LIBMAS/ImageCollect/CameraComponent/DisplayCameraControl.cs

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Drawing;
using System.Data;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using PaintDotNet.Base.SettingModel;
using TUCAMAPI;
using TUCamera;
using OpenCvSharp;
using PaintDotNet.CustomControl;

namespace PaintDotNet.Setting.LabelComponent
{
    public class DisplayCameraControl : UserControl
    {
        #region 初始化变量
        private AppWorkspace AppWorkspace;
        /// <summary>
        /// 一个矩阵数组，用来接收直方图,记得全部初始化
        /// </summary>
        Mat[] oldHists = new Mat[] { new Mat(), new Mat(), new Mat() };
        /// <summary>
        /// 相机采集的图像
        /// </summary>
        Mat OldMat;
        /// <summary>
        /// 灰度图
        /// </summary>
        private bool isGray = true;
        /// <summary>
        /// BGR线条颜色
        /// </summary>
        private Scalar[] color = new Scalar[] { new Scalar(255, 0, 0, 255), new Scalar(0, 255, 0, 255), new Scalar(0, 0, 255, 255) };
        public int trackBar1Value = -50;//亮度
        public int trackBar2Value = 100;//对比度
        public int trackBar3Value = 100;//gamma值
        #endregion

        #region 控件

        private Label label0005;


        /// <summary>
        /// 相机参数的Model
        /// </summary>
        private CameraParamModel m_cameraParamModel;
        private bool m_immediately;
        private TUCamera.TUCamera m_camera;
        private Label label7;
        private TriangleTrackBar trackBar3;
        private Label label6;
        private TriangleTrackBar trackBar2;
        private Label label5;
        private TriangleTrackBar trackBar1;
        private Label label4;
        private NumericUpDown numericUpDown1;
        private Label label3;
        private Label label2;
        private Label label1;
        public Button button5;
        public Button button4;
        public Button button3;
        public Button button2;
        private UCTrackBar ucTrackBar1;
        private SelectButton logButton;
        private SelectButton skipButton;
        private PictureBox pictureBox1;
        private System.Timers.Timer m_aeTimer;

        #endregion
        public DisplayCameraControl(CameraParamModel model, bool immediately)
        {
            m_cameraParamModel = model;
            m_immediately = immediately;
            m_camera = TUCameraManager.GetInstance().GetCurrentCamera();

            InitializeComponent();
            InitializeLanguageText();
            InitColorAdjustRange();
            InitializeControlData();
        }
        /// <summary>
        /// 设置直方图的图像
        /// </summary>
        /// <param name="mat"></param>
        public void resetMat(OpenCvSharp.Mat mat)
        {
            OldMat = mat;
            if (this.OldMat == null)
            {
                isGray = true;
                return;
            }
            isGray = true;

            Mat[] mats = Cv2.Split(this.OldMat);//一张图片，将panda拆分成3个图片装进mat

            Mat[] mats0 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
            Mat[] mats1;
            if (mats.Length < 2)
                mats1 = new Mat[] { mats[0] };//panda的第二个通道，也就是G
            else
                mats1 = new Mat[] { mats[1] };//panda的第二个通道，也就是G
            Mat[] mats2;
            if (mats.Length < 3)
                mats2 = new Mat[] { mats[0] };//panda的第三个通道，也就是R
            else
                mats2 = new Mat[] { mats[2] };//panda的第三个通道，也就是R

            int[] channels0 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
            int[] channels1 = new int[] { 0 };
            int[] channels2 = new int[] { 0 };
            int[] histsize = new int[] { 256 };//一个通道，初始化为256箱子
            Rangef[] range = new Rangef[1];//一个通道，范围
            range[0].Start = 0.0F;//从0开始(含)
            range[0].End = 256.0F;//到256结束(不含)
            Mat mask = new Mat();//不做掩码

            Cv2.CalcHist(mats0, channels0, mask, oldHists[0], 1, histsize, range);//对被拆分的图片单独进行计算
            Cv2.CalcHist(mats1, channels1, mask, oldHists[1], 1, histsize, range);//对被拆分的图片单独进行计算
            Cv2.CalcHist(mats2, channels2, mask, oldHists[2], 1, histsize, range);//对被拆分的图片单独进行计算

            for (int h = 0; h < oldHists[0].Rows; h++)
            {
                for (int j = oldHists.Length - 1; j >= 1; j--)
                {
                    if (oldHists[j].At<float>(h) != oldHists[0].At<float>(h))
                    {
                        isGray = false;
                        break;
                    }
                }
                if (!isGray)
                    break;
            }
            //if (this.AppWorkspace.ActiveDocumentWorkspace != null)
            //{
            //    //获取gamma值
            //    double gamma = this.AppWorkspace.ActiveDocumentWorkspace.HistogramGamma;
            //    this.trackBar3.Value = Math.Min(299, (int)(gamma * 100));//备注：需要与AxioVision进行比较效果，调试一致
            //    label7.Text = gamma.ToString("f2");
            //    //获取对比度
            //    double alpha = this.AppWorkspace.ActiveDocumentWorkspace.HistogramAlpha;
            //    //获取亮度
            //    double beta = this.AppWorkspace.ActiveDocumentWorkspace.HistogramBeta;
            //    //计算对比度
            //    label6.Text = alpha.ToString("f2");
            //    trackBar2.Value = Math.Min(trackBar2.Maximum, (int)(double.Parse(label6.Text) * 100));
            //    //计算亮度
            //    if (beta != double.Parse(label5.Text))
            //    {
            //        label5.Text = beta.ToString("f2");
            //        trackBar1.Value = (int)(double.Parse(label5.Text) * 100);
            //    }
            //    this.numericUpDown1.Value = new decimal(new int[] {
            //(int)this.AppWorkspace.ActiveDocumentWorkspace.HistogramPercent,
            //0,
            //0,
            //0});
            //    //numericUpDown1.Value = (decimal)this.AppWorkspace.ActiveDocumentWorkspace.SurfaceBox.HistogramPercent;
            //    skipButton.BtnSelect = this.AppWorkspace.ActiveDocumentWorkspace.HistogramSkipEnabled;
            //    logButton.BtnSelect = this.AppWorkspace.ActiveDocumentWorkspace.HistogramLogEnabled;
            //}

            updateHistImg(null);
            updateHistogramRect(true);
        }

        public void ResetCameraParamModel(CameraParamModel model)
        {
            m_cameraParamModel = model;
            InitColorAdjustRange();
            InitializeControlData();
        }

        int resolution_width = 2448;
        int resolution_height = 2048;

        private void InitColorAdjustRange()
        {
            //if (m_camera.IsOpen())
            //{
            //    // 曝光时间
            //    double aeMinVal = 0;
            //    double aeMaxVal = 0;
            //    double aeDftVal = 0;
            //    m_camera.GetExposureTimeRange(ref aeMinVal, ref aeMaxVal, ref aeDftVal);


            //    m_camera.m_drawAllHandler += new DrawAllHandler(CallbackDraw);
            //    m_camera.StartWaitForFrame();
            //    m_process = m_camera.StartDrawing(ref resolution_width, ref resolution_height);
            //}

        }

        ////#17904
        //private OpenCvSharp.Mat m_mat;
        ///// <summary>
        ///// 一个矩阵数组，用来接收直方图,记得全部初始化
        ///// </summary>
        //Mat[] oldHists = new Mat[] { new Mat(), new Mat(), new Mat() };
        ///// <summary>
        ///// 灰度图
        ///// </summary>
        //private bool isGray = true;
        ///// <summary>
        ///// BGR线条颜色
        ///// </summary>
        //private Scalar[] color = new Scalar[] { new Scalar(255, 0, 0, 255), new Scalar(0, 255, 0, 255), new Scalar(0, 0, 255, 255) };

        ///// <summary>
        ///// skip按钮
        ///// 显示直方图时，忽略黑色的灰度或颜色值。 
        ///// 这使您可以为背景为黑色的图像实现有意义的直方图显示。
        ///// </summary>
        ///// <param name="sender"></param>
        ///// <param name="e"></param>
        //private void skipButton_Click(object sender, EventArgs e)
        //{
        //    if (m_mat == null)
        //    {
        //        //MessageBox.Show("请打开图片");
        //        return;
        //    }
        //    //设置按钮的选中/非选择的状态
        //    skip000Button.BtnSelect = !skip000Button.BtnSelect;
        //    //this.AppWorkspace.ActiveDocumentWorkspace.HistogramSkipEnabled = skipButton.BtnSelect;
        //    updateHistImg(null);
        //}

        ///// <summary>
        ///// log按钮
        ///// 以对数比例显示直方图
        ///// </summary>
        ///// <param name="sender"></param>
        ///// <param name="e"></param>
        //private void logButton_Click(object sender, EventArgs e)
        //{
        //    if (m_mat == null)
        //    {
        //        //MessageBox.Show("请打开图片");
        //        return;
        //    }
        //    //设置按钮的选中/非选择的状态
        //    log000Button.BtnSelect = !log000Button.BtnSelect;
        //    //this.AppWorkspace.ActiveDocumentWorkspace.HistogramLogEnabled = logButton.BtnSelect;
        //    updateHistImg(null);
        //}

        ///// <summary>
        ///// 绘制
        ///// </summary>
        ///// <param name="pBuf"></param>
        ///// <param name="obj"></param>
        //public void CallbackDraw(byte[] pBuf, object obj, int channel)
        //{
        //    lock (obj)
        //    {
        //        //m_isWaiting = false;
        //        Console.WriteLine("接收全图buf");
        //        m_camera.m_drawAllHandler -= new DrawAllHandler(CallbackDraw);
        //        OpenCvSharp.Mat mat;
        //        if (channel == 1)
        //        {
        //            mat = new OpenCvSharp.Mat(resolution_height, resolution_width/*m_resolution.Height, m_resolution.Width*/, OpenCvSharp.MatType.CV_8UC1, pBuf);
        //        }
        //        else
        //        {
        //            mat = new OpenCvSharp.Mat(resolution_height, resolution_width/*m_resolution.Height, m_resolution.Width*/, OpenCvSharp.MatType.CV_8UC3, pBuf);
        //        }
        //        OpenCvSharp.Cv2.Flip(mat, mat, 0);
        //        if (m_mat != null)
        //        {
        //            m_mat.Dispose();
        //        }
        //        m_mat = mat.Clone();
        //        //this.pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);

        //        stopDrawing();
        //        if (mat != null)
        //        {
        //            mat.Dispose();
        //        }
        //        this.showHistImg(m_mat);
        //    }
        //}

        //private void showHistImg(Mat BoxMat)
        //{
        //    Mat[] mats = Cv2.Split(BoxMat);//一张图片，将panda拆分成3个图片装进mat
        //    if (mats.Count() == 1)
        //    {
        //        isGray = true;
        //    }
        //    else
        //    {
        //        isGray = false;
        //    }
        //    if (isGray)
        //    {
        //        Mat[] mats011 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
        //        int[] channels011 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
        //        int[] histsize11 = new int[] { 256 };//一个通道，初始化为256箱子
        //        Rangef[] range11 = new Rangef[1];//一个通道，范围
        //        range11[0].Start = 0.0F;//从0开始(含)
        //        range11[0].End = 256.0F;//到256结束(不含)
        //        Mat mask11 = new Mat();//不做掩码

        //        Cv2.CalcHist(mats011, channels011, mask11, oldHists[0], 1, histsize11, range11);//对被拆分的图片单独进行计算

        //        updateHistImgGray(oldHists);

        //        return;
        //    }
        //    Mat[] mats0 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
        //    Mat[] mats1 = new Mat[] { mats[1] };//panda的第二个通道，也就是G
        //    Mat[] mats2 = new Mat[] { mats[2] };//panda的第三个通道，也就是R

