dotnet new console -n OpenCvSharpDemo
cd OpenCvSharpDemo

dotnet add package OpenCvSharp4
dotnet add package OpenCvSharp4.runtime.win

using OpenCvSharp;
using System;

class Program
{
    static void Main()
    {
        var src = Cv2.ImRead("input.jpg"); // 讀取圖像
        Cv2.ImShow("原圖", src);
        Cv2.WaitKey();
    }
}

var src = Cv2.ImRead("document.jpg");

// 模擬已檢測的四個(gè)角點(diǎn)（順時(shí)針）
Point2f[] srcPoints = {
    new Point2f(320, 150),
    new Point2f(700, 130),
    new Point2f(750, 600),
    new Point2f(280, 620)
};

// 目標(biāo)矩形區(qū)域
Point2f[] dstPoints = {
    new Point2f(0, 0),
    new Point2f(500, 0),
    new Point2f(500, 700),
    new Point2f(0, 700)
};

Mat matrix = Cv2.GetPerspectiveTransform(srcPoints, dstPoints);
Mat corrected = new Mat();
Cv2.WarpPerspective(src, corrected, matrix, new Size(500, 700));

Cv2.ImShow("糾偏后", corrected);
Cv2.WaitKey();

var src = Cv2.ImRead("person.jpg");
var mask = new Mat();
var bgdModel = new Mat();
var fgdModel = new Mat();

// 定義前景區(qū)域（大致框住主體）
Rect rect = new Rect(50, 50, src.Width - 100, src.Height - 100);

// 使用 GrabCut 算法
Cv2.GrabCut(src, mask, rect, bgdModel, fgdModel, 5, GrabCutModes.InitWithRect);

// 提取前景
mask = (mask == 1) + (mask == 3);
Mat foreground = new Mat();
src.CopyTo(foreground, mask);

Cv2.ImShow("背景擦除結(jié)果", foreground);
Cv2.WaitKey();

var src = Cv2.ImRead("input.jpg");
Rect roi = new Rect(100, 50, 200, 200); // x, y, width, height
Mat cropped = new Mat(src, roi);
Cv2.ImShow("裁切區(qū)域", cropped);
Cv2.WaitKey();

var src = Cv2.ImRead("flower.jpg");
Mat hsv = new Mat();
Cv2.CvtColor(src, hsv, ColorConversionCodes.BGR2HSV);

// 定義顏色范圍（例如紅色）
Scalar lower = new Scalar(0, 100, 100);
Scalar upper = new Scalar(10, 255, 255);

Mat mask = new Mat();
Cv2.InRange(hsv, lower, upper, mask);

// 提取目標(biāo)區(qū)域
Mat result = new Mat();
Cv2.BitwiseAnd(src, src, result, mask);

Cv2.ImShow("顏色分割結(jié)果", result);
Cv2.WaitKey();

var src = Cv2.ImRead("scene.jpg");
Mat samples = src.Reshape(1, src.Rows * src.Cols);
samples.ConvertTo(samples, MatType.CV_32F);

int K = 3;
Mat labels = new Mat();
Mat centers = new Mat();

Cv2.Kmeans(samples, K, labels,
    new TermCriteria(CriteriaTypes.Eps | CriteriaTypes.MaxIter, 10, 1.0),
    3, KMeansFlags.PpCenters, centers);

centers.ConvertTo(centers, MatType.CV_8U);
var segmented = new Mat(src.Size(), src.Type());

for (int y = 0; y < src.Rows; y++)
{
    for (int x = 0; x < src.Cols; x++)
    {
        int label = labels.Get<int>(y * src.Cols + x);
        var color = centers.At<Vec3b>(label);
        segmented.Set(y, x, color);
    }
}

Cv2.ImShow("KMeans 圖像分割", segmented);
Cv2.WaitKey();