C# MCvScalar类代码示例

 /// <summary>
 /// Applies an affine transformation to an image.
 /// </summary>
 /// <param name="src">Source image</param>
 /// <param name="dst">Destination image</param>
 /// <param name="mapMatrix">2x3 transformation matrix</param>
 /// <param name="dsize">Size of the output image.</param>
 /// <param name="interpMethod">Interpolation method</param>
 /// <param name="warpMethod">Warp method</param>
 /// <param name="borderMode">Pixel extrapolation method</param>
 /// <param name="borderValue">A value used to fill outliers</param>
 public static void WarpAffine(IInputArray src, IOutputArray dst, IInputArray mapMatrix, Size dsize, CvEnum.Inter interpMethod = CvEnum.Inter.Linear, CvEnum.Warp warpMethod = CvEnum.Warp.Default, CvEnum.BorderType borderMode = CvEnum.BorderType.Constant, MCvScalar borderValue = new MCvScalar())
 {
    using (InputArray iaSrc = src.GetInputArray())
    using (OutputArray oaDst = dst.GetOutputArray())
    using (InputArray iaMapMatrix = mapMatrix.GetInputArray())
       cveWarpAffine(iaSrc, oaDst, iaMapMatrix, ref dsize, (int)interpMethod | (int)warpMethod, borderMode, ref borderValue);
 }

        public static VectorOfPoint FindLargestContour(IInputOutputArray cannyEdges, IInputOutputArray result)
        {
            int largest_contour_index = 0;
            double largest_area = 0;
            VectorOfPoint largestContour;

            using (Mat hierachy = new Mat())
            using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
            {
                IOutputArray hirarchy;

                CvInvoke.FindContours(cannyEdges, contours, hierachy, RetrType.Tree, ChainApproxMethod.ChainApproxNone);

                for (int i = 0; i < contours.Size; i++)
                {
                    MCvScalar color = new MCvScalar(0, 0, 255);

                    double a = CvInvoke.ContourArea(contours[i], false);  //  Find the area of contour
                    if (a > largest_area)
                    {
                        largest_area = a;
                        largest_contour_index = i;                //Store the index of largest contour
                    }

                    CvInvoke.DrawContours(result, contours, largest_contour_index, new MCvScalar(255, 0, 0));
                }

                CvInvoke.DrawContours(result, contours, largest_contour_index, new MCvScalar(0, 0, 255), 3, LineType.EightConnected, hierachy);
                largestContour = new VectorOfPoint(contours[largest_contour_index].ToArray());
            }

            return largestContour;
        }

        public void DrawCrosshair( int x, int y, MCvScalar color, Image<Bgr, Byte> src )
        {
            Point point_laser, point_left, point_right, point_top, point_bottom;

            /*
            point_laser = new Point( x, y );
            point_left = new Point( 0, y );
            point_top = new Point( x, 0 );
            point_right = new Point( CvInvoke.cvGetSize( src ).Width, y );
            point_bottom = new Point( x, CvInvoke.cvGetSize( src ).Height );
            */
            // If image center tracking is desired
            /*
            point_laser = new Point( 320, 240 );
            point_left = new Point( 0, 240 );
            point_top = new Point( 320, 0 );
            point_right = new Point( CvInvoke.cvGetSize( src ).Width, 240 );
            point_bottom = new Point( 320, CvInvoke.cvGetSize( src ).Height );
            */

            // Draw a crosshair centered on the laser pointer on the webcam feed
            CvInvoke.cvCircle( src, point_laser, 5, color, 1,
                               Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, 0 );
            CvInvoke.cvLine( src, point_left, point_right, color, 1,
                             Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, 0 );
            CvInvoke.cvLine( src, point_top, point_bottom, color, 1,
                             Emgu.CV.CvEnum.LINE_TYPE.EIGHT_CONNECTED, 0 );
        }

 private static MCvScalar GetScalar(double hue, double sat, double value)
 {
     var scalar = new MCvScalar();
     scalar.V0 = hue;
     scalar.V1 = sat;
     scalar.V2 = value;
     return scalar;
 }