        //    int[] channels0 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
        //    int[] channels1 = new int[] { 0 };
        //    int[] channels2 = new int[] { 0 };
        //    int[] histsize = new int[] { 256 };//一个通道，初始化为256箱子
        //    Rangef[] range = new Rangef[1];//一个通道，范围
        //    range[0].Start = 0.0F;//从0开始(含)
        //    range[0].End = 256.0F;//到256结束(不含)
        //    Mat mask = new Mat();//不做掩码

        //    Cv2.CalcHist(mats0, channels0, mask, oldHists[0], 1, histsize, range);//对被拆分的图片单独进行计算
        //    Cv2.CalcHist(mats1, channels1, mask, oldHists[1], 1, histsize, range);//对被拆分的图片单独进行计算
        //    Cv2.CalcHist(mats2, channels2, mask, oldHists[2], 1, histsize, range);//对被拆分的图片单独进行计算

        //    for (int h = 0; h < oldHists[0].Rows; h++)
        //    {
        //        for (int j = oldHists.Length - 1; j >= 1; j--)
        //        {
        //            if (oldHists[j].At<float>(h) != oldHists[0].At<float>(h))
        //            {
        //                isGray = false;
        //                break;
        //            }
        //        }
        //        if (!isGray)
        //            break;
        //    }
        //    updateHistImg(oldHists);
        //}
        ///// <summary>
        ///// 绘制直方图-单通道
        ///// </summary>
        ///// <param name="hist"></param>
        ///// <returns></returns>
        //private unsafe void updateHistImgGray(Mat[] hists)
        //{
        //    if (m_mat == null)
        //    {
        //        return;
        //    }
        //    Mat[] mats = Cv2.Split(m_mat);//一张图片，将panda拆分成3个图片装进mat
        //    //if (mats.Count() == 1)
        //    //{
        //    //    isGray = true;
        //    //}
        //    //else
        //    //{
        //    //    isGray = false;
        //    //}
        //    Mat[] mats0 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
        //    //Mat[] mats1 = new Mat[] { mats[1] };//panda的第二个通道，也就是G
        //    //Mat[] mats2 = new Mat[] { mats[2] };//panda的第三个通道，也就是R

        //    if (hists == null)
        //    {
        //        hists = new Mat[] { new Mat()/*, new Mat(), new Mat()*/ };//一个矩阵数组，用来接收直方图,记得全部初始化
        //        int[] channels0 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
        //        //int[] channels1 = new int[] { 0 };
        //        //int[] channels2 = new int[] { 0 };
        //        int[] histsize = new int[] { 256 };//一个通道，初始化为256箱子
        //        Rangef[] range = new Rangef[1];//一个通道，范围
        //        range[0].Start = 0.0F;//从0开始(含)
        //        range[0].End = 256.0F;//到256结束(不含)
        //        Mat mask = new Mat();//不做掩码

        //        Cv2.CalcHist(mats0, channels0, mask, hists[0], 1, histsize, range);//对被拆分的图片单独进行计算
        //        //Cv2.CalcHist(mats1, channels1, mask, hists[1], 1, histsize, range);//对被拆分的图片单独进行计算
        //        //Cv2.CalcHist(mats2, channels2, mask, hists[2], 1, histsize, range);//对被拆分的图片单独进行计算

        //        if (log000Button.BtnSelect)
        //        {
        //            //取对数
        //            for (int j = 0; j < hists.Length; j++)
        //            {
        //                List<float> ProbPixel = new List<float>();

        //                for (int i = 0; i < hists[j].Rows; i++)
        //                {
        //                    if (((float*)hists[j].Ptr(0))[i] == 0)
        //                    {
        //                        ((float*)hists[j].Ptr(0))[i] = ((float*)hists[j].Ptr(0))[i];
        //                        ProbPixel.Add(0);
        //                    }
        //                    else
        //                    {
        //                        ((float*)hists[j].Ptr(0))[i] = (float)Math.Log10(((float*)hists[j].Ptr(0))[i]/* + 9*/);
        //                        ProbPixel.Add(1);
        //                    }
        //                }

        //                double max1jVal = 0;
        //                double min1jVal = 0;
        //                //找到直方图中的最大值和最小值
        //                Cv2.MinMaxLoc(hists[j], out min1jVal, out max1jVal);

        //                if (min1jVal >= max1jVal)
        //                {
        //                    continue;
        //                }
        //                //归一化到0~255，并根据AxioVision添加偏置值
        //                for (int i = 0; i < hists[j].Rows; i++)
        //                {
        //                    ((float*)hists[j].Ptr(0))[i] = (float)((((float*)hists[j].Ptr(0))[i] - 0) * 255.0 / (max1jVal - 0)) + (float)(min1jVal > 0 ? (ProbPixel[i] * min1jVal * 255.0 / max1jVal) : ProbPixel[i] * 5);
        //                }
        //            }
        //        }
        //        if (skip000Button.BtnSelect)
        //        {
        //            //去掉黑色部分 和 白色部分
        //            for (int j = 0; j < hists.Length; j++)
        //            {
        //                ((float*)hists[j].Ptr(0))[0] = 0;
        //                ((float*)hists[j].Ptr(0))[255] = 0;
        //            }
        //        }
        //    }

        //    //double max2Val = 0;
        //    //double max1Val = 0;
        //    double max0Val = 0;
        //    double minVal = 0;
        //    //为了更好显示直方图细节，使用4倍尺寸绘制直方图
        //    int histSize = hists[0].Rows * 4;
        //    Mat histImg = new Mat(histSize, histSize, MatType.CV_8UC3, new Scalar(255, 255, 255));
        //    //找到直方图中的最大值和最小值
        //    //Cv2.MinMaxLoc(hists[2], out minVal, out max2Val);
        //    //Cv2.MinMaxLoc(hists[1], out minVal, out max1Val);
        //    Cv2.MinMaxLoc(hists[0], out minVal, out max0Val);
        //    double maxVal = max0Val;// Math.Max(max2Val, Math.Max(max0Val, max1Val));

        //    if (maxVal < 1)
        //        return;

        //    // 设置最大峰值为图像高度的90%
        //    double hpt = 0.9 * histSize;

        //    //灰度图的显示直方图较为简单，显示一个通道即可
        //    if (isGray)
        //    {
        //        Mat hist = hists[0];
        //        int lastY2 = histSize - 1;
        //        for (int h = 0; h < hists[0].Rows; h++)
        //        {
        //            int intensity = (int)(hist.At<float>(h) * hpt / maxVal);
        //            int lastY1 = lastY2;
        //            lastY2 = histSize - intensity - 1 - (hist.At<float>(h) > 0 ? 4 : 0);
        //            if (lastY2 < lastY1)
        //            {
        //                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lastY1 - 2)), new Scalar(0, 0, 0, 255), 2, LineTypes.Link4);
        //            }
        //            else
        //            {
        //                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lastY1 + 2)), new Scalar(0, 0, 0, 255), 2, LineTypes.Link4);
        //            }
        //        }
        //    }
        //    //彩度图显示BGR三个通道的直方图
        //    else
        //    {
        //        int lineWidth = 2;
        //        for (int j = hists.Length - 1; j >= 0; j--)
        //        {
        //            Mat hist = hists[j];
        //            int lastY2 = histSize - 1;
        //            for (int h = 0; h < hists[0].Rows; h++)
        //            {
        //                int intensity = (int)(hist.At<float>(h) * hpt / maxVal);
        //                int lastY1 = lastY2;
        //                lastY2 = histSize - intensity - 1 - (hist.At<float>(h) > 0 ? 4 : 0);
        //                if (h > 0)
        //                {
        //                    //显示0.5位置的直方图，这样与AxioVision效果更加近似
        //                    int lasty12 = (lastY1 + lastY2) / 2;
        //                    if (lasty12 < lastY1)
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4 - 2, lastY1), new OpenCvSharp.Point(h * 4 - 2, Math.Min(lasty12, lastY1 - 2)), color[j], lineWidth, LineTypes.Link4);
        //                    }
        //                    else
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4 - 2, lastY1), new OpenCvSharp.Point(h * 4 - 2, Math.Max(lasty12, lastY1 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
        //                    }
        //                    if (lastY2 < lasty12)
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lasty12), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lasty12 - 2)), color[j], lineWidth, LineTypes.Link4);
        //                    }
        //                    else
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lasty12), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lasty12 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
        //                    }
        //                }
        //                else
        //                {
        //                    //灰度值为0的线的绘制
        //                    if (lastY2 < lastY1)
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lastY1 - 2)), color[j], lineWidth, LineTypes.Link4);
        //                    }
        //                    else
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lastY1 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
        //                    }
        //                }
        //            }
        //        }
        //    }

        //    this.picture000Box1.BackgroundImage/*Image*/ = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(histImg);
        //}

        ///// <summary>
        ///// 绘制直方图
        ///// </summary>
        ///// <param name="hist"></param>
        ///// <returns></returns>
        //private unsafe void updateHistImg(Mat[] hists)
        //{
        //    if (m_mat == null)
        //    {
        //        return;
        //    }
        //    Mat[] mats = Cv2.Split(m_mat);//一张图片，将panda拆分成3个图片装进mat
        //    if (mats.Count() == 1)
        //    {
        //        isGray = true;
        //    }
        //    else
        //    {
        //        isGray = false;
        //    }
        //    if (isGray)
        //    {
        //        updateHistImgGray(hists);
        //        return;
        //    }
        //    Mat[] mats0 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
        //    Mat[] mats1 = new Mat[] { mats[1] };//panda的第二个通道，也就是G
        //    Mat[] mats2 = new Mat[] { mats[2] };//panda的第三个通道，也就是R

        //    if (hists == null)
        //    {
        //        hists = new Mat[] { new Mat(), new Mat(), new Mat() };//一个矩阵数组，用来接收直方图,记得全部初始化
        //        int[] channels0 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
        //        int[] channels1 = new int[] { 0 };
        //        int[] channels2 = new int[] { 0 };
        //        int[] histsize = new int[] { 256 };//一个通道，初始化为256箱子
        //        Rangef[] range = new Rangef[1];//一个通道，范围
        //        range[0].Start = 0.0F;//从0开始(含)
        //        range[0].End = 256.0F;//到256结束(不含)
        //        Mat mask = new Mat();//不做掩码

        //        Cv2.CalcHist(mats0, channels0, mask, hists[0], 1, histsize, range);//对被拆分的图片单独进行计算
        //        Cv2.CalcHist(mats1, channels1, mask, hists[1], 1, histsize, range);//对被拆分的图片单独进行计算
        //        Cv2.CalcHist(mats2, channels2, mask, hists[2], 1, histsize, range);//对被拆分的图片单独进行计算

        //        if (log000Button.BtnSelect)
        //        {
        //            //取对数
        //            for (int j = 0; j < hists.Length; j++)
        //            {
        //                List<float> ProbPixel = new List<float>();

        //                for (int i = 0; i < hists[j].Rows; i++)
        //                {
        //                    if (((float*)hists[j].Ptr(0))[i] == 0)
        //                    {
        //                        ((float*)hists[j].Ptr(0))[i] = ((float*)hists[j].Ptr(0))[i];
        //                        ProbPixel.Add(0);
        //                    }
        //                    else
        //                    {
        //                        ((float*)hists[j].Ptr(0))[i] = (float)Math.Log10(((float*)hists[j].Ptr(0))[i]/* + 9*/);
        //                        ProbPixel.Add(1);
        //                    }
        //                }

