diff --git a/8thWeek/Canny/BlobDetection.pde b/8thWeek/Canny/BlobDetection.pde
index f01af67..a58e705 100644
--- a/8thWeek/Canny/BlobDetection.pde
+++ b/8thWeek/Canny/BlobDetection.pde
@@ -1,104 +1,112 @@
 import java.util.ArrayList;
 import java.util.List;
 import java.util.TreeSet;
 
 class BlobDetection {
   PImage findConnectedComponents(PImage img, boolean onlyBiggest) {
     PImage img0=img.copy();
     int [] labels= new int [img0.width*img0.height];
     List<TreeSet<Integer>> labelsEquivalences= new ArrayList<TreeSet<Integer>>();
     ArrayList<Integer> count=new ArrayList<Integer>();
 
     int currentLabel=0;
 
     for (int line=0; line < img0.height; ++line) {
       TreeSet<Integer> adjColor=new TreeSet<Integer>();
       for (int x=0; x < img0.width; x++) {
-        if (img0.pixels[line*img0.width+x]==Integer.MAX_VALUE) {
+        if (img0.pixels[line*img0.width+x]==color(255)) {
           adjColor.clear();
 
           if (line>0) {
             for (int i=x-1; i <= x+1; i++) {//check the three
               if (0<=i && i<img0.width && labels[(line-1)*img0.width+i]!=0) {
                 adjColor.add(labels[(line-1)*img0.width+i]);
               }
             }
           }
 
           if (0<x && labels[line*img0.width+x-1]!=0) {
             adjColor.add(labels[line*img0.width+x-1]);
           }
 
           //println(minColor==1000?1:minColor);
           if (adjColor.isEmpty()) {
             TreeSet ts=new TreeSet<Integer>();
             ts.add(++currentLabel);
             labelsEquivalences.add(ts);
             count.add(1);
             labels[line*img0.width+x]=currentLabel;
           } else {
             if (adjColor.size()>1) {
               for (Integer i : adjColor) {
                 labelsEquivalences.get(i-1).addAll(adjColor);
               }
             }
             int first=adjColor.first();
             count.set(first-1, count.get(first-1)+1);
             labels[line*img0.width+x]=first;
           }
         }
       }
     }
 
     //merge labelsEquivalences
     for (int j=0; j<labelsEquivalences.size(); ++j) {
       TreeSet<Integer> ts=labelsEquivalences.get(j);
       if (ts.size()>1) {
+        TreeSet<Integer> acc=new TreeSet<Integer>();
         for (Integer i : ts) {
-          labelsEquivalences.get(i-1).addAll(ts);
-          labelsEquivalences.set(i-1, ts);
+          TreeSet<Integer> other=labelsEquivalences.get(i-1);
+          if(ts!=other){ acc.addAll(other);}
+        }
+        ts.addAll(acc);
+        for (Integer i : ts) {
+          labelsEquivalences.set(i-1,ts);
         }
       }
     }
 
     //assess blob size
     int[] blobSize=new int[labelsEquivalences.size()];
     for (int j=0; j<labelsEquivalences.size(); ++j) {
       TreeSet<Integer> ts=labelsEquivalences.get(j);
       int sum=0;
       for (Integer i : ts) {
         sum+=count.get(i-1);
       }
       blobSize[j]=sum;
     }
 
     //create ColorMap
     int[] colorMap=new int[blobSize.length];
     if (onlyBiggest) {
       int max =-1;
       for(int i=0; i < blobSize.length; i++){
          max=max(max,blobSize[i]);
       }
       for(int i=0; i < blobSize.length; i++){
         colorMap[i]=blobSize[i]==max?color(255):color(0);
       }
     } else {//List<TreeSet<Integer>> labelsEquivalences
       for(TreeSet<Integer> ts:labelsEquivalences){
         int rndColor=color(random(255),255,200);
         for(Integer i:ts){
           colorMap[i-1]=rndColor;
         }
       }
     }
 
     //colorize
     for (int i=0; i < img0.width*img0.height; ++i) {
       //int co=(labels[i])*256/7;
       //labels[i]=color(co, 255, 200);
       if(labels[i]!=0){
         img0.pixels[i]=colorMap[labels[i]-1];
       }
     }
+    
+    println(labelsEquivalences);
+    
     return img0;
   }
 }
diff --git a/8thWeek/Canny/Canny.pde b/8thWeek/Canny/Canny.pde
index 197e112..02ff1ed 100644
--- a/8thWeek/Canny/Canny.pde
+++ b/8thWeek/Canny/Canny.pde
@@ -1,177 +1,177 @@
 PImage img;
-PImage board1Thresholded;
-HScrollbar thresholdBar0;
-HScrollbar thresholdBar1;
+PImage board1Thresholded, board1Blurred, board1Scharr;
+int minHThreshold = 100, maxHThreshold =140;
+int minSThreshold = 100, maxSThreshold = 255;
+int minBThreshold = 45, maxBThreshold = 170;
+//HScrollbar thresholdBar0;
+//HScrollbar thresholdBar1;
+enum kernelType { scale , blur , gaussian};
 