      /// <summary>
      /// Transforms source image using the specified matrix
      /// </summary>
      /// <param name="src">Source image</param>
      /// <param name="dst">Destination image</param>
      /// <param name="mapMatrix">2x3 transformation matrix</param>
      /// <param name="flags"> flags </param>
      /// <param name="fillval">A value used to fill outliers</param>
#if ANDROID
      public static void cvWarpAffine(
          IntPtr src,
          IntPtr dst,
          IntPtr mapMatrix,
          int flags,
          MCvScalar fillval)
      {
         cvWarpAffine(src, dst, mapMatrix, flags, fillval.v0, fillval.v1, fillval.v2, fillval.v3);
      }

 /// <summary>
 /// Create a Laplacian filter.
 /// </summary>
 /// <param name="ksize">Either 1 or 3</param>
 /// <param name="scale">Optional scale. Use 1.0 for default</param>
 /// <param name="borderType">The border type.</param>
 /// <param name="borderValue">The border value.</param>
 public CudaLaplacianFilter(
    DepthType srcDepth, int srcChannels,
    DepthType dstDepth, int dstChannels,
    int ksize = 1, double scale = 1.0, 
    CvEnum.BorderType borderType = BorderType.Default, MCvScalar borderValue = new MCvScalar())
 {
    _ptr = CudaInvoke.cudaCreateLaplacianFilter(
       CvInvoke.MakeType(srcDepth, srcChannels), CvInvoke.MakeType(dstDepth, dstChannels), 
       ksize, scale, borderType, ref borderValue);
 }

        public void DrawMeasurements( int x, int y, double dist, double pfc,
                                      MCvScalar color, Image<Bgr, Byte> src )
        {
            // Measurement text content, position, and font
            string text_size, text_posn, text_pfc, text_dist;
            Point point_size, point_posn, point_pfc, point_dist;
            MCvFont font = new MCvFont( Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_SIMPLEX, 0.5, 0.5 );

            // Fill size string
            text_size = "Size (pix): ";
            text_size += Convert.ToString( CvInvoke.cvGetSize( src ).Width );
            text_size += ", ";
            text_size += Convert.ToString( CvInvoke.cvGetSize( src ).Height );

            // Start position, pfc, and distance strings
            text_posn = "Position (pix):  ";
            text_pfc  = "PFC (pix): ";
            text_dist = "Distance (cm): ";

            // If the laser point WAS found
            if ( ( x > 0 ) && ( y > 0 ) )
            {
                // Fill position string
                text_posn += Convert.ToString( x );
                text_posn += ", ";
                text_posn += Convert.ToString( y );

                // Fill pfc string
                text_pfc += Convert.ToString( pfc );

                // Fill distance string
                text_dist += String.Format( "{0:F1}", dist );
            }
            // If the laser pointer was NOT found
            else
            {
                // Fill measurement strings with NULL readings
                text_posn += "NULL, NULL";
                text_pfc += "NULL";
                text_dist += "NULL";
            }

            // Initialize text position
            point_size = new Point( 10, 400 );
            point_posn = new Point( 10, 420 );
            point_pfc  = new Point( 10, 440 );
            point_dist = new Point( 10, 460 );

            // Draw text on image
            CvInvoke.cvPutText( src, text_size, point_size, ref font, color );
            CvInvoke.cvPutText( src, text_posn, point_posn, ref font, color );
            CvInvoke.cvPutText( src, text_pfc,  point_pfc,  ref font, color );
            CvInvoke.cvPutText( src, text_dist, point_dist, ref font, color );
        }

 /// <summary>
 /// Create a Gpu LinearFilter
 /// </summary>
 /// <param name="kernel">Convolution kernel, single-channel floating point matrix (e.g. Emgu.CV.Matrix). If you want to apply different kernels to different channels, split the gpu image into separate color planes and process them individually</param>
 /// <param name="anchor">The anchor of the kernel that indicates the relative position of a filtered point within the kernel. The anchor shoud lie within the kernel. The special default value (-1,-1) means that it is at the kernel center</param>
 /// <param name="borderType">Border type. Use REFLECT101 as default.</param>
 /// <param name="borderValue">The border value</param>
 public CudaLinearFilter(
    DepthType srcDepth, int srcChannels,
    DepthType dstDepth, int dstChannels,
    IInputArray kernel,
    System.Drawing.Point anchor,
    CvEnum.BorderType borderType = BorderType.Default, MCvScalar borderValue = new MCvScalar())
 {
    using (InputArray iaKernel = kernel.GetInputArray())
       _ptr = CudaInvoke.cudaCreateLinearFilter(
          CvInvoke.MakeType(srcDepth, srcChannels), CvInvoke.MakeType(dstDepth, dstChannels),
          iaKernel, ref anchor, borderType, ref borderValue);
 }