        //                double max1jVal = 0;
        //                double min1jVal = 0;
        //                //找到直方图中的最大值和最小值
        //                Cv2.MinMaxLoc(hists[j], out min1jVal, out max1jVal);

        //                if (min1jVal >= max1jVal)
        //                {
        //                    continue;
        //                }
        //                //归一化到0~255，并根据AxioVision添加偏置值
        //                for (int i = 0; i < hists[j].Rows; i++)
        //                {
        //                    ((float*)hists[j].Ptr(0))[i] = (float)((((float*)hists[j].Ptr(0))[i] - 0) * 255.0 / (max1jVal - 0)) + (float)(min1jVal > 0 ? (ProbPixel[i] * min1jVal * 255.0 / max1jVal) : ProbPixel[i] * 5);
        //                }
        //            }
        //        }
        //        if (skip000Button.BtnSelect)
        //        {
        //            //去掉黑色部分 和 白色部分
        //            for (int j = 0; j < hists.Length; j++)
        //            {
        //                ((float*)hists[j].Ptr(0))[0] = 0;
        //                ((float*)hists[j].Ptr(0))[255] = 0;
        //            }
        //        }
        //    }

        //    double max2Val = 0;
        //    double max1Val = 0;
        //    double max0Val = 0;
        //    double minVal = 0;
        //    //为了更好显示直方图细节，使用4倍尺寸绘制直方图
        //    int histSize = hists[0].Rows * 4;
        //    Mat histImg = new Mat(histSize, histSize, MatType.CV_8UC3, new Scalar(255, 255, 255));
        //    //找到直方图中的最大值和最小值
        //    Cv2.MinMaxLoc(hists[2], out minVal, out max2Val);
        //    Cv2.MinMaxLoc(hists[1], out minVal, out max1Val);
        //    Cv2.MinMaxLoc(hists[0], out minVal, out max0Val);
        //    double maxVal = Math.Max(max2Val, Math.Max(max0Val, max1Val));

        //    if (maxVal < 1)
        //        return;

        //    // 设置最大峰值为图像高度的90%
        //    double hpt = 0.9 * histSize;

        //    //灰度图的显示直方图较为简单，显示一个通道即可
        //    if (isGray)
        //    {
        //        Mat hist = hists[0];
        //        int lastY2 = histSize - 1;
        //        for (int h = 0; h < hists[0].Rows; h++)
        //        {
        //            int intensity = (int)(hist.At<float>(h) * hpt / maxVal);
        //            int lastY1 = lastY2;
        //            lastY2 = histSize - intensity - 1 - (hist.At<float>(h) > 0 ? 4 : 0);
        //            if (lastY2 < lastY1)
        //            {
        //                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lastY1 - 2)), new Scalar(0, 0, 0, 255), 2, LineTypes.Link4);
        //            }
        //            else
        //            {
        //                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lastY1 + 2)), new Scalar(0, 0, 0, 255), 2, LineTypes.Link4);
        //            }
        //        }
        //    }
        //    //彩度图显示BGR三个通道的直方图
        //    else
        //    {
        //        int lineWidth = 2;
        //        for (int j = hists.Length - 1; j >= 0; j--)
        //        {
        //            Mat hist = hists[j];
        //            int lastY2 = histSize - 1;
        //            for (int h = 0; h < hists[0].Rows; h++)
        //            {
        //                int intensity = (int)(hist.At<float>(h) * hpt / maxVal);
        //                int lastY1 = lastY2;
        //                lastY2 = histSize - intensity - 1 - (hist.At<float>(h) > 0 ? 4 : 0);
        //                if (h > 0)
        //                {
        //                    //显示0.5位置的直方图，这样与AxioVision效果更加近似
        //                    int lasty12 = (lastY1 + lastY2) / 2;
        //                    if (lasty12 < lastY1)
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4 - 2, lastY1), new OpenCvSharp.Point(h * 4 - 2, Math.Min(lasty12, lastY1 - 2)), color[j], lineWidth, LineTypes.Link4);
        //                    }
        //                    else
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4 - 2, lastY1), new OpenCvSharp.Point(h * 4 - 2, Math.Max(lasty12, lastY1 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
        //                    }
        //                    if (lastY2 < lasty12)
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lasty12), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lasty12 - 2)), color[j], lineWidth, LineTypes.Link4);
        //                    }
        //                    else
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lasty12), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lasty12 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
        //                    }
        //                }
        //                else
        //                {
        //                    //灰度值为0的线的绘制
        //                    if (lastY2 < lastY1)
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lastY1 - 2)), color[j], lineWidth, LineTypes.Link4);
        //                    }
        //                    else
        //                    {
        //                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lastY1 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
        //                    }
        //                }
        //            }
        //        }
        //    }

        //    this.picture000Box1.BackgroundImage/*Image*/ = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(histImg);
        //}

        //private void stopDrawing()
        //{
        //    //创建一个委托，用于封装一个方法，在这里是封装了 控制更新控件 的方法
        //    Action invokeAction = new Action(stopDrawing);
        //    //判断操作控件的线程是否创建控件的线程
        //    //调用方调用方位于创建控件所在的线程以外的线程中，如果在其他线程则对控件进行方法调用时必须调用 Invoke 方法
        //    if (this.InvokeRequired)
        //    {
        //        //与调用线程不同的线程上创建(说明您必须通过 Invoke 方法对控件进行调用)
        //        this.Invoke(invokeAction);
        //    }
        //    else
        //    {
        //        m_camera.StopDrawing(m_process);
        //        m_camera.StopWaitForFrame();
        //        ////窗体线程，即主线程
        //        //shuaxinButton.Enabled = true;
        //    }
        //}


        private string UpdateExposureTime(UInt64 exposureTime)
        {
            string str = "";
            int sec = 0;
            int msec = 0;
            int usec = 0;
            m_camera.UpdateExposureTime(ref sec, ref msec, ref usec, exposureTime);
            if (sec > 0)
            {
                str += sec + "s";
            }
            else if (msec > 0)
            {
                str += msec + "ms";
            }
            else if (usec > 0)
            {
                str += usec + "μs";
            }

            return str;
        }

        private void UpdateExposureUI(int autoExposure)
        {
            if (autoExposure == 1)
            {
                // 设置到相机
                if (m_immediately)
                {
                    // 自动曝光
                    m_camera.SetExposureMode(ExposureMode.AUTO);
                    m_aeTimer.Start();
                }
            }
            else
            {
                // 设置到相机
                if (m_immediately)
                {
                    m_camera.SetExposureMode(ExposureMode.MANUAL);
                    m_aeTimer.Stop();
                }
            }
        }
        /// <summary>
        /// 设置下拉等数据源
        /// </summary>
        private void InitializeControlData()
        {
            m_aeTimer = new System.Timers.Timer(1000);
            m_aeTimer.Elapsed += new System.Timers.ElapsedEventHandler(OnTimerAutoExposure);
            m_aeTimer.AutoReset = true;
            m_aeTimer.SynchronizingObject = this;

            UpdateExposureUI(m_cameraParamModel.parame.ATExposure);

            // 增益值调整
            int gainValue = m_cameraParamModel.parame.GlobalGain;

            //// 白平衡
            //if (m_cameraParamModel.parame.WhiteBalance == 1)
            //{
            //    AutoWhiteBalance(true);
            //}
            //else
            //{
            //    if (m_cameraParamModel.parame.FMExposure == 1)
            //    {
            //        m_camera.SetColorTemperatureByString("3200K");
            //    }
            //    else if (m_cameraParamModel.parame.FMExposure == 2)
            //    {
            //        m_camera.SetColorTemperatureByString("5500K");
            //    }
            //    AutoWhiteBalance(false);
            //}

        }

        private void InitializeLanguageText() 
        {
            this.label0005.Text = PdnResources.GetString("Menu.timeofexposure.text")+":";
            this.label3.Text = PdnResources.GetString("Menu.Gammavalue.text")+":";
            this.label2.Text = PdnResources.GetString("Menu.Contrast.text")+":";
            this.label1.Text = PdnResources.GetString("Menu.luminance.text")+":";
            this.button5.Text = PdnResources.GetString("Menu.Originalstate.text");
            this.button4.Text = PdnResources.GetString("Menu.Gammavalue.text")+"0.45";
            this.button2.Text = PdnResources.GetString("Menu.optimal.text");
        }
        private void InitializeComponent()
        {
            this.label0005 = new System.Windows.Forms.Label();
            this.label7 = new System.Windows.Forms.Label();
            this.trackBar3 = new PaintDotNet.CustomControl.TriangleTrackBar();
            this.label6 = new System.Windows.Forms.Label();
            this.trackBar2 = new PaintDotNet.CustomControl.TriangleTrackBar();
            this.label5 = new System.Windows.Forms.Label();
            this.trackBar1 = new PaintDotNet.CustomControl.TriangleTrackBar();
            this.label4 = new System.Windows.Forms.Label();
            this.numericUpDown1 = new System.Windows.Forms.NumericUpDown();
            this.label3 = new System.Windows.Forms.Label();
            this.label2 = new System.Windows.Forms.Label();
            this.label1 = new System.Windows.Forms.Label();
            this.button5 = new System.Windows.Forms.Button();
            this.button4 = new System.Windows.Forms.Button();
            this.button3 = new System.Windows.Forms.Button();
            this.button2 = new System.Windows.Forms.Button();
            this.ucTrackBar1 = new PaintDotNet.CustomControl.UCTrackBar();
            this.logButton = new PaintDotNet.CustomControl.SelectButton();
            this.skipButton = new PaintDotNet.CustomControl.SelectButton();
            this.pictureBox1 = new System.Windows.Forms.PictureBox();
            ((System.ComponentModel.ISupportInitialize)(this.numericUpDown1)).BeginInit();
            ((System.ComponentModel.ISupportInitialize)(this.pictureBox1)).BeginInit();
            this.SuspendLayout();
            // 
            // label0005
            // 
            this.label0005.AutoSize = true;
            this.label0005.Location = new System.Drawing.Point(24, 39);
            this.label0005.Margin = new System.Windows.Forms.Padding(4, 0, 4, 0);
            this.label0005.Name = "label0005";
            this.label0005.Size = new System.Drawing.Size(82, 15);
            this.label0005.TabIndex = 12;
            this.label0005.Text = "曝光时间：";
      
