直方圖可以反映圖片的整體統計信息, 使用函數 CalcHist() 實現.
但CalcHist() 統計出的數量信息和圖像大小相關, 如果要剔除圖像大小因素, 需要做歸一化處理, 歸一化處理后的信息, 反映出各個顏色值得占比情況, 這樣更方便不同size圖像做對比, 歸一化的函數為 Normalize().

        /// <summary>
        /// computes the joint dense histogram for a set of images.
        /// </summary>
        /// <param name="images">要統計直方圖的Mat</param>
        /// <param name="channels">需要統計的通道Id, 為了理解方便, 一般僅統計一個通道</param>
        /// <param name="mask">掩碼Mat, 如果是整張圖片統計直方圖, 傳null即可</param>
        /// <param name="hist">統計后的hist mat</param>
        /// <param name="dims">輸出直方圖的維度, 灰度為1, 彩色為3</param>
        /// <param name="histSize">直方圖橫坐標的區間數, 即直方圖每一維數組的大小</param>
        /// <param name="ranges">執直方圖每個bin上下浮動的數值范圍</param>
        /// <param name="uniform">直方圖是否均勻, 一般取值為true</param>
        /// <param name="accumulate">累計標志, 多次進行直方圖統計時是否需要累計, 一般取值為false</param>
        public static void CalcHist(Mat[] images, 
            int[] channels, InputArray? mask,
            OutputArray hist, int dims, int[] histSize,
            Rangef[] ranges, bool uniform = true, bool accumulate = false)
        {}  

        /// <summary>
        /// scales and shifts array elements so that either the specified norm (alpha) 
        /// or the minimum (alpha) and maximum (beta) array values get the specified values
        /// </summary>
        /// <param name="src">直方圖hist mat</param>
        /// <param name="dst">歸一化后的Mat, 歸一化前后的mat具有相同的size</param>
        /// <param name="alpha">如果beta參數為0, alpha值為歸一化后的下限值;  如果beta值>0; alpha值為歸一化后的上限值;</param>
        /// <param name="beta">如果beta>0, 即指定歸一化后的上限值</param>
        /// <param name="normType">歸一化的算法</param>
        /// <param name="dtype"> 如dtype<0, 歸一化后的數據類型同歸一化之前的數據類型, 一般取-1即可</param>
        /// <param name="mask">掩碼區</param>
        public static void Normalize(InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0,
            NormTypes normType = NormTypes.L2, int dtype = -1, InputArray? mask = null)

歸一化有兩類算法:

• 范圍歸一化, 上下限為[alpha, beta] , 算法需要使用 norm_type= NormTypes.MinMax, 函數會進行比例變換, 將數值從[min(src),max(src)]變換到[alpha,beta]區間.
• 范數歸一化, 上下限為 [0, alpha],
  . 算法取值為NORM_INF，此時函數normalize()會把src矩陣所有元素的最大絕對值調整為參數alpha的值。
  . 算法取值為NORM_L1，此時函數normalize()會把src矩陣所有元素的絕對值之和調整為參數alpha的值。
  . 算法取值為NORM_L2，此時函數normalize()會把src矩陣所有元素的絕對值的平方和進行開方后的值調整為參數alpha的值。

示例代碼

private void calcHistTest()
{
    string fileName = @"D:\my_workspace\opencv\images\lena2.jpg";
    var lena = Cv2.ImRead(fileName, ImreadModes.Color);
    //var lena = new Mat(500, 500, MatType.CV_8UC3, Scalar.Blue);

    //分離BGR通道
    Mat[] bgr = lena.Split();

    //分別對BGR mat進行直方圖統計
    int binCount = 256;
    Mat blueHist = new Mat();
    Cv2.CalcHist(new Mat[] { bgr[0] }, channels: new int[] { 0 },
        mask: null, hist: blueHist, dims: 1, histSize: new int[] { binCount }, ranges: new Rangef[] { new Rangef(0, 256) });
    Mat greenHist = new Mat();
    Cv2.CalcHist(new Mat[] { bgr[1] }, channels: new int[] { 0 },
        mask: null, hist: greenHist, dims: 1, histSize: new int[] { binCount }, ranges: new Rangef[] { new Rangef(0, 256) });
    Mat redHist = new Mat();
    Cv2.CalcHist(new Mat[] { bgr[2] }, channels: new int[] { 0 },
        mask: null, hist: redHist, dims: 1, histSize: new int[] { binCount }, ranges: new Rangef[] { new Rangef(0, 256) });

    //分別做歸一化, 歸一化到 [0,1]
    Cv2.Normalize(blueHist, blueHist, 0, 1, NormTypes.MinMax);
    Cv2.Normalize(greenHist, greenHist, 0, 1, NormTypes.MinMax);
    Cv2.Normalize(redHist, redHist, 0, 1, NormTypes.MinMax);

    //繪制直方圖
    int histWidth = 500;
    int histHeight = 500;
    int binWidth = histWidth / binCount;
    var histImage = new Mat(histWidth, histHeight, MatType.CV_8UC3, Scalar.Black);
    for (int i = 1; i < binCount; i++)
    {
        histImage.Line(new OpenCvSharp.Point((i - 1) * binWidth, blueHist.At<float>(i - 1) * histHeight),
            new OpenCvSharp.Point(i * binWidth, blueHist.At<float>(i) * histHeight), Scalar.Blue);
        histImage.Line(new OpenCvSharp.Point((i - 1) * binWidth, greenHist.At<float>(i - 1) * histHeight),
            new OpenCvSharp.Point(i * binWidth, greenHist.At<float>(i) * histHeight), Scalar.Green);
        histImage.Line(new OpenCvSharp.Point((i - 1) * binWidth, redHist.At<float>(i - 1) * histHeight),
            new OpenCvSharp.Point(i * binWidth, redHist.At<float>(i) * histHeight), Scalar.Red);
    }

    //比較兩個歸一化的直方圖
    var anotherBlueHist = blueHist.Clone();
    var compareResult = Cv2.CompareHist(anotherBlueHist, blueHist, HistCompMethods.Correl);
    Console.WriteLine($"compareResult:{compareResult}");

    Cv2.ImShow("lena", lena);
    Cv2.ImShow("histImage", histImage);
    Cv2.WaitKey();
    Cv2.DestroyAllWindows();
}

參考:

https://zhuanlan.zhihu.com/p/258118645
https://blog.csdn.net/wenhao_ir/article/details/125619073
https://blog.csdn.net/weixin_42207434/article/details/134020709
https://blog.csdn.net/lweiyue/article/details/105775814