本文整理匯總了C#中OpenCvSharp.CPlusPlus.Mat.ToCvMat方法的典型用法代碼示例。如果您正苦於以下問題：C# Mat.ToCvMat方法的具體用法？C# Mat.ToCvMat怎麽用？C# Mat.ToCvMat使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類OpenCvSharp.CPlusPlus.Mat的用法示例。
在下文中一共展示了Mat.ToCvMat方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的C#代碼示例。
示例1: Extract
/// <summary>
/// 高速なマルチスケール Hesian 検出器を用いて keypoint を検出します．
/// </summary>
/// <param name="img"></param>
/// <param name="mask"></param>
/// <returns></returns>
#else
/// <summary>
/// detects keypoints using fast multi-scale Hessian detector
/// </summary>
/// <param name="img"></param>
/// <param name="mask"></param>
/// <returns></returns>
#endif
public KeyPoint[] Extract(Mat img, Mat mask)
{
if (img == null)
throw new ArgumentNullException("img");
CvMat imgMat = img.ToCvMat();
CvMat maskMat = (mask == null) ? null : mask.ToCvMat();
CvSURFPoint[] keypoints;
float[][] descriptors;
Cv.ExtractSURF(imgMat, maskMat, out keypoints, out descriptors, this);
KeyPoint[] result = new KeyPoint[keypoints.Length];
for (int i = 0; i < result.Length; i++)
{
CvSURFPoint kpt = keypoints[i];
result[i] = new KeyPoint(kpt.Pt, (float) kpt.Size, kpt.Dir, kpt.Hessian, GetPointOctave(kpt, this));
}
return result;
}示例2: Transform
private void Transform(double[] srcPoints)
{
const int POINT_COUNT = 8;
System.Diagnostics.Debug.Assert(srcPoints.Length == POINT_COUNT);
double leftOffset = (srcGrid.Width - imgRaw.Source.Width) / 2;
double topOffset = (srcGrid.Height - imgRaw.Source.Height) / 2;
CvMat srcPointsMat = new CvMat(4, 2, MatrixType.F64C1, srcPoints);
CvMat dstPointsMat = new CvMat(4, 2, MatrixType.F64C1,
new double[POINT_COUNT] {
dstGrid.Width * 1 / 4, dstGrid.Height * 1 / 4, dstGrid.Width * 3 / 4, dstGrid.Height * 1 / 4,
dstGrid.Width * 3 / 4, dstGrid.Height * 3 / 4, dstGrid.Width * 1 / 4, dstGrid.Height * 3 / 4 });
CvMat viewerHomographyMatrix = new CvMat(3, 3, MatrixType.F64C1, new double[9]);
Cv.FindHomography(srcPointsMat, dstPointsMat, viewerHomographyMatrix);
CV.Mat src = WriteableBitmapConverter.ToMat((WriteableBitmap)imgRaw.Source);
CV.Mat dst = new CV.Mat((int)srcGrid.Height, (int)srcGrid.Width, src.Type());
Cv.WarpPerspective(src.ToCvMat(), dst.ToCvMat(), viewerHomographyMatrix);
imgTransformed.Source = WriteableBitmapConverter.ToWriteableBitmap(dst);
srcPointsMat.Dispose();
dstPointsMat.Dispose();
src.Dispose();
dst.Dispose();
}示例3: Extract
/// <summary>
/// MSERのすべての輪郭情報を抽出する
/// </summary>
/// <param name="image"></param>
/// <param name="mask"></param>
/// <returns></returns>
#else
/// <summary>
/// Extracts the contours of Maximally Stable Extremal Regions
/// </summary>
/// <param name="image"></param>
/// <param name="mask"></param>
/// <returns></returns>
#endif
public CvPoint[][] Extract(Mat image, Mat mask)
{
if(image == null)
throw new ArgumentNullException("image");
CvMat _image = image.ToCvMat();
IntPtr pmask = (mask == null) ? IntPtr.Zero : mask.ToCvMat().CvPtr;
IntPtr pcontours = IntPtr.Zero;
using(CvMemStorage storage = new CvMemStorage(0))
{
CvInvoke.cvExtractMSER(_image.CvPtr, pmask, ref pcontours, storage.CvPtr, Struct);
if (pcontours == IntPtr.Zero)
{
return new CvPoint[0][];
}
CvSeq<IntPtr> seq = new CvSeq<IntPtr>(pcontours);
CvContour[] contours = Array.ConvertAll<IntPtr, CvContour>(seq.ToArray(), delegate(IntPtr p) { return new CvContour(p); });
CvPoint[][] result = new CvPoint[contours.Length][];
for (int i = 0; i < contours.Length; i++)
{
result[i] = contours[i].ToArray();
}
return result;
}
}