            // 
            // label7
            // 
            this.label7.AutoSize = true;
            this.label7.Location = new System.Drawing.Point(332, 237);
            this.label7.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label7.Name = "label7";
            this.label7.Size = new System.Drawing.Size(29, 12);
            this.label7.TabIndex = 59;
            this.label7.Text = "1.00";
            this.label7.TextChanged += new System.EventHandler(this.label7_Changed);
            // 
            // trackBar3
            // 
            this.trackBar3.Location = new System.Drawing.Point(127, 229);
            this.trackBar3.Maximum = 299;
            this.trackBar3.Minimum = 0;
            this.trackBar3.Name = "trackBar3";
            this.trackBar3.Size = new System.Drawing.Size(200, 30);
            this.trackBar3.TabIndex = 58;
            this.trackBar3.Value = 100;
            this.trackBar3.TrackBarScroll += new System.EventHandler(this.trackBar3_Scroll);
            // 
            // label6
            // 
            this.label6.AutoSize = true;
            this.label6.Location = new System.Drawing.Point(332, 201);
            this.label6.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label6.Name = "label6";
            this.label6.Size = new System.Drawing.Size(29, 12);
            this.label6.TabIndex = 57;
            this.label6.Text = "1.00";
            this.label6.TextChanged += new System.EventHandler(this.label6_Changed);
            // 
            // trackBar2
            // 
            this.trackBar2.Location = new System.Drawing.Point(127, 194);
            this.trackBar2.Maximum = 999;
            this.trackBar2.Minimum = 0;
            this.trackBar2.Name = "trackBar2";
            this.trackBar2.Size = new System.Drawing.Size(200, 30);
            this.trackBar2.TabIndex = 56;
            this.trackBar2.Value = 100;
            this.trackBar2.TrackBarScroll += new System.EventHandler(this.trackBar2_Scroll);
            // 
            // label5
            // 
            this.label5.AutoSize = true;
            this.label5.Location = new System.Drawing.Point(332, 165);
            this.label5.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label5.Name = "label5";
            this.label5.Size = new System.Drawing.Size(29, 12);
            this.label5.TabIndex = 55;
            this.label5.Text = "-0.5";
            this.label5.TextChanged += new System.EventHandler(this.label5_Changed);
            // 
            // trackBar1
            // 
            this.trackBar1.Location = new System.Drawing.Point(127, 158);
            this.trackBar1.Maximum = 200;
            this.trackBar1.Minimum = -200;
            this.trackBar1.Name = "trackBar1";
            this.trackBar1.Size = new System.Drawing.Size(200, 30);
            this.trackBar1.TabIndex = 54;
            this.trackBar1.Value = -50;
            this.trackBar1.TrackBarScroll += new System.EventHandler(this.trackBar1_Scroll);
            // 
            // label4
            // 
            this.label4.AutoSize = true;
            this.label4.Location = new System.Drawing.Point(356, 279);
            this.label4.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label4.Name = "label4";
            this.label4.Size = new System.Drawing.Size(17, 12);
            this.label4.TabIndex = 53;
            this.label4.Text = "‰";
            // 
            // numericUpDown1
            // 
            this.numericUpDown1.Location = new System.Drawing.Point(302, 275);
            this.numericUpDown1.Margin = new System.Windows.Forms.Padding(2);
            this.numericUpDown1.Maximum = new decimal(new int[] {
            499,
            0,
            0,
            0});
            this.numericUpDown1.Name = "numericUpDown1";
            this.numericUpDown1.Size = new System.Drawing.Size(50, 21);
            this.numericUpDown1.TabIndex = 52;
            this.numericUpDown1.TextAlign = System.Windows.Forms.HorizontalAlignment.Right;
            this.numericUpDown1.Value = new decimal(new int[] {
            200,
            0,
            0,
            0});
            // 
            // label3
            // 
            this.label3.AutoSize = true;
            this.label3.Location = new System.Drawing.Point(73, 237);
            this.label3.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label3.Name = "label3";
            this.label3.Size = new System.Drawing.Size(53, 12);
            this.label3.TabIndex = 51;
            this.label3.Text = "伽马值：";
           
            // 
            // label2
            // 
            this.label2.AutoSize = true;
            this.label2.Location = new System.Drawing.Point(73, 201);
            this.label2.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label2.Name = "label2";
            this.label2.Size = new System.Drawing.Size(53, 12);
            this.label2.TabIndex = 50;
            this.label2.Text = "对比度：";
            
            // 
            // label1
            // 
            this.label1.AutoSize = true;
            this.label1.Location = new System.Drawing.Point(85, 165);
            this.label1.Margin = new System.Windows.Forms.Padding(2, 0, 2, 0);
            this.label1.Name = "label1";
            this.label1.Size = new System.Drawing.Size(41, 12);
            this.label1.TabIndex = 49;
            this.label1.Text = "亮度：";
          
            // 
            // button5
            // 
            this.button5.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Right)));
            this.button5.Location = new System.Drawing.Point(184, 305);
            this.button5.Name = "button5";
            this.button5.Size = new System.Drawing.Size(75, 23);
            this.button5.TabIndex = 48;
            this.button5.Text = "原始状态";
            
            this.button5.UseVisualStyleBackColor = true;
            this.button5.Click += new System.EventHandler(this.button5_Click);
            // 
            // button4
            // 
            this.button4.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Right)));
            this.button4.Location = new System.Drawing.Point(70, 305);
            this.button4.Name = "button4";
            this.button4.Size = new System.Drawing.Size(75, 23);
            this.button4.TabIndex = 47;
            this.button4.Text = "伽马值0.45";
            
            this.button4.UseVisualStyleBackColor = true;
            this.button4.Click += new System.EventHandler(this.button4_Click);
            // 
            // button3
            // 
            this.button3.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Right)));
            this.button3.Location = new System.Drawing.Point(299, 305);
            this.button3.Name = "button3";
            this.button3.Size = new System.Drawing.Size(75, 23);
            this.button3.TabIndex = 46;
            this.button3.Text = "MIN/MAX";
            this.button3.UseVisualStyleBackColor = true;
            this.button3.Click += new System.EventHandler(this.button3_Click);
            // 
            // button2
            // 
            this.button2.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Right)));
            this.button2.Location = new System.Drawing.Point(184, 272);
            this.button2.Name = "button2";
            this.button2.Size = new System.Drawing.Size(75, 23);
            this.button2.TabIndex = 45;
            this.button2.Text = "最佳";
            
            this.button2.UseVisualStyleBackColor = true;
            this.button2.Click += new System.EventHandler(this.button2_Click);
            // 
            // ucTrackBar1
            // 
            this.ucTrackBar1.DcimalDigits = 0;
            this.ucTrackBar1.LineColor = System.Drawing.Color.FromArgb(((int)(((byte)(255)))), ((int)(((byte)(77)))), ((int)(((byte)(59)))));
            this.ucTrackBar1.LineWidth = 8F;
            this.ucTrackBar1.Location = new System.Drawing.Point(68, 134);
            this.ucTrackBar1.MaxValue = 255F;
            this.ucTrackBar1.MinValue = 0F;
            this.ucTrackBar1.Name = "ucTrackBar1";
            this.ucTrackBar1.Size = new System.Drawing.Size(274, 20);
            this.ucTrackBar1.TabIndex = 44;
            this.ucTrackBar1.Text = "ucTrackBar1";
            this.ucTrackBar1.Value1 = 0F;
            this.ucTrackBar1.Value2 = 127F;
            this.ucTrackBar1.Value3 = 255F;
            this.ucTrackBar1.Value1Changed += new System.EventHandler(this.ucTrackBar1_Value1Changed);
            this.ucTrackBar1.Value2Changed += new System.EventHandler(this.ucTrackBar1_Value2Changed);
            this.ucTrackBar1.Value3Changed += new System.EventHandler(this.ucTrackBar1_Value3Changed);
            // 
            // logButton
            // 
            this.logButton.BackColor = System.Drawing.SystemColors.ControlDark;
            this.logButton.BtnSelect = false;
            this.logButton.BtnText = "log";
            this.logButton.Location = new System.Drawing.Point(339, 43);
            this.logButton.Name = "logButton";
            this.logButton.Size = new System.Drawing.Size(41, 21);
            this.logButton.TabIndex = 43;
            this.logButton.Click += new System.EventHandler(this.logButton_Click);
            // 
            // skipButton
            // 
            this.skipButton.BackColor = System.Drawing.SystemColors.ControlDark;
            this.skipButton.BtnSelect = false;
            this.skipButton.BtnText = "skip";
            this.skipButton.Location = new System.Drawing.Point(339, 74);
            this.skipButton.Name = "skipButton";
            this.skipButton.Size = new System.Drawing.Size(41, 21);
            this.skipButton.TabIndex = 42;
            this.skipButton.Click += new System.EventHandler(this.skipButton_Click);
            // 
            // pictureBox1
            // 
            this.pictureBox1.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom) 
            | System.Windows.Forms.AnchorStyles.Left) 
            | System.Windows.Forms.AnchorStyles.Right)));
            this.pictureBox1.BackColor = System.Drawing.Color.White;
            this.pictureBox1.BackgroundImageLayout = System.Windows.Forms.ImageLayout.Stretch;
            this.pictureBox1.BorderStyle = System.Windows.Forms.BorderStyle.FixedSingle;
            this.pictureBox1.Location = new System.Drawing.Point(76, 9);
            this.pictureBox1.Name = "pictureBox1";
            this.pictureBox1.Size = new System.Drawing.Size(256, 124);
            this.pictureBox1.SizeMode = System.Windows.Forms.PictureBoxSizeMode.StretchImage;
            this.pictureBox1.TabIndex = 41;
            this.pictureBox1.TabStop = false;
            // 
            // DisplayCameraControl
            // 
            this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 12F);
            this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
            this.Controls.Add(this.label7);
            this.Controls.Add(this.trackBar3);
            this.Controls.Add(this.label6);
            this.Controls.Add(this.trackBar2);
            this.Controls.Add(this.label5);
            this.Controls.Add(this.trackBar1);
            this.Controls.Add(this.label4);
            this.Controls.Add(this.numericUpDown1);
            this.Controls.Add(this.label3);
            this.Controls.Add(this.label2);
            this.Controls.Add(this.label1);
            this.Controls.Add(this.button5);
            this.Controls.Add(this.button4);
            this.Controls.Add(this.button3);
            this.Controls.Add(this.button2);
            this.Controls.Add(this.ucTrackBar1);
            this.Controls.Add(this.logButton);
            this.Controls.Add(this.skipButton);
            this.Controls.Add(this.pictureBox1);
            this.Name = "DisplayCameraControl";
            this.Size = new System.Drawing.Size(490, 345);
            ((System.ComponentModel.ISupportInitialize)(this.numericUpDown1)).EndInit();
            ((System.ComponentModel.ISupportInitialize)(this.pictureBox1)).EndInit();
            this.ResumeLayout(false);
            this.PerformLayout();

        }

        public void OnTimerAutoExposure(object source, System.Timers.ElapsedEventArgs e)
        {
            double paramValue = m_camera.GetExposureTime();

            decimal txtVale = 0;
            int sec = 0;
            int msec = 0;
            int usec = 0;
            m_camera.UpdateExposureTime(ref sec, ref msec, ref usec, (UInt64)(paramValue * 1000));

            //UpdateExposureTime((UInt64)paramValue * 1000);
        }


        /// <summary>
        /// 最佳
        /// </summary>
        public void BestButtonMethod()
        {
            OpenCvSharp.Point startIndex = this.getStartIndex(this.OldMat, oldHists, (float)numericUpDown1.Value);

            //计算对比度
            label6.Text = (256.0 / (startIndex.Y - startIndex.X)).ToString("f2");
            trackBar2.Value = Math.Min(trackBar2.Maximum, (int)(double.Parse(label6.Text) * 100));
            //计算亮度
            label5.Text = ((0 - startIndex.X - startIndex.Y) / 510.0).ToString("f2");
            trackBar1.Value = (int)(double.Parse(label5.Text) * 100);

            this.scrollMethod();
            updateHistogramRect(true);
        }

        /// <summary>
        /// 最大最小
        /// </summary>
        public void MaxMinButtonMethod()
        {
            OpenCvSharp.Point startIndex = this.getStartIndex(this.OldMat, oldHists, 0);

            //计算对比度
            label6.Text = (256.0 / (startIndex.Y - startIndex.X)).ToString("f2");
            trackBar2.Value = Math.Min(trackBar2.Maximum, (int)(double.Parse(label6.Text) * 100));
            //计算亮度
            label5.Text = ((0 - startIndex.X - startIndex.Y) / 510.0).ToString("f2");
            trackBar1.Value = (int)(double.Parse(label5.Text) * 100);