 public static void DrawCircle(IInputOutputArray image,
     Point center,
     int radius,
     MCvScalar color,
     int thickness = 1,
     LineType lineType = LineType.EightConnected,
     int shift = 0)
 {
     using (InputOutputArray array = image.GetInputOutputArray())
     {
         cveCircle(array, ref center, radius, ref color, thickness, lineType, shift);
     }
 }

 public static void DrawLine(IInputOutputArray image,
     Point start,
     Point end,
     MCvScalar color,
     int thickness = 1,
     LineType lineType = LineType.EightConnected,
     int shift = 0)
 {
     using (InputOutputArray array = image.GetInputOutputArray())
     {
         cveLine(array, ref start, ref end, ref color, thickness, lineType, shift);
     }
 }

        public static void DrawEllipse(IInputOutputArray image,
            RotatedRect box,
            MCvScalar color,
            int thickness = 1,
            LineType lineType = LineType.EightConnected,
            int shift = 0)
        {
            int width = (int)Math.Round(box.Size.Height * 0.5F);
            int height = (int)Math.Round(box.Size.Width * 0.5F);
            Size axesSize = new Size(width, height);
            Point center = Point.Round(box.Center);

            DrawEllipse(image, center, axesSize, box.Angle, 0.0D, 360.0D, color, thickness, lineType, shift);
        }

 /// <summary>
 /// Draw the matched keypoints between the model image and the observered image.
 /// </summary>
 /// <param name="modelImage">The model image</param>
 /// <param name="modelKeypoints">The keypoints in the model image</param>
 /// <param name="observerdImage">The observed image</param>
 /// <param name="observedKeyPoints">The keypoints in the observed image</param>
 /// <param name="matchColor">The color for the match correspondence lines</param>
 /// <param name="singlePointColor">The color for highlighting the keypoints</param>
 /// <param name="mask">The mask for the matches. Use null for all matches.</param>
 /// <param name="flags">The drawing type</param>
 /// <param name="result">The image where model and observed image is displayed side by side. Matches are drawn as indicated by the flag</param>
 /// <param name="matches">Matches. Each matches[i] is k or less matches for the same query descriptor.</param>
 public static void DrawMatches(
    IInputArray modelImage, VectorOfKeyPoint modelKeypoints,
    IInputArray observerdImage, VectorOfKeyPoint observedKeyPoints,
    VectorOfVectorOfDMatch matches,
    IInputOutputArray result,
    MCvScalar matchColor, MCvScalar singlePointColor,
    IInputArray mask = null,
    KeypointDrawType flags = KeypointDrawType.Default)
 {
    using (InputArray iaModelImage = modelImage.GetInputArray())
    using (InputArray iaObserverdImage = observerdImage.GetInputArray())
    using (InputOutputArray ioaResult = result.GetInputOutputArray())
    using (InputArray iaMask = mask == null ? InputArray.GetEmpty() : mask.GetInputArray())
    CvInvoke.drawMatchedFeatures(iaObserverdImage, observedKeyPoints, iaModelImage,
       modelKeypoints, matches, ioaResult, ref matchColor, ref singlePointColor, iaMask , flags);
 }

        public List<FaceScored> FindFaces(Emgu.CV.Image<Emgu.CV.Structure.Bgr, byte> image, CascadeClassifier cascadeClassifierFace, CascadeClassifier cascadeClassifierEye)
        {
            List<FaceScored> currentFaces = new List<FaceScored>();
            using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>())
            {
            gray._EqualizeHist();
            Size minFaceSize = new Size(minSizeFace , minSizeFace );
            Size maxFaceSize =  new Size(maxSizeFace , maxSizeFace );
            Size minEyeSize = new Size(minSizeEye , minSizeEye );
            Size maxEyeSize =  new Size(maxSizeEye , maxSizeEye );
            Rectangle[] facesDetected = cascadeClassifierFace.DetectMultiScale(gray, scaleFace , neighborsFace , minFaceSize,maxFaceSize);

            foreach (Rectangle f in facesDetected)
            {
                if (f.Width<35)
                    break;
                gray.ROI = f;

