本文整理汇总了C#中Mat.Line方法的典型用法代码示例。如果您正苦于以下问题：C# Mat.Line方法的具体用法？C# Mat.Line怎么用？C# Mat.Line使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Mat的用法示例。
在下文中一共展示了Mat.Line方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: SampleCpp
/// <summary>
/// sample of new C++ style wrapper
/// </summary>
private void SampleCpp()
{
// (1)画像の読み込み
using (Mat imgGray = new Mat(Const.ImageGoryokaku, LoadMode.GrayScale))
using (Mat imgStd = new Mat(Const.ImageGoryokaku, LoadMode.Color))
using (Mat imgProb = imgStd.Clone())
{
// ハフ変換のための前処理
CvCpp.Canny(imgGray, imgGray, 50, 200, ApertureSize.Size3, false);
// (3)標準的ハフ変換による線の検出と検出した線の描画
CvLineSegmentPolar[] segStd = CvCpp.HoughLines(imgGray, 1, Math.PI / 180, 50, 0, 0);
int limit = Math.Min(segStd.Length, 10);
for (int i = 0; i < limit; i++ )
{
float rho = segStd[i].Rho;
float theta = segStd[i].Theta;
double a = Math.Cos(theta);
double b = Math.Sin(theta);
double x0 = a * rho;
double y0 = b * rho;
CvPoint pt1 = new CvPoint { X = Cv.Round(x0 + 1000 * (-b)), Y = Cv.Round(y0 + 1000 * (a)) };
CvPoint pt2 = new CvPoint { X = Cv.Round(x0 - 1000 * (-b)), Y = Cv.Round(y0 - 1000 * (a)) };
imgStd.Line(pt1, pt2, CvColor.Red, 3, LineType.AntiAlias, 0);
}
// (4)確率的ハフ変換による線分の検出と検出した線分の描画
CvLineSegmentPoint[] segProb = CvCpp.HoughLinesP(imgGray, 1, Math.PI / 180, 50, 50, 10);
foreach (CvLineSegmentPoint s in segProb)
{
imgProb.Line(s.P1, s.P2, CvColor.Red, 3, LineType.AntiAlias, 0);
}
// (5)検出結果表示用のウィンドウを確保し表示する
using (new CvWindow("Hough_line_standard", WindowMode.AutoSize, imgStd.ToIplImage()))
using (new CvWindow("Hough_line_probabilistic", WindowMode.AutoSize, imgProb.ToIplImage()))
{
CvWindow.WaitKey(0);
}
}
}示例2: SampleCpp
/// <summary>
/// sample of new C++ style wrapper
/// </summary>
private void SampleCpp()
{
// (1) Load the image
using (Mat imgGray = new Mat(FilePath.Image.Goryokaku, LoadMode.GrayScale))
using (Mat imgStd = new Mat(FilePath.Image.Goryokaku, LoadMode.Color))
using (Mat imgProb = imgStd.Clone())
{
// Preprocess
Cv2.Canny(imgGray, imgGray, 50, 200, 3, false);
// (3) Run Standard Hough Transform
CvLineSegmentPolar[] segStd = Cv2.HoughLines(imgGray, 1, Math.PI / 180, 50, 0, 0);
int limit = Math.Min(segStd.Length, 10);
for (int i = 0; i < limit; i++ )
{
// Draws result lines
float rho = segStd[i].Rho;
float theta = segStd[i].Theta;
double a = Math.Cos(theta);
double b = Math.Sin(theta);
double x0 = a * rho;
double y0 = b * rho;
Point pt1 = new Point { X = Cv.Round(x0 + 1000 * (-b)), Y = Cv.Round(y0 + 1000 * (a)) };
Point pt2 = new Point { X = Cv.Round(x0 - 1000 * (-b)), Y = Cv.Round(y0 - 1000 * (a)) };
imgStd.Line(pt1, pt2, Scalar.Red, 3, LineType.AntiAlias, 0);
}
// (4) Run Probabilistic Hough Transform
CvLineSegmentPoint[] segProb = Cv2.HoughLinesP(imgGray, 1, Math.PI / 180, 50, 50, 10);
foreach (CvLineSegmentPoint s in segProb)
{
imgProb.Line(s.P1, s.P2, CvColor.Red, 3, LineType.AntiAlias, 0);
}
// (5) Show results
using (new Window("Hough_line_standard", WindowMode.AutoSize, imgStd))
using (new Window("Hough_line_probabilistic", WindowMode.AutoSize, imgProb))
{
CvWindow.WaitKey(0);
}
}
}示例3: ThresholdStairs
public static Mat ThresholdStairs(this Mat src)
{
var dst = new Mat(src.Rows, src.Cols, src.Type());
var partCount = 10;
var partWidth = src.Width / partCount;
for (var i = 0; i < partCount; ++i)
{
var th_mat = new Mat();
Cv2.Threshold(src, th_mat, 255 / 10 * (i + 1), 255, ThresholdType.Binary);
th_mat.Rectangle(new Rect(0, 0, partWidth * i, src.Height), new Scalar(0), -1);
th_mat.Rectangle(new Rect(partWidth * (i + 1), 0, src.Width - partWidth * (i + 1), src.Height), new Scalar(0), -1);
Cv2.Add(dst, th_mat, dst);
}
var color_dst = new Mat();
Cv2.CvtColor(dst, color_dst, ColorConversion.GrayToRgb);
for (var i = 0; i < partCount; ++i)
{
color_dst.Line(partWidth * i, 0, partWidth * i, src.Height, new CvScalar(50, 200, 50), thickness: 2);
}
return color_dst;
}