            //this.parentf

            //this.scrollMethod();
            updateHistogramRect(true);
        }

        /// <summary>
        /// 原始状态
        /// </summary>
        public void OriginButtonMethod()
        {
            this.trackBar1.Value = -50;
            this.trackBar2.Value = 100;
            this.trackBar3.Value = 100;
            label5.Text = (trackBar1.Value / 100.0).ToString("f2");
            label6.Text = (trackBar2.Value / 100.0).ToString("f2");
            label7.Text = (trackBar3.Value / 100.0).ToString("f2");
            if (this.OldMat == null)
            {
                //MessageBox.Show("请打开图片");
                return;
            }
            updateHistogramRect(true);
            /**待处理**///this.AppWorkspace.ActiveDocumentWorkspace.SurfaceBox.ResetBoxBitmap();
            //this.AppWorkspace.ActiveDocumentWorkspace.SurfaceBox.Surface = Surface.CopyFromBitmap(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(oldMat));
        }

        /// <summary>
        /// 伽马0.45
        /// </summary>
        public void Gamma45ButtonMethod()
        {
            this.trackBar3.Value = 45;
            label7.Text = (trackBar3.Value / 100.0).ToString("f2");

            this.scrollMethod();
            updateHistogramRect(true);
        }

        /// <summary>
        /// 最佳
        /// 确定当前直方图中排除图像中包含的像素的1‰的值，
        /// 并在屏幕上显示以此方式确定的灰度或颜色范围。 
        /// (您可以将1‰的值调整为适合您的需求。)
        /// https://blog.csdn.net/qq_20095389/article/details/83658878
        /// https://blog.csdn.net/lantishua/article/details/46377325
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button2_Click(object sender, EventArgs e)
        {
            if (this.OldMat == null)
            {
                return;
            }
            this.BestButtonMethod();
        }

        /// <summary>
        /// 最大最小
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button3_Click(object sender, EventArgs e)
        {
            if (this.OldMat == null)
            {
                return;
            }
            this.MaxMinButtonMethod();
        }

        /// <summary>
        /// 伽马0.45
        /// 需要调整直线的弧度
        /// 直方图曲线根据伽马值进行变化
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button4_Click(object sender, EventArgs e)
        {
            if (this.OldMat == null)
            {
                return;
            }
            this.Gamma45ButtonMethod();
        }

        /// <summary>
        /// 原始状态
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void button5_Click(object sender, EventArgs e)
        {
            this.OriginButtonMethod();
        }

        /// <summary>
        /// skip按钮
        /// 显示直方图时，忽略黑色的灰度或颜色值。 
        /// 这使您可以为背景为黑色的图像实现有意义的直方图显示。
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void skipButton_Click(object sender, EventArgs e)
        {
            if (this.OldMat == null)
            {
                return;
            }
            //设置按钮的选中/非选择的状态
            skipButton.BtnSelect = !skipButton.BtnSelect;
            //this.AppWorkspace.ActiveDocumentWorkspace.HistogramSkipEnabled = skipButton.BtnSelect;
            updateHistImg(null);
        }

        /// <summary>
        /// log按钮
        /// 以对数比例显示直方图
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void logButton_Click(object sender, EventArgs e)
        {
            if (this.OldMat == null)
            {
                return;
            }
            //设置按钮的选中/非选择的状态
            logButton.BtnSelect = !logButton.BtnSelect;
            //this.AppWorkspace.ActiveDocumentWorkspace.HistogramLogEnabled = logButton.BtnSelect;
            updateHistImg(null);
        }

        /// <summary>
        /// 最佳设置的数值改变
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void numericUpDown1_ValueChanged(object sender, EventArgs e)
        {
            //this.AppWorkspace.ActiveDocumentWorkspace.HistogramPercent = (int)numericUpDown1.Value;
        }

        /// <summary>
        /// 亮度改变
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void trackBar1_Scroll(object sender, EventArgs e)
        {
            trackBar1Value = trackBar1.Value;
            //this.AppWorkspace.ActiveDocumentWorkspace.SurfaceBox.HistogramBeta = trackBar1.Value / 100.0;
            label5.Text = (trackBar1.Value / 100.0).ToString("f2");
            updateHistogramRect(true);
        }

        /// <summary>
        /// 亮度改变
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void label5_Changed(object sender, EventArgs e)
        {
            //this.AppWorkspace.ActiveDocumentWorkspace.HistogramBeta = double.Parse(label5.Text);
            this.scrollMethod();
        }

        /// <summary>
        /// 对比度改变
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void trackBar2_Scroll(object sender, EventArgs e)
        {
            trackBar2Value = trackBar2.Value;
            label6.Text = (trackBar2.Value / 100.0).ToString("f2");
            updateHistogramRect(true);
        }

        /// <summary>
        /// 对比度改变
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void label6_Changed(object sender, EventArgs e)
        {
            //this.AppWorkspace.ActiveDocumentWorkspace.HistogramAlpha = double.Parse(label6.Text);
            this.scrollMethod();
        }

        /// <summary>
        /// gamma改变
        /// gamma值小于1时，会拉伸图像中灰度级较低的区域，同时会压缩灰度级较高的部分
        /// gamma值大于1时，会拉伸图像中灰度级较高的区域，同时会压缩灰度级较低的部分
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void trackBar3_Scroll(object sender, EventArgs e)
        {
            trackBar3Value = trackBar3.Value;
            label7.Text = (trackBar3.Value / 100.0).ToString("f2");
            updateHistogramRect(true);
        }

        /// <summary>
        /// gamma改变
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void label7_Changed(object sender, EventArgs e)
        {
            //this.AppWorkspace.ActiveDocumentWorkspace.HistogramGamma = double.Parse(label7.Text);
            this.scrollMethod();
        }

        /// <summary>
        /// 绘制直方图
        /// </summary>
        /// <param name="hist"></param>
        /// <returns></returns>
        private unsafe void updateHistImg(Mat[] hists)
        {
            if (this.OldMat == null)
            {
                return;
            }
            Mat[] mats = Cv2.Split(this.OldMat);//一张图片，将panda拆分成3个图片装进mat
            Mat[] mats0 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
            Mat[] mats1;
            if (mats.Length < 2)
                mats1 = new Mat[] { mats[0] };//panda的第二个通道，也就是G
            else
                mats1 = new Mat[] { mats[1] };//panda的第二个通道，也就是G
            Mat[] mats2;
            if (mats.Length < 3)
                mats2 = new Mat[] { mats[0] };//panda的第三个通道，也就是R
            else
                mats2 = new Mat[] { mats[2] };//panda的第三个通道，也就是R

            if (hists == null)
            {
                hists = new Mat[] { new Mat(), new Mat(), new Mat() };//一个矩阵数组，用来接收直方图,记得全部初始化
                int[] channels0 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
                int[] channels1 = new int[] { 0 };
                int[] channels2 = new int[] { 0 };
                int[] histsize = new int[] { 256 };//一个通道，初始化为256箱子
                Rangef[] range = new Rangef[1];//一个通道，范围
                range[0].Start = 0.0F;//从0开始(含)
                range[0].End = 256.0F;//到256结束(不含)
                Mat mask = new Mat();//不做掩码

                Cv2.CalcHist(mats0, channels0, mask, hists[0], 1, histsize, range);//对被拆分的图片单独进行计算
                Cv2.CalcHist(mats1, channels1, mask, hists[1], 1, histsize, range);//对被拆分的图片单独进行计算
                Cv2.CalcHist(mats2, channels2, mask, hists[2], 1, histsize, range);//对被拆分的图片单独进行计算

                if (logButton.BtnSelect)
                {
                    //取对数
                    for (int j = 0; j < hists.Length; j++)
                    {
                        List<float> ProbPixel = new List<float>();

                        for (int i = 0; i < hists[j].Rows; i++)
                        {
                            if (((float*)hists[j].Ptr(0))[i] == 0)
                            {
                                ((float*)hists[j].Ptr(0))[i] = ((float*)hists[j].Ptr(0))[i];
                                ProbPixel.Add(0);
                            }
                            else
                            {
                                ((float*)hists[j].Ptr(0))[i] = (float)Math.Log10(((float*)hists[j].Ptr(0))[i]/* + 9*/);
                                ProbPixel.Add(1);
                            }
                        }

                        double max1jVal = 0;
                        double min1jVal = 0;
                        //找到直方图中的最大值和最小值
                        Cv2.MinMaxLoc(hists[j], out min1jVal, out max1jVal);

                        if (min1jVal >= max1jVal)
                        {
                            continue;
                        }
                        //归一化到0~255，并根据AxioVision添加偏置值
                        for (int i = 0; i < hists[j].Rows; i++)
                        {
                            ((float*)hists[j].Ptr(0))[i] = (float)((((float*)hists[j].Ptr(0))[i] - 0) * 255.0 / (max1jVal - 0)) + (float)(min1jVal > 0 ? (ProbPixel[i] * min1jVal * 255.0 / max1jVal) : ProbPixel[i] * 5);
                        }
                    }
                }
                if (skipButton.BtnSelect)
                {
                    //去掉黑色部分 和 白色部分
                    for (int j = 0; j < hists.Length; j++)
                    {
                        ((float*)hists[j].Ptr(0))[0] = 0;
                        ((float*)hists[j].Ptr(0))[255] = 0;
                    }
                }
            }

            double max2Val = 0;
            double max1Val = 0;
            double max0Val = 0;
            double minVal = 0;
            //为了更好显示直方图细节，使用4倍尺寸绘制直方图
            int histSize = hists[0].Rows * 4;
            Mat histImg = new Mat(histSize, histSize, MatType.CV_8UC3, new Scalar(255, 255, 255));
            //找到直方图中的最大值和最小值
            Cv2.MinMaxLoc(hists[2], out minVal, out max2Val);
            Cv2.MinMaxLoc(hists[1], out minVal, out max1Val);
            Cv2.MinMaxLoc(hists[0], out minVal, out max0Val);
            double maxVal = Math.Max(max2Val, Math.Max(max0Val, max1Val));

            if (maxVal < 1)
                return;

            // 设置最大峰值为图像高度的90%
            double hpt = 0.9 * histSize;

            //灰度图的显示直方图较为简单，显示一个通道即可
            if (isGray)
            {
                Mat hist = hists[0];
                int lastY2 = histSize - 1;
                for (int h = 0; h < hists[0].Rows; h++)
                {
                    int intensity = (int)(hist.At<float>(h) * hpt / maxVal);
                    int lastY1 = lastY2;
                    lastY2 = histSize - intensity - 1 - (hist.At<float>(h) > 0 ? 4 : 0);
                    if (lastY2 < lastY1)
                    {
                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lastY1 - 2)), new Scalar(0, 0, 0, 255), 2, LineTypes.Link4);
                    }
                    else
                    {
                        Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lastY1 + 2)), new Scalar(0, 0, 0, 255), 2, LineTypes.Link4);
                    }
                }
            }
            //彩度图显示BGR三个通道的直方图
            else
            {
                int lineWidth = 2;
                for (int j = hists.Length - 1; j >= 0; j--)
                {
                    Mat hist = hists[j];
                    int lastY2 = histSize - 1;
                    for (int h = 0; h < hists[0].Rows; h++)
                    {
                        int intensity = (int)(hist.At<float>(h) * hpt / maxVal);
                        int lastY1 = lastY2;
                        lastY2 = histSize - intensity - 1 - (hist.At<float>(h) > 0 ? 4 : 0);
                        if (h > 0)
                        {
                            //显示0.5位置的直方图，这样与AxioVision效果更加近似
                            int lasty12 = (lastY1 + lastY2) / 2;
                            if (lasty12 < lastY1)
                            {
                                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4 - 2, lastY1), new OpenCvSharp.Point(h * 4 - 2, Math.Min(lasty12, lastY1 - 2)), color[j], lineWidth, LineTypes.Link4);
                            }
                            else
                            {
                                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4 - 2, lastY1), new OpenCvSharp.Point(h * 4 - 2, Math.Max(lasty12, lastY1 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
                            }
                            if (lastY2 < lasty12)
                            {
                                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lasty12), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lasty12 - 2)), color[j], lineWidth, LineTypes.Link4);
                            }
                            else
                            {
                                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lasty12), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lasty12 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
                            }
                        }
                        else
                        {
                            //灰度值为0的线的绘制
                            if (lastY2 < lastY1)
                            {
                                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Min(lastY2, lastY1 - 2)), color[j], lineWidth, LineTypes.Link4);
                            }
                            else
                            {
                                Cv2.Line(histImg, new OpenCvSharp.Point(h * 4, lastY1), new OpenCvSharp.Point(h * 4, Math.Max(lastY2, lastY1 + 2)), color[j], lineWidth, LineTypes.Link4/*AntiAlias*/);
                            }
                        }
                    }
                }
            }

            this.pictureBox1.BackgroundImage/*Image*/ = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(histImg);
        }