                Rectangle[] eyesDetected = cascadeClassifierEye.DetectMultiScale(gray, scaleEye, neighborsEye, minEyeSize, maxEyeSize);
                if (eyesDetected.Count() >0){
                    FaceScored faceModel = new FaceScored();
                    faceModel.FaceImage = gray.Bitmap;
                    faceModel.FaceImageFullColr = image.GetSubRect(f).Bitmap;
                    faceModel.Height = faceModel.FaceImage.Height;
                    faceModel.Width = faceModel.FaceImage.Width;
                    faceModel.EyesCount = eyesDetected.Count();

                    Gray avgf = new Gray();
                    MCvScalar avstd = new MCvScalar();
                    gray.AvgSdv(out avgf, out avstd);
                    faceModel.StdDev = avstd.V0;

                    currentFaces.Add(faceModel);
                    if(currentFaces.Count%5==0)
                        Console.WriteLine("FaceDetect Add every 5 faceModel" + faceModel.Width);
                    break;
                }
                gray.ROI = Rectangle.Empty;
                }
            }
            return currentFaces;
        }

 public static void DrawContours(IInputOutputArray image,
     IInputArray contours,
     int contourIdx,
     MCvScalar color,
     int thickness = 1,
     LineType lineType = LineType.EightConnected,
     IInputArray hierarchy = null,
     int maxLevel = int.MaxValue,
     Point offset = default(Point))
 {
     using (InputOutputArray imageArray = image.GetInputOutputArray())
     {
         using (InputArray contoursArray = contours.GetInputArray())
         {
             using (InputArray hierarchyArray = (hierarchy != null) ? hierarchy.GetInputArray() : EmptyArray<InputArray>.Value)
             {
                 cveDrawContours(imageArray, contoursArray, contourIdx, ref color, thickness, lineType, hierarchyArray, maxLevel, ref offset);
             }
         }
     }
 }

    public void ApplyFilter(Mat src)
    {
        CvInvoke.CvtColor(src, src, ColorConversion.Bgr2Hsv);

        Mat threshold = new Mat(src.Height, src.Width, src.Depth, src.NumberOfChannels);
        MCvScalar min = new MCvScalar(m_hmin, m_smin, m_vmin);
        MCvScalar max = new MCvScalar(m_hmax, m_smax, m_vmax);

        CvInvoke.InRange(src, new ScalarArray(min), new ScalarArray(max), threshold);

        Mat element = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3,3), Point.Empty);
        CvInvoke.Erode(threshold, threshold, element, Point.Empty, 1, BorderType.Constant, new MCvScalar(1.0f));
        CvInvoke.Canny(threshold, threshold, 100, 255);

        VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
        Mat hierarchy = new Mat();

         CvInvoke.FindContours(threshold, contours, hierarchy, RetrType.Tree, ChainApproxMethod.ChainApproxSimple, Point.Empty);

        Mat draw = new Mat(src.Height, src.Width, src.Depth, 1);
        draw.SetTo(new MCvScalar(0.0));
        int i = 0;

        //Debug.Log("CONTOURS");

        var contoursArray = contours.ToArrayOfArray();
        foreach(Point[] contour in contoursArray)
        {
            CvInvoke.DrawContours(draw, contours, i, new MCvScalar(255.0), 1, LineType.EightConnected, null, int.MaxValue, Point.Empty);

         	double a = CvInvoke.ContourArea(new VectorOfPoint(contour));
            //Debug.Log("Contour: " + a);
            i++;
        }

        //Emgu.CV.UI.ImageViewer.Show(draw, "test");

        if(m_onFrame != null) m_onFrame.Invoke(draw);
    }