using OpenCvSharp;
using System;
using System.Drawing;
using System.Text;
using System.Windows.Forms;
 
namespace OpenCvSharp_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }
 
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
 
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
 
        Mat image;
        Mat result_image;
 
        StringBuilder sb = new StringBuilder();
 
        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
 
            pictureBox1.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";
 
            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
            //test
            image_path = "test_img/1.jpg";
            image = new Mat(image_path);
            pictureBox1.Image = new Bitmap(image_path);
        }
 
        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "檢測(cè)中，請(qǐng)稍等……";
            pictureBox2.Image = null;
            Application.DoEvents();
 
            result_image = image.Clone();
 
            //二值化操作
            Mat grayimg = new Mat();
            Cv2.CvtColor(image, grayimg, ColorConversionCodes.BGR2GRAY);
            Mat BinaryImg = new Mat();
            Cv2.Threshold(grayimg, BinaryImg, 240, 255, ThresholdTypes.Binary);
            //Cv2.ImShow("二值化", BinaryImg);
 
            //腐蝕
            Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(15, 15));
            Mat morhImage = new Mat();
            Cv2.Dilate(BinaryImg, morhImage, kernel, null, 2);
            //Cv2.ImShow("morphology", morhImage);
 
            //距離變換：用于二值化圖像中的每一個(gè)非零點(diǎn)距自己最近的零點(diǎn)的距離，距離變換圖像上越亮的點(diǎn)，代表了這一點(diǎn)距離零點(diǎn)的距離越遠(yuǎn)
            Mat dist = new Mat();
            Cv2.BitwiseNot(morhImage, morhImage);
            /*
            OpenCV中，函數(shù)distanceTransform()用于計(jì)算圖像中每一個(gè)非零點(diǎn)像素與其最近的零點(diǎn)像素之間的距離，
            輸出的是保存每一個(gè)非零點(diǎn)與最近零點(diǎn)的距離信息，圖像上越亮的點(diǎn)，代表了離零點(diǎn)的距離越遠(yuǎn)。
            用途：
            可以根據(jù)距離變換的這個(gè)性質(zhì)，經(jīng)過(guò)簡(jiǎn)單的運(yùn)算，用于細(xì)化字符的輪廓和查找物體質(zhì)心（中心）。
            */
            /*
            距離變換的處理圖像通常都是二值圖像，而二值圖像其實(shí)就是把圖像分為兩部分，即背景和物體兩部分，物體通常又稱(chēng)為前景目標(biāo)。
            通常我們把前景目標(biāo)的灰度值設(shè)為255（即白色），背景的灰度值設(shè)為0（即黑色）。
            所以定義中的非零像素點(diǎn)即為前景目標(biāo)，零像素點(diǎn)即為背景。
            所以圖像中前景目標(biāo)中的像素點(diǎn)距離背景越遠(yuǎn)，那么距離就越大，如果我們用這個(gè)距離值替換像素值，那么新生成的圖像中這個(gè)點(diǎn)越亮。
            */
            //User：用戶自定義
            //L1：  曼哈頓距離
            //L2：  歐式距離
            //C：   棋盤(pán)距離
            Cv2.DistanceTransform(morhImage, dist, DistanceTypes.L1, DistanceTransformMasks.Mask3);
            Cv2.Normalize(dist, dist, 0, 1.0, NormTypes.MinMax);   //范圍在0~1之間
            //Cv2.ImShow("distance", dist);
 
            //形態(tài)學(xué)處理
            Mat MorphImg = new Mat();
            dist.ConvertTo(MorphImg, MatType.CV_8U);
            Cv2.Threshold(MorphImg, MorphImg, 0.99, 255, ThresholdTypes.Binary);  //上圖像素值在0~1之間
            kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(7, 3), new OpenCvSharp.Point(-1, -1));
            Cv2.MorphologyEx(MorphImg, MorphImg, MorphTypes.Open, kernel);  //開(kāi)操作
            //Cv2.ImShow("t-distance", MorphImg);
 
            //找到種子的輪廓區(qū)域
            OpenCvSharp.Point[][] contours;
            HierarchyIndex[] hierarchly;
            Cv2.FindContours(MorphImg, out contours, out hierarchly, RetrievalModes.External, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0));
            Mat markers = Mat.Zeros(image.Size(), MatType.CV_8UC3);
            int x, y, w, h;
            Rect rect;
            for (int i = 0; i < contours.Length; i++)
            {
                // Cv2.DrawContours(markers, contours, i, Scalar.RandomColor(), 2, LineTypes.Link8, hierarchly);
                rect = Cv2.BoundingRect(contours[i]);
                x = rect.X;
                y = rect.Y;
                w = rect.Width;
                h = rect.Height;
                Cv2.Circle(result_image, x + w / 2, y + h / 2, 20, new Scalar(0, 0, 255), -1);
                if (i >= 9)
                {
                    Cv2.PutText(result_image, (i + 1).ToString(), new OpenCvSharp.Point(x + w / 2 - 18, y + h / 2 + 8), HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 255, 0), 2);
                }
                else
                {
                    Cv2.PutText(result_image, (i + 1).ToString(), new OpenCvSharp.Point(x + w / 2 - 8, y + h / 2 + 8), HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 255, 0), 2);
                }
            }
 
            textBox1.Text = "number of corns: " + contours.Length;
            pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
 
 
        }
 
        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }
 
        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}