        /// <summary>
        /// 测试曲线拟合的代码
        /// </summary>
        private void testCurveFit()
        {
            //创建用于绘制的深蓝色背景图像	
            Mat image = new Mat(480, 640, MatType.CV_8UC3, new Scalar(0/*255*/, 0, 0, 255));// cv::Mat image = cv::Mat::zeros(480, 640, CV_8UC3);	image.setTo(cv::Scalar(100, 0, 0)); 	
            //输入拟合点  	
            List<OpenCvSharp.Point> points = new List<OpenCvSharp.Point>();// std::vector<cv::Point> points;
            points.Add(new OpenCvSharp.Point(0, 254));
            points.Add(new OpenCvSharp.Point(127, 222/*32*/));
            points.Add(new OpenCvSharp.Point(254, 0));
            //points.Add(new OpenCvSharp.Point(100, 58));
            //points.Add(new OpenCvSharp.Point(150, 70));
            //points.Add(new OpenCvSharp.Point(200, 90));
            ////points.push_back(cv::Point(100., 58.));	points.push_back(cv::Point(150., 70.));	points.push_back(cv::Point(200., 90.));
            //points.Add(new OpenCvSharp.Point(252, 140));
            //points.Add(new OpenCvSharp.Point(300, 220));
            //points.Add(new OpenCvSharp.Point(350, 400));
            //points.push_back(cv::Point(252., 140.));	points.push_back(cv::Point(300., 220.));	points.push_back(cv::Point(350., 400.));
            List<List<OpenCvSharp.Point>> pointsIn = new List<List<OpenCvSharp.Point>>()
            {
                points
            };
            //将拟合点绘制到空白图上  	
            for (int i = 0; i < points.Count; i++)
            {
                Cv2.Circle(image, points[i].X, points[i].Y, 5, new Scalar(0, 0, 255), 2, LineTypes.Link8, 0);
                //cv::circle(image, points[i], 5, cv::Scalar(0, 0, 255), 2, 8, 0);
            }
            //绘制折线	
            Cv2.Polylines(image, pointsIn/*(IEnumerable<OpenCvSharp.Point>)(points.ToArray())*/, false, new Scalar(0, 255, 0), 1, LineTypes.Link8, 0); //cv::polylines(image, points, false, cv::Scalar(0, 255, 0), 1, 8, 0);
            Mat A = new Mat(3 + 1/*480*/, 1/*640*/, MatType.CV_64FC1, new Scalar(0));// cv::Mat A;
            polynomial_curve_fit(points.ToArray(), 3, A);
            //std::cout << "A = " << A << std::endl;
            List<OpenCvSharp.Point> points_fitted = new List<OpenCvSharp.Point>(); //std::vector<cv::Point> points_fitted;
            List<double> dataFitted = new List<double>();
            dataFitted.Add(A.At<double>(0, 0));
            dataFitted.Add(A.At<double>(1, 0));
            dataFitted.Add(A.At<double>(2, 0));
            dataFitted.Add(A.At<double>(3, 0));
            for (int x = 0; x < 255; x++)
            {
                double y = A.At<double>(0, 0) + A.At<double>(1, 0) * x + A.At<double>(2, 0) * Math.Pow(x, 2) + A.At<double>(3, 0) * Math.Pow(x, 3);
                //    double y = A.at<double>(0, 0) + A.at<double>(1, 0) * x + A.at<double>(2, 0)*std::pow(x, 2) + A.at<double>(3, 0)*std::pow(x, 3);
                points_fitted.Add(new OpenCvSharp.Point(x, y));
                //    points_fitted.push_back(cv::Point(x, y));
            }
            List<List<OpenCvSharp.Point>> points_fittedIn = new List<List<OpenCvSharp.Point>>()
            {
                points_fitted
            };
            //黄色线表示拟合后的曲线，成功
            Cv2.Polylines(image, points_fittedIn, false, new Scalar(0, 255, 255), 1, LineTypes.Link8, 0); //cv::polylines(image, points_fitted, false, cv::Scalar(0, 255, 255), 1, 8, 0);
            Cv2.ImShow("image", image);// cv::imshow("image", image);
            //this.pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(histImg);
        }

        /// <summary>
        /// 将拟合点绘制到空白图上
        /// //原文链接：https://blog.csdn.net/guduruyu/article/details/72866144
        /// </summary>
        /// <param name="key_point">拟合点</param>
        /// <param name="n">拟合矩阵的？？数量</param>
        /// <param name="A">空白图</param>
        /// <returns></returns>
        private unsafe bool polynomial_curve_fit(OpenCvSharp.Point[] key_point, int n, Mat A)
        {
            //Number of key points	
            int N = key_point.Length;
            //构造矩阵X	
            Mat X = new Mat(n + 1, n + 1, MatType.CV_64FC1, new Scalar(0));// cv::Mat X = cv::Mat::zeros(n + 1, n + 1, CV_64FC1);
            for (int i = 0; i < n + 1; i++)
            {
                for (int j = 0; j < n + 1; j++)
                {
                    ((double*)X.Ptr(i))[j] = 0;//111
                    for (int k = 0; k < N; k++)
                    {
                        ((double*)X.Ptr(i))[j] = X/*[i]*/.At<double>(i, j) + (double)Math.Pow(key_point[k].X, i + j);// X.at<double>(i, j) = X.at<double>(i, j) + std::pow(key_point[k].x, i + j);
                    }
                }
            }
            //构造矩阵Y	
            Mat Y = new Mat(n + 1, 1, MatType.CV_64FC1, new Scalar(0));// cv::Mat Y = cv::Mat::zeros(n + 1, 1, CV_64FC1);
            for (int i = 0; i < n + 1; i++)
            {
                ((double*)Y.Ptr(i))[0] = 0;//111
                for (int k = 0; k < N; k++)
                {
                    ((double*)Y.Ptr(i))[0] = Y.At<double>(i, 0) + (double)(Math.Pow(key_point[k].X, i) * key_point[k].Y);// Y.at<double>(i, 0) = Y.at<double>(i, 0) + std::pow(key_point[k].x, i) * key_point[k].y;
                }
            }
            //A = new Mat(n + 1, 1, MatType.CV_64FC1, new Scalar(0));// A = cv::Mat::zeros(n + 1, 1, CV_64FC1);

            //List<double> dataFitted = new List<double>();
            //dataFitted.Add(Y.At<double>(0, 0));
            //dataFitted.Add(Y.At<double>(1, 0));
            //dataFitted.Add(Y.At<double>(2, 0));
            //dataFitted.Add(Y.At<double>(3, 0));

            //for (int i = 0; i < n + 1; i++)
            //{
            //    for (int j = 0; j < n + 1; j++)
            //    {
            //        dataFitted.Add(X.At<double>(i, j));
            //    }
            //}

            //求解矩阵A	
            Cv2.Solve(X, Y, A, DecompTypes.LU);//Cv2.Solve(X, Y, A, DecompTypes.LU); cv::solve(X, Y, A, cv::DECOMP_LU);

            //dataFitted.Add(A.At<double>(0, 0));
            //dataFitted.Add(A.At<double>(1, 0));
            //dataFitted.Add(A.At<double>(2, 0));
            //dataFitted.Add(A.At<double>(3, 0));

            return true;
        }

        /// <summary>
        /// 通过颜色通道去除像素点的千分比计算有效阈值
        /// </summary>
        /// <param name="hists"></param>
        /// <param name="percentValue"></param>
        /// <returns></returns>
        private OpenCvSharp.Point getStartIndex(Mat mat, Mat[] hists, float percentValue)
        {
            //灰度化图片取第一通道
            Mat histGray = hists[0];
            //彩色图片取灰度化后的通道
            if (!isGray)
            {
                OpenCvSharp.Point startIndex = new OpenCvSharp.Point(0, histGray.Rows);

                //计算三次
                OpenCvSharp.Point startIndex0 = this.getStartRange(mat, hists[0], percentValue);
                OpenCvSharp.Point startIndex1 = this.getStartRange(mat, hists[1], percentValue);
                OpenCvSharp.Point startIndex2 = this.getStartRange(mat, hists[2], percentValue);

                startIndex.X = Math.Min(startIndex0.X, Math.Min(startIndex1.X, startIndex2.X));
                startIndex.Y = Math.Max(startIndex0.Y, Math.Max(startIndex1.Y, startIndex2.Y));

                startIndex.Y = Math.Min(255 - 1, startIndex.Y);
                startIndex.X = Math.Max(startIndex.X, 1);
                startIndex.Y = Math.Max(startIndex.X + 1, startIndex.Y);

                return startIndex;
            }
            return this.getStartRange(mat, histGray, percentValue);
        }

        private OpenCvSharp.Point getStartRange(Mat mat, Mat histGray, float percentValue)
        {
            if (this.OldMat == null)
            {
                return new OpenCvSharp.Point(1, 254); ;
            }
            //计算灰度分布密度
            float totalPoints = this.OldMat.Cols * this.OldMat.Rows;
            OpenCvSharp.Point startIndex = new OpenCvSharp.Point(0, histGray.Rows);
            bool foundStartTag = false;
            bool foundEndTag = false;
            //根据累计直方图分布计算左右阈值
            float equalizeHists = 0;
            for (int i = 0; i < histGray.Rows; ++i)
            {
                equalizeHists += ((float)(histGray.At<float>(i) / (totalPoints * 1.0))) * 1000;

                if (!foundStartTag && equalizeHists > percentValue)
                {
                    foundStartTag = true;
                    startIndex.X = i;
                }
                else if (equalizeHists >= 1000 - percentValue)
                {
                    foundEndTag = true;
                    startIndex.Y = i;
                }

                if (foundStartTag && foundEndTag)
                    break;
            }
            startIndex.Y = Math.Min(255 - 1, startIndex.Y);
            startIndex.X = Math.Max(startIndex.X, 1);
            startIndex.Y = Math.Max(startIndex.X + 1, startIndex.Y);
            return startIndex;
        }

        /// <summary>
        /// 更新直方图特征曲线
        /// </summary>
        /// <param name="updateThreePoints">是否需要更新三个控制点的位置</param>
        private void updateHistogramRect(bool updateThreePoints)
        {
            //为了更好显示直方图细节，使用4倍尺寸绘制直方图
            int histSize = 256/*oldHists[0].Rows*/ * 4;

            //创建用于绘制的深蓝色背景图像	
            Mat image = new Mat(histSize, histSize, MatType.CV_8UC4, new Scalar(0, 0, 0, 0));
            //输入拟合点  	
            List<OpenCvSharp.Point> points = new List<OpenCvSharp.Point>();