 int variable=0;
 
 void settings() {
   size(1600, 600);
 }
 
 void setup() {
-  thresholdBar0 = new HScrollbar(0, 560, 800, 20);
-  thresholdBar1 = new HScrollbar(0, 580, 800, 20);
+  //thresholdBar0 = new HScrollbar(0, 560, 800, 20);
+  //thresholdBar1 = new HScrollbar(0, 580, 800, 20);
   img = loadImage("src/board1.jpg");
   board1Thresholded = loadImage("src/board1Thresholded.bmp");
-  frameRate(4);
-  //noLoop(); // no interactive behaviour: draw() will be called only once.
+  colorMode(HSB);
+  //board1Blurred = loadImage("src/board1Blurred.bmp");
+  //board1Scharr = loadImage("src/board1Scharr.bmp");
+  
+  noLoop(); // no interactive behaviour: draw() will be called only once.
 }
 
 void draw() {
-  variable=mouseX*255/width;
+  variable=(int)mouseX*255/width;
   
   image(img, 0, 0);//show image
-  PImage img2 = img.copy();//make a deep copy
-  img2.loadPixels();// load pixels
-
-  //img2=threshold(img2, (int)map(thresholdBar.getPos(),0,1,0,255), false);
-  //img2=HueMap(img2, thresholdBar0.getPos(), thresholdBar1.getPos());
-  img2=thresholdHSB(img2, 100, 140, 100, 255, 45, 170 );
 
-  img2.updatePixels();//update pixels
-  image(new BlobDetection().findConnectedComponents(img2,true), img.width, 0);
-  /*thresholdBar0.display();
-   thresholdBar0.update();
-   thresholdBar1.display();
-   thresholdBar1.update();*/
-   
-   //println(imagesEqual(img2,board1Thresholded));
+  image(img, 0, 0);//show image
+  int I = 100;
+  PImage img1=img.copy();
+  img1 = thresholdHSB(img1, minHThreshold, maxHThreshold, minSThreshold, maxSThreshold, minBThreshold, maxBThreshold );
+  img1 = convolute(img1, kernelType.gaussian);
+  img1 = scharr(img1);
+  img1 = threshold(img1, I , false);
+  img1 = new BlobDetection().findConnectedComponents(img1,true);
+  
+  image(img1, img.width, 0);//show processed image
 }
 
+//if inverted = false take values for 0 to threshol
 PImage threshold(PImage img, int threshold, boolean inverted) {
   PImage result = createImage(img.width, img.height, RGB);
   for (int i = 0; i < img.width * img.height; i++) {
     result.pixels[i]=brightness(img.pixels[i])>threshold^inverted?color(255):color(0);
   }
   return result;
 }
 
 PImage thresholdHSB(PImage img, int minH, int maxH, int minS, int maxS, int minB, int maxB) {
   PImage result = createImage(img.width, img.height, RGB);
   for (int i = 0; i < img.width * img.height; i++) {
     if (hue(img.pixels[i])<minH||maxH<hue(img.pixels[i])||
       saturation(img.pixels[i])<minS||maxS<saturation(img.pixels[i])||
       brightness(img.pixels[i])<minB||maxB<brightness(img.pixels[i])) {
       result.pixels[i]=0;
     } else {
       result.pixels[i]=Integer.MAX_VALUE;
     }
   }
   return result;
 }
 
-
 boolean imagesEqual(PImage img1, PImage img2) {
   if (img1.width != img2.width || img1.height != img2.height)
     return false;
   for (int i = 0; i < img1.width*img1.height; i++)
     //assuming that all the three channels have the same value
-    if (red(img1.pixels[i]) != red(img2.pixels[i]))
+    if (red(img1.pixels[i]) != red(img2.pixels[i])){
+      //if((i%img1.width)!=0 ||(i%img1.width)!=img1.width-1 ||(i/img1.width)!=0 ||(i/img1.width)!=img1.width-1 )
+        //println(i%img1.width+" "+i/img1.width + (red(img1.pixels[i]) - red(img2.pixels[i])) );
       return false;
+    }
   return true;
 }
 
