Win8 Metro (C #) Digital image processing--2.52 image K-mean clustering

Original: Win8 Metro (C #) Digital image processing--2.52 image K-mean clustering

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?? Image Kmeans clustering?????? kmeanscluster (writeablebitmapsrc,int.k)

<summary>//Kmeans Cluster process. </summary>//<param name= "src" >the source image.</param>//<param name= "K" >c Luster Threshould, from 2 to 255.</param>//<returns></returns> public static Writeableb                Itmap Kmeanscluster (WriteableBitmap src,int k)////kmeanscluster {if (src! = null) { int w = src.                Pixelwidth; int h = src.                Pixelheight;                WriteableBitmap dstimage = new WriteableBitmap (w, h); byte[] temp = src.                Pixelbuffer.toarray (); Byte[] Tempmask = (byte[]) temp.                Clone ();                int b = 0, g = 0, r = 0;                Defining grayscale image information storage variables byte[] ImageData = new byte[w * h];                Define the cluster mean store variable (store the mean value of each cluster) double[] Meancluster = new Double[k];                Define a cluster tag variable (mark the current pixel belongs to which class) int[] Markcluster = new int[w * h]; //Define the cluster pixels and storage variables (the sum of the stored values of each class of pixels) double[] Sumcluster = new Double[k];                Defines the cluster pixel statistic variable (the number of pixels stored in each class) int []countcluster = new Int[k];                Define a clustered RGB component storage variable (storing RGB three-component size for each Class) int[] Sumr = new Int[k];                int[] sumg = new Int[k];                int[] Sumb = new Int[k];                temporary variable int sum = 0;                Cyclic control variable bool s = true;                double[] Mjduge = new Double[k];                Double TEMPV = 0;                int cou = 0; Gets the grayscale image information for (int j = 0; J < H; j + +) {for (int i = 0; i < W; i+                        +) {b = tempmask[i * 4 + J * W * 4];                        g = tempmask[i * 4 + 1 + J * W * 4];                        r = tempmask[i * 4 + 2 + J * W * 4];                    Imagedata[i + J * W] = (byte) (b * 0.114 + G * 0.587 + R * 0.299);          }      } while (s) {sum = 0;  Initialize cluster mean for (int i = 0; i < K; i++) {Meancluster[i] = I                    * 255.0/(K-1);                        }//Compute cluster attribution for (int i = 0; i < imagedata.length; i++) {                        TEMPV = Math.Abs (double) imagedata[i]-meancluster[0]);                        cou = 0; for (int j = 1; j < K; J + +) {Double T = math.abs (double) imagedata[i                            ]-meancluster[j]);                                if (Tempv > t) {tempv = t;                            Cou = j;                        }} countcluster[cou]++;                        Sumcluster[cou] + = (double) imagedata[i]; MarkcluSter[i] = cou;                        }//Update cluster mean for (int i = 0; i < K; i++) {                        Meancluster[i] = Sumcluster[i]/(double) countcluster[i];                        sum + = (int) (Meancluster[i]-mjduge[i]);                    Mjduge[i] = Meancluster[i];                    } if (sum = = 0) {s = false;                    }}//Compute the cluster RGB component for (int j = 0; J < H; j + +) { for (int i = 0; i < W; i++) {sumb[markcluster[i + J * W]] + = tempmask[                        I * 4 + J * W * 4];                        Sumg[markcluster[i + J * W]] + = tempmask[i * 4 + 1 + J * W * 4];                    Sumr[markcluster[i + J * W]] + = tempmask[i * 4 + 2 + J * W * 4]; }} for (int j = 0; J < H; j + +) {                    for (int i = 0; i < W; i++) {Temp[i * 4 + J * 4 * W] = (                        BYTE) (Sumb[markcluster[i + J * W]]/countcluster[markcluster[i + J * W]]);                        Temp[i * 4 + 1 + J * 4 * W] = (byte) (Sumg[markcluster[i + J * W]]/countcluster[markcluster[i + J * W]]);                    Temp[i * 4 + 2 + J * 4 * W] = (byte) (Sumr[markcluster[i + J * W]]/countcluster[markcluster[i + J * W]]);                }} Stream Stemp = DstImage.PixelBuffer.AsStream ();                Stemp.seek (0, Seekorigin.begin);                Stemp.write (temp, 0, W * 4 * h);            return dstimage;            } else {return null; }        }

Win8 Metro (C #) Digital image processing--2.52 image K-mean clustering