            //根据亮度和对比度计算左右阈值
            int grayMin = (int)(0 - (256.0 / (double.Parse(label6.Text) == 0 ? 0.01 : double.Parse(label6.Text)) + double.Parse(label5.Text) * 510.0) / 2.0);
            int grayMax = (int)((256.0 / (double.Parse(label6.Text) == 0 ? 0.01 : double.Parse(label6.Text)) - double.Parse(label5.Text) * 510.0) / 2.0);
            if (grayMin >= grayMax)
            {
                return;
            }
            if (updateThreePoints)
            {
                ucTrackBar1.Value1 = grayMin;
                ucTrackBar1.Value3 = grayMax;
                //ucTrackBar1.Value2 = (float)((grayMin + grayMax) / 2.0);
            }

            //gamma运算，gamma值根据AxioVision对比 取最小值0.01进行计算
            double gamma = Math.Max(0.01, double.Parse(label7.Text));

            //计算Value2
            ucTrackBar1.Value2 = (float)Math.Max(grayMin, Math.Min(grayMax, Math.Pow(0.5, 1.0 / gamma) * (grayMax - grayMin) + grayMin));
            ucTrackBar1.Refresh();

            int lastY2 = histSize + 0;// histSize - 1;
            for (int h = 0; h <= 256; h++)
            {
                if (h < grayMin)
                {
                    points.Add(new OpenCvSharp.Point(h * 4, lastY2));
                    continue;
                }
                else if (h > grayMax)
                {
                    lastY2 = -1;// 0;
                    points.Add(new OpenCvSharp.Point(h * 4, lastY2));
                    continue;
                }
                int lastY1 = (int)Math.Round((1.0 - Math.Pow((h - grayMin) * 1.0 / (grayMax - grayMin), gamma)) * (histSize + 1/*histSize - 1*/)) - 1;
                lastY1 = Math.Min(lastY2, Math.Max(0, lastY1));
                if (lastY2 == lastY1)
                {
                    if (h == 0 || h == 256)
                    {
                        points.Add(new OpenCvSharp.Point(h * 4, lastY1));
                    }
                }
                else
                {
                    points.Add(new OpenCvSharp.Point(h * 4, lastY1));
                    lastY2 = lastY1;
                }
            }
            lastY2 = histSize + 0;// histSize - 1;
            //将第一个点的位置设置最底部，保证曲线平滑性
            //if (points[0].X > 4 && points[0].Y < lastY2)
            //{
            //    points.Insert(0, new OpenCvSharp.Point(points[0].X - 4, lastY2));
            //}
            List<List<OpenCvSharp.Point>> pointsIn = new List<List<OpenCvSharp.Point>>()
                {
                    points
                };
            //绘制折线	
            Cv2.Polylines(image, pointsIn/*(IEnumerable<OpenCvSharp.Point>)(points.ToArray())*/, false, new Scalar(0, 0/*255*/, 0, 255), 4/*1*/, LineTypes.Link8, 0); //cv::polylines(image, points, false, cv::Scalar(0, 255, 0), 1, 8, 0);
            //this.pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image);
            Mat A = new Mat(3 + 1, 1, MatType.CV_64FC1, new Scalar(0));
            polynomial_curve_fit(points.ToArray(), 3, A);
            List<OpenCvSharp.Point> points_fitted = new List<OpenCvSharp.Point>(); //std::vector<cv::Point> points_fitted;
            List<double> dataFitted = new List<double>();
            dataFitted.Add(A.At<double>(0, 0));
            dataFitted.Add(A.At<double>(1, 0));
            dataFitted.Add(A.At<double>(2, 0));
            dataFitted.Add(A.At<double>(3, 0));
            for (int x = 0; x < histSize + 1; x++)
            {
                if (x < grayMin * 4)
                {
                    points_fitted.Add(new OpenCvSharp.Point(x, lastY2));
                    continue;
                }
                else if (x > grayMax * 4)
                {
                    lastY2 = -1;// 0;
                    points_fitted.Add(new OpenCvSharp.Point(x, lastY2));
                    continue;
                }
                double y = A.At<double>(0, 0) + A.At<double>(1, 0) * x + A.At<double>(2, 0) * Math.Pow(x, 2) + A.At<double>(3, 0) * Math.Pow(x, 3);
                points_fitted.Add(new OpenCvSharp.Point(x, y));
            }
            //List<List<OpenCvSharp.Point>> points_fittedIn = new List<List<OpenCvSharp.Point>>()
            //{
            //    points_fitted
            //};
            ////绘制折线	
            //Cv2.Polylines(image, points_fittedIn/*(IEnumerable<OpenCvSharp.Point>)(points.ToArray())*/, false, new Scalar(0, 255/*255*/, 0, 255), 4/*1*/, LineTypes.Link8, 0); //cv::polylines(image, points, false, cv::Scalar(0, 255, 0), 1, 8, 0);
            this.pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image);
            //    //黄色线表示拟合后的曲线，成功
            //    Cv2.Polylines(image, points_fittedIn, false, new Scalar(0, 255, 255), 1, LineTypes.Link8, 0); //cv::polylines(image, points_fitted, false, cv::Scalar(0, 255, 255), 1, 8, 0);
            //    Cv2.ImShow("image", image);// cv::imshow("image", image);
            //this.pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(histImg);
        }

        /// <summary>
        /// 
        /// </summary>
        /// <param name="oldmat">原图</param>
        /// <param name="label5_Text">亮度</param>
        /// <param name="label6_Text">对比度</param>
        /// <param name="label7_Text">gamma值</param>
        /// <returns></returns>
        public static Mat scrollMethod(Mat oldmat, string label5_Text, string label6_Text, string label7_Text)
        {
            if (oldmat == null)
            {
                return null;
            }
            Mat[] mats = Cv2.Split(oldmat);//一张图片，将panda拆分成3个图片装进mat
            bool isGray = true;
            /// <summary>
            /// 一个矩阵数组，用来接收直方图,记得全部初始化
            /// </summary>
            Mat[] oldHists = new Mat[] { new Mat(), new Mat(), new Mat() };

            Mat[] mats0 = new Mat[] { mats[0] };//panda的第一个通道，也就是B
            Mat[] mats1;
            if (mats.Length < 2)
                mats1 = new Mat[] { mats[0] };//panda的第二个通道，也就是G
            else
                mats1 = new Mat[] { mats[1] };//panda的第二个通道，也就是G
            Mat[] mats2;
            if (mats.Length < 3)
            {
                mats2 = new Mat[] { mats[0] };//panda的第三个通道，也就是R

                mats = new Mat[] { mats0[0], mats0[0], mats0[0] };
            }
            else
                mats2 = new Mat[] { mats[2] };//panda的第三个通道，也就是R


            int[] channels0 = new int[] { 0 };//一个通道,初始化为通道0,这些东西可以共用设置一个就行
            int[] channels1 = new int[] { 0 };
            int[] channels2 = new int[] { 0 };
            int[] histsize = new int[] { 256 };//一个通道，初始化为256箱子
            Rangef[] range = new Rangef[1];//一个通道，范围
            range[0].Start = 0.0F;//从0开始(含)
            range[0].End = 256.0F;//到256结束(不含)
            Mat mask = new Mat();//不做掩码

            Cv2.CalcHist(mats0, channels0, mask, oldHists[0], 1, histsize, range);//对被拆分的图片单独进行计算
            Cv2.CalcHist(mats1, channels1, mask, oldHists[1], 1, histsize, range);//对被拆分的图片单独进行计算
            Cv2.CalcHist(mats2, channels2, mask, oldHists[2], 1, histsize, range);//对被拆分的图片单独进行计算

            for (int h = 0; h < oldHists[0].Rows; h++)
            {
                for (int j = oldHists.Length - 1; j >= 1; j--)
                {
                    if (oldHists[j].At<float>(h) != oldHists[0].At<float>(h))
                    {
                        isGray = false;
                        break;
                    }
                }
                if (!isGray)
                    break;
            }

            //根据亮度和对比度计算左右阈值
            int grayMin = (int)(0 - (256.0 / (double.Parse(label6_Text) == 0 ? 0.01 : double.Parse(label6_Text)) + double.Parse(label5_Text) * 510.0) / 2.0);
            int grayMax = (int)((256.0 / (double.Parse(label6_Text) == 0 ? 0.01 : double.Parse(label6_Text)) - double.Parse(label5_Text) * 510.0) / 2.0);
            if (grayMin >= grayMax)
            {
                return null;
            }
            Mat dst = new Mat();
            //如果是灰度化图片，则只需计算一个通道。对灰度值进行映射，BGR根据左右阈值做缩放操作
            if (isGray)
            {
                Cv2.Min(mats[0], new Mat(mats[0].Rows, mats[0].Cols, MatType.CV_8UC1, new Scalar(grayMax)), dst);
                Cv2.Max(dst, new Mat(mats[0].Rows, mats[0].Cols, MatType.CV_8UC1, new Scalar(grayMin)), dst);
                Cv2.Normalize(dst, dst, 0, 255, NormTypes.MinMax);
            }
            else
            {
                if (mats.Length < oldHists.Length)
                    for (int j = 0; j < oldHists.Length; j++)
                    {
                        Cv2.Min(mats[j], new Mat(mats[j].Rows, mats[j].Cols, MatType.CV_8UC1, new Scalar(grayMax)), mats[j]);
                        Cv2.Max(mats[j], new Mat(mats[j].Rows, mats[j].Cols, MatType.CV_8UC1, new Scalar(grayMin)), mats[j]);
                        Cv2.Normalize(mats[j], mats[j], 0, 255, NormTypes.MinMax);
                    }
                else
                    for (int j = 0; j < oldHists.Length; j++)
                    {
                        Cv2.Min(mats[j], new Mat(mats[j].Rows, mats[j].Cols, MatType.CV_8UC1, new Scalar(grayMax)), mats[j]);
                        Cv2.Max(mats[j], new Mat(mats[j].Rows, mats[j].Cols, MatType.CV_8UC1, new Scalar(grayMin)), mats[j]);
                        Cv2.Normalize(mats[j], mats[j], 0, 255, NormTypes.MinMax);
                    }
                Cv2.Merge(mats, dst);
            }
            //灰度归一化
            dst.ConvertTo(dst, MatType.CV_64F, 1.0 / 255, 0);
            //gamma运算，gamma值根据AxioVision对比 取最小值0.01进行计算
            Cv2.Pow(dst, Math.Max(0.01, double.Parse(label7_Text)), dst);
            dst.ConvertTo(dst, MatType.CV_8U, 255, 0);

            //Document document = Document.FromImage(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));
            //this.AppWorkspace.ActiveDocumentWorkspace.setDoc(document, false);
            //this.AppWorkspace.ActiveDocumentWorkspace.Refresh();
            ////this.AppWorkspace.ActiveDocumentWorkspace.CompositionSurface = Surface.CopyFromBitmap(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));