 PImage scharr(PImage img) {
   float[][] vKernel = {
-            { 3, 0, -3 }, 
-            { 10, 0, -10 }, 
-            { 3, 0, -3 } };
+    { 3, 0, -3 }, 
+    { 10, 0, -10 }, 
+    { 3, 0, -3 } };
   float[][] hKernel = {
-            { 3, 10, 3 }, 
-            { 0, 0, 0 }, 
-            { -3, -10, -3 } };
+    { 3, 10, 3 }, 
+    { 0, 0, 0 }, 
+    { -3, -10, -3 } };
   PImage result = createImage(img.width, img.height, ALPHA);
+  
   // clear the image
   for (int i = 0; i < img.width * img.height; i++) {
     result.pixels[i] = color(0);
   }
   float max=0;
-  float normFactor = 1.f;
   float[] buffer = new float[img.width * img.height];
+  
   // *************************************
   // Implement here the double convolution
   int N = 3; //kernel size
-  for (int x = 0; x< img.width; ++x) {
-    for (int y = 0; y<img.height; y++) { 
+  for (int x = 1; x<img.width-1; ++x) {
+    for (int y = 1; y<img.height-1; y++) { 
       float sum_h = 0, sum_v = 0;
       for (int i = 0; i< N; ++i) {
         for (int j = 0; j<N; ++j) {
-          int row = ((row = y-N/2+i) < 0)? 0 : row;
-          row = (row >= img.height)? img.height-1 : row;
-          int column = ((column = x-N/2+i) < 0)? 0 : column;
-          column = (column >= img.width)? img.width-1: column;
-          float brightness = brightness(img.pixels[column + img.width *row]);
+          float brightness = brightness(img.pixels[(x-N/2+i)+ img.width *(y-N/2+j)]);
           sum_h += hKernel[i][j] * brightness;
           sum_v += vKernel[i][j] * brightness;
         }
       }
-      // - sum all these intensities and divide it by normFactor
-      // - set result.pixels[y * img.width + x] to this value
       float value = sqrt(pow(sum_h, 2) + pow(sum_v, 2));
       max = (value > max )? value : max;
-      buffer[x+y*img.width] = value / normFactor;
+      buffer[x+y*img.width] = value;
     }
   }
-  
   // *************************************
+  
   for (int y = 1; y < img.height - 1; y++) { // Skip top and bottom edges
     for (int x = 1; x < img.width - 1; x++) { // Skip left and right
       int val=(int) ((buffer[y * img.width + x] / max)*255);
       result.pixels[y * img.width + x]=color(val);
     }
   }
   return result;
 }
-PImage convolute(PImage img) {
+PImage convolute(PImage img, kernelType kt) {
   int N = 3; //kernel size
-  float[][] kernel1 = {{ 0, 0, 0 }, 
-    { 0, 2, 0 }, 
-    { 0, 0, 0 }};
-  float[][] kernel2 = {{ 0, 1, 0 }, 
-    { 1, 0, 1 }, 
-    { 0, 1, 0 }};
-  float[][] gaussianKernel = 
-    {{ 9, 12, 9 }, 
-    { 12, 15, 12 }, 
-    { 9, 12, 9 }};
-  float[][] kernel = gaussianKernel;
-  float normFactor = 1.f;
-  /* For the gaussian kernel
-   for(int i = 0; i< N; ++i){
-   for(int j = 0; j<N; ++j){
-   normFactor += kernel[i][j];
-   }
-   }
-   */
+  float[][] kernel;
+  float normFactor ;
+  if(kt == kernelType.scale ) {
+    float[][] kernel1 = {{ 0, 0, 0 }, 
+      { 0, 2, 0 }, 
+      { 0, 0, 0 }};
+      kernel = kernel1;
+      normFactor = 1f;
+  }else if(kt == kernelType.blur){
+    float[][] kernel2 = {{ 0, 1, 0 }, 
+      { 1, 0, 1 }, 
+      { 0, 1, 0 }};
+      kernel = kernel2;
+      normFactor = 1f;
+  }else{
+    float[][] gaussianKernel = 
+      {{ 9, 12, 9 }, 
+      { 12, 15, 12 }, 
+      { 9, 12, 9 }};
+    kernel = gaussianKernel;
+      normFactor = 0f;
+    for (int i = 0; i< N; ++i) {
+      for (int j = 0; j<N; ++j) {
+        normFactor += kernel[i][j];
+      }
+    }
+  }
   // create a greyscale image (type: ALPHA) for output
   PImage result = createImage(img.width, img.height, ALPHA);
   //
   // for each (x,y) pixel in the image:
-  for (int x = 0; x< img.width; ++x) {
-    for (int y = 0; y<img.height; y++) { 
-      // - multiply intensities for pixels in the range 
-      //(x - N/2, y - N/2) to (x + N/2, y + N/2) by the
-      //corresponding weights in the kernel matrix
+  for (int x = 1; x< img.width-1; x++) {
+    for (int y = 1; y<img.height-1; y++) { 
       int sum = 0;
       for (int i = 0; i< N; ++i) {
         for (int j = 0; j<N; ++j) {
-          int row = ((row = y-N/2+i) < 0)? 0 : row;
-          row = (row >= img.height)? img.height-1 : row;
-          int column = ((column = x-N/2+i) < 0)? 0 : column;
-          column = (column >= img.width)? img.width-1: column;
-          sum += kernel[i][j] * brightness(img.pixels[column + img.width *row]);
+          sum += kernel[i][j] * brightness(img.pixels[(x-N/2+i)+ img.width *(y-N/2+j)]);
         }
       }
-      // - sum all these intensities and divide it by normFactor
-      // - set result.pixels[y * img.width + x] to this value
       result.pixels[x+y*img.width] = color((int)(sum / normFactor));
     }
   }
 
   return result;
 }
 
 void mousePressed(){
   println(variable);
 }