout<<"SSIM"
            <<"R"<<setiosflags(ios::fixed)<<setprecision(3)<<ssimV[2]*100<<"%"
            <<"G"<<setiosflags(ios::fixed)<<setprecision(3)<<ssimV[1]*100<<"%"
            <<"B"<<setiosflags(ios::fixed)<<setprecision(3)<<ssimV[0]*100<<"%"<<endl;
        imshow(WIN_SOURCE, sourceImage);
        imshow(WIN_TEST, testImage);

        char c=(char)waitKey(500);
        if(c==27)
        {
            break;
        }

    }
    return 0;
}

// 對照着具體公式即可很容易理解該部分代碼
double getPSNR(const Mat& I1, const Mat& I2)
{
    Mat s1;
    absdiff(I1, I2, s1);       // |I1 - I2|
    s1.convertTo(s1, CV_32F);  // cannot make a square on 8 bits
    s1 = s1.mul(s1);           // |I1 - I2|^2
    Scalar s = sum(s1);        // sum elements per channel
    double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels
    if( sse <= 1e-10) // for small values return zero
        return 0;
    else
    {
        double mse  = sse / (double)(I1.channels() * I1.total());
        double psnr = 10.0 * log10((255 * 255) / mse);
        return psnr;
    }
}
Scalar getSSIM( const Mat& i1, const Mat& i2)
{
    const double C1 = 6.5025, C2 = 58.5225;
    /***************************** INITS **********************************/
    int d = CV_32F;
    Mat I1, I2;
    i1.convertTo(I1, d);            // cannot calculate on one byte large values
    i2.convertTo(I2, d);
    Mat I2_2   = I2.mul(I2);        // I2^2
    Mat I1_2   = I1.mul(I1);        // I1^2
    Mat I1_I2  = I1.mul(I2);        // I1 * I2
    /*************************** END INITS **********************************/
    Mat mu1, mu2;                   // PRELIMINARY COMPUTING
    GaussianBlur(I1, mu1, Size(11, 11), 1.5);
    GaussianBlur(I2, mu2, Size(11, 11), 1.5);
    Mat mu1_2   =   mu1.mul(mu1);
    Mat mu2_2   =   mu2.mul(mu2);
    Mat mu1_mu2 =   mu1.mul(mu2);
    Mat sigma1_2, sigma2_2, sigma12;
    GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
    sigma1_2 -= mu1_2;
    GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
    sigma2_2 -= mu2_2;
    GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
    sigma12 -= mu1_mu2;
    Mat t1, t2, t3;
    t1 = 2 * mu1_mu2 + C1;
    t2 = 2 * sigma12 + C2;
    t3 = t1.mul(t2);                 // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
    t1 = mu1_2 + mu2_2 + C1;
    t2 = sigma1_2 + sigma2_2 + C2;
    t1 = t1.mul(t2);                 // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
    Mat ssim_map;
    divide(t3, t1, ssim_map);        // ssim_map =  t3./t1;
    Scalar mssim = mean(ssim_map);   // mssim = average of ssim map
    return mssim;
}

opencv進行簡單的視頻生成

一個視頻文件的內容可以從後綴名中得到，主要包括 video feeds, audio feeds or other tracks， video track存儲方式是由編碼器決定的，opencv主要面對圖像處理，因而其中僅支持.avi文件，也僅能生成.avi文件

要生成視頻文件首先要建立一個視頻流對象，包括視頻名稱，編碼格式，fps，視頻尺寸等，我們再次以建立一個與輸入文件相同格式相同尺寸的視頻爲例，如果已知要存儲的視頻編碼格式，和fps，尺寸就只需要構造一個VideoWriter對象即可,

// 獲取輸入視頻源
VideoCapture inputVideo(source); 
// CAP_PROP_FOURCC代表編碼格式，但get屬性獲得是double類型8字節，編碼器類型4字節，存儲在低四字節，故轉換爲int類型
int ex = static_cast<int>(inputVideo.get(CAP_PROP_FOURCC));     // Get Codec Type- Int form
 // 通過位運算將數字轉換爲對應的字符串，每1個字節，8位代表一個字符，故通過&運算分別取出每個字節，並在最後加上'\0'
 char EXT[] = {(char)(ex & 0XFF) , (char)((ex & 0XFF00) >> 8),(char)((ex & 0XFF0000) >> 16),(char)((ex & 0XFF000000) >> 24), 0};
 // 獲取視頻尺寸
 Size S = Size((int) inputVideo.get(CAP_PROP_FRAME_WIDTH),    // Acquire input size
                  (int) inputVideo.get(CAP_PROP_FRAME_HEIGHT));
 VideoWriter outputVideo;
 outputVideo.open(NAME, ex, inputVideo.get(CAP_PROP_FPS), S, true);

將每一幀圖片存儲你進行視頻源對象中即可

// res即爲要存儲的幀圖片
outputVideo.write(res);  //or
outputVideo << res;

opencv學習歷程4

創建一個Trackbar

Reading Geospatial Raster files with GDAL

opencv讀取視頻或調用攝像頭

opencv進行簡單的視頻生成

Mat矩陣初始化和訪問

APAP多圖拼接代碼

OpenGL編程需要了解的一些概念

OpenGL創建窗口

DoG算子和LoG算子

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

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