            #region 均衡化的方法备份
            ////对灰度值进行映射(均衡化)
            //float[] totalPoints = new float[] { oldMat.Cols * oldMat.Rows, oldMat.Cols * oldMat.Rows, oldMat.Cols * oldMat.Rows };
            ////去掉黑色部分 和 白色部分
            //for (int j = 0; j < oldHists.Length; j++)
            //{
            //    for (int i = 0; i < grayMin; i++)
            //    {
            //        totalPoints[j] = totalPoints[j] - oldHists[j].At<float>(i);
            //    }
            //    for (int i = grayMax; i < oldHists[j].Rows; i++)
            //    {
            //        totalPoints[j] = totalPoints[j] - oldHists[j].At<float>(i);
            //    }
            //}
            //for (int j = 0; j < (isGray ? 1 : oldHists.Length); j++)
            //{
            //    //计算灰度分布密度
            //    //List<float> ProbPixel = new List<float>();
            //    //计算累计直方图分布
            //    List<float> equalizeHists = new List<float>();
            //    Mat hist = oldHists[j];
            //    //计算累计直方图分布
            //    for (int i = 0; i < hist.Rows; ++i)
            //    {
            //        //去掉黑色部分 和 白色部分
            //        if (i < grayMin || i > grayMax)
            //        {
            //            if (i == 0)
            //            {
            //                equalizeHists.Add(0);
            //            }
            //            else
            //            {
            //                equalizeHists.Add(equalizeHists[i - 1]);
            //            }
            //        }
            //        else if (i == 0)
            //        {
            //            equalizeHists.Add((float)(hist.At<float>(i) / (totalPoints[j] * 1.0)));
            //        }
            //        else
            //        {
            //            equalizeHists.Add(equalizeHists[i - 1] + (float)(hist.At<float>(i) / (totalPoints[j] * 1.0)));
            //        }
            //    }
            //    //累计分布取整
            //    for (int i = 0; i < equalizeHists.Count; ++i)
            //    {
            //        float numHist = equalizeHists[i];
            //        equalizeHists[i] = Math.Min(255, (int)(numHist * 255.0 + 0.5));
            //    }
            //    for (int y = 0; y < h; ++y)
            //    //对灰度值进行映射，BGR根据左右阈值做缩放操作
            //    {
            //        byte* imageData = (byte*)(mats[j].Ptr(y));
            //        //对灰度值进行映射
            //        for (int x = 0; x < w; ++x)
            //        {
            //            int oldvalue = imageData[x];
            //            if (oldvalue < grayMin)
            //            {
            //                imageData[x] = 0;
            //            }
            //            else if (oldvalue > grayMax)
            //            {
            //                imageData[x] = 255;
            //            }
            //            else
            //            {
            //                imageData[x] = (byte)equalizeHists[oldvalue];
            //            }
            //        }
            //    }
            //}
            //Mat dst = new Mat();
            ////灰度归一化
            //if (isGray)
            //{
            //    mats[0].ConvertTo(dst, MatType.CV_64F, 1.0 / 255, 0);
            //}
            //else
            //{
            //    Cv2.Merge(mats, dst);
            //    dst.ConvertTo(dst, MatType.CV_64F, 1.0 / 255, 0);
            //}
            ////gamma运算，gamma值根据AxioVision对比 取最小值0.01进行计算
            //Cv2.Pow(dst, Math.Max(0.01, double.Parse(label7.Text)), dst);
            //dst.ConvertTo(dst, MatType.CV_8U, 255, 0);
            //this.AppWorkspace.ActiveDocumentWorkspace.SurfaceBox.Surface = Surface.CopyFromBitmap(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));
            #endregion
            return dst;
        }

        /// <summary>
        /// 根据亮度、对比度、gamma对图片进行处理
        /// </summary>
        private void scrollMethod()
        {
            trackBar1Value = trackBar1.Value;
            trackBar2Value = trackBar2.Value;
            trackBar3Value = trackBar3.Value;
            //if (this.OldMat == null)
            //{
            //    return;
            //}
            //Mat[] mats = Cv2.Split(this.OldMat);//一张图片，将panda拆分成3个图片装进mat
            ////根据亮度和对比度计算左右阈值
            //int grayMin = (int)(0 - (256.0 / (double.Parse(label6.Text) == 0 ? 0.01 : double.Parse(label6.Text)) + double.Parse(label5.Text) * 510.0) / 2.0);
            //int grayMax = (int)((256.0 / (double.Parse(label6.Text) == 0 ? 0.01 : double.Parse(label6.Text)) - double.Parse(label5.Text) * 510.0) / 2.0);
            //if (grayMin >= grayMax)
            //{
            //    return;
            //}
            //Mat dst = new Mat();
            ////如果是灰度化图片，则只需计算一个通道。对灰度值进行映射，BGR根据左右阈值做缩放操作
            //if (isGray)
            //{
            //    Cv2.Min(mats[0], new Mat(mats[0].Rows, mats[0].Cols, MatType.CV_8UC1, new Scalar(grayMax)), dst);
            //    Cv2.Max(dst, new Mat(mats[0].Rows, mats[0].Cols, MatType.CV_8UC1, new Scalar(grayMin)), dst);
            //    Cv2.Normalize(dst, dst, 0, 255, NormTypes.MinMax);
            //}
            //else
            //{
            //    for (int j = 0; j < oldHists.Length; j++)
            //    {
            //        Cv2.Min(mats[j], new Mat(mats[j].Rows, mats[j].Cols, MatType.CV_8UC1, new Scalar(grayMax)), mats[j]);
            //        Cv2.Max(mats[j], new Mat(mats[j].Rows, mats[j].Cols, MatType.CV_8UC1, new Scalar(grayMin)), mats[j]);
            //        Cv2.Normalize(mats[j], mats[j], 0, 255, NormTypes.MinMax);
            //    }
            //    Cv2.Merge(mats, dst);
            //}
            ////灰度归一化
            //dst.ConvertTo(dst, MatType.CV_64F, 1.0 / 255, 0);
            ////gamma运算，gamma值根据AxioVision对比 取最小值0.01进行计算
            //Cv2.Pow(dst, Math.Max(0.01, double.Parse(label7.Text)), dst);
            //dst.ConvertTo(dst, MatType.CV_8U, 255, 0);

            //Document document = Document.FromImage(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));
            //this.AppWorkspace.ActiveDocumentWorkspace.setDoc(document, false);
            //this.AppWorkspace.ActiveDocumentWorkspace.Refresh();
            ////this.AppWorkspace.ActiveDocumentWorkspace.CompositionSurface = Surface.CopyFromBitmap(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));

            #region 均衡化的方法备份
            ////对灰度值进行映射(均衡化)
            //float[] totalPoints = new float[] { oldMat.Cols * oldMat.Rows, oldMat.Cols * oldMat.Rows, oldMat.Cols * oldMat.Rows };
            ////去掉黑色部分 和 白色部分
            //for (int j = 0; j < oldHists.Length; j++)
            //{
            //    for (int i = 0; i < grayMin; i++)
            //    {
            //        totalPoints[j] = totalPoints[j] - oldHists[j].At<float>(i);
            //    }
            //    for (int i = grayMax; i < oldHists[j].Rows; i++)
            //    {
            //        totalPoints[j] = totalPoints[j] - oldHists[j].At<float>(i);
            //    }
            //}
            //for (int j = 0; j < (isGray ? 1 : oldHists.Length); j++)
            //{
            //    //计算灰度分布密度
            //    //List<float> ProbPixel = new List<float>();
            //    //计算累计直方图分布
            //    List<float> equalizeHists = new List<float>();
            //    Mat hist = oldHists[j];
            //    //计算累计直方图分布
            //    for (int i = 0; i < hist.Rows; ++i)
            //    {
            //        //去掉黑色部分 和 白色部分
            //        if (i < grayMin || i > grayMax)
            //        {
            //            if (i == 0)
            //            {
            //                equalizeHists.Add(0);
            //            }
            //            else
            //            {
            //                equalizeHists.Add(equalizeHists[i - 1]);
            //            }
            //        }
            //        else if (i == 0)
            //        {
            //            equalizeHists.Add((float)(hist.At<float>(i) / (totalPoints[j] * 1.0)));
            //        }
            //        else
            //        {
            //            equalizeHists.Add(equalizeHists[i - 1] + (float)(hist.At<float>(i) / (totalPoints[j] * 1.0)));
            //        }
            //    }
            //    //累计分布取整
            //    for (int i = 0; i < equalizeHists.Count; ++i)
            //    {
            //        float numHist = equalizeHists[i];
            //        equalizeHists[i] = Math.Min(255, (int)(numHist * 255.0 + 0.5));
            //    }
            //    for (int y = 0; y < h; ++y)
            //    //对灰度值进行映射，BGR根据左右阈值做缩放操作
            //    {
            //        byte* imageData = (byte*)(mats[j].Ptr(y));
            //        //对灰度值进行映射
            //        for (int x = 0; x < w; ++x)
            //        {
            //            int oldvalue = imageData[x];
            //            if (oldvalue < grayMin)
            //            {
            //                imageData[x] = 0;
            //            }
            //            else if (oldvalue > grayMax)
            //            {
            //                imageData[x] = 255;
            //            }
            //            else
            //            {
            //                imageData[x] = (byte)equalizeHists[oldvalue];
            //            }
            //        }
            //    }
            //}
            //Mat dst = new Mat();
            ////灰度归一化
            //if (isGray)
            //{
            //    mats[0].ConvertTo(dst, MatType.CV_64F, 1.0 / 255, 0);
            //}
            //else
            //{
            //    Cv2.Merge(mats, dst);
            //    dst.ConvertTo(dst, MatType.CV_64F, 1.0 / 255, 0);
            //}
            ////gamma运算，gamma值根据AxioVision对比 取最小值0.01进行计算
            //Cv2.Pow(dst, Math.Max(0.01, double.Parse(label7.Text)), dst);
            //dst.ConvertTo(dst, MatType.CV_8U, 255, 0);
            //this.AppWorkspace.ActiveDocumentWorkspace.SurfaceBox.Surface = Surface.CopyFromBitmap(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));
            #endregion
        }

        /// <summary>
        /// 拖动左侧三角形
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void ucTrackBar1_Value1Changed(object sender, EventArgs e)
        {
            OpenCvSharp.Point startIndex = new OpenCvSharp.Point(ucTrackBar1.Value1, ucTrackBar1.Value3);
            //计算对比度
            label6.Text = (256.0 / (startIndex.Y - startIndex.X)).ToString("f2");
            trackBar2.Value = Math.Min(trackBar2.Maximum, (int)(double.Parse(label6.Text) * 100));
            //计算亮度
            label5.Text = ((0 - startIndex.X - startIndex.Y) / 510.0).ToString("f2");
            trackBar1.Value = (int)(double.Parse(label5.Text) * 100);
            //备注：这里是可以优化用户体验的地方(避免操作事件引起多次计算发生)
            //this.scrollMethod();
            updateHistogramRect(false);
        }

        /// <summary>
        /// 拖动中间三角形
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void ucTrackBar1_Value2Changed(object sender, EventArgs e)
        {
            //计算gamma值
            double gamma = 1.0 / Math.Log((ucTrackBar1.Value2 - ucTrackBar1.Value1) / (ucTrackBar1.Value3 - ucTrackBar1.Value1), 0.5);
            gamma = Math.Max(0.01, gamma);
            this.trackBar3.Value = Math.Min(299, (int)(gamma * 100));//备注：需要与AxioVision进行比较效果，调试一致
            label7.Text = gamma.ToString("f2");
            //备注：这里是可以优化用户体验的地方(避免操作事件引起多次计算发生)
            updateHistogramRect(false);
        }

        /// <summary>
        /// 拖动右侧三角形
        /// </summary>
        /// <param name="sender"></param>
        /// <param name="e"></param>
        private void ucTrackBar1_Value3Changed(object sender, EventArgs e)
        {
            OpenCvSharp.Point startIndex = new OpenCvSharp.Point(ucTrackBar1.Value1, ucTrackBar1.Value3);// this.getStartIndex(oldMat, oldHists, (float)numericUpDown1.Value);

            //计算对比度
            label6.Text = (256.0 / (startIndex.Y - startIndex.X)).ToString("f2");
            trackBar2.Value = Math.Min(trackBar2.Maximum, (int)(double.Parse(label6.Text) * 100));
            //计算亮度
            label5.Text = ((0 - startIndex.X - startIndex.Y) / 510.0).ToString("f2");
            trackBar1.Value = (int)(double.Parse(label5.Text) * 100);

            //this.scrollMethod();
            updateHistogramRect(false);
        }
    }
}