OpenCV直方圖的繪制

1.直方圖的繪制

直方圖是通過將整個變量值范圍劃分為小的值范圍，然后計算每個間隔中落入多少個值來創建的。
使用calcHist函數計算直方圖，函數原型：

void calcHist( const Mat* images, int nimages,
                          const int* channels, InputArray mask,
                          SparseMat& hist, int dims,
                          const int* histSize, const float** ranges,
                          bool uniform = true, bool accumulate = false );

參數說明
/*
* 輸入圖像
* 輸入圖像數
* 數字通道尺寸
* 可選的 掩碼矩陣
* 存儲計算結果
* 直方圖 維度（圖像 取值的 空間維度）
* 要計算的 區間數（每個像素值一個區間）
* 輸入變量的范圍， 可能的 像素值 ，范圍0~255
*/
響應按鈕的回調函數

void showHistoCallback(int state, void* userData)
{
    vector<Mat> bgr;
    split( img, bgr );
    int numbins= 256;
    float range[] = { 0, 256 } ;
    const float* histRange = { range };

    Mat b_hist, g_hist, r_hist;
    calcHist( &bgr[0], 1, 0, Mat(), b_hist, 1, &numbins, &histRange );
    calcHist( &bgr[1], 1, 0, Mat(), g_hist, 1, &numbins, &histRange );
    calcHist( &bgr[2], 1, 0, Mat(), r_hist, 1, &numbins, &histRange );

    int width= 512;
    int height= 300;
    Mat histImage( height, width, CV_8UC3, Scalar(20,20,20) );

    normalize(b_hist, b_hist, 0, height, NORM_MINMAX );
    normalize(g_hist, g_hist, 0, height, NORM_MINMAX );
    normalize(r_hist, r_hist, 0, height, NORM_MINMAX );

    int binStep= cvRound((float)width/(float)numbins);
    for( int i=1; i< numbins; i++)
    {
        line( histImage, Point( binStep*(i-1), height-cvRound(b_hist.at<float>(i-1) ) ), Point( binStep*(i), height-cvRound(b_hist.at<float>(i) ) ),Scalar(255,0,0) );
        line( histImage,  Point( binStep*(i-1), height-cvRound(g_hist.at<float>(i-1) ) ),Point( binStep*(i), height-cvRound(g_hist.at<float>(i) ) ),Scalar(0,255,0) );
        line( histImage, Point( binStep*(i-1), height-cvRound(r_hist.at<float>(i-1) ) ), Point( binStep*(i), height-cvRound(r_hist.at<float>(i) ) ),Scalar(0,0,255) );
    }
    imshow("Histogram", histImage);
}

結果如下：

2.圖像均衡（直方圖均衡化）

均衡彩色圖像，可獲取均勻分布的直方圖，均衡的結果是圖像對比度的增加。均衡能夠使對比度較低的局部區域獲得高對比度，從而分散最頻繁的強度。
兩個主要缺點：背景噪聲的增加以及隨之而來的有用信號的減少。

為了均衡彩色圖像，只需均衡亮度通道。

void equalizeCallback(int state, void* userData)
{
    Mat result;
    // Convert BGR image to YCbCr
    Mat ycrcb;
    cvtColor( img, ycrcb, COLOR_BGR2YCrCb);

    // Split image into channels
    vector<Mat> channels;
    split( ycrcb, channels );
    
    // Equalize the Y channel only
    equalizeHist( channels[0], channels[0] );

    // Merge the result channels
    merge( channels, ycrcb );

    // Convert color ycrcb to BGR
    cvtColor( ycrcb, result, COLOR_YCrCb2BGR );

    // Show image
    imshow("Equalized", result);
}

均衡后的